Relevant Codes Research Articles

Utilization of endovascular thrombectomy in patients with antiphospholipid syndrome and acute ischemic stroke

Abstract Background Antiphospholipid syndrome (APS) is a prothrombotic state and can cause acute ischemic stroke (AIS) particularly in younger individuals. Purpose Given the recent widespread adoption of endovascular thrombectomy (EVT) in patients with AIS, we aimed to evaluate the current trends and utilization of this technique in patients with APS. Methods We used National Readmission Database (NRD) to identify patients admitted between 2016 and 2020 with a primary diagnosis of AIS and a concurrent diagnosis of APS using relevant ICD-10 CM codes. Results Our analysis identified a total of 2,535,777 patients admitted for AIS, of which 7209 had a concomitant diagnosis of APS. The median age in patients with APS was 55 compared to 71 in non-APS patients (p < 0.001). APS patients also exhibited a lower prevalence of comorbidities such as coronary artery disease (4% vs. 28%; p < 0.001), congestive heart failure (CHF) (15% vs. 19%; p < 0.001), atrial fibrillation (14% vs. 26%), and diabetes (12% vs. 18%; p < 0.001). Systemic lupus erythematosus (SLE) was the most prevalent concurrent autoimmune disease in the APS cohort (19%). Patients with APS were significantly more likely to undergo EVT compared to their non-APS counterparts (5.2% vs. 3.5%; p < 0.001). Mortality was higher in non-APS likely due to older age compared to non-APS but didn’t reach statistical significance (4.4% vs 3.7%, p=0.070). Notably, the length of hospital stay was significantly higher for patients with APS (median: 6 days) compared to the non-APS group (median: 3 days; p < 0.001). Furthermore, we observed a steady increase in the utilization of EVT in patients with APS and AIS from 2016 (3.7%) to 2020 (6.8%; p = 0.02). Conclusion Our findings demonstrate that patients with AIS and concurrent APS are more likely to undergo endovascular thrombectomy, potentially due to a higher stroke burden. Additionally, the study revealed a progressive increase in the use of EVT for this specific patient population.

Problem-Based Learning in Science Education: A Mixed Meta Method Study

A mixed-meta analysis of research on problem-based learning in science education was the goal of this study. In this instance, document analysis utilizing the mixed-meta method—which combines meta-analysis and meta-thematic analysis procedures-was used to investigate both quantitative and qualitative data. Using CMA and MetaWin programs, studies from certain databases that fit predetermined criteria were analyzed for a meta-analysis. Of the 78 studies that were included in the study, the effect size was found to be large, with g = 0.909. It has been suggested that science education benefits from the problem-based learning approach. Within the framework of the established criteria, 15 researches based on document analysis were subjected to a meta-thematic analysis using the Maxqda program in accordance with content analysis. Codes were acquired based on specific topics. By calculating the compliance values of the relevant codes under the relevant themes, a good degree of agreement was found regarding the effects of problem-based learning in science education on academic achievement, the affective-social dimension, 21st century skills, and the negative aspects of problem-based learning in science education. The research results indicate that there is a consensus among the researchers regarding the benefits of using problem-based learning techniques in science education.

The influence of Muscle Wasting on Patient Outcomes among Burn Patients: A Burn Care Quality Platform Study.

The hypermetabolism that occurs after large burns leads to muscle wasting that can result in weakness, difficulty fighting infections, and other challenges to healing. Overall, rates of muscle wasting are not well established across burn centers. The Burn Care Quality Platform (BCQP), the largest available dataset on burn patients globally, was used to explore the rate of Muscle Wasting using data collected from more than 100 burn centers. "Muscle Wasting" was defined from relevant ICD codes (sarcopenia, muscle cachexia, protein malnutrition). Binomial and logistic regression were used to analyze the role of Muscle Wasting in burn mortality and other outcomes after controlling for demographic and comorbid characteristics. In total, the BCQP provided data from 84,438 adult and pediatric burn patients injured between 2000-2018. Only 2.6% (N=2,159) of the patients in the BCQP were diagnosed with having Muscle Wasting at some point during their admission. While Muscle Wasting was not associated with a statistically significant impact on mortality, it remained an independent predictor of inability to discharge to independent living, longer inpatient days, as well as an average of 10 additional surgical procedures (P<0.001 for all) as compared to patients who did not have Muscle Wasting. Muscle Wasting is an independent risk factor for several adverse outcomes in burn patients, but not mortality in the BCQP. Confidence in these findings would be improved with more accurate data collection, as the diagnosis of Muscle Wasting is likely under-reported, under-diagnosed, or both.

