A stacked CNN and random forest ensemble architecture for complex nursing activity recognition and nurse identification.

Nursing activity recognition has immense importance in the development of smart healthcare management and is an extremely challenging area of research in human activity recognition. The main reasons are an extreme class-imbalance problem and intra-class variability depending on both the subject and the recipient. In this paper, we apply a unique two-step feature extraction, coupled with an intermediate feature 'Angle' and a new feature called mean min max sum to render the features robust against intra-class variation.

Plant Species Richness and the Root Economics Space Drive Soil Fungal Communities.

Trait-based approaches have been increasingly used to relate plants to soil microbial communities. Using the recently described root economics space as an approach to explain the structure of soil-borne fungal communities, our study in a grassland diversity experiment reveals distinct root trait strategies at the plant community level. In addition to significant effects of plant species richness, we show that the collaboration and conservation gradient are strong drivers of the composition of the different guilds of soil fungi.

High incidence of anterior cruciate ligament injuries in professional men's handball: Analysis of the 'ACL registry in German Sports' over seven consecutive seasons.

Purpose: The purpose of this study was to generate knowledge about the epidemiology and risk factors for anterior cruciate ligament (ACL) injuries in handball. It was hypothesised that the incidence of ACL injuries is high and comparable to other pivoting sports like football.

Methods: This study is based on the prospective 'ACL registry in German Sports' implemented in the 2016-2017 season.

Accountability and objectivity: Humanitarian narratives at the intersection of evidence and localisation.

In the last decade, there has been a push for greater evidence-based practice within the humanitarian sector, alongside an increasing turn towards localising humanitarian assistance. Humanitarian actors and organisations have been increasing their production and use of evidence, while also being encouraged to reflect more critically on power hierarchies and decolonise humanitarian aid. This paper explores the intersection of these two narratives, examining how the use of evidence in humanitarian decision-making fits within a localisation agenda.

Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound.

We propose in this paper a texture-invariant 2D keypoints descriptor specifically designed for matching preoperative Magnetic Resonance (MR) images with intraoperative Ultrasound (US) images. We introduce a strategy, where intraoperative US images are synthesized from MR images accounting for multiple MR modalities and intraoperative US variability. We build our training set by enforcing keypoints localization over all images then train a patient-specific descriptor network that learns texture-invariant discriminant features in a supervised contrastive manner, leading to robust keypoints descriptors.

Distinct regulation of Tau Monomer and aggregate uptake and intracellular accumulation in human neurons.

Background: The prion-like spreading of Tau pathology is the leading cause of disease progression in various tauopathies. A critical step in propagating pathologic Tau in the brain is the transport from the extracellular environment and accumulation inside naïve neurons. Current research indicates that human neurons internalize both the physiological extracellular Tau (eTau) monomers and the pathological eTau aggregates.

Everyday life and boredom of people living with dementia in residential long-term care: a merged methods study.

Background: Everyday life in residential long-term care is widely portrayed as boring. However, empirical evidence on this topic remains limited, particularly for the vulnerable group of people living with dementia. A better understanding of everyday life and the associated experiences of boredom could facilitate the development of practical strategies to reduce boredom in this target group.

The MuCAYA Study-Influence of Physical Activity and Metabolic Parameters on the Structure and Function of the Cardiovascular System in Young Athletes.

Exercise has a significant impact on the cardiovascular (CV) health of children and adolescents, with resultant alterations in CV structure and function being evident, even at an early age. Engagement in regular, moderate physical activity (PA) is associated with long-term CV health benefits and a reduced risk of CV disease and mortality later in life. However, competitive sports often involve PA training intensities that are beyond recommended levels for young athletes, potentially leading to adverse CV outcomes.

Wastewater-based epidemiology: deriving a SARS-CoV-2 data validation method to assess data quality and to improve trend recognition.

