A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI.

The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain's medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigation. This research focuses on automatic hippocampus segmentation from Magnetic Resonance (MR) images of a human head with high accuracy and fewer false positive and false negative rates.

Predicting Color Development and Texture Changes in Tomatoes Treated With Hot Water and Exposed to High-Temperature Ethylene Using Support Vector Regression.

This study investigated the impacts of hot water treatment (HWT) at 50°C or 25°C for 5 min and high-temperature ethylene (HTE) exposure at varying temperatures (20°C, 30°C, or 35°C) and durations (24, 48, or 72 h) on the postharvest quality and antioxidant properties of mature green tomatoes (MG). Color changes, physicochemical characteristics, antioxidant compounds, and overall antioxidant ability were assessed. HWT increased β-carotene levels and oxygen radical absorbance capacity (ORAC) while preserving color metrics, despite later HTE exposure.

Feasibility of long-range telesurgical robotic radical gastrectomy in a live porcine model.

Background: Telesurgery has been made increasingly possible with the advancements in robotic surgical platforms and network connectivity. However, long-distance transnational complex robotic surgeries such as gastrectomy have yet to be attempted.

Methods: Multiple transnational network connections by Science Innovation Network (SINET), Japan Gigabit Network (JGN), and Arterial Research and Education Network in Asia-Pacific (ARENA-PAC) were established and tested by multiple surgeons in a dry box model.

Radiation tolerance and biodegradation performance of a marine bacterium Acinetobacter sp. Y9 in radioactive composite oil-contaminated wastewater.

The tolerance and degradation characteristics of a marine oil-degrading strain Acinetobacter sp. Y9 were investigated in the presence of diesel oil and simulated radioactive nuclides (Mn, Co, Ni, Sr, Cs) at varying concentrations, as well as exposure to γ-ray radiation (Co-60). The maximum tolerable concentrations for Coand Ni were found to be 5 mg/l and 25 mg/l, respectively, while the tolerable concentrations for Mn, Sr, and Cs exceeded 400 mg/l, 1000 mg/l, and 1000 mg/l, respectively.

Comparative analysis of regression algorithms for drug response prediction using GDSC dataset.

Background: Drug response prediction can infer the relationship between an individual's genetic profile and a drug, which can be used to determine the choice of treatment for an individual patient. Prediction of drug response is recently being performed using machine learning technology. However, high-throughput sequencing data produces thousands of features per patient.

Mitochondrial dysfunction is a major cause of thromboinflammation and inflammatory cell death in critical illnesses.

Background: Mitochondria generate the adenosine triphosphate (ATP) necessary for eukaryotic cells, serving as their primary energy suppliers, and contribute to host defense by producing reactive oxygen species. In many critical illnesses, including sepsis, major trauma, and heatstroke, the vicious cycle between activated coagulation and inflammation results in tissue hypoxia-induced mitochondrial dysfunction, and impaired mitochondrial function contributes to thromboinflammation and cell death.

Methods: A computer-based online search was performed using the PubMed and Web of Science databases for published articles concerning sepsis, trauma, critical illnesses, cell death, mitochondria, inflammation, coagulopathy, and organ dysfunction.

Prevalence of processed foods in major US grocery stores.

The offering of grocery stores is a strong driver of consumer decisions. While highly processed foods such as packaged products, processed meat and sweetened soft drinks have been increasingly associated with unhealthy diets, information on the degree of processing characterizing an item in a store is not straightforward to obtain, limiting the ability of individuals to make informed choices. GroceryDB, a database with over 50,000 food items sold by Walmart, Target and Whole Foods, shows the degree of processing of food items and potential alternatives in the surrounding food environment.

Efficacy and safety of indocyanine green fluorescence navigation versus conventional laparoscopic hepatectomy for hepatocellular carcinoma: a systematic review and meta-analysis.

Background: Indocyanine green (ICG) fluorescence imaging technology is increasingly widely used in laparoscopic hepatectomy. However, previous studies have produced conflicting results regarding whether it is truly superior to traditional laparoscopic hepatectomy. This study investigated the clinical effect of laparoscopic hepatectomy for hepatocellular carcinoma (HCC) using ICG imaging technology.

Video-based robotic surgical action recognition and skills assessment on porcine models using deep learning.

