Deep neural networks (DNNs) have gained prominence in addressing regression problems, offering versatile architectural designs that cater to various applications. In the field of earthquake engineering, seismic response prediction is a critical area of study. Simplified models such as single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) systems have traditionally provided valuable insights into structural behavior, known for their computational efficiency facilitating faster simulations. However, these models have notable limitations in capturing the nuanced nonlinear behavior of structures and the spatial variability of ground motions. This study focuses on leveraging ambient vibration (AV) measurements of buildings, combined with earthquake (EQ) time-history data, to create a predictive model using a neural network (NN) in image format. The primary objective is to predict a specific building's earthquake response accurately. The training dataset consists of 1,197 MDOF 2D shear models, generating a total of 32,319 training samples. To evaluate the performance of the proposed model, termed MLPER (Machine Learning based Prediction of building structures' Earthquake Response), several metrics are employed. These include mean absolute percentage error (MAPE) and mean deviation angle (MDA) for comparisons in the time domain. Additionally, we assess magnitude-squared coherence values and phase differences (Δφ) for comparisons in the frequency domain. This study underscores the potential of MLPER as a reliable tool for predicting building earthquake response, addressing the limitations of simplified models. By integrating AV measurements and EQ time-history data into a neural network framework, MLPER offers a promising avenue for enhancing our understanding of structural behavior during seismic events, ultimately contributing to improved earthquake resilience in building design and engineering.

An abdominal aortic aneurysm (AAA) is usually asymptomatic until rupture, which is associated with extremely high mortality. Consequently, early detection of AAAs is of paramount importance in reducing mortality; however, most AAAs are detected by medical imaging incidentally. The aim of this study was to investigate the feasibility of machine learning-based pulse wave (PW) analysis for the early detection of AAAs using a database of in silico PWs. PWs in the large systemic arteries were simulated using one-dimensional blood flow modelling. A database of in silico PWs representative of subjects (aged 55, 65 and 75 years) with different AAA sizes was created by varying the AAA-related parameters with major impacts on PWs – identified by parameter sensitivity analysis – in an existing database of in silico PWs representative of subjects without AAA. Then, a machine learning architecture for early detection of AAAs was proposed, which was trained and tested using the new in silico PW database. The parameter sensitivity analysis revealed that the AAA maximum diameter and stiffness of the large systemic arteries were the dominant AAA-related biophysical properties that significantly influence the PW. The simulated PW indexes extracted from the database showed that the PW was not only influenced by the presence of an AAA but was also significantly affected by multiple cardiovascular parameters that compromised the detection of AAAs by using individual PW indexes. Alternatively, the trained machine learning model performed well in classifying normal and AAA conditions using digital photoplethysmogram PWs from the database. These findings suggest that machine learning-based PW analysis is a promising approach for AAA screening using PW signals acquired by wearable devices.

The Bayes filters, such as Kalman and particle filters, have been used in sensor fusion to integrate two sources of information and obtain the best estimate of the unknowns. Efficient integration of multiple sensors requires deep knowledge of their error sources and it is not trivial for complicated sensors, such as Inertial Measurement Unit (IMU). Therefore, IMU error modelling and efficient integration of IMU and Global Navigation Satellite System (GNSS) observations has remained a challenge. In this paper, we develop deep Kalman filter to model and remove IMU errors and consequently, improve the accuracy of IMU positioning. In other words, we add modelling step to the prediction and update steps of Kalman filter and the IMU error model is learned during integration. Therefore, our deep Kalman filter outperforms Kalman filter and reaches higher accuracy.

Background: This study aimed to evaluate survival rate, marginal bone levels, and full arch prosthetic success on short implants when placed in areas of severely resorbed and edentulous mandibles. Methods: This is a systematic review of all randomized controlled trials of at least 10 patients with a control group in which bone augmentations were performed that were published between January 2010 and February 2023. Only 3 relevant studies met the inclusion criteria. Results: It obtained demonstrate that short-term dental implant survival rates ranged from 94.2% to 97.4% with a 5-year follow-up and the prosthetic success rate varied by 62% during the same follow-up. The mean marginal bone level values ​​of the affected short implants ranged from 0.2 mm to 0.6 mm. Conclusions: The data obtained demonstrated that short dental implants positioned with criterion and precision as a full-arch fixed support are a valid therapeutic choice for the medium-long term rehabilitation of severe edentulous mandibular atrophy.

This work addresses the challenge of EEG visual multiclass classification into 40 classes for Brain-Computer interface applications, by using deep learning architectures. The visual multiclass classification approach offers BCI applications a significant advantage, since it allows the supervision of more than one BCI interaction, considering that each class label supervises a BCI task. However, because of the nonlinearity and nonstationarity of EEG signals, performing multiclassifiers based on EEG features remains a significant challenge for BCI systems. In the present work, mutual information-based discriminant channel selection and Minimum-Norm Estimate algorithms are implemented to select discriminant channels and enhance EEG data. Hence, deep EEGNet and Convolutional-recurrent neural networks are implemented separately to classify EEG data of image visualization into 40 labels. By using the k-fold cross-validation approach, average classification accuracies of 94.8% and 89.8% were obtained by implementing the aforementioned network architectures, respectively. Satisfactory results obtained with this method offer a new implementation opportunity for multi-task BCI applications by utilizing a reduced number of channels (<50%), compared to those presented in the related literature where the whole set of channels is used.

Deep Learning (DL) oriented document processing is widely used in different fields for extraction, recognition, and classification processes from raw corpora of data. The article examines the application of deep learning approaches, based on different neural network methods, including Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN). The compared models were combined with two different word embedding techniques, namely: Bidirectional Encoder Representations from Transformers BERT and Gensim Word2Vec. The models are designed to evaluate the performance of architectures based on neural network techniques for the classification of CVs of Moroccan engineering students at ENSAK(National School of Applied Sciences of Kenitra, Ibn Tofail University). The used dataset included resumes collected from engineering students at ENSAK in 2023 for a project on the employability of Moroccan engineers in which new approaches were applied, especially machine learning, deep learning, and big data. Accordingly, 867 resumes were collected from five specialties of study (Electrical Engineering, Networks and Systems Telecommunications, Computer Engineering, Automotive Mechatronics Engineering, Industrial Engineering). The results revealed good performance of the proposed models based on the BERT embedding approach compared to models based on the Gensim Word2Vec embedding approach. Accordingly, the CNN-GRU/Bert model achieved slightly better accuracy with 0.9251 compared to other hybrid models.

The present study examines the long-term care service awareness, needs, and usage intention of elderly male homosexuals in Taiwan and their ideal long-term care service model. This study of five elderly male homosexual subjects aged 66 to 73 years was intended as a preliminary exploration. Interviews were used to collect data. The study determined that the five subjects had high awareness of long-term care services, as they had actual experience of long-term care services or even participated in care service staff training to obtain certificates. Some even had experience in applying for home care services and experienced problems during use. The subjects perceived that they were very likely to require long-term care services in the future and tended to use home care services if they required long-term care services. Due to their personal experiences, the subjects had negative awareness of long-term care services and worried that long-term care service staff had poor attitudes toward homosexuals. The subjects considered the most important aspects of long-term care to be basic medical care and lifestyle care. However, they worried that long-term care staff would delay or refuse to provide such services due to the subjects’ sexual orientation or stereotypes, and they were concerned above all about the “friendly attitude” of long-term care staff. They hoped that long-term care staff were friendly toward homosexuals and did not discriminate against them, and they felt that it would be more appropriate for homosexual long-term care staff to provide assistance. In terms of vision, while preferring organizations with homosexual employees, the subjects worried that they would be stigmatized and discriminated against . With regard to ideal long-term care services, while considering institutions with homosexual staff to be ideal, the subjects also worried that these would be labeled as institutions that were dedicated to homosexuals, potentially resulting in discrimination. Therefore, they hoped that the sexuality sensitivity of long-term care staff could be improved and that they would undergo professional continuing education to learn about homosexuals, the situation and care needs of homosexuals, and care techniques for homosexuals.

Stress has emerged as a major concern in modern society, significantly impacting human health and well-being. Statistical evidence underscores the extensive social influence of stress, especially in terms of work-related stress and associated healthcare costs. This work addresses the critical need for accurate stress detection, emphasising its far-reaching effects on health and social dynamics. Focusing on remote stress monitoring, this work proposes an efficient deep learning framework to discern stress from facial videos. In contrast to research on wearable devices, this paper investigates the application of *dl for stress detection based on *rppg. The methodology involves selecting suitable *dl models (*lstm, *gru, *1dcnn), optimising hyperparameters, and investigating augmentation techniques. 1D-CNNv1 model achieved the best performance compared to other approaches, particularly with augmentation techniques such as linear interpolation and white noise, achieving a stress classification accuracy of 95.83% while maintaining excellent computational efficiency. The experimental results demonstrate the use of *dl for stress detection based on *rppg, with the potential to make significant contributions to the international standard in the field.

Extensive limb lengthening (ELL) was completed in 75 patients: 66 achondroplasia and 9 hypochondroplasia. The average lengthening was 27cm for achondroplasia (12-40cm) and 17cm for hypochondroplasia (range 10-25cm). There were 48 females and 27 males. Lengthening was done either by 2-segment (14 patients; both tibias and/or both femurs) or by serial 4-segment lengthenings (64 patients; both femurs and tibias same time). Most patients also had bilateral humeral lengthening. Lengthenings were either juvenile-onset (31), adolescent-onset (38) or adult-onset (6). The average age at final follow-up was 26 years old (range 17-43 years). There were few permanent sequelae of complications. The most serious was one paraparesis. All patients returned to activities of normal living and only one was made worse by the surgery (paraparesis). This is the first study to show that ELL can lead to increase of height into the normal height range. Previous studies showed mean increases of height of up to 20cm, while this study consistently showed an average increase of 30 cm (range 15-40cm) for juvenile-onset and increase of 26cm (range 15-30cm) for adolescent-onset. This results in lower normal height at skeletal maturity for males and females. The adult-onset had a mean increase of 16.8 (range 12-22cm). This long-term follow-up study shows ELL can be done safely even with large lengthenings and that 4-segment lengthening may offer advantages over 2-segment lengthening. While the majority of cases were performed using external fixation, implantable limb lengthening promises to be an excellent alternative and perhaps an improvement.

Cultural Tourism (CT), which enhances the economic development of a region, aids a country in reinforcing its identities, enhancing cross-cultural understanding, and preserving the heritage culture of an area. Designing a proper tourism model assists the tourists in understanding the point of interest without the help of a local guide. However, owing to the need for the analysis of different factors, designing such a model is a complex process. Therefore, this article proposes a CT model for Riyadh for peak visitor time. Primarily, the map data and cultural event dataset are processed for location, such as grouping with Kriging Interpolation-based Chameleon (KIC), tree forming, and feature extraction. After that, the event dataset’s attributes are processed with word embedding. Meanwhile, the Social Network Sites (SNS) data like reviews and news are extracted with an external Application Programming Interface (API). The review data is processed with keyword extraction and word embedding, whereas the news data is processed with score value estimation. Lastly, the data are fused corresponding to a historical site and given to the Multi-Quadratic-Long Short Term Memory (MQ-LSTM) Recommendation System (RS); also, the recommended result with the map is stored in a database. Lastly, the database security is maintained with Locality Sensitive Hashing (LSH); moreover, by attaining higher performance values, the proposed model is experimentally verified.

Introduction: Intrauterine fetal death (IFD) is death of fetus after the 20th week of gestation. Despite regular monitoring, the incidence of IFD remains high. This study aims to assess the incidence and risk factors for IFD in term pregnancies for better prevention and reduction of neonatal morbidity and mortality. Materials and Methods: A retrospective cohort study was conducted with term pregnant women diagnosed with IFD. The analysis included the number of deliveries, live births, and stillbirths. Statistical analysis involves descriptive and analytical statistical methods. Results: Average age was 30 years. Most patients had intermediate and high education, and one-third of patients had regular pregnancy monitoring. 53.33% were primiparous, and pregnancies occurred spontaneously. IFD mainly occurred in the 39th week of gestation. The average birth weight was approximately 3000g. 38.3% had one to two associated diseases, 5% more than three, and 56.7% were healthy. Recurrence of IFD was reported by 10% of patients, while 8.33% had a history of spontaneous abortion. Over 80% of placental histopathological findings indicated some pathology (infarction, infections, abruptio placenta). Conclusion: The most significant risk factors were pregnancy-induced hypertensive disorders, obesity, and diabetes, followed by hypothyroidism, maternal tachycardia, and anemia.

Background: To analyze long-term outcomes in patients undergoing thoracic endovascular aortic repair (TEVAR). Methods: All consecutive 97 patients undergoing TEVAR between September 2014 and September 2022 were included in the study. Primary outcome was long-term incidence of overall death and major adverse cardiovascular and cerebrovascular events (MACCE). Results: Mean age was 70.4 years, and 22(23.2%) had cerebrovascular disease (CBVD). A total of 49(51.6%) of patients had prior cardiac surgery intervention and 8(8.5%) had prior aortic valve replacement. Twenty-eight patients(28.8%) presented with aortic dissection, 60(61.8%) had aortic aneurysm, 4(4.1%) had intramural hematoma, and 5(5.1%) had other presentations. An emergent procedure was performed in 6(6.2%) patients, an urgent procedure in 37(38.1%) patients and 54(55.7%) patients had an elective procedure. Intraoperatively, 78.3% had percutaneous TEVAR, 5.1% had ministernotomy TEVAR, while 10.3% had concomitant full sternotomy TEVAR repair. Hospital mortality was 7 patients(7.2%). At 8-years follow-up, 76% were alive, 25.8% had MACCE, 21.6% were diagnosed with endoleaks(13 patients type II and 2 patients type 1) while 10.3% un-derwent repeat intervention. Conclusions: This single center experience in patients undergoing TEVAR evidenced good short and long-term survival and MACCE. Nonetheless, almost half of the patients underwent an ur-gent/emergent procedure, clinical results were favourable for TEVAR.

