bluuebunny/crossref_metadata_2025 huggingface dataset:
Active Monitoring Mechanism for Control-Based Self-Adaptive Systems
Authors: Qin Yi , Tong Yanxiang , Xu Yifei , Cao Chun , Ma Xiaoxing
Published: July 2024

Control-based self-adaptive systems (control-SAS) are susceptible to deviations from their pre-identified nominal models. If this model deviation exceeds a threshold, the optimal performance and theoretical guarantees of the control-SAS can be compromised. Existing approaches detect these deviations by locating the mismatch between the control signal of the managing system and the response output of the managed system. However,
vague observations
may mask a potential mismatch where the explicit system behavior does not reflect the implicit variation of the nominal model. In this paper, we propose the Active Monitoring Mechanism (AMM for short) as a solution to this issue. The basic intuition of AMM is to stimulate the control-SAS with an active control signal when vague observations might mask model deviations. To determine the appropriate time for triggering the active signals, AMM proposes a stochastic framework to quantify the relationship between the implicit variation of a control-SAS and its explicit observation. Based on this framework, AMM’s monitor and remediator enhance model deviation detection by generating active control signals of well-designed timing and intensity. Results from empirical evaluations on three representative systems demonstrate AMM’s effectiveness (33.0% shorter detection delay, 18.3% lower FN rate, 16.7% lower FP rate) and usefulness (19.3% lower abnormal rates and 88.2% higher utility).
Predicting Failures of Autoscaling Distributed Applications
Authors: Denaro Giovanni , El Moussa Noura , Heydarov Rahim , Lomio Francesco , Pezzè Mauro , Qiu Ketai
Published: July 2024

Predicting failures in production environments allows service providers to activate countermeasures that prevent harming the users of the applications. The most successful approaches predict failures from error states that the current approaches identify from anomalies in time series of fixed sets of KPI values collected at runtime. They cannot handle time series of KPI sets with size that varies over time. Thus these approaches work with applications that run on statically configured sets of components and computational nodes, and do not scale up to the many popular cloud applications that exploit autoscaling. This paper proposes Preface, a novel approach to predict failures in cloud applications that exploit autoscaling. Preface originally augments the neural-network-based failure predictors successfully exploited to predict failures in statically configured applications, with a Rectifier layer that handles KPI sets of highly variable size as the ones collected in cloud autoscaling applications, and reduces those KPIs to a set of
rectified-KPIs
of fixed size that can be fed to the neural-network predictor. The Preface Rectifier computes the
rectified-KPIs
as descriptive statistics of the original KPIs, for each logical component of the target application. The descriptive statistics shrink the highly variable sets of KPIs collected at different timestamps to a fixed set of values compatible with the input nodes of the neural-network failure predictor. The neural network can then reveal anomalies that correspond to error states, before they propagate to failures that harm the users of the applications. The experiments on both a commercial application and a widely used academic exemplar confirm that Preface can indeed predict many harmful failures early enough to activate proper countermeasures.
Sharing Software-Evolution Datasets: Practices, Challenges, and Recommendations
Authors: Broneske David , Kittan Sebastian , Krüger Jacob
Published: July 2024

Sharing research artifacts (e.g., software, data, protocols) is an immensely important topic for improving transparency, replicability, and reusability in research, and has recently gained more and more traction in software engineering. For instance, recent studies have focused on artifact reviewing, the impact of open science, and specific legal or ethical issues of sharing artifacts. Most of such studies are concerned with artifacts created by the researchers themselves (e.g., scripts, algorithms, tools) and processes for quality assuring these artifacts (e.g., through artifact-evaluation committees). In contrast, the practices and challenges of sharing software-evolution datasets (i.e., republished version-control data with person-related information) have only been scratched in such works. To tackle this gap, we conducted a meta study of software-evolution datasets published at the International Conference on Mining Software Repositories from 2017 until 2021 and snowballed a set of papers that build upon these datasets. Investigating 200 papers, we elicited what types of software-evolution datasets have been shared following what practices and what challenges researchers experienced with sharing or using the datasets. We discussed our findings with an authority on research-data management and ethics reviews through a semi-structured interview to put the practices and challenges into context. Through our meta study, we provide an overview of the sharing practices for software-evolution datasets and the corresponding challenges. The expert interview enriched this analysis by discussing how to solve the challenges and by defining recommendations for sharing software-evolution datasets in the future. Our results extend and complement current research, and we are confident that they can help researchers share software-evolution datasets (as well as datasets involving the same types of data) in a reliable, ethical, and trustworthy way.
MirrorFair: Fixing Fairness Bugs in Machine Learning Software via Counterfactual Predictions
Authors: Xiao Ying , Zhang Jie M. , Liu Yepang , Mousavi Mohammad Reza , Liu Sicen , Xue Dingyuan
Published: July 2024

With the increasing utilization of Machine Learning (ML) software in critical domains such as employee hiring, college admission, and credit evaluation, ensuring fairness in the decision-making processes of underlying models has emerged as a paramount ethical concern. Nonetheless, existing methods for rectifying fairness issues can hardly strike a consistent trade-off between performance and fairness across diverse tasks and algorithms. Informed by the principles of counterfactual inference, this paper introduces MirrorFair, an innovative adaptive ensemble approach designed to mitigate fairness concerns. MirrorFair initially constructs a counterfactual dataset derived from the original data, training two distinct models—one on the original dataset and the other on the counterfactual dataset. Subsequently, MirrorFair adaptively combines these model predictions to generate fairer final decisions.

