SMAD specific E3 ubiquitin protein ligase 1 accelerates diabetic macular edema progression by WNT inhibitory factor 1

BACKGROUND Diabetic macular edema(DME)is the most common cause of vision loss in people with diabetes.Tight junction disruption of the retinal pigment epithelium(RPE)cells has been reported to induce DME development.SMAD-specific E3 ubiquitin protein ligase(SMURF)1 was associated with the tight junctions of cells.However,the mechanism of SMURF1 in the DME process remains unclear.AIM To investigate the role of SMURF1 in RPE cell tight junction during DME.METHODS ARPE-19 cells treated with high glucose(HG)and desferrioxamine mesylate(DFX)for establishment of the DME cell model.DME mice models were constructed by streptozotocin induction.The trans-epithelial electrical resistance and permeability of RPE cells were analyzed.The expressions of tight junction-related and autophagy-related proteins were determined.The interaction between insulin like growth factor 2 mRNA binding protein 2(IGF2BP2)and SMURF1 mRNA was verified by RNA immunoprecipitation(RIP).SMURF1 N6-methyladenosine(m6A)level was detected by methylated RIP.RESULTS SMURF1 and vascular endothelial growth factor(VEGF)were upregulated in DME.SMURF1 knockdown reduced HG/DFX-induced autophagy,which protected RPE cell tight junctions and ameliorated retinal damage in DME mice.SMURF1 activated the Wnt/β-catenin-VEGF signaling pathway by promoting WNT inhibitory factor(WIF)1 ubiquitination and degradation.IGF2BP2 upregulated SMURF1 expression in an m6A modification-dependent manner.CONCLUSION M6A-modified SMURF1 promoted WIF1 ubiquitination and degradation,which activated autophagy to inhibit RPE cell tight junctions,ultimately promoting DME progression.

Efficacy of a Diabetes Specific Nutrition Supplement on Glycemic, Anthropometric, Dietary and Gut Health Markers in Adults with Type 2 Diabetes: An RCT

With increasing incidence of diabetes, use of diabetes specific nutrition supplements (DSNS) is common for better management of the disease. To study effect of 12-week DSNS supplementation on glycemic markers, anthropometry, lipid profile, SCFAs, and gut microbiome in individuals with diabetes. Markers studied were glycemic [Fasting Blood Glucose (FBG), Post Prandial Glucose (PPG), HbA1c, Incremental Area under curve (iAUC), Mean Amplitude of Glycemic Excursions (MAGE), Time in/above Range (TIR/TAR)], anthropometry [weight, Body Mass Index (BMI), waist circumference (WC)], lipid profile, diet and gut health [plasma short chain fatty acids (SCFAs)]. N = 210 adults were randomized to receive either DSNS with standard care (DSNS + SC;n = 105) or standard care alone (SC alone;n = 105). After 12 weeks, significant differences between DSNS + SC versus SC alone was observed in FBG [−3 ± 6 vs 14 ± 6 mg/dl;p = 0.03], PPG [−35 ± 9 vs −3 ± 9 mg/dl;p = 0.01], weight [−0.6 ± 0.1 vs 0.2 ± 0.1 kg;p = 0.0001], BMI [−0.3 ± 0.1 vs 0.1 ± 0.1 kg/m2;p = 0.0001] and WC [−0.3 ± 0.2 vs 0.2 ± 0.2 cm;p = 0.01]. HbA1C and low-density lipoprotein (LDL) were significantly reduced in DSNS + SC [−0.2 ± 0.9;p = 0.04 and −5 mg/dl;p = 0.03] respectively with no change in control. Continuous Glucose Monitoring (CGM) reported significant differences between DSNS + SC versus SC alone for mean glucose [−12 ± 65 vs 28 ± 93 mg/dl;p < 0.01], TAR 180 [−9 ± 42 vs 7 ± 45 mg/dl;p = 0.04], TAR 250 [−3 ± 27 vs 9 ± 38 mg/dl;p = 0.05], iAUC [−192 (1.1) vs −48 (1.1) mg/dl;p = 0.03]. MAGE was significantly reduced for both DSNS + SC (−19 ± 67;p < 0.001) and SC alone (−8 ± 70;p = 0.04), with reduction being more pronounced for DSNS + SC. DSNS + SC reported a decrease in carbohydrate energy % [−9.4 (−11.3, −7.6) %;p < 0.0001] and amount [−47.4 (−67.1, −27.7) g;p < 0.0001], increased dietary fiber [9.5 (7.2, 11.8) g;p < 0.0001] and protein energy % [0.9 (0.5, 1.3) %;p < 0.0001] versus SC alone. DSNS + SC reported significant increases versus SC alone in total (0.3 ng/ml;p = 0.03) and individual plasma SCFAs. The consumption of DSNS significantly improves the glycemic, anthropometric, dietary, and gut health markers in diabetes.

