Database Access Research Articles

An Empirical Study on the Characteristics of Database Access Bugs in Java Applications

An Empirical Study on the Characteristics of Database Access Bugs in Java Applications

A deep neural network and transfer learning combined method for cross-task classification of error-related potentials.

Error-related potentials (ErrPs) are electrophysiological responses that naturally occur when humans perceive wrongdoing or encounter unexpected events. It offers a distinctive means of comprehending the error-processing mechanisms within the brain. A method for detecting ErrPs with high accuracy holds significant importance for various ErrPs-based applications, such as human-in-the-loop Brain-Computer Interface (BCI) systems. Nevertheless, current methods fail to fulfill the generalization requirements for detecting such ErrPs due to the high non-stationarity of EEG signals across different tasks and the limited availability of ErrPs datasets. This study introduces a deep learning-based model that integrates convolutional layers and transformer encoders for the classification of ErrPs. Subsequently, a model training strategy, grounded in transfer learning, is proposed for the effective training of the model. The datasets utilized in this study are available for download from the publicly accessible databases. In cross-task classification, an average accuracy of about 78% was achieved, exceeding the baseline. Furthermore, in the leave-one-subject-out, within-session, and cross-session classification scenarios, the proposed model outperformed the existing techniques with an average accuracy of 71.81, 78.74, and 77.01%, respectively. Our approach contributes to mitigating the challenge posed by limited datasets in the ErrPs field, achieving this by reducing the requirement for extensive training data for specific target tasks. This may serve as inspiration for future studies that concentrate on ErrPs and their applications.

ExMove: An open-source toolkit for processing and exploring animal-tracking data in R.

Ongoing technological advances have led to a rapid increase in the number, type and scope of animal-tracking studies. In response, many software tools have been developed to analyse animal movement data. These tools generally focus on movement modelling, but the steps required to clean raw data files from different tracking devices have been largely ignored. Such pre-processing steps are often time-consuming and involve a steep learning curve but are crucial for the creation of high-quality, standardised and shareable data. Moreover, decisions made at this early stage can substantially influence subsequent analyses, and in the current age of reproducibility crisis, the transparency of this process is vital. Here we present an open-access, reproducible toolkit written in the programming language R for processing raw data files into a single cleaned data set for analyses and upload to online tracking databases (found here: https://github.com/ExMove/ExMove). The toolkit comprises well-documented and flexible code to facilitate data processing and user understanding, both of which can increase user confidence and improve the uptake of sharing open and reproducible code. Additionally, we provide an overview website (found here: https://exmove.github.io/) and a Shiny app to help users visualise tracking data and assist with parameter determination during data cleaning. The toolkit is generalisable to different data formats and device types, uses modern 'tidy coding' practices, and relies on a few well-maintained packages. Among these, we perform spatial manipulations using the package sf. Overall, by collating all required steps from data collection to archiving on open access databases into a single, robust pipeline, our toolkit provides a valuable resource for anyone conducting animal movement analyses and represents an important step towards increased standardisation and reproducibility in animal movement ecology.

Three-dimensional characterization of particle size, shape, and internal porosity for Apollo 11 and Apollo 14 lunar regolith and JSC-1A lunar regolith soil simulant