We have shown in previous work that the equivalence of the Heisenberg and Schrödinger pictures of quantum mechanics requires the use of the Born and Jordan quantization rules. In the present work we give further evidence that the Born--Jordan rule is the correct quantization scheme for quantum mechanics. For this purpose we use correct short-time approximations to the action functional, initially due to Makri and Miller, and show that these lead to the desired quantization of the classical Hamiltonian.

Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepfake detection method using upper body language analysis. Specifically, a many-to-one LSTM network was designed and trained as a classification model for deepfake detection. Different models were trained by varying the hyperparameters to build a final model with benchmark accuracy. We achieved 94.39% accuracy on the deepfake test set. The experimental results showed that upper body language can effectively detect deepfakes.

Predicting change from multivariate time series has relevant applications ranging from medical to engineering fields. Multisensory stimulation therapy in patients with dementia aims to change the patient’s behavioral state. For example, patients who exhibit a baseline of agitation may be paced to change their behavioral state to relaxed. This study aims to predict changes in behavioral state from the analysis of the physiological and neurovegetative parameters to support the therapist during the stimulation session. In order to extract valuable indicators for predicting changes, both handcrafted and learned features were evaluated and compared. The handcrafted features were defined starting from the CATCH22 feature collection, while the learned ones were extracted using a Temporal Convolutional Network, and the behavioral state was predicted through Bidirectional Long Short-Term Memory Auto-Encoder, operating jointly. From the comparison with the state-of-the-art, the learned features-based approach exhibits superior performance with accuracy rates of up to 99.42% with a time window of 70 seconds and up to 98.44% with a time window of 10 seconds.

The rationalization and modeling of an electric discharge in a fluid require the knowledge of electron swarm parameters. In this work, transport coefficients of alcohols, including methanol, ethanol, propanol, and butanol are reported. The electron energy distribution function, mean energy, reduced mobility, and diffusion coefficient, are calculated using the two-term expansion of the electron Boltzmann equation in the frame of BOLSIG+ and the Monte Carlo collision code METHES. For such, experimental and theoretical values of the electron-alcohol collision cross-section, covering a range of electron impact energy from 1 eV to 1000 eV, are employed. A temperature of 300K, reduced fields E/N between $1-10^4$ Td (1Td = $10^{-21}~\rm Vm^2$), and Plasma density of $10^{19}~\rm m^{-3}$ were used in simulations. The calculated coefficients are compared with data available from previous studies.

Abstract Background: Massive resection of the small intestine leading to short bowel syndrome (SBS) deprives organism of many immunocompetent cells concentrated in gut associated lymphoid tissue- the largest immune organ in humans. We have aimed to access the influence of bowel resection on adaptive immunity in children, basing on peripheral lymphocyte subsets and serum immunoglobulins. Methods: 15 children, who underwent bowel resection in the first month of life and required further home parenteral nutrition were enrolled into the study.. Based on flow cytometry the following subsets of lymphocytes were evaluated: T, B, NK, CD4+, C8+and activated T cells. Results: Significantly statistical differences were found for the rates of lymphocytes B, T, CD8 + and NK cells. Absolute count of NK cells was lower in SBS group than in the control group. Absolute counts of lymphocytes, lymphocytes B, T, CD4+ and percentages of lymphocytes CD4+, and activated T cells inversely correlated with the time after resection. Conclusions: Children with SBS do not present with clinical signs of immunodeficiency as well as deficits in peripheral lymphocyte subsets and serum immunoglobulins. The tendency of the lymphocyte subpopulations to decrease over time after resection points out the necessity for longer follow- up.

Nonsteroidal anti-inflammatory drugs (NSAIDs), which block the activity of cyclooxygenase (COX) isoenzymes and inhibit the synthesis of prostaglandin, have been used for pain relief. We have developed a method to separate a mixture of three NSAIDs, such as aspirin, paracetamol, and naproxen, using reverse-phase high-performance liquid chromatography (RP-HPLC). An isocratic mobile phase consisting of acidic water and acetonitrile was selected to run at a low flow rate, such as 0.8 mL/min. The mixture of three NSAIDs was injected at a low volume into a C18 column that was 150 mm in length and characterized using a UV detector at 230 nm. We identified three peaks in the chromatogram indicating the three compounds. The elution time of the peaks was less than 10 minutes. To identify multiple peaks on the isocratic flow using a short column, further studies are required regarding the proposed method to generate microfluidic devices for nanoLC.

The main goal of the review is to provide a summary and an assessment of the potential of fast-growing tree species for suitable transformation of agroforestry areas for biomass production in the Baltic Sea region. The article summarizes the research on the management process of agroforestry zones by establishing short rotation plantations with tree species Salix spp., Populus spp., Alnus spp. and looks at the perspectives of planning of these zones as biomass producers. Short rotation forestry (SRF) with a combination of species and a rotation time of 15 to 30 years, depending on the species used, is the most suitable approach for management of these agroforestry zones. Willows (Salix spp.) and poplars (Populus spp.) are suitable for short rotation coppice (SRC), as these tree species can be harvested at much shorter intervals, respectively, 1–5 and 4–10 years, facilitating their use in agricultural systems. In Alnus spp. short rotation plantation the life cycle for energy wood production is assumed to be 15-30 years. The black alder plantations in agroforestry zones are used for sawnwood and firewood production, with a rotation span of 20–40 years. Calculated economic agroforestry zone repayment period is about 10-15 years, if costs and prices as in 2021 are used.

Deep learning (DL) algorithms have recently emerged from machine learning and soft computing techniques. Since then, several deep learning (DL) algorithms have been recently introduced to scientific communities and are applied in various application domains. Today the usage of DL has become essential due to their intelligence, efficient learning, accuracy and robustness in model building. However, in the scientific literature, a comprehensive list of DL algorithms has not been introduced yet. This paper provides a list of the most popular DL algorithms, along with their applications domains.

We conducted a hybrid-design study on a historical cohort of 359 adult recipients of a liver graft who received transplants between 1996 and 2002. The study aimed to identify predictors of survival and investigate the prevalence of co-morbidities among long-term survivors. The actuarial (95% CI) patient survival of the overall study cohort was 96% (94.6%-98.3%), 69% (64.2%-73.6%), 55% (49.8%-59.9%), 42.8% (37.6%-47.8%), and 34% (29.2%-38.9%) at 1, 5, 10, 15, and 20 years, respectively. The actuarial (95% CI) graft survival was 93.8% (91.6%-97.2%), 67.6% (62.2%-71.5%), 54.3% (49.6%-59.3%), 42.1% (37.3%-47.4%), and 33.8% (29.0%-38.4%) at 1, 5, 10, 15, and 20 years, respectively. The leading cause of death was hepatitis C virus recurrence (24.6%), followed by extrahepatic malignancies (16.9%), infection (14.4%), and hepatocellular carcinoma recurrence (14.4%). The Cox proportional hazards analysis revealed that several independent factors were associated with the survival probability. These factors include younger donor and recipient ages (p=0.001 and 0.004, respectively), female recipient sex (p<0.001), absence of HCV (p<0.01), absence of HCC (p=0.001), and absence of diabetes mellitus at one year (p<0.01). At the latest follow-up, the leading comorbidities in the long-term survivors were hypertension (53.6%), obesity (18.7%), diabetes mellitus (17.1%), hyperlipidemia (14.7%), chronic kidney dysfunction (14.7%), and extrahepatic malignancies (13.8%), with 73.9% of patients having more than one complication. The Kaplan-Meier probability (95% CI) of complication-free survival was 65.4% (60.2%-70.3%), 38.4% (33.4%-43.7%), 17.8% (14.1%-22.2%), 7.2% (4.8%-10.5%), and 1.94% (0.85%-4.15%) at 1, 5, 10, 15, and 20 years, respectively. Aging with a liver graft is associated with an increased risk of complications and requires ongoing care to reduce the long-term attrition rate resulting from chronic immunosuppression.

Background: There are ongoing workforce challenges with the delivery of long-term care (LTC), such as staffing decisions based on arbitrary standards. The Synergy tool, a resident-centered approach to staffing, pro-vides objective, real-time acuity and dependency scores (Synergy scores) for residents. The purpose of this study was to implement and evaluate the impact of the Synergy tool on LTC delivery. Methods: A longitudinal mixed methods study took place within two publicly-funded LTC homes in British Columbia, Canada. Quantitative data included weekly Synergy scores for residents (24 weeks), monthly aggregated resident falls data (18 months) and a six-month economic evaluation. Qualitative data were gathered from family caregivers and thematically analyzed. Results: Quantitative findings from Synergy scores revealed considerable variability for resident acuity/dependency needs within and across units; and falls decreased during implementation. The six-month economic evaluation demonstrated some cost savings by comparing Synergy tool training and implementation costs with savings from resident fall rates reductions. Qualitative analyses yielded three positive impacts themes (improved care delivery, better communication, and improved resident-family-staff relationships), and two negative structural themes (language barrier and staff shortages). Conclusions: The Synergy tool provides useful data for enhancing a ‘fit’ between resident needs and available staff.

Conclusive evidence that specic long-term memory formation relies on den- dritic growth and structural synaptic changes has proven elusive. Connec- tionist models of memory based on this hypothesis are confronted with the so-called plasticity stability dilemma or catastrophic interference. Other fun- damental limitations of these models are the feature binding problem, the speed of learning, the capacity of the memory, the localisation in time of an event and the problem of spatio-temporal pattern generation. This paper suggests that the generalisation and long-term memory mechanisms are not correlated. Only the development and the improvement of the feature ex- tractors in the cortex involves structural synaptic changes. We suggest the long-term memory has a separate mechanism which involves protein synthe- sis to encode the information into the structure of these proteins. A model of memory should be capable of explaining the dierence between memorisation and learning. Learning has in our approach two dierent mechanisms. The generalisation in the brain is handled by the proper development of the links between neurons via synapses. The Hebbian learning rule could be applied only for this part of learning. Storing an internal ring pattern involves, in our approach, a new mechanism which puts the information regarding this ring pattern into the structure of special proteins in such a way that it can be retrieved later. The hypotheses introduced in this article includes a physiological assumption which has not been yet verified because it is not currently experimentally accessible. Keywords: Waves, Protein Synthesis, Resonance, Long Term Memory Preprint submitted to Neural Networks

The rainfall study in the long term is essential for climatic change understanding and socioeconomic development. The main goal of this study is to explore the spatial and temporal variations of precipitation in different time scales (seasonal and annual) in Cameroon. The Mann–Kendall and Pettitt tests were applied to analyze the precipitation variability. On temporal plan, the different regions of Cameroon have recorded significant drops in annual rainfall that Pet-titt's test generally situates around the 1970s. The decreases observed for the northern part of Cameroon regions are between –5.4% (Adamawa) and –7.4% (Far North). Those of west-ern part regions oscillate between –7.5% (South-West) and –12.5% (West). The southern part of Cameroon regions recorded decreases varying between –4.3% (East) and –5.9% (Center). On spatial plan, the divisions of the northern, western and southern parts of Cameroon respectively recorded after the 1970s (a pivotal period in the evolution of precipitation on temporal plan), a precipitation decrease towards the South, the South-West and the West. This study's findings could be helpful for planning and managing water resources in Cameroon.

In this study, 6 residents of a long-term care facility were asked to try on Virtual Reality glasses and report their first experiences with Virtual Reality. The results show that Virtual Reality is of great interest to elderly residents of in-patient long-term care facilities. The wearing period was longer than expected and no symptoms of cyber sickness occurred. For the residents it was exciting to explore the virtual environments. Austrian destinations, nature scenes in the mountains and forests but also trips to the zoo, the museum, in churches or even densely populated areas like shopping streets or train stations would be places for the residents, they would like to explore virtually. Far-off destinations such as Rio de Janeiro or the Caribbean are more of an exception. Biographically relevant places such as the parental home or the location of their wedding were not named. Concerning the usability, an adjustment of the VR glasses is necessary for a longer-term use in any case.

Muscarinic acetylcholine receptors are well-known for their crucial involvement in hippocampus-dependent learning and memory, but the exact roles of the various receptor subtypes (M1-M5) are still not fully understood. Here, we studied how M1 and M3 receptors affect plasticity at the mossy fiber (MF)-CA3 pyramidal cell synapse. In hippocampal slices from M1/M3 receptor double knockout (M1/M3-dKO) mice, the signature short-term plasticity of the MF-CA3 synapse was not significantly affected. However, the rather unique, NMDA receptor-independent and presynaptic form of long-term potentiation (LTP) of this synapse was much larger in M1/M3-deficient slices compared to wild type slices, in both field potential and whole-cell recordings. Consistent with its presynaptic origin, induction of MF-LTP strongly enhanced the excitatory drive onto single CA3 pyramidal cells, with the effect being more pronounced in M1/M3-dKO cells. In an earlier study [1], we found that deletion of M2 receptors in mice disinhibits MF-LTP in a similar fashion, suggesting that endogenous acetylcholine employs both M1/M3 and M2 receptors to constrain MF-LTP. Importantly, such synergism was not observed for MF long-term depression (LTD). Low-frequency stimulation, which reliably induced LTD of MF synapses in control slices, failed to do so in M1/M3-dKO slices and gave rise to LTP instead. In striking contrast, loss of M2 augmented LTD when compared to control slices. Taken together, our data demonstrate convergence of M1/M3 and M2 receptors on MF-LTP, but functional divergence on MF-LTD, the net effect being well-balanced bidirectional plasticity of the MF-CA3 pyramidal cell synapse.