MKAN-MMI: empowering traditional medicine-microbe interaction prediction with masked graph autoencoders and KANs

The growing microbial resistance to traditional medicines necessitates in-depth analysis of medicine-microbe interactions (MMIs) to develop new therapeutic strategies. Widely used artificial intelligence models are limited by sparse observational data and prevalent noise, leading to over-reliance on specific data for feature extraction and reduced generalization ability. To address these limitations, we integrate Kolmogorov-Arnold Networks (KANs), independent subspaces, and collaborative decoding techniques into the masked graph autoencoder (Mask GAE) framework, creating an innovative MMI prediction model with enhanced accuracy, generalization, and interpretability. First, we apply Bernoulli distribution to randomly mask parts of the medicine-microbe graph, advancing self-supervised training and reducing noise impact. Additionally, the independent subspace technique enables graph neural networks (GNNs) to learn weights independently across different feature subspaces, enhancing feature expression. Fusing the multi-layer outputs of GNNs effectively reduces information loss caused by masking. Moreover, using KANs for advanced nonlinear mapping enhances the learnability and interpretability of weights, deepening the understanding of complex MMIs. These measures significantly enhanced the accuracy, generalization, and interpretability of our model in MMI prediction tasks. We validated our model on three public datasets with results showing that our model outperformed existing leading models. The relevant data and code are publicly accessible at: https://github.com/zhuoninnin1992/MKAN-MMI.

Elastic–plastic solutions of reserved deformation for soft rock circular tunnel under high stress

The determination of reserved deformation is typically based on referring to relevant codes or the engineering analogy method, lacking a certain theoretical approach. The purpose of this study is to provide a theoretical basis for determining reserved deformation and to analyze the variation law of the surrounding rock affected by reserved deformation. Considering the reserved deformation under the condition of asymmetric load, the expression of optimal reserved deformation, the expression of support resistance reflecting the strength of surrounding rock, the strength of support material and the magnitude of in situ stress, the displacement expression of surrounding rock are derived by approximate solution based on the classical elastic&amp;ndash;plastic theory. Numerical simulation software is used to simulate the displacement expression of the surrounding rock considering the reserved deformation and the expression of the optimum reserved deformation under the condition of asymmetric load. The results of the numerical simulation were compared with those of the analytical solutions, and the analytical results show that the errors between the two are within 12% and that the consistency is good.

Organizational innovations related to Primary Care Access Points (GAP) for unattached patients in Quebec: a multi-case qualitative study

BackgroundBeing attached to a primary care (PC) provider is at the core of a strong primary health care system. Centralized waiting lists (CWL) for unattached patients have been implemented in eight provinces of Canada to support the attachment process. In Quebec, the Ministry of Health mandated the implementation of Primary Care Access Points (GAP) across the province to help unattached patients navigate the health system while awaiting attachment through the CWL. Several local health territories developed complementary innovations to the GAP to respond to local population needs. This paper aims to describe five organizational innovations implemented locally.MethodsThis multi-case qualitative study was conducted in four local health territories in the province of Quebec. Fifty-two semi-structured interviews with healthcare managers, nurses, physicians, other health professionals and administrative staff were conducted between April 2023 and April 2024. An interview guide was developed based on existing frameworks on the implementation of innovations and the evaluation of the GAP. Thematic analysis was conducted using NVivo software. Inductive and deductive approaches were used to develop relevant codes and themes. Logic models were built to describe the organizational innovations.ResultsFive organizational innovations are described. First, a multidisciplinary clinic aimed at responding to patients with mental health issues was implemented. Second, a nurse clinic was implemented to provide temporary care for patients with unstable chronic illnesses. The third innovation is a mobile proximity clinic where unattached GAP patients are first evaluated by a paramedic before receiving care from a nurse. Fourth, a pharmacist trajectory was implemented to increase engagement of community pharmacists to respond to GAP patients. The last innovation is a decentralized GAP offering in-person nursing care to unattached GAP patients.ConclusionsDescriptions of these five innovations are key to inform other territories and provinces on ways to improve access for unattached patients while they are waiting to be attached. The introduction of the GAP and the organizational innovations, suggests a transition where access to PC services does not rely solely on attachment status.

