Miracle-dz/UniFAR-FTUs huggingface dataset:
No Association of Angiotensin-Converting Enzyme Insertion/Deletion (ACE I/D) Gene Polymorphism in the Susceptibility to Diabetic Retinopathy in Type 2 Diabetes Mellitus Patients: An Updated Meta-Analysis

Diabetic retinopathy (DR) is a complex and multifactorial pathology encompassing environmental, metabolic, and polygenic influences. Among the genes possibly involved in the development and progression of DR, the Angiotensin I-converting enzyme (ACE) gene stands out, which presents an insertion (I) or deletion (D) polymorphism of a 287 bp Alu repetitive sequence in intron 16. Thus, this study aimed to perform a systematic review with meta-analysis to elucidate the relationship between the ACE gene (I/D) polymorphism (rs1799752) and the development and progression of DR in type 2 diabetic patients. PubMed/MEDLINE, Embase, Web of Science, and Scopus databases were systematically searched to retrieve articles that investigated the association between ACE gene (I/D) polymorphism in DR patients. Sixteen articles were included in the systematic review. The results describe no significant association between the polymorphism and DR risk (OR = 1.12; CI = 0.96–1.31; and p = 0.1359) for genotypic analysis by the dominant model (II vs. ID+DD). Moreover, we also observed no significant association between the D allele on the allele frequency analysis (I vs. D) and the DR risk (OR = 1.10; CI = 0.98–1.23; and p = 0.1182). Forest plot analysis revealed that the discrepancy between previous studies most likely arose from variations in their sample sizes. In conclusion, I/D polymorphism appears to be not involved in the susceptibility to and progression of the DR in type 2 diabetic patients.
Arthroscopic capsular release for the treatment of post-stroke frozen shoulder

Abstract Background: This study will assess the efficacy and safety of arthroscopic capsular release (ACR) for the treatment of post-stroke frozen shoulder (PSFS). Methods: We will carry out a systematic study of randomized controlled trials that assess the efficacy and safety of ACR for PSFS. We will search all potential records for any eligible trials from selected electronic databases (MEDLINE, EMBASE, Cochrane Library, Web of Science, Chinese Biomedical Literature Database, WANGFANG, and China National Knowledge Infrastructure) and grey literature sources from inception to the present. Two authors will independently perform study selection, data extraction, and study quality assessment. Any disagreement will be solved by a third author via consultation. Statistical analysis will be carried out by RevMan 5.3 software. Results: This study will comprehensively summarize current eligible studies to systematically assess the efficacy and safety of ACR for PSFS. Conclusion: This study will provide evidence to determine whether ACR is an effective management for patients with PSFS.
Alteration of Mitochondrial Transcript Expression in Arabidopsis thaliana Using a Custom-Made Library of Pentatricopeptide Repeat Proteins

Pentatricopeptide repeat (PPR) proteins are considered a potential tool for manipulating organelle gene expression in plants because they can recognise a wide range of different RNA sequences, and the molecular basis for this sequence recognition is partially known and understood. A library of redesigned PPR proteins related to restorer-of-fertility proteins was created and transformed into plants in order to target mitochondrial transcripts. Ninety different variants tested in vivo showed a wide range of phenotypes. One of these lines, which displayed slow growth and downward curled leaves, showed a clear reduction in complex V. The phenotype was due to a specific cleavage of atp1 transcripts induced by a modified PPR protein from the library, validating the use of this library as a source of mitochondrial ‘mutants’. This study is a step towards developing specific RNA targeting tools using PPR proteins that can be aimed at desired targets.
Evaluation of Arctic Sea Ice Thickness Simulated by AOMIP Models