Inside hippocampal circuits, neuroplasticity events that individual cells may undergo during synaptic transmissions occur in the form of Long Term Potentiation (LTP) and Long Term Depression (LTD). The high density of NMDA receptors expressed on the surface of the dendritic CA1 spines confers to hippocampal CA3-CA1 synapses, the ability to easily undergo NMDA-mediated LTP and LTD, that is essential for some forms of explicit learning in mammals. Providing a comprehensive kinetic model that can be used for running computer simulations of the synaptic transmission process is currently a major challenge. Here, we propose a compartmentalized kinetic model for CA3-CA1 synaptic transmission. Our major goal was to tune our model in order to predict the functional impact caused by disease associated variants of NMDA receptors related to severe cognitive impairment. Indeed, for variants Glu413Gly and Cys461Phe, our model predicts negative shifts in the glutamate affinity and changes in the kinetic behavior, consistent with experimental data. These results pinpoint to the predictive power of this multiscale viewpoint, which aims to integrate the quantitative kinetic description of large interaction networks typical of system biology approaches with a focus on the quality of few, key, molecular interactions typical of structural biology ones.

Short bowel syndrome (SBS) in adults is defined as having less than 180 to 200 cm of remaining small bowel [1–4]. Many literature sources do not provide precise epidemiological data, challenges in estimating the prevalence of SBS include its multifactorial etiology and varying definitions [1–7]. The most common pathologies leading to SBS include Crohn disease, mesenteric ischemia, radiation enteritis, post-surgical adhesions, and post-operative complications [1,2,4–6]. In advanced SBS, parenteral nutrition may be required to ensure that all vital nutrients are delivered directly through the venous system, bypassing the gastrointestinal tract [1,8–10]. In this article, we present a clinical case of a patient who underwent parenteral nutrition for four months due to SBS. The surgical team performed reconstructive re-anastomosis of both the small and large bowel, leading to the discontinuation of parenteral nutrition and the resumption of a regular diet.

For adolescents, high aggression is often associated with suicide, physical injury, worse academic performance, and crime. Therefore, there is a need for early identification and intervention for highly aggressive adolescents. The Buss-Warren Aggression Questionnaire (BWAQ) consists of 34 items, and the longer the scale, the more likely participants are to make an insufficient effort response (IER), which reduces the credibility of the results and increases the cost of implementation. The study aimed to develop a shorter BWAQ using machine learning (ML) techniques to reduce the frequency of IER and decrease implementation costs meantime. First, an initial version of the short-form questionnaire was determined using Stepwise Regression and ANOVA F-test. Then, a machine learning algorithm determined the optimal short-form questionnaire (BWAQ-ML). Finally, the reliability and validity of the optimal short-form questionnaire were tested using independent samples. The BWAQ-ML has 88% fewer items than the BWAQ. It has AUC, accuracy, recall, precision, and F1 scores of 0.85, 0.85, 0.89, 0.83, and 0.86, respectively, and good psychometric properties. The BWAQ-ML can effectively measure individual aggression and can be used as a simplified version of BWAQ.

The microbiome is critical to the function of higher organisms and the gastrointestinal microbiome in chief among them. We do not know what impact the spaceflight GI microbiome has on organismal health in the absence of other confounding factors. We used a fecal transplant model to address this question and we found that we can recapitulate some of the functional aspects of the spaceflight microbiome on the ground but the impact on organismal health was ambiguous showing neither a defined improvement nor a impairment to lymph transport, growth, or immune populations. Histidine treatment was able to alter some of the functional aspects of the microbiome. Overall it we can conclude that the spaceflight microbiome is not pathological but has distinct impacts on organismal health.

TNF cytokine receptor TNFR1 protein plays an important role in immune response and autoimmune diseases. Previously, we demonstrated that the peptide of the innate immunity protein Tag7 (PGLYRP1), designated as 17.1, is able to perform the functions of a full-size Tag7 protein, namely, to inhibit the transmission of a signal through the TNFR1 receptor and activate this receptor in complex with the major heat shock protein Hsp70. In this study, we show that two peptides derived from 17.1 – 17.1A and 17.1B have different affinities to the TNFR1 receptor and the Hsp70 protein, and each of them is able to perform only one of the above functions: peptide 17.1A is only able to inhibit signal conduction through the TNFR1 receptor, and peptide 17.1B can only activate this receptor is in complex with Hsp70. Thus, it is possible to modulate the activity of TNFR1 receptor and further perform its specific inhibition or activation in the treatment of various autoimmune or oncological diseases.

This pilot study of Streptococcus mutans ATCC 35668 grown in media with and without polyols (erythritol) measured the resultant metabolites, including the short chain fatty acids by using head space analysis. Brain Heart Infusion Broth (BHI2 or BHI10) supplemented with 2% or 10% sucrose containing no polyols or either erythritol or xylitol and Streptococcus mutans (ATCC 35668) was grown aerobically. After 48 hours of growth the supernatant were harvested and centrifuged to pellet bacteria. Supernatants were removed from bacterial pellets then submitted for Short Chain Fatty Acid (SCFA) analysis with an Agilent Technologies (Santa Clara, CA 95051) system configured from three components, a 5973 mass selective detector, a 6890N gas chromatographer, and a 7697A headspace sampler. Streptococcus mutans growing in Brain Heart Infusion Broth (BHI2 or BHI10) supplemented with 2% or 10% sucrose but containing no polyols produced the following short chain fatty acids: methyl isovalerate, acetic acid, propionic acid, butanoic acid, pentanoic acid, ethyl butaric acid, 4-methylvaleric acid, hexanoic acid. When the Brain Heart Infusion Broth (BHI2 or BHI10) supplemented with 2% or 10% sucrose containing erythritol was used as media for this Streptococcus mutans strain, the following were produced: ethanol, acetoin, and acetic acid. Our results would suggest that constituents of the media may affect the bacterial metabolite production.

Introduction: Congenital heart defects (CHD) are the most frequent group of major congenital anomalies, accounting for almost 1% of all births. They comprise a very heterogeneous group of birth defects in terms of their severity, clinical management, epidemiology and embryologic origins. Taking this heterogeneity into account is an important imperative to provide reliable prognostic information to patients and their caregivers, as well as to compare results between centers or to assess alternative diagnostic and treatment strategies. The Anatomic and Clinical Classification of CHD (ACC-CHD) aims to facilitate both the CHD coding process and data analysis in clinical and epidemiological studies. Objectives: 1) To describe the long-term childhood survival of newborns with CHD and 2) To develop and validate predictive models of infant mortality based on the ACC-CHD. Materials and Methods: This study is based on data from a population-based, prospective cohort study: Epidemiological Study of Children with Congenital Heart Defects (EPICARD). The final study population comprised 1881 newborns with CHD after excluding cases that were associated with chromosomal and other anomalies. Statistical analysis included non-parametric survival analysis and flexible parametric survival models. The predictive performance of models was assessed by Harrell’s C index and the Royston-Sauerbrei R2 with internal validation by bootstrap. Results: The overall 8-year survival rate for newborns with isolated CHD was 0.96 [0.93- 0.95]. There was a substantial difference between the survival rate of the categories of ACC-CHD. The highest and lowest 8-year survival rates were 0.995 [0.989-0.997] and 0.34 [0.21-0.50] for “interatrial communication abnormalities and ventricular septal defects” and "functionally univentricular heart", respectively. Model discrimination as measured by Harrell’s C was 87% and 89% for the model with ACC-CHD alone and the full model, which included other known predictors of infant mortality, respectively. The predictive performance as measured by R2 was 45% and 50%, for the ACC-CHD alone and the full model. These measures were essentially the same after internal validation by bootstrap. Conclusion: The ACC-CHD classification provided the basis of a highly discriminant survival model with good predictive ability for the 8-year survival of newborns with CHD. Prediction of individual outcomes remains an important clinical and statistical challenge.

Landsat and Sentinel-2 data archives provide ever-increasing amounts of satellite data for studying land cover and land use change (LCLUC) over the past four decades. However, the availability of cloud-, shadow-, and snow-free observations varies spatially and temporally due to climate and satellite data acquisition schemes. Spatio-temporal heterogeneity poses a major issue for some time-series analysis approaches, but can be addressed with pixel-based compositing that generates temporally equidistant cloud-free or near-cloud free synthetic images. Although much consideration is given to methods identifying the ‘best’ pixel value for each composite, determining the aggregation period receives less attention and is often done arbitrary, or based on expert intuition. Here, we evaluated data compositing windows ranging from five days to one year for 1984-2021 Landsat and 2015-2021 Sentinel‑2 time series across Europe. We considered separate and joint use of both data archives and analyzed spatio-temporal availability of composites during each calendar year and pixel-specific growing season. We reported mean annual composites’ availability investigating differences among biogeographical regions, checked feasibility of pan‑European analyses for three LCLUC applications based on annual, monthly and 10-day composites, and analyzed the shortest feasible compositing window ensuring ≥50% temporal data availability and interpolation of the remaining composites for individual years and across a variety of medium- and long‑term time windows. Our results highlighted low data coverage in the 1980s, 1990s, and in 2012, as well as spatial variability in data availability driven by climate and orbit overlaps, which altogether impact spatio-temporal consistency of medium- and long-term time series, limiting feasibility of some LCLUC analyses. We demonstrated that prior to 2011 monthly composites ensured overall 50-62% data coverage for each calendar year, and ~75% afterwards, with further increase to ~82% when Landsat and Sentinel-2 were combined. Temporal consistency of monthly composites was overall low and temporal interpolation augmenting up to 50% missing data each year and across a time window of interest, ensured feasibility of analyses. Applications based on shorter than monthly composites were challenging without joining Landsat and Sentinel‑2 archives after 2015, and beyond the Mediterranean biogeographical region. Using pixel-specific growing season data typically boosted data availability in most geographies and diminished most of the latitudinal differences, but feasibility of complete time series with sub-monthly compositing windows was still restricted to the most recent years, and required data interpolation. Overall, our analyses provided a detailed assessment of Landsat and Sentinel-2 data availability over Europe, and based on selected application examples, highlighted often lacking spatio-temporal consistency of time series with sub-monthly compositing windows and long-time periods, which might hinder feasibility of some LCLUC applications.

We study the divisibility properties of the constant terms of certain meromorphic modular forms for Hecke groups. We relate those properties to those of some sequences that have already appeared in the literature. For possible use in later drafts, we show how to invert the map taking a Laurent series $f(x) = 1/x + \sum_{n=0}^{\infty}a_{n+1} x^n$ to the sequence of constant terms of its positive powers. At the end of the article, we construct from elementary arithmetic functions some meromorphic but not necessarily modular functions and study their constant terms.

Memory is probably one of the most complex cognitive functions of the human, and in many years, thousands of studies have helped us to better recognize this brain function. One of the reference textbooks in neuroscience, which has also elaborated on the memory function, is written by Prof. Kandel and his colleagues. In this book, I encountered a number of ambiguities when it was explaining the memory system. Here, I am sharing those points, either to find an answer for them, or to let them be a suggestion for our future works. Prof. Kandel has spent most of his meritorious lifetime on studying the memory system; however, the brain is extremely complex, and as a result, we still have many years to comprehensively understand the neural mechanisms of brain functions.

The Eastern Mediterranean is a highly populated area with air quality problems as well where climate change already is noticed by higher temperatures and changing precipitation pattern. The anthropogenic aerosol affects health and changing concentra-tions and properties of the atmospheric aerosol affect radiation balance and clouds. Continuous long-term observations are essential in assessing the influence of anthro-pogenic aerosols on climate and health. We present 6 years of observations from Navarino Environmental Observatory (NEO), a new station located at the south west tip of Pelo-ponnese, Greece. The two sites at NEO, were evaluated to show the influence of the local meteorology but also to assess the general background aerosol possible. It was found that the background aerosol was originated from aged European aerosols and was strongly influenced by biomass burning, fossil fuel combustion, and industry. When subsiding into the boundary layer, local sources contributed in the air masses moving south. Mesoscale meteorology determined the diurnal variation of aerosol properties such as mass and number by means of typical sea breeze circulation, giving rise to pronounced morning and evening peaks in pollutant levels. While synoptic scale meteorology, mainly large-scale air mass transport and precipitation, strongly influenced the season-ality of the aerosol properties.

Great Britain’s stocks of coal, natural gas, and petroleum have seen major changes to the levels of stored energy over the years 2005 to 2019, a reduction of 200 TWh (35%) from 570 TWh to 370 TWh. The transformation of its electrical system over this timeframe saw a reduction in coal generation, leading to a corresponding reduction of the levels of stockpiled coal of 85 TWh (68%), partially offset by an increase in the stocks of biomass for electrical generation. The reduction in natural gas storage of 24 TWh (44%) was primarily due to the closure of Britain’s only long-term seasonal natural gas storage facility in January 2018. This was partially offset by the construction of medium-term natural gas storage facilities and the use of LNG storage in the years preceding its closure. For stocks of crude oil and oil products the reduction was 35 TWh (21%), linked to the overall reduction in demand.