Effective Hard Negative Mining for Contrastive Learning-based Code Search

Background. Code search aims to find the most relevant code snippet in a large codebase based on a given natural language query. An accurate code search engine can increase code reuse and improve programming efficiency. The focus of code search is how to represent the semantic similarity of code and query. With the development of code pre-trained models, the pattern of using numeric feature vectors (embeddings) to represent code semantics and using vector distance to represent semantic similarity has replaced traditional string matching methods. The quality of semantic representations is critical to the effectiveness of downstream tasks such as code search. Currently, the state-of-the-art (SOTA) learning method uses the contrastive learning paradigm. The objective of contrastive learning is to maximize the similarity between matching code and query (positive samples) and minimize the similarity between mismatched pairs (negative samples). To increase the reusing of negative samples, prior contrastive learning approaches use a large queue (memory bank) to store embeddings. Problem. However, there is still a lot of room for improvement in using negative examples for code search: ① Due to the random selection of negative samples, semantic representations learned by existing models cannot distinguish similar codes well. ② Since semantic vectors in the memory bank are reused from previous inference results and then directly used for loss function calculation without gradient descent, the model cannot effectively learn the negative sample semantic information. Method. To solve the above problems, we propose a contrastive learning code search model with hard negative mining called CoCoHaNeRe: ❶ To enable the model to distinguish similar codes, we introduce hard negative examples into contrastive training, which are negative examples in the codebase that are most similar to positive examples. As a result, hard negative examples are most likely to make the model make mistakes. ❷ To improve the learning efficiency of negative samples during training, we add all hard negative examples to the model's gradient descent process. Result. To verify the effectiveness of CoCoHaNeRe, we conducted experiments on large code search datasets with six programming languages, as well as similar retrieval tasks code clone detection and code question answering. Experimental results show that our model achieves SOTA performance. In the code search task, the average MRR score of CoCoHaNeRe exceeds CodeBERT, GraphCodeBERT, and UniXcoder by 11.25%, 8.13%, and 7.38%, respectively. It has also made great progress in code clone detection and code question answering. In addition, our method performs well in different programming languages and code pre-training models. Furthermore, qualitative analysis shows that our model effectively distinguishes high-order semantic differences between similar codes.

Development and Validation of Claims-based Algorithms for Identifying Hospitalized Patients With COVID-19 and Their Severity in 2020 and 2021.

This study aimed to develop and validate claims-based algorithms for identifying hospitalized patients with coronavirus disease 2019 (COVID-19) and disease severity. We used claims data including all patients at the National Center for Global and Medicine Hospital between January 1, 2020, and December 31, 2021. The claims-based algorithms for three statuses with COVID-19 (hospitalizations, moderate or higher status, and severe status) were developed using diagnosis codes (International Classification of Diseases, 10th revision code: U07.1, B34.2) and relevant medical procedure code. True cases were determined using the COVID-19 inpatient registry and electronic health records. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each algorithm at 6-month intervals. Of the 75,711 total patients, the number of true cases was 1,192 for hospitalizations, 622 for moderate or higher status, and 55 for severe status. The diagnosis code-only algorithm for hospitalization had sensitivities 90.4% to 94.9% and PPVs 9.3% to 19.4%. Among the algorithms consisting of both diagnosis codes and procedure codes, high sensitivity and PPV were observed during the following periods: 93.9% and 97.1% for hospitalization (January-June 2021), 90.4% and 87.5% for moderate or higher status (July-December 2021), and 92.3% and 85.7% for severe status (July-December 2020), respectively. Almost all algorithms had specificities and NPVs of approximately 99%. The diagnosis code-only algorithm for COVID-19 hospitalization showed low validity throughout the study period. The algorithms for hospitalizations, moderate or higher status, and severe status with COVID-19, consisting of both diagnosis codes and procedure codes, showed high validity in some periods.

