High-power laser facilities necessitate predicting incremental damage to final optics to identify evolving damage trends. In this study, we propose a surface damage detection method utilizing image segmentation employing ResNet-18, and a damage area estimation network employing U-Net++. Paired sets of online and offline images of optics obtained from a large laser facility is used to train the network. The trends of varying damage could be identified by incorporating additional experimental parameters. A key advantage of the proposed method is that the network can be trained end-to-end on small samples, eliminating the need for manual labeling or feature extraction. The software developed based on these models can facilitate the daily inspection and maintenance of optics in large laser facilities.

Pneumonia is a leading cause of death worldwide, and one of the most significant approaches to diagnose pneumonia is Chest X-ray (CXR) since it was used in clinical scenes. Convolutional neural networks (CNNs) have been widely used in computer vision community. Along with the development of CNNs, we want to make use of CNNs to recognize CXR of people who get pneumonia and make classification. It is important, especially during epidemic period. In this paper, we present a new type of residual learning framework, PEPX-Resnet, which makes use of a type of lightweight residual, and apply this network to CXR dataset. The result shows that PEPX-Resnet is easier to optimize and can have better results, especially for COVID-19 cases. PEPX-Resnet could reach higher accuracy, f1 score and some other evaluations for CXR dataset.

Background: CK-18 is a serological marker of apoptosis that has been widely associated with fibrosis and steatosis in NASH. Many studies have showed association CK-18 levels with NAFLD. To date, there is a profound lack of rheumatology studies on the effect of MTX therapy with regard to CK-18 levels. The relationship between CK-18 levels and cumulative MTX dose, MTX treatment duration, weekly MTX dose is not available in the literature. Objectives: The main purpose of this study was to determine the relationship between serum cytokeratin-18 (CK-18) and cumulative methotrexate dose in rheumatology patients on methotrexate (MTX) therapy. Besides, we studied the correlations between CK-18 and clinical parameters including age, disease duration , duration of MTX therapy, cumulative steroid dose and biochemical parameters such as alanine transaminase (ALT) and aspartate aminotransferase AST). Methods : We recruited 79 rheumatology patients on methotrexate (MTX) therapy as MTX group and 38 patients not on MTX therapy as non-MTX group . All subjects were tested for their serum CK-18 levels using an enzyme-linked immunosorbent assay (ELISA) test. We identified 20 patients with the highest CK-18 levels and 20 patients with the lowest CK-18 levels and had ultrasound liver performed on them. Results : The median serum CK-18 levels were marginally higher among the patients on MTX (1.20 ng/mL [0.19-37.15]) compared to the non-MTX group (1.17 ng/mL [0.37-34.32]). There was a significantly positive relationship between serum CK-18 levels and cumulative MTX dose (r = 0.329, p = 0.003) and total duration of MTX therapy (r = 0.284, p = 0.011). Apart from the above-mentioned variables, the CK-18 levels in MTX group significantly correlated with age (r = 0.265, p = 0.018), ALT (r = 0.440, p = <0.001) and AST (r = 0.478, p= 0.004). Ultrasound liver findings showed median CK-18 levels was higher among patients with abnormal liver findings although statistical significance was not reached. Conclusion : Among patients on MTX therapy, serum CK-18 levels correlated positively with cumulative MTX dose , total duration of MTX treatment , ALT and AST levels. These findings are preliminary and requires validation by future studies in the field of rheumatology.

Sign language recognition technology can help people with hearing impairments to communicate with those who are hearing impaired. At present, with the rapid development of society, deep learning technology also provided certain technical support for sign language recognition work. In sign language recognition tasks, the use of traditional convolutional neural networks to extract spatio-temporal features from sign language videos suffers from insufficient feature extraction, resulting in low recognition rates. Nevertheless, video-based sign language datasets are very large and require a lot of computational resources for training and generalisation must be ensured, which poses a challenge for recognition. In this paper, we have presented a video-based sign language recognition method based on resnet and lstm. As the number of network layers increases, the ResNet network can effectively solve the granularity explosion problem and obtain better time series features. We use the Resnet convolutional network as the backbone model. At the initialisation stage, we obtain sign language features using ResNet; then, the learned feature space is used as the input of LSTM network to obtain long sequence features. The experimental results show that the accuracy of the above model is better than the mainstream model, and it can effectively extract the spatio-temporal features in sign language videos and improve the recognition rate of sign language actions.

Defect recognition in ballastless track structures, based on distributed acoustic sensors (DASs), was researched in order to improve detection efficiency and ensure the safe operation of trains on high-speed railways. A line in southern China was selected, and equipment was installed and debugged to collect the signals of trains and events along it. Track vibration signals were extracted by identifying a train track, denoising, framing and labeling to build a defect dataset. Time–frequency-domain statistical features, wavelet packet energy spectra and the MFCCs of vibration signals were extracted to form a multi-dimensional vector. An XGBoost model was trained and its accuracy reached 89.34%. A time-domain residual network (ResNet) that would expand the receptive field and test the accuracies obtained from convolution kernels of different sizes was proposed, and its accuracy reached 94.82%. In conclusion, both methods showed good performance with the built dataset. Additionally, the ResNet delivered more effective detection of DAS signals compared to conventional feature engineering methods.

Currently, face recognition technologies are the most widely used methods for verifying 1an individual’s identity. Nevertheless, it has increased in popularity, raising concerns about face spoofing attacks, in which a photo or video of an authorized person’s face is used to get access to services. Based on a combination of Background Subtraction (BS) and Convolutional Neural Networks (CNN), as well as an ensemble of classifiers, we propose an efficient and more robust face spoof detection algorithm. This algorithm includes a Fully Connected (FC) classifier with a Majority Vote (MV) algorithm, which uses different face spoof attacks (e.g., printed photo and replayed video). By including a majority vote to determine whether the input video is genuine or not, the proposed method significantly enhances the performance of the Face Anti-Spoofing (FAS) system. For evaluation, we considered the MSU MFSD, REPLAY-ATTACK, and CASIA-FASD databases. The obtained results by our proposed approach are better than those obtained by state of the art methods. On the REPLAY-ATTACK database, we were able to attain a Half Total Error Rate (HTER) of 0.62% and an Equal Error Rate (EER) of 0.58%. It was possible to attain an EER of 0% on both the CASIA-FASD and the MSU FAS databases.