Objectives: This study aimed to develop an automated skills assessment tool for surgical trainees using deep learning.

Background: Optimal surgical performance in robot-assisted surgery (RAS) is essential for ensuring good surgical outcomes. This requires effective training of new surgeons, which currently relies on supervision and skill assessment by experienced surgeons.

Could Empathy Be Taught? The Role of Advanced Technologies to Foster Empathy in Medical Students and Healthcare Professionals: A Systematic Review.

The physician-patient relationship relies mostly on doctors' empathetic abilities to understand and manage patients' emotions, enhancing patient satisfaction and treatment adherence. With the advent of digital technologies in education, innovative empathy training methods such as virtual reality, simulation training systems, mobile apps, and wearable devices, have emerged for teaching empathy. However, there is a gap in the literature regarding the efficacy of these technologies in teaching empathy, the most effective types, and the primary beneficiaries -students or advanced healthcare professionals-.

A combinatory approach of non-chain ring and henon map for image encryption application.

Algebraic structures play a vital role in securing important data. These structures are utilized to construct the non-linear components of block ciphers. Since constructing non-linear components through algebraic structures is crucial for the confusion aspects of encryption schemes, relying solely on these structures can result in limited key spaces.

Development of a model for detection and analysis of inclusions in tomographic images of iron castings using decision trees.

CT images of castings made of ductile iron were analyzed in the paper. On these images, objects can be identified that can be considered as graphite precipitates or indicate the presence of a defect in the casting. Research conducted in this area is described, based on experimental data that allows to determine whether the indicated components present in the casting are graphite precipitation.

Basal forebrain innervation of the amygdala: an anatomical and computational exploration.

Theta oscillations of the mammalian amygdala are associated with processing, encoding and retrieval of aversive memories. In the hippocampus, the power of the network theta oscillation is modulated by basal forebrain (BF) GABAergic projections. Here, we combine anatomical and computational approaches to investigate if similar BF projections to the amygdaloid complex provide an analogous modulation of local network activity.

Hierarchical graph-based integration network for propaganda detection in textual news articles on social media.

During the Covid-19 pandemic, the widespread use of social media platforms has facilitated the dissemination of information, fake news, and propaganda, serving as a vital source of self-reported symptoms related to Covid-19. Existing graph-based models, such as Graph Neural Networks (GNNs), have achieved notable success in Natural Language Processing (NLP). However, utilizing GNN-based models for propaganda detection remains challenging because of the challenges related to mining distinct word interactions and storing nonconsecutive and broad contextual data.

Assessing the impact of energy coaching with smart technology interventions to alleviate energy poverty.

Energy poverty affects 550,000 homes in the Netherlands yet policy interventions to alleviate this issue are rare. Therefore, we test two energy coaching interventions in Amsterdam: a static information group (n = 67) which received energy efficient products and one energy-use report, and a smart information group (n = 50), which also had a display providing real-time feedback on energy-use. Results across both groups, show a 75% success rate for alleviating energy poverty.

Investigating heavy quarkonia binding in an anisotropic-dense quark-gluon plasma with topological defects in the framework of fractional non-relativistic quark model.

The quark-gluon plasma analysis relies on the heavy quark potential, which is influenced by the anisotropic plasma parameter temperature (t), and baryonic chemical potential (μ). Employing the generalized fractional derivative Nikiforov-Uvarov (GFD-NU) method, we solved the topologically-fractional Schrödinger equation. Two scenarios were explored: the classical model (α = β = 1) and the fractional model (α, β < 1).

Probabilistic nested model selection in pharmacokinetic analysis of DCE-MRI data in animal model of cerebral tumor.

Best current practice in the analysis of dynamic contrast enhanced (DCE)-MRI is to employ a voxel-by-voxel model selection from a hierarchy of nested models. This nested model selection (NMS) assumes that the observed time-trace of contrast-agent (CA) concentration within a voxel, corresponds to a singular physiologically nested model. However, admixtures of different models may exist within a voxel's CA time-trace.

All-angle unidirectional flat-band acoustic metasurfaces.

Flat bands have empowered novel phenomena such as robust canalization with strong localization, high-collimation and low-loss propagation. However, the spatial symmetry protection in photonic or acoustic lattices naturally forces flat bands to manifest in pairs aligned at an inherently specific angle, resulting in a fixed bidirectional canalization. Here, we report an acoustic flat-band metasurface, allowing not only unidirectional canalization at all in-plane angles but also robust tunability in band alignment.

