This project investigates the behavior of multi-head attention in Transformer models, specifically focusing on the differences between benign and trojan models in the context of sentiment analysis. Trojan attacks cause models to perform normally on clean inputs but exhibit misclassifications when presented with inputs containing predefined triggers. We characterize attention head functions in trojan and benign models, identifying specific 'trojan' heads and analyzing their behavior.

Traditional 3D reconstruction models have consistently encountered a challenge: attaining high recall of object edges while preserving precision. In this paper, we introduce a post-processing method Multi-Head Attention Refiner (MA-R) aimed at tackling this challenge by integrating a multi-head attention mechanism within the U-Net style refiner module. The 3D reconstruction model applying this method demonstrates excellence in parsing intricate image details, resulting in significant enhancement of boundary predictions and increased recall rates. In our experiments, our method significantly enhances the reconstruction performance of Pix2Vox++ when multiple images are utilized as input. Specifically, it achieves an enhanced Intersection Over Union score of approximately 1.1% compared to the original Pix2Vox++ model when 16-view images are employed.

：Non-Euclidean data, such as social networks, and citation relationships between documents, has node information and structural information. Graph Convolutional Network(GCN) can automatically learn node features and association information between nodes. The core ideology of the graph convolutional network is to aggregate node information by using edge information, thereby generating a new node feature. In the process of updating node features, there are two core influencing factors. One is the number of neighboring nodes of the central node, the other is the contribution of the neighboring nodes to the central node. Due to the previous GCN methods not simultaneously considering the numbers and different contributions of neighboring nodes to the central node, we design the Adaptive Attention Mechanism(AAM). To further enhance the representational capability of the model, we utilize Multi-Head Graph Convolution(MHGC). Finally, we adopt cross-entropy(CE) loss function to describe the difference between the predicted results of node categories and the ground truth (GT). Combined with backpropagation, this ultimately achieves accurate node classification. Based on AAM, MHGC and CE, we contrive the novel Graph Adaptive Attention Network (GAAN). Experiments show that the classification accuracy has achieved outstanding performances on Cora, Citeseer and Pubmed datasets.

With the development of deep learning, several Graph Neural Networks (GNN)-based approaches have been utilized for text classification. However, GNNs encounter challenges in capturing contextual text information within a document sequence. To address this, a novel text classification model RB-GAT is proposed by combining RoBERTa-BiGRU embedding and a multi-head Graph ATtention Network (GAT). First, the pre-trained RoBERTa model is exploited to learn word and text embeddings in different contexts. Second, the Bidirectional Gated Recurrent Unit (BiGRU) is employed to capture long-term dependencies and bidirectional sentence information from the text context. Next, the multi-head graph attention network is applied to analyze this information, which serves as a node feature for the document. Finally, the classification results are generated through a Softmax layer. Experimental results on three benchmark datasets demonstrate that our method can achieve an accuracy of 71.48%, 98.45%, and 80.32% on Ohsumed, R8, and MR, which is superior to the existing nine text classification approaches.

Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multiscale hybrid model. This model integrates advanced methodologies including Improved Intrinsic Mode Function with Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Permutation Entropy(PE), Least Squares Support Vector Regression (LSSVR), Regularized Extreme Learning Machine(RELM), Multi-head Attention (MHA), and Bidirectional Gated Recurrent Unit (BiGRU). Firstly, the ICEEMDAN technique is employed to decompose the non-stationary raw wind power data into multiple relatively stable sub-modes, while concurrently utilizing PE to assess the complexity of each sub-mode. Secondly, the dataset is reconstituted into three distinct components: high-frequency, mid-frequency, and low-frequency, to alleviate data complexity. Following this, the LSSVR, RELM, and MHA-BiGRU models are individually applied to predict the high, mid,and low-frequency components, respectively. Thirdly, the parameters of the low-frequency prediction model are optimized utilizing the Dung Betele Optimizer(DBO) algorithm. Ultimately, the predicted results of each component are aggregated to derive the final prediction. Empirical findings illustrate the exceptional predictive performance of the multiscale hybrid model incorporating LSSVR, RELM,and MHA-BiGRU. In comparison to other benchmark models, the proposed model exhibits a reduction in Root Mean Square Error(RMSE) values of over 10%, conclusively affirming its superior predictive accuracy.

Using traditional methods based on detection rules written by human security experts, there is a significant challenge in accurately detecting many network threats that are increasingly becoming sophisticated. In order to deal with the limitation of the traditional methods, network threat detection techniques utilizing artificial intelligence technologies such as machine learning are being extensively studied. Research has also been conducted on analyzing various string patterns in network packet payloads through natural language processing techniques to detect attack intent. However, due to the nature of packet payloads containing binary data as well as text data, a new approach is needed that goes beyond typical natural language processing techniques. In this paper, we study a token extraction method optimized for payloads using n-gram and byte pair encoding techniques. Furthermore, we generate embedding vectors that can understand the context of the packet payload using algorithms such as Word2Vec and FastText. We also compute the embedding of various header data associated with packets, such as IP addresses and ports. Given the combination of these features, we ensemble a text 1D-CNN and a multi-head attention network in a novel fashion. We validated the effectiveness of our classification technique with the CICIDS2017 open dataset and over half a million data collected by The Education Cyber Security Center (ECSC) currently operating in South Korea. The proposed model showed remarkable progress compared to previous studies, demonstrating a highly accurate classification performance with an F1-Score of 0.998. Our model can also preprocess and classify 150,000 network threats per minute to help security agents in the field divert their time to analyzing more complex attack patterns.

Vehicle logo detection plays a crucial role in various computer vision applications, such as vehicle classification and detection. In this research, we propose an improved vehicle logo detection method leveraging the MAMBA structure. The MAMBA structure integrates multiple attention mechanisms and bidirectional feature aggregation to enhance the discriminative power of the detection model. Specifically, we introduce the Multi-Head Attention for Multi-Scale Feature Fusion (MHAMFF) module to capture multi-scale contextual information effectively. Moreover, we incorporate the Bidirectional Aggregation Mechanism (BAM) to facilitate information exchange between different layers of the detection network. Experimental results on a benchmark dataset (VLD-45 dataset) demonstrate that our proposed method outperforms baseline models in terms of both detection accuracy and efficiency. Furthermore, extensive ablation studies validate the effectiveness of each component in the MAMBA structure. Overall, the proposed MAMBA-based vehicle logo detection approach shows promising potential for real-world applications in intelligent transportation systems.

Deep convolutional neural network (CNN) has shown their great advantages in the single image deraining task. However, most existing CNN-based single image deraining methods still suffer from residual rain streaks and details lost. In this paper, we propose a deep neural network including the Multi-scale feature extraction module and the channel attention module, which are embed in the feature extraction sub-network and the rain removal sub-network respectively. In the feature extraction sub-network, the Multi-scale feature extraction module is constructed by a Multi-layer Laplacian pyramid, and is then integrated multi-scale feature maps by a feature fusion module. In the rain removal sub-network, the channel attention module, which assigns different weights to the different channels, is introduced for preserving image details. Experimental results on visually and quantitatively comparison demonstrate that the proposed method performs favorably against other state-of-the-art approaches

Cerebrovascular and airway structures are tubular structures used for transporting blood and gases, respectively, providing essential support for the normal activities of the human body. Accurately segmenting these tubular structures is the basis of morphology research and patho-logical detection. Nevertheless, accurately segmenting these structures from images presents great challenges due to their complex morphological and topological characteristics. To address this challenge, this paper proposes a framework UARAI based on the U-Net multi-scale reverse at-tention network and sparse convolution network. The framework utilizes a multi-scale structure to effectively extract the global and deep detail features of vessels and airways. Further, it enhances the extraction ability of fine-edge features by a joint reverse attention module. In addition, the sparse convolution structure is introduced to improve the features' expression ability without increasing the model's complexity. Finally, the proposed training sample cropping strategy re-duces the influence of block boundaries on the accuracy of tubular structure segmentation. The experimental findings demonstrate that the UARAI-based metrics, namely Dice and IoU, achieve impressive scores of 90.31% and 82.33% for cerebrovascular segmentation and 93.34% and 87.51% for airway segmentation, respectively. Compared to commonly employed segmentation tech-niques, the proposed method exhibits remarkable accuracy and robustness in delineating tubular structures such as cerebrovascular and airway. These results hold significant promise in facilitating medical image analysis and clinical diagnosis, offering invaluable support to healthcare profes-sionals.

Due to the inherent limitations of a single viewpoint, reconstructing 3D human meshes 1 from a single image has long been a challenging task. While deep learning networks enable us to 2 approximate the shape of unseen sides, capturing the texture details of the non-visible side remains 3 difficult with just one image. Traditional methods utilize Generative Adversarial Networks (GANs) 4 to predict the normal maps of the non-visible side, thereby inferring detailed textures and wrinkles 5 on the model’s surface. However, we have identified challenges with existing normal prediction 6 networks when dealing with complex scenes, such as a lack of focus on local features and insufficient 7 modeling of spatial relationships.To address these challenges, we introduce EMAR—Enhanced 8 Multi-scale Attention-driven Single Image 3D Human Reconstruction. This approach incorporates 9 a novel Enhanced Multi-Scale Attention (EMSA) mechanism, which excels at capturing intricate 10 features and global relationships in complex scenes. EMSA surpasses traditional single-scale attention 11 mechanisms by adaptively adjusting the weights between features, enabling the network to more 12 effectively leverage information across various scales.Furthermore, we have improved the feature 13 fusion method to better integrate representations from different scales. This enhanced feature fusion 14 allows the network to more comprehensively understand both fine details and global structures 15 within the image. Finally, we have designed a hybrid loss function tailored to the introduced attention 16 mechanism and feature fusion method, optimizing the network’s training process and enhancing the 17 quality of reconstruction results.Our network demonstrates significant improvements in performance 18 for 3D human model reconstruction. Experimental results show that our method exhibits greater 19 robustness to challenging poses compared to traditional single-scale approaches.

Land cover mapping provides spatial information on the physical properties of the Earth’s surface, for various classes of wetlands, artificial surface and constructions, vineyards, water bodies, etc. Having reliable information on land cover is crucial to developing solutions to a variety of environmental problems such as destruction of important wetlands/forests, and loss of fish and wildlife habitats. This has made land cover mapping one of the most widespread application areas in remote sensing computational imaging. However, due to the differences between modalities in terms of resolutions, content, and sensors, integrating complementary information that multi-modal remote sensing imagery exhibits into a robust and accurate system still remains challenging, and classical segmentation approaches generally do not give satisfactory results for land cover mapping. In this paper, we propose a novel dynamic deep network architecture, AMM-FuseNet, that promotes the use of multi-modal remote sensing images for the purpose of land cover mapping. The proposed network exploits the hybrid approach of the Channel Attention mechanism and Densely Connected Atrous Spatial Pyramid Pooling (DenseASPP). In the experimental analysis, in order to to verify the validity of the proposed method, we test AMM-FuseNet applied to four datasets whilst comparing it to the 6 state-of-the-art models of DeepLabV3+, PSPNet, UNet, SegNet, DenseASPP, and DANet. In addition, we also demonstrate the capability of AMM-FuseNet under minimal training supervision (reduced number of training samples) compared to the state-of-the-art, achieving less accuracy loss even for the case with 1/20 of the training samples.

Speech emotion recognition is a critical component for achieving natural human-robot interaction. The modulation-filtered cochleagram is a feature based on auditory modulation perception, which contains multi-dimensional spectral-temporal modulation representation. In this study, we propose an emotion recognition framework that utilizes a multi-level attention network to recognize emotions from the modulation-filtered cochleagram. The channel-level attention and spatial-level attention modules are used to capture emotional saliency maps of channel and spatial feature representations from the 3D convolution feature maps, respectively. Furthermore, the temporal-level attention module captures significant emotional regions from the concatenated feature sequence of the emotional saliency maps. Our experiments on the IEMOCAP dataset demonstrate that the modulation-filtered cochleagram significantly improves the prediction performance of categorical emotion compared to other evaluated features. Moreover, our emotion recognition framework achieves a better unweighted accuracy of 71% in categorical emotion recognition than several existing approaches in the experiments. In summary, our study demonstrates the effectiveness of the modulation-filtered cochleagram in speech emotion recognition, and our proposed multi-level attention framework provides a promising direction for future research in this field.

To improve the effect of multi-point stretch forming of sheet metal, it is proposed in this paper to replace fixed ball head with swinging ball head. According to the multi-point dies with different arrangements, this research establishes the finite element models of the following stretch forming, i.e. fixed ball heads with conventional arrangement, swinging ball heads with conventional arrangement, swinging ball heads with declining staggered arrangement, and swinging ball heads with parallel staggered arrangement and then numerical simulation is performed. The simulation results show that by replacing fixed ball head with swinging ball head, the surface indentation of the formed part was effectively suppressed, the stress and tension strain distribution of the formed part was improved and the forming quality was improved; that the thickness of the elastic pad was reduced, the springback was reduced and the forming accuracy was improved; and that when the ball head was applied to multi-point die with staggered arrangement, better forming result was achieved, where the best forming result was achieved in combining the swinging ball heads with the multi-point die with parallel staggered arrangement. The forming experiments were carried out, and the experimental results were consistent with the trend of numerical simulation results, which verified the correctness of the numerical simulation.

Target detection technology plays a crucial role in fishery ecological monitoring, fishery diversity research, and intelligent aquaculture. Deep learning, with its distinct advantages, provides significant convenience to the fishery industry. However, it still faces various challenges in practical applications, such as significant differences in image species and image blurring. To address these issues, this study proposes a multi-scale, multi-level, and multi-stage cross-domain feature fusion model. In order to train the model more effectively, a new dataset called Fish52 (Multi-scene Fish dataset) was constructed, on which the model achieved an mAP of 82.57%. Furthermore, we compared prevalent one-stage and two-stage detection methods on the Lahatan (single-scene fish dataset) and Fish30 dataset and tested them on the F4k and FishNet dataset. The mAP of our proposed model on the Fish30, Lahatan, F4k, and FishNet datasets reaches 91.72%, 98.7%, 88.6%,and 81.5% respectively, outperforming existing mainstream models. Comprehensive empirical analysis indicates that our model possesses high generalization ability and has reached advanced performance levels. In this study, the depth of the model backbone is deepened, a novel neck structure is proposed, and anew module is embedded therein. To enhance the fusion ability of the model, a new attention mechanism module is introduced. In addition, in the adaptive decoupling detection header module, introducing classes with independent parameter sand regression adapters reduces interaction between different tasks. The proposed model can better monitor fishery resources and enhance aquaculture efficiency. It not only provides an effective approach for fish detection but also has certain reference significance for the identification of similar target sin other environments, and offers assistance for the construction of smart fisheries and digital fisheries.