Background: Glioblastomas (GBM) is generally burdened, to date, by a dismal prognosis, although Long Term Survivors have a relatively significant incidence. Our specific aim was to determine the exact impact of many surgery-, patient- and tumor-related variable on Survival parameters. Methods: The surgical, radiological and clinical outcomes of patients have been retrospectively reviewed for the present study. All the patients have been operated on in our Institution and classified according their Overall Survival in LTS (Long Term Survivors) and STS (Short Term Survivors). A thorough Review of our surgical series was conducted to compare the oncologic results of the patients in regards to 1. Surgical , 2. Molecular, and 3.Treatment related features. Results: A total of 177 patients were included in the final cohort. Extensive statistical analysis by means of univariate, multivariate and survival analyses disclosed a survival advantage for patients presenting a younger age, a smaller lesion and a better functional status at presentation. From the Histochemical point of view, Ki67(%) was the strongest predictor of better oncologic outcomes. A stepwise analysis of variance outlines the existence of 8 prognostic subgroups according to the molecular patterns of Ki67 overexpression and EGFR, p53 and IDH mutations. Conclusions: On the ground of our statistical analyses we can affirm that the following factors were significant predictors of survival advantage: KPS, Age, Volume of the lesion, Motor disorder at presentation, a Ki67 overexpression. A fine molecular profiling is feasible to precisely stratify the prognosis of GBM patients.

The fermentation of syngas is an attractive technology that can be integrated with gasification of lignocellulosic biomass. The coupling of these two technologies allows for treating a great variety of raw materials. Lignin usually hinders microbial fermentations; thus, the thermal decomposition of the whole material into small molecules allows for the production of fuels and other types of molecules using syngas as substrate, a process performed at mild conditions. Syngas contains mainly hydrogen, carbon monoxide and carbon dioxide in varying proportions. These gases have a low volumetric energy density, resulting more interesting its conversion into higher energy density molecules. Syngas can be transformed by microorganisms, thus avoiding the use of expensive catalysts, which may be subject to poisoning. However, the fermentation is not free of suffering from inhibitory problems. The presence of trace components in syngas may cause a decrease in fermentation yields or cause a complete cessation of bacteria growth. The presence of tar and hydrogen cyanide are just examples of this fermentation's challenges. Syngas cleaning impairs significant restrictions in technology deployment. The technology may seem promising, but it is still far from large-scale application due to several aspects that still need to find a practical solution.

The shortwave infrared Ge-Si photodetector will become the core device of the LiDAR optical receiver. In order to meet the urgent demand for photodetectors in the LiDAR field, we have designed and produced a 32×32 pixel Ge-Si photodetector array proposed and developed to meet the performance requirements of the detector array. A dark current detection system for fast scanning and detecting large-scale Ge-Si detector arrays is proposed and developed to achieve rapid detection of dark current in each pixel of the detector. The system was used to validate the main performance indicators of the detector array we designed, achieving rapid discrimination of array performance and rapid localization of damaged pixels. The scanning test results show that the average dark current of the detector array chip we designed is at the nano ampere level, and the proportion of bad points is less than 1%. The consistency of the array chip is high, which can meet the requirements of light detection at the receiving end of the LiDAR. This work laid the foundation for our subsequent development of a LiDAR prototype system.

For the numerical simulation of components made of short fiber-reinforced composites the correct prediction of the deformation including the elastic and plastic behavior and its spatial distribution is essential. When using purely deterministic modeling approaches the information of the probabilistic microstructure is not included in the simulation process. One possible approach for the integration of stochastic information is the use of random fields. In this study numerical simulations of tensile test specimens are conducted utilizing a finite deformation elastic-ideal plastic material model. A selection of the material parameters covering the elastic and plastic domain are represented by cross-correlated second-order Gaussian random fields to incorporate the probabilistic nature of the material parameters. To validate the modeling approach tensile tests until failure are carried out experimentally, that confirm the assumption of spatially distributed material behavior in both the elastic and plastic domain. Since the correlation lengths of the random fields cannot be determined by pure analytic treatments, additionally numerical simulations are performed for different values of the correlation length. The numerical simulations endorse the influence of the correlation length on the overall behavior. For a correlation length of 5mm a good conformity with the experimental results is obtained. Therefore, it is concluded, that the presented modeling approach is suitable to predict the elastic and plastic deformation of a set of tensile test specimens made of short fiber-reinforced composite sufficiently.

Students are regarded as frequent users of mobile phones which has proven to be a convenient and acceptable method to promote healthy lifestyle. Students usually engage in relatively high levels of risky behavior and make unhealthy lifestyle choices, a study that investigates how health students access health information is necessary. The study adopted a descriptive cross-sectional study which was undertaken among third-year nursing students from three nurses training institutions in Ghana. A total of 270 students participated in the study. Most of the respondents who were currently subscribers of the health messages reported that they usually received health information on reproductive health issues, nutrition, and practicing safe sex. Most of the health students revealed that they needed more information on safe sex, diet, managing weight, and stress management. The results also show that health students are likely to remember and share short messages with friends. The findings serve as an ‘eye-opener’ for health educators and mobile service providers concerning factors that should be taken into consideration when framing health text messages that will attract health students.

Research background: Relationship between inflation rate, unemployment rate, interest rate and real gross domestic product per capita in Nigeria. However, there seems to be a short-run or long-run relationship among the macroeconomic variables.Purpose: This study investigated the impact of the inflation rate, unemployment rate and interest rate on real gross domestic product per capita (RGDPPC) (proxy for economic growth) and proffered recommendations towards enhancing economic growth and to reduce the distasteful effects of inflation rate, unemployment rate and interest rate in Nigeria in this present time economic challenges.Research methodology: This study applied a linear dynamic model Autoregressive Distributed Lag (ARDL) modeling technique to analyze the short-run dynamics and long-run relationship of the economic growth in Nigeria over the sample period between 1984 and 2017 using annual secondary data extracted from World Bank Development Indicators Report (last updated January 2019).Results: The empirical results showed that there was long-run relationship between inflation rate, unemployment rate and interest rate on real gross domestic product per capita (proxy for economic growth) in Nigeria. The result further revealed that only unemployment rate had a significant positive impact on real gross domestic product per capita in the long-run and inflation rate had a significant negative impact on real gross domestic product per capita in the short-run.Novelty: Therefore, the study concluded that unemployment rate and inflation rate proved to have significant impacts on economic growth in the long-run and short-run respectively. Formulation of policies to reduce unemployment through the adoption of labour concentrated technique of production, entrepreneurship development and policy to keep the inflation rate at single digit.

Rainfall is a major climate parameter whose variation in space and time influences activities in different weather sensitive sectors such as agriculture, transport, and energy among others. Therefore, accurately forecasting rainfall is of paramount importance to the development of these sectors. In this regard, this study sought to contribute to quantitative forecasting of rainfall over Eastern Uganda through assessing the Weather Research and Forecasting model’s ability to simulate the intra–seasonal characteristics of the September to December rain season. These were: onset and cessation dates; wet days and lengths of the wet spells. The data used in the study included daily ground rainfall observations and lateral and boundary conditions data from the National Centers for Environmental Prediction (NCEP) final analysis at 1 0 horizontal resolution and at a temporal resolution of 6 hours for the entire study period were used to initialize the Weather Research and Forecasting (WRF) model. The study considered four weather synoptic weather stations namely; Jinja, Serere, Soroti and Tororo. The results show that the WRF model generally simulated fewer wet days at each station except for Tororo. Also, the WRF model simulated earlier onset and cessation dates of the rainfall season and overestimated the length of the wet spells.

Increased sugar intake is implicated in Type-2 diabetes and fatty liver disease. Mechanisms by which glucose and fructose components promote these conditions are unclear. We hypothesize that alterations in intestinal metabolite and microbiota profiles specific to each monosaccharide are involved. Two groups of six adult C57BL/6 mice were fed for 10-weeks with a diet where either glucose or fructose was the sole carbohydrate component (G and F, respectively). A third group was fed with normal chow (N). Fecal metabolites were profiled every 2-weeks by ¹H NMR and microbial composition was analysed by real-time PCR (qPCR). Glucose tolerance was also periodically assessed. N, G and F mice had similar weight gains and glucose tolerance. Multivariate analysis of NMR profiles indicated that F mice were separated from both N and G, with decreased butyrate and glutamate and increased fructose, succinate, taurine, tyrosine and xylose. Compared to N and G, F mice showed a shift in microbe populations from gram-positive Lactobacillus spp. to gram-negative Enterobacteria species. Substitution of normal chow carbohydrate mixture by either pure glucose or fructose for 10 weeks did not alter adiposity or glucose tolerance. However, F G and N mice generated distinctive fecal metabolite signatures with incomplete fructose absorption as a dominant feature of F mice.

Time-series analysis is a widely used technique across various fields and industries, as it helps in understanding, predicting, and forecasting the behavior of data points over time. These fields include but are not limited to finance, economics, healthcare, transportation, etc. In the case of this paper, we have focused on finance. Predicting future values of financial time series offers several benefits. Accurate forecasts can help investors make better decisions about their investments. To predict future values, deep learning algorithms are commonly used since it is an effective method with complex data. In this study, we conduct a study that investigates the use of different data pre-processing techniques on deep learning algorithms in predicting the time series values. To conduct this experimental study, we utilize an open source software, which using long short-term memory technique as the representative deep learning technique, published in github software code repository platform. With this study, we investigate the effects of autoencoder and discrete wavelet transform data pre-processing techniques in time-series prediction. We discuss the details of the experimental study and report our results. The results show that time series prediction (using backtesting methodology) without any data pre-processing leads to 12.6% for mean absolute percentage error. The results also show that, time series prediction with the data preprocessing techniques (Wavelet Transform and Stacked Autoencoder) lead to 3.4%

Background: Severe eosinophilic asthma (SEA) represents a substantial global health burden. Benralizumab has been shown to restore good control of asthma. Robust data on benralizumab effectiveness over periods longer than 2 years are still scarce. Methods: This retrospective multicentric study was conducted on 108 Italian SEA patients treated with benralizumab for up to 36 months. Clinical outcomes were evaluated at 6, 12, 24 and 36 months. Partial and complete clinical remission (CR) were also assessed. Data were analysed either with descriptive statistics or using linear, logistic, and negative binomial mixed-effect regression models. Results: Benralizumab induced a persistent reduction in exacerbation rates (p

The atmospheric concentrations of sodium, aluminum, silicon, sulphur, chlorine, potassium, calcium, titanium, vanadium, chromium, manganese, iron, nickel, copper, zinc, bromine and lead were measured in air filters at the Finnish Meteorological Institute station, in Helsinki, Finland, during a period of 44 years (1962-2005). The mean annual concentrations were calculated and are presented from the lowest values to the highest ones Cr&lt;Ni&lt;Ti&lt;Br&lt;V&lt;Mn&lt;Cu&lt;Zn&lt;Cl&lt;Al&lt;Fe&lt;K&lt;Ca&lt;Na&lt;Pb&lt;Si&lt;S. Most of the elements (Fe, Si, Ti, K, Ca, Zn, Br, Pb, V, Ni, S, Cr, Na, Al, Cl) present higher values during spring and winter season while in summer the elements (Ti, Ca, S, Na) are found in higher concentrations. There is a strong correlation between the elements (V-Ni, Si-Pb, Fe-Ca, V-Cr, Si-K, K-Ca, Fe-Ti, K-Na, Si-Ca, V-S), indicating their common source. The identification of the sources of trace elements was performed based on positive matrix factorization analysis, using SoFi software. Four PM sources were identified: road dust (due to usage of leaded fuel), heavy oil combustion/secondary sulfates, traffic emissions and natural dust (soil). For the total of 44 years studied, significant decreases in concentrations were observed for all trace elements, most of which were over 50%: Na (-74%), Al (-86%), Si (-88%), S (-82%), K (-82%), Ca (-89%), Ti (-80%), V (-89%), Cr (-82%), Mn (-77%), Fe (-77%), Ni (-61%), Zn (-72%), Pb (-95%). The current data are consistent with previous air studies covering the whole territory of Finland. Finally, a significant decline has been observed in the majority of the elemental concentrations since the end of 70s, underlying the effectiveness of different environmental policies that have been applied during the last decades.

Background: this RCT assesses the 18 months clinical outcomes after regenerative therapy of periimplantitis lesions using either an electrolytic method (EC) to remove biofilms or a combination of powder spray and electrolytic method (PEC). Materials and Methods: Twenty-four patients (24 implants) suffering from periimplantitis were randomly treated by EC or PEC followed by augmentation and submerged healing. Probing pocket depth (PPD), Bleeding on Probing (BoP), suppuration and standardized radiographs were assessed before surgery (T0), 6 months after augmentation (T1), 6 (T2) and 12 (T3) months after replacement of the restoration. Results: Mean of PPD changed from 5.8 ± 1.6 mm (T0) to 3.1 ± 1.4 mm (T3). While BoP and suppuration at T0 was 100 % BoP decreased at T2 to 36.8 % and at T3 to 35.3 %. Suppuration could be found 10.6% at T2 and 11.8% at T3. Radiologic bone level measured from the implant shoulder to the first visible bone to implant contact was 4.9 ± 1.9 mm at me-sial and 4.4 ± 2.2 mm at distal sites (T0) and 1.7 ± 1.7 mm and 1.5 ± 17 mm at T3. Conclusions: Significant radiographic bone fill and improvement of clinical parameters were demonstrated 18 months after therapy.