Efficacy of a Diabetes Specific Nutritional Supplement (DSNS) on Glycemic Response in Prediabetic Adults: A Two-Armed, Open-Labelled Randomized Controlled Study

It is well known that Diabetes Specific Nutritional Supplements (DSNSs) are linked to improved glycemic control in individuals with diabetes. However, data on efficacy of DSNSs in prediabetics is limited. This was a two-armed, open-labelled, randomized controlled six-week study on 199 prediabetics [30 - 65 years;Glycosylated Hemoglobin (HbA1c) 5.7% - 6.4% and/or Fasting Blood Glucose (FBG) 100-125 mg/dl]. Two parallel phases were conducted: Acute Blood Glucose Response (ABGR) and Intervention phase. Prediabetic participants were randomized into test (n = 100) and control (n = 99). The primary objective was to assess the ABGR of DSNS versus an isocaloric snack, measured by incremental Area under the Curve (iAUC). Test and control received 60 g of DSNS and 56 g of isocaloric snack (cornflakes) respectively, both in 250 ml double-toned milk on visit days 1, 15, 29 and 43. Postprandial Blood Glucose (PPG) was estimated at 30, 60, 90, 120, 150 and 180 minutes. During the 4 weeks intervention phase, the test group received DSNS with lifestyle counselling (DSNS + LC) and was compared with the control receiving lifestyle counselling alone (LC alone). Impact was studied on FBG, HbA1C, anthropometry, body composition, blood pressure, nutrient intake, and physical activity. The impact of DSNS was also studied using CGM between two 14-day phases: CGM1 baseline (days 1 - 14) and CGM2 endline (days 28 - 42). DSNS showed significantly lower PPG versus isocaloric snack at 30 (p 12, and chromium were reported by DSNS + LC versus LC alone. No other significant changes were reported between groups. It may be concluded that DSNS may be considered as a snack for prediabetic or hyperglycemic individuals requiring nutritional support for improved glycemic control.

Karyotype establishment and development of specific molecular markers of Aegilops geniculata Roth based on SLAF-seq

The constant evolution of pathogens poses a threat to wheat resistance against diseases,endangering food security.Developing resistant wheat varieties is the most practical approach for circumventing this problem.As a close relative of wheat,Aegilops geniculata,particularly accession SY159,has evolved numerous beneficial traits that could be applied to improve wheat.In this study,we established the karyotype of SY159 by fluorescence in situ hybridization(FISH)using the oligonucleotide probes Oligo-pTa535 and Oligo-pSc119.2 and a complete set of wheat–Ae.geniculata accession TA2899 addition lines as a reference.Using specific-locus amplified fragment sequencing(SLAF-seq)technology,400 specific markers were established for detecting the SY159 chromosomes with efficiencies reaching 81.5%.The SY159-specific markers were used to classify the different homologous groups of SY159 against the wheat-Ae.geniculata addition lines.We used these specific markers on the 7Mg chromosome after classification,and successfully confirmed their suitability for studying the different chromosomes of SY159.This study provides a foundation for accelerating the application of SY159 in genetic breeding programs designed to improve wheat.

Design of Fully Automatic Specification Selection System for Resistance Welding Equipment

A system for fully automatic selection of welding specifications in resistance welding equipment has been developed to address the problem of workers frequently choosing the wrong specifications during manual welding of multiple parts on a single machine in automobile factories. The system incorporates an automatic recognition system for different workpiece materials using the added machine fixture,visual detection system for nuts and bolts,and secondary graphical confirmation to ensure the correctness of specification calling. This system achieves reliable,fully automatic selection of welding specifications in resistance welding equipment and has shown significant effects in improving welding quality for massproduced workpieces,while solving the problem of specification calling errors that can occur with traditional methods involving process charts and code adjustments. This system is particularly suitable for promoting applications in manual welding of multiple parts on a single machine in automobile factories,ensuring correct specification calling and welding quality.

Enhancing Relational Triple Extraction in Specific Domains:Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.