With the recent advances in Machine Learning (ML), several models have been successfully applied to financial and accounting data to predict the likelihood of companies’ bankruptcy. However, time series have received little attention in literature with a lack of studies on the application of Deep Learning sequence models as Recurrent Neural Networks (RNN) and the recent Attention-based models in general. In this research work, we investigated the application of Long Short Term Memory (LSTM) networks to exploit time series of accounting data for bankruptcy prediction. The main contributions of our work are the following: a) we have proposed a Multi-head LSTM that models each financial variable in a time window independently and compared it with a single-input LSTM and other traditional ML models. The Multi-head LSTM outperforms all the other models; b) We identify the optimal time series length for bankruptcy prediction to be equal to 4 years of accounting data; c) We made public the dataset we used for the experiments that include data from 8262 different public companies in the American stock market generated in the period between 1999-2018. Furthermore, we prove the efficacy of the Multi-head LSTM models in terms of lower false positives and better division of the two classes.

Purposes: To provide abdominal contrast-enhanced MR image synthesis, we developed an image gradient regularized multi-modal multi-discrimination sparse-attention fusion generative adversarial network (GRMM-GAN) to avoid repeated contrast injections to patients and facilitate adaptive monitoring. Methods: With IRB approval, 165 abdominal MR studies from 61 liver cancer patients were retrospectively solicited from our institutional database. Each study included T2, T1 pre-contrast (T1pre), and T1 contrast-enhanced (T1ce) images. The GRMM-GAN synthesis pipeline consists of a sparse attention fusion network, an image gradient regularizer (GR), and a generative adversarial network with multi-discrimination. The studies were randomly divided into 115 for training, 20 for validation, and 30 for testing. The two pre-contrast MR modalities, T2 and T1pre images, were adopted as inputs in the training phase. The T1ce image at the portal venous phase was used as an output. The synthesized T1ce images were compared with the ground truth T1ce images. The evaluation metrics include peak signal-to-noise ratio (PSNR), structural-similarity-index (SSIM), and mean-squared-error (MSE). A Turing test and experts’ contours evaluated the image synthesis quality. Results: The proposed GRMM-GAN model achieved a PSNR of 28.56, an SSIM of 0.869, and an MSE of 83.27. The proposed model showed statistically significant improvements in all metrics tested with p-values <0.05 over the state-of-the-art model comparisons. The average Turing test score was 52.33%, close to random guessing, supporting the model's effectiveness for clinical application. In the tumor-specific region analysis, the average tumor contrast-to-noise ratio (CNR) of the synthesized MR images was not statistically significant from the real MR images. The average DICE from real vs. synthetic images was 0.90, compared to the inter-operator DICE of 0.91. Conclusion: We demonstrated the function of a novel multi-modal MR image synthesis neural network, GRMM-GAN, for T1ce MR synthesis based on pre-contrast T1 and T2 MR images. GRMM-GAN shows promise for avoiding repeated contrast injections during radiation therapy treatment.

This study presents the development and evaluation of a Multi-Task Long Short-Term Memory (LSTM) model with an Attention Mechanism designed to predict students' academic performance. The model concurrently addresses two tasks: predicting overall performance (total score) as a regression task and categorizing performance levels (remarks) as a classification task. By processing both tasks simultaneously, the model optimizes computational efficiency and resource use. The dataset includes detailed student performance records across various metrics such as Continuous Assessment, Practical Skills, Demeanor, Presentation Quality, Attendance, and Participation. The model's performance was evaluated using comprehensive metrics. For the regression task, it achieved a Mean Absolute Error (MAE) of 0.0249, Mean Squared Error (MSE) of 0.0012, and Root Mean Squared Error (RMSE) of 0.0346. For the classification task, it attained perfect scores with an accuracy, precision, recall, and F1 score of 1.0. These results highlight the model's high accuracy and robustness in predicting both continuous and categorical outcomes. The Attention Mechanism enhances the model's capabilities by identifying and focusing on the most relevant features. This study demonstrates the effectiveness of the Multi-Task LSTM with Attention Mechanism in educational data analysis, offering a reliable tool for predicting student performance and potential broader applications in similar multi-task learning contexts. Future work will explore further enhancements and wider applications to improve predictive accuracy and efficiency.

The root system plays an irreplaceable role in plant growth. Its improvement can increase crop productivity. However, such system is still mysterious for us. The underlying mechanism has not been fully uncovered. The investigation on proteins related to the root system is an important means to complete this task. In the previous time, lack of root-related proteins makes it impossible to adopt machine learning methods for designing efficient models for the discovery of novel root-related proteins. Recently, a public database on root-related proteins was set up and machine learning methods can be applied in this field. In this study, we proposed a machine learning based model, named Graph-Root, for identification of root-related proteins. The features derived from protein sequences and one network were extracted, where the former features were processed by graph convolutional neural network and multi-head attention, and the later features abstracted the linkage between proteins. These features were fed into the fully connected layer to make prediction. The 5-fold cross-validation and independent tests suggested its good performance. It also outperformed the only one previous model, SVM-Root. Furthermore, the importance of each feature type and component in the proposed model was investigated.

Pedestrian object detection plays a significant role in intelligent systems such as intelligent traffic and monitoring. Traditional machine learning methods on pedestrian detection have shown various drawbacks, e.g., low accuracy, slow speed, etc. The Convolutional Neural Network (CNN) based object detection algorithms have demonstrated remarkable advantages in the field of pedestrian detection. However, the mainstream CNNs still face the problems of slow speed and low detection accuracy, especially on small and occluded targets from aerial perspective. In this paper, we propose Multi-Scale Attention YOLO (MSA-YOLO) detection algorithm to address the above issues. MSA-YOLO includes a Squeeze, Excitation and Cross Stage Partial (SECSP) channel attention module for CNNs to extract richer pedestrian features with a small number of extra parameters. It also contains a multi-scale prediction module to capture the information among different pedestrian scales, which can recognize the small objects with higher accuracy and significantly reduce the missed detection. To sufficiently evaluate our proposed model, we manually collect and annotate a new benchmark dataset, Aerial Pedestrian Dataset, which has much more sample annotations, features, scenes and image view angles than the existing benchmark datasets. In addition, the images in our dataset have higher resolution than most of benchmark pedestrian detection datasets, which can provide more detailed features of pedestrians and thus improve the model performance. When tested on Aerial Pedestrian Dataset, our proposed MSA-YOLO algorithm significantly outperforms the most used baseline models with almost the same model size. This shows the efficiency of our proposed model. (The code and new dataset will be released to the public later.)

In order to improve real-time performance of gesture recognition by micro-Doppler map of mmWave Radar, the point cloud based gesture recognition for mmWave Radar is proposed in this paper.Two steps are carried out for mmWave Radar based gesture recognition. The first step is to estimate the point cloud of the gestures by 3D-FFT and the peak grouping. The second step is to train the TRANS-CNN model by combining the multi-head self-attention and the 1D-convolutional network, so as to extract the features in the point cloud data at a deeper level to categorize the gestures. In the experiments, TI mmWave Radar sensor IWR1642 is used as benchmark to evaluate the feasibility of the proposed approach. The results show that the accuracy of the gesture recognition reaches 98.5%. In order to prove the effectiveness of our approach, a simply 2Tx2Rx Radar sensor is developed in our lab and the accuracy of recognition reaches 97.1%. The results show that our proposed gesture recognition approach achieves the best performance in real-time with limited training data, in comparison with the existing methods.

Classification of pre-processed fMRI dataset using the functional connectivity(FC) based features is considered a challenging task due to high dimensional FC features set and smaller dataset size. To tackle this specific FC high dimensional features set and a smaller dataset size , We have proposed here a GAN based dataset augmenter to firstly train the GAN on the NYU Connectivity Features dataset and used the trained GAN to generate synthetic features per category and after getting sufficient number of features per category , A multi-head attention mechanism was used as a head for the classification . We name our proposed approach as "ASD-GANNet" which is End-To-End and does not require hand-crafted features as the multi-head attention mechanism focuses on the features that are more relevant . Moreover , We compared our results with the 06 available state-of-the-art techniques from the literature and Our proposed approach results using "NYU" site as a training set for generating GAN based synthetic dataset are promising. We achieved an overall 10-fold cross validation based accuracy of 82% ,sensitivity 82% and specificity 81% outperforming the available state-of-the art approaches. Sitewise comparison of our proposed approach also outperformed the the available state-of-the-art as out of the 17 sites our proposed approach has better results in the 10 sites.

In response to reducing the energy cost of unmanned surface vehicles (USVs) while overcoming the low accuracy problem in surface target detection, a lightweight detection algorithm with multi-scale feature fusion is proposed. Based on the popular one-stage lightweight Yolov7-tiny target detection model, a lightweight extraction module is designed first by introducing the multiscale residual module to reduce the number of parameters and computational complexity while improving accuracy. The Mish and SiLU activation functions are used to enhance network feature extraction. Second, the path aggregation network employs coordinate convolution to strengthen spatial information perception. Finally, the dynamic head, which is based on the at-tention mechanism, improves the representation ability of object detection heads without any computational overhead. According to the experimental findings, the proposed model has 22.1% fewer parameters than the original model, 15% fewer GFLOPs, a 6.2% improvement in mAP@0.5, a 4.3% rise in mAP@0.5:0.95, and it satisfies the real-time criteria. According to the research, the suggested lightweight water surface detection approach includes a lighter model, a simpler computational architecture, more accuracy, and a wide range of generalizability. It performs bet-ter in a variety of difficult water surface circumstances.

Attention deficit hyperactivity disorder (ADHD) results in various functioning impairments in children’s lives and families. Parents of children with ADHD report high levels of parenting stress, low levels of parental self-efficacy and use of more authoritarian and/or permissive parenting practices than parents of typically developing children. Intervention programs need to address both children’s and parents’ needs and multimodal intervention programs could cover this demand. The aim of this study was to examine the efficacy of “Child ViReal Support Program” - a multi-level evidence-based comprehensive program - on parenting stress, parental self-efficacy, parenting practices, and the core symptoms of children’s ADHD. Families with a child diagnosed with ADHD (n = 16) were randomly allocated to two groups (PC and CP; P = parent training, C = child training) and a cross-over design was utilized. Participating parents completed, in four different times during the study, the Parenting Stress Index-Short Form, the Parenting Sense of Competence Scale, the Parenting Styles and Dimensions Questionnaire-Short Version and the parent form of the ADHD Rating Scale-IV. Parents from both groups, after their participation in the parent training, demonstrated reduced parenting stress, enhanced parental self-efficacy and increased the employ of democratic parenting practices. More than that, they reported decreased levels of inattention and impulsivity/hyperactivity for their children. Evidence-based multi-level intervention programs could produce positive effects in parents and children by incorporating effective methods and tools in accordance with the needs and the demands of the family context.

Bidirectional gene promoters affect the transcription of two genes, leading to the hypothesis that they should exhibit protection against genetic or epigenetic changes in cancer. Therefore, they provide an excellent opportunity to learn about promoter susceptibility to somatic alteration in tumors. We tested this hypothesis using data from genome-scale DNA methylation (14 cancer types), simple somatic mutation (10 cancer types), and copy number variation profiling (14 cancer types). For DNA methylation, the difference in rank differential methylation between tumor and tumor-adjacent normal matched samples based on promoter type was tested by Wilcoxon rank sum test. Logistic regression was used to compare differences in simple somatic mutations. For copy number alteration, a mixed effects logistic regression model was used. The change in methylation between non-diseased tissues and their tumor counterparts was significantly greater in single compared to bidirectional promoters across all 14 cancer types examined. Similarly, the extent of copy number alteration was greater in single gene compared to bidirectional promoters for all 14 cancer types. Furthermore, among 10 cancer types with available simple somatic mutation data, bidirectional promoters were slightly more susceptible. These results suggest that selective pressures related with specific functional impacts during carcinogenesis drive the susceptibility of promoter regions to somatic alteration.

In the remote sensing domain, it is crucial to complete semantic segmentation on the raster images, e.g., river, building, forest, etc, on raster images. A deep convolutional encoder--decoder (DCED) network is the state-of-the-art semantic segmentation method for remotely sensed images. However, the accuracy is still limited, since the network is not designed for remotely sensed images and the training data in this domain is deficient. In this paper, we aim to propose a novel CNN for semantic segmentation particularly for remote sensing corpora with three main contributions. First, we propose applying a recent CNN called a global convolutional network (GCN), since it can capture different resolutions by extracting multi-scale features from different stages of the network. Additionally, we further enhance the network by improving its backbone using larger numbers of layers, which is suitable for medium resolution remotely sensed images. Second, "channel attention'' is presented in our network in order to select the most discriminative filters (features). Third, "domain-specific transfer learning'' is introduced to alleviate the scarcity issue by utilizing other remotely sensed corpora with different resolutions as pre-trained data. The experiment was then conducted on two given datasets: (i) medium resolution data collected from Landsat-8 satellite and (ii) very high resolution data called the ISPRS Vaihingen Challenge Dataset. The results show that our networks outperformed DCED in terms of $F1$ for 17.48% and 2.49% on medium and very high resolution corpora, respectively.