Identified by the eponym “Edwards’ Syndrome,” trisomy 18 (T18) represents the second most common autosomal trisomy after T21. The pathophysiology underlying the extra chromosome 18 is a nondisjunction error, mainly linked with the advanced maternal age. More frequent in female fetuses, the syndrome portends high mortality, reaching a rate of 80% of miscarriages or stillbirths. The three-step evaluation includes first trimester screening for fetal aneuploidy using a combination of maternal age, fetal nuchal translucency thickness, fetal heart rate and maternal serum free ß-hCG and PAPP-A; followed by the research for fragments of fetal DNA in maternal blood; and, finally, invasive techniques leave to the established diagnosis. Starting with the first trimester scan, selected ultrasound findings should be investigated to define not only the impact of the genetic problem on the fetus, but also to address the prenatal counselling. Previous series underline that T18 is not uniformly lethal. An active dialogue on the choices in the management of infants with T18 has emerged, sustained by the transition from the comfort care to the intervention attitude. Survival rates for individuals with supposedly fatal conditions have increased. In this novel scenario, an ad hoc counselling is pivotal. To support it, a comparative analysis by pictorial assays between ultrasound and autopsy findings could be beneficial. We provide an illustrative tool from a clinical case managed in early second trimester, with the purpose to strive a balanced approach in the hard choice faced by couples of fetuses with T18.

Worldwide, hundreds of thousands of people are diagnosed with kidney cancer and this disease is more common in developed and industrialized countries. Previously, kidney cancer was known as an elderly disease and was seen in people over a certain age; nowadays it is also seen in younger individuals and it is easier to diagnose thanks to new radiological diagnostic methods. A kidney tumor is a type of cancer that is extremely aggressive and needs surgical treatment rapidly. Today, approximately 30% of patients diagnosed with kidney cancer are unfortunately noticed at the stage of metastatic disease (spread to distant organs). The biggest factor that pushes us to this study is that kidney tumors progress unlike other cancer types with little or no symptoms. Therefore, conducting such studies is extremely important for early diagnosis. In this study, we compare the Unet3D models in order to help people who are dealing with difficulties in the diagnosis of kidney cancer. Unet, Unet+ResNet and Unet++ models were compared for image segmentation.

Atopic dermatitis and psoriasis are prevalent inflammatory skin conditions with diverse treatment options. Despite advances in understanding their underlying mechanisms, recent research highlights the significance of interleukins IL-18 and IL-37 as potential therapeutic targets. This review synthesizes knowledge on these interleukins, their roles in atopic dermatitis and psoriasis, and emerging treatment strategies. Findings of a literature search up to May 30, 2024 underscore a research gap in IL-37-targeted therapies. Conversely, IL-18-focused treatments have demonstrated promise in adult-onset Still’s Disease, warranting further exploration for their potential efficacy in psoriasis and atopic dermatitis.

Maize mosaic virus (MMV) is a plant virus that is transmitted by the corn planthopper, Peregrinus maidis Ashmead (Hemiptera: Delphacidae) in a persistent and propagative manner. MMV is the causal agent for mosaic disease that can cause significant loss in corn yield. Management is focused on controlling insect vector population. The molecular mechanisms of virus transmission by the insect vector are not fully understood. Previously, we identified a receptor for MMV, the P. maidis syntaxin 18 (PmStx18). PmStx18 interacts with the viral glycoprotein and potentially facilitates MMV recognition and entry into the vector, enabling virus proliferation. Stx18 is a key component of the vesicular trafficking machinery and also plays a critical role in neurotransmitter release in insects. In this work, we investigated the expression of PmStx18 in different developmental stages and major parts of the insect vector. Primers were designed to generate templates for synthesis of double-stranded RNA fragments. A 374-bp amplicon was selected and gene-specific sequences were flanked by T7 promoter sequences for in vitro transcription. Results showed a significant difference in the expression of Stx18 in the different developmental stages of P. maidis but not between male and female adults and major body parts. In vitro transcription products indicated optimal reaction conditions and protocol suitability. The findings establish the initial steps for developing RNAi-based approach in controlling MMV transmission by targeting Syntaxin-18 in P. maidis. Further investigations are needed to evaluate the efficacy of this approach.

During the 1950s, the Gros Michel species of bananas were nearly wiped out by the incurable Fusarium Wilt, also known as Panama Disease. Originating in Southeast Asia, Fusarium Wilt is a banana pandemic that has been threatening the multi-billion-dollar banana industry worldwide. The disease is caused by a fungus that spreads rapidly throughout the soil and into the roots of banana plants. Currently, the only way to stop the spread of this disease is for farmers to manually inspect and remove infected plants as quickly as possible, whereas it is a time-consuming process. The main purpose of this study is to build a deep Convolutional Neural Network (CNN) using a transfer learning approach to rapidly identify fusarium wilt infections on banana crop leaves. We chose to use the ResNet50 architecture as the base CNN model for our transfer learning approach owing to its remarkable performance in image classification, which was demonstrated through its victory in the ImageNet competition. After its initial training and fine-tuning on a data set consisting of 300 healthy and diseased images, the CNN model achieved near-perfect accuracy of 0.99 and was fine-tuned to adapt the ResNet base model. ResNet50’s distinctive residual block structure could be the reason behind these results. To evaluate this CNN model, 500 test images, consisting of 250 diseased and healthy banana leaf images, were classified by the model. The deep CNN model was able to achieve an accuracy of 0.98 and an F-1 score of 0.98 by correctly identifying the class of 492 of the 500 images. These results show that this DCNN model outperforms existing models such as Sangeetha et al., 2023’s deep CNN model by at least 0.07 in accuracy and is a viable option for identifying Fusarium Wilt in banana crops.

Microglia are the resident immune cells of the central nervous system. Upon stimulus presentation microglia polarize from a resting to an activated state. Microglial translocator protein 18 kDa (TSPO) is considered as a marker of inflammation. Here, we characterized the role of TSPO by investigating the impact of TSPO deficiency on human microglia. We used TSPO knockout (TSPO-/-) variants of the human C20 microglia cell line. We found a significant reduction in the TSPO-associated protein VDAC1 in TSPO-/- compared to control cells. Moreover, we assessed the impact of TSPO deficiency on calcium levels and the mitochondrial membrane potential. Cytosolic and mitochondrial calcium concentrations were increased in TSPO-/- cell lines, whereas the mitochondrial membrane potential tended to be lower. Assessment of the mitochondrial DNA copy number via RT-PCR revealed a decreased amount of mtDNA in the TSPO-/- cells when compared to controls. Moreover, metabolic profiles of C20 cells were strongly dependent on the glycolytic pathway. However, TSPO depletion did not affect the cellular metabolic profile. Measurement of the mRNA expression levels of the pro-inflammatory mediators revealed an attenuated response to proinflammatory stimuli in TSPO-depleted cells, implying a role of the TSPO protein in the process of microglial polarization.