Samples of soils collected by the Apollo 11 mission (10084,2036) and the Apollo 14 mission (14163,940) were obtained from the NASA Curation and Analysis Planning Team for Extraterrestrial Materials (CAPTEM) program. The particle size, shape, and internal porosity were characterized in three dimensions (3D) using a combination of X-ray computed tomography (XCT) and mathematical analysis, with various size and shape parameters measured and calculated for each particle. High-resolution scanning electron microscopy (SEM) was used to image the particles that were too small for the two XCT instruments used. Similar characterization was carried out on samples of JSC-1A lunar soil simulant, updating a previous analysis. Approximately 14,000 lunar regolith particles and 128,000 JSC1-A particles, covering a wide range of size and shape, were characterized for this paper and the results stored in a publicly accessible database. This large number of particles enabled, for the first time, statistically valid particle shape distributions to be generated. The 3D shape distributions of the two regoliths and JSC-1A were quantitatively compared and it was found that the way particle shape and porosity depends on particle size was different between regolith and simulant. The measured size distribution of particles in the lunar soils was applied to estimate the relative contributions of different sizes to the ensemble average particle single scattering albedo and phase function. By linking our particle counts to published sieve weight fractions for the lunar samples, we find that ∼80% of the total cross-section area is contributed by particles <20 μm diameter and ∼ 50% by particles <8 μm diameter. The orientation-averaged two-dimensional projected areas of the actual regolith particles were computed so that this estimate was also based on real particle shapes. Such small sizes dominating the total cross-section area suggest that calculations of the elements of the scattering matrix for individual particles may be possible with modest computing capabilities leading to the development of improved models for the quantitative interpretation of remote sensing spectrophotometry and polarimetry. This 3D characterization and database will enable other computational work to be done with real lunar regolith particle shapes, including discrete element method mechanical modeling, packing simulations, further light scattering calculations, dust contamination modeling, and modeling of lunar rover interactions with collected and packed regolith particles.

Integrating Renewable Energy Solutions in Small-Scale Industrial Facilities

The purpose of this study was to analyze the economical suitability of numerous on-site renewable electricity generation technologies which were intended to be used in a recently built industrial facility designed and utilized as a warehouse. The facility was located in the vicinity of Riga, Latvia. Data were collected and calculations were performed within the scope of the project “Mitigating Energy Poverty through Innovative Solutions” as part of several planned activities to address the broad spectrum of energy poverty and self-reliance issues in both the residential sector and small-scale industrial facilities. During the project, evaluations of various renewable energy technologies, including PV installations, wind energy installations, battery storage solutions, and hybrid technologies, were carried out. The aim of these evaluations was to develop an electricity production–consumption model for efficient and cost-effective energy use and to reduce greenhouse gas emissions from the test facility. A model was created and subsequent research scenarios were developed based on a payback period instead of the net present value criterion. The project was carried out over several steps to develop a calculation methodology. The open access databases of energy resource providers were used to evaluate statistical data and make forecasts for the analysis of the electricity consumption of companies. MATLAB/Simulink 23/2 was used for the data analysis, and the H-TEC method was employed. This made it possible to modulate the required production capacity as the model allowed for the addition of new modules to modules already installed. The project results proved that despite high initial investment costs, renewable energy sources and efficient storage systems can provide cost-effective solutions and reduce dependence on fossil fuels in the long term.

BIOMX-DB: A web application for the BIOFACQUIM natural product database.

Natural product databases are an integral part of chemoinformatics and computer-aided drug design. Despite their pivotal role, a distinct scarcity of projects in Latin America, particularly in Mexico, provides accessible tools of this nature. Herein, we introduce BIOMX-DB, an open and freely accessible web-based database designed to address this gap. BIOMX-DB enhances the features of the existing Mexican natural product database, BIOFACQUIM, by incorporating advanced search, filtering, and download capabilities. The user-friendly interface of BIOMX-DB aims to provide an intuitive experience for researchers. For seamless access, BIOMX-DB is freely available at www.biomx-db.com.

The Application of Cathodoluminescence (CL) for the Characterization of Blue Pigments