Introduction: Accurate and consistent data play a critical role in enabling health officials to make informed decisions regarding emerging trends in SARS-CoV-2 infections. Alongside traditional indicators such as the 7-day-incidence rate, wastewater-based epidemiology can provide valuable insights into SARS-CoV-2 concentration changes. However, the wastewater compositions and wastewater systems are rather complex.

Increasing hub disruption parallels dementia severity in autosomal dominant Alzheimer's disease.

Hub regions in the brain, recognized for their roles in ensuring efficient information transfer, are vulnerable to pathological alterations in neurodegenerative conditions, including Alzheimer's disease (AD). Computational simulations and animal experiments have hinted at the theory of activity-dependent degeneration as the cause of this hub vulnerability. However, two critical issues remain unresolved.

Combination of Coevolutionary Information and Supervised Learning Enables Generation of Cyclic Peptide Inhibitors with Enhanced Potency from a Small Data Set.

Computational generation of cyclic peptide inhibitors using machine learning models requires large size training data sets often difficult to generate experimentally. Here we demonstrated that sequential combination of Random Forest Regression with the pseudolikelihood maximization Direct Coupling Analysis method and Monte Carlo simulation can effectively enhance the design pipeline of cyclic peptide inhibitors of a tumor-associated protease even for small experimental data sets. Further studies showed that such -evolved cyclic peptides are more potent than the best peptide inhibitors previously developed to this target.

Long-range data transmission in a fault-tolerant quantum bus architecture.

We propose a fault-tolerant scheme for generating long-range entanglement at the ends of a rectangular array of qubits of length with a square cross-section of qubits. It is realized by a constant-depth circuit producing a constant-fidelity Bell-pair (independent of ) for local stochastic noise of strength below an experimentally realistic threshold. The scheme can be viewed as a quantum bus in a quantum computing architecture where qubits are arranged on a rectangular 3D grid, and all operations are between neighboring qubits.

- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

Environmental and social correlates of the plumage color polymorphism in an urban dweller, feral pigeon (Columba livia f. domestica).

We examined how urban environments affect the abundance, proportion, and diversity of plumage color morphs in feral pigeons. Five major plumage color morphs (black, blue, white, red, and mixed) were counted in sixty 25-ha plots in Poznań City (Poland). Generalized additive models were used to study the correlations among abundance, proportion of morphs, and environmental factors.

Objective and Automated Facial Palsy Grading and Outcome Assessment After Facial Palsy Reanimation Surgery - A Prospective Observational Study.

Background: Facial palsy (FP) is a widespread condition affecting over 3 million people annually, with a complex etiology requiring tailored, multidisciplinary management. Despite advancements, there remains a lack of reliable, automated tools for objective pre- and postoperative assessment, limiting progress in treatment optimization. This study introduces the AI Research Metrics Model (CAARISMA ® ARMM) to evaluate FP severity and outcomes following microsurgical gracilis muscle transfer.

Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research.

Psychiatric disorders are highly comorbid, heritable, and genetically correlated [1-4]. The primary objective of cross-disorder psychiatric genetics research is to identify and characterize both the shared genetic factors that contribute to convergent disease etiologies and the unique genetic factors that distinguish between disorders [4, 5]. This information can illuminate the biological mechanisms underlying comorbid presentations of psychopathology, improve nosology and prediction of illness risk and trajectories, and aid the development of more effective and targeted interventions.

Hyperreactive B cells instruct their elimination by T cells to curb autoinflammation and lymphomagenesis.

B cell immunity carries the inherent risk of deviating into autoimmunity and malignancy, which are both strongly associated with genetic variants or alterations that increase immune signaling. Here, we investigated the interplay of autoimmunity and lymphoma risk factors centered around the archetypal negative immune regulator TNFAIP3/A20 in mice. Counterintuitively, B cells with moderately elevated sensitivity to stimulation caused fatal autoimmune pathology, while those with high sensitivity did not.

Chemically defined and growth factor-free system for highly efficient endoderm induction of human pluripotent stem cells.