We conduct an extensive evaluation of MirrorFair and compare it with 15 existing methods across a diverse range of decision-making scenarios. Our findings reveal that MirrorFair outperforms all the baselines in every measurement (i.e., fairness improvement, performance preservation, and trade-off metrics). Specifically, in 93% of cases, MirrorFair surpasses the fairness and performance trade-off baseline proposed by the benchmarking tool Fairea, whereas the state-of-the-art method achieves this in only 88% of cases. Furthermore, MirrorFair consistently demonstrates its superiority across various tasks and algorithms, ranking first in balancing model performance and fairness in 83% of scenarios. To foster replicability and future research, we have made our code, data, and results openly accessible to the research community.
Less Cybersickness, Please: Demystifying and Detecting Stereoscopic Visual Inconsistencies in Virtual Reality Apps
Authors: Li Shuqing , Gao Cuiyun , Zhang Jianping , Zhang Yujia , Liu Yepang , Gu Jiazhen , Peng Yun , Lyu Michael R.
Published: July 2024

The quality of Virtual Reality (VR) apps is vital, particularly the rendering quality of the VR Graphical User Interface (GUI). Different from traditional two-dimensional (2D) apps, VR apps create a 3D digital scene for users, by rendering two distinct 2D images for the user’s left and right eyes, respectively. Stereoscopic visual inconsistency (denoted as “SVI”) issues, however, undermine the rendering process of the user’s brain, leading to user discomfort and even adverse health effects. Such issues commonly exist in VR apps but remain underexplored. To comprehensively understand the SVI issues, we conduct an empirical analysis on 282 SVI bug reports collected from 15 VR platforms, summarizing 15 types of manifestations of the issues. The empirical analysis reveals that automatically detecting SVI issues is challenging, mainly because: (1) lack of training data; (2) the manifestations of SVI issues are diverse, complicated, and often application-specific; (3) most accessible VR apps are closed-source commercial software, we have no access to code, scene configurations, etc. for issue detection. Our findings imply that the existing pattern-based supervised classification approaches may be inapplicable or ineffective in detecting the SVI issues.

To counter these challenges, we propose an unsupervised black-box testing framework named StereoID to identify the stereoscopic visual inconsistencies, based only on the rendered GUI states. StereoID generates a synthetic right-eye image based on the actual left-eye image and computes distances between the synthetic right-eye image and the actual right-eye image to detect SVI issues. We propose a depth-aware conditional stereo image translator to power the image generation process, which captures the expected perspective shifts between left-eye and right-eye images. We build a large-scale unlabeled VR stereo screenshot dataset with larger than 171K images from real-world VR apps, which can be utilized to train our depth-aware conditional stereo image translator and evaluate the whole testing framework StereoID. After substantial experiments, depth-aware conditional stereo image translator demonstrates superior performance in generating stereo images, outpacing traditional architectures. It achieved the lowest average L1 and L2 losses and the highest SSIM score, signifying its effectiveness in pixel-level accuracy and structural consistency for VR apps. StereoID further demonstrates its power for detecting SVI issues in both user reports and wild VR apps. In summary, this novel framework enables effective detection of elusive SVI issues, benefiting the quality of VR apps.
An Empirical Study on Code Review Activity Prediction and Its Impact in Practice
Authors: Olewicki Doriane , Habchi Sarra , Adams Bram
Published: July 2024

During code reviews, an essential step in software quality assurance, reviewers have the difficult task of understanding and evaluating code changes to validate their quality and prevent introducing faults to the codebase. This is a tedious process where the effort needed is highly dependent on the code submitted, as well as the author’s and the reviewer’s experience, leading to median wait times for review feedback of 15-64 hours. Through an initial user study carried with 29 experts, we found that re-ordering the files changed by a patch within the review environment has potential to improve review quality, as more comments are written (+23%), and participants’ file-level hot-spot precision and recall increases to 53% (+13%) and 28% (+8%), respectively, compared to the alphanumeric ordering. Hence, this paper aims to help code reviewers by predicting which files in a submitted patch need to be (1) commented, (2) revised, or (3) are hot-spots (commented or revised). To predict these tasks, we evaluate two different types of text embeddings (i.e., Bag-of-Words and Large Language Models encoding) and review process features (i.e., code size-based and history-based features). Our empirical study on three open-source and two industrial datasets shows that combining the code embedding and review process features leads to better results than the state-of-the-art approach. For all tasks, F1-scores (median of 40-62%) are significantly better than the state-of-the-art (from +1 to +9%).
DeciX: Explain Deep Learning Based Code Generation Applications
Authors: Chen Simin , Li Zexin , Yang Wei , Liu Cong
Published: July 2024

Deep learning-based code generation (DL-CG) applications have shown great potential for assisting developers in programming with human-competitive accuracy. However, lacking transparency in such applications due to the uninterpretable nature of deep learning models makes the automatically generated programs untrustworthy.

In this paper, we develop DeciX, a first explanation method dedicated to DL-CG applications. DeciX is motivated by observing two unique properties of DL-CG applications: output-to-output dependencies and irrelevant value and semantic space. These properties violate the fundamental assumptions made in existing explainable DL techniques and thus cause applying existing techniques to DL-CG applications rather pessimistic and even incorrect.

