Rheological investigation of complex lignocellulosic suspensions during hydrolysis using pure and cocktail of enzymes.

Enzymatic hydrolysis of lignocellulosic materials at high dry matter content is of crucial interest in bioindustry, and namely for biorefinery. Physical limitations linked to high concentrations must be understood and surpassed. This study used online and in-situ measurements to examine the rheological properties of different cellulosic suspensions and its evolution during enzymatic hydrolysis.

Mechanisms and effects of gas intercalation into ionic liquids confined within charged nanoscale volumes.

Understanding the behavior of gas within confined ionic liquids (ILs) is important for a wide range of emerging energy, separation, and sensing technologies. However, the mechanisms governing gas solubility and molecular structure within these systems remain largely unknown. Here, we investigate the factors that dictate the intercalation and arrangement of CO, N and O, in a commonly used IL (1-butyl-3-methylimidazolium hexafluorophosphate, [BMIM][PF]) confined within neutral and charged 2.

FDG-PET-based brain network analysis: a brief review of metabolic connectivity.

Background: Over the past decades, the analysis metabolic connectivity patterns has received significant attention in exploring the underlying mechanism of human behaviors, and the neural underpinnings of brain neurological disorders. Brain network can be considered a powerful tool and play an important role in the analysis and understanding of brain metabolic patterns. With the advantages and emergence of metabolic-based network analysis, this study aims to systematically review how brain properties, under various conditions, can be studied using Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) images and graph theory, as well as applications of this approach.

Superior effect of mini-implant anchorage in the treatment of skeletal class II malocclusion.

Objectives: To evaluate the effectiveness of mini-implant (MI) anchorage versus conventional anchorage for the treatment of skeletal class II malocclusion.

Materials And Methods: The study was conducted on 64 patients with skeletal class II malocclusion. The patients were divided into two groups: 1) 32 patients underwent conventional anchorage, and 2) 32 patients underwent MI anchorage.

Atomic-scale understanding of oxide growth and dissolution kinetics of Ni-Cr alloys.

Aqueous corrosion of metals is governed by formation and dissolution of a passivating, multi-component surface oxide. Unfortunately, a detailed atomistic description is challenging due to the compositional complexity and the need to consider multiple kinetic factors simultaneously. To this end, we combine experiments with a first-principles-derived, multiscale computational framework that transcends thermodynamic descriptions to explicitly simulate the kinetic evolution of surface oxides of Ni-Cr alloys as a function of composition, temperature, pH, and applied voltage.

Effects of clustering heterogeneity on the spectral density of sparse networks.

We derive exact equations for the spectral density of sparse networks with an arbitrary distribution of the number of single edges and triangles per node. These equations enable a systematic investigation of the effects of clustering on the spectral properties of the network adjacency matrix. In the case of heterogeneous networks, we demonstrate that the spectral density becomes more symmetric as the fluctuations in the triangle-degree sequence increase.

The research explores the predictive capacity of the shear strength of reinforced concrete walls with different cross-sectional shapes using the XGBoost model.

Structurally, the lateral load-bearing capacity mainly depends on reinforced concrete (RC) walls. Determination of flexural strength and shear strength is mandatory when designing reinforced concrete walls. Typically, these strengths are determined through theoretical formulas and verified experimentally.

Multistep Growth Pathway of Covalent Organic Framework Onion Nanostructures.

The growth of complex organic macromolecular materials in solution is a pervasive phenomenon in both natural and synthetic systems, yet the underlying growth mechanisms remain largely unresolved. Using liquid-phase transmission electron microscopy (TEM), we elucidate the real-time growth pathways of covalent organic framework (COF) onion nanostructures, which involve graphitic layer formation, subsequent layer attachment, onion ring closure, and structural relaxation. This process is marked by variations in orientation and curvature, driven by the dynamic formation of the COF structure, which further regulates order-disorder transition and defect generation within the framework.

Multiplatform metabolomic interlaboratory study of a whole human stool candidate reference material from omnivore and vegan donors.

Introduction: Human metabolomics has made significant strides in understanding metabolic changes and their implications for human health, with promising applications in diagnostics and treatment, particularly regarding the gut microbiome. However, progress is hampered by issues with data comparability and reproducibility across studies, limiting the translation of these discoveries into practical applications.

Objectives: This study aims to evaluate the fit-for-purpose of a suite of human stool samples as potential candidate reference materials (RMs) and assess the state of the field regarding harmonizing gut metabolomics measurements.

How Do Craniometric Measurements and Skull Morphology Influence Subthalamic Nucleus Target Coordinations in Deep Brain Stimulation Surgery for Parkinson Disease?