Basic regulatory science behind drug substance and drug product specifications of monoclonal antibodies and other protein therapeutics

In this review,we focus on providing basics and examples for each component of the protein therapeutic specifications to interested pharmacists and biopharmaceutical scientists with a goal to strengthen understanding in regulatory science and compliance.Pharmaceutical specifications comprise a list of important quality attributes for testing,references to use for test procedures,and appropriate acceptance criteria for the tests,and they are set up to ensure that when a drug product is administered to a patient,its intended therapeutic benefits and safety can be rendered appropriately.Conformance of drug substance or drug product to the specifications is achieved by testing an article according to the listed tests and analytical methods and obtaining test results that meet the acceptance criteria.Quality attributes are chosen to be tested based on their quality risk,and consideration should be given to the merit of the analytical methods which are associated with the acceptance criteria of the specifications.Acceptance criteria are set forth primarily based on efficacy and safety profiles,with an increasing attention noted for patient-centric specifications.Discussed in this work are related guidelines that support the biopharmaceutical specification setting,how to set the acceptance criteria,and examples of the quality attributes and the analytical methods from 60 articles and 23 pharmacopeial monographs.Outlooks are also explored on process analytical technologies and other orthogonal tools which are on-trend in biopharmaceutical characterization and quality control.

Comparative Analysis of Statistical Thickness Models for the Determination of the External Specific Surface and the Surface of the Micropores of Materials: The Case of a Clay Concrete Stabilized Using Sugar Cane Molasses

In this work, four empirical models of statistical thickness, namely the models of Harkins and Jura, Hasley, Carbon Black and Jaroniec, were compared in order to determine the textural properties (external surface and surface of micropores) of a clay concrete without molasses and clay concretes stabilized with 8%, 12% and 16% molasses. The results obtained show that Hasley’s model can be used to obtain the external surfaces. However, it does not allow the surface of the micropores to be obtained, and is not suitable for the case of simple clay concrete (without molasses) and for clay concretes stabilized with molasses. The Carbon Black, Jaroniec and Harkins and Jura models can be used for clay concrete and stabilized clay concrete. However, the Carbon Black model is the most relevant for clay concrete and the Harkins and Jura model is for molasses-stabilized clay concrete. These last two models augur well for future research.

Development of donor specific antibodies after SARS-CoV-2 vaccination:What do we know so far?

Vaccination against Coronavirus disease-19(COVID-19)was pivotal to limit spread,morbidity and mortality.Our aim is to find out whether vaccines against COVID-19 lead to an immunological response stimulating the production of de novo donor specific antibodies(DSAs)or increase in mean fluorescence intensity(MFI)of pre-existing DSAs in kidney transplant recipients(KTRs).This study involved a detailed literature search through December 2nd,2023 using PubMed as the primary database.The search strategy incorporated a combination of relevant Medical Subject Headings terms and keywords:"COVID-19","SARS-CoV-2 Vaccination","Kidney,Renal Transplant",and"Donor specific antibodies".The results from related studies were collated and analyzed.A total of 6 studies were identified,encompassing 460 KTRs vaccinated against COVID-19.Immunological responses were detected in 8 KTRs of which 5 had increased MFIs,1 had de novo DSA,and 2 were categorized as either having de novo DSA or increased MFI.There were 48 KTRs with pre-existing DSAs prior to vaccination,but one study(Massa et al)did not report whether pre-existing DSAs were associated with post vaccination outcomes.Of the remaining 5 studies,35 KTRs with pre-existing DSAs were identified of which 7 KTRs(20%)developed de novo DSAs or increased MFIs.Overall,no immunological response was detected in 452(98.3%)KTRs.Our study affirms prior reports that COVID-19 vaccination is safe for KTRs,especially if there are no pre-existing DSAs.However,if KTRs have pre-existing DSAs,then an increased immunological risk may be present.These findings need to be taken cautiously as they are based on a limited number of patients so further studies are still needed for confirmation.

RFFsNet-SEI:a multidimensional balanced-RFFs deep neural network framework for specific emitter identification

Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.