DeciX addresses these two limitations by constructing a causal inference dependency graph, containing a novel method leveraging causal inference that can accurately quantify the contribution of each dependency edge in the graph to the end prediction result.

Proved by extensive experiments assessing popular, widely-used DL-CG applications and several baseline methods, DeciX is able to achieve significantly better performance compared to state-of-the-art in terms of several critical performance metrics, including correctness, succinctness, stability, and overhead.

Furthermore, DeciX can be applied to practical scenarios since it does not require any knowledge of the DL-CG model under explanation.

We have also conducted case studies that demonstrate the applicability of DeciX in practice.
Bounding Random Test Set Size with Computational Learning Theory
Authors: Walkinshaw Neil , Foster Michael , Rojas José Miguel , Hierons Robert M.
Published: July 2024

Random testing approaches work by generating inputs at random, or by selecting inputs randomly from some pre-defined operational profile. One long-standing question that arises in this and other testing contexts is as follows: When can we stop testing? At what point can we be certain that executing further tests in this manner will not explore previously untested (and potentially buggy) software behaviors? This is analogous to the question in Machine Learning, of how many training examples are required in order to infer an accurate model. In this paper we show how probabilistic approaches to answer this question in Machine Learning (arising from Computational Learning Theory) can be applied in our testing context. This enables us to produce an upper bound on the number of tests that are required to achieve a given level of adequacy. We are the first to enable this from only knowing the number of coverage targets (e.g. lines of code) in the source code, without needing to observe a sample test executions. We validate this bound on a large set of Java units, and an autonomous driving system.
Detecting, Creating, Repairing, and Understanding Indivisible Multi-Hunk Bugs
Authors: Xin Qi , Wu Haojun , Tang Jinran , Liu Xinyu , Reiss Steven P. , Xuan Jifeng
Published: July 2024

This paper presents our approach proposed to detect and create indivisible multi-hunk bugs, an evaluation of existing repair techniques based on these bugs, and a study of the patches of these bugs constructed by the developers and existing tools. Multi-hunk bug repair aims to deal with complex bugs by fixing multiple locations of the program. Previous research on multi-hunk bug repair is severely misguided, as the evaluation of previous techniques is predominantly based on the Defects4J dataset containing a great deal of divisible multi-hunk bugs. A divisible multi-hunk bug is essentially a combination of multiple bugs triggering different failures and is uncommon while debugging, as the developer typically deals with one failure at a time. To address this problem and provide a better basis for multi-hunk bug repair, we propose an enumeration-based approach IBugFinder, which given a bug dataset can automatically detect divisible and indivisible bugs in the dataset and further isolate the divisible bugs into new indivisible bugs. We applied IBugFinder to 281 multi-hunk bugs from the Defects4J dataset. IBugFinder identified 139 divisible bugs and created 249 new bugs among which 105 are multi-hunk.

We evaluated existing repair techniques with the indivisible multi-hunk bugs detected and created by IBugFinder and found that these techniques repaired only a small number of bugs suggesting weak multi-hunk repair abilities. We further studied the patches of indivisible multi-hunk bugs constructed by the developers and the various tools with a focus on understanding the relationships of the partial patches made at different locations. The study has led to the identification of 8 partial patch relationships, which suggest different strategies for multi-hunk patch generation and provide important implication for multi-hunk bug repair.
Carboxymethyl Cellulose – Polyvinyl Alcohol Composite-Based Gel Polymer Electrolyte for Rechargeable Zn-Air Batteries
Authors: Choudhary Deepika , Bala Ritu , Dhiman Rajnish
Published: July 2024

The high ionic conductivity, lower interfacial contact resistance, enhanced safety, non-toxicity, and biodegradability bring the gel polymer electrolytes (GPEs) as a prospective electrolyte for applications in high-energy density flexible Zn-air batteries (ZABs). The present study comprehensively optimizes the procedures to obtain carboxymethyl cellulose (CMC)–polyvinyl alcohol (PVA) composite-based GPEs holding a maximum KOH amount in the polymer matrix. Optimization of the GPE has been performed and demonstrated by an in-house-developed rechargeable ZAB cells using MnO2-based air cathode and Zn anode. The optimization parameters include the ratio of PVA:CMC, concentration of PVA-CMC in DI water, and thickness of the gel polymer electrolyte. Results show that a 4mm thick GPE prepared from a polymer membrane synthesized using PVA:CMC ratio of 5:2 at a concentration of 0.063 g ml−1 in DI water displayed the highest 6M KOH uptake, least charge transfer resistance of the device, higher discharge plateau, and 5–6 times more cycling compared to GPE made of PVA only. The “as-synthesized GPE” demonstrates high stability of GPE over 100h for a Zn-air battery device. The findings of this work shall speed up the development of Zn air batteries for applications as energy storage systems.
Multidrug Resistant Enteric Bacteria from Cancer Patients Admitted in Douala Laquintinie Hospital, Littoral Region of Cameroon
Authors: Kengne Michael F. , Tsobeng Ornella D. , Dadjo Ballue S. T. , Kuete Victor , Mbaveng Armelle T.
Published: July 2024