Background and Objectives: The deltopectoral (DP) flap represents a reconstructive option for head and neck. It is a fasciocutaneous flap raised from the anterior chest wall below the clavicle. Its role partially declined with the arise of free flaps. However, it still remains a valid option in patients that could not undergo a reconstruction with free flaps. The aim of this retrospective study was to evaluate the role of the DP flap in head and neck reconstruction, with a focus on surgical outcomes and complications. Materials and Methods: Thirty-one patients who underwent head and neck reconstruction with DP flap were included in the study. The delayed technique was not used in any procedure to harvest the flap beyond the deltopectoral groove. The patients’ characteristics, the recipient site, the closure of the donor site and the flap-related complications were recorded. Results: The median time to autonomization of DP flap was 23 days. Post-operative complications were observed in 10 subjects (32.3%). A partial necrosis was seen in 5 patients (16.1%), while a complete necrosis developed only in one case (3.2%). The diabetes mellitus was the only parameter associated to post-operative complications. In particular, the percentage of necrosis in subjects with or without diabetes was 66.7% and 8.0%, respectively. Conclusions: The DP has a wide range of applications in head and neck reconstruction and a low complication rate can be observed. The delayed technique does not necessarily need to be applied, and the flap can be extended beyond the deltopectoral groove when necessary. However, patients with diabetes mellitus have a higher risk of postoperative necrosis of the distal portion of the flap.

In recent decades, with the ever-growing scale of video data, near-duplicate videos continue to emerge. Data quality issue caused by near-duplicate videos is becoming more and more prominent, which has affected the application of normal videos. Although current studies on near-duplicate video detection can be helpful to uncover data quality issues for videos, they still lack a process of automatic merging for the video data represented by high-dimensional features, which are difficult to automatically clean the near-duplicate videos to improve data quality for video datasets. At present, there are few studies on near-duplicate video data cleaning. The existing studies have the sensitive problems of video data orderliness and clustering initial center under a condition that prior distribution is unknown, which seriously affect the accuracy of near-duplicate video data cleaning. To address the above issues, an automatic near-duplicate video data cleaning method based on a consistent feature hash ring is proposed in this paper. First, a residual network with convolutional block attention modules, a long short-term memory deep network, and an attention model are integrated to construct an RCLA deep network with the multi-headed attention mechanism to extract spatiotemporal features of video data. Then, a consistent feature hash ring is constructed, which can effectively alleviate the sensitivity of video data orderliness while providing a condition of near-duplicate video merging. To reduce the sensitivity of the initial cluster centers to results of near-duplicate video cleansing, an optimized feature distance-means clustering algorithm is constructed by utilizing a mountain peak function on a consistent feature hash ring, which can implement automatic cleaning of near-duplicate video data. Finally, experiments are conducted based on a commonly used dataset named CC_WEB_VIDEO and a coal mining video dataset. Compared with some existing works, simulation results demonstrate that the performance of the proposed method.

The delineation of the clinical target volumes (CTVs) for radiation therapy is time-consuming, requires intensive training and shows high inter-observer variability. Supervised deep learning methods depend heavily on consistent training data, thus state-of-the-art research focuses on making CTV labels more homogeneous and strictly bounding them to current standards. International consensus expert guidelines standardize CTV delineation by conditioning the extension of the clinical target volume on surrounding anatomical structures. Training strategies that directly following the construction rules given in the expert guidelines or the possibility to quantify the conformance of manually drawn contours with the guidelines are still missing. 71 anatomical structures that are relevant for CTV delineation in head and neck cancer patients according to the expert guidelines were segmented on 104 CT scans to assess the possibility to automate their segmentation by state-of-the-art deep learning methods. nnU-Net models were trained to automatically segment those 71 structures on planning CT scans. We report the DICE, HD and sDICE for 71 + 5 anatomical structures for most of which no previous segmentation accuracies have been reported. For those structures, for that prediction values have been reported, our segmentation accuracy matched or exceeded the reported values. The predictions from our models were always better than those predicted by the TotalSegmentator. The sDICE with 2mm margin was larger than 80% for almost all structures. Individual structures with decreased segmentation accuracy are analyzed and discussed with respect to their impact on the CTV delineation following the expert guidelines. No deviation is expected to affect the rule-based automation of the CTV delineation.

Head trauma or any traumatic injury can cause damage to parts of the brain. The most important part regulating the body are the hormones and the two centers of brain which are main ones for this regulation are the most prone to getting injured. The hypothalamus and pituitary regulate a lot many functions and the loss of these organs can cause Adrenal Insufficiency, SIADH, Diabetes Insipidus, and many more.

Stereotactic body radiation therapy (SBRT) is a precise and conformal radiation therapy (RT) that aims to deliver a high dose of radiation to the tumor with sparing surrounding normal tissue, making it an attractive option for head and neck cancer (HNC) patients who are not suitable for traditional long course of RT with comprehensive RT target volume. Definitive SBRT for HNC has been investigated in different settings, including early stage glottis cancer, and as alternative to brachytherapy boost after external beam RT. It also used as a primary treatment option for elderly or medically unfit patients. More recently, SBRT combination with immunotherapy in the neoadjuvant setting for HNC showed promising results. Salvage or adjuvant SBRT for HNC can be used in appropriately selected cases. Future studies are warranted to determine the optimum dose and fractionation schedules in any of these indications.

Spending on out-patient health care by citizens in limited resource countries has received little attention.The purpose of this study is to determine the predictors of household spending on out-patient expenses in a cross-sectional study in Kenya. We applied the GEE methods to determine the effect of various variables on outpatient care. We established that the best predictors for outpatient spending in Kenya are Age of the household head, wealth index, marital status and education, which had the lowest QICu of 976341.2. There were no differences on age in mean spending on outpatient care and was changing in a sinusoidal manner. The rich spend more on outpatient care, due to financial ability. Spending increased across the wealth quantiles while gender had a significant effect in the general performance of the models, it didn’t assist in lowering the QICu

Recently, the destructive impact of wildfires has proliferated; for instance, the August Complex wildfire in 2020 burned around 4% of California's landmass. This has caused increased economic damage and risk to human life. Additionally, climate change is anticipated to increase the severity of wildfires, making it imperative for accurate and efficient detection of wildfires. Machine Learning approaches allow for the automatic detection of wildfires, simultaneously prioritizing accuracy and efficiency, with minimal human intervention, thus decreasing the likelihood of increased economic damage and increasing firefighting responses. Convolutional Neural Networks (CNNs), while showing promise, are often limited by their inability to learn and capture deep spatial dependencies in satellite imagery tasks. In this paper, we propose FlameViT, a novel wildfire detection architecture based on Vision Transformers (ViT). Satellite images are more efficient to obtain and can cover wide areas prone to wildfires. We obtain a dataset of 40K+ satellite images from Canada's Open Government Portal, allowing FlameViT to be optimized to detect wildfires in satellite imagery. FlameViT uses Patch Extractor and Patch Embedding layers, followed by multiple Transformer Encoder layers with Multi-Head Self-Attention and feed-forward neural networks. FlameViT is hyperparameter-tuned and achieves a validation accuracy of 95%, outperforming various baselines in the wildfire detection task. FlameViT shows the power of Vision Transformers for wildfire detection tasks, and in conjunction with the use of satellite imagery, can provide an efficient and accurate way of detecting wildfires.

Patients with locally advanced head and neck squamous cell carcinoma (HNSCC) frequently require primary radiochemotherapy (RCT). Despite intensity-modulation, desired radiation-induced effects observed in HNSCC, may also be observed as side-effects in healthy tissue e.g. the sternocleidomastoideus muscle (SCM). These side-effects (e.g. tissue fibrosis) depend on the interval between completion of RCT and restaging-CT. For salvage surgery, the optimal time window for surgery is currently postulated between 6 and 12 weeks after completion of RCT. Thus, no extensive tissue fibrosis is to be expected. This interval is based on studies exploring surgical complications. Studies directly exploring radiation-induced changes of the SCM in HNSCC-patients are sparse. The present study quantifies tissue alterations in SCM and paravertebral musculature (PVM) after RCT applying radiomics to determine the optimal time window for salvage surgery. Three radiomic key parameters 1) volume, 2) mean positivity of pixels (MPP) and 3) uniformity were extracted with mint lesionTM in the staging-CTs and restaging-CTs of 98 HNSCC-patients. Of these, 25 were female, the mean age was 62 (±9.6) years and 80.9% were UICC Stage IV. The mean restaging-interval was 55 (±28; range29-229) days. Only the mean volume significantly decreased after RCT from 9.0 to 8.4 and 96.5 to 91.9 ml for SCM and PVM, respectively (both p=0.007, both Cohen’s d=0.28). In addition, the mean body mass index (BMI) decreases from 23.9 (±4.2) to 21.0 (±3.6) kg/m² (p<0.001); Cohen's d = 0.9). The mean BMI-decrease significantly correlated with the volume decrease for SCM (r=0.27; p=0.007) and PVM (r=0.41; p<0.001). If t-test p-values were adjusted for the BMI-decrease, no significant change in volumes for SCM and PVM was observed (both p>0.05). The present data supports the postulated optimal interval for salvage surgery of 6 to 12 weeks. Irrespective of the remaining risk-benefit ratio of salvage surgery, the risk of additional surgical complications due to fibrosis is not supported by the present observations.

Single Nucleotide Polymorphisms (SNPs) are the most common type of genetic variation found in individual’s DNA sequences. SNPs can occur in both coding and non-coding regions of the genome and can affect gene expression, protein function, and disease susceptibility. In this systematic review we evaluate the influence of SNPs as a biomarker in the assessment of oral mucositis (OM) in head and neck cancer (HNC) patients. The study selection process involved screening 66 articles from different platforms, and after removing duplicates and excluding irrelevant articles, 23 articles were included for full-text evaluation. The main characteristics and findings of the selected articles are presented and discussed. We conclude that SNPs can be used as a reliable biomarker for the assessment of OM in HNC patients, and further research is needed to explore the potential of SNPs in personalized medicine for HNC treatment.

Passive dynamics is an aspect of locomotion which is entirely dependent on the mechanical configuration and linkages of adjacent body segments. Tension distribution along mechanical linkages enables execution of movement patterns with reduced need for complex neurological pathways and may play a role in reestablishing postural stability following external disturbances. Here we demonstrate a uni-directional mechanical relationship between the equine forelimb, head and neck, which may have implications for balance and forelimb loading in the horse. These observations suggest that forelimb, head and neck movement coordination (observed in the horse during unrestrained locomotion) is largely controlled by the mechanical linkages between body segments, rather than being entirely dependent on neurological input as previously thought. This highlights the potential significance of research directed at investigating passively induced movements in understanding common locomotory patterns. Additionally, it suggests a mode of postural control which may provide instantaneous adjustments to postural disturbances, thus promoting rapid and efficient locomotion.

Background: We aimed to study the frequency, management, and outcomes of patients with blunt pancreatic trauma. Methods: We reviewed the medical records for all patients admitted with pancreatic injuries between 2011 and 2017 at the only level 1 trauma center in the country. Results: There were 71 patients admitted with pancreatic trauma (0.6% of trauma admissions and 3.4% of abdominal injury admissions) with a mean age of 31 years. Sixty-two patients had pancreatic injury grade I-II and 9 had grade III-IV. Thirty-eight percent had GCS <9 and 73% had ISS >16. The level of pancreatic enzymes was significantly proportional to the grade of injury. Over half of patients required a laparotomy, of them 12 patients had an intervention on the pancreas. Eight patients developed complications related to pancreatic injuries ranging from pancreatitis to pancreatico-cutaneous fistula while 35% developed hemorrhagic shock. Mortality was 31% and regardless of the grade of injury, the mortality was associated with high ISS, low GCS and presence of hemorrhagic shock. Conclusion: Pancreatic injuries following blunt trauma are rare and the injured subjects are usually young male. However, most injuries are of low-grade severity. This study shows that shock, higher ISS and lower GCS are associated with worse in-hospital out-comes. Non-operative management may suffice in patients with lower grade injuries, which may not be the case in patients with higher grade injuries unless carefully selected

Importance: The rapidly expanding 2019 novel coronavirus pandemic (COVID-19, caused by the SARS-CoV-2 virus) has challenged the medical community to an unprecedented degree. Physicians and healthcare workers are at added risk of exposure and infection during the course of the patient care. Due to the rapid spread of this disease through respiratory droplets, healthcare providers such as otolaryngologists-head & neck surgeons who come in close contact with the upper aerodigestive tract during diagnostic and therapeutic procedures are particularly at risk. Here we present a set of safety recommendations based on our review of literature and communications with physicians with first-hand knowledge of safety procedures during the 2019 COVID-19 pandemic.Observations: A high number of healthcare providers were infected during the first phase of the pandemic in Wuhan province. Subsequently, by adopting strict safety precautions, other regions were able to achieve high levels of safety for healthcare providers without jeopardizing the care of patients. We reviewed the most common procedures related to the examination and treatment of upper aerodigestive tract diseases. Each category was reviewed based on the potential risk imposed to healthcare workers. Specific recommendations were made, based on the literature, when available, or consensus best practices. Specific safety recommendations were made for performing tracheostomy in COVID-19 patients. Conclusions and Relevance: Preserving highly skilled healthcare work force is a top priority for any community and healthcare system. Based on the experience of healthcare systems in Asia and Europe, by following strict safety guidelines, the risk of exposure and infection of healthcare providers could be significantly reduced, while providing high levels of care. The provided recommendations could be used as broad guidance for all healthcare workers who are involved with the care of COVID-19 patients.

Head and neck cancers arise from mucosa lining the oral cavity, oropharynx, hypopharynx, larynx, sinonasal tract, and nasopharynx and the etiology of head and neck cancers is complex and involves many factors, among which oncogenic viruses are also enumerated. Nevertheless, this type of cancers are among the most common cancers around the world. The thorough knowledge of the pathogenesis of viral infection is needed to fully understand its impact on cancer development.

In daily travel and activities, pathfinding is a significant process. They are often used in transportation routes calculation. They have now evolved to be able to solve most situations of the pathfinding and its related problems. This review describes previous and recent studies on the pathfinding algorithms. It reviews the development of pathfinding algorithms in a classification base on their usage. The aim is to summarize the application of the pathfinding algorithms for the readers interested in the subject that can be used as a supplement.

