Electrochemical Sensors and Biosensors for Identification of Viruses: A Critical Review.

Due to their life cycle, viruses can disrupt the metabolism of their hosts, causing diseases. If we want to disrupt their life cycle, it is necessary to identify their presence. For this purpose, it is possible to use several molecular-biological and bioanalytical methods.

Exploring bacterial diversity and antimicrobial resistance gene on a southern Brazilian swine farm.

The bacterial composition of and the circulation of antimicrobial resistance genes (ARGs) in waste from Brazilian swine farms are still poorly understood. Considering that antimicrobial resistance (AMR) is one of the main threats to human, animal, and environmental health, the need to accurately assess the load of ARGs released into the environment is urgent. Therefore, this study aimed to characterize the microbiota in a swine farm in southern Brazil and the resistome in swine farm wastewater treated in a series of waste stabilization ponds (WSPs).

Enhancing reclaimed water distribution network resilience with cost-effective meshing.

Water Distribution Networks (WDNs) are critical infrastructures that ensure a continuous supply of safe water to homes. In the face of challenges, like water scarcity, establishing resilient networks is imperative, especially in regions vulnerable to water crises. This study evaluates the resilience of network designs through graph theory, including its hydraulic feasibility using EPANET software, an aspect often overlooked.

Temporal dynamics of coordinated online behavior: Stability, archetypes, and influence.

Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of . State-of-the-art methods for detecting coordinated behavior perform analyses, disregarding the temporal dynamics of coordination. Here, we carry out a analysis of coordinated behavior.

Deep learning-based method for sentiment analysis for patients' drug reviews.

This article explores the application of deep learning techniques for sentiment analysis of patients' drug reviews. The main focus is to evaluate the effectiveness of bidirectional long-short-term memory (LSTM) and a hybrid model (bidirectional LSTM-CNN) for sentiment classification based on the entire review text, medical conditions, and rating scores. This study also investigates the impact of using GloVe word embeddings on the model's performance.

Sodium propionate oral supplementation ameliorates depressive-like behavior through gut microbiome and histone 3 epigenetic regulation.

Major depressive disorder (MDD) is a global health concern, affecting over 250 million individuals worldwide. In recent years, the gut-brain axis has emerged as a promising field for understanding the pathophysiology of MDD. Microbial metabolites, such as short-chain fatty acids (SCFAs)-acetate, butyrate, and propionate-, have gained attention for their potential to influence epigenetic modifications within the host brain.

Critical assessment of variant prioritization methods for rare disease diagnosis within the rare genomes project.

Background: A major obstacle faced by families with rare diseases is obtaining a genetic diagnosis. The average "diagnostic odyssey" lasts over five years and causal variants are identified in under 50%, even when capturing variants genome-wide. To aid in the interpretation and prioritization of the vast number of variants detected, computational methods are proliferating.

Gross morphology and adhesion-associated physical properties of Drosophila larval salivary gland glue secretion.

One of the major functions of the larval salivary glands (SGs) of many Drosophila species is to produce a massive secretion during puparium formation. This so-called proteinaceous glue is exocytosed into the centrally located lumen, and subsequently expectorated, serving as an adhesive to attach the puparial case to a solid substrate during metamorphosis. Although this was first described almost 70 years ago, a detailed description of the morphology and mechanical properties of the glue is largely missing.

Call to Action: Investigating Interaction Delay in Smartphone Notifications.

Notifications are an essential part of the user experience on smart mobile devices. While some apps have to notify users immediately after an event occurs, others can schedule notifications strategically to notify them only on opportune moments. This tailoring allows apps to shorten the users' interaction delay.

The Biodistribution of Replication-Defective Simian Adenovirus 1 Vector in a Mouse Model.

The administration route affects the biodistribution of a gene transfer vector and the expression of a transgene. A simian adenovirus 1 vector carrying firefly luciferase and GFP reporter genes (SAdV1-GFluc) were constructed, and its biodistribution was investigated in a mouse model by bioluminescence imaging and virus DNA tracking with real-time PCR. Luciferase activity and virus DNA were mainly found in the liver and spleen after the intravenous administration of SAdV1-GFluc.

Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [Rb] PET for MACE prediction.