Definitive endoderm (DE) derived from human pluripotent stem cells (hPSCs) holds great promise for cell-based therapies and drug discovery. However, current DE differentiation methods required undefined components and/or expensive recombinant proteins, limiting their scalable manufacture and clinical use. Homogeneous DE differentiation in defined and recombinant protein-free conditions remains a major challenge.

On the transition between autonomous and nonautonomous systems: The case of FitzHugh-Nagumo's model.

This work deals with a parametric linear interpolation between an autonomous FitzHugh-Nagumo model and a nonautonomous skewed problem with the same fundamental structure. This paradigmatic example allows us to construct a family of nonautonomous dynamical systems with an attracting integral manifold and a hyperbolic repelling trajectory located within the nonautonomous set enclosed by the integral manifold. Upon the variation of the parameter the integral manifold collapses, the hyperbolic repelling solution disappears and a unique globally attracting hyperbolic solution arises in what could be considered yet another pattern of nonautonomous Hopf bifurcation.

The compact line-focus X-ray tube for microbeam radiation therapy - Focal spot characterisation and collimator design.

Purpose: Microbeam radiation therapy (MRT) has shown superior healthy tissue sparing at equal tumour control probabilities compared to conventional radiation therapy in many preclinical studies. The limitation to preclinical research arises from a lack of suitable radiation sources for clinical application of MRT due to high demands on beam quality. To overcome these limitations, we developed and built the first prototype of a line-focus X-ray tube (LFXT).

Simulated low-dose dark-field radiography for detection of COVID-19 pneumonia.

Background: Dark-field radiography has been proven to be a promising tool for the assessment of various lung diseases.

Purpose: To evaluate the potential of dose reduction in dark-field chest radiography for the detection of the Coronavirus SARS-CoV-2 (COVID-19) pneumonia.

Materials And Methods: Patients aged at least 18 years with a medically indicated chest computed tomography scan (CT scan) were screened for participation in a prospective study between October 2018 and December 2020.

New Label-Free DNA Nanosensor Based on Top-Gated Metal-Ferroelectric-Metal Graphene Nanoribbon on Insulator Field-Effect Transistor: A Quantum Simulation Study.

In this paper, a new label-free DNA nanosensor based on a top-gated (TG) metal-ferroelectric-metal (MFM) graphene nanoribbon field-effect transistor (TG-MFM GNRFET) is proposed through a simulation approach. The DNA sensing principle is founded on the dielectric modulation concept. The computational method employed to evaluate the proposed nanobiosensor relies on the coupled solutions of a rigorous quantum simulation with the Landau-Khalatnikov equation, considering ballistic transport conditions.

Accuracy and Reliability of Grip Strength Measurements: A Comparative Device Analysis.

Grip strength is widely used as a surrogate parameter for functional status. The current gold standard, the JAMAR Hydraulic Dynamometer (JAMAR Hydraulic), presents challenges for individuals with painful finger joints or low grip strength. Therefore, the objective of this observational study was to assess comparability across the JAMAR Smart, the Martin Vigorimeter and the gold standard.

Corrigendum: TUM4Health, a holistic student health promotion program. Screening of cardiovascular risk factors in university students.

[This corrects the article DOI: 10.3389/fcvm.2024.

Identification of key factors for malnutrition diagnosis in chronic gastrointestinal diseases using machine learning underscores the importance of GLIM criteria as well as additional parameters.

Introduction: Disease-related malnutrition is common but often underdiagnosed in patients with chronic gastrointestinal diseases, such as liver cirrhosis, short bowel and intestinal insufficiency, and chronic pancreatitis. To improve malnutrition diagnosis in these patients, an evaluation of the current Global Leadership Initiative on Malnutrition (GLIM) diagnostic criteria, and possibly the implementation of additional criteria, is needed.

Aim: This study aimed to identify previously unknown and potentially specific features of malnutrition in patients with different chronic gastrointestinal diseases and to validate the relevance of the GLIM criteria for clinical practice using machine learning (ML).