Risk of Venous Thromboembolism in Adolescents Undergoing Pelvic Osteotomy: Insights From a Propensity-matched Retrospective Cohort Study.

Pelvic osteotomies are surgical procedures that are commonly performed in adolescents and young adults to improve stability or correct various deformities. Venous thromboembolism (VTE), including pulmonary embolism (PE) and deep vein thrombosis (DVT), are dreaded complications of any major procedure including pelvic osteotomies. Unlike adults, the incidence of DVT and need for prophylactic measures are not well understood in the adolescents. The purpose of this study is to understand the need for VTE prophylaxis in adolescents and determine if their risk profile aligns with adults, who have more established VTE prophylactic guidelines in place for these surgeries. This retrospective cohort study utilized data from the TriNetX Research Network, which includes records from over 80 healthcare organizations and more than 120 million patients. De-identified patient data from January 1, 2003, to March 1, 2024, were extracted using relevant ICD-9 and ICD-10 procedural codes. The study included 2 cohorts: patients aged 13 to 17 years (adolescents) and patients 18 years and older (adults) who underwent pelvic osteotomies. To control for confounding variables, propensity score matching was employed based on sex and relevant comorbidities, including diabetes mellitus, tobacco use, and overweight/obesity. Statistical significance was set at P<0.01. A total of 2374 patients successfully matched in each cohort. Within 90 days following surgical intervention, adult patients had overall risks of 4.5%, 1.9%, and 5.9%, whereas adolescent patients had overall risks of 1.3%, 0.5%, and 1.4% for DVT (RR: 3.419), PE (RR: 4.182), and either DVT or PE (RR: 4.118), respectively. This study examines the rate of VTE in a large cohort of adolescents undergoing pelvic osteotomies. Although we found the rates to be lower in adolescents compared with adults, the high absolute risk in adolescents-above 1%, the threshold established in the literature for considering prophylaxis-underscores the need for tailored risk stratification strategies and targeted thromboprophylaxis protocols for this population. Level III: case-control study or retrospective cohort study.

Морально-етичні норми в юриспруденції

It was found that the moral and ethical norms in jurisprudence were reflected in the relevant Codes, Instructions, Rules of Conduct, etc., which are aimed at ensuring the principle of peoplecenteredness and contribute to the formation and development of a morally mature specialist with high value orientations in the field of professional duties and interpersonal communication Compliance with moral and ethical standards in jurisprudence is a guarantee of maintaining stable professional relationships in the legal team and contributes to maintaining a positive image of the legal profession. It was emphasized that professional activity implemented in the “person-to-person” format requires not only proper expertise, but also perfect standards of moral and ethical interaction to prevent violation of human rights and freedoms and personal values. Jurisprudence is one of the areas where the official-business interaction between representatives of this profession and citizens is tangential to a person’s private life and personal boundaries. That is why it is important to regulate the ethical aspect of such interaction for full cooperation and the most positive resolution of problematic issues. It is noted that ethical codes define a system of value orientations of representatives of a certain professional direction, in particular, persons who perform specific job duties and are empowered to cooperate with their colleagues or subordinates, as well as all physical and legal entities with whom they cooperate. Attention is focused on the fact that the proper level of moral and ethical culture of a lawyer contributes to the prevention and avoidance of his professional deformation, because a high level of professionalism, unfortunately, is not an obstacle to the development of professional deformation. A developed moral and ethical culture and the desire for personal development do not allow signs of antivalue behavior of a person to manifest. When forming and implementing moral and ethical norms in Ukrainian jurisprudence, the experience and practice of the world legal community is taken into account. Recognition and observance of international standards enables the active cooperation of relevant competent authorities with international organizations to ensure law and order and prevent violations of human rights.