Introduction Early prediction of long term outcomes in patients resuscitated after cardiac arrest (CA) is still challenging. Guidelines suggested a multimodal approach combining multiple predictors. We evaluated whether the combination of the electroencephalography (EEG) reactivity, somatosensory evoked potentials (SSEPs) cortical complex and Gray to White matter ratio (GWR) on brain computed tomography (CT) at different temperatures could predict survival and good outcome at hospital discharge and after six months. Methods We performed a retrospective cohort study including consecutive adult, non-traumatic patients resuscitated from out-of-hospital CA who remained comatose on admission to our intensive care unit from 2013 to 2017. We acquired SSEPs and EEGs during the treatment at 36°C and after rewarming at 37°C, Gray to white matter ratio (GWR) was calculated on the brain computed tomography scan performed within six hours of the hospital admission. We primarily hypothesized that SSEP was associated with favorable functional outcome at distance and secondarily that SSEP provides independent information from EEG and CT. Outcomes were evaluated using the Cerebral Performance Category (CPC) scale at six months from discharge. Results Of 171 resuscitated patients, 75 were excluded due to missing of data or uninterpretable neurophysiological findings. EEG reactivity at 37 °C has been shown the best single predictor of good outcome (AUC 0.803) while N20P25 was the best single predictor for survival at each time point. (AUC 0.775 at discharge and AUC 0.747 at six months follow up) Predictive value of a model including EEG reactivity, average GWR, and SSEP N20P25 amplitude was superior (AUC 0.841 for survival and 0.920 for good outcome) to any combination of two tests or any single test. Conclusion Our study, in which life-sustaining treatments were never suspended, suggests SSEP cortical complex N20P25, after normothermia ad off sedation, is a reliable predictor for survival at any time. When SSEP cortical complex N20P25 is added into a model with GWR average and EEG reactivity, the predictivity for good outcome and survival at distance is superior than each single test alone.

Objectives: A wealth of literature has established risk factors for social isolation among older people, however much of this research has focused on community-dwelling populations. Relatively little is known about how risk of social isolation is experienced among those living in long-term care (LTC) homes. We conducted a scoping review to identify possible risk factors for social isolation among older adults living in LTC homes. Methods: A systematic search of five online databases retrieved 1535 unique articles. Eight studies met the inclusion criteria. Results: Thematic analyses revealed that possible risk factors exist at three levels: individual (e.g., communication barriers), systems (e.g., location of LTC facility), and structural factors (e.g., discrimination). Discussion: Our review identified several risk factors for social isolation that have been previously documented in literature, in addition to several risks that may be unique to those living in LTC homes. Results highlight several scholarly and practical implications.

The present work studies a swelling porous-elastic system with viscoelastic damping. We establish a general and optimal decay estimate which generalizes some recent results in the literature. Our result is established without imposing the usual equal-wave-speed condition associated with similar problems in literature.

Refractive surgery is an increasingly popular procedure to decrease spectacle or contact lens dependency. The two most commonly used surgical techniques to correct myopia is Photorefractive keratectomy (PRK) and Femtosecond- Lasik (FS-LASIK)There are few publications that gathers such a long term follow up of both surgical techniques (2) Methods It has been performed a retrospective non-randomized study 509 PRK eyes and 310 FS-LASIK surgeries were followed for 10 years for the treatment of myopia and compound myopic astigmatism. Patients were followed up three months, one year, 2 years, 5 and 10 years. The safety index of both procedures was defined as a quotient between the postoperative BCVA (Best Corrected Visual Acuity) and the preoperative BCVA. The predictability is calculated as difference between the expected spherical equivalent and the achieved spherical equivalent. The efficacy index was calculated as a quotient between postoperative UCVA divided by the preoperative BCVA (3) Results. The results were: a safety index higher than 100% (109%) and an efficacy index of 82.4% after 10 years of PRK surgery in both groups. FS-LASIK was the safest surgery after 10 years and the most efficacy technique although in this case there were no statistically significant differences (4) Conclusions. All these data demonstrated better indexes for FS-LASIK

Anxiety, chronical stress and depression during pregnancy are considered to affect the offspring, presumably through placental dysregulation. We have studied the term placentae of pregnancies clinically monitored with the Beck’s Anxiety Inventory (BAI) and Edinburgh Postnatal Depression Scale (EPDS). A cutoff threshold for BAI/EPDS of 10 classed patients into an Index group (>10, n=23) and a Control group (<10, n=23). Cortisol concentrations in hair (HCC) were periodically monitored throughout pregnancy and delivery. Expression differences of main glucocorticoid pathway genes: i.e. corticotropin-releasing hormone (CRH), 11β-hydroxysteroid dehydrogenase (HSD11B2), glucocorticoid receptor (NR3C1), as well as other key stress biomarkers (Arginine Vasopressin, AVP and O-GlcNAc transferase, OGT) were explored in medial placentae using real-time qPCR and western blotting. Moreover, gene expression changes were considered for their association with HCC, offspring, gender and birthweight. A significant dysregulation of gene expression for CRH, AVP and HSD11B2 genes was seen in the Index group, compared to controls, while OGT and NR3C1 expression remained similar between groups. Placental gene expression of the stress-modulating enzyme 11β-hydroxysteroid dehydrogenase (HSD11B2) was related to both hair cortisol levels (Rho= 0.54; p<0.01) and the sex of the newborn in pregnancies perceived as stressful (Index, p<0.05). Gene expression of CRH correlated with both AVP (Rho= 0.79; p<0.001) and HSD11B2 (Rho= 0.45; p<0.03), and also between AVP with both HSD11B2 (Rho= 0.6; p<0.005) and NR3C1 (Rho= 0.56; p<0.03) in the Control group but not in the Index group; suggesting a possible loss of interaction in the mechanisms of action of these genes under stress circumstances during pregnancy.

The fundamental theory of nuclear interactions, Quantum Chromodynamics (QCD), operates in terms of quarks and gluons at higher resolution. At low resolution the relevant degrees of freedom are nucleons. Two-nucleon Short-Range Correlations (SRC) help to interconnect these two descriptions. SRCs are temporary fluctuations of strongly interacting close pairs of nucleons. The distance between the two nucleons is comparable to their radii and their relative momenta are larger than the fermi sea level. According to the electron scattering experiments held in the last decade, SRCs have far-reaching impacts on many-body systems, the nucleon-nucleon interactions, and nuclear substructure. The modern experiments with ion beams and cryogenic liquid hydrogen target make it possible to study properties of the nuclear fragments after quasi-elastic knockout of a single nucleon or an SRC pair. The first SRC experiment at BM@N (2018) with a carbon beam has shown that detection of an intact 11B nucleus after interaction selects out the quasi-elastic knockout reaction with minimal contribution of initial- and final-state interactions. Also, 25 events of SRC-breakups showed agreement in SRC properties as knows from electron beam experiments. The analysis of the second measurement of SRC at BM@N held in 2022 with an improved setup is currently ongoing. The SRC project at JINR moved to a new experimental area in 2023, where the next measurement is being planned in terms of experimental setup and physics goals.

In semi-arid regions, climate change has affected crop growing season length and sowing time, potentially causing low yield of the rainfed staple crop pearl millet (Pennisetum glaucum L.) and food insecurity among smallholder farmers. In this study, we used 1994–2023 rainfall data from Namibia's semi-arid North-Central Region (NCR), receiving November–April summer rainfall, to analyze rainfall patterns and trends and their implications on the growing season to propose climate adaptation options for the region. The results revealed high annual and monthly rainfall variabilities, with nonsignificant negative trends for November–February rainfalls, implying a shortening growing season. Furthermore, we determined the effects of sowing date on grain yields of the early-maturing Okashana-2 and local landrace Kantana pearl millet varieties and the optimal sowing window for the region, using data from a two-year split-plot field experiment conducted at the University of Namibia-Ogongo Campus, NCR, during the rainy season. Cubic polynomial regression models were applied to grain yield data sets to predict grain production for any sowing date between January and March. Both varieties produced the highest grain yields under January sowings, with Kantana exhibiting a higher yield potential than Okashana-2. Kantana, sown by 14 January, had a yield advantage of up to 36.0% over Okashana-2, but its yield gradually reduced with delays in sowing. Okashana-2 exhibited higher yield stability across January sowings, surpassing Kantana’s yields by up to 9.4% following the 14 January sowing. We determined the pearl millet optimal sowing window for the NCR from 1–7 and 1–21 January for Kantana and Okashana-2, respectively. These results suggest that co-cultivation of early and late pearl millet varieties and growing early-maturing varieties under delayed seasons could stabilize grain production in northern Namibia and enhance farmers' climate adaptation. Semi-arid agro-region policymakers could utilize this information to adjust local seed systems and extension strategies.

Processing of the RNA polymerase I pre-rRNA transcript into the mature 18S, 5.8S, and 25S rRNAs requires removing the “spacer” sequences. The canonical pathway for the removal of the ITS1 spacer, located between 18S and 5.8S rRNAs in the primary transcript, involves cleavages at the 3’ end of 18S rRNA and at two sites inside ITS1. The process generates a long and a short 5.8S rRNA that differ in the number of ITS1 nucleotides retained at the 5.8S 5’ end. Here we document a novel pathway that generates the long 5.8S for ITS1 while bypassing cleavage within ITS1. It entails a single endonuclease cut at the 3’-end of 18S rRNA followed by exonuclease Xrn1 degradation of ITS1. Mutations in RNase MRP increase the accumulation of long relative to short 5.8S rRNA; traditionally this is attributed to a decreased rate of RNase MRP cleavage at its target in ITS1, called A3. In contrast, we report here that the MRP induced switch between long and short 5.8S rRNA formation occurs even when the A3 site is deleted. Based on this and our published data, we propose that the switch may depend on RNase MRP processing RNA molecules other than pre-rRNA.

Short stature has been associated with increased various disease and all-cause death, but no reliable data exist the association between height and end-stage renal disease (ESRD) in diabetic patients. We investigated the relationship between short stature, development of ESRD, and mortality in type 2 diabetes. This study analyzed clinical data using the National Health Insurance Database in Korea. Height was stratified by five groups according to age and sex. Risk of ESRD and all-cause mortality was analyzed with Cox proportional hazards models. During a 6.9-year follow-up period, 220,457 subjects (8.4%) died and 28,704 subjects (1.1%) started dialysis. Short stature significantly increased the incidence of ESRD and all-cause mortality in the overall cohort analysis. In multivariable analysis, hazard ratios (HR) for development of ESRD comparing the highest versus lowest quartiles of adult height were 0.86 (95% confidence interval (CI), 0.83–0.89). All-cause mortality also decreased with highest height compared to patients with lowest height after fully adjusting for confounding variables (HR 0.79, 95% CI, 0.78–0.81). Adult height had an inverse relationship with newly diagnosed ESRD and all-cause in both males and females. Short stature is strongly associated with an increased risk of ESRD and all-cause mortality in type 2 diabetes.

The time-dependent behavior and long-term stability of deep-buried tunnels in soft rocks have received lots of considerations in tunnel engineering and allied sciences. To better explore and deepen the engineering application of rock creep, extensive research studies are still needed, although fruitful outcomes have already obtained in many related investigations. In this article, the Weilai Tunnel in China’s Guangxi province is studied taking its host rocks as the main research object. In fact, aiming at forecasting the time-varying deformation of this tunnel, a novel elasto-visco-plastic creep constitutive model with two variants is proposed, by exploiting the typical complex load-unload process of rock excavation. The model is well validated and good agreements are found with the relevant experimental data. Moreover, the time-dependent de-formation rules are properly established for the surrounding rocks, by designing two new closed-form solutions based on the proposed creep model and the Hoek-Brown criterion. The convergence deformations calculated from the closed-form solutions conform well to the on-site monitoring data. In only 27 days after excavation, the creep deformation of the Weilai tunnel overtakes 400 mm, which is enormous. To guarantee the long-term stability of this tunnel, a ro-bust support scheme and its long-term monitoring with appropriate remote sensors are strongly suggested.

Newborn screening (NBS) is hailed as a public health success, but little is known about long-term outcomes following a positive newborn screen. There has been difficulty gathering long-term follow-up (LTFU) data consistently, reliably, and with minimal effort. Six programs developed and tested a core set of minimal LTFU data elements. After an iterative data collection process and the development of a data collection tool, the group agreed on minimal LTFU data elements. The denominator captured all infants with an NBS diagnosis, accounting for children who moved or died prior to the follow-up year. They also agreed on three LTFU outcomes: if the child was still alive, had contact with a specialist, and received appropriate care specific to their diagnosis within the year. The six programs representing NBS public health programs, clinical providers, and research programs provided data across multiple NBS disorders. In 2022, 83.8% (563/672) of the children identified by the LTFU programs were alive and living in the jurisdiction; of those, 92.0% (518/563) saw a specialist, and 87.7% (494/563) received appropriate care. The core LTFU data elements can be applied as a foundation to address the impact of early diagnosis by NBS within and across jurisdictions.

Objective: The long-term care insurance (LTCI) system was introduced in 2000 to provide financial support for long-term care services to older adults. What is known on this topic report focuses on a comparative analysis of how the LTCI system has affected older people in Japan. Methods: We obtained data from the literature about the LTCI system, compared and analysed the collected articles, and concluded. Results: The LTCI system has improved access to long-term care services, with more older adults receiving care than before the system’s implementation. This service has improved the quality of life for many elderly individuals who require assistance with daily activities. Furthermore, the LTCI system has alleviated the financial burden of long-term care services since the insurance covers a large portion of the costs. It has made older adults maintain their independence and dignity, as they can afford the necessary care without relying solely on their savings or burdening their families. Conclusions: Japan’s LTCI system has positively impacted older adults by improving access to long-term care services and alleviating financial burdens. However, challenges such as increasing demand and variations in service quality need to be addressed to ensure the system effectively supports older people in Japan.

Genetic parameters of overreach, term and acuteness in Peruvian Paso horses (PPH) have not been determined to date. It is important to estimate these parameters for application in PPH breeding, therefore, the aim of this study was to estimate the heritability, repeatability, and genetic correlations under field conditions of overreach, term, and acuteness of PPHs. The study included records of up to 137 stallions and mares. All measurements were recorded in MP4 video format with a resolution of 1920 x 1080 megapixels and at 60 frames per second. All traits were measured three times (once per stride), and each trait was analyzed. KINOVEA software version 0.9.5 was used to analyze the measurements. A multivariate repeated measures animal model with sex effect was used to estimate the variance components for each trait using WOMBAT software. The results showed heritability of 0.411, 0.476 and 0.405, for the traits of overreach, term, and acuteness, respectively. Repeatability was high in all traits (&gt; 0.78). Genetic additive correlations ranged from -0.30 to 0.49. It can be concluded that overreach and term have high heritability values, which allows these traits to respond better in a selection process, unlike acuteness which has a moderate heritability value.