16 We compare results from six AOMIP model simulations with estimates of sea ice thickness 17 obtained from ICESat, moored and submarine-based upward looking sensors, airborne 18 electromagnetic measurements and drill holes. Our goal is to find patterns of model performance 19 to guide model improvement. The satellite data is pan-arctic from 2004–2008, ice-draft data is 20 from moored instruments in Fram Strait, the Greenland Sea and the Beaufort Sea from 1992– 21 2008 and from submarines from 1975-2000. The drill hole data are from the Laptev and East 22 Siberian marginal seas from 1982–1986 and from coastal stations from 1998-2009. While there 23 are important caveats when comparing modeled results with measurements from different 24 platforms and time periods such as these, the models agree well with moored ULS data. In 25 general, the AOMIP models underestimate the thickness of measured ice thicker than about 2 m 26 and overestimate thickness of ice thinner than 2 m. The simulated results are poor over the fast 27 ice and marginal seas of the Siberian shelves. Averaging over all observational data sets, the 28 better correlations and smaller differences from observed thickness are from the ECCO2 and 29 UW models. 30
Combination treatment of pembrolizumab with DC-CIK cell therapy for advanced hepatocellular carcinoma: A case report

Background Recently, immunotherapy has emerged as a promising method for advanced HCC treatment. There are several clinical trials and meta-analyses of immune checkpoint inhibitors and immune cell therapy, but clinical evidence on the combination of these two therapies is lacking. Case description A 66-year-old man with chronic hepatitis B-related cirrhosis complained of acute abdominal pain in an emergency department of a hospital. On exams, there was a palpable mass in the right upper quadrant of his abdomen. Contrast-enhanced abdominal computed tomography showed a large tumor in the right lobe, 13 cm × 17 cm in size, and right portal vein thrombosis. The alpha-fetoprotein (AFP) level was 30,905 mg/dL. Therefore this patient was diagnosed with BCLC stage C hepatocellular carcinoma (HCC). He underwent trans-arterial chemo-embolization (TACE), abdominal radiotherapy, nivolumab, and lenvatinib. His disease had been under control until two years later, the disease progressed with multiple lung metastases, and his AFP level rose from around 1000 to 17,000 ng/ml. At this stage, he underwent new combination immunotherapy in January 2022. He used pembrolizumab (100 mg) first, and the AFP level decreased by 600 ng/ml daily. Then he received DC-CIK cell therapy two weeks after using pembrolizumab, and the AFP level declined to 900 ng/ml a day. Unfortunately, severe pneumonitis and tension pneumothorax developed after therapy. The patient denied undergoing further treatment and expired peacefully. Conclusion The previous in-vivo study found that combination immunotherapy can improve tumor control in the mice model. Besides, in previous clinical studies, the level of AFP may be a surrogate marker of tumor response. Therefore we thought the more rapidly declined level of AFP was the clinical evidence of the synergistic effect of checkpoint inhibitors combined with cell therapy in HCC treatment.
Aesthetic Visual Question Answering of Photographs

Aesthetic assessment of images can be categorized into two main forms: numerical assessment and language assessment. In this paper, we propose a new task of aesthetic language assessment: aesthetic visual question and answering (AVQA) of images. We use images from www.flickr. com. The objective QA pairs are generated by the proposed aesthetic attributes analysis algorithms. Moreover, we introduce subjective QA pairs that are converted from aesthetic numerical labels and sentiment analysis from large-scale pre-train models. We build the first aesthetic visual question answering dataset, AesVQA, that contains 72,168 high-quality images and 324,756 pairs of aesthetic questions. This is the first work that both addresses the task of aesthetic VQA and introduces subjectiveness into VQA tasks. The experimental results reveal that our methods outperform other VQA models on this new task.
The accuracy of one-stop CTA in predicting valve size before TAVI

At present, one-stop CTA is considered as the gold standard in the preoperative evaluation of TAVI. It analyzes and measures the anatomical structure of the aortic root to match the best artificial aortic valve, which is very helpful for the smooth progress of TAVI surgery. Compared with Echocardiography, CTA has a higher prediction accuracy.
Electric Vehicle Energy Demand Prediction Techniques: An In-Depth and Critical Systematic Review