Text recognition is an important area of the pattern recognition field. Natural language processing (NLP) and pattern recognition have been utilized efficiently in script recognition. Much research has been done on handwritten script recognition. However, the research on the Arabic language for handwritten text recognition received little attention compared with other languages. Therefore, it is crucial to develop a new model that can recognize Arabic handwritten text. Most of the existing models used to acknowledge Arabic text are based on traditional machine learning techniques. Therefore, we implemented a new model using deep machine learning techniques by integrating two deep neural networks. The architecture of the Residual Network (ResNet) model is used to extract features from raw images. Then, the Bidirectional Long Short-Term Memory (BiLSTM) and connectionist temporal classification (CTC) are used for sequence modeling. Our system improved the recognition rate of Arabic handwritten text compared to other models of similar type with a character error rate of 13.2% and character error rate of 27.31%. In conclusion, the domain of Arabic handwritten recognition is advancing swiftly with the use of sophisticated deep learning methods.

In mammals, the role of interleukin-18 (IL-18) in the immune response is to drive inflammatory and, normally therefore, anti-viral responses. IL-18 also shows promise as a vaccine adjuvant in mammals. Chicken IL-18 (chIL-18) has been cloned. The aim of this study was to investigate the potential of chIL-18 to act as a vaccine adjuvant in the context of a live recombinant Fowlpox virus vaccine (fpIBD1) against Infectious bursal disease virus (IBDV). fpIBD1 protects against mortality, but not against damage to the bursa of Fabricius caused by IBDV infection. The Fowlpox virus genome itself contains several candidate immunomodulatory genes, including potential IL-18 binding proteins (IL-18bp). We have knocked out (Δ) the potential IL-18bp genes in fpIBD1 and inserted (::) the cDNA encoding chIL-18 into fpIBD1 in the non-essential ORF030, generating five new viral constructs –fpIBD1::chIL-18, fpIBD1ΔORF073, fpIBD1ΔORF073::chIL-18, fpIBD1ΔORF214 and fpIBD1ΔORF214::chIL-18. The subsequent protection from challenge with virulent IBDV, as measured by viral load and bursal damage, given by these altered fpIBD1 strains, was compared to that given by the original fpIBD1. The results show that chIL-18 can act as a vaccine adjuvant, giving complete protection against challenge, with no detectable virus or damage in the bursa of Fabricius.

In the field of 18F-chemistry for the development of radiopharmaceuticals for positron emission tomography (PET), various labeling strategies by the use of prosthetic groups have been im-plemented, including chemoselective 18F-labeling of biomolecules. Among those, chemoselec-tive 18F-fluoroglycosylation methods focus on the sweetening of pharmaceutical radiochemistry by offering a highly valuable tool for the synthesis of 18F-glycoconjugates with suitable in vivo properties for PET imaging studies. A previous review covered the various 18F-fluoroglycosylation methods that have been developed and applied as of 2014 [Maschauer and Prante, BioMed. Res. Int. 2014, 214748]. This paper is an updated review, providing the recent progress in 18F-fluoroglycosylation reactions and the preclinical application of 18F-glycoconjugates, including small molecules, peptides, and high-molecular-weight proteins.

Pattern recognition receptors such as nucleotide-binding oligomerization domain (NOD)-containing protein receptors (NLRs) and the pyrin and HIN domain (PYHIN) receptors initiate the inflammatory response following cell stress or pathogenic challenge. When activated some of these receptors oligomerize to form the structural backbone of a signalling platform known as the inflammasome. The inflammasome promotes the activation of caspase-1 and the maturation of the proinflammatory cytokines, interleukin (IL)-1β and IL-18. In the gut dysregulation of the inflammasome complex is thought to be a contributing factor in the development of inflammatory bowel diseases (IBD), such as ulcerative colitis (UC) and Crohn’s disease (CD). The importance of inflammasomes to intestinal health has been emphasized by various inflammasome deficient mice in dextran sulphate sodium (DSS) models of intestinal inflammation and by the identification of novel potential candidate genes in population based human studies. In this review we summarise the most recent finding with regard to formation, sensing and regulation of the inflammasome complex and highlight their importance in maintaining intestinal health.

Radiolabelled tracers targeting the prostate-specific membrane antigen (PSMA) have become important radiopharmaceuticals for the PET-imaging of prostate cancer. In this connection we recently developed the fluorine-18 labelled PSMA-ligand [¹⁸F]PSMA-1007 as next generation radiofluorinated Glu-ureido PSMA inhibitor after [¹⁸F]DCFPyL and [¹⁸F]DCFBC. Since radiosynthesis so far was suffering for rather poor yields novel procedures for the automated radiosyntheses of [¹⁸F]PSMA-1007 have been developed. We herein report on both the two-step and the novel one-step procedures, which have been performed on different commonly used radiosynthesisers. Using the novel one-step procedure the [¹⁸F]PSMA-1007 was produced in good radiochemical yields ranging from 30-70 % and synthesis times less than 55 minutes. Furthermore, upscaling to product activities up to 50 GBq per batch was successfully conducted. All batches passed quality control according to European Pharmacopoeia standards. Therefore, we were able to disclose a new, simple and at the same time high yielding production pathway for the next generation PSMA radioligand [¹⁸F]PSMA-1007. Actually it turned out that the radiosynthesis is as easily realised as the well-known [¹⁸F]FDG synthesis and thus transferable onto all currently available radiosynthesisers. Using the new procedures, clinical daily routine can be sustainably supported in-house even in larger hospitals by a single production batch.

Cytokines play a substantial role in the pathophysiology of infectious diseases. Consequently, genetic polymorphisms in cytokine genes can have profound effect on the susceptibility to or protection from infections and also influence the clinical manifestations, disease severity as well as outcome. The objective of this brief narrative review is to provide an update on the recent research findings on the role of genetic polymorphisms of IL-18, a recently discovered cytokine on certain infectious diseases of public health relevance. The current review suggests there is a significant impact of IL-18 polymorphisms on different infectious diseases. The presence of G and T alleles at position -656 is suggested to have a probable protective effect against VL. The −137G/−607C (GC) and −137C/−607C (CC) haplotypes in the promoter region are associated with increased susceptibility to severe malarial anemia. SNPs rs544354 and rs574429 (GC) are linked to a higher risk of P. falciparum infection and increased parasitemia. The -137 C/C genotype and -137 G allele (with G/G genotype) are associated with faster progression to AIDS. In contrast, the IL-18 genotype does not seem to affect overall HIV susceptibility but influences disease progression. The G and GG genotypes at position -137 and allele A at position -607 are likely associated with protection against HBV infection. Conversely, the C allele at position -137 is linked to increased susceptibility to HBV infections. The GG genotype of IL-18 -137 G > C variant is associated with increased susceptibility to pulmonary tuberculosis (PTB). The AC genotype of IL-18 -607A>C has been linked to PTB susceptibility, particularly in patients with co-morbid diabetes mellitus. SNPs −137G/C, 113T/G, and +127C/T are associated with susceptibility to H. pylori infection, indicating that these genetic variants may influence the risk of H. pylori-related diseases. There are conflicting results in studies conducted in different populations (e.g., genetic polymorphism, studies conducted in India and China against HBV infection) highlighting the need for further research with larger sample sizes to resolve discrepancies. Due to the variability in findings, extensive studies in diverse populations are needed to better understand the impact of IL-18 polymorphisms.