The cyclooxygenase-2 (COX-2) is a potent enzyme that converts arachidonic acid to prostaglandins (PG), including PGE2, a key mediator of inflammation and angiogenesis. Importantly, COX-2 is activated in response to inflammatory stimuli, where it is also believed to promote the development and progression of head and neck cancers (HNC). COX-2 can mediate its protumorigenic effect through various mechanisms such as inducing cell proliferation, inhibition of apoptosis, and suppressing the host’s immune response. Furthermore, COX-2 can induce the production of vascular endothelial growth factors, hence promoting angiogenesis. Indeed, the ability of COX-2 inhibitors to selectively restrict the proliferation of tumor cells and mediating apoptosis provides promising therapeutic targets for cancer patients. Thus, in this comprehensive review, we summarized the reported differential expression patterns of COX-2 in different stages of head and neck carcinogenesis—from potentially premalignant lesions to invasive carcinomas. Furthermore, we examined the available meta-analysis evidence for COX-2 role in the carcinogenesis of HNC. Finally, further understanding of the biological processes of COX-2 and its role in orchestrating cell proliferation, apoptosis and angiogenesis may give therapeutically beneficial insight to develop the management plan of HNC patients and improve their clinical outcomes.

Multi-Faith Spaces (MFS) are a relatively recent invention that quickly gained in significance. On the one hand, they offer a convenient solution for satisfying needs of people with diverse beliefs in the institutional context of hospitals, schools, airports, etc. On the other hand, as Andrew Crompton pointed out, they are politically significant because the multi-faith paradigm “is replacing Christianity as the face of public religion in Europe” (2012, p. 493). Due to their ideological entanglement, MFS are often used as the means to promote either a more privatised version of religion, or a certain denominational preference. Two distinct designs are used to achieve these means: negative in the case of the former, and positive in the latter. Neither is without problems, and neither adequately fulfils its primary purpose of serving diverse groups of believers. Both, however, seem to follow the biases and main problems of secularism. In this paper, I analyse recent developments of MFS to detail their main problems and answer the question, whether the MFS, and the underlying Multi-Faith Paradigm, can be classified as a continuation of secularism.

Multi-channel and multi-parameter near-infrared spectrometers (NIRS) have gradually become a new research direction and hot spot due to their ability to provide real-time, continuous, comprehensive indicators of multiple parameters. However, multi-channel and multi-parameter detection may lead to crosstalk between signals. There is still a lack of benchmarks on the evaluation of reliability, sensitivity, stability and response consistency of the NIRS instrument. In this study, a set of test methods (the ink drop test, multi-channel crosstalk test and multi-parameter crosstalk test) for analysis crosstalk and verifying the reliability of NIRS were conducted to test experimental verification on a multi-channel (8-channel), multi-parameter (4-parameter) NIRS instrument independently developed by our team. Results show that these tests can be used to analyze the signal crosstalk and verify the reliability, sensitivity, stability and response consistency in the NIRS instrument. This study contributes to the establishment of benchmarks for the NIRS instrument crosstalk and reliability testing. These novel tests have the potential to become the benchmark for the NIRS instrument reliability testing.

Background: Infantile hemangiomas (IHs) are the most common benign vascular tumors of infancy. Methods: We report our experience of 248 patients with head and neck IHs. Results: The median age on admission was 4 months, and female/male ratio was 2.17. Among the cases, 45% were followed without treatment. No local complications were observed in any of these patients. Propranolol was given to all patients who received medical treatment. The median duration of treatment was 12 months (1-30 months), and the median follow-up period of all patients was 14 months (0-118 months). The treatment response was 98%. Complication rate was 17% and children aged between 3 and 9 months accounted for 60% of patients who developed complications. Most of the complications were local complications such as bleeding, ulcer, and infection. Conclusion: Although most of them regress spontaneously, complications may occur. Propranolol alone is an effective treatment option, and early treatment initiation increases success rates.

The twelve cranial nerves play a crucial role in nervous system, orchestrating a myriad of functions vital for our everyday life. These nerves are each specialized for particular tasks. Cranial Nerve I, known as the olfactory nerve, is responsible for our sense of smell, allowing us to perceive and distinguish various scents. Cranial Nerve II, or the optic nerve, is dedicated to vision, transmitting visual information from the eyes to the brain. Eye movements are governed by Cranial Nerves III, IV, and VI, ensuring our ability to track objects and focus. Cranial Nerve V controls facial sensation and jaw movement, while Cranial Nerve VII, the facial nerve, facilitates facial expressions and taste perception. Cranial Nerve VIII, or the vestibulocochlear nerve, plays a critical role in hearing and balance. Cranial Nerve IX, the glossopharyngeal nerve, affects throat sensation and taste perception. Cranial Nerve X, the vagus nerve, is a far-reaching nerve, influencing numerous internal organs such as the heart, lungs, and digestive system. Cranial Nerve XI, the accessory nerve, is responsible for neck muscle control, contributing to head movement. Finally, Cranial Nerve XII, the hypoglossal nerve, manages tongue movement, essential for speaking, swallowing, and breathing. Understanding these cranial nerves is fundamental in comprehending the intricate workings of our nervous system and the functions that sustain our daily lives.

Engine Overheating is a condition where the engine temperature has increased above the working temperature. This analysis aims to analyze the damage, determine the factors causing the damage, identify corrective steps and prevent engine overheating. The inspection procedure is carried out by checking the cooling system's components, cylinder head, and cylinder block. Further analysis using a fishbone diagram to analyze the causes of engine overheating troubleshooting. Damage analysis results Engine overheating occurs because the thermostat is damaged, namely, the thermostat valve which cannot open when the temperature reaches the standard temperature (71oC), and also due to damage to the cylinder gasket which causes bubbles due to compression leaks and mixing of radiator coolant and oil. Repair steps are taken in the form of replacing damaged components with new ones. Preventive measures are taken by not using the unit on an over-running basis, checking coolant levels, carrying out daily checks properly and correctly, and carrying out preventive maintenance regularly.

Cytoglobin has been implicated in a range of pathological conditions including fibrosis and cancer, where cytoglobin expression is linked to hypoxia- and drug-resistance. One disease where there is evidence of a role for cytoglobin is head and neck squamous cell carcinoma, a disease with a poor prognosis and where clinical resistance to platinum-based drugs is common. In the current study, we demonstrate that 3D-culture of head and neck cancer cells (PE/CA-PJ41, Liv-22K) and normal oral keratinocyte cells (NOK-hTERT) results in changes in sensitivity to cisplatin cytotoxicity compared to 2D-cultures. Relative to 2D-cultures, 3D-cultures of PE/CA-PJ41 and NOK-hTERT cells demonstrated increased cisplatin resistance. In contrast, 3D-cultures of Liv-22K cells were more sensitive to cisplatin. Evidence for cisplatin genotoxicity was observed in the form of GADD45A activation in PE/CA-PJ41 cells but not the other two cell types investigated. Furthermore, DNA-strand breaks were also detected by the alkaline comet assay in PE/CA-PJ41 cells although levels were not significantly affected by 3D-culture. Cytoglobin expression levels were elevated by 3D-culture in all three cell lines investigated and there were also changes in expression of genes related to cell division (MKI57, GJB6), cell adhesion (CDH1), stress response (NFkB, NQO1) and apoptosis (Casp3) but these changes were cell line specific. We also observed consistent transcriptional activation of HIF1a in 3D-cultures of all three cell lines, suggestive of hypoxic conditions in spheroids. In support of a direct role of hypoxia in cytoglobin induction, the HIF1a stabiliser cobalt chloride also induced cytoglobin expression in spheroids. Transcriptomic profiling of PE/CA-PJ41 cells over-expressing cytoglobin identified 121 differentially regulated genes, when cells were cultured under hypoxic conditions. Major changes identified included, upregulation of G1/S cell cycle regulation as well as wnt- and RhoGTPase-signalling pathways. In conclusion, 3D-cultures are a useful model to further study the biological function of cytoglobin in head and neck cancer and we provide evidence that further supports a role for cytoglobin in hypoxia-dependent cellular proliferation and phenotypic changes that could contribute to cisplatin resistance in vivo.

Deoxynivalenol (DON) is a mycotoxin produced by Fusarium spp. that causes Fusarium head blight (FHB) disease in cereal crops. Ingestion of food contaminated with DON poses serious human health complications. However, the DON cytotoxicity has been mostly deduced from animal studies. In this study, we used the nematode Caenorhabditis elegans (C. elegans) as a tractable animal model to dissect the toxic effect of DON. Our results indicate that DON reduces the fecundity and lifespan of C. elegans. The real-time RT-PCR analysis showed that DON upregulates innate immunity-related genes including C17H12.8 and K08D8.5 encoding PMK-1 (mitogen activated protein kinase-1)-regulated immune effectors, and F35E12.5 encoding a CUB-like domain-containing protein. Furthermore, our RNAseq data demonstrate that out of ~ 17,000 C. elegans genes, 313 are upregulated and 166 were downregulated by DON treatment. Among the DON-upregulated genes, several are ugt genes encoding UDP-glucuronosyl transferase (UGTs) which are known to be involved in chemical detoxification. The three upregulated genes, F52F10.4 (oac-32), C10H11.6 (ugt-26) and C10H11.4 (ugt-28) encoding the O-acyltransferase homolog, UGT26 and UGT 28, respectively, are shown to contribute to DON tolerance by RNAi bacterial feeding experiment. The results of this study provide insights to the targets of DON cytotoxicity and potential mitigation measures.

The proliferation of deep learning in natural language processing (NLP) has led to the development and release of innovative technologies capable of understanding and generating human language with remarkable proficiency. Atinuke, a Transformer-based neural network, optimises performance across various language tasks by utilising a unique configuration. The architecture interweaves layers for processing sequential data with attention mechanisms to draw meaningful affinities between inputs and outputs. Due to the configuration of its topology and hyperparameter tuning, it can emulate human-like language by extracting features and learning complex mappings. Atinuke is modular, extensible, and integrates seamlessly with existing machine learning pipelines. Advanced matrix operations like softmax, embeddings, and multi-head attention enable nuanced handling of textual, acoustic, and visual signals. By unifying modern deep learning techniques with software design principles and mathematical theory, the system achieves state-of-the-art results on natural language tasks whilst remaining interpretable and robust.

Although there are several access control systems in the literature for flexible policy management in multi-authority and multi-domain environments, achieving interoperability &amp; scalability, without relying on strong trust assumptions, is still an open challenge. We present HMBAC, a distributed fine-grained access control model for shared and dynamic multi-authority and multi-domain environments, along with Janus, a practical system for HMBAC policy enforcement. The proposed HMBAC model supports: (a) dynamic trust management between different authorities; (b) flexible access control policy enforcement, defined at domain and cross-domain level; (c) a global source of truth for all entities, supported by an immutable, audit-friendly mechanism. Janus implements the HMBAC model and relies on the effective fusion of two core components. First, a Hierarchical Multi-Blockchain architecture that acts as a single access point that cannot be bypassed by users or authorities. Second, a Multi-Authority Attribute Based Encryption protocol that supports flexible shared multi-owner encryption, where attribute keys from different authorities are combined to decrypt data distributedly stored in different authorities. Our approach was implemented using Hyperledger Fabric as the underlying blockchain, with the system components placed in Kubernetes Docker container pods. We experimentally validated the effectiveness and efficiency of Janus, while fully reproducible artifacts of both our implementation and our measurements are provided.

Terpenes and terpenoids content in cannabis plant was already studied in the past with three used methods. Since these works did not compare the content of these substances under the same conditions, we tried to make this comparison exactly. Three different gas chromatography/mass spectrometry (GS/MS) methods - hexane based liquid extraction (Liq), static head-space extraction (HS), and head-space solid phase microextraction (SPME) – were compared to identify volatile compounds in four different cannabis chemotypes - Green fields, Titan chemotype, Black Domina chemotype, and Neptune chemotype. The main compounds focused on were monoterpenes/monoterpenoids and sesquiterpenes/sesquiterpenoids. Extraction with hexane gave comparable results, however the other two methods allowed for the identification of more substances. For the final evaluation of the comparison of the three methods of analysis, extraction with hexane gives comparable results (which is advantageous for quantitative analysis), although the other two methods allowed for the identification of more substances. This means that the same method should be used everywhere for the quantitative evaluation of constituents in cannabis.

Background: The laryngeal mask airway Vision Mask (LMA VM) is a supraglottic airway device (SAD) with vision guidance system. The ideal head and neck position for direct laryngoscopy is known, but the ideal position for placing a LMA is not. The objective of this study is to evaluate and compare the optimal position for placement of a video laryngeal mask airway. Methods: This was a prospective observational, transversal and analytical study of 72 consecutive patients. In the same patient, laryngeal vision was first assessed with the head and neck in the sniffing position and then with the head in the neutral position. Procedures were filmed and performed by the same investigator. Assessment of the laryngeal view was performed using two classifications: modified Cormack-Lehane classification and Brimacombe classification. Device placement was considered adequate when the Cormack-Lehane rating was scored from 1 to 2 and the Brimacombe rating was from 2 to 4. Results: 72 patients were included in this study. In the assessment of the glottis using the Cormack Lehane classification for fiberoptic view, laryngeal visibility was adequate in 64 (88.89%) patients in the neutral position and in 65 (90,28%) patients in the sniffing position (p>0,05). In the fiberoptic view of the glottis, assessed using the Brimacombe classification, laryngeal visibility was adequate in 68 (93%) patients in the neutral position and in 69 (95%) patients in the sniffing position, (p>0,05). There was no statistically significant difference in the rate of success between the sniffing position (70 patients, 97.22% success rate) and the neutral position (67 patients, 93.06% success rate) during the first insertion attempt. Two patients required a second attempt in the sniffing position, while five patients required a second attempt in the neutral position. Conclusion: An adequate sniffing position did not result in a better glottic view than neutral position, and the number and type of additional maneuvers were equal in both positions.