Experimental study on the stabilization of marine soft clay as subgrade filler using binary blending of calcium carbide residue and fly ash

This study endeavors to realize the concurrent utilization of marine soft clay (MSC) and industrial waste, specifically calcium carbide residue (CCR) and fly ash (FA), through a series of experimental investigations. The optimal ratio between CCR and FA, as well as the efficacy of the composite agent (CF–1), were examined, and an empirical equation associating the unconfined compressive strength (qu) of stabilized MSC was developed through unconfined compressive strength (UCS) tests. Microscopic analyses, including X–ray diffraction (XRD), scanning electron microscopy (SEM), and energy–dispersive spectroscopy (EDS), were employed to unveil the intrinsic mechanisms underlying CF–1 stabilized MSC. Subsequently, the suitability of CF–1 solidified MSC as a roadbed filler was ascertained through laboratory tests. Results revealed the optimum CCR:FA ratio for CF–1 to be 4:1, demonstrating superior curing effects compared to individual components such as Portland cement (PC), CCR, and FA, with commendable environmental and economic benefits. The developed empirical equation exhibited effectiveness in predicting the qu of CF–1 solidified MSC under varying curing dates (T) and dosages (Wg) conditions. Characterization through XRD, SEM, and EDS identified the primary products formed within the stabilized MSC matrix with CF–1 as comprising calcium–silicate–hydrate (C–S–H) gel, calcium–aluminate–hydrate (C–A–H) gel, and a minor amount of calcite. As T and Wg increased, the reduction in pores between soil particles enhanced the structural integrity and macro–strength of the cured MSC. The failure pattern of CF–1–solidified MSC elementary samples depended on the CF–1 dosage and curing duration. The solidification mechanism of CF–1 on MSC involved pozzolanic, ion exchange, and carbonation reactions. CF–1 solidified MSC satisfied all the specified requirements for roadbed filler in the relevant code, demonstrating substantial potential for in–situ solidification projects involving MSC.

A survey of deep causal models and their industrial applications

The notion of causality assumes a paramount position within the realm of human cognition. Over the past few decades, there has been significant advancement in the domain of causal effect estimation across various disciplines, including but not limited to computer science, medicine, economics, and industrial applications. Given the continous advancements in deep learning methodologies, there has been a notable surge in its utilization for the estimation of causal effects using counterfactual data. Typically, deep causal models map the characteristics of covariates to a representation space and then design various objective functions to estimate counterfactual data unbiasedly. Different from the existing surveys on causal models in machine learning, this review mainly focuses on the overview of the deep causal models based on neural networks, and its core contributions are as follows: (1) we cast insight on a comprehensive overview of deep causal models from both timeline of development and method classification perspectives; (2) we outline some typical applications of causal effect estimation to industry; (3) we also endeavor to present a detailed categorization and analysis on relevant datasets, source codes and experiments.

Risk factors and recurrence of hyperemesis gravidarum: A population-based record linkage cohort study.

There are limited contemporary population-based studies on the risk factors for hyperemesis gravidarum (HG), a severe type of nausea and vomiting in pregnancy. This study aimed to determine the prevalence and trend of HG over time, identify risk factors for any and multiple HG health service visits during pregnancy, and investigate HG recurrence across pregnancies. This population-based record linkage cohort study featured births in New South Wales, Australia from 2010 to 2019. Hospital and emergency data collections were used to identify health service visits for HG using relevant diagnosis codes and were linked to the corresponding pregnancy on the birth data set. Outcomes included any HG and multiple HG visits during pregnancy, and HG recurrence across pregnancies. Annual HG prevalence was calculated, and negative binomial regression was used to examine standardized prevalence trends. Risk factors for any HG and multiple HG visits within a pregnancy were examined using Robust Poisson models with generalized estimating equations and Prentice-Williams-Peterson Gap Time models, respectively. Rates and risk of recurrence were calculated for women with a second and third pregnancy. Of the 955 107 pregnancies, 21 702 (2.3%) were classified as HG. There was an average annual increase of 6.8% (95% CI 5.3-8.3) in HG prevalence. Younger maternal age, multiple pregnancies, and selected preexisting conditions were associated with an increased risk of HG, with the strongest factor being HG in any previous pregnancy (risk ratio 8.92, 99% CI 8.43-9.44). Hyperemesis gravidarum recurrence at the second (28.9%) and third (54.7%) pregnancies was high. Hyperemesis gravidarum history is the strongest risk factor for HG, which has implications for counseling and care that women receive around pregnancy.