Objective: This study investigates the correlation among craniometric measurements, skull morphology, and subthalamic nucleus (STN) target coordinates in DBS surgery.

Methods: We conducted a retrospective study of 77 Parkinson disease patients who underwent bilateral STN-DBS surgery at Nguyen Tri Phuong Hospital and University Medical Center in Ho Chi Minh City, Vietnam. Craniometric measurements and STN target coordinates were analyzed using Pearson correlation and multivariate linear regression.

Age- and gender-dependent impact of titanium vertebral augmentation implants combined with cementing on subsequent vertebral fracture incidence: A comparative study with cementing alone.

Purpose: To compare vertebroplasty (VP) and kyphoplasty (KP) with a titanium implantable vertebral augmentation device (TIVAD) in symptomatic subsequent vertebral compression fracture (SVCF) incidence among osteoporotic vertebral compression fracture (OVCF) patients stratified by age and sex.

Methods: This retrospective cohort study involved OVCF patients aged ≥ 50, who underwent KP with TIVAD or VP in our hospital from 2014 to 2019. Subgroup analysis was conducted to evaluate the efficacy of KP with TIVAD and VP in patients stratified by age and sex.

Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions.

Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning and natural language processing, and their applications in HNC. The integration of AI with imaging techniques, genomics, and electronic health records is explored, emphasizing its role in early detection, biomarker discovery, and treatment planning.

Risk Factors and Profiles of Falls Among Inpatients in Vietnam: A Multicenter Nested Case-Control Study.

Purpose: Falls among inpatients represent a significant global health concern and are among the leading causes of accidental death. However, hospital falls are context- and population dependent. This study aimed to investigate the risk factors contributing to falls and the fall profiles among Vietnamese inpatients.

Spatial prediction of groundwater salinity in multiple aquifers of the Mekong Delta region using explainable machine learning models.

Groundwater salinization is a prevalent issue in coastal regions, yet accurately predicting and understanding its causal factors remains challenging due to the complexity of the groundwater system. Therefore, this study predicted groundwater salinity in multi-layered aquifers spanning the entire Mekong Delta (MD) region using machine learning (ML) models based on an in situ dataset and using three indicators (Cl, pH, and HCO). We applied nine different decision tree-based models and evaluated their prediction performances.

Machine learning demonstrates the impact of proton transfer and solvent dynamics on CO capture in liquid ammonia.

Direct air capture of CO using supported amines provides a promising means to achieve the net-zero greenhouse gas emissions goal; however, many mechanistic details regarding the CO adsorption process in condensed phase amines remain poorly understood. This work combines machine learning potentials, enhanced sampling and grand-canonical Monte Carlo simulations to directly compute experimentally relevant quantities to elucidate the mechanism of CO chemisorption in liquid ammonia as a model system. Our simulations suggest that CO capture in the liquid occurs in a sequential fashion, with the formation of a metastable zwitterion intermediate.

Uterine Angioleiomyoma: Clinical and Histopathologic Differentiation of an Underrecognized Mimicker of Uterine Leiomyoma.

Angioleiomyoma is an uncommon benign neoplasm of mesenchymal origin that arises from perivascular smooth muscle cells. This soft tissue neoplasm usually occurs in the dermal or subcutaneous tissues of the extremities, head and neck, or trunk with fewer than 40 reported angioleiomyomas arising in the uterine corpus. Herein we report a uterine angioleiomyoma in a 44-year-old G5P4 Hispanic woman with a longstanding history of recurrent abdominal pain, pelvic organ prolapse, abnormal uterine bleeding, anemia, and hypertension.

Exploring Extravasation in Cancer Patients.

Extravasation, the unintended leakage of intravenously administered substances, poses significant challenges in cancer treatment, particularly during chemotherapy and radiotherapy. This comprehensive review explores the pathophysiology, incidence, risk factors, clinical presentation, diagnosis, prevention strategies, management approaches, complications, and long-term effects of extravasation in cancer patients. It also outlines future directions and research opportunities, including identifying gaps in the current knowledge and proposing areas for further investigation in extravasation prevention and management.

Implementation of Sound Direction Detection and Mixed Source Separation in Embedded Systems.

In recent years, embedded system technologies and products for sensor networks and wearable devices used for monitoring people's activities and health have become the focus of the global IT industry. In order to enhance the speech recognition capabilities of wearable devices, this article discusses the implementation of audio positioning and enhancement in embedded systems using embedded algorithms for direction detection and mixed source separation. The two algorithms are implemented using different embedded systems: direction detection developed using TI TMS320C6713 DSK and mixed source separation developed using Raspberry Pi 2.