nipalsMCIA: Flexible Multi-Block Dimensionality Reduction in R via Nonlinear Iterative Partial Least Squares.

Summary: With the increased reliance on multi-omics data for bulk and single cell analyses, the availability of robust approaches to perform unsupervised learning for clustering, visualization, and feature selection is imperative. We introduce nipalsMCIA, an implementation of multiple co-inertia analysis (MCIA) for joint dimensionality reduction that solves the objective function using an extension to Non-linear Iterative Partial Least Squares (NIPALS). We applied nipalsMCIA to both bulk and single cell datasets and observed significant speed-up over other implementations for data with a large sample size and/or feature dimension.

Research on vehicle scheduling for forest fires in the northern Greater Khingan Mountains.

In the face of forest fire emergencies, fast and efficient dispatching of rescue vehicles is an important means of mitigating the damage caused by forest fires, and is an effective method of avoiding secondary damage caused by forest fires, minimizing the damage caused by forest fires to the ecosystem, and mitigating the losses caused by economic development. this paper takes the actual problem as the starting point, constructs a reasonable mathematical model of the problem, for the special characteristics of the emergency rescue vehicle scheduling problem of forest fires, taking into account the actual road conditions in the northern pristine forest area, through the analysis of the cost of paths between the forest area and the highway, to obtain the least obstructed rescue paths, to narrow the gap between the theoretical model and the problem of the actual. Improvement of ordinary genetic algorithm, design of double population strategy selection operation, the introduction of chaotic search initialization population, to improve the algorithm's solution efficiency and accuracy, through the northern pristine forest area of Daxing'anling real forest fire cases and generation of large-scale random fire point simulation experimental test to verify the effectiveness of the algorithm, to ensure that the effectiveness and reasonableness of the solution to the problem of forest fire emergency rescue vehicle scheduling program.

Surrogate-assisted global and distributed local collaborative optimization algorithm for expensive constrained optimization problems.

This paper presents a surrogate-assisted global and distributed local collaborative optimization (SGDLCO) algorithm for expensive constrained optimization problems where two surrogate optimization phases are executed collaboratively at each generation. As the complexity of optimization problems and the cost of solutions increase in practical applications, how to efficiently solve expensive constrained optimization problems with limited computational resources has become an important area of research. Traditional optimization algorithms often struggle to balance the efficiency of global and local searches, especially when dealing with high-dimensional and complex constraint conditions.

PbsNRs: predict the potential binders and scaffolds for nuclear receptors.

Nuclear receptors (NRs) are a class of essential proteins that regulate the expression of specific genes and are associated with multiple diseases. In silico methods for prescreening potential NR binders with predictive binding ability are highly desired for NR-related drug development but are rarely reported. Here, we present the PbsNRs (Predicting binders and scaffolds for Nuclear Receptors), a user-friendly web server designed to predict the potential NR binders and scaffolds through proteochemometric modeling.

transCAE: Enhancing cell type annotation in single-cell RNA-seq data with transfer learning and convolutional autoencoder.

Single-cell RNA sequencing (scRNA-seq) analysis offers tremendous potential for addressing various biological questions, with one key application being the annotation of query datasets with unknown cell types using well-annotated external reference datasets. However, the performance of existing supervised or semi-supervised methods largely depends on the quality of source data. Furthermore, these methods often struggle with the batch effects arising from different platforms when handling multiple reference or query datasets, making precise annotation challenging.

Alterations of subcortical structural volume in pediatric bipolar disorder patients with and without psychotic symptoms.

Background: Pediatric bipolar disorder (PBD) with psychotic symptoms may predict more severe impairment in social functioning, but the underlying biological mechanisms remain unclear. The aim of this study was to investigate alterations in subcortical structural volume in PBD with and without psychotic symptoms.

Methods: We recruited 24 psychotic PBD (P-PBD) patients, 24 non-psychotic PBD (NP-PBD) patients, and 18 healthy controls (HCs).