In the past decade, it has been discovered that intestinal flora can influence various organ-specific cancers by altering the body's energy balance, increasing obesity, synthesizing genetic toxins and small signaling molecules, initiating and modulating immune responses, and metabolizing indigestible dietary components, exogenous substances, and drugs. In this review, we will focus on elucidating the role of gut flora based on its molecular mechanisms and its potential therapeutic implications in head and neck tumors in the near future.

Arthroscopic capsular release (ACR) is used for persistent shoulder stiffness after an index surgery. No cases of post-ACR humeral head osteonecrosis have been reported to date. A 56-year-old male patient underwent open reduction and internal fixation using a hook plate for acromioclavicular joint dislocation. Despite hardware removal, the patient presented with unresolved shoulder pain and range-of-motion (ROM) limitation. He had a history of hypertension, chronic hepatitis B infection, and alcohol consumption. Preoperative ROM was forward flexion 90°and internal rotation sacral level. Preoperative functional status was visual analog scale (VAS) score 4, American Shoulder and Elbow Surgeons (ASES) score 51, and Constant–Murley (CMS) score 48, and normal radiography and magnetic resonance imaging. Standard ACR was performed with 360° release of the joint capsule via electrocautery ablation. Six-months post-ACR, ROM (forward flexion: 135°, abduction: 135°, external rotation: 70°, internal rotation: T10 vertebra) and functional outcomes (VAS 2; ASES79; CMS 75) were significantly improved, without interval change in radiographic assessment. However, 21 months later, he had recurrent shoulder pain and decreased ROM. Radiography revealed humeral head osteonecrosis. Patients with intrinsic or extrinsic risk factors related to humeral head circulation disturbance should be monitored for humeral head osteonecrosis post-ACR.

The Atlantic puffin is a medium-sized seabird with black and white plumage and orange feet. It is distributed mainly along the northern Atlantic Ocean and due, among other reasons, to human activities, it is in a threatened situation and classified as a vulnerable species according to the International Union of Conservation of Nature (IUCN). In this study, we used a total of 20 carcasses of juvenile Atlantic puffins to perform anatomical cross sections and magnetic resonance images. Thus, an adequate description of the head was made, providing valuable information that could be helpful as a diagnostic tool for veterinary clinicians, who increasingly treat these birds in zoos, rehabilitation centers and even in the wild.

This study assessed the relationship between head posture displacements and biomechanical parameters in three different tasks. One hundred male and female students (20 ± 3yrs) were assessed via the PostureScreen Mobile app to quantify postural displacements of head rotations and translations including: 1) the cranio-vertebral angle (CVA) (°), 2) anterior head translation (AHT) (cm), 3) lateral head translation in the coronal plane (cm), and 4) lateral head side bending (°). Biomechanical parameters during gait and jumping were measured using the G-Walk sensor. The assessed gait spatiotemporal parameters were cadence (steps / min), speed (m / s), symmetry index, % left and right stride length (% height), and right and left propulsion index. The pelvic movement parameters were: 1) tilt symmetry index, 2) tilt left and right range, 3) obliquity symmetry index, 4) obliquity left and right range, 5) rotation symmetry index, and 6) rotation left and right range. The jump parameters measured were: 1) flight height (cm), 2) take off force (kN), 3) impact Force (kN), 4) take off speed (m / s), 5) peak speed (m / s), 6) average speed concentric phase (m / s), 7) maximum concentric power (kW), 8) average concentric power (kW) during the counter movement jump (CMJ), and 9) CMJ with arms thrust (CMJAT). At a significance-level of p ≤ 0.001, moderate to high correlations (0.4 &lt; r &lt; 0.8) were found between CVA, AHT, lateral translation head and all the gait and jump parameters. Weak correlations (0.2 &lt; r &lt;  0.4) were ascertained for lateral head bending and all the gait and jump parameters except for gait symmetry index and pelvic symmetry index, where moderate correlations were identified (0.4 &lt; r &lt; 0.6). The findings indicate moderate to high correlations between specific head posture displacements, such as CVA, lateral head translation and AHT with the various gait and jump parameters. These findings highlight the importance of considering head posture in the assessment and optimization of movement patterns during gait and jumping. Our findings contribute to the existing body of knowledge and may have implications for clinical practice and sports performance training. Further research is warranted to elucidate the underlying mechanisms and establish causality in these relationships, which could potentially lead to the development of targeted interventions for improving movement patterns and preventing injuries.

Cancers of the head and ne¬ck and their treatment can cause disfigurement and loss of functioning, with a profound negative impact on the person’s self-image and psychosocial wellbeing. This can lead to experiences of shame and stigma, which are important targets for psychosocial interventions. Accurate measurement and identification of these problems enables clinicians to offer appropriate interventions and monitor patients’ progress. This study aimed to validate the Canadian version of the Shame and Stigma Scale (SSS) among French and English speaking head and neck cancer patients. Data from 258 patients from two major Canadian hospitals was analysed. The existing 4-factor structure of the SSS was supported, with the following subscales: Shame with Appearance, Sense of Stigma, Regret, and Social/Speech Concerns. The Canadian SSS showed adequate convergent and divergent validity and test-retest reliability. Rasch analysis suggested scale improvement by removing two misfitting item and two items with differential functioning between French and English speaking patients. The final 16-item scale version had adequate fit to the Rasch model. The SSS provides more accurate measures for people with high levels of shame and stigma, and thus has utility in identifying patients with more severe symptoms who may be in need of psychosocial interventions.

Chemoradiotherapy (CRT) is the standard treatment for locally advanced head and neck cancer; however, CRT may cause post-treatment dysphagia. Transcutaneous electrical sensory stimulation (TESS), developed in recent years for swallowing rehabilitation, has been used at many medical facilities. Although TESS has been used for dysphagia in several fields, its safety and efficacy in patients with head and neck cancer remain to be clarified. Therefore, this study evaluated the safety of TESS in ten patients with head and neck cancers undergoing CRT. Swallowing rehabilitation intervention and TESS implementation were performed for all patients during CRT. Non-blood toxicity adverse events (AEs), such as dermatitis and mucositis, occurred during CRT; however, the severity was less than Grade 3. No patient experienced pain due to TESS. As survival time analysis using the Kaplan–Meier method for interferential current device implementation rates revealed a feasibility of 100% for up to 60 Gy and a feasibility of 78% for up to 70 Gy, it concludes that TESS was feasible until 70 Gy. This study confirmed the feasibility and safety of TESS in the head and neck region, even during CRT. Although the precise mechanism of TESS on dysphagia is not yet clear, its continued use has great potential for improving sensory disturbance.

Chondrosarcoma, represents approximately 0.1% of all neoplasms of the head and neck, being considered a rare disease, but with a relatively good prognosis. The 5-year overall survival (OS) rate is estimated at 70-80%, being considered a disease with a low growth rate. About 13% of all cases of chondrosarcoma, are located in the region of the head and neck. We present the case of a young patient treated multimodally, lost from the oncological records less than two years after the diagnosis, without evidence of disease progression. The early loss from the oncological follow-up programs of patients with a lower risk of early repalpse, without evidence of a recurrence or metastasis could be the consequence of a major complication among which we could assume an aspiration pneumonia secondary to a dysphagia, associated with an aggressive multidisciplinary treatment. Large tumor size and positive resection margins (R1 resection) are risk factors that support an intensive adjuvant approach in order to reduce the risk of recurrence, but the low grade of tumor, associating a lower risk of recurrence as well as the adverse events (AE) of adjuvant radiotherapy and chemotherapy justifies a more reserved therapeutic approach. Taking into account the longer life expectancy of these patients, it is recommended to use a more conformal irradiation technique in order to reduce doses to radiosensitive structures as well as to omit elective neck irradiation, taking into account the lower risk of lymph node involvement. The lack of guidelines that include very rare tumors including low grade chondrosarcoma of head and neck make a unified approach difficult, but the data presented even in case reports could contribute to choosing the regimen that offers the best therapeutic ratio.

Oculomotor Dysfunctions (OMDs) are problems with eye-musculature relating to coordination and accuracy of eye movements. Eye-tracking (ET) technologies show great promise in the identification of OMDs. Current computer technologies for vision screening are specialized devices or laptop based technologies with limited by screen size and the inability to measure depth. In this experimental study, we examine the possibilities of immersive virtual reality (VR) technologies for increased user experiences, presence, immersiveness, and the use of serious games. Further results of an experimental study present increased interest in VR-based screening, its ability to focus better, and aspects motivating for its use, despite the actual limitations of current technologies. These limitations currently include lower performance and confidence in results of the used HMDs. Some users also describe being more focused when screening in VR, free from outside distractions. Using serious games for screening in VR is also estimated to have great potential for developing a more robust vision screening tool, especially for younger children.

Most head and neck cancers are diagnosed in advanced stages, when the curative interventions are no longer possible. The assessment and the multidisciplinary therapeutic approach of the locally advanced cases are difficult since the onset of the disease becomes a significant challenge for the whole attending team (including both the patient and his family) because they have to deal with to an extensive symptomatology (abundant secretions, refractory pain, mutilations predominantly in the cephalic area), with the cure of stomas (tracheostomy, gastrostomy) and with various post-therapeutic sequelae. The present paper reflects the impact of late diagnosis, the complexity of the multimodal specific oncological treatment (chemotherapy, radiotherapy, immunotherapy, pal-liative and terminal treatment), the adaptation of medical care to the psycho-emotional structure, having as an example the case of 54-year old patient (with his consent) diagnosed with pharyn-golaryngeal neoplasm who presented himself to “St. Luca” Chronic Disease Hospital, the Oncol-ogy-Palliative Care Department, in September 2021. In patients with locally advanced head and neck cancers, besides the specifically oncological therapeutic decisions, the empathic-emotional connection between the medical team with the patient and his family plays a key role in the ther-apeutic conduct.

Fusarium Head Blight (FHB) is a destructive disease affecting the grain yield and quality of wheat, barley, rye and triticale. Developing varieties with genetic resistance is integral to successfully managing FHB. However, significant knowledge gap exists in the genetic diversity present in triticale for FHB resistance. This information is critical for breeding new varieties of triticale as its production continues to increase. In the present study, a set of 298 winter triticale accessions from a worldwide collection were screened for their type-2 FHB resistance in an artificially inoculated misted nursery with high levels of inoculum density. Most of the triticale accessions were susceptible to FHB, and only 8% of accessions showed resistance in the field nursery screening. The resistant accessions identified in the nursery screening were selected and further screened for three years in greenhouse conditions. Seven accessions were found to show robust FHB resistance over the three years of greenhouse testing. Thirteen accessions showed significantly lower levels of Deoxynivalenol accumulation when compared to the susceptible triticale control. The accessions identified in the study will be useful in triticale and wheat breeding programs for enhancing FHB resistance and reducing DON accumulation.

Abstract: Background: Intensity modulated radiotherapy (IMRT) is still a standard of care for radiotherapy in locally advanced head and neck cancer (LA-HNSCC). Simultaneous integrated boost (SIB) and moderate hypofractionation offer an opportunity for individual dose painting and a reduction in overall treatment time. We present retrospective data on toxicity and locore-gional control of a patient cohort with LA-HNSCC treated with an IMRT-SIB concept in compar-ison to normofractionated 3D-conformal radiotherapy (3D-RT) after a long-term follow-up. Methods: Between 2012 and 2014, n=67 patients with HNSCC (stages III/IV without distant me-tastases) were treated with IMRT-SIB either definitively (single/total doses: 2.2/66 Gy, 2.08/62.4 Gy, 1.8/54 Gy in 30 fractions) or in the postoperative setting (2.08/62.4 Gy, 1.92/57.6 Gy, 1.8/54 Gy). These patients' clinical courses were matched (for sex, primary, and treatment concept) as part of a matched-pair analysis with patients treated before mid-2012 with normofractionated 3D-CRT (definitive: 2/50 Gy followed by a sequential boost up to 70 Gy; postoperative: 2/60-64 Gy). Chemotherapy/ immunotherapy was given concomitantly in both groups in the definitive situation (postoperative dependent on risk factors). The primary endpoints were acute and late toxicity; the secondary endpoint was locoregional control (LRC). Results: Sixty-seven patients treated with IMRT-SIB (n = 20 definitive, n = 47 adjuvant) were matched with 67 patients treated with 3D-RT. There were minor imbalances between the groups concerning nonmatching variables such as extracapsular extension (ECE) and chemotherapy in IMRT-SIB. Significantly less toxicity was found in favor of IMRT-SIB concerning dysphagia, ra-diation dermatitis, xerostomia, fibrosis, and lymphedema. After a median follow-up of 63 months, the median LRC was not reached (IMRT-SIB) vs. 69.5 months (3D-RT) (p=0.63). Conclusions: This moderately hypofractionated IMRT-SIB concept was shown to be feasible with less toxicity than conventional 3D-RT in this long-term follow-up observation.

Background: The main factors to influence on the stroking performance of club head are alloy and spring-like effect. Design the structure of new club face to create the fairway wood which can show best stroking feeling and let driving distance become longer. Purpose: The club face of fairway wood is processed through the iron alloy heating procedure and CNC to innovate the club head with high spring-like effect (characteristic time). Method: (1) Use 455 stainless steel processed by heating and aging treatment, to do the analysis on microstructure and mechanical properties. (2) Through CNC to design seven different patterns for the back of club face, each pattern for three club head. The total amount is twenty-one. (3) Make finished product be test the characteristic time and the ability of strike by the practical measurement. (4) To analyze after the compare between three-way ANOVA and LSD. Results: After 455 stainless steel was heated in procedure S850℃-A550℃, mechanical properties became superior and had the better malleability, it is suitable for developing the club face of high spring-like effect. Before and after grinding among three-way ANOVA (pattern*thickness*place), did not achieve the significant level on the figure of characteristic time. Through two-way ANOVA (pattern*place and thickness*place), showed the significant deviation before and after grinding. After doing the crash test by 45m/s, all the results can reach the higher limited standard. Summary: 455 stainless steel was designed with the different club face’s thicknesses of fairway wood can make spring-like effect achieve high characteristic time. The much thinner club face let the characteristic time perform highly. But there was no obvious effects from the patterns of the back club face.