Landsat and Sentinel-2 acquisitions are among the most widely used medium-resolution optical data adopted for terrestrial vegetation applications, such as land cover and land use mapping, vegetation condition and phenology monitoring, and disturbance and change mapping. When combined, both data archives provide over 40 years, and counting, of continuous and consistent observations. Although the spatio-temporal availability of both data archives is well-known at the scene level, information on the actual availability of cloud-, snow-, and shade-free observations at the pixel level is lacking and should be explored individually for each study to correctly parametrize subsequent analyses. However, data exploration is time and resource consuming, thus is rarely performed a-priori. Consequently, the spatio-temporal heterogeneity of usable data is often inadequately accounted for in the analysis design, risking ill-advised selection of algorithms and hypotheses, and thus inferior quality of final results. Here we present precomputed data on the daily 1982‑2023 availability of usable Landsat and Sentinel-2 acquisitions across the globe, sampled at a pixel-level in a regular 0.18°-point grid. The dataset comprises separate Landsat- and Sentinel‑2‑specific data records, and is accompanied by a pixel-specific growing season information to facilitate data exploration. The dataset was derived based on freely available 1982-2023 Landsat surface reflectance (Collection 2) and Sentinel-2 top-of-the-atmosphere reflectance (pre‑Collection-1 and Collection-1) scenes from 2015 through 2023, following the methodology developed in the recent study on data availability over Europe [1]. Growing season information was derived based on 2001‑2019 time series of the yearly 500 m MODIS land cover dynamics product (MCD12Q2; Collection 6) [1]. As such, the dataset presents a unique overview of the spatio-temporal availability of usable daily Landsat and Sentinel-2 data at the global scale, hence offering much needed a-priori information aiding identification of appropriate methods and challenges for terrestrial vegetation analyses at local to global scale.

In view of the expected climate changes, a paradigm shift in soil management, through reduced tillage and/or a different use of crop residues, can be a key point in the mitigation of the climate impacts. The transition from a traditional cropping system (i.e., durum wheat in continuous cropping system under conventional tillage and/or straw removal) to those undergoing the conservative practices require to be evaluated in a medium to long time frame to reach an equilibrium and thus to be properly investigated. In this regard, cropping system simulation models are fundamental tools for the in-silico evaluation of the response of crop growth and soil organic matter dynamics to varying cropping scenarios. This paper reports the evaluations on the parameterization and reliability of the ARMOSA crop simulation model calibrated and validated on experimental datasets collected on durum wheat grown in continuous cropping system under several straw and soil management strategies in a Mediterranean environment.

Training has a significative effect on physiology of blood coagulation both in humans and in horses. Several hemostatic changes have been reported after exercise in the horse but data available are inconclusive. The aim of this study was to investigate the platelet activation and primary platelet-related hemostasis modifications in young untrained Thoroughbred racehorses in the first 4-month timeframe incremental training period in order to improve knowledge on this topic. Twenty-nine clinically healthy, untrained, 2-year-old Thoroughbred racehorses. Never trained Thoroughbreds were followed during their incremental 4-month sprint exercise schedule. Blood collection was performed once a month (T-30, T0, T30, T60, and T90). Platelet aggregation was measured by light transmission aggregometer in response to various agonists: adenosine diphosphate (ADP), collagen and calcium ionophore A23187. Platelet function was evaluated using the Platelet Function Analyzer (PFA-100®) using collagen/ADP and collagen/adrenaline cartridges. Nitrite-nitrate (NOx) plasma concentrations were measured by a colorimetric assay to assess in vivo nitric oxide bioavailability. Platelet activation was investigated also through gene expression analyses (selectin P-SELP, ectonucleotidase CD39-ENTPD1, prostaglandin I2 synthase-PTGIS, endothelial nitric oxide synthase 3-NOS3). Differences among the time points were analyzed and mean±SEM were calculated. Significant modifications were identified compared with T-30 with increase of platelet aggregation (collagen:32.6±4.8 vs 21.6±4.9%; ADP: 35.5±2.0 vs 24.5±3.1%; A23187: 30±4.7 vs 23.8±4%) and a shorter closure time of C-ADP cartridges (75.6±4.4 vs 87.7±3.4 sec) that tended to return to baseline value at T90. NOx concentrations in plasma significantly increased after 30 days of training program compared with baseline. The first long-term training period induce platelet hyperactivity already after 30 days in untrained Thoroughbreds. Regular physical training blunts the negative effects of acute efforts on platelets activation.

The chemically synthesized polymer polyacrylamide (HPAM) in conventional reservoirs has achieved excellent oil displacement, but its oil displacement is poor in extreme reservoir environments. To develop biopolymer oil flooding agent under extreme reservoir conditions, the viscosity increasing and rheological properties of three biopolymers, diutan gum, xanthan gum, and scleroglucan, were studied under extreme reservoir conditions (high salt, high temperature, strong acid, and alkali), and the effects of temperature, mineralization, pH, and other factors on their viscosities and long-term stability properties were analyzed and compared. The results showed that under the temperature of 90℃ and below, the three kinds of biopolymers had the best viscosity-increasing ability. The viscosity of the three kinds of biopolymers was 80.94, 11.57, and 59.83mPa·s, respectively, when the concentration was 1500mg/L and the salinity was 220g/L. At the shear rate of 250s-1,100℃~140℃, scleroglucan has the best viscosification. At 140℃, the solution viscosity is 19.74mPa·s, and the retention rate can reach 118.27%. The results of the long-term stability study showed that the solution viscosity of scleroglucan with mineralization level of 220 mg/L was 89.54% viscosity retention in 40 days, and the diutan gum could be stabilized for 10 days, and the viscosity was maintained at 90 mPa·s. All three biopolymers are highly acid and alkali-resistant, with viscosity variations of less than 15% in the pH3~10 range. Rheological tests have shown that both of them have better viscoelastic properties than xanthan gum due to the peculiar double-helix structure of xanthan gum and the rigid triple-helix structure of scleroglucan. Therefore, the two biopolymers, diutan gum, and scleroglucan have the potential for extreme reservoir oil displacement applications, and it is recommended to use diutan gum for oil displacement in reservoirs up to 90℃ and scleroglucan for oil displacement in reservoirs between 100℃ and 140℃.

Humeral resurfacing arthroplasty (HRA) entails the substitution of the articular surface alone with a prosthetic cap without stem. It is a more conservative procedure which can be easily converted in a total arthroplasty if necessary. The present study aimed to evaluate the clinical and radiographical outcomes in a series of patients treated with HRA. 33 patients with a mean fol-low-up of 11 years were clinically (Constant score; Disability of the Arm, Shoulder and Hand score, DASH) and radiographically assessed before and after surgery. Constant and DASH score improved significantly after surgery, and only 2 cases needed revision surgery. HRA represents a valid therapeutic option in selected cases to improve the quality of life and delaying the need for more invasive procedures.

Glioblastoma (GBM) is a very aggressive malignant brain tumor with the vast majority of patients surviving less than 12 months (Short-term survivors [STS]). Only around 2% of patients survive more than 36 months (Long-term survivors [LTS]). Studying these extreme survival groups might help in better understanding GBM biology. This work aims at exploring application of machine learning methods in predicting survival groups(STS, LTS). We used age and gene expression profiles belonging to 249 samples from publicly available datasets. 10 Machine learning methods have been implemented and compared for their performances. Hyperparameter tuned random forest model performed best with accuracy of 80% (AUC of 74% and F1_score of 85%). The performance of this model is validated on external test data of 16 samples. The model predicted the true survival group for 15 samples achieving an accuracy of 93.75%. This classification model is deployed as a web tool GlioSurvML. The top 1500 features which retained classification efficiency (Accuracy of 80%, AUC of 74%) were studied for enriched pathways and disease-causal biomarker associations using the HumanPSD^TM database. We identified 199 genes as possible biomarkers of GBM and/or similar diseases (like Glioma, astrocytoma, and others). 57 of these genes are shown to be differentially expressed across survival groups and/or have impact on survival. This work demonstrates the application of machine learning methods in predicting survival groups of GBM.

In fisheries monitoring, catch is assumed to be a product of fishing intensity, catchability, and availability, where availability is defined as the number or biomass of fish present and catchability refers to the relationship between catch rate and the true population. Ecological monitoring programs use catch per unit of effort (CPUE) to standardize catch and monitor changes in fish populations; however, CPUE is proportional to the portion of the population that is vulnerable to the type of gear that is used in sampling, which is not necessarily the entire population. Programs often deal with this problem by assuming that catchability is constant, but if catchability is not constant, it is not possible to separate the effects of catchability and population size using monitoring data alone. This study uses individual-based simulation to separate the effects of changing environmental conditions on catchability and availability in environmental monitoring data. The simulation combines a module for sampling conditions with a module for individual fish behavior to estimate the proportion of available fish that would escape from the sample. The method is applied to the case study of the well-monitored fish species Delta Smelt (Hypomesus transpacificus) in the San Francisco Estuary, where it has been hypothesized that changing water clarity may affect catchability for long-term monitoring studies. Results of this study indicate that given constraints on Delta Smelt swimming ability, it is unlikely that the apparent declines in Delta Smelt abundance are due to an effect of changing water clarity on catchability.

Recently, several reports have emerged describing the long-term consequences of COVID-19 that may affect multiple systems, suggesting its chronicity. As further research is needed, we conducted a longitudinal observational study to report the prevalence and associated risk factors of long-term health consequences of COVID-19 by symptom clusters in patients discharged from the Temporary COVID-19 Hospital (TCH) in Mexico City. Self-reported clinical symptom data were collected via telephone calls over 90 days post-discharge. Among 4670 patients discharged from the TCH, we identified 45 symptoms across eight symptom clusters (neurological; mood disorders; systemic; respiratory; musculoskeletal; ear, nose, and throat; dermatological; and gastrointestinal). We observed that the neurological, dermatological, and mood disorder symptom clusters persisted in >30% of patients at 90 days post-discharge. Although most symptoms decreased in frequency between day 30 and 90, alopecia and the dermatological symptom cluster significantly increased (p<0·00001). Women were more prone than men to develop long-term symptoms and invasive mechanical ventilation also increased the frequency of symptoms at 30-days post-discharge. Overall, we observed that symptoms often persisted regardless of disease severity. We hope these findings will help promote public health strategies that ensure equity in the access to solutions focused on the long-term consequences of COVID-19.

Multicomponent training is recommended for people with dementia living in long-term care homes. Nevertheless, evidence is limited and people with severe dementia are often excluded from trials. Hence, the aim of this study was to investigate (1) the feasibility and (2) the requirements regarding a multicomponent training for people with moderate to severe dementia. The study was conducted as an uncontrolled single arm pilot study with a mixed methods approach. 15 nursing home residents with a mean age of 82 years (range: 75-90 years; female: 64%) with moderate to severe dementia received 16 weeks of multicomponent training. Feasibility and requirements of the training were assessed by a standardized observation protocol. Eleven participants regularly attended the intervention. The highest active participation was observed during gait exercises (64%), the lowest during strength exercises (33%). It was supportive if exercises were task-specific or related to everyday life. This study confirms that a multicomponent training for the target group is (1) feasible and well accepted. To enhance active participation (2) individual instructions and the implementation of exercises related to everyday life is required. The effectiveness of the adapted training should be tested in future randomized controlled trials.

The population dynamics of Hazel Grouse was studied by presence/ absence recording at stationary sites along fixed routes (110 km) during 1972-2019 in the central part of the Bohemian Forest (Šumava, Czech Republic). The 100-km² study area covered altitudes between 600 m (Rejstejn) and 1,253 m a.s.l., (mount Sokol). Our data base contained indices of Hazel Grouse occupancy: positive sites/ controlled sites for a yearly increasing number of Hazel Grouse occurrence sites (N = 134) for 48 years. We used a loglinear Poisson-regression method to analyze the long-term population trend for Hazel Grouse in the study area. In the period 1972 to 2006 we found a stable Hazel Grouse population (p = 0.83). From 2006-2007 to 2019, the population index dropped (-3.8% per year, p &lt; 0.05) for the last 13 years. This decline is assumed to be influenced by habitat loss due to succession resulting in older, more open forest stands, by strongly increasing forestry and windstorm “Kyrill” followed by clear cutting, bark-beetle damage, and removal of pioneer trees in spruce plantations, which diminished buds and catkins, the dominant winter food. The influence of disturbance by increasing touristic activities and/or predation is discussed. Our results could help to optimize conservation efforts for Hazel Grouse in the Bohemian Forest.

Metastatic pancreatic ductal adenocarcinoma pancreatic (PDAC) is characterized by poor prognosis and short survival. Today, the use of new polytherapeutic regimens increases clinical outcome of these patients opening new clinical scenario. A crucial issue related to the actual improvement achieved with these new regimens is represented by the occasional possibility to observe a radiological complete response of metastatic lesions in patients with synchronous primary tumor. What could be the best therapeutic management of these patients? Could surgery represent an indication? Herein we reported a case of a patient with a PDAC of the head with multiple liver metastasis, who underwent first line chemotherapy with mFOLFIRINOX. After 10 cycles, he achieved a complete radiological response of liver metastases and a partial response of pancreatic lesion. A, duodenocephalopancreasectomy was performed. Due to liver a lung metastases after 8 months from surgery, a second line therapy was started with a disease free survival and overall survival of 8 months and 45 months, respectively. Improvement in the molecular characterization of PDAC could help in the selection of patients suitable for multimodal treatments.