The combined application of Cathodoluminescence (CL) with Scanning Electron Microscopy (SEM) on paintings and painted surfaces has the potential to identify both organic and inorganic pigments on a micrometre or even nanometre scale. Additionally, the combination with Energy-Dispersive Spectrometry (EDS) allows for a more holistic, elemental, and mineralogical characterization of pigments. In addressing the need for the creation of a robust, open access database of characteristic CL spectra of pigments, a large project has been undertaken, focusing primarily on common organic and inorganic pigments. The present paper focuses on the CL characterization of 10 significant blue pigments in pure powder form: cerulean blue, Egyptian blue, Han blue, indigo, lapis lazuli, Maya blue, phthalo blue, vivianite, ultramarine blue, and zirconium blue. The CL spectra present characteristic bands for most of the pigments, allowing their secure identification, especially when combining the results with the EDS analyses. The effect of binding media and of the mixture of different pigments was also studied, via the analysis of mixtures of pigments with oil painted over canvas. Overall, both the binding medium and the mixture of pigments do not appear to create significant differences in the occurring spectra, thus allowing the identification of individual pigments. EDS and RAMAN spectra are included in order to facilitate comparison with other databases.

A nineteenth-century urban Ottoman population micro dataset: Data extraction and relational database curation from the 1840s pre-census Bursa population registers

In recent decades, the “big microdata revolution” has transformed access to transcribed historical census data for social science research. However, the population records of the Ottoman Empire, spanning Southeastern Europe, Western Asia, and Northern Africa, remained inaccessible to the big microdata ecosystem due to their prolonged unavailability. This publication marks the inaugural release of complete population data for an Ottoman urban center, Bursa, derived from the 1839 population registers. The dataset presents originally non-tabulated register data in a tabular format integrated into a relational Microsoft Access database. Thus, we showcase the extensive and diverse data found in the Ottoman population registers, demonstrating a level of quality and sophistication akin to the censuses conducted worldwide in the nineteenth century. This valuable resource, whose potential has been massively underexploited, is now presented in an accessible format compatible with global microdata repositories. Our aim with this dataset is to enable historical demographic studies for the Ottoman realm and beyond, while also broadening access to the datasets constructed by our large research team.

Analyzing Social Factors to Enhance Suicide Prevention Across Population Groups.

Social factors like family background, education level, financial status, and stress can impact public health outcomes, such as suicidal ideation. However, the analysis of social factors for suicide prevention has been limited by the lack of up-to-date suicide reporting data, variations in reporting practices, and small sample sizes. In this study, we analyzed 172,629 suicide incidents from 2014 to 2020 utilizing the National Violent Death Reporting System Restricted Access Database (NVDRS-RAD). Logistic regression models were developed to examine the relationships between demographics and suicide-related circumstances. Trends over time were assessed, and Latent Dirichlet Allocation (LDA) was used to identify common suicide-related social factors. Mental health, interpersonal relationships, mental health treatment and disclosure, and school/work-related stressors were identified as the main themes of suicide-related social factors. This study also identified systemic disparities across various population groups, particularly concerning Black individuals, young people aged under 24, healthcare practitioners, and those with limited education backgrounds, which shed light on potential directions for demographic-specific suicidal interventions.

Integrating evidence and causal mapping of help-seeking behaviour of migrant women with postpartum depression in Shanghai, China: a scoping review protocol

IntroductionThe estimated prevalence of postpartum depression (PPD) worldwide, in China, and Shanghai is 17.2%, 18.0% and 23.2%, respectively. In 2021, Shanghai housed a population of 3.2 million childbearing-age migrant women,...

