The Effect of Protective Mat Thickness on the Upper Limb Strike Force Simulation in Combat Sports and Self Defense.

The strike force in combat sports strongly depends on the protective material's mechanical properties and energy absorption capacity. Therefore, this study aimed to estimate the effect of the protective layer thickness and repeated loading on the net force in a falling weight impact test. A falling 8-kg weight dropped from 15 cm, 25 cm, and 50 cm was used to simulate impact peak forces in an upper limb strike.

Boosting predictive models and augmenting patient data with relevant genomic and pathway information.

The recurrence of low-stage lung cancer poses a challenge due to its unpredictable nature and diverse patient responses to treatments. Personalized care and patient outcomes heavily rely on early relapse identification, yet current predictive models, despite their potential, lack comprehensive genetic data. This inadequacy fuels our research focus-integrating specific genetic information, such as pathway scores, into clinical data.

Synergy between imputed genetic pathway and clinical information for predicting recurrence in early stage non-small cell lung cancer.

Objective: Lung cancer exhibits unpredictable recurrence in low-stage tumors and variable responses to different therapeutic interventions. Predicting relapse in early-stage lung cancer can facilitate precision medicine and improve patient survivability. While existing machine learning models rely on clinical data, incorporating genomic information could enhance their efficiency.

Extraction, Labeling, Clustering, and Semantic Mapping of Segments From Clinical Notes.

This work is motivated by the scarcity of tools for accurate, unsupervised information extraction from unstructured clinical notes in computationally underrepresented languages, such as Czech. We introduce a stepping stone to a broad array of downstream tasks such as summarisation or integration of individual patient records, extraction of structured information for national cancer registry reporting or building of semi-structured semantic patient representations that can be used for computing patient embeddings. More specifically, we present a method for unsupervised extraction of semantically-labeled textual segments from clinical notes and test it out on a dataset of Czech breast cancer patients, provided by Masaryk Memorial Cancer Institute (the largest Czech hospital specialising exclusively in oncology).

Performance Level and Strike Type during Ground and Pound Determine Impact Characteristics and Net Force Variability.

The evaluation of strike impact is important for optimal training, conditioning and tactical use. Therefore, the aim of this study was to evaluate ground and pound strikes, in terms of net force variability, across genders and performance levels. Eighty-one participants, professional men ( = 8, 37 ± 6 years, 195 ± 7 cm, 113 ± 27 kg), advanced men ( = 47, 26 ± 8 years, 180 ± 7 cm, 76 ± 11 kg), and advanced women ( = 26, 21 ± 1 years, 167 ± 6 cm, 61 ± 7 kg) performed three strikes from a kneeling position into a force plate on the ground.

Boundary heat diffusion classifier for a semi-supervised learning in a multilayer network embedding.

The scarcity of high-quality annotations in many application scenarios has recently led to an increasing interest in devising learning techniques that combine unlabeled data with labeled data in a network. In this work, we focus on the label propagation problem in multilayer networks. Our approach is inspired by the heat diffusion model, which shows usefulness in machine learning problems such as classification and dimensionality reduction.

Analysis of the Contact Area for Three Types of Upper Limb Strikes.

Performance in strike combat sports is mostly evaluated through the values of the net force, acceleration, or speed to improve efficient training procedures and/or to assess the injury. There are limited data on the upper limb striking area, which can be a useful variable for contact pressure assessment. Therefore, the aim of this study was to determine the contact area of the upper limb in three different strike technique positions.

On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer.

Early detection and mitigation of disease recurrence in non-small cell lung cancer (NSCLC) patients is a nontrivial problem that is typically addressed either by rather generic follow-up screening guidelines, self-reporting, simple nomograms, or by models that predict relapse risk in individual patients using statistical analysis of retrospective data. We posit that machine learning models trained on patient data can provide an alternative approach that allows for more efficient development of many complementary models at once, superior accuracy, less dependency on the data collection protocols and increased support for explainability of the predictions. In this preliminary study, we describe an experimental suite of various machine learning models applied on a patient cohort of 2442 early stage NSCLC patients.

Mechanical characterization and modeling of knitted textile implants with permanent set.

Textile-based implant (mesh) treatment is considered as a standard of care for abdominal wall hernia repair. Computational models and simulations have appeared as one of the most promising approach to investigate biomechanics related to hernia repair and to improve clinical outcomes. This paper presents a novel anisotropic hypo-elastoplastic constitutive model specifically established for surgical knitted textile implants.