Math anxiety mediates the association between gender and STEM-related attitudes: Evidence from a large-scale study.

Math anxiety (MA) is a prevalent academic anxiety that can affect student mental health and academic performance worldwide. Using a very large sample of students in grades 7 to 10 (N = 7172) in Qatar, the present study examined the associations among gender, math anxiety, and attitudes towards Science, Technology, Engineering, and Mathematics (STEM). In addition, the factorial structure of the "Student Attitudes towards STEM Survey" (STEM-S) was examined.

Immunogenicity of yellow fever vaccine co-administered with 13-valent pneumococcal conjugate vaccine in rural Gambia: A cluster-randomised trial.

Introduction: Because booster doses of pneumococcal conjugate vaccine (PCV) may be given at a similar time to yellow fever vaccine (YF), it is important to assess the immune response to YF when co-administered with PCV. This has been investigated during a reduced-dose PCV trial in The Gambia.

Methods: In this phase 4, parallel-group, cluster-randomized trial, healthy infants aged 0-10 weeks were randomly allocated to receive either a two-dose schedule of PCV13 with a booster dose co-administered with YF vaccine at age 9 months (1 + 1 co-administration) or YF vaccine administered separately at age 10 months (1 + 1 separate) or the standard three early doses of PCV13 with YF vaccine at age 9 months (3 + 0 separate).

A smooth gradient approximation neural network for general constrained nonsmooth nonconvex optimization problems.

Nonsmooth nonconvex optimization problems are pivotal in engineering practice due to the inherent nonsmooth and nonconvex characteristics of many real-world complex systems and models. The nonsmoothness and nonconvexity of the objective and constraint functions bring great challenges to the design and convergence analysis of the optimization algorithms. This paper presents a smooth gradient approximation neural network for such optimization problems, in which a smooth approximation technique with time-varying control parameter is introduced for handling nonsmooth nonregular objective functions.

Monitoring of indoor air quality at a large sailing cruise ship to assess ventilation performance and disease transmission risk.

Large passenger ships are characterised as enclosed and crowded indoor spaces with frequent interactions between travellers, providing conditions that facilitate disease transmission. This study aims to provide an indoor ship CO dataset for inferring thermal comfort, ventilation and infectious disease transmission risk evaluation. Indoor air quality (IAQ) monitoring was conducted in nine environments (three cabins, buffet, gym, bar, restaurant, pub and theatre), on board a cruise ship voyaging across the UK and EU, with the study conducted in the framework of the EU HEALTHY SAILING project.

Transfer of congruency effects between Stroop and multiplication tasks: Evidence that retrieval of multiplication facts requires inhibitory control.

Inhibitory control is classically considered a domain-general process, yet recent findings suggest it may operate in context-specific ways. This has important implications for theories in other cognitive domains, such as mathematics, in which inhibitory control is proposed to play a key role. Inhibitory control has been implicated in resolving interference between competing number facts when retrieving them from memory, yet clear evidence for this is lacking.

Cognitive and home predictors of precocious reading and math before formal education.

Children start formal schooling with substantial individual differences in their early literacy and numeracy abilities, but little is known about predictors of precocious (i.e., early advanced) reading and math.

CircWaveDL: Modeling of optical coherence tomography images based on a new supervised tensor-based dictionary learning for classification of macular abnormalities.

Modeling Optical Coherence Tomography (OCT) images is crucial for numerous image processing applications and aids ophthalmologists in the early detection of macular abnormalities. Sparse representation-based models, particularly dictionary learning (DL), play a pivotal role in image modeling. Traditional DL methods often transform higher-order tensors into vectors and then aggregate them into a matrix, which overlooks the inherent multi-dimensional structure of the data.

Light-Dependent Chemoselective Amide or Thioamide Ligation of Acylsilanes with Amines using Elemental Sulfur.