Assessing the individual risk of Major Adverse Cardiac Events (MACE) is of major importance as cardiovascular diseases remain the leading cause of death worldwide. Quantitative Myocardial Perfusion Imaging (MPI) parameters such as stress Myocardial Blood Flow (sMBF) or Myocardial Flow Reserve (MFR) constitutes the gold standard for prognosis assessment. We propose a systematic investigation of the value of Artificial Intelligence (AI) to leverage [ Rb] Silicon PhotoMultiplier (SiPM) PET MPI for MACE prediction.

Reducing Automotive Cooling System Complexity through an Adaptive Biomimetic Air Control Valve.

Future automotive mobility is predominantly electric. Compared to existing systems, the requirements of subsystems change. Air flow for cooling components is needed predominantly when the car is in rest (i.

Method for cycle detection in sparse, irregularly sampled, long-term neuro-behavioral timeseries: Basis pursuit denoising with polynomial detrending of long-term, inter-ictal epileptiform activity.

Numerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries.

A study on formalizing the knowledge of data curation activities across different fields.

In recent years, with the trend of open science, there have been many efforts to share research data on the internet. To promote research data sharing, data curation is essential to make the data interpretable and reusable. In research fields such as life sciences, earth sciences, and social sciences, tasks and procedures have been already developed to implement efficient data curation to meet the needs and customs of individual research fields.

Towards a GDPR-compliant cloud architecture with data privacy controlled through sticky policies.

Data privacy is one of the biggest challenges facing system architects at the system design stage. Especially when certain laws, such as the General Data Protection Regulation (GDPR), have to be complied with by cloud environments. In this article, we want to help cloud providers comply with the GDPR by proposing a GDPR-compliant cloud architecture.

RegWSI: Whole slide image registration using combined deep feature- and intensity-based methods: Winner of the ACROBAT 2023 challenge.

Background And Objective: The automatic registration of differently stained whole slide images (WSIs) is crucial for improving diagnosis and prognosis by fusing complementary information emerging from different visible structures. It is also useful to quickly transfer annotations between consecutive or restained slides, thus significantly reducing the annotation time and associated costs. Nevertheless, the slide preparation is different for each stain and the tissue undergoes complex and large deformations.

Overview and Discussion of the Competition on Legal Information, Extraction/Entailment (COLIEE) 2023.

We summarize the 10th Competition on Legal Information Extraction and Entailment. In this tenth edition, the competition included four tasks on case law and statute law. The case law component includes an information retrieval task (Task 1), and the confirmation of an entailment relation between an existing case and a selected unseen case (Task 2).

Revisiting instrument segmentation: Learning from decentralized surgical sequences with various imperfect annotations.

This paper focuses on a new and challenging problem related to instrument segmentation. This paper aims to learn a generalizable model from distributed datasets with various imperfect annotations. Collecting a large-scale dataset for centralized learning is usually impeded due to data silos and privacy issues.

Towards better laparoscopic video segmentation: A class-wise contrastive learning approach with multi-scale feature extraction.

The task of segmentation is integral to computer-aided surgery systems. Given the privacy concerns associated with medical data, collecting a large amount of annotated data for training is challenging. Unsupervised learning techniques, such as contrastive learning, have shown powerful capabilities in learning image-level representations from unlabelled data.

Shock index and its variants as predictors of mortality in severe traumatic brain injury.

Background: The increase in severe traumatic brain injury (sTBI) incidence is a worldwide phenomenon, resulting in a heavy disease burden in the public health systems, specifically in emerging countries. The shock index (SI) is a physiological parameter that indicates cardiovascular status and has been used as a tool to assess the presence and severity of shock, which is increased in sTBI. Considering the high mortality of sTBI, scrutinizing the predictive potential of SI and its variants is vital.

Transcriptomic analysis of HEK293A cells with a CRISPR/Cas9-mediated TDP1 knockout.

Tyrosyl-DNA phosphodiesterase 1 (TDP1) is a human DNA repair protein. It is a member of the phospholipase D family based on structural similarity. TDP1 is a key enzyme of the repair of stalled topoisomerase 1 (TOP1)-DNA complexes.

Reverse Engineering of Radical Polymerizations by Multi-Objective Optimization.

Reverse engineering is applied to identify optimum polymerization conditions for the synthesis of polymers with pre-defined properties. The proposed approach uses multi-objective optimization (MOO) and provides multiple candidate polymerization procedures to achieve the targeted polymer property. The objectives for optimization include the maximal similarity of molar mass distributions (MMDs) compared to the target MMDs, a minimal reaction time, and maximal monomer conversion.