Accurate prediction of kinase-substrate networks using knowledge graphs.

Phosphorylation of specific substrates by protein kinases is a key control mechanism for vital cell-fate decisions and other cellular processes. However, discovering specific kinase-substrate relationships is time-consuming and often rather serendipitous. Computational predictions alleviate these challenges, but the current approaches suffer from limitations like restricted kinome coverage and inaccuracy.

Upper Limb Strikes Reactive Forces in Mix Martial Art Athletes during Ground and Pound Tactics.

Athletes of mixed martial arts use a ground and pound strategy with the strikes in the dominant ground position. The aim of this study was to compare the average peak force (F) among three punches and to estimate the probability of achieving a skull bone fracture force of 5.1 kN for each type of strike in male and female athletes.

Predicting Polypharmacy Side-effects Using Knowledge Graph Embeddings.

Polypharmacy is the use of drug combinations and is commonly used for treating complex and terminal diseases. Despite its effectiveness in many cases, it poses high risks of adverse side effects. Polypharmacy side-effects occur due to unwanted interactions of combined drugs, and they can cause severe complications to patients which results in increasing the risks of morbidity and leading to new mortalities.

Biological applications of knowledge graph embedding models.

Complex biological systems are traditionally modelled as graphs of interconnected biological entities. These graphs, i.e.

Discovering protein drug targets using knowledge graph embeddings.

Motivation: Computational approaches for predicting drug-target interactions (DTIs) can provide valuable insights into the drug mechanism of action. DTI predictions can help to quickly identify new promising (on-target) or unintended (off-target) effects of drugs. However, existing models face several challenges.

Characterization of the anisotropic mechanical behavior of human abdominal wall connective tissues.

Abdominal wall sheathing tissues are commonly involved in hernia formation. However, there is very limited work studying mechanics of all tissues from the same donor which prevents a complete understanding of the abdominal wall behavior and the differences in these tissues. The aim of this study was to investigate the differences between the mechanical properties of the linea alba and the anterior and posterior rectus sheaths from a macroscopic point of view.

Facilitating prediction of adverse drug reactions by using knowledge graphs and multi-label learning models.

Timely identification of adverse drug reactions (ADRs) is highly important in the domains of public health and pharmacology. Early discovery of potential ADRs can limit their effect on patient lives and also make drug development pipelines more robust and efficient. Reliable in silico prediction of ADRs can be helpful in this context, and thus, it has been intensely studied.

Using Drug Similarities for Discovery of Possible Adverse Reactions.

We propose a new computational method for discovery of possible adverse drug reactions. The method consists of two key steps. First we use openly available resources to semi-automatically compile a consolidated data set describing drugs and their features (e.

Material model calibration from planar tension tests on porcine linea alba.

The purpose of this study was to determine biomechanical properties of linea alba subjected to transverse planar tension and to compare its behavior at different locations of the abdominal wall. Samples of linea alba from five different porcine abdominal walls were tested in planar tension. During these tests, strain maps were measured for the first time ever using the stereo-digital image correlation (S-DIC) technique.

SKIMMR: facilitating knowledge discovery in life sciences by machine-aided skim reading.

Background. Unlike full reading, 'skim-reading' involves the process of looking quickly over information in an attempt to cover more material whilst still being able to retain a superficial view of the underlying content. Within this work, we specifically emulate this natural human activity by providing a dynamic graph-based view of entities automatically extracted from text.

Rheological properties of myometrium: experimental quantification and mathematical modeling.

This work answers some questions related to detection of rheological properties of soft tissues exemplified in myometrium, stressed by external tensile force. In the first stage of the experiment the tissue samples were ciclically stressed and response loops were recorded. This test proved severe plastical deformation of samples, which is not usually being stated for living tissues.

Infrastructure for dynamic knowledge integration--automated biomedical ontology extension using textual resources.

We present a novel ontology integration technique that explicitly takes the dynamics and data-intensiveness of e-health and biomedicine application domains into account. Changing and growing knowledge, possibly contained in unstructured natural language resources, is handled by application of cutting-edge Semantic Web technologies. In particular, semi-automatic integration of ontology learning results into a manually developed ontology is employed.