Background: Cytokines are multifunctional glycoproteins playing a vital role in the tumor microenvironment and in the progression of breast cancer. The immune responses in the tumors restrained by pro- and anti-inflammatory cytokine expressions could be influenced by genetic polymorphisms. Hence, the present study evaluated the contribution of IL6 (rs1800797, rs1800796, and rs1800795), and IL18 (rs1946518, rs187238, and rs549908) genotypes and its haplotypes with risk and progression of breast cancer in south Indian population. Methods: The polymorphisms of IL6 gene -597G>A, -572G>C, -174G>C and IL18 gene -607C>A, -137G>C, and 105A>T were genotyped through PCR-RFLP and As-PCR assays in blood DNA of 600 subjects. We have performed haplotype, LD, univariate, multivariate logistic regression and Kaplan-Meier analyses for the obtained data. Results: The frequency of AA genotype & A-allele of IL6 -597G>A, and CC genotype & C allele of IL6 -174G>C polymorphism was higher in breast cancer patients and was found to be significantly associated with TNM staging, late (advanced) stage, metastasis, etc. While, CG and GG genotypes of IL6 -572 C>G polymorphism had a protective role against breast cancer. Further, IL18 -607C>A, -137G>C & 105A>T polymorphisms were found to be associated with lobular carcinoma subtype, PR-ve and HER2+ve breast cancer patients. Perfect LD was observed between all SNPs of IL6 & IL18 genes under study; G-C-C, A-G-G and A-C-C haplotype combination of IL6 genes had conferred 2.09, 2.25 and 4.72 folds risk for breast cancer respectively. In survival analysis, we observed that the C allele of rs 1800795 was found to be significantly associated with 5years overall survival in breast cancer subjects. Conclusions: Overall, our results suggest the importance of genotypic and haplotype analysis of IL6, and IL18 gene variants in progression and risk identification of breast cancer.

Stable isotope concentrations in the soil, rain and ground water have been used to trace the water extraction zones of plants in different environments. The need to identify the plant water use by plants in afforestation programs to control desertification increases the importance of sap water partitioning of plants in sand dune areas. However, the introduction of new plant covers exerts pressure on the water resources and can affect the local soil water conditions. In this study, we analyzed the isotope concentrations in rain, soil, sap, and ground water after the summer of 2010. Two experimental plots established in the Hailiutu catchment (Shaanxi province, northwest China) were selected to gather the water samples between September and October 2010. One plot is dominated by Salix bushes (Salix psammophila C. Wang \& Chang Y. Yang) and the other by the tree species Willow (Salix matsudana Koidz.). The total precipitation at the experimental site was 401 mm/yr during 2010, while 88.7 mm was collected in total for the period September to October. Willow trees transpired 12.82 kg/d being almost three times larger than Salix shrubs (4.57 kg/d). Despite the transpiration rates of both plant species and the few rain events in the region, the soil water beneath the plant covers is not depleted. Stable isotope signature of soil water beneath both covers shows the fractionation front in Salix at 20 cm depth and at Willow at 40 cm depth. However, soil water signature is closer to the groundwater than the collected rain water.

The difficulty in detecting tumors in earlier stages is the major cause of mortalities of patients, despite the advancements in treatment and research regarding ovarian cancer. Deep Learning algorithms are applied to serve the purpose of a diagnostic tool by applying them on CT scan images of the ovarian region. The images go through a series of pre-processing techniques and further the tumor is segmented using the UNet model. Instances are then classified into two categories – benign and malignant tumors. Classification is performed using Deep Learning models like CNN, ResNet, DenseNet, Inception-ResNet, VGG16 and Xception along with Machine Learning models such as Random Forest, Gradient Boosting, AdaBoosting, XGBoosting. DenseNet 121 emerges as the best model on this dataset even after applying optimization on the Machine Learning models by obtaining an accuracy of 95.7%. The current work demonstrates the comparison of multiple CNN architectures among themselves and with common Machine Learning algorithms, with and without optimization techniques applied.

Single crystal nickel-based superalloys are extensively used in turbine blade applications 1 due to their superior creep resistance compared to their polycrystalline counterparts. With the high 2 creep resistance, High Cycle Fatigue (HCF) and Low Cycle Fatigue (Single crystal nickel-based 3 superalloys are extensively used in turbine blade applications due to their superior creep resistance 4 compared to polycrystalline counterparts. As a result, fatigue becomes the primary failure mechanism 5 in such applications. Specifically, High Cycle Fatigue (HCF) and Low Cycle Fatigue (LCF) are of 6 significant concern under these conditions. This study investigates the fatigue life prediction of 7 CMSX-4 using a combined crystal plasticity and lifetime assessment model. The constitutive crystal 8 plasticity model simulates the anisotropic, rate-dependent deformation behavior of CMSX-4, while 9 a modified Chaboche damage model is used for lifetime assessment, incorporating anisotropy by 10 focusing on cleavage stresses on active slip planes. Both qualitative and quantitative data obtained 11 from HCF experiments on single crystal superalloys with notched geometry were used to validate 12 the model. Furthermore, artificial neural networks (ANNs) were employed to enhance the accuracy 13 of lifetime predictions across varying temperatures by analyzing the stress-strain data obtained from 14 simulations. The integration of crystal plasticity, damage mechanics, and ANNs resulted in accurate 15 predictions of fatigue life and crack initiation points under complex loading conditions of single 16 crystal superalloys.

Drinking water contamination of lead from various environmental sources, leaching consumer products and intrinsic water-pipe infrastructure is still today a matter of great concern. Therefore, new highly sensitive and convenient Pb²⁺ measurement schemes are necessary, especially for in-situ measurements at a low-cost. Within this work dye/ionophore/Pb²⁺ co-extraction and effective water phase de-colorization was utilized for highly sensitive lead measurements and sub-ppb naked-eye detection. A low-cost ionophore Benzo-18-Crown-6-ether was used, and a simple test-tube mix and separate procedure was developed. Instrumental detection limits were in the low ppt region (LOD=3, LOQ=10), and naked-eye detection was 500 ppt. Note, however, that this sensing scheme still has improvement potential as concentrations of fluorophore and ionophore were not optimized. Artificial tap-water samples, leached by a standardized method, demonstrated drinking water application. Implications for this method are convenient in-situ lead ion measurements.