Patients with cancer have weakened immune systems, making them more vulnerable to infections. This study was carried out to determine the bacterial origins of enteric disorders in cancer patients and noncancer patients at the Oncology Department of Laquintinie Hospital in Douala. A cross‐sectional study was conducted from October 2021 to March 2023. Stool samples from 307 cancer patients with enteric disorders and 200 noncancer patients with enteric disorders were examined to diagnose the presence of bacteria using various techniques. Among all participants in this study, 62.13% were female and 37.87% were male. The average age of the participants was 46.38 ± 15.81 years, with a minimum age of 10 years and a maximum age of 84 years. The average age of participants was significantly higher (p < 0.000) in cancer patients (49.54 ± 14.65 years) compared to noncancer patients (41.53 ± 16.33 years). Proteus mirabilis, Proteus vulgaris, Salmonella typhi, Enterobacter cloacae, Klebsiella pneumoniae, Yersinia intemedia, and Klebsiella oxytoca were more frequently isolated in cancer patients than in noncancer patients, with the respective percentages of 56.25% versus 43.75%, 50.00% versus 50.00%, 61.66% versus 38.34%, 66.66% versus 33.34%, 72.22% versus 27.78%, 80.00 versus 20.00%, and 100% versus 0.00%. Most isolates were sensitive to imipenem (IMP), gentamicin (GEN), and amikacin (AMK). Proteus vulgaris, the most prevalent isolate, showed significantly high resistance (with p < 0.05) in cancer patients compared to noncancer patients at amoxicillin/clavuranic acid (AMC) (89.13% versus 41.30%), ceftriaxone (CTR) (63.04% versus 39.13%), ciprofloxacin (CIP) (65.22% versus 34.18%), and tetracycline (TET) (93.48% versus 63.04%). Multidrug resistance was observed in cancer patients compared to noncancer patients for Klebsiella pneumoniae (85.00% versus 60.00%), Salmonella typhi (84.62% versus 60.00%), and Klebsiella oxytoca (86.49% versus 43.48%). The increase in the number of Gram‐negative infections among cancer patients, as shown in the present study, highlights the need for broad‐spectrum therapy and effective planning of control programs to reduce bacterial diseases among cancer patients.
Evaluation of In Vivo Antidiarrheal Activities of 80% Methanol Extract and Solvent Fractions of Peels of Colocasia esculenta (Araceae)
Authors: Obsa Terfa Temesgen , Abera Kebede Fekadu , Kitessa Beyene Monas , Tesfaye Abebe Tilahun , Basazn Mekuria Abebe , Seid Yesuf Jibril
Published: July 2024

Background. Diarrhea is the common gastrointestinal disorder accounting for 2.5 billion episodes and 1.5 million deaths annually. Limitations and inaccessibility of currently available medications are the main problem associated with treatment of diarrhea. Hence, medicinal plants are usually preferred to manage diarrhea because they may contain constituents with high activity and fewer side effects. Even though the dose, safety, and efficacy of Colocasia esculenta (L.) Schott are not substantiated scientifically, several societies use it for the treatment of diarrhea. Objective. This study was targeted at exploring the in vivo antidiarrheal activities of 80% methanol extract and solvent fractions of peels of Colocasia esculenta (L.) Schott in Swiss albino mice. Methods. The plant was collected and extracted with 80% methanol, followed by fractionation with distilled water, chloroform, and diethyl ether. Castor oil‐induced diarrhea, enteropooling, and motility tests were used to evaluate antidiarrheal activity. The test groups received graded doses of 100 mg/kg, 200 mg/kg, and 400 mg/kg. Negative controls received 10 ml/kg of 2% Tween 80 while positive controls received loperamide (3 mg/kg) or atropine (5 mg/kg). Results. The crude and solvent fractions of the plant extract have induced significant effects in reduction of the number and weight of wet stools at all tested doses. However, delay in onset of diarrhea was observed only at 400 mg/kg (P < 0.001) for both crude extract and solvent fractions. In antienteropooling test, 80% methanol extract and solvent fractions have significantly reduced the weight and volume of intestinal contents, especially at 200 mg/kg and 400 mg/kg. Regarding the antimotility test, the crude extract reduced motility at all tested doses, whereas the solvent fractions reduced intestinal motility mainly at 400 mg/kg (P < 0.001). Conclusion. The study has revealed that the 80% methanol extract and solvent fractions of the plant possess antidiarrheal activities supporting the traditional antidiarrheal claims of the plant.
Electricity Theft Detection in a Smart Grid Using Hybrid Deep Learning‐Based Data Analysis Technique
Authors: Mbey Camille Franklin , Bikai Jacques , Yem Souhe Felix Ghislain , Foba Kakeu Vinny Junior , Boum Alexandre Teplaira
Published: July 2024