A long-term investigation of D. suzukii dynamics in wild blueberry fields from 2012 - 2018 demonstrates relative abundance is still increasing seven years after initial invasion. Relative abundance is determined by physiological date of first detection and air temperatures the previous winter. Date of first detection of flies does not determine date of fruit infestation. The level of fruit infestation is determined by year, fly pressure, and insecticide application frequency. Frequency of insecticide application is determined by production system. Non-crop wild fruit and predation influences fly pressure; increased wild fruit abundance results in increased fly pressure. Increased predation rate reduces fly pressure, but only at high abundance of flies, or when high levels of wild fruit are present along field edges. Male sex ratio might be declining over the seven years. Action thresholds were developed from samples of 92 fields from 2012 - 2017 that related cumulative adult male trap capture to the following week likelihood of fruit infestation. A two-parameter gamma density function describing this probability was used to develop a risk-based gradient action threshold system. The action thresholds were validated from 2016-2018 in 35 fields and were shown to work well in two of three years (2016 and 2017).

Due to its devastating consequences, late life depression is an important public health problem. The aim of the study was the analysis of variables which may potentially influence risk of depression (GDS-SF). Furthermore, the aim was to study possible mediating effect of given variables on the relationship between the total protein concentration and risk of depression in older-adults with chronic diseases, and physical function impairment. The research sample included a total of 132 older adults with chronic conditions and physical function impairments, remaining under a long-term care in residential environment. Negative linear correlation was observed between patients’ physical functionality, total protein concentration, concentration of HDL cholesterol, arm circumference, and the risk of depression. Considerably stronger relationship was observed between total protein concentration, and GDS-SF, in elderly suffering from sensory dysfunction (b = −6.42, 95% CI = −11.27; −1.58). The effect of the mediation between depression risk is correlated to total protein concentration in blood serum, and the mediators are probably low function impairment and low levels of 25 (OH)D vitamin. Cohort control research is suggested to confirm the hypothesis.

The rapid growth rate of the elderly population is a serious current issue in most countries, affecting them economically through needed medical treatment and healthcare planning. The priority concern is how to reduce the number of elderly people requiring long-term healthcare and raise the number who are able to live independently. This study executed a behavior assessment of elderly person’s self-reported use of electric scooters and analyzed their degree of acceptance of these assisted living tools, partly through a related factor analysis of our survey instrument. We used this questionnaire survey as our research method, applying SPSS22 software for factor analysis that revealed five survey facets.

The paper explores the Kullback-Leibler divergence formalism (KLDF) applied to the stable MG1 queue manifold. It explores the analytic forms of state probabilities and their maximization based on entropy functionals, subject to normalization and mean value constraints. The credibility of KLDF is justified through consistency axioms, and the application of Rényi's and Tsallis's formalisms on a stable M/G/1 queue is examined, resulting in novel state probabilities and insights into information theory of Queue Learning.

Understanding the implications of organic acids in animal diets on physiological markers is critical for developing viable strategies for improving overall health and performance. The aim of this study was to evaluate the influence of dietary organic acids inclusion on rabbits’ growth performance and physiological status by determining blood and intestine development indicators. Forty weaned rabbits aged 28-d randomly assigned into 2 groups and fed with standard compound diet (SCD) and SCD + 0.2% organic acids mixture (butyric, propionic, sorbic; OAM). Growth performance was evaluated between 28-77 d of age, physiological status at 77 d of age. OAM’s rabbits final body weight, ADG, DFI, growth rate indicators were higher, but FCR lower compared to SCD (P &lt; 0.05). OAM increased total protein and albumin levels in blood plasma; reduced pH, increased DM, achieved greater viscosity in caecum’s content (P &lt; 0.05). Compared to SCD better gut development observed in OAM, as total intestinal weight was significantly higher (P &lt; 0.05). Acetic and propionic acids concentrations were increased, but butyric reduced in OAM caecum’s content (P &lt; 0.05). OAM had significant effect on the most caecum histomorphometric parameters: increased villus height, crypt depth and their ratio (P &lt; 0.05). According to our results OAM can improve rabbits’ growth performance, nutrient intake, and feed digestibility by significantly improving physiological status.

The current study aims to develop a methodology for obtaining topology-optimal structures made of short fiber-reinforced polymers, which include the fluid-structure interaction. Accounting for the molding process during the internal iterations of topology optimization makes it possible to enhance the weight efficiency of structures—a crucial aspect, especially in aerospace. Anisotropy is considered through the fiber orientation tensor, which is modeled by solving the plastic molding equations for non-Newtonian fluids, and then introduced as a variable of the stiffness matrix during the structural analysis. The materials used were non-linearly modeled using an exponential-and-linear hardening law. The evaluation of weight efficiency in structures composed of short-reinforced composite materials using a dimensionless criterion is addressed. Experimental verification was performed to confirm the validity of the developed methodology. The evidence illustrates that considering anisotropy leads to stiffer structures and structural elements should be oriented in the direction of maximal stiffness. The load-carrying factor can be expressed in terms of failure criteria. The presented multidisciplinary methodology can be used to improve the quality of the design of structures made of short fiber-reinforced composites (SFRC) where high stiffness, high strength, and minimum mass are the primary required structural characteristics.

Salmonella enterica includes enteric pathogens of zoonotic potential, possessing one of the largest pools of temperate phages in their genomes. One such class of phages previously called Podoviridae (Now genera placed directly under class Caudoviricetes temporarily) is the largest group of temperate phages that lysogenize clinically and economically important Salmonella enterica serovars. These phages are capable of generalized transduction and are well known for carrying foreign DNA (mobile genetic elements and resistance genes) to new bacterial species that play a major role in the evolution, pathogenicity, and host adaptability of Salmonella serovars. Here we report a novel species of podo viruses; Salmonella phage BIS08P22 (BIS08) that infects Salmonella enterica serovar Typhimurium strain SE-BS17 (Acc. NO: MZ503545). Phage BIS08 was viable only at biological pH and temperature (pH7 and 37 °C) and features 41,574 -base pair (bp) linear ds DNA genome with 47% GC content. It encodes 73 putative Open Reading Frames (ORFs), has a mosaic arrangement, and shares only 16 core genes with its closest homologs. BIS08 genome possesses only 52 % homology (genome-wide Nucleotide homology by VIRIDIC analysis) with members of the genus Lederbergvirus which is not sufficient to place it in the same genus. This phage has a unique terminase enzyme (DNA packaging motor) with no nucleotide and protein homology with any known member of the Lederbergvirus genus. We carried out a detailed phylogenetic and genome analysis of this novel phage and proposed its placement in a new genus. Our study also suggests a revision in the classification of the genus Lederbergvirus. We also performed in silico structural analysis of the BIS08 unique terminase enzyme comparing it to its close homologs.

Liquid biopsy is a rapidly emerging field which involves the minimal/non-invasive assessment of signature somatic mutations through analysis of circulating tumor DNA (ctDNA) shed by tumor cells in bodily fluids. The broad, unmet need for liquid biopsy lung cancer detection is the lack of multiplex platform that can detect a mutation panel of lung cancer genes using minimum amount of sample, especially for ultra-short ctDNA (usctDNA). Here we developed a non-PCR and non-NGS-based single droplet based multiplexing microsensor technology “Electric Field-Induced Released and Measurement (EFIRM) Liquid Biopsy” (m-eLB) for lung can-cer-associated usctDNA. The m-eLB provides multiplexable assessment of usctDNA within a single droplet of biofluid in only one well of micro-electrodes, as each electrode coated with dif-ferent probes for the ctDNA. In this m-eLB prototype, it demonstrates accuracy for 3 tyrosine ki-nase inhibitor related EGFR target sequences in synthetic nucleotides. The accuracy for the multi-plexing assay has an AUC of 0.98 for L858R, Ex19del, AUC of 0.94 for Ex19 deletion and AUC of 0.93 for T790M. By combining the 3 EGFR assay together, the AUC was 0.97 for the multiplexing assay.

The aim of the study was to find the effect of polyethylene (PE) coatings on the short-circuit current of silicon photovoltaic cells covered with glass, in order to improve the short-circuit current of the cells. Various combinations of PE films (thicknesses ranging from 9 to 23 µm, number of layers ranging from 2 to 6) with glasses (greenhouse, float, optiwhite and acrylic glass) were investigated. The best current gain of 4.05% was achieved for the coating combining a 1.5 mm-thick acrylic glass with 2 × 12 µm-thick PE films. This effect can be related to the formation of an array of micro-wrinkles and micrometer-sized air bubbles with a diameter of 50 to 600 µm in the films, which serve as micro-lenses and enhance light trapping.

In recent years, with the development of social media, people are more and more inclined to upload text, pictures and videos on the platform to express their personal emotions, thus the number of short videos is increasing and becoming the first choice for people to socialize. Unlike the traditional way, people can convey their personal emotions and opinions through media other than words, such as video images, etc. for external information. Therefore, the expression and analysis of emotions is not only through text, but also through the analysis of emotional needs in images and videos, and the research scholars have customized products for individual users. Compared with pure text content, video information can more intuitively express users' happiness, anger and sorrow, thus short video-related applications have gained more and more popularity among Internet users in recent years. However, not all short videos on social networking sites can accurately express users' emotions, and related text information can more accurately assist sentiment analysis and thus improve accuracy. However, short video sentiment analysis based on video frame images is inaccurate in some scenarios, such as when expressing tears of joy, the sentiment expressed by the user's facial expression and voice are different, which will cause errors in the analysis of sentiment. As a result, researchers began to consider multimodal sentiment analysis to reduce the impact of the above scenarios on short video sentiment analysis. This paper focuses on proposing a sentiment analysis method for short videos. We first propose a residual attention model to make full use of the information in audio to classify the emotions contained in them. Then the text information in the dataset is classified by feature extraction. The key to extract features from text information is not only to retain the semantic information of the text, but also to explore the potential emotional information in the text, so as to ensure the integrity of the text information features. The experiments show that the sentiment analysis model proposed in this paper is more superior than the baselines.

Axial compression tests were carried out on 6 square steel tube confined concrete short columns and 6 BFRP square pipe confined concrete axial compression tests. The concrete strength grades were C30, C40, and C50. The test results show that the failure modes of steel pipe and BFRP pipe are obviously different, and the BFRP pipe undergoes brittle failure. Compared with the short columns of concrete confined by BFRP pipes, the ultimate bearing capacity of axial compression is increased by -76.46%, -76.01%, and -73.06%, and the ultimate displacements are -79.20%, -80.78%, -71.71%.

Axial compression tests were carried out on 6 square steel tube confined concrete short columns and 6 BFRP square pipe confined concrete axial compression tests. The concrete strength grades were C30, C40, and C50. The test results show that the failure modes of steel pipe and BFRP pipe are obviously different, and the BFRP pipe undergoes brittle failure. Compared with the short columns of concrete confined by BFRP pipes, the ultimate bearing capacity of axial compression is increased by -76.46%, -76.01%, and -73.06%, and the ultimate displacements are -79.20%, -80.78%, -71.71%.

Body acceleration due the heartbeat-induced reaction forces can be measured as smartphone accelerometer (m-ACC) signals. Our aim was to test the feasibility of using m-ACC to detect changes induced by stress by ultra-short heart rate variability (USV) indices (SDNN and RMSSD). Sixteen healthy volunteers were recruited; m-ACC was recorded while in supine position, during spontaneous breathing (REST) and during one minute of mental stress (MS) induced by arithmetic serial subtraction task, simultaneous with conventional ECG. Beat occurrences were extracted from both ECG and m-ACC and used to compute USV indices using 60, 30 and 10s durations, both for REST and MS. A feasibility of 93.8% in the beat-to-beat m-ACC heart rate series extraction was reached. In both ECG and m-ACC series, compared to REST, in MS the mean beat duration was reduced by 15% and RMSSD decreased by 38%. These results show that short term recordings (up to 10 sec) of cardiac activity using smartphone’s accelerometers are able to capture the decrease in parasympathetic tone, in agreement with the induced stimulus.

Agavins consumption has lead to accelerate body weight loss in mice. We investigated the changes on cecal microbiota and short chain fatty acids (SCFA) associated to body weight loss in overweight mice. Firstly, mice were fed with standard (ST5) or high fat (HF5) diet for 5 weeks. Secondly, overweight mice were shifted to standard diet alone (HF-ST10) or supplemented with agavins (HF-ST+A10) or oligofructose (HF-ST+O10), five more weeks. Cecal contents were collected before and after supplementation to determine microbiota and SCFA concentrations. At the end of first phase, HF5 mice showed a significant increase of body weight, which was associated with reduction of cecal microbiota diversity (PD whole tree; non-parametric t-test, P < 0.05), increased Firmicutes/Bacteroidetes ratio and reduced SCFA concentrations (t-test, P < 0.05). After diet shifted, HF-ST10 normalized its microbiota, increase its diversity and SCFA levels, whereas agavins (HF-ST+A10) or oligofructose (HF-ST+O10) led to partial microbiota restoration, with normalization of the Firmicutes/Bacteroides ratio as well as higher SCFA levels (P < 0.1). Moreover, agavins noticeably enriched Klebsiella and Citrobacter (LDA > 3.0); this enrichment has not been reported previously under a prebiotic treatment. In conclusion, agavins or oligofructose modulated cecal microbiota composition, reduced extent of diversity and increased SCFA. Furthermore, identification of bacteria enriched by agavins, opens opportunities to explore new probiotics.