Genetic regulation of cell type-specific chromatin accessibility shapes brain disease etiology

Nucleotide variants in cell type-specific gene regulatory elements in the human brain are risk factors for human disease.We measured chromatin accessibility in 1932 aliquots of sorted neurons and non-neurons from 616 human postmortem brains and identified 34,539 open chromatin regions with chromatin accessibility quantitative trait loci(caQTLs).Only 10.4%of caQTLs are shared between neurons and non-neurons,which supports cell type-specific genetic regulation of the brain regulome.Incorporating allele-specific chromatin accessibility improves statistical fine-mapping and refines molecular mechanisms that underlie disease risk.Using massively parallel reporter assays in induced excitatory neurons,we screened 19,893 brain QTLs and identified the functional impact of 476 regulatory variants.Combined,this comprehensive resource captures variation in the human brain regulome and provides insights into disease etiology.

Identity Study of English for Specific Purposes Teachers in the Context of Shifts in Public English Teaching Positioning

This study investigates the identity of English for Specific Purposes(ESP)teachers within the dynamic landscape of shifts in public English teaching positioning.Adopting a mixed-methods approach,qualitative and quantitative analyses were employed to explore the complexities of ESP teacher identity construction and adaptation.Qualitative findings revealed key themes including strong professional identity grounded in specialized expertise,the impact of changing educational policies and curriculum reforms,and the importance of cultural competence and intercultural communication.Quantitative analysis of survey data indicated high levels of job satisfaction among ESP teachers,with significant correlations between variables such as professional development participation,perceived efficacy in technology integration,and self-perceptions of identity as ESP educators.

Effective interventions to improve domain-specific social, emotional, or academic outcomes for twice-exceptional individuals who are gifted with ADHD as a disability

The demand for effective interventions to improve domain-specific academic outcomes for individuals with special needs at either end of the spectrum has existed for some time.Since the earlier contributions to the literature documenting gifted individuals who were simultaneously exhibiting disabilities,there has been some progress in our understanding.We now know that in individuals with both gifts and disabilities,potentially,either or both of the exceptionalities can obscure the effects of the other,which significantly delays the average time to receive a diagnosis.Such delays in diagnosis detrimentally impact the the form of effective interventions without a diagnosis.The purpose of this paper is t quality of life across various domains because there can be no opportunity to receive help in o determine whether effective interventions exist to improve domain-specific(i.e.,social,emotional,or academic)outcomes for people with both gifts and disabilities.A query was performed using evidence databases TRIP and PDQ for“twice-exceptional,”“Giftedness,”“Disability,”and“intervention.”The four most relevant,freely available studies in English were selected for critique.Despite identifying potential threats to validity among the studies,methodological similarities among them were strong enough to confidently conclude that not only do effective interventions exist for the population of gifted with ADHD,but the outcomes of these interventions may also carry over into other domains resulting in indirect effects.

Comparative Analysis of Machine Learning Models for Customer Churn Prediction in the U.S. Banking and Financial Services: Economic Impact and Industry-Specific Insights

Customer churn poses a significant challenge for the banking and finance industry in the United States, directly affecting profitability and market share. This study conducts a comprehensive comparative analysis of machine learning models for customer churn prediction, focusing on the U.S. context. The research evaluates the performance of logistic regression, random forest, and neural networks using industry-specific datasets, considering the economic impact and practical implications of the findings. The exploratory data analysis reveals unique patterns and trends in the U.S. banking and finance industry, such as the age distribution of customers and the prevalence of dormant accounts. The study incorporates macroeconomic factors to capture the potential influence of external conditions on customer churn behavior. The findings highlight the importance of leveraging advanced machine learning techniques and comprehensive customer data to develop effective churn prevention strategies in the U.S. context. By accurately predicting customer churn, financial institutions can proactively identify at-risk customers, implement targeted retention strategies, and optimize resource allocation. The study discusses the limitations and potential future improvements, serving as a roadmap for researchers and practitioners to further advance the field of customer churn prediction in the evolving landscape of the U.S. banking and finance industry.

Building a Productive Domain-Specific Cloud for Big Data Processing and Analytics Service

Cloud Computing as a disruptive technology, provides a dynamic, elastic and promising computing climate to tackle the challenges of big data processing and analytics. Hadoop and MapReduce are the widely used open source frameworks in Cloud Computing for storing and processing big data in the scalable fashion. Spark is the latest parallel computing engine working together with Hadoop that exceeds MapReduce performance via its in-memory computing and high level programming features. In this paper, we present our design and implementation of a productive, domain-specific big data analytics cloud platform on top of Hadoop and Spark. To increase user’s productivity, we created a variety of data processing templates to simplify the programming efforts. We have conducted experiments for its productivity and performance with a few basic but representative data processing algorithms in the petroleum industry. Geophysicists can use the platform to productively design and implement scalable seismic data processing algorithms without handling the details of data management and the complexity of parallelism. The Cloud platform generates a complete data processing application based on user’s kernel program and simple configurations, allocates resources and executes it in parallel on top of Spark and Hadoop.