With the popularization of smart meters around the world and the appearance of a large amount of electricity consumption data, the analysis of smart meter data is of major interest to electricity distributors around the world. Therefore, we proposed a hybrid artificial intelligence (AI) technique considering sudden changes of consumption in order to accurately predict fraudulent consumers in the smart network. Thus, the proposed hybrid model is based on the support vector machine (SVM) and a particle swarm optimization (PSO) algorithm to detect energy fraudsters in the network. In addition, a real smart grid dataset is used to verify the effectiveness of the proposed algorithm. Moreover, a smart calendar context is modeled showing the scheduling of energy consumption. The effectiveness of the proposed technique is evaluated using performance coefficients such as precision, recall, F1‐score, and area under ROC curve (AUC). We also perform sensitivity analysis through regression, variance, and variogram analysis. The results obtained give a performance of 98.9% in the detection of irregular consumers in the smart power grid. These results demonstrate the effectiveness of the proposed method compared to that in the literature.
A Retrospective Analysis of the Characteristics and Clinical Outcomes of Dental Implants Placed in Romanian Patients
Authors: Sandu Bogdan Gabriel , Juncar Raluca Iulia , Moca Abel Emanuel , Țenț Paul Andrei , Juncar Mihai
Published: July 2024

This study aimed to assess the favorable or unfavorable progress of dental implants in a patient sample from Oradea, Romania, considering different variables. A secondary objective was to describe the characteristics of the study sample, as well as different aspects regarding the implants used. This retrospective study analyzed medical records from 1 January 2018, to 1 January 2022, of patients at a dental clinic in Oradea, Romania, who received at least one dental implant. Variables examined included gender, age, systemic diseases, implant location, implant type, and timing of insertion. Clinical and radiological evaluations, using CBCT immediately postplacement and at six months, assessed implant stability and absence of complications. A standardized three‐month loading time postinsertion was followed. Exclusion criteria included lack of follow‐up, implants placed elsewhere, or incomplete medical records. Quantitative variables were assessed using the Shapiro–Wilk test and nonparametric tests (Mann–Whitney U and Kruskal–Wallis H tests). Fisher’s Exact test analyzed qualitative variables, with Z‐tests and Dunn–Bonferroni tests providing detailed results. The study included 344 implants, with 153 (44.5%) placed in female patients and 191 (55.5%) in male patients. Most implants were bone level (61.6%, n = 212) and had a delayed placement (82.6%, n = 284). The majority were situated in the upper posterior dental arch (39.2%, n = 135). Notably, 96.5% (n = 332) of the implants exhibited a positive outcome, irrespective of age, gender, or associated systemic diseases. Immediate postextraction implants displayed a significantly higher association with a negative outcome (41.7% vs. 16.6%), whereas delayed placements were more frequently linked with a positive outcome (83.4% vs. 58.3%) (p = 0.040). Employing established techniques and protocols for dental implant insertion led to consistently positive outcomes, irrespective of the implant type, insertion timing, or location. Patient’s age, gender, and associated diseases did not significantly impact implant outcome, highlighting the robustness of these methods in achieving favorable results.
IFNγ Production by Functionally Reprogrammed Tregs Promotes Antitumor Efficacy of OX40/CD137 Bispecific Agonist Therapy
Authors: Imianowski Charlotte J. , Kuo Paula , Whiteside Sarah K. , von Linde Teresa , Wesolowski Alexander J. , Conti Alberto G. , Evans Alexander C. , Baird Tarrion , Morris Benjamin I. , Fletcher Nicole E. , Yang Jie , Poon Edmund , Lakins Matthew A. , Yamamoto Masahiro , Brewis Neil , Morrow Michelle , Roychoudhuri Rahul
Published: July 2024

Regulatory T cells (Treg) are highly enriched within many tumors and suppress immune responses to cancer. There is intense interest in reprogramming Tregs to contribute to antitumor immunity. OX40 and CD137 are expressed highly on Tregs, activated and memory T cells, and NK cells. In this study, using a novel bispecific antibody targeting mouse OX40 and CD137 (FS120m), we show that OX40/CD137 bispecific agonism induces potent antitumor immunity partially dependent upon IFNγ production by functionally reprogrammed Tregs. Treatment of tumor-bearing animals with OX40/CD137 bispecific agonists reprograms Tregs into both fragile Foxp3+ IFNγ+ Tregs with decreased suppressive function and lineage-instable Foxp3− IFNγ+ ex-Tregs. Treg fragility is partially driven by IFNγ signaling, whereas Treg instability is associated with reduced IL2 responsiveness upon treatment with OX40/CD137 bispecific agonists. Importantly, conditional deletion of Ifng in Foxp3+ Tregs and their progeny partially reverses the antitumor efficacy of OX40/CD137 bispecific agonist therapy, revealing that reprogramming of Tregs into IFNγ-producing cells contributes to the anti-tumor efficacy of OX40/CD137 bispecific agonists. These findings provide insights into mechanisms by which bispecific agonist therapies targeting costimulatory receptors highly expressed by Tregs potentiate antitumor immunity in mouse models.