Deep learning has increasingly been employed to identify minerals. However, deep learning can only be used to identify minerals in the distribution of the training set, while any mineral outside the spectrum of the training set is inevitably categorized erroneously within a predetermined class from the training set. To solve this problem, the study introduces the approach that amalgamates One-Class Support Vector Machines (OCSVM) with the ResNet architecture for the out-of-distribution mineral detection. Initially, ResNet undergoes training using a training set comprising well-defined minerals. Subsequently, the earlier layers of the trained ResNet are employed to extract the discriminative features of the mineral under consideration. These extracted mineral features then become the input for OCSVM. When OCSVM discerns the mineral in the training set's distribution, it triggers the subsequent layers within the trained ResNet, facilitating the accurate classification of the mineral into one of the predefined categories encompassing the known minerals. In the event OCSVM identifies the mineral out of the training set's distribution, it is unequivocally categorized as an unclassified or 'unknown' mineral. Empirical results substantiate the method's capability to identify out-of-distribution minerals while concurrently maintaining a commendably high accuracy rate for the classification of the 36 in-distribution minerals.

The utilization of Pig Emotion Recognition (PER) driven by Artificial Intelligence (AI) promises to mitigate labor costs and alleviate stress among domestic pigs, thereby minimizing the need for consistent human intervention. Nevertheless, this research acknowledges the inherent limitations within the raw PER datasets, which often include irrelevant porcine features, hence impeding genuine progress in real-world evaluations. A significant proportion of PER datasets derive from sequential pig imagery obtained from video recordings, and a common pitfall in these studies is the unregulated shuffling of data. This lack of control can result in the overlap of data samples between training and testing groups, thereby yielding skewed experimental evaluations. To address these challenges, this paper introduces a novel solution in the form of the Semi-Shuffle-Pig Detector (SSPD) for PER datasets, with the intent to facilitate a less biased experimental output. By applying the SSPD, we can ensure that all testing data samples remain distinct from the training datasets, and any superfluous information from raw images is systematically discarded. This optimized method enhances the true performance of classification, providing unbiased experimental evaluations. Notably, our approach has led to a remarkable improvement in the Isolation After Feeding (IAF) metric by 20.2\% and achieved higher accuracy in segregating IAF and Paired After Feeding (PAF) classifications exceeding 92\%. This methodology, thereby, ensures the preservation of pertinent data within the PER system and eliminates potential biases in experimental evaluations. Consequently, it elevates the accuracy and reliability of real-world PER applications, resulting in a tangible positive impact on both pig welfare management and food safety standards.

Early stable deep molecular response (DMR) of nilotinib is associated with goal of treatment-free remission (TFR) for patients with chronic-phase chronic myeloid leukemia (CML-CP). It is significant to early distinguish patients who can obtain DMR and fit for TFR. We performed a multicenter study to explore early cumulative MR4.5 rate by 18 months with nilotinib in patients with newly diagnosed CML-CP (ND-CML-CP) in China. From 29 institutes, 106 ND-CML-CP patients received nilotinib (300mg BID). The cumulative MR4.5 rate of nilotinib by 18 months was 69.8% (74/106). The cumulative MMR and MR4.0 rates of nilotinib by 18 months were 94.3% (100/106) and 84.9% (90/106), respectively. Patients with ultra-early molecular response (u-EMR) at 6 weeks were not significantly different to obtain DMR or MMR by 24 months compared to those without u-EMR (P=0.7584, P=0.9543, respectively). Our study demonstrated that treatment with nilotinib in patients with ND-CML-CP contributed to obtain high early MR4.5.

One of the critical problems in multiclass classification tasks is the imbalance of the dataset. This is especially true when using contemporary pre-trained neural networks, where, in fact, the last layers of the neural network are retrained. Therefore, the large datasets with highly unbalanced classes are not good for models’ training since the use of such a dataset leads to overfitting and, accordingly, poor metrics on test and validation datasets. In this paper the sensitivity to a dataset imbalance of Xception, ViT-384, ViT-224, VGG19, ResNet34, ResNet50, ResNet101, Inception_v3, DenseNet201, DenseNet161, DeIT was studied using a highly imbalanced dataset of 20,971 images sorted into 7 classes. It is shown that the best metrics were obtained when using a cropped dataset with augmentation of missing images in classes up to 15% of the initial number. So, the metrics can be increased by 2-6% compared to the metrics of the models on the initial unbalanced data set. Moreover, the metrics of the rare classes' classification also improved significantly – the TruePositive value can be increased by 0.3 and more. As result, the best approach to train considered networks on an initially unbalanced dataset was formulated.

In the era of the post-quantum Internet of Things (IoT), the implementation of anti-quantum cryptographic algorithms in numerous terminals can successfully defend against prospective quantum computing assaults. Lattice-based cryptography can withstand quantum computing attacks, making it a viable substitute for the currently prevalent classical public-key cryptographic technique. Nevertheless, the algorithm’s significant time complexity will result in a substantial computational burden on the edge computing chip in the IoT terminal. The computation of polynomial multiplication is the most demanding task in lattice-based cryptographic algorithms. Therefore, the investigation into efficient methods for calculating polynomial multiplication is highly important. Quick number theory transformations (NTT) are a widely employed technique to accelerate polynomial multiplication. This study presents a hardware implementation of an efficient number theory transformation. We utilize a multi-level pipeline architecture in the design to accomplish parallel calculations and execute it on a low-profile Artix7-XC7A100T FPGA device. The performance evaluation results demonstrate that our implementation significantly enhances performance and reduces resource usage compared to other existing proposals on the same platform. We suggested NTT core can be implemented in edge computing chips to enhance computational speed due to its small and low-latency design. The experimental results show that the proposed design, which supports both NTT and inverse NTT, achieves 417-Megahertz and consumes only 541-LUTs on Artix-7 XC7A100T.

Downward short-wave (SW) solar radiation is the only essential energy source powering the atmospheric dynamics, ocean dynamics, biochemical processes, etc. on our planet. Clouds are the main factor limiting the SW flux over the land and the Ocean. For the accurate meteorological measurements of the SW flux one needs expensive equipment - pyranometers. For some cases where one does not need golden-standard quality of measurements, we propose estimating incoming SW radiation flux using all-sky optical RGB imagery which is assumed to incapsulate the whole information about the SW flux. We used DASIO all-sky imagery dataset with corresponding SW downward radiation flux measurements registered by an accurate pyranometer. The dataset has been collected in various regions of the World Ocean during several marine campaigns from 2014 to 2022, and it will be updated. We demonstrate the capabilities of several machine learning models in this problem, which are multilinear regression, Random Forests, Gradient Boosting and convolutional neural networks (CNN). We also applied the inverse target frequency (ITF) re-weighting of the training subset and the approach of pre-classification in order to raise the SW flux approximation quality. We found that the CNN is capable of approximating downward SW solar radiation with higher accuracy compared to existing empiric parameterizations. We also found that the ITF reweighting of the training subset increases the approximation quality substantially. The estimates of the SW radiation flux using all-sky imagery may be of particular use in case of the need for the fast radiative budgets assessment of a site.