Accurate forecast of electric vehicle energy demand is vital for maintaining the stability and reliability of power systems. With the increasing prevalence of electric vehicles in transportation systems, the anticipating demand surges with precision in terms of timing and location is becoming ever more critical for utilities to guarantee sufficient supply. The intermittent and stochastic nature of electric vehicle electricity consumption is a significant challenge in accurately forecasting of electric vehicles demand. As a result, there is a growing field of research focused on developing models that can effectively capture and interpret such complex data. In improving the potential of accurate prediction models, conducting a comprehensive review of literature, examining current research overviews, and exploring potential expansions and extensions of models are all critical components. In this review, a comprehensive overview of prior research conducted for forecasting electric vehicle energy demand is presented, including a detailed examination of the benefits and drawbacks of the methods used. Additionally, potential gaps in the field are identified, and recommendations for future research directions are provided.
The AquaSPICE Industrial Symbiosis Enabling Platform

The role of the AquaSPICE Symbiosis Enabling platform is to facilitate the development of one-to-one waste reuse value chains, through the collaboration of an industrial waste source and a potential waste user. This can be achieved by adopting a three-step process: (i) map and characterize the potential sources and sinks of waste in a region; (ii) match them based on technical criteria; and (iii) identify and assess potential business models for opportunity exploitation.
Toxicity of microplastics and nanoplastics: invisible killers of female fertility and offspring health

Microplastics (MPs) and nanoplastics (NPs) are emergent pollutants, which have sparked widespread concern. They can infiltrate the body via ingestion, inhalation, and cutaneous contact. As such, there is a general worry that MPs/NPs may have an impact on human health in addition to the environmental issues they engender. The threat of MPs/NPs to the liver, gastrointestinal system, and inflammatory levels have been thoroughly documented in the previous research. With the detection of MPs/NPs in fetal compartment and the prevalence of infertility, an increasing number of studies have put an emphasis on their reproductive toxicity in female. Moreover, MPs/NPs have the potential to interact with other contaminants, thus enhancing or diminishing the combined toxicity. This review summarizes the deleterious effects of MPs/NPs and co-exposure with other pollutants on female throughout the reproduction period of various species, spanning from reproductive failure to cross-generational developmental disorders in progenies. Although these impacts may not be directly extrapolated to humans, they do provide a framework for evaluating the potential mechanisms underlying the reproductive toxicity of MPs/NPs.
Moving Target Defense Strategy Selection against Malware in Resource-Constrained Devices

Internet-of-Things (IoT) devices have become critical assets to be protected due to increased adoption for emerging use cases. As such, these devices are confronted with a myriad of malware-based threats. To combat malware in IoT, Moving Target Defense (MTD) is a viable defense layer, since MTD does not rely on a low breach probability - aiming to increase security in a dynamic way. Although evidence supports the usefulness of MTD for IoT, the current state of the art suffers from unrealistic deployments, including the problem of operating multiple MTD techniques. Especially, there is a commonly observed gap in determining and deploying one of a set of locally available MTD techniques. This paper addresses this gap by relying on a rule-based selection mechanism. For that, a risk-driven methodology to establish this selection agent with a well-defined architecture is followed. As an input, the device's behavior, as expressed through its resource consumption, serves as a selection criterion. This architecture was implemented for a Raspberry Pi and evaluated against seven malware, given four existing MTD techniques. The resulting prototype highlights that a rule-based system can efficiently mitigate the malware samples.
EEG Classification Using Reinforcement Learning and Swarm-Based Deep Sparse Autoencoder

Analysis of electroencephalographic (EEG) data has shown to be a useful tool in the fields of Brain-computer interfaces (BCIs), and even business. Recently, deep learning architectures have been implemented, particularly in the interpretation of EEG readings and the knowledge it may impart about mental processes, thanks to the availability of huge EEG data sets and advancements in deep learning. In order to find useful data for brain classification and neuroimaging, Various Analytical Techniques utilized in EEG investigations have turned to machine learning. This research study combines sparse auto-encoders (SAEs) with a post-processing system, which is comprised of a linear system model dependent on the Particle Swarm Optimization (PSO) method as a unique data categorization framework. To boost classification precision, it is to prune datasets of superfluous characteristics. The Q-Learning method is used to built on reinforcement learning to reduce the overall number of features and have obtained the resulting accuracy. Evidence of this strategy leading to better classification performance is shown by experiments on both simulated and actual data. The PSO method provides a metaheuristic strategy for estimating the linear model's parameters. Using publicly available datasets, the suggested methodology is verified; When contrasted to current best practices, the results are favorable. With some adjustments to the input characteristics, hidden neurons, and output classes, the system may be utilized to address any data categorization problem.
A Multidimensionality Reduction Approach to Rainfall Prediction