Evaluation of an AI-Based Voice Biomarker Tool to Detect Signals Consistent With Moderate to Severe Depression.

Purpose: Mental health screening is recommended by the US Preventive Services Task Force for all patients in areas where treatment options are available. Still, it is estimated that only 4% of primary care patients are screened for depression. The goal of this study was to evaluate the efficacy of machine learning technology (Kintsugi Voice, v1, Kintsugi Mindful Wellness, Inc) to detect and analyze voice biomarkers consistent with moderate to severe depression, potentially allowing for greater compliance with this critical primary care public health need.

Changes and persistence in heart rate variability before and during social stress: A comparison of individuals with and without social anxiety disorder.

Individuals with social anxiety disorder (SAD) experience significant and persistent fear of social situations as they anticipate rejection, scrutiny, and embarrassment. Given that physiological reactions to social situations may shape emotional experience in SAD, understanding psychophysiological changes operating in SAD may be important to address this potentially key perpetuating factor. This study compared the patterns of change (via contrasts of estimated marginal means) and persistence (via autoregressive models) of two indices of heart rate variability (HRV; Root Mean Square of Successive Differences between normal heartbeats, and High-Frequency absolute units) as physiological measures of emotion regulation, between individuals with SAD (n = 94) and without (n = 59) using the Trier Social Stress Test phases (TSST).

Critical behavioral traits foster peer engagement in Online Mental Health Communities.

Online Mental Health Communities (OMHCs), such as Reddit, have witnessed a surge in popularity as go-to platforms for seeking information and support in managing mental health needs. Platforms like Reddit offer immediate interactions with peers, granting users a vital space for seeking mental health assistance. However, the largely unregulated nature of these platforms introduces intricate challenges for both users and society at large.

Rural Roads to cognitive Resilience (RRR): A prospective cohort study protocol.

Background: Ambient air pollution, detrimental built and social environments, social isolation (SI), low socioeconomic status (SES), and rural (versus urban) residence have been associated with cognitive decline and risk of Alzheimer's disease and related dementias (ADRD). Research is needed to investigate the influence of ambient air pollution and built and social environments on SI and cognitive decline among rural, disadvantaged, ethnic minority communities. To address this gap, this cohort study will recruit an ethnoracially diverse, rural Florida sample in geographic proximity to seasonal agricultural burning.

Enhancing covert communication in NOMA systems with joint security and covert design.

The explosion of Internet-of-Thing enables several interconnected devices but also gives rise chance for unauthorized parties to compromise sensitive information through wireless communication systems. Covert communication therefore has emerged as a potential candidate for ensuring data privacy in conjunction with physical layer transmission to render two lines of defense. In this paper, we aim to enhance the individual transmission of nearby users in non-orthogonal multiple access (NOMA) systems under scenarios of an eavesdropper who monitors covert transmission before decoding covert information.

Drug-Drug interactions prediction calculations between cardiovascular drugs and antidepressants for discovering the potential co-medication risks.

Predicting Drug-Drug Interactions (DDIs) enables cost reduction and time savings in the drug discovery process, while effectively screening and optimizing drugs. The intensification of societal aging and the increase in life stress have led to a growing number of patients suffering from both heart disease and depression. These patients often need to use cardiovascular drugs and antidepressants for polypharmacy, but potential DDIs may compromise treatment effectiveness and patient safety.

Development and effectiveness analysis of a safety management model for SMEs integrating lean management innovation with SQCDP framework.

This study organically integrates the safety, quality, cost, delivery, and people (SQCDP) management mode and lean management to create the SQCDP + lean safety management (SLSM) mode, which addresses certain problems faced by China's small- and medium-sized enterprises (SMEs), such as imperfect safety management systems, poor regulatory implementation, challenging problem correction, and perfunctory management. It then explains the benefits proposed mode for SMEs and the establishment of a novel mode that aligns with the current safety production and operation management of SMEs. The application of the SLSM mode to a private machinery manufacturing company in Wenzhou resulted in the effective shouldering of safety responsibilities by the company, a year-on-year decrease in accident rates, and a significant increase in production efficiency, thereby providing corporate managers with guidance and suggestions for making improvements.