Significance:

The bispecific antibody FS120, an immunotherapy currently being tested in the clinic, partially functions by inducing anti-tumor activity of Tregs, which results in tumor rejection.
The 21 August 2021 Catastrophic Flash Flood at Waverly, Tennessee: Harnessing the Warn-on-Forecast System for Confident Prewarning Messaging of Extreme Rainfall
Authors: Burke Patrick C. , Barnwell Joshua , Reagan Matthew , Rose Mark A. , Galarneau Thomas J. , Otto Richard , Orrison Andrew
Published: July 2024

On the morning of 21 August 2021, extreme rainfall spurred a flood wave on Trace Creek that ravaged Waverly, Tennessee, causing 19 fatalities. Peak 24-h rainfall of 526 mm was recorded just upstream at McEwen, setting the Tennessee 24-h state rainfall record. A Slight Risk of excessive rainfall and a Flash Flood Watch were issued 16 and 8 h, respectively, before rain began; however, predicting mesobeta scale extreme rainfall remains an elusive skill for models and humans alike. Operational convection-allowing models not only suggested pockets of heavy rain but also displayed 1) peak values generally less than half of those observed, 2) widely ranging solutions, and 3) erroneous similarly heavy rain elsewhere. Future use of storm-scale ensembles which use rapid data assimilation promises to help forecasters anticipate extrema that may only be predictable at shorter time scales. This work will examine compelling forecasts from a retrospective run of the experimental Warn-on-Forecast System (WoFS). The authors, who include National Weather Service forecasters who worked the event, discuss how WoFS and its probabilistic framework could influence services during low-predictability, high-impact flash floods.
Anxiety and Depression Among Adults and Children With Celiac Disease: A Meta‐Analysis of Different Psychiatry Scales
Authors: Moawad Mostafa Hossam‐Eldin , Serag Ibrahim , Shalaby Mahmoud Mohamed , Aissani Mohamed Smail , Sadeq Mohammed Ahmed , Hendi Nada Ibrahim , Elawfi Bashaer , Ghorab Reem Mohamed Farouk , Mostafa Naydeen , Ibrahim Nancy , Abdelrhem Hasnaa Ali Hassan , Rady Ahmed Hassan A. , Alkasaby Muhammad
Published: July 2024

Background

Celiac disease (CD) is an autoimmune disorder in which genetically susceptible individuals cannot digest gluten (wheat) and its homologs such as Scalin (rye) and Hordein (barley).

Aim

This systematic review and meta‐analysis aimed to investigate the measures of associations between CD and psychiatric disorders, specifically anxiety and depression, and explore the relationship between adherence to a Gluten‐Free Diet (GFD) and the psychiatric aspects of the disease.

Methods

We searched PubMed, Scopus and Web of Science for articles investigating anxiety and depression in CD patients. The following inclusion criteria were implemented: Primary research articles (either observational or experimental) that include participants with a CD diagnosis ‐confirmed either serologically, with anti‐endomysial antibodies, anti‐tissue transglutaminase antibodies, or with duodenum biopsy, whether on a GFD or not,—who have depression or anxiety symptoms identified through self‐report or clinician‐administered scales.

Results

CD patients are at a higher odds of developing anxiety, as the odds ratio was (OR: 2.26, 95% CI: [1.10, 4.67]) and depression symptoms (OR: 3.36, 95% CI: [1.36, 8.32]). Results of both State‐Trait Anxiety Inventory Y‐1 and Y‐2 improved after 1 year of GFD with mean difference of 3.48, 95% CI: (0.26, 6.71), and MD: 3.45, 95% CI: (1.39, 5.52), respectively.

Conclusion

Anxiety and depression are prevalent among adults and children CD patients as they are observed to have high odds of anxiety and depression as expressed by various scales. It is reported that GFD is associated with decreased levels of anxiety and depression, however, further studies are required to confirm these findings and to investigate the main mechanism of psychiatric disorders among CD patients.
The OHStat Guidelines for Reporting Observational Studies and Clinical Trials in Oral Health Research: Explanation and Elaboration
Authors: Best A.M. , Lang T.A. , Greenberg B.L. , Gunsolley J.C. , Ioannidou E. ,   Task Force on Design and Analysis in Oral Health Research
Published: July 2024

Adequate and transparent reporting is necessary for critically appraising research. Yet, evidence suggests that the design, conduct, analysis, interpretation, and reporting of oral health research could be greatly improved. Accordingly, the Task Force on Design and Analysis in Oral Health Research—statisticians and trialists from academia and industry—empaneled a group of authors to develop methodological and statistical reporting guidelines identifying the minimum information needed to document and evaluate observational studies and clinical trials in oral health: the OHstat Guidelines. Drafts were circulated to the editors of 85 oral health journals and to Task Force members and sponsors and discussed at a December 2020 workshop attended by 49 researchers. The final version was subsequently approved by the Task Force in September 2021, submitted for journal review in 2022, and revised in 2023. The checklist consists of 48 guidelines: 5 for introductory information, 17 for methods, 13 for statistical analysis, 6 for results, and 7 for interpretation; 7 are specific to clinical trials. Each of these guidelines identifies relevant information, explains its importance, and often describes best practices. The checklist was published in multiple journals. The article was published simultaneously in JDR Clinical and Translational Research, the Journal of the American Dental Association, and the Journal of Oral and Maxillofacial Surgery. Completed checklists should accompany manuscripts submitted for publication to these and other oral health journals to help authors, journal editors, and reviewers verify that the manuscript provides the information necessary to adequately document and evaluate the research.
Comprehensive analysis of the Xya riparia genome uncovers the dominance of DNA transposons, LTR/Gypsy elements, and their evolutionary dynamics
Authors: Khan Hashim , Yuan Huang , Liu Xuanzeng , Nie Yimeng , Majid Muhammad
Published: July 2024