The rainfall has an impact on various fields and industries, including transportation, construction, tourism, health, and wildlife preservation. Accurate rainfall prediction is essential for mitigating the negative impact of rainfall on these sectors. However, previous studies on rainfall prediction have been mainly based on datasets from North America, Europe, Australia, and Central Asia, covering different periods. This study proposes using weather datasets covering the past 5 to 10 years to capture recent patterns in weather data. Additionally, the curse of dimensionality can impact model performance and lead to overfitting. Therefore, this study proposes utilizing dimensionality reduction techniques to ensure that only the significant features are used for rainfall prediction. Multiple Linear Regression (MLR) with dimensionality reduction is applied to improve the accuracy of rainfall prediction. The experimental result shows that UMAP+MLR and t-SNE+MLR have lower MSEs of 57.27 and 56.74 and higher r2 scores of 0.130 and 0.138, respectively. The proposed approach can be valuable in optimizing resource utilization and mitigating the impacts of rainfall on various fields and industries. The source code for our research is available on GitHub repository: https://github.com/Prasanjit-Dey/Dimension_Reduction.
A Proposed Framework for Robot-Mediated Healthcare and Response Systems

The study proposes an experimental model for robot-mediated systems in healthcare. The framework outline and required modules are discussed successively alongside the methodology needed to achieve the system's base functioning. The concept of response systems and their importance in building an efficient agent for healthcare support are also discussed. The study looks at the concept of human-machine interfaces, medical robotic systems, methods of implementation and design to achieve the conception, the underlying effects, and the public's opinion on technology in healthcare. The proposed system derives from the issues faced in conveying the full capabilities of a healthcare network. The idea behind the proposal is to provide a means to leverage different components of healthcare systems to attain a holistic model capable of serving various patient and staff needs.
Collaborative Active Learning Based on Improved Capsule Networks for Hyperspectral Image Classification

For hyperspectral image classification (HIC) tasks, most uncertainty-based active learning (AL) methods only consider the uncertainty, without considering the diversity of actively selected samples and the budget of expert labeling. In this article, we propose a collaborative AL (CAL) framework to address this problem. The proposed framework consists of two well-designed base classifiers and an ingenious CAL scheme that takes into account both the uncertainty and diversity of actively selected samples and the cost of expert annotation. Specifically, getting benefit from the ability of capsule networks (CapsNets) to accurately identify and locate features, we design two improved CapsNets. For these two networks, we call the first one Capsule Vision Transformer (CapsViT), which introduces vision Transformer (ViT) into the CapsNet to learn the global relationship between the capsule features. We call the second one Capsule GLOM (CapsGLOM); the basic structure of this network is derived from the GLOM system proposed by Geoffrey Hinton; and we learn from the way CapsNet constructs the primary capsules to improve its implementation details. CapsViT and CapsGLOM are used as the two base classifiers in the proposed CAL framework to select the most informative samples according to the CAL scheme under the premise of fully considering the cost of expert annotation. Experimental results on four benchmark hyperspectral image datasets show that our proposed CAL framework can achieve satisfactory classification results. At the same time, compared with other advanced deep models, our proposed CapsViT and CapsGLOM are also competitive in the supervised HIC tasks. The source code is available online (https://github.com/swiftest/CAL).
Sources et dynamiques spatiales et temporelles des contaminations en éléments traces et hydrocarbures aromatiques polycycliques du continuum atmosphère - sol - rivière d'un bassin versant contrasté