Artificial Intelligence: A Quick Overview of Speech Recognition System

Abstract: The study of intelligent behaviour is known as artificial intelligence. Wagner said, "We build intelligent machines in science." By this, he implied that the primary goal is to teach these sophisticated devices, such as computers, to examine and comprehend human conduct. numerous believed that human intelligence might be understood through the construction of several programmes, while other researchers believed that numerous basics should be required in order to accomplish the intended objective. We can carry out or replicate any type of task we conduct using a computer. Therefore, computers are the ideal devices for developing artificial intelligence. In the hopes that they will outperform computers in several other areas, some researchers created additional computing devices. Billions of dollars were invested in creating new devices that surpassed the speed of the computer used to simulate software. However, the computers and the code both need to be extremely quick. As you learn about artificial intelligence, you may wonder, what really is intelligence? How is intelligence measured? Turn become something quite significant. In this case, the programme that evaluates its surroundings acts as the intelligent agent. Modern digital computers and human minds are comparable to one other in ways such as symbolic information processing systems. Both use symbolic data as input, change it in accordance with predetermined guidelines, and then use the results to solve issues. In order to mechanically replicate intelligent human activity, artificial intelligence researchers have programmed some algorithms to recognise intelligent human behaviour. Human conduct in language processing, chess, and other games, as well as medical diagnostics, may be used to monitor this activity. Voice recognition is a key component of AI. A certain voice may be recognised using voice recognition technology. The cornerstone for speaker identification is voice signals. Voice targeting is applicable to a wide range of applications, including voice mail, database access, phone banking, and phone purchasing. The ability to enter one's voice for verification is among the most potent uses of voice recognition for security. The fundamental means of interpersonal communication is speech. The technique of translating voice sounds into appropriate text is known as speech recognition. Over the past few years, speech recognition technology has advanced significantly. Nonetheless, there are other significant study obstacles, such as variations in speaker and language, ambient sound, word size, etc. This study aims to give a comprehensive overview of speech acceptance by summarising the several approaches utilised in the standard speech system and describing the numerous processes involved

Phospholipid Signaling in Crop Plants: A Field to Explore.

In plant models such as Arabidopsis thaliana, phosphatidic acid (PA), a key molecule of lipid signaling, was shown not only to be involved in stress responses, but also in plant development and nutrition. In this article, we highlight lipid signaling existing in crop species. Based on open access databases, we update the list of sequences encoding phospholipases D, phosphoinositide-dependent phospholipases C, and diacylglycerol-kinases, enzymes that lead to the production of PA. We show that structural features of these enzymes from model plants are conserved in equivalent proteins from selected crop species. We then present an in-depth discussion of the structural characteristics of these proteins before focusing on PA binding proteins. For the purpose of this article, we consider RESPIRATORY BURST OXIDASE HOMOLOGUEs (RBOHs), the most documented PA target proteins. Finally, we present pioneering experiments that show, by different approaches such as monitoring of gene expression, use of pharmacological agents, ectopic over-expression of genes, and the creation of silenced mutants, that lipid signaling plays major roles in crop species. Finally, we present major open questions that require attention since we have only a perception of the peak of the iceberg when it comes to the exciting field of phospholipid signaling in plants.

Exploring the gender gap: A nationwide comparative analysis of general surgery residency program leadership

BackgroundThe gender disparity in surgery leadership roles is well-reported. However, the effect of program type and region on mean number of men or women occupying a particular leadership role has yet to be explored. This study aims to investigate the gender disparity of leadership positions in different types of General Surgery Residency Programs (GSRPs). MethodsLeadership roles of the general surgery departments were collected from the Fellowship and Residency Electronic Interactive Database Access System (FREIDA) database. Each GSRP was categorized by region and program type using FRIEDA. Analysis of the mean number of men and women holding various leadership positions by program type and region was conducted using one-way ANOVA with post-hoc tests. ResultsA total of 345 GSRPs were analyzed. The mean number of women occupying various leadership roles was significantly higher at university-based programs when compared to community-based programs. No significant difference in mean number of women leaders was observed by region. ConclusionsWomen consistently occupy a lower number of GSRP leadership positions when compared to men, regardless of program type or region. University-based GSRP leadership positions have significantly greater gender inclusion compared to community-based GSRPs. Key messagesUniversity-based general surgery residency programs had a higher mean number of women in all leadership roles compared to other program types. In comparison, region did not appear to be a significant factor impacting the leadership gender disparity. Improvement is needed in community-based general surgery residency programs to bridge the gender gap in leadership roles.

Protein Adsorption on Solid Surfaces: Data Mining, Database, Molecular Surface-Derived Properties, and Semiempirical Relationships.