Recent studies, typically using patient cerebrospinal fluid (CSF), have suggested that different autoantibodies (Aabs) acting on their respective receptors, may underlie neuropsychiatric disorders. The NR1 subunit of the N-methyl-D-aspartate receptor (NMDAR) has been identified as a target of anti-NMDAR Aabs in a number of central nervous system (CNS) diseases including encephalitis and autoimmune epilepsy. However, the role or the nature of Aabs responsible for effects on neuronal excitability and synaptic plasticity is yet to be established fully. Patient-derived anti-NMDAR Aabs have been shown to bind to specific regions within the NR1 subunit; here, peptide immunisation was used to generate Aabs against selected specific NR1 extracellular sequences. ‘Protein A’ purification was used to obtain total IgG and further peptide purification used to obtain a greater percentage of NMDAR-target specific IgG Aabs. Binding and specificity of these anti-NMDAR Aabs was determined using a range of methodologies including enzyme-linked immunosorbent assay, immunocytochemistry and immunoblotting. Functional effects were determined using different in vitro electrophysiology techniques: two-electrode voltage-clamp in Xenopus oocytes and measures of long-term potentiation (LTP) in ex vivo hippocampal brain slices using multi-electrode arrays. We show that anti-NMDAR Aabs generated from peptide immunisation had specificity for NR1 immunisation peptides as well as target-specific binding to the native protein. Anti-NMDAR Aabs had no clear effect on isolated NMDARs in an oocyte expression system. However, we show that peptide-purified anti-NMDAR Aabs prevented the induction of LTP at Schaffer collateral-CA1 synapses in ex vivo brain slices. Our data demonstrate that anti-NMDAR Aabs cause synaptic NMDAR hypofunction at a network level. This work provides a solid basis to address outstanding questions regarding anti-NMDAR Aabs mechanisms of action and, potentially, development of therapies against CNS diseases.

In patients with prior Takotsubo syndrome (TTS), long lasting functional cardiac limitations were described as compared with normal subjects. Emotions-triggered Takotsubo syndrome (E-TTS) has more favorable outcomes than TTS preceded by a physical trigger or by no identifiable fac-tors. The aim of the present study was to assess long-term cardiac functional limitations in a co-hort of asymptomatic E-TTS patients. We enrolled n=39 asymptomatic patients with a diagnosis of E-TTS. Cardiopulmonary exercise tests (CPET) were performed at 30 [12-40] months median fol-low-up from the acute event. A cohort of n=39 individuals matched for age, sex, body mass index and comorbidities served as control. Despite recovery of left ventricular ejection fraction, patients with prior E-TTS had lower peak VO2 and percentage of predicted peak VO2 (17.8 ± 3.6 vs 22.5 ± 6.5; P < 0.001 and 75.2 ± 14.1 % vs 100.6 ± 17.1%, P <0.001), VO2 at anaerobic threshold (AT) (11.1 [10.1-12.9] vs 14.4 [12.5-18.7]; P <0.001), peak O2 pulse (9.7 ± 2.5 vs 13.1 ± 3.5; P <0.001) and higher VE/VCO2 slope (30.4 ± 3.7 vs 27.2 ± 3.5; P <0.001) compared with matched controls. We found no statistically significant differences in heart rate reserve (HRR), respiratory equivalent ratio (RER), mean blood pressure and peak PetCO2 between patients and controls. Despite its favorable out-come, patients with E-TTS in our population were found to have subclinical long-term functional cardiac limitations as compared with a control cohort.

This research work deals with the formulation of numerical scheme with algorithm to compute singularly perturbed partial differential equations(SP-PDEs) involving a significant negative shift. The problem involves small parameter which causes a rapidly changing boundary layers in the vicinity of a body, and the negative shift term causes interior layer. Such appearance of an abruptly varying layers causes difficulties to find the exact solution and it is not adequate to employ classical numerical methods. The technique presented in this research work simplifies these challenges and yields accurate numerical solution.The stability and convergence analyses of the methods are examined and proven. To test the developed technique, numerical experiments are carried out and confirmed with theoretical analysis.

Background: PFO (Patent foramen ovale) is a common defect that affects about 25% of the population. Although its presence is asymptomatic in the majority of the cases, the remaining part becomes overt with different symptoms, including cryptogenic stroke. The PFO closure procedure is widely available to date with the most used Amplatzer PFO Occluder, also in complex anatomy, but the performance of another device, the GORE Septal Occluder (GSO), has not been completely explored with regard to different septal anatomies. Methods: From March 2012 to June 2020, 118 consecutive patients with an indication for PFO closure were treated using the GSO system and included in a prospective analysis and followed. After 12 months, every patient underwent transcranial Doppler to evaluate the effectiveness of treatment. Results: of 111 patients evaluated, 107 showed effective PFO closure (96,4%) and 4 showed a residual shunt (3,6%). To better evaluate the device performance, the overall population was sorted into 2 clusters based on the echocardiographic characteristics. The main difference between groups was for PFO width (4,85 ± 1,8 vs 2,9 ± 1 mm, p <0,001) and PFO tunnel length (12,6 ± 3,8 vs 7,2 ± 2 respectively, p <0,001), allowing identification of complex anatomy and simple anatomy, respectively. Regardless of the aforementioned cluster, the GSO performance to reach an effective closure was independent of anatomy type and the chosen device size. Conclusion: GSO device showed efficacy and safety at 1-year follow-up in patients with at least 1 anatomical factor of complexity of PFO, irrespectively from the level of complexity itself.

The natural frequency of buildings decreases during a strong-motion earthquake, and the structure loses its stiffness. As a result, understanding the damaging process in the structure owing to changes in structural properties is critical during a seismic excitation. The time-frequency technique can detect the damaged building’s time-varying frequency contents. Wigner distributions (WD), Wigner-Ville distributions (WVD), pseudo-Wigner-Ville distributions (PWVD), smoothed pseudo-Wigner-Ville distributions (SPWVD), and synchrosqueezing transforms (SST) have all become popular in recent years for a variety of earthquake engineering applications, including building damage detection. This study proposes the local maximum synchrosqueezing transform (LMSST) for detecting frequency shifts in buildings during strong earthquakes. The data presented in the research show that the suggested method outperforms as compared to the conventional time-frequency methods for detecting frequency shifting in earthquake-damaged structures.

This article presents the research results on developing professional capital in Lithuanian schools during the national project "Time for leaders". The longitudinal national initiative aimed to develop professional capital as the synergy of human, social and decisional components (Hargreaves and Fullan, 2019) of schools through the various cooperation-based learning experiences of educational professionals. The article provides an overview of project interventions (activities that stimulated cooperative learning of educational professionals) in the light of cooperative learning principles. The assessment of change over two project years in education professionals' perceptions of professional capital is presented using Cohen's d effect size measure. The measurement sample consisted of teachers (n1=5105; n2=4683) and school leaders (n1=439; n2=405) from 189 schools in 30 Lithuanian municipalities. The findings show a statistically significant medium positive change in professional capital. The most considerable change was estimated in the social and decisional capital dimensions and the relatively smallest - in the field of human capital.

Coincident changes in abundance and behavior pose a challenge for interpreting abundance data from monitoring programs. In the San Francisco Estuary, long-term monitoring documented the declines of many species including the anadromous Longfin Smelt (Spirinchus thaleichthys). We identified seasonal patterns in reginal presence of Longfin Smelt through its life cycle using monitoring data and generalized additive modelling. We then investigated the year-to-year variability in the seasonal patterns of presence using functional data analysis (FDA). FDA separated the variability due to population size from variability due to differences in timing of presence. We found that Longfin Smelt have consistent seasonal distribution patterns and that two trawl types were needed to accurately describe those patterns. After accounting for variability due to year-class strength, shifts in the timing of presence were evident in three regions. The most variable period for the upstream regions Suisun Bay and West Delta was for age-0 fish in summer and for the downstream region Central Bay was for age-0 fish in late fall. This manifested as a delay in the typical fall re-occupation of upstream regions that comprise the study area for another monitoring study (Fall Midwater Trawl). Thus, a portion of the recent reductions in Fall Midwater Trawl abundance of Longfin Smelt resulted from changes in behavior rather than a decline in abundance. The presence of multiple monitoring surveys allowed analysis of distribution from one data set to aid interpretation of patterns in abundance from another monitoring survey. This study highlights how identifying portions of the life cycle with the most and least variability in distribution can help inform the types of management strategies that will be most effective. It also illustrates an analytical method that can be used to address the problem of confounded effects of abundance and behavior on patterns in monitoring data.

While the ongoing climate change is well documented, the impacts exhibit a substantial variability, both in direction and magnitude, visible even at regional and local scales. However, the knowledge of regional impacts is crucial for the design of mitigation and adaptation measures, particularly when changes in the hydrological cycle are concerned. In this paper we present hydro-meteorological trends based on observations from a hydrological research basin in Eastern Austria between 1979-2019. The analysed state variables include the air temperature, the precipitation, and the catchment runoff. Additionally, trends for the catchment evapotranspiration were derived. The analysis shows that while the mean annual temperature was decreasing and annual temperature minima remained constant, the annual maxima were rising. The long-term trends indicate a shift of precipitation to the summer with minor variations observed for the remaining seasons and at an annual scale. Observed precipitation intensities mainly increased in spring and summer between 1979-2019. The catchment evapotranspiration, computed based on catchment precipitation and outflow, showed an increasing trend for the observed time period.

Dietary components are important for the structural and functional development of the brain. Among these, docosahexaenoic acid,22:6n-3 (DHA) is critically required for the structure and development of the growing fetal brain in utero. DHA is the major n-3 long-chain fatty acid in brain gray matter representing about 15% of all fatty acids in the human frontal cortex. DHA affects neurogenesis, neurotransmitter, synaptic plasticity & transmission, and signal transduction in the brain. Studies in animals and humans show that adequate levels of DHA in neural membranes are important for cortical astrocyte maturation and vascular coupling, and helps cortical glucose uptake and metabolism. In addition, specific metabolites of DHA are bioactive molecules that protect tissues from oxidative injury and stress in the brain. A low DHA level in the brain results in behavior changes and is associated with learning problems and memory deficits. In humans, the third trimester-placental supply of maternal DHA to the growing fetus is critically important as the growing brain obligatory requires DHA during this window period. Besides, DHA is also involved in the early placentation process, essential for placental development. This underscores the critical importance of maternal DHA intake for the structural and functional development of the brain. This review describes DHA's multiple roles during gestation, lactation, and the consequences of its lower intake during pregnancy and postnatally on the children's brain development and function.

Background: The associations between long-term exposure to particulate matters (PM) in residential ambiance and obesity are comparatively less elucidated among young adults. Methods: Using 2017 Community Health Survey data with aged 19−29 participants in 25 communities, Seoul, the relationship between obesity and long−term PM10 levels of living district was examined. We defined obesity as overweight (25≤BMI&lt;30) or obese (30≤BMI) using Body Mass Index (BMI) from self-reported anthropometric information. Analysis was conducted sampling weighted logistic regression models by fitting municipal PM10 levels according to individual residence periods with 10 years and more residing in a current municipality. Socio-demographic factors were adjusted over all models and age−specific effect was explored among aged 19–24 and 25–29. Results: Total study population are 3,655 [men 1,680 (46.0%) and aged 19–24 1,933 (52.9%)] individuals. Among the communities with greater level of PM10; 2001–2005, associations with obesity were increased for overall with residence period; 10 years ≤ [Odds ratio, OR 1.071, 95% Confidence interval (CI) 0.969–1.185], 15 years ≤ [OR 1.118, 95% CI 1.004–1.245], and 20 years ≤ [OR 1.156, 95% CI 1.032–1.294]. However, decreased associations were detected for PM10; 2006–2010, and age–specific effects were modified according to the residence period. Conclusions: Although currently PM10 levels are decreasing, higher levels of PM10 exposure at the residential area during the earlier life-time may contribute in increasing obesity among young adults.

COVID-19 patients can recover with a median SARS-CoV-2 clearance of 20 days post initial symptoms (PIS). However, we observed some COVID-19 patients with existing SARS-CoV-2 for more than 50 days PIS. This study aimed to investigate the cause of viral clearance delay and the infectivity in these patients. Demographic data and clinical characteristics of 22 long-term COVID-19 patients were collected. SARS-CoV-2 nucleic acid, peripheral lymphocyte count, and functionality were assessed. SARS-CoV-2-specific and neutralization antibodies were detected, followed by virus isolation and genome sequencing. The median age of the studied cohort was 59.83±12.94 years. All patients were clinically cured after long-term SARS-CoV-2 infection ranging from 53 to 112 days PIS. Peripheral lymphocytes counts were normal. Interferon gamma (IFN-ƴ)-generated CD4+ and CD8+ cells were normal as 24.68±9.60% and 66.41±14.87%. However, the number of IFN-ƴ-generated NK cells diminished (58.03±11.78%). All patients presented detectable IgG, which positively correlated with mild neutralizing activity (ID50=157.2, P=0.05). SARS-CoV-2 was not isolated, and a cytopathic effect was lacking. Only three synonymous variants were identified in spike protein coding regions. In conclusion, decreased IFN-γ production by NK cells and low neutralizing antibodies might favor SARS-CoV-2 long-term existence. Further, low viral load and weak viral pathogenicity was observed in COVID-19 patients with long-term SARS-CoV-2 infection.

This paper examines whether the Great Recession has altered the disparities of the US regional health care expenditures. We test the null hypothesis of convergence for the US real per capita health expenditure for the period 1980-2014. Our results indicate that the null hypothesis of convergence is clearly rejected for the total sample as well as for the pre-Great Recession period. Thus, no changes are found in this regard. However, we find that the Great Recession has modified the composition of the estimated convergence clubs, offering a much more concentrated picture in 2014 than in 2008, with most of the states included in a big club, and only 5 (Nevada, Utah, Arizona, Colorado and Georgia) exhibiting a different pattern of behavior. These two estimated clubs diverge and, consequently, the disparities in the regional health sector have increased.