Le developpement des activites humaines, notamment industrielles, depuis le 19ieme siecle a engendre une contamination massive de l’atmosphere a la riviere, en passant par la biosphere et les sols. Si les contaminations ont fortement diminue depuis la fin des annees 1960, elles persistent notamment en milieu urbain, ou se concentrent un grand nombre d’activites humaines. Afin de pouvoir gerer au mieux la pollution, il est necessaire de comprendre la dynamique de transfert des contaminants, ainsi que leurs sources, a l’echelle du continuum atmosphere – sol – riviere. Cette etude a donc pour objectif d’evaluer les variations temporelles et spatiales de deux types de contaminants historiques, les hydrocarbures aromatiques polycycliques (HAP) et certains elements traces (ET), a l’echelle du bassin versant de l’Orge (950 km2), presentant une urbanisation croissante d’amont en aval. Plusieurs approches ont ete utilisees pour tracer les transferts de particules en riviere (radionucleides ⁷Be, ²¹⁰Pb, ¹³⁷Cs), et determiner les sources de Pb (isotopes du Pb), et les sources de HAP (rapports de molecules). Les resultats ont mis en evidence un impact important des zones urbaines sur la qualite de la riviere Orge en aval du bassin : contamination importante en metaux (Cu, Zn, Sb, Pb) et en HAP de la phase particulaire, et des niveaux eleves en SO42⁻, Na⁺ et Cl⁻, Cu, Zn et Pb dans la phase dissoute. Le tracage des sediments par les radionucleides a permis d’observer un apport de particules provenant du ruissellement urbain en aval, et d’identifier les particules de route (ou Road Deposited Sediment) comme source principale. Les signatures isotopiques du Pb ainsi que les signatures en HAP permettent de confirmer que le ruissellement urbain est le principal vecteur de contamination. Les estimations des flux d’ET et HAP annuels et saisonniers entrants (retombees atmospheriques) et sortants (exportes par la riviere) ont mis en evidence une accumulation globale a l’echelle du bassin, qui comporte deja un stock important de contaminants dans ses sols. Cette etude montre ainsi l’importance d’etudier la Zone Critique dans son ensemble afin d’evaluer la dynamique des contaminations au sein et entre ses differents compartiments, et met en evidence l’efficacite du couplage de plusieurs approches afin de comprendre le systeme entier. Ces resultats pourraient a terme permettre l’etablissement d’un modele de transfert de contaminations au sein d’un bassin urbain.
From Bounded to Unbounded: Privacy Amplification via Shuffling with Dummies

In recent years, the shuffling model has been garnering attention in the realm of differential privacy (DP). This study focuses on the fact that the shuffling model follows bounded DP rather than unbounded DP. This characteristic causes a privacy issue in which participation itself is not protected. To address this issue, we propose a framework, called unbounded shuffling, which follows unbounded DP in addition to bounded DP under the trust assumption of the shuffling model. The main difference from the conventional shuffling model is the inclusion of dummies, which some users add to pose that perturbed records are sent by other users. We also analyze the privacy and utility of our proposed framework. The analysis shows that our framework achieves almost the same utility and privacy as that of the traditional shuffling model while guaranteeing unbounded DP. Additionally, we apply the technique of individual privacy accounting, which is built solely on unbounded DP, to stochastic gradient descent (SGD) using our framework. This approach approximately halves the value of $\varepsilon$ of a baseline.
Information technology infrastructure and earnings management strategy: evidence from a quasi-natural experiment

ABSTRACT Taking the ‘Broadband China’ programme as a quasi-natural experiment, we intend to explore the effect of information technology infrastructure (Hereinafter, ITI) on earnings management strategy. Our results show that with the office location city selected as the pilot city, the firm will strategically reduce the accrual-based earnings management and increase real earnings management. In addition, we find that ITI can reduce audit delay, decrease financial restatement, increase analyst coverage, improve analyst forecast accuracy, promote the media to release original news, and increase investor postings, which is consistent with the view that ITI can improve stakeholders’ monitoring efficiency. Cross-sectional analyses show that the impact of ITI on earnings management strategy is more pronounced for firms with low corporate governance efficiency. This study is helpful to further understand the externalities of ITI to firms’ behaviour.