Protein adsorption on solid surfaces is a process relevant to biological, medical, industrial, and environmental applications. Despite this wide interest and advancement in measurement techniques, the complexity of protein adsorption has frustrated its accurate prediction. To address this challenge, here, data regarding protein adsorption reported in the last four decades was collected, checked for completeness and correctness, organized, and archived in an upgraded, freely accessible Biomolecular Adsorption Database, which is equivalent to a large-scale, ad hoc, crowd-sourced multifactorial experiment. The shape and physicochemical properties of the proteins present in the database were quantified on their molecular surfaces using an in-house program (ProMS) operating as an add-on to the PyMol software. Machine learning-based analysis indicated that protein adsorption on hydrophobic and hydrophilic surfaces is modulated by different sets of operational, structural, and molecular surface-based physicochemical parameters. Separately, the adsorption data regarding four "benchmark" proteins, i.e., lysozyme, albumin, IgG, and fibrinogen, was processed by piecewise linear regression with the protein monolayer acting as breakpoint, using the linearization of the Langmuir isotherm formalism, resulting in semiempirical relationships predicting protein adsorption. These relationships, derived separately for hydrophilic and hydrophobic surfaces, described well the protein concentration on the surface as a function of the protein concentration in solution, adsorbing surface contact angle, ionic strength, pH, and temperature of the carrying fluid, and the difference between pH and the isoelectric point of the protein. When applying the semiempirical relationships derived for benchmark proteins to two other "test" proteins with known PDB structure, i.e., β-lactoglobulin and α-lactalbumin, the errors of this extrapolation were found to be in a linear relationship with the dissimilarity between the benchmark and the test proteins. The work presented here can be used for the estimation of operational parameters modulating protein adsorption for various applications such as diagnostic devices, pharmaceuticals, biomaterials, or the food industry.

#787 Kidney genetics—a reproducible and curated database of kidney-disease associated genes

Abstract Background and Aims Genetic insights are becoming increasingly influential in the understanding and treatment of various kidney diseases (KD). Hundreds of genes associated with monogenic kidney disease have been identified, providing valuable insights into their diagnosis, management, and monitoring. However, the lack of a unified and standardized database of genes assigned to kidney diseases has led to diagnostic blind spots and comparability issues among current studies of kidney genetics. To address this gap, we created “Kidney-Genetics”, a regularly updated, automated and publicly accessible database, which aims to provide a comprehensive list of all relevant genes associated with kidney disease. Methods Kidney disease-associated gene information was compiled from various sources, including: (1) Genomics England and Australia PanelApp [1], (2) a comprehensive literature review of published gene lists, (3) clinical diagnostic panels for kidney disease, (4) a Human Phenotype Ontology (HPO)-based [2] search in rare disease databases (OMIM, Orphanet), and (5) a PubTator [3] API-based automated literature extraction from PubMed. An evidence-scoring system was developed to distinguish high- evidence genes from low evidence genes and candidate genes. High-evidence genes were defined as those present in two or more of the five resources and were manually curated based on predetermined criteria or, in the case of existing ClinGen [4] curation, their data and scores were used. Genes with a score of one or less were accordingly classified as candidate genes. Additionally, genes were grouped into different categories for later genotype-phenotype correlation matching (see Fig. 1). Results The Kidney-Genetics database currently contains detailed information on 3025 kidney-associated genes with detailed annotations on gene expression, kidney phenotype, inheritance mode, disease onset, possible syndromic disease mode and genetic variation. The number of genes extracted from the five analyzed sources of information are as follows: (1) 534, (2) 822, (3) 956, (4) 789, and (5) 2158 (see Fig. 2). Notably, 598 genes (19.8%) of the total 3025 genes are present in two or more of the analyzed information sources, thus meeting our evidence criteria, indicating high confidence and their potential for diagnostic use. Of these high-evidence genes, 526 (88.0%) are present in at least one, and 56 (9.4%) are present in all 10 comprehensive diagnostic laboratory panels. To ensure currency, Kidney-Genetics will be updated regularly and automatically on a monthly basis. We will also provide phenotypic and functional clustering results to facilitate gene grouping. Conclusion By utilizing Kidney-Genetics, clinicians, geneticists, and researchers can examine genomic data and improve their understanding of the genetic components of diverse KDs. The code and results are freely available on GitHub (kidney-genetics.org) [5]. A standardized pipeline and automated system keep our database on the cutting edge of kidney research and diagnostics. Screening efforts toward manual curation (such as through the ClinGen initiative) and assignment of diagnostic genes to kidney disease groups (e.g., syndromic vs. isolated; adult vs. pediatric; cystic, nephrotic, etc.) are currently under development.