Lecturers’ perceptions of the influence of AI on a blended learning approach in a South African higher education institution

In this study, the researchers explore lecturers’ perspectives on the impact artificial intelligence (AI) has on blended learning within the context of South African higher education. AI is transforming traditional teaching and learning by enabling academic institutions to offer computerised, effective, and objective educational processes. The research was conducted to address the growing need to understand lecturers’ viewpoints on how AI can enhance educational practices and overcome existing challenges in blended learning environments. To investigate this phenomenon, the researchers applied the Substitution, Augmentation, Modification, and Redefinition (SAMR) model as theoretical framework for the study. Their qualitative research undertaking employed a singular case study design focusing on 15 lecturers from the College of Education at a selected academic institution, to arrive at an in-depth understanding of lecturers’ experiences and perceptions of how AI is integrated in blended learning. The researchers examined both the benefits and challenges associated with a blended teaching and learning mode, in the context of AI integration. The data collection process involved semi-structured focus group interviews that allowed for in-depth discussions to be conducted. This was complemented by detailed document analysis to analyse the course materials and teaching methods used by the lecturers. Homogeneous, purposeful sampling was applied to select participating lecturers who shared specific characteristics relevant to the study. Data analysis involved coding through the induction method, which helped to reveal relevant codes that were subsequently categorised. The study also included a comprehensive literature review of recent research findings, which were correlated with the collected data. The findings underscored the critical need for supportive measures, such as management backing, enhanced training opportunities, professional development initiatives, reliable technological infrastructure, improved internet connectivity, and additional time allocation, for the successful implementation of blended learning which integrates AI. This study contributes valuable insights into, and discussions on, the implications of adopting AI in a hybrid learning environment.

Assessing property value impacts near utility-scale solar in the Midwestern United States

Utility-scale solar energy project proposals have been accelerating exponentially in the United States (U.S.) as the energy transition from fossil fuels to renewables continues to unfold. While the emissions and economic related benefits of deploying large-scale solar photovoltaics (PV) for electricity generation are well documented, relatively less is known about their impact on nearby property values. This paper investigates the location of utility-scale solar facilities in the U.S. Midwest, the average home value in each relevant zip code, and whether the presence of a utility-scale solar project affects nearby property values in any manner. Our study includes 70 utility-scale solar facilities built in the Midwest from 2009 to 2022 using data from the Lawrence Berkeley National Laboratory. Alongside housing value data from Zillow (i.e., Zestimate), we incorporate additional data, including solar project size in installed capacity, rurality, and state. Using the difference-in-differences method, our results indicate that utility-scale solar projects increase nearby property values by roughly 0.5–2.0 %. Moreover, our results show that smaller projects have more of a positive impact on nearby property values than projects that are 20 megawatts or larger. Ultimately, having a better understanding of how these larger-scale solar projects impact property values is essential for a variety of stakeholders – especially local officials and property owners – as they are increasingly faced with making decisions about whether to permit the construction of these facilities in their communities.

Estimating the compressive strength of lightweight foamed concrete using different machine learning-based symbolic regression techniques