Effect of specific pressure on fabrication of 2D-C_f/Al composite by vacuum and pressure infiltration

Carbon fiber reinforced aluminum matrix （Cf/Al） composite has many excellent properties, and it has received more and more attention. Two-dimensional （2D） Cf/Al composites were fabricated by vacuum and pressure infiltration, which was an integrated technique and could provide high vacuum and high infiltration pressure. The effect of specific pressure on the infiltration quality of the obtained composites was comparatively evaluated through microstructure observation. The experimental results show that satisfied Cf/Al composites could be fabricated at the specific pressure of 75 MPa. In this case, the preform was infiltrated much more completely by aluminum alloy liquid, and the residual porosity was seldom found. It is found that the ultimate tensile strength of the obtained Cf/Al composite reached maximum at the specific pressure of 75 MPa, which was improved by 138.9% compared with that of matrix alloy.

Preparation of Na specific absorbent and application of sodium removal from ammonium tungstate solution

An adsorbent, Na1.6Al0.6Ti1.4（PO4）3 （or NATP）, was prepared by controlled crystallization of glasses in the Na2O-Al2O3-CaO-TiO2-P2O5 system. The crystalline phases characterized by X-ray diffraction （XRD） show that the sample glasses crystallizes into two phases, i.e. NATP and Ca9Al（PO4）7, while the Ca9Al（PO4）7 phase can be leached selectively with HCl, leaving a massive number of pores in the material. Through the experimental research, the effects of contact time, solution pH, and the initial concentration of Na＋on the cation exchange properties were investigated. The batch sorption kinetics and equilibria can be described by Pseudo-second-order kinetic equations and Langmuir isotherm equations respectively. Furthermore, the experiments with an industrial solution show that the removal rate of sodium from industrial （NH4）2WO4 is higher than 97%. Cycle experiment also shows that the NATP has a good cyclic performance.

Cloning of cDNA Encoding COMT from Chinese White Poplar ( Populus tomentosa ), Sequence Analysis and Specific Expression

The cDNA fragment encoding caffeic acid 3_O_methyltransferase (COMT) in Chinese white poplar ( Populus tomentosa Carr.) was isolated and cloned by RT_PCR technique. The size of the cDNA fragment is 1 080 bp, which almost covers the whole cDNA_encoding region. Authors’ cDNA fragment in P. tomentosa shares 98.7% homology with the reported corresponding cDNA in the P. tremuloids at nucleotide level, 99.4% homology at amino acid level, respectively. The analysis of Northern dot hybridization showed that COMT is expressed specifically in the developing secondary xylem of stem during the season of xylem differentiation, which means the linkage between the gene expression for a monolignol biosynthetic enzyme and seasonal regulation of xylem development in woody plant.

渐近平均伪轨跟踪性质、弱specification性质和分布混沌

证明了有渐近平均伪轨跟踪性质的非平凡紧致动力系统具有一致分布混沌或者按序列分布混沌。此外,在具有渐近平均伪轨跟踪性质系统中的分布混沌在测度中心是一致和稠密的,即有一个不可数的一致分布混沌集是由这样的点组成,它们的轨道闭包包含测度中心。作为一个推论,具有弱specification性质的系统也有类似的结果。

Coherence cube enhancement based on local histogram specification

Coherence analysis is a powerful tool in seismic interpretation for imaging geological discontinuities such as faults and fractures. However, subtle faults or fractures of one stratum are difficult to be distinguished on coherence sections （time slices or profiles） due to interferences from adjacent strata, especially these with strong reflectivity. In this paper, we propose a coherence enhancement method which applies local histogram specification （LHS） techniques to enhance subtle faults or fractures in the coherence cubes. Unlike the traditional histogram specification （HS） algorithm, our method processes 3D coherence data without discretization. This method partitions a coherence cube into many sub-blocks and self-adaptively specifies the target distribution in each block based on the whole distribution of the coherence cube. Furthermore, the neighboring blocks are partially overlapped to reduce the edge effect. Applications to real datasets show that the new method enhances the details of subtle faults and fractures noticeably.