Transposable elements (TEs) are DNA sequences that can move or replicate within a genome, and their study has become increasingly important in understanding genome evolution and function. The Tridactylidae family, including Xya riparia (pygmy mole cricket), harbors a variety of transposable elements (TEs) that have been insufficiently investigated. Further research is required to fully understand their diversity and evolutionary characteristics. Hence, we conducted a comprehensive repeatome analysis of X. riparia species using the chromosome-level assembled genome. The study aimed to comprehensively analyze the abundance, distribution, and age of transposable elements (TEs) in the genome. The results indicated that the genome was 1.67 Gb, with 731.63 Mb of repetitive sequences, comprising 27% of Class II (443.25 Mb), 16% of Class I (268.45 Mb), and 1% of unknown TEs (19.92 Mb). The study found that DNA transposons dominate the genome, accounting for approximately 60% of the total repeat size, with retrotransposons and unknown elements accounting for 37% and 3% of the genome, respectively. The members of the Gypsy superfamily were the most abundant amongst retrotransposons, accounting for 63% of them. The transposable superfamilies (LTR/Gypsy, DNA/nMITE, DNA/hAT, and DNA/Helitron) collectively constituted almost 70% of the total repeat size of all six chromosomes. The study further unveiled a significant linear correlation (Pearson correlation: r = 0.99, p-value = 0.00003) between the size of the chromosomes and the repetitive sequences. The average age of DNA transposon and retrotransposon insertions ranges from 25 My (million years) to 5 My. The satellitome analysis discovered 13 satellite DNA families that comprise about 0.15% of the entire genome. In addition, the transcriptional analysis of TEs found that DNA transposons were more transcriptionally active than retrotransposons. Overall, the study suggests that the genome of X. riparia is complex, characterized by a substantial portion of repetitive elements. These findings not only enhance our understanding of TE evolution within the Tridactylidae family but also provide a foundation for future investigations into the genomic intricacies of related species.
Woven coronary artery with acquired etiology: the natural history documented by serial angiography and optical coherence tomography
Authors: Li Jiannan , Wang Xiaoli , Liu Chen , Zhou Peng , Zhao Hanjun
Published: July 2024

Woven coronary artery (WCA) is a rare anomaly and its etiology remains speculative. Both congenital and acquired factors are considered to be concerned with the pathogenesis. In a 35-year-old man, the tissue characteristics of WCA were evaluated by optical coherence tomography. Serial coronary angiography indicated that acquired factor is the cause, and thrombus recanalization is the most likely pathological mechanism.
Non-emergency department (ED) interventions to reduce ED utilization: a scoping review
Authors: Nummedal Målfrid A. , King Sarah , Uleberg Oddvar , Pedersen Sindre A. , Bjørnsen Lars Petter
Published: July 2024

Background

Emergency department (ED) crowding is a global burden. Interventions to reduce ED utilization have been widely discussed in the literature, but previous reviews have mainly focused on specific interventions or patient groups within the EDs. The purpose of this scoping review was to identify, summarize, and categorize the various types of non-ED-based interventions designed to reduce unnecessary visits to EDs.

Methods

This scoping review followed the JBI Manual for Evidence Synthesis and the PRISMA-SCR checklist. A comprehensive structured literature search was performed in the databases MEDLINE and Embase from 2008 to March 2024. The inclusion criteria covered studies reporting on interventions outside the ED that aimed to reduce ED visits. Two reviewers independently screened the records and categorized the included articles by intervention type, location, and population.

Results

Among the 15,324 screened records, we included 210 studies, comprising 183 intervention studies and 27 systematic reviews. In the primary studies, care coordination/case management or other care programs were the most commonly examined out of 15 different intervention categories. The majority of interventions took place in clinics or medical centers, in patients’ homes, followed by hospitals and primary care settings - and targeted patients with specific medical conditions.

Conclusion

A large number of studies have been published investigating interventions to mitigate the influx of patients to EDs. Many of these targeted patients with specific medical conditions, frequent users and high-risk patients. Further research is needed to address other high prevalent groups in the ED - including older adults and mental health patients (who are ill but may not need the ED). There is also room for further research on new interventions to reduce ED utilization in low-acuity patients and in the general patient population.
GP’s GP, general practitioner’s health and willingness to contract family doctors in China: a national cross-sectional study
Authors: He Guoshu , Ren Jingjing , Chen Xiaoyang , Pan Qi , Pan Tianyuan
Published: July 2024

Objectives

General practitioners are trained to care for patients with a high level of responsibility and professional competency. However, there are few reports on the physical and mental health status of general practitioners (GPs) in China, particularly regarding help seeking and self-treatment. The primary aims of this study were to explore GPs’ expectations of their own family doctors and their reflection on role positioning, and to explore the objective factors that hinder the system of family doctors.

Study design

Cross-sectional study.

Methods

We conducted an online survey of Chinese GPs. Descriptive statistics were used to summarize the findings.

Results

More than half of the participants (57.20%) reported that their health was normal over the past year. A total of 420 participants (23.35%) reported having chronic diseases. For sleep duration, 1205 participants (66.98%) reported sleeping 6–8 h per day; 473 participants (26.29%) reported chronic insomnia. Two hundred thirty-one participants (12.84%) had possible depression. A total of 595 (33.07%) participants reported that they had contracted a fixed family doctor. In terms of preventing themselves from contracting for a family doctor, the following factors were identified: lack of sufficient time (54.81%), could solve obstacles themselves (50.97%), and embarrassment (24.24%). The proportion of the contract group (12.44%) taking personal relationship as a consideration was higher than that of the non-contract group (7.64%) (χ2 = 10.934P = 0.01). Most participants (79.90%) in the non-signed group reported never having seen a family doctor. In terms of obstacles, more than half of the signed group thought that they could solve obstacles themselves, while the non-signed group (39.20%) was less confident in the ability of family doctors than the signed group (29.75%) (χ2 = 15.436, P < 0.01).