Fusarium head blight (FHB) disease is extensively distributed worldwide. This disease damages grain quality and reduces yield. The detection of this disease in a high throughput way is crucial to planters and breeders. Our study focused on developing a method for processing wheat color images and accurately detecting disease areas using deep learning and image processing techniques. The color images of wheat at the milky stage were collected and processed to construct datasets, which were used to retrain a deep convolutional neural network model using transfer learning. Testing results showed that the model can detect spikes, and the coefficient of determination of the number of spikes between the manual count and the detection was 0.80. The model was assessed, and the mean average precision for the testing dataset was 0.9201. On the basis of the results of spike detection, a new color feature was applied to obtain the gray image of each spike. Then, a modified region growing algorithm was implemented to segment and detect the diseased areas of each spike. Results show that the region growing algorithm performs better than K-means and Otsu’s method in segmenting the FHB disease. Overall, this study demonstrates that deep learning techniques enable the accurate detection of FHB in wheat using color images, and the proposed method can effectively detect spikes and diseased areas, thereby improving the efficiency of FHB detection.

The aim of the study was to determine the presence Fusarium species and mycotoxins in winter wheat grain in Poland. Grain samples from different locations in Poland in 2009 and 2010 were analysed for the content of biomass of Fusarium species and mycotoxins. In 2009 biomass of F. graminearum and F. poae was present in all samples, F. culmorum in 82% of samples, F. avenaceum in 55% of samples. F. sporotrichioides, F. tricinctum and F. equiseti were found only in individual samples. F. langsethiae was not detected. In 2010, five Fusarium species were detected with the exception of F. sporotrichioides. The highest content of biomass was found for F. graminearum followed by F. avenaceum, F. poae and F. langsethiae. The amount of F. culmorum biomass was very low. The most frequently occurring species was F. poae and F. graminearum. In 2009, deoxynivalenol was detected in all samples. In 2010, the average content of deoxynivalenol was lower than in 2009. Nivalenol was detected at very low concentration in both years. Significant correlations between content of F. graminearum biomass and deoxynivalenol concentration in grain and between content of F. poae biomass and nivalenol concentration in grain in 2009 were found. The most important finding of this study was that main Fusarium species infecting wheat kernels in Poland in both years was F. graminearum. The amount of biomass of F. graminearum was the highest in both years. It was present in the most samples. The other frequently detected species was F. poae, which in 2010 appeared in more samples than F. graminearum. However, the amount of F. poae biomass was lower. F. culmorum, species that was previously dominating as wheat pathogen in Poland, was found less frequently than F. graminearum. The amount of biomass of this species was the lowest in 2010.

To define the optimistic multiple-granulation (OMG) supra interior operator and the OMG-supra closure operator associated with a set A in a multiple-granulation approximation space, we used the concepts of the OMG-lower set OL (A) and the OMG-upper set OU (A). These operators create an approximate topological space known as optimistic multiple-granulation supratopological spaces. OMG-supra-open and OMG-supra-closed approximation sets are also defined, followed by OMG-supra-continuous approximation and OMG-irresolute approximation.

The paper proposes TOPSIS method for solving multi-objective multi-level programming problem (MO-MLPP) with fuzzy parameters via fuzzy goal programming (FGP). At first, - cut method is used to transform the fuzzily described MO-MLPP into deterministic MO-MLPP. Then, for specific , we construct the membership functions of distance functions from positive ideal solution (PIS) and negative ideal solution (NIS) of all level decision makers (DMs). Thereafter, FGP based multi-objective decision model is established for each level DM for obtaining individual optimal solution. A possible relaxation on decisions for all DMs is taken into account for satisfactory solution. Subsequently, two FGP models are developed and compromise optimal solutions are found by minimizing the sum of negative deviational variables. To recognize the better compromise optimal solution, the concept of distance functions is utilized. Finally, a novel algorithm for MO-MLPP involving fuzzy parameters is provided and an illustrative example is solved to verify the proposed procedure.

In a meta-analysis, we examined the behavioral portfolio of invasive brown anoles (Anolis sagrei) and native green anoles (Anolis carolinensis) in urban and non-urban environments. We hypothesized that invasive anoles should display more agonistic and bold signals (head bobbing, dewlap extensions and pushups) than their native range counterparts, and their native competitors. We found that in urban settings, anoles of both species signaled more with dewlap extensions than with head-bobs. The brown anoles displayed significantly higher in non-urban habitats and their native range compared to urban habitats and invasive ranges. The outcome of our analysis suggests that brown anoles have plastic behavioral portfolios, whereas the green anole have relatively balanced preferences for head-bobbing irrespective of the habitat in which the populations were collected. We attribute the success of the brown anole invasion to the flexible strategy in face of higher mate competition, higher predation risks, and less resource competition in the urban environments and invasive ranges. Lastly, we observed publication biases. More studies are conducted with urban and invasive brown anoles and specifically in manipulative mesocosm experimental settings - transplanting populations from native field settings. We show this altered the display rates across all studied signals.

This study presents head losses (hL) and loss coefficients (K) obtained experimentally for schedule 40 PVC pipe elbows, with 90o and 45o in small commercial diameters of ½ to 1½ inches that are commonly used in small networks. The investigation was conducted on a system constructed with short pipes and fittings necessary to measure, direct, and control flow, as well as to evaluate pressure head losses at elbows. Simple water flow was used with rates of 0.342 to 0.980 lps (Q), which generated moderate Reynolds numbers (Re) of 13337 to 80221. It was found that hL showed dependence on the velocity head in all elbows, as well as that hL are greater as the diameter increases for same velocity head. The K values in the elbows tended to be constant when Q &gt; 0.75 lps, but for Q &lt; 0.75 lps the K coefficient increased inversely proportional to Q, becoming larger as the diameter increased. Likewise, the K coefficient tended to increase as Re decreased, while K tended to be constant as Re increased. The results of the experimental coefficient were compared against K values reported in the literature, where significant similarities and discrepancies were found.

Abstract. Leiomyoma is a benign tumour of smooth muscle cells classified into three groups: solid leiomyoma, angioleiomyoma and leiomyoblastoma. Angioleiomyoma arises from the tunica media of veins and arteries of vascular smooth muscle.Nasal cavity, paranasal sinus and oral cavity are the most frequent locations in Head and Neck region. Here we present the case of a tongue base angioleiomyoma with an emphasis on clinical data, pathologic findings and surgical approach. The patient complained about a one-month of globus sensation, dysphagia and hemoptysis without other systemic symptoms. Laryngoscopy revealed the presence of a 2 cm solitary submucosal mass located on the left tongue base covered with normal mucosa. The patient underwent direct laryngoscopy to perform a biopsy for the purpose of achieving the conclusive diagnosis. Treatment options were discussed, included surgical resection. Transoral robot-assisted resection was performed without complications. Clearly the description of our case does not want to have a purpose in terms of indications but wants to describe the possibility of considering TORS in the case of benign tumours of the tongue base, especially in cases like this where a standard technique can difficulty be achieved. Current literature review was provided and discussed.

Weight and age interact to modify cognitive effects of head acceleration events after two seasons of youth football Abstract This is a follow-up study of youth American football players in a second season of play. Season-one findings identified a relationship between a head acceleration measure (HITsp) and cognitive score changes with weight modifying the effect in 9 to 10 year-olds. Sixty-eight youth completed a second season of play wearing helmet-mounted sensors and were assessed with neuropsychological tests pre- and post-season. Regression analysis of the full sample demonstrated a small but significant negative effect of HAE on cognition as indexed by test score changes: R2 = .06, F = 4.06, p = .024. Outcome differences between those who started playing at ages nine to 10, compared to those who started between 11 and 13 were identified: t(66) = -3.39, p<.01, d = -.84, 95th CI -2.77 to -.72. Regression models including players’ weights found that the relationship of weight to outcome was different by group: greater negative cognitive effects were found in younger-heavier players (R2 = 0.21, F =3.21, p = 0.03) and older-lighter players (R2 = 0.18, F =4.26, p <.001). These findings confirmed a negative relationship of HAE and cognitive change and point to player weight as an important developmental factor to consider in understanding concussion biomechanics.

BACKGROUND: Head and Neck Sarcomas (HNSs) are rare malignant tumors derived from mesenchymal cells characterized by rapid growth, local aggressive behavior, and multiple re-lapses. The Rhabdomyosarcoma (RMS) and the Osteosarcoma (OS) are the more frequent HNS. The present study aims to evaluate the clinical impact of multimodal treatment in patients diagnosed with HNS. METHODS: A retrospective analysis of medical records from patients diagnosed with HNS multimodal treated, four groups were formed: a) surgery and chemotherapy (CTX); b) surgery, CTX, and RT; c) surgery and RT; d) CTX and RT exclusively. The overall survival (OSS), disease-free survival (DFS) and progression-free survival (PFS) rates were calculated using the Kaplan-Meier test. RESULTS: Among 39 patients, 23.1% were diagnosed with RMS , 66.7% with OS and 10.3% with other subtypes of HNS. Moreover, 59% showed initial clinical staging (T1/T2) and of those 35.9% were T1. Surgery was performed in 87.2% patients, RT in 35.9% and CTX in 84.7%. RT performed showed not statistically significant clinical role regarding OSS (p=0.74), DFS (p=0.09) and PFS (p=0.90) rates independent of histological subtype. Finally, OSS (p=0.89), DFS (p=0.13) and PFS (p=0.50) rates were not statistically different for patients treated with monotherapy or multimodal therapy. CONCLUSION: RT has not showed a clinically important role referred to improve OSS, DFS and PFS rates in patients with HNS, regardless of the histological subtype of the primary tumor. Multimodal treatment could influence in survival rates and locoregional control of the disease; however, longitudinal studies such as randomized clinical trials must be conducted.

Researchers achieved high torque volume ratios in magnetorheological (MR) fluid braking devices by introducing multiple layers and multiple poles concepts. The number of magnetic poles, pole geometry and placement have a significant effect on these multi-pole MR brake performances. This research work focused on the effect of the pole head geometry in a multi-pole dual-disc MR brake for the enhancement of braking torque. The magnetic field generated by the electromagnetic coil has to penetrate through pole heads and travel orthogonally through the rotor and MR fluid layers to achieve maximum brake torque. This flow depends on the profiles of the magnetic pole head area and corresponding core dimensions. This study considered pentagon, square, hexagon, trapezoid and ellipse pole head profiles in place of the conventional circular pole and the best possible core dimensions were used for each geometry. A three-dimensional electromagnetic analysis of the MR brake is conducted to simulate the brake performance. Results showed that the hexagonal and elliptical pole heads are having better magnetic field area, magnetic intensity distribution and higher magnetic flux lines piercing through the MR fluid perpendicularly than the circular and square pole heads. An increment of 3.88% for braking torque performance is obtained, and the braking torque is significantly enhanced.

Detecting human head movement during sleep is important as it can help doctor to access some physical or mental health problems like infantile eczema, calcium deficiency, insomnia, anxiety disorder and even Parkinson’s disease, and provide useful clues for scientific diagnosing. To obtain the information of sleep head movement, current solutions either use the camera or require the user to wear intrusive sensors to collect the image or motion data. However, the vision-based schemes depend on the environmental light conditions and also bring privacy concerns. Some people including the elderly and infant may be reluctant to wear wearable devices during sleep. In this paper, we propose a novel system called Wi-Senser to address the issues mentioned above. Our Wi-Senser directly reuses the existing WiFi infrastructure and exploits the fine-grained channel state information (CSI) of WiFi signals to capture the minute human head movement during sleep without attaching any sensors to the human body. We design and implement our system with just one commercial off-the-shelf (COTS) router and one laptop equipped with Intel 5300 network interface card (NIC). We evaluate the performance of Wi-Senser with 6 volunteers (4 adults and 2 children). Extensive experiments demonstrate that Wi-Senser can achieve a 98.5% accuracy for head movement detection during sleep. Wi-Senser provides a new solution for achieving noninvasive, continuous and accurate human minute movement detection without any additional cost.

In foggy weather scenarios, the scattering and absorption of light by water droplets and particulate matter cause object features in images to become blurred or lost, presenting a significant challenge for target detection in autonomous driving vehicles. To tackle this problem, this study proposes a foggy weather detection method, YOLOv5s-Fog, based on the YOLOv5s framework. The model enhances the feature extraction and expression capabilities of YOLOv5s by introducing a novel target detection layer, SwinFocus. Additionally, this research incorporates decoupled head into the model and replaces the conventional non-maximum suppression method with Soft-NMS. Experimental results demonstrate that these improvements effectively enhance the detection performance for blurry objects and small targets in foggy weather conditions. Compared to the baseline model YOLOv5s, YOLOv5s-Fog achieves a 5.4% increase in mAP on the RTTS dataset, reaching 73.4%. This method provides technical support for rapid and accurate target detection in adverse weather conditions, such as foggy weather, for autonomous driving vehicles.

Oral mucositis (OM) is inflammation of the mouth caused by damage to the mucous membranes that line the mouth and throat. It is a side effect of cancer treatment, particularly in patients with head and neck squamous cell carcinoma (HNSCC) who undergo radiotherapy, chemotherapy, and/or immunotherapy with immune checkpoint inhibitors. The etiology and pathogenic mechanisms of OM is complex and multifaceted, involving cytotoxicity (cell death), inflammation, infection, change in microbiome, and immune-mediated cytotoxicity. We summarize the literature about attempts to use various omics methodologies (genomics, transcriptomics, microbiomics and metabolomics) to elucidate the biological pathways associated with the development or the severity of OM. Integrating different omics into multi-omics approaches carries the potential to discover links among host factors (genomics), host responses (transcriptomics, metabolomics), and local environment (microbiomics).