A novel Bayesian generative approach for estimating tumor dynamics from published studies.

Tumor growth inhibition (TGI) modeling attempts to describe the time course changes in tumor size for patients undergoing cancer therapy. TGI models present several advantages over traditional exposure-response models that are based explicitly on clinical end points and have become a popular tool in the pharmacometrics community. Unfortunately, the data required to fit TGI models, namely longitudinal tumor measurements, are sparse or often not available in literature or publicly accessible databases. On the contrary, common end points such as progression-free survival (PFS) and objective response rate (ORR) are directly derived from longitudinal tumor measurements and are routinely published. To this end, a Bayesian generative model relating underlying tumor dynamics to summary PFS and ORR data is introduced to learn TGI model parameters using only published summary data. The parameterized model can describe the tumor dynamics, quantify treatment effect, and account for differences in the study population. The utility of this model is shown by applying it to several published studies, and learned parameters are combined to simulate an in silico trial of a novel combination therapy in a novel setting.

Assessment of the relationship between gut microbiota and bone mineral density: a two-sample Mendelian randomization study.

Emerging evidence from observational studies and clinical trials suggests a connection between the gut microbiota and variations in bone mineral density (BMD). Nonetheless, the specific association between gut microbiota and BMD alterations at different skeletal sites has not been comprehensively explored. To address this, we employed Genome-Wide Association Study (GWAS) summary statistics from a publicly accessible database, conducting a two-sample Mendelian Randomization analysis to elucidate the potential causal relationship between gut microbiota composition and BMD. This study utilized two distinct thresholds for screening instrumental variables (IVs), followed by an extensive series of quality control procedures to identify IVs that were significantly related to exposure. Gut microbiota were classified into two sets based on hierarchical levels: phylum, class, order, family, and genus. Bone mineral density (BMD) data were systematically collected from four skeletal sites: femoral neck, lumbar spine, forearm, and heel. For Mendelian Randomization (MR) analysis, robust methods including Inverse-Variance Weighting (IVW) and the Wald Ratio Test were employed. Additional analytical tests such as the Outlier Test, Heterogeneity Test, 'Leave-One-Out' Test, and Pleiotropy Test were conducted to assess the impact of horizontal pleiotropy, heterogeneities, and the genetic variation stability of gut microbiota on BMD causal associations. The MR Steiger Directionality Test was applied to exclude studies with potential directional biases. In this two-sample Mendelian randomization analysis, we utilized five sets of exposure GWAS (Genome-Wide Association Studies) summary statistics and four sets of outcome GWAS summary statistics. The initial analysis, applying a threshold of p < 5 × 10-6, identified 48 significant causal relationships between genetic liability in the gut microbiome and bone mineral density (BMD). A subsequent analysis with a more stringent threshold of p < 5 × 10-8 uncovered 14 additional causal relationships. Upon applying the Bonferroni correction, 9 results from the first analysis and 10 from the second remained statistically significant. Our MR analysis revealed a causal relationship between gut microbiota and bone mineral density at all sites, which could lead to discoveries in future mechanistic and clinical studies of microbiota-associated osteoporosis.