The development of concrete with excellent water and frost resistance providing high level of sound and thermal insulation has triggered the formulation of foamed concrete. However, multiple laboratory studies are required to produce reasonable data to design the relevant codes and mathematics with which design of mixes is made easier at low cost. In this research paper, the artificial intelligence (AI)-based symbolic regression technique estimation of the compressive strength of foamed concrete has been reported. Foamed concrete has been a subject of serious research in sustainable built-environment due to its lightweight and structural functionality. In this research work, data gathering method was applied to gather a globally representative data base comprising concrete density to water density (concrete density g/cm3) (γ/γw), water-cement ratio (W/C), and sand-cement ratio (S/C) as input variable and the compressive strength (Fc) as the study output. The dimensionless factors have been derived to eliminate data handling complexities and improve model performances. The 230 data entries from foamed concrete mixes were partitioned into 75% and 25% for training and validation data, respectively. At the end of the model execution, it was found that the response surface methodology (RSM) produced a symbolic closed-form equation like the genetic programming (GP), evolutionary polynomial regression (EPR), and the group method of data-handling-neural network (GMDH-NN). Even though the RSM closed with a minimum error, the GP, EPR and GMDH-NN were faster in runtime. The overall outcomes show that the GP outclassed the EPR, RSM and the GMDH-NN, though with minor margin. Meanwhile the EPR produced the highest outliers from the ±25% test of accuracy envelope. Overall, the present models outperformed those reported in the literature due the parameter reduction through dimensionless factors derivation and provided a decisive model to predict the Fc of foamed concrete.

Computer-controlled electrical stimulation of facial muscles by facial neuromuscular electrical stimulation (fNMES): Hardware and software solutions

BackgroundComputer controlled electrical stimulation of facial muscles is a promising method to study facial feedback effects, though little guidance is available for new adopters. New MethodFacial Neuromuscular Electrical Stimulation (fNMES) offers a spatially and temporally precise means of manipulating facial muscles during experiments, and can be combined with EEG to study the neurological basis of facial feedback effects. Precise delivery of stimulation requires hardware and software solutions to integrate stimulators and a stimulus-presenting computer. We provide open-source hardware schematics and relevant computer code in order to achieve this integration, so as to facilitate the use of fNMES in the laboratory. ResultsHardware schematics are provided for the building of a bespoke control module, which allows researchers to finely control stimulator output whilst participants complete computer tasks. In addition, we published code that new adopters of NMES can use within their experiments to control the module and send event triggers to another computer. These hard- and software solutions were successfully used to investigate the effects of facial muscle activation on felt and perceived emotion. We summarise these findings and discuss the integration of fNMES with EEG and peripheral physiological measures. Comparison with existing methodsOur inexpensive hardware solution allows fNMES parameters to be computer controlled, and thus allows to stimulate facial muscles with high precision. This opens up new possibilities to investigate, for example, facial feedback effects. ConclusionsWe provide tools and guidance to build a control module in order to precisely deliver electrical stimulation to facial muscles using a stimulus computer (while recording EEG or other peripheral physiology).

A Segment Anything Model based weakly supervised learning method for crop mapping using Sentinel-2 time series images

Automatic crop mapping is essential for various agricultural applications. Although fully convolutional networks (FCNs) have shown effectiveness in crop mapping, they rely on labor-intensive pixel-level annotations. Weakly supervised semantic segmentation (WSSS) methods offer a solution by enabling pixel-level segmentation with less costly annotations, such as point, bounding box (bbox), and image-level annotations. However, these weak annotations often lack complete object information, leading to reduced accuracy. Moreover, there is limited exploration of WSSS methods in medium-resolution satellite imagery, such as Sentinel-2. Therefore, this study proposes SAMWS, a WSSS method based on the Segment Anything Model (SAM) for crop mapping using Sentinel-2 time series images. Leveraging SAM, our method can generate high-quality pseudo labels and accommodate various weak annotations without extensive adjustments. SAMWS comprises three stages: 1) finetuning SAM with adapters; 2) generating pseudo labels using weak annotations and finetuned SAM; and 3) training a segmentation network using pseudo labels for crop mapping. Experiments conducted on PASTIS and Munich datasets demonstrate the superiority of our approach. After SAM was finetuned with adapters, the F1-scores for parcel segmentation using point and bbox prompts increased by 75.0 % and 14.4 %, respectively, reaching 0.880 and 0.931. Additionally, our method achieves classification results closest to fully supervised learning when using point, bbox, and image-level annotations, with F1-scores of 0.810, 0.817, and 0.779, respectively. Our approach offers valuable insights into leveraging foundation models in the remote sensing domain and contains significant potential for crop monitoring. The relevant code will be made publicly available at https://github.com/Nick0317Sun/SAMWS.