PET imaging with [¹⁸F]F-DOPA has demonstrated high potential for the evaluation and management of pediatric brain gliomas. Manual extraction of PET parameters is time-consuming, lacks reproducibility, and varies with operator experience. In this study, we tested whether a semi-automated image processing framework could overcome these limitations. Pediatric patients with available static and/or dynamic [¹⁸F]F-DOPA PET studies were evaluated retrospectively. We developed a Python software to automate clinical index calculations, including preprocessing to delineate tumor volumes from structural MRI, accounting for lesions with low [¹⁸F]F-DOPA uptake. 73 subjects with treatment-naïve low and high-grade gliomas, who underwent brain MRI within two weeks of [¹⁸vF]F-DOPA PET, were included and analyzed. Static analysis was conducted on all subjects, while dynamic analysis was performed on 32 patients. For 68 subjects, the Intraclass Correlation Coefficient for T/S between manual and ground truth segmentation was 0.91. Using our tool, ICC improved to 0.94. Our method demonstrated good reproducibility in extracting static tumor-to-striatum ratio (p = 0.357), though significant differences were observed in tumor slope (p < 0.05). No significant differences were found in time-to-peak (p = 0.167) and striatum slope (p = 0.36). Our framework aids in analyzing [¹⁸F]F-DOPA PET images of pediatric brain tumors by automating clinical score extraction, simplifying segmentation and Time Activity Curve extraction, reducing user variability, and enhancing reproducibility.

Agriculture stands as the cornerstone of Africa’s economy, supporting over 60% of the continent’s labor force. Despite its significance, the quality assessment of agricultural products remains a challenging task, particularly at large scale, consuming valuable time and resources. The African plum is an agricultural fruit that is widely consumed across West and Central Africa but remains underrepresented in AI research. In this paper, we collected a dataset of 2,892 African plum samples from fields in Cameroon representing the first dataset of its kind for training AI models. The dataset contains images of plums annotated with quality grades. We then trained and evaluated various state-of-the-art object detection and image classification models, including YOLOv5, YOLOv8, YOLOv9, Fast R-CNN, Mask R-CNN, VGG16, Detectron-121, MobileNet and ResNet, on this African plum dataset. Our experimentation resulted in mean average precision scores ranging from 88.2% to 89.9% and accuracies between 86% and 91% for the object detection models and the classification models respectively. We then performed model pruning to reduce model sizes while preserving performance, achieving up to 93.6% mean average precision and 99.09% accuracy after pruning YOLOv5, YOLOv8 and ResNet by 10-30%. We deployed the high-performing YOLOv8 system in a web application, offering an accessible AI-based quality assessment tool tailored for African plums. To the best of our knowledge, this represents the first such solution for assessing this underrepresented fruit, empowering farmers with efficient tools. Our approach integrates agriculture and AI to fill a key gap.

Background/Objectives: Although the role of F-18 FDG PET/CT imaging is well established in oncology, its diagnostic value in routine monitoring for recurrent colorectal cancer (CRC) is still controversial. The aim was to evaluate the diagnostic value of FDG PET/CT in detecting recurrent CRC in correlation with CEA, CA 19-9 levels, and conventional imaging modalities (CIM). Methods: Between 2009 and 2023, we retrospectively studied 134 CRC patients referred for FDG PET/CT imaging on the suspicion of recurrence based on elevated CEA and/or CA 19-9, and/or equivocal radiological findings. According to the Institutional Tumor Board CRC protocol, after initial treatment which was dependent on TNM stage (neoadjuvant therapy, primary resection or adjuvant treatment), patients underwent a standard 5-year surveillance including CEA and CA 19-9 measurements, CIM, and colonoscopy every 6 months. The statistics including univariate and multivariable analyses were conducted using the IBM SPSS 20.0 statistical software. P-values <0.05 were considered statistically significant. Results: Recurrent CRC was confirmed in 54/134 (40.3%) patients with elevated tumor markers. FDG PET/CT shows high diagnostic performance in detecting recurrent CRC with sensitivity, specificity, PPV, NPV, and accuracy of 94.4%, 82.5%, 78.5%, 95.7%, and 87.3%, respectively. The CEA shows a high sensitivity of 98.1%, but both low specificity and accuracy of 15% and 48.5%, respectively. The sensitivity, specificity, and accuracy, for CA 19-9 and CIM for the correct diagnosis in CRC patients were: 44.4%, 67.5%, 58.2%, and 51.9%, 98.8%, 79.9%, respectively. The AUC for FDG PET/CT, elevated CEA levels, CIM, and elevated CA 19-9 levels was 0.885 (95% CI: 0.824-0.946; p<0.001), 0.844 (95% CI: 0.772-0.916; p<0.001), 0.753 (95% CI: 0.612-0.844; p<0.001); and 0.547 (95% CI: 0.442-0.652; p=0.358). Univariate analysis shows that both FDG PET/CT- and CIM-positive results were highly associated with CRC recurrence (p<0.001 and p<0.001, respectively), while gender, mucinous tumor type, presence of initial lymph node metastasis (N+), and presence of initial distant metastasis (M+) have no significance (p=0.211, p=0.158, p=0.583, and p=0.201, respectively). Our multivariate analysis shows that independent predictors for CRC recurrence are: positive FDG PET/CT scans (p<0.001), positive CIM results (p=0.001) and elevated CA19-9 levels (p=0.023). Although CA 19-9 was not detected as a statistically significant predictor in the univariate analysis (p=0.358), in a multivariate analysis it was recognized as a significant predicting factor in detecting CRC recurrence (p=0.023). Conclusions: FDG PET/CT shows a high diagnostic efficacy in CRC recurrence detection, in correlation with CEA levels, CA 19-9 levels, and CIM. This imaging modality should be routinely integrated into the postoperative follow-op in patients with elevated tumor markers.

The AT-HOOK MOTIF NUCLEAR LOCALIZED PROTEIN (AHL) gene family encodes embryophyte-specific nuclear proteins with DNA binding activity. They modulate gene expression and affect various developmental processes in plants. We identify AHL18 (At3G60870) as a developmental modulator of root system architecture and growth. AHL18 regulates the length of the proliferation domain and number of dividing cells in the root apical meristem and thereby, cell production. Both primary root growth and lateral root development respond according to AHL18 transcription level. The ahl18 knock-out plants show reduced root systems due to a shorter primary root and a lower number of lateral roots. This change results from a higher number of arrested and non-developing lateral root primordia (LRP) rather than from decreased initiation. Overexpression of AHL18 results in a more extensive root system, longer primary roots, and increased density of lateral root initiation events. Formation of lateral roots is affected during the initiation of LRP and later development. AHL18 regulate root apical meristem activity, lateral root initiation and emergence, which is in accord with localization of its expression.

