Predicting lower body joint moments and electromyography signals using ground reaction forces during walking and running: An artificial neural network approach.

Background: This study leverages Artificial Neural Networks (ANNs) to predict lower limb joint moments and electromyography (EMG) signals from Ground Reaction Forces (GRF), providing a novel perspective on human gait analysis. This approach aims to enhance the accessibility and affordability of biomechanical assessments using GRF data, thus eliminating the need for costly motion capture systems.

Research Question: Can ANNs use GRF data to accurately predict joint moments in the lower limbs and EMG signals?

Methods: We employed ANNs to analyze GRF data and to use them to predict joint moments (363-trials; 4-datasets) and EMG signals (63-trials; 2-datasets).

Murine Models and Human Cell Line Models to Study Altered Dynamics of Ovarian Follicles in Polycystic Ovary Syndrome.

Polycystic ovary syndrome is one of the most common endocrine disorders in women of reproductive age, characterized by functional and structural alterations of the female reproductive organs. Due to the unknown underlying molecular mechanisms, in vivo murine models and in vitro human cellular models are developed to study the syndrome. These models are used to analyze various aspects of the pathology by replicating the conditions of the syndrome.

Ground Reaction Forces and Joint Moments Predict Metabolic Cost in Physical Performance: Harnessing the Power of Artificial Neural Networks.

Understanding metabolic cost through biomechanical data, including ground reaction forces (GRFs) and joint moments, is vital for health, sports, and rehabilitation. The long stabilization time (2-5 min) of indirect calorimetry poses challenges in prolonged tests. This study investigated using artificial neural networks (ANNs) to predict metabolic costs from the GRF and joint moment time series.

A mutant ASXL1-EHMT complex contributes to heterochromatin dysfunction in clonal hematopoiesis and chronic monomyelocytic leukemia.

Unlabelled: is one of the three most frequently mutated genes in age-related clonal hematopoiesis (CH), alongside and . CH can progress to myeloid malignancies including chronic monomyelocytic leukemia (CMML), and is also strongly associated with inflammatory cardiovascular disease and all-cause mortality in humans. DNMT3A and TET2 regulate DNA methylation and demethylation pathways respectively, and loss-of-function mutations in these genes reduce DNA methylation in heterochromatin, allowing de-repression of silenced elements in heterochromatin.

Uncertainty estimation in diagnosis generation from large language models: next-word probability is not pre-test probability.

Objective: To evaluate large language models (LLMs) for pre-test diagnostic probability estimation and compare their uncertainty estimation performance with a traditional machine learning classifier.

Materials And Methods: We assessed 2 instruction-tuned LLMs, Mistral-7B-Instruct and Llama3-70B-chat-hf, on predicting binary outcomes for Sepsis, Arrhythmia, and Congestive Heart Failure (CHF) using electronic health record (EHR) data from 660 patients. Three uncertainty estimation methods-Verbalized Confidence, Token Logits, and LLM Embedding+XGB-were compared against an eXtreme Gradient Boosting (XGB) classifier trained on raw EHR data.