Enhancing Transparency in Optimum Patient Care Research Database Access Procedures: A Multifaceted Approach Addressing Improvements in Transparency Standards

Enhancing Transparency in Optimum Patient Care Research Database Access Procedures: A Multifaceted Approach Addressing Improvements in Transparency Standards

Reduced Tolerogenic Program Death-Ligand 1-Expressing Conventional Type 1 Dendritic Cells Are Associated with Rapid Decline in Chronic Obstructive Pulmonary Disease.

Chronic obstructive pulmonary disease (COPD) is characterized, at least in part, by autoimmunity through amplified T helper 1 and 17 (Th1 and Th17) immune responses. The loss of immune tolerance controlled by programmed death-ligand 1 (PD-L1) may contribute to this. We studied the tolerogenic role of PD-L1+ dendritic cells (DCs) and their subtypes in relation to specific T cell immunity and the clinical phenotypes of COPD. We used flow cytometry to analyze PD-L1 expression by the DCs and their subtypes in the peripheral blood mononuclear cells (PBMCs) from normal participants and those with COPD. T cell proliferation and the signature cytokines of T cell subtypes stimulated with elastin as autoantigens were measured using flow cytometry and enzyme-linked immunosorbent assays (ELISA), respectively. A total of 83 participants were enrolled (normal, n = 29; COPD, n = 54). A reduced PD-L1+ conventional dendritic cell 1 (cDC1) ratio in the PBMCs of the patients with COPD was shown (13.7 ± 13.7%, p = 0.03). The decrease in the PD-L1+ cDC1 ratio was associated with a rapid decline in COPD (p = 0.02) and correlated with the CD4+ T cells (r = -0.33, p = 0.02). This is supported by the NCBI GEO database accession number GSE56766, the researchers of which found that the gene expressions of PD-L1 and CD4, but not CD8 were negatively correlated from PBMC in COPD patients (r = -0.43, p = 0.002). Functionally, the PD-L1 blockade enhanced CD4+ T cell proliferation stimulated by CD3/elastin (31.2 ± 22.3%, p = 0.04) and interleukin (IL)-17A production stimulated by both CD3 (156.3 ± 54.7, p = 0.03) and CD3/elastin (148 ± 64.9, p = 0.03) from the normal PBMCs. The PD-L1 blockade failed to increase IL-17A production in the cDC1-depleted PBMCs. By contrast, there was no significant change in interferon (IFN)-γ, IL-4, or IL-10 after the PD-L1 blockade. Again, these findings were supported by the NCBI GEO database accession number GSE56766, the researchers of which found that only the expression of RORC, a master transcription factor driving the Th17 cells, was significantly negatively correlated to PD-L1 (r = -0.33, p = 0.02). Circulating PD-L1+ cDC1 was reduced in the patients with COPD, and the tolerogenic role was suppressed with susceptibility to self-antigens and linked to rapid decline caused by Th17-skewed chronic inflammation.

The Performance of Environmental and Health Impact Assessment Implementation: A Case Study in Eastern Thailand.

Environmental impact assessment (EIA) performance has remained of interest, and over the past ten years, the evaluation technique has evolved. Thailand implemented an EIA with a health impact assessment (HIA) as an environmental health impact assessment (EHIA), which necessitated investigating and developing these instruments; however, its implementation performance has been questioned. The main goal of this study is to comparatively assess how well EIAs and EHIAs are performed in projects in an area in Thailand. Six projects in various sectors that were implemented in Eastern Thailand were studied. The 162 residents (nine local authorities and 153 villagers) closest to the project completed a survey and evaluated the performance according to three aspects (i.e., substantive, procedural, and transactive), using a rating scale and evaluation checklists. The results were presented as a percentage of the total scores and interpreted according to the five scales. The overall performance reached a satisfactory level, albeit not significantly different between cases; however, it was pointed out that the shortcomings of EHIAs and EIAs, particularly their dependability, lack of public involvement, and the need for more transparency, could be addressed through the establishment of an open access database, which would help to simplify the assessment of all stages of EIAs and EHIAs.