Hate Speech Detection in Arabic presents a multifaceted challenge due to the broad and diverse linguistic terrain. With its multiple dialects and rich cultural subtleties, Arabic requires particular measures to address hate speech online successfully. To address this issue, academics and developers have used natural language processing (NLP) methods and machine learning algorithms adapted to the complexities of Arabic text. However, many proposed methods were hampered by a lack of a comprehensive dataset/corpus of Arabic hate speech. In this research, we propose a novel multi-class public Arabic dataset comprised of 403,688 annotated tweets categorized as extremely positive, positive, neutral, or negative based on the presence of hate speech. Using our developed dataset, we additionally characterize the performance of multiple machine learning models for Hate speech identification in Arabic Jordanian dialect tweets. Specifically, the Word2Vec, TF-IDF, and AraBert text representation models have been applied to produce word vectors. With the help of these models, we can provide classification models with vectors representing text. After that, seven Machine learning classifiers have been evaluated: Support Vector Machine (SVM), Logistic Regression (LR), Naive Bays (NB), Random Forest (RF), AdaBoost (Ada), XGBoost (XGB), and CatBoost (CatB). In light of this, the experimental evaluation revealed that, in this challenging and unstructured setting, our gathered and annotated datasets were rather efficient and generated encouraging assessment outcomes. This will enable academics to delve further into this crucial field of study.

Background and aim: To explore the discriminant accuracy of Cytokeratin 18 (CK18, including M65 and M30 forms) for an elevated fatty liver index (FLI) as a validated proxy of Non-Alcoholic Fatty Liver Disease (NAFLD), and cardiovascular disease (CVD) risk in the general population. Methods: Both serum CK18 forms were measured using a commercial immunoassay in randomly selected samples from 312 participants of the PREVEND general population cohort. FLI ≥60 was used to indicate NAFLD. Framingham Risk Score (FRS) and the SCORE2 were used to estimate the 10-year risk of CVD. Receiver Operating Characteristic (ROC) curve, linear/logistic regression models and Spearman's correlations were used. Results: Intricate associations were found between CK18, FLI and CVD risk scores. While M30 was the only independent predictor of FLI≥60, M65 discriminated best NAFLD individuals at very-high 10-years CVD risk according to SCORE2 (AUC:0.71; p=0.001). Values above the predefined manufacturer cut-off (400 U/l), were associated with an independent 5-fold increased risk (adjusted odds ratio: 5.44, p=0.01), with negative predictive value of 93%. Conclusions: Confirming that NAFLD is associated with an increased CVD risk, our results point to CK18 M65 as a candidate biomarker to identify NAFLD individuals at low CVD risk in European general population.

The cytokine-producing nucleotide-binding domain leucine-rich repeat and pyrin-containing receptor 3 (NLRP3) inflammasome is generally recognized to play a central role in the immune response to vaccination and NLRP3 activity is known to be increased by both aluminum adjuvants and by oxidative stress. Thus, the presence of oxidative stress may place individuals at higher risk for exaggerated or prolonged inflammatory responses and related side effects from vaccination. We here examine the relationship between oxidative stress and NLRP3 inflammasome activity, particularly in the context of aluminum-adjuvanted vaccines, and its implications for subpopulations of individuals with restricted antioxidant capacity.

Metandienone and methyltestosterone are orally active anabolic-androgenic steroids with a 17α-methyl structure that are prohibited in sports but are frequently detected in anti-doping analysis. After the previously reported detection of long-term metabolites with a 17ξ-hydroxymethyl-17ξ-methyl-18-nor-5ξ-androst-13-en-3ξ-ol structure in the chlorinated metandienone analog dehydrochloromethyltestosterone (“Oral Turinabol”), in this study we investigated the formation of similar metabolites of metandienone and 17α-methyltestosterone with a rearranged D-ring and a fully reduced A-ring. Using a semi-targeted approach including synthesis of reference compounds, two diastereomeric substances, viz. 17α-hydroxymethyl-17β-methyl-18-nor-5β-androst-13-en-3α-ol and its 5α-analog, were identified following an administration of methyltestosterone. In post-administration urines of metandienone, only the 5β-metabolite was detected. Additionally, 3α,5β-tetrahydro-epi-methyltestosterone was identified in the urines of both administrations besides the classical metabolites included in the screening procedures. Besides their applicability for anti-doping analysis, the results provide new insights on the metabolism of 17α-methyl steroids with respect to the order of reductions in the A-ring, the participation of different enzymes, and alterations of the D-ring.

Toxoplasmosis is a zoonosis of warm-blooded animals, affecting also humans, caused by Toxo-plasma gondii, an obligate intracellular parasite with a complex biological cycle. Toxoplasmosis is considered a re-emerging zoonosis and an important public health threat, also for the absence, up to now, of efficient vaccines and preventive measures. Sources of transmission to humans are environmental or food-borne, mainly through the consumption of raw and undercooked meat, predominantly of Sus scrofa, considered highly susceptible to T. gondii. Mainly in the last dec-ades, an effective strategy to counter animal infectious diseases was based on the discovery of resistance immuno-genetic markers, possibly to be used for association studies between geno-typic and phenotypic traits and consequently in Marker Assisted Selection. Until now, this gen-otyping approach has never been adopted for T. gondii infection, so this study aimed, for the first time, to fill this gap. In particular, pigs, reared in different farms of Central Italy, were serologically characterized by an ELISA assay performed on diaphragm meat juice. Out of 179 tested animals, 98 resulted seropositive (54.74%), 57 seronegative (31.84%) and 24 doubtful (10.6%), underlining a possible re-emerging diffusion of this protozoan in the investigated areas. Atten-tion was firstly focused on IL-18 gene, encoding for a pro-inflammatory cytokine that plays a key role starting from the upstream steps of the immunity pathway towards T. gondii. A RT-PCR assay followed by Sanger sequencing of IL-18 cDNA was de novo developed. For this interleukin, at this stage, genetic analysis has not highlighted significant polymorphic variations compared to the reference sequence, except for a seropositive animal. However, the outcomes of this pre-liminary innovative study will be investigated in depth analyzing other relevant genetic re-gions and other target genes.

Fecal contamination is a common cause of impairment of surface waters. In monitoring studies, it usually is assessed by measuring concentrations of fecal indicator bacteria such as Escherichia coli (E. coli), a common monitoring target in freshwater systems. In this study, we assess the advantages and disadvantages of two common methods for monitoring E. coli concentrations at freshwater beaches: Colilert-18®, with a turnaround time of ca. 18 h, and real-time quantitative PCR (qPCR), with a turnaround time of ca. 3–4 h. Based on data comprising 3081 pairs of Colilert-18 and qPCR estimates of E. coli concentrations in split samples from Michigan’s annual beach monitoring program in 2019 and 2020, we found that qPCR monitoring detected a high percentage of exceedances of the state’s water quality standard for E. coli contamination that went undetected on the day of sampling with Colilert-18 monitoring, because qPCR concentration estimates were available on the day of sampling but Colilert-18 estimates were not. However, Colilert-18 data were more useful than qPCR data for statistical comparison of contamination levels at different beaches, probably in part because Colilert-18 data showed a much lower percentage of censored concentration estimates (estimates outside the range of quantification).