Background: Sinonasal squamous cell carcinoma (SNSCC) is rare in the general population. No clear and consistent etiologic correlation between human papillomavirus and SNSCC has yet been delineated in literature. p16 is a tumour suppressor protein used as a surrogate marker for HPV. This study aims to evaluate the relationship between p16 overexpression in SNSCC and its role in prognosis and survival. Methods: A population-based retrospective analysis was performed using prospectively collected data from the Northern Alberta Head and Neck Tumour Board, Alberta Cancer Registry, and Alberta Cancer Research Biobank. p16 overexpression was analyzed from pathologic sample of patients meeting study criteria, and participants were dichotomized by status. Subsequently, nonparametric analysis of demographics, initial staging, and initial treatment were performed, and a Kapan-Meier curve was developed to assess differences in survival. Results: 16 patients were included in analysis. p16 overexpression was seen in 68.8% of patients. p16 positive and negative groups were comparable for age, gender, smoking status, stage, and treatment. A statistically significant five-year survival advantage was observed in patients with p16 positive SNSCC (p = 0.013). Conclusions: This is the first Canadian study to demonstrate a high prevalence of p16 positivity in SNSCC and its presence denoting a statistically significant survival advantage. Results demonstrate a previously unconfirmed role of oncogenic HPV in SNSCC.

Fusarium head blight (FHB) can cause contamination of cereal grain with mycotoxins. Triticale is also infected with FHB; however, it is more resistant than wheat to head infection. The aim of this study was to identify triticale lines that combine low head infection with low toxin contamination. Resistance to FHB of 15 winter triticale and three winter wheat lines was evaluated over a three-year experiment established in two locations. At the anthesis stage, heads were inoculated with Fusarium culmorum isolates. The FHB index was scored and the percentage of Fusarium-damaged kernels (FDKs) assessed. The grain was analysed for type B trichothecenes (deoxynivalenol and derivatives, nivalenol) and zearalenone content. The average FHB index was 10.7%. The proportion of FDK was 18.1% (weight) and 21.6% (number). An average content of deoxynivalenol for wheat amounted to 7.258 mg/kg and nivalenol to 5.267 mg/kg. In total, it was 12.788 m/kg of type B trichothecenes. The zearalenone content in the grain was 0.805 mg/kg. Relationships between FHB index, FDK and mycotoxin contents were statistically significant for triticale lines; however, they were stronger for FDK versus mycotoxins. Lines combing all types of FHB resistance were found, and two of them had resistance similar to that of wheat lines with the Fhb1 gene.

This paper examines the spatial and temporal distribution of all COVID-19 cases from January to June 2020 against the underlying distribution of population in the United States. It is found that, as time passes, COVID-19 cases become a power law with cut-off, resembling the underlying spatial distribution of populations. The power law implies that many states and counties have a low number of cases, while only a few highly populated states and counties have a high number of cases. To further differentiate patterns between the underlying populations and COVID-19 cases, we derived their inherent hierarchy characterized by the ht-index. We found that the ht-index of COVID-19 cases persistently approaches that of the populations; that is, 5 and 7 at the state and county levels, respectively. Mapping the ht-index of COVID-19 cases against that of populations provides new insights into the development of the pandemic in the United States.

Conceived and developed by Christopher Alexander through his life’s work: The Nature of Order, wholeness is defined as a mathematical structure of physical space in our surroundings. Yet, there was no mathematics, as Alexander admitted then, that was powerful enough to capture his notion of wholeness. Recently, a mathematical model of wholeness, together with its topological representation, has been developed that is capable of addressing not only why a space is good, but also how much goodness the space has. This paper develops a structural perspective on goodness of space – both large- and small-scale – in order to bridge two basic concepts of space and place through the very concept of wholeness. The wholeness provides a de facto recursive definition of goodness of space from a holistic and organic point of view. A space is good, genuinely and objectively, if its adjacent spaces are good, the larger space to which it belongs is good, and what is contained in the space is also good. Eventually, goodness of space – sustainability of space – is considered a matter of fact rather than of opinion under the new view of space: space is neither lifeless nor neutral, but a living structure capable of being more living or less living, or more sustainable or less sustainable. Under the new view of space, geography or architecture will become part of complexity science, not only for understanding complexity, but also for making and remaking complex or living structures.

Authorities define cities – or human settlements in general – through imposing top-down rules in terms of whether buildings belong to cities. Emerging geospatial big data makes it possible to define cities from the bottom up, i.e., buildings determine themselves whether they belong to a city based on the notion of natural cities that is defined based on head/tail breaks, a classification and visualization tool for data with a heavy-tailed distribution. In this paper, we used 125 million building locations – all building footprints of America (mainland) or their centroids more precisely – to derive 2.1 million natural cities in the country (http://lifegis.hig.se/uscities/). These natural cities – in contrast to government defined city boundaries – constitute a valuable data source for city-related research.

Purpose: The aim of present study was to compare the treatment results of daily cisplatin (CDDP), weekly docetaxel (DOC) intra-arterial infusion chemotherapy combined with radiotherapy (DIACRT) regimen and weekly CDDP intra-arterial infusion chemotherapy combined with radiotherapy (WIACRT) for patients with tongue cancer. Materials and Methods: Between January 2007 and December 2016, a total of 11 patients treated with WIACRT and 45 patients treated with DIACRT were enrolled in present study. In DIACRT group, 25 patients had T2, 20 patients had T3. A total of 9 patients had T2 and 2 had T3 in WIACRT (p = NS). In DIACRT, the treatment schedule consisted of intra-arterial chemotherapy (DOC, total 60 mg/m²; CDDP, total 150 mg/m²) and daily concurrent radiotherapy (RT) (total, 60 Gy). In WIACRT, the treatment schedule consisted of intra-arterial chemotherapy (CDDP, total 360 mg/m²) and daily concurrent RT (total, 60 Gy). Results: The median follow-up periods for DIACRT and WIACRT were 61 and 66 months respectively. The 5-year local control (LC) and overall survival (OS) rate were 94.5% and 89.6% for DIACRT group, 60.6% and 63.6% for WIACRT group respectively. The LC rate and OS of DIACRT group were significantly higher than that of WIACRT group. As regards toxicities, no treatment-related deaths were observed during the follow-up periods both in two groups. Conclusions: DIACRT was found to be feasible and effective for patients with tongue cancer and could become a new treatment modality.

This work focuses on the design improvement of a tri-axial piezoresistive accelerometer specifically designed for head injuries monitoring where medium-G impacts are common, for example in sports such as racing cars or American Football. The device requires the highest sensitivity achievable with a single proof mass approach, and a very low error (<1%) as the accuracy for these types of applications is paramount. The optimization method differs from previous work as it is based on the progressive increment of the sensor mass moment of inertia (MMI) in all three axes. The work numerically demonstrates that an increment of MMI determines an increment of device sensitivity with a simultaneous reduction of cross-talk in the particular axis under study. The final device shows a sensitivity increase of about 80% in the Z-axis and a reduction of cross-talk of 18% respect to state-of-art sensors available in the literature. Sensor design, modelling and optimization are presented, concluding the work with results, discussion and conclusion.

As Heterogeneous Wireless Sensor Network (HWSN) fulfill the requirements of researchers in the design of real life application to resolve the issues of unattended problem. But, the main constraint face by researchers is energy source available with sensor nodes. To prolong the life of sensor nodes and hence HWSN, it is necessary to design energy efficient operational schemes. One of the most suitable routing scheme is clustering approach, which improves stability and hence enhances performance parameters of HWSN. A novel solution proposed in this article is to design energy efficient clustering protocol for HWSN, to enhance performance parameters by EECPEP-HWSN. Propose protocol is designed with three level nodes namely normal, advance and super node respectively. In clustering process, for selection of cluster head we consider three parameters available with sensor node at run time, i.e., initial energy, hop count and residual energy. This protocol enhance the energy efficiency of HWSN, it improves performance parameters in the form of enhance energy remain in the network, force to enhance stability period, prolong lifetime and hence higher throughput. It is been found that proposed protocol outperforms than LEACH, DEEC and SEP with about 188, 150 and 141 percent respectively.

We develop an interactive likelihood (ILH) for sequential Monte-Carlo (SMC) methods for image-based multiple target tracking applications. The purpose of the ILH is to improve tracking accuracy by reducing the need for data association. In addition, we integrate a recently developed deep neural network for pedestrian detection along with the ILH with a multi-Bernoulli filter. We evaluate the performance of the multi-Bernoulli filter with the ILH and the pedestrian detector in a number of publicly available datasets (2003 PETS INMOVE, AFL, and TUD-Stadtmitte) using standard, well-known multi-target tracking metrics (OSPA and CLEAR MOT). In all datasets, the ILH term increases the tracking accuracy of the multi-Bernoulli filter.

Background: Recent studies have found forward head posture (FHP) to be associated with altered physiology, however, there is a lack of research into whether FHP is associated with altered gait parameters when cognitively challenged. The current study was undertaken to investigate the changes in gait parameters in asymptomatic individuals with FHP when a cognitive dual task is introduced during walking. Methods: Ninety asymptomatic participants were recruited, 45 having FHP defined as having a craniovertebral angle (CVA) &lt; 50, and 45 sex and age-matched controls having normal head posture (NHP) defined as CVA &gt; 55. All participants walked along a 10 meter platform under a control condition (no cognitive load) with 3 consecutive trials while an optical motion-capture system consisting of 8 infrared cameras was used to assess gait kinematics. Secondly, participants were assessed under a dual task cognitive load condition (3 trials while performing simple addition, counting backwards, and answering yes or no questions) to identify the impact on gait kinematics compared to the control condition. Results: Under the single task condition, there were no significant differences for any gait parameter, however, under the dual task condition, 12/13 gait parameters were significantly altered for the FHP group compared to the NHP group (p &lt; .01). A calculation of dual task cost (DTC) percentage showed significant increases in all gait parameters in participants with FHP (p &lt; .02). Correlations between the CVA and gait parameters were not significant for the single task condition, but all gait parameters were correlated to CVA for the dual task condition (p &lt; .01). The correlation between CVA and DTC for all gait variables were also significant (p &lt; .04). Conclusions: This study demonstrates that FHP significantly increases the cognitive cost during walking, highlighting the importance of proper postural alignment for maintaining cognitive function under a dual task condition. Addressing FHP through targeted interventions could improve both physical and cognitive health, enhancing overall well-being and daily functioning.

Previously, pseudomyogenic hemangioendothelioma (PMHE) is regarded as epithelioid sarcoma-like hemangioendothelioma due to significant histologic overlapping and diffuse cytokeratin expression since the first description in 1992. After considerable controversy and long-term debating, it is currently defined as a distinct intermediate malignant, rarely metastasizing neoplasm with vascular/endothelial differentiation in nature. PMHE is characterized by young age (typically < 40 years old) and extremity location (approximately ~80%) with t(7:19) SERPINE1-FOSB fusion as the most common molecular alteration. Here we reported a 59-year-old male presenting with multifocal lesions, including right temporalis muscle, right frontoparietal calvarium, right pterygoid muscles, and right mandibular condyle. Histologic examination of the lesion from right temporal mass revealed a multinodular biphasic lesion composed of sheets and fascicles of elongated spindle cells, and epithelioid small round to ovoid cells infiltrating into the adjacent skeletal muscle admixed with abundant neutrophilic infiltration. No rhabdomyoblast-like cells, prominent nuclear pleomorphism and mitosis were identified. IHC staining showed that the tumor cells were diffusely and strongly positive for FOSB, pan-cytokerain (AE1/AE3), CD31, ERG, and molecular testing further demonstrated a t(9:19) EGFL7::FOSB fusion mRNA. This constellation of morphological, IHC and molecular findings was consistent with the diagnosis of PMHE. This is the first reported case of multiple PMHEs with EGFL7::FOSB fusion in a patient aged more than 50 years old in the head and neck area. Since the vast majority of the differential diagnoses of PMHE are high-grade malignancies with aggressive clinical behavior and PMHE is rarely reported in the head and neck region, awareness of this tumor will avoid misdiagnosis and over-treatments of this entity.

Taxanes, particularly docetaxel (DTX), has been widely used for combination therapy of head and neck squamous cell carcinoma (HNSCC). For locally advanced unresectable HNSCC, DTX combined with cisplatin and 5-fluorouracil as a revolutionary treatment revealed an advantage in improvement of patient outcome. In addition. DTX plus immune check inhibitors (ICIs) showed low toxicity and increased response of patients with recurrent or metastatic HNSCC (R/M HNSCC). Accumulating data indicate that taxanes not only function as antimitotics but also impair diverse oncogenic signalings including angiogenesis, inflammatory response, ROS production, and apoptosis induction. However, despite an initial response, development of resistance remains a major obstacle to treatment response. Taxane resistance could result from intrinsic mechanisms such as enhanced DNA/RNA damage repair, increased drug efflux, and apoptosis inhibition, and extrinsic effects such as angiogenesis and interactions between tumor cells and immune cells. This review provides an overview of taxanes therapy applied in different stages of HNSCC and describe the mechanisms of taxane resistance in HNSCC. Through a detailed understanding the mechanisms of resistance may help developing the potential therapeutic methods and the effective combination strategies to overcome drug resistance.

This study aimed to investigate the influence of cranio-cervical position on the electromyographical activity of the core muscles during the prone plank exercise. Twenty healthy participants were enrolled in this study. Muscle activation was registered using surface electromyography (sEMG) in the rectus abdominis (RA), external oblique (EO), internal oblique (IO) and lumbar portion of erector spinae (LES). Three prone plank conditions were randomly evaluated, varying the position of the cranio-cervical segment during the prone plank performance (NEUTRAL, FLEXION and EXTENSION). sEMG signals of each individual muscle, Total Intensity (TI) and ratings of perceived exertion (RPE) were analysed with statistical significance set at P&lt;0.05. FLEXION revealed significantly higher TI values compared to NEUTRAL and EXTENSION (p = 0.000; effect size (ES) &gt; 0.90). The RA presented larger activation in FLEXION compared to the other variations (p &lt; 0.05; ES &gt;0.70). FLEXION elicited a greater response compared to NEUTRAL for both sides of EO when compared to EXTENSION, and also for both sides of IO compared to the other conditions. Both FLEXION and EXTENSION reported higher RPE values compared to NEUTRAL. Cranio-cervical segment position influenced the electromyographical activation of core musculature, showing highest values when performing prone plank in a cranio-cervical flexion.