Conclusions

GPs work under great pressure and lack of self-care awareness, resulting in an increased prevalence of health conditions. Most GPs did not have a regular family doctor. Having a family doctor with a fixed contract is more conducive to the scientific management of their health and provides a reasonable solution to health problems. The main factors hindering GPs from choosing a family doctor were time consumption, abilities to solve obstacles themselves, and trust in the abilities of GPs. Therefore, simplifying the process of family doctor visits, Changing the GPs' medical cognition, and strengthening the policy of GP training would be conducive to promoting a family doctor system that enhances hierarchical diagnosis and treatment. International collaboration could integrate GP health support into global healthcare system.
Application of color doppler ultrasound and US shear wave elastography with connective tissue growth factor in the risk assessment of papillary thyroid carcinoma
Authors: Leng Xiaoling , Liu Jinhui , Zou Qiao , Wang Changchun , Yang Sen
Published: July 2024

Background

This study aims to investigate the role of shear wave elastography (SWE) and connective tissue growth factor (CTGF) in the assessment of papillary thyroid carcinoma (PTC) prognosis.

Methods

CTGF expression was detected with immunohistochemistry. Clinical and pathological data were collected. Parameters of conventional ultrasound combined with SWE were also collected. The relationship among CTGF expression, ultrasound indicators, the elastic modulus and the clinicopathological parameters were analyzed.

Results

Univariate analysis showed that patients with high risk of PTC were characterized with male, Uygur ethnicity, increased expression of CTGF, convex lesions, calcified, incomplete capsule, intranodular blood flow, rear echo attenuation, cervical lymph node metastasis, lesions larger than 1 cm, psammoma bodies, advanced clinical stage, increased TSH and high value in the shear modulus (P < 0.05). Multivariate analysis demonstrated that the risk factors of high expression of CTGF according to contribution size order were irregular shape, aspect ratio ≥ 1, and increased TSH. The logistic regression model equation was Logit (P) = 1.153 + 1.055 × 1 + 0.926 × 2 + 1.190 × 3 and the Area Under Curve value of the logistic regression was calculated to be 0.850, with a 95% confidence interval of 0.817 to 0.883.

Conclusion

SWE and CTGF are of great value in the risk assessment of PTC. The degree of fibrosis of PTC is closely related to the prognosis. The hardness of PTC lesions and the expression level of CTGF are correlated with the main indexes of conventional ultrasound differentiating benign or malignant nodules. Irregular shape, aspect ratio ≥ 1, and increased TSH are independent factors of CTGF.
Effect of rehabilitation on renal outcomes after acute kidney injury associated with cardiovascular disease: a retrospective analysis
Authors: Hirano Yuma , Fujikura Tomoyuki , Kono Kenichi , Yamaguchi Tomoya , Ohashi Naro , Yokoyama Yurina , Toda Masahiro , Yamauchi Katsuya , Yasuda Hideo
Published: July 2024

Background

Acute kidney injury (AKI) incidence is extremely high worldwide, and patients who develop AKI are at increased risk of developing chronic kidney disease (CKD), CKD progression, and end-stage kidney disease (ESKD). However, there is no established treatment strategy for AKI. Based on the idea that exercise has a stabilizing effect on hemodynamics, we hypothesized that rehabilitation would have beneficial renal outcomes in patients with AKI associated with cardiovascular disease. Therefore, the purpose of this study was to determine whether rehabilitation can stabilize hemodynamics and positively impact renal outcomes in patients with AKI associated with cardiovascular disease.

Methods

In total, 107 patients with AKI associated with cardiovascular disease were enrolled in this single-center retrospective study and were either assigned to the exposure group (n = 36), which received rehabilitation at least once a week for at least 8 consecutive weeks, or to the control group (n = 71). Estimated glomerular filtration rate was assessed at baseline before admission, at the lowest value during hospitalization, and at 3, 12, and 24 months after enrolment. Trends over time (group × time) between the two groups were compared using generalized estimating equations. Moreover, congestive status was assessed by amino-terminal pro-B-type natriuretic peptide (NT-proBNP), and the effect of rehabilitation on congestion improvement was investigated using logistical regression analysis.

Results

The time course of renal function after AKI, from baseline to each of the three timepoints suggested significant differences between the two groups (p < 0.01). However, there was no significant difference between the two groups at any time point in terms of percentage of patients who experienced a 40% estimated glomerular filtration rate reduction from that at baseline. The proportion of patients with improved congestion was significantly higher in the exposure group compared with that in the control group (p = 0.018). Logistic regression analysis showed that rehabilitation was significantly associated with improved congestion (p = 0.021, OR: 0.260, 95%CI: 0.083–0.815).

Conclusion

Our results suggest that rehabilitation in patients with AKI associated with cardiovascular disease correlates with an improvement in congestion and may have a positive effect on the course of renal function.