Mortality and economic cost in common primary cancer admitted with COVID-19: Analysis of NIS 2020.

182 Background: Recent data showed the detrimental effects of COVID-19 infection on various conditions, resulting in a wide range of health disparities and worsening outcomes, as well as an increased burden of cost. However, limited information exists on the impacts of COVID-19 infection on the most commonly admitted with primary cancer, regarding clinical and economic impact. Methods: We analyzed the 2020 U.S. National Inpatient Sample (NIS) to investigate the effects of COVID-19 infection on cases primarily admitted with the most common admitted with primary cancer, using relevant ICD-10 CM codes. Adjusted odds ratios (aORs) for specified outcomes were calculated through multivariable logistic and linear regression analyses. The primary outcome was inpatient mortality, with secondary outcomes including the cost of hospitalization and length of stay. Statistical significance was established at a p-value of 0.05. Results: We estimated 919,104 hospitalizations with a primary discharge diagnosis of cancer conditions. Of these, 0.54% (4,985/919,104) had a concurrent diagnosis of COVID-19 infection and 46.1% were female. The mean age was 63.6 years old and Caucasians dominated the majority of the records (69.7%), followed by Hispanics (13.1%). In a multivariable logistic and linear regression model adjusting for patient and hospital factors, COVID-19 infection was associated with higher in-hospital mortality for all cancer conditions and other outcome. The txable denotes all-type and subpopulation analysis. Conclusions: In conclusion, our study encompasses all most common admitted primary cancer conditions and shed light how COVID-19 infection is associated with higher in-hospital mortality, prolonged hospital stays, and increased economic burden in various condition. Future longitudinal studies are warranted to comprehensively assess the long-term health sequelae in this population. [Table: see text]

Comparative analysis of mortality and hospital expenditure in patients with gastroenterological cancer: Thailand and United States.

79 Background: Concerns about rising health care cost and quality of care has been a major health issue worldwide. Gastrointestinal (GI) cancer accounts for one-quarter of the global cancer incidence. However, there are limited evidence, which demonstrates the differences in clinical outcome and economic burden of GI cancer when comparing between health care setting of developed and developing countries. Methods: Two data sources were analyzed: (i) The 2020 U.S. National Inpatient Sample (NIS) involving patients that were primarily discharged with gastroenterological cancer, and (ii) data at tertiary care center in Thailand used for this comparative study, with similar period and conditions. Participants were identified using relevant ICD-10 CM codes. Adjusted odds ratios (aORs) for specified outcomes were calculated through multivariate logistic and linear regression analyses. Participants are divided into those who experienced mortality and those who survived. We report crude mortality rates and expenditure outcomes. Statistical significance was established at a p-value of 0.05. Results: We identified 9,299 Thai records and estimated 216,859 US records with a primary diagnosis of GI cancer at discharge. Of these, 2.15% (200/9,299) of the Thai records and 3.82% (8,285/216,859) of the US records died. In a multivariate analysis, death during hospitalization is associated with higher length of stay (Thai: b = 18.10, 95% CI (16.71, 19.49), p &lt; 0.001), (US: b = 3.38, 95% CI 3.38 (2.14, 4.63), p &lt; 0.001) and higher hospitalization charges (Thai: b = 155,530.02 THB, 95% CI (141,805.10, 169,254.80), p &lt; 0.001), (US: b = 46,882.44 USD, 95% CI (31,629.51, 62,135.37), p &lt;0.001). Subpopulation analysis revealed similar significant results. We observed higher increased length of stay in Thailand, whereas higher crude mortality rate and hospitalization charges was observed in the United States. Conclusions: In both Thailand and United States, mortality in patient with GI cancer is associated with increase in length of stay and higher hospitalization expenses. However, the magnitude of difference in each outcome differs between two countries. Future studies investigating health care costs, cultural role and clinical outcomes may provide insights for cost-reduction strategies, while maintaining or improving the current standard of care in this population group. [Table: see text]