Head and neck squamous cell carcinoma (HNSCC) takes the sixth place among the most common cancers in the world. Abnormal methylation can be one of the reasons for this cancer. The aim of this study was to investigate the DNA promotor methylation status of cancer-associated genes (ATM, APC, CDO1, RB1, TP53, WIF1) in patients with HNSCC. Bisulfite Conversion and Methylation-Sensitive High-Resolution Melting was used for analysis of the DNA methylation level of normal and tumor tissues in 44 patients. There were significant differences in DNA methylation level between patient’s tumor and normal tissues for CDO1 and WIF1 genes in all subjects and subgroups (p<0.05). In T3 subgroup there was significant correlation between CDO1 gene methylation and age in the normal tissue. The same correlation was detected also for the WIF1 gene methylation in tumor tissue samples in the subgroup with T3 and in normal tissue samples in the subgroup with T4 (p<0,05). In all genes no significant differences were found between the subgroups (T2, T3, T4 stage, primary/recurrent lesion, non-keratinizing/keratinizing SCC, age before/ after 50, smokers/non-smokers) of the patients. Thus, changes in the expression of the CDO1 and WIF1 genes can affect mechanisms of the occurrence and development of HNSCC.

Introduction: The identification of some prognostic markers in head and neck cancers (HNC) is necessary for the stratification of the therapeutic approach with benefits in increasing treatment response rates and limiting the toxic side effects of treatments. Neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR) and pallets-to-lymphocyte ratio (PLR) are currently validated as cheap and accessible biomarkers in different types of solid tumors. The purpose of the study was to evaluate the possible biomarker value of NLR, PLR and MLR recorded pre-treatment (radiotherapy/chemotherapy) for prognostic purposes in HNC. Materials and methods: The study included 190 patients with HNC included in the oncology record in the oncology outpatient clinic of the Craiova County Emergency Hospital starting from January 2002 and deceased until December 2022. Among them, 40 patients treated multimodally in the Clinic were included in the study of Oncology of the Craiova County Emergency Hospital for which the pre-treatment value (chemotherapy/radiotherapy) of NLR, PLR and MLR could be calculated. Overall survival (OS) values were correlated with NLR, PLR and MLR. Results: Median values for NLR, PLR and MLR were 6.15 (1.24-69), 200.79 (61.3- 1775.0) and 0.53 (0.12-5.5) respectively. In the study, the mean values for NLR, PLR and MLR of 2.88, 142.97 and 0.36 respectively were obtained. Median OS in the study group was 11 months (1-120). Although a negative Pearson`s correlation, the relationship between your variables was only weak, values R =.07, p=.67; R=.02, p=.31 and R=07, p=.62 being related with NLR, PLR and MLR respectively correlation with overall survival (OS). The median values of NLR, PLR and MLR were calculated (1.53, 90.32 and 0.18 respectively) for HNC cases with pre-treatment values of NLR <2 and for HNC cases with NLR values ≥6 (23.5, 232.78 and 0.79 respectively). Median OS for cases with NLR<2 and NLR≥6 were 17.4 and 13 months, respectively. Conclusions: The negative correlations of NLR, PLR and MLR with OS reported in the study are in accordance with the data reported in the literature for locally advanced recurrent and metastatic HNSCC cases. The comparative analysis of the data in the group with NLR<2 and NLR≥6 highlights an advantage of 4.4 months in median OS in favor of the group with low values of NLR. Being cheap and accessible, these markers could change the therapeutic approach even in centers with limited resources. The role of borderline NLR values as a prognostic factor in HNSCC must also be defined. PLR and MLR are less evaluated as biomarkers but the study demonstrates their potential to be used as prognostic biomarkers, it remains to be clarified if their inclusion in multivariable models along with NLR would bring a benefit

Head and neck squamous cell carcinoma (HNSCC) is a type of cancer that arises from the epithelium lining of the oral cavity, hypopharynx, oropharynx, and larynx. Despite advancement of the current treatment to HNSCC, such as surgery, chemotherapy, and radiotherapy, the overall survival rate of HNSCC remains poor due to late diagnosis and acquired resistance to treatment. Natural products have been extensively explored as a safer and more acceptable alternative to current treatments, with numerous studies displaying their potential against HNSCC. This review highlights preclinical studies in the past 5 years involving natural products against HNSCC and explores the signalling pathways altered by these products. This review also addresses challenges and future directions of the use of natural products as chemotherapeutic and chemoprevention agents against HNSCC.

.Purpose: This paper presents a comparative analysis of state-of-the-art DIR algorithms in the context of head and neck squamous cell carcinoma (HNSCC) radiation therapy. Materials and Methods: We used a dataset of head and neck cancer patients within the Cancer Image Archive, which includes 31 patients with 3D CT image data from pre-treatment, mid-treatment, and post-treatment stages of radiotherapy. We applied DIR algorithms to two datasets, one for pre-treatment to mid-treatment registration and another for mid-treatment to post-treatment registration, by aligning one CT image to another CT image. To quantitatively analyze the patient data, we focused on the spinal cord, optic nerve, brain stem, cochlea, and PTV as the OAR datasets. To verify the DIR algorithm, we used two non-learning-based methods, SyN and NiftyReg, as well as three deep learning-based methods, Voxelmorph, Cyclemorph, and Transmorph. We trained a deep learning model by adjusting the ratio of the training dataset, validation dataset, and test dataset to 7:1:2. Results: The average DSCs of SyN and NiftyReg, the non-learning-based methods, were 0.74±0.06 and 0.70±0.12, respectively. The deep learning-based methods, Voxelmorph, Cyclemorph, and Transmorph, had average DSCs of 0.72±0.08, 0.68±0.11, and 0.69±0.13, respectively. The deep learning DIR algorithm produced transformed outputs of OAR/PTV in a shorter time than other models, including commercial and conventional mathematical algorithms (At Inference(sec/images), Voxelmorph: 0.36, Cyclemorph: 0.3, Transmorph: 5.1, SyN: 140, NiftyReg: 40.2). Conclusions: In conclusion, our study provides a comprehensive comparison of commercial DIR algorithms with traditional and deep learning-based DIR methods. Our results demonstrate that commercial DIR algorithms outperform traditional DIR methods in terms of accuracy and efficiency. However, we also show that deep learning-based DIR methods have the potential to achieve similar performance to commercial algorithms with proper training and optimization. Our findings suggest that both commercial and deep learning-based DIR methods have their respective advantages and limitations, and the choice of method should depend on the specific requirements of the clinical application. Overall, our study contributes to ongoing efforts to improve the accuracy and efficiency of DIR methods for better patient outcomes.

This paper aimed to describe an atlas of the head of the crested porcupine (Hystrix cristata), applying advanced imaging techniques such as MRI. Furthermore, by combining the images acquired through these techniques with anatomical gross sections, we obtained an adequate description of the structures that form the CNS and associated structures of this species. This anatomical information could serve as a valuable diagnostic tool for the clinical evaluation of different pathological processes in porcupines such as abscesses, skull malformations, fractures, and neoplasia.

In this paper, the sequential prime numbers are used as variables for the galactic spiral equations which were developed from the ROTASE model. Special spiral patterns are produced when prime numbers are treated with the unit of radian. The special spiral patterns produced with the first 1000 prime numbers have 20 spirals arranged in two groups. The two groups have perfect central symmetry with each other and are separated with two spiral gaps. The special spiral pattern produced with natural numbers from 1 to 7919 shows 6 spirals in the central area and 44 spirals in the outer area. The whole 7919 spiral points can be plotted with either 6-spiral pattern or 44-spiral pattern. For the spirals only produced by the prime numbers in the 6-spiral pattern plotting, the spiral 2 and spiral 3 each has only one spiral point produced by prime number 2 and 3, respectively, all other spiral points produced by other prime numbers are located on the spiral 1 and spiral 5. The special spiral pattern is well explained with careful analysis, it is concluded that all prime numbers greater than 3 must meet one of the equations: P₁ = 1 + 6 * n (n > 0) P₅ = 5 + 6 * n (n ≥ 0) In other words, every prime number greater than 3 is either a P₁ prime number or a P₅ prime number, no exception. Matching one of the equations is a necessary condition for a number to be a prime number, not a sufficient condition. Hope such sufficient condition can be found in the future. The number of P₁ prime numbers roughly equal the number of P₅ prime number in the first 2 billion prime numbers. The galactic spiral equations with golden angle can duplicate Vogel’s result for the simulation of sunflower seed head pattern, and a pinwheel pattern can be produced also with galactic spiral equations and 1 degree more than golden angle.

Objective: To investigate postacute laryngeal injuries and dysfunctions (PLID) in coronavirus disease 2019 (COVID-19) patients. Methods: Three independent investigators performed a systematic review of the current literature studying PLID in patients with a history of COVID-19. The review was performed according to PRISMA Statement. Epidemiological, clinical, hospitalization features, laryngeal diseases and voice outcomes were extracted from the included papers. Results: Eight papers met our inclusion criteria (393 patients) corresponding to 5 uncontrolled prospective and 3 retrospective studies. The most prevalent PLID were vocal fold dysmotility (65%), vocal fold edema (35%), laryngopharyngeal reflux (21%), and muscle tension dysphonia (21%). Posterior glottic stenosis (12%), granuloma (14%), and posterior glottic diastasis (12%) were the most common injuries. Most patients with PLID were obese and had a history of intensive care unit hospitalization, and orotracheal intubation. The delay between the discharge and the laryngology office consultation ranged from 51 to 122 days. The mean duration of intubation ranged from 10 to 34 days. Seventy-eight (49%) intubated patients were in prone position. The proportion of patients requiring surgical treatment ranged from 39% to 70% (mean=48%). There was an important heterogeneity between studies about inclusion, exclusion criteria and outcomes. Conclusion: COVID-19 appeared to be associated with PLID, especially in patients with a history of intubation. However, future controlled studies are needed to evaluate if intubated COVID-19 patients reported more frequently PLID than patients who were intubated for other conditions.

Understanding neck pain is an important societal issue. Kinematic data from sensors may help to gain insight on the pathophysiological mechanisms associated with neck pain through a quantitative sensorimotor assessment of one patient. The objective of this study was to evaluate the potential usefulness of artificial intelligence with several Machine Learning (ML) algorithms in assessing neck sensorimotor performance. Angular velocity and acceleration measured by an inertial sensor placed on the forehead during the DidRen laser test in thirty-eight acute and subacute non-specific neck pain (ANSP) patients were compared to forty-two healthy control participants (HCP). Seven supervised ML algorithms were chosen for the predictions. The most informative kinematic features were computed using Sequential Feature Selection methods. The best performing algorithm is the Linear Support Vector Machine with an accuracy of 82% and Area Under Curve of 84%. The best discriminative kinematic feature between ANSP patients and HCP is the first quartile of head pitch angular velocity. This study has shown that supervised ML algorithms could be used to classify ANSP patients and identify discriminatory kinematic features potentially useful for the clinicians in the assessment and monitoring of the neck sensorimotor performance in ANSP patients.

Otolith morphology analysis is one of the main tools used for fish or fish stock identification. Moreover, otolith shape can also be used in fish dietary studies (stomach content) for the identi-fication of prey fishes and their size according to the relationship between fish and otolith sizes. In the present study, the relationship between fish length and otolith morphological dimensions was investigated for the sabre squirrelfish, Sargocentron spiniferum (Forsskål, 1775) (family: Hol-ocentridae). Samples of 185 fish were collected from the coast of the Red Sea, Egypt. To analyze the relationship between fish and otolith, otolith morphometric measurements (length, width, area, perimeter, weight, sulcus, and ostium) and shape factors (aspect ratio, compactness, form factor, rectangularity, roundness, ellipticity, squareness) describing outline shape were extracted using image analysis. Generalized linear models were applied for the relationship between body length and each otolith morphology feature. From the relationships between the total length of fish and fourteen morphology features, only otolith length, caudal length, and squareness were significantly correlated with fish size. Our results provide more information for the relationship between fish length and otolith morphometric features.

Otolith morphology analysis is one of the main tools used for fish or fish stock identification. Moreover, otolith shape can also be used in fish dietary studies (stomach content) for the identification of prey fishes and their size according to the relationship between fish and otolith sizes. In the present study, the relationship between fish length and otolith morphological dimensions was investigated for the sabre squirrelfish, Sargocentron spiniferum (Forsskål, 1775) (family: Holocentridae). Samples of 185 fish were collected from the coast of the Red Sea, Egypt. To analyze the relationship between fish and otolith, otolith morphometric measurements (length, width, area, perimeter, weight, sulcus, and ostium) and shape factors (aspect ratio, compactness, form factor, rectangularity, roundness, ellipticity, squareness) describing outline shape were extracted using image analysis. Generalized linear models were applied for the relationship between body length and each otolith morphology feature. From the relationships between the total length of fish and fourteen morphology features, only otolith length, caudal length, and squareness were significantly correlated with fish size. Our results provide more information for the relationship between fish length and otolith morphometric features.

The problem of drug resistance in terms of antifungal therapy, unknown until a few years ago, is assuming increasing importance. Particularly in immunosuppressed patients and subject to chemotherapy and radiotherapy. In the last years the use of essential oils as approach to improving the effectiveness of antifungal agents and reducing the antibiotic resistant has been proposed. Our research aimed to evaluate the antifungal activity of Colombian essential oil of Ruta graveolens (REO) against clinical strains of Candida albicans, Candida parapsilopsis, C. glabrata and Candida tropicalis. The data obtained showed that Candida tropicalis and Candida albicans were most sensible strains showing minimum inhibitory concentrations (MIC) of 0.5 and 1.0 µg/ml of REO. The Time Kill Kinetics assay demonstrated that REO showed fungicide effect against C. tropicalis and fungistatic effect against C. albicans. In addition, the 40% of the biofilm formed by C. albicans was eradicated using 1% of REO after 1 hour of exposure.