For esophageal cancer patients treated with neoadjuvant chemoradiotherapy (nCRT), restaging using F-18-fluorodeoxyglucose (18F-FDG) positron emission tomography computed tomography (PET-CT) following nCRT can detect interval metastases, including liver metastases, in almost 10% of patients. However, in clinical practice, focal FDG liver uptake, unrelated to liver metastases, is observed after chemoradiotherapy. This radiation-induced liver injury (RILI) can potentially lead to overstaging. Liver radiation damage is also a concern during distal esophageal cancer radiotherapy due to proximity to the left liver lobe, typically included in the radiation field. Post-CRT, if FDG activity appears in the left or caudate liver lobes, thorough investigation is needed to confirm or rule out distant metastases. The increased FDG uptake in liver lobes post-CRT often presents a diagnostic dilemma. Distinguishing between radiation-induced liver disease and metastasis is vital for appropriate patient management, necessitating a combination of imaging techniques and an understanding of the factors influencing radiation response. Diagnosis involves identifying new foci of hepatic FDG avidity on PET/CT scans. Geographic regions of hypoattenuation on CT and well-demarcated regions with specific enhancement patterns on contrast-enhanced CT scan and MRI are characteristic of radiation-induced liver disease (RILD). Lack of mass effect on all three modalities (CT, MRI, PET) indicates RILD. Resolution of abnormalities on subsequent examinations also helps in diagnosing RILD. Moreover, it can also help to rule out occult metastases, thereby excluding those patients from further surgery who will not benefit from esophagectomy with curative intent.

It is known that knockdown of the mitochondrial 18 kDa translocator protein (TSPO) as well as TSPO ligands modulate various functions, including functions related to cancer. To study the ability of TSPO to regulate gene expression regarding such functions, we applied microarray analysis of gene expression to U118MG glioblastoma cells. Within 15 minutes, the classical TSPO ligand PK 11195 induced changes in expression of immediate early genes and transcription factors. These changes also included gene products that are part of the canonical pathway serving to modulate general gene expression. These changes are in accord with reverse transcriptase (RT) real-time -PCR. At the time points of 15, 30, 45, and 60 minutes, as well as 3 and 24 hours of PK 11195 exposure, the functions associated with the changes in gene expression in these glioblastoma cells covered well known TSPO functions. These functions included cell viability, proliferation, differentiation, adhesion, migration, tumorigenesis, and angiogenesis. This was corroborated microscopically for cell migration, cell accumulation, adhesion, and neuronal differentiation. Changes in gene expression at 24 hours of PK 11195 exposure were related to downregulation of tumorigenesis and upregulation of programmed cell death. In the vehicle treated as well as PK 11195 exposed cell cultures, our triple labeling showed intense TSPO labeling in the mitochondria but no TSPO signal in the cell nuclei. Thus, mitochondrial TSPO appears to be part of the mitochondria-to-nucleus signaling pathway for modulation of nuclear gene expression. The novel TSPO ligand 2-Cl-MGV-1 appeared to be very specific regarding modulation of gene expression of immediate early genes and transcription factors.

We report a case of colloid carcinoma (CC) arising from an intestinal-type intraductal papillary mucinous neoplasm with high-grade dysplasia (IPMNHGD) of the pancreas, diagnosed with serial pancreatic juice aspiration cytological examination (SPACE). A rapidly growing intraductal papillary mucinous neoplasm (IPMN) in a 71-year-old Japanese man accelerated his hospitalization in our institute. Clinically, a large, ruptured pancreatic cyst was suspected. Cytologically, several mucin-positive signet-ring cells were scattered in the inflammatory, necrotic, or mucinous background. Signet-ring cells in cell block specimens were immunoreactive for MUC2, MUC5AC, maspin, S100P, and claudin-18. The final cytologic diagnosis was CC arising in an intestinal-type IPMNHGD with intraperitoneal penetration. The patient died two months postoperatively. The cytologic diagnosis was achieved through SPACE, and the presence of signet-ring cells was characteristic. Anti-claudin-18.2-specific monoclonal antibody therapy will likely be used to treat patients with IPMNHGD in the future. To our knowledge, this is the first report in English on mucin-positive signet-ring cells of CC arising in an intestinal-type IPMNHGD evaluated by SPACE cytology.

Mild traumatic brain injury mTBI is a common occurrence around the world, associated with a variety of blunt force and torsion injuries affecting all age groups. Most never reach medical attention, and identification of acute injury and later clearance to return to usual activities is relegated to clinical evaluation particularly in sports injuries. Advanced structural imaging is rarely performed due to the usual absence of associated acute anatomic/hemorrhagic changes. This review targets physiologic imaging techniques available to identify subtle blood brain barrier dysfunction and white matter tract shear injury, and their association with chronic traumatic encephalopathy. These techniques provide needed objective measures to assure recovery from injury in those patients with persistent cognitive/emotional symptoms and in the face of repetitive mTBI.

A contribution of Epstein-Barr Virus (EBV), Human Cytomegalovirus (HCMV), and certain types of Human Papilloma Virus (HPV) to the overall pathogenesis of upper airways cancers seems a priori reasonably conceivable. The existing evidence mostly relates them to nasopharyngeal squamous and undifferentiated carcinomas; indications emerge about their involvement in other upper airways cancers too. Markers of EBV, HCMV, HPV16, HPV18, (and Mycoplasma pneumoniae as a negative control) were searched in a series of 68 samples of neoplastic tissues from 57 patients diagnosed with different upper airways cancers. The results provide clues in favor of an actual role of EBV, HCMV, and HPV18, in the web of causation of upper airways squamous and undifferentiated carcinomas, possibly of upper airways adenocarcinomas too.

This study advances the field of solar irradiance nowcasting by introducing a discrete-level classification approach, diverging from traditional continuous measurement methods. Grounded in a need for cost-effective and modular solutions, the research employs high-resolution computer vision coupled with a deep learning framework to predict Direct Normal Irradiance (DNI). By harnessing the capabilities of a Logitech C900 1080p HD camera and an NVIDIA Jetson module, the study achieves real-time data processing, pivotal for CSP systems' operational efficiency. The core of the methodology is the ResNet-50 convolutional neural network, refined via transfer learning on a bespoke dataset, culminating in a predictive accuracy of 85.78%. This discrete classification model contrasts with conventional, costly instruments like the MS-57, offering a novel and accessible alternative for DNI estimation. Such innovation not only demonstrates high predictive accuracy but also signifies a shift towards less resource-intensive and more adaptable solar energy forecasting tools, contributing a significant leap towards optimizing renewable energy systems.