Due to the diverse chemical and physical properties of functional groups, mild and controllable ligation methods are often required to construct complex drugs and functional materials. To make diverse sets of products with tunable physicochemical properties, it is also useful to employ complimentary ligation methods that adopt the same starting materials. Here, we disclose the efficient and modular synthesis of amides or thioamides through the chemical ligation of acyl silanes with amines, simply by turning a light on or off.

Biocompatibility of Phosphorus Dendrimers and Their Antibacterial Properties as Potential Agents for Supporting Wound Healing.

Dendrimers are a wide range of nanoparticles with desirable properties that can be used in many areas of medicine. However, little is known about their potential use in wound healing. This study examined the properties of phosphorus dendrimers that were built on a cyclotriphosphazene core and pyrrolidinium (DPP) or piperidinium (DPH) terminated groups, to be used as potential factors that support wound healing ().

Improving Molecular Design with Direct Inverse Analysis of QSAR/QSPR Model.

Recent advances in machine learning have significantly impacted molecular design, notably the molecular generation method combining the chemical variational autoencoder (VAE) with Gaussian mixture regression (GMR). In this method, a mathematical model is constructed with X as the latent variable of the molecule and Y as the target properties and activities. Through direct inverse analysis of this model, it is possible to generate molecules with the desired target properties.

Deciphering repair pathways of clustered DNA damage in human TK6 cells: insights from atomic force microscopy direct visualization.

Ionizing radiation induces various types of DNA damage, and the reparability and lethal effects of DNA damage differ depending on its spatial density. Elucidating the structure of radiation-induced clustered DNA damage and its repair processes will enhance our understanding of the lethal impact of ionizing radiation and advance progress toward precise therapeutics. Previously, we developed a method to directly visualize DNA damage using atomic force microscopy (AFM) and classified clustered DNA damage into simple base damage clusters (BDCs), complex BDCs and complex double-strand breaks (DSBs).

Integrating Model-Informed Drug Development With AI: A Synergistic Approach to Accelerating Pharmaceutical Innovation.

The pharmaceutical industry constantly strives to improve drug development processes to reduce costs, increase efficiencies, and enhance therapeutic outcomes for patients. Model-Informed Drug Development (MIDD) uses mathematical models to simulate intricate processes involved in drug absorption, distribution, metabolism, and excretion, as well as pharmacokinetics and pharmacodynamics. Artificial intelligence (AI), encompassing techniques such as machine learning, deep learning, and Generative AI, offers powerful tools and algorithms to efficiently identify meaningful patterns, correlations, and drug-target interactions from big data, enabling more accurate predictions and novel hypothesis generation.

Room-Temperature CsPbI-Quantum-Dot Reinforced Solid-State Li-Polymer Battery.

A novel polymer electrolyte based on CsPbI quantum dots (QDs) reinforced polyacrylonitrile (PAN), named as PIL, is exploited to address the low room-temperature (RT) ion conductivity and poor interfacial compatibility of polymer solid-state electrolytes. After optimizing the content of CsPbI QDs, RT ion conductivity of PIL largely increased from 0.077 to 0.

Protein-Guided Biomimetic Calcification Constructing 3D Nitrogen-Rich Core-Shell Structures Realizing High-Performance Lithium-Sulfur Batteries.

Biomimetic calcification is a micro-crystallization process that mimics the natural biomineralization process, where biomacromolecules regulate the formation of inorganic minerals. In this study, it is presented that a protein-assisted biomimetic calcification method for the in situ synthesis of nitrogen-doped metal-organic framework (MOF) materials. A series of unique core-shell structures are created by utilizing proteins as templates and guiding agents in the nucleation step, creating ideal conditions for shell growth.

3D Digital Anatomical Models Based on Computed Tomographic Morphometric Analysis of C1 and C2 for Surgical Navigation.

Injuries involving the Atlas (C1) and Axis (C2) vertebrae of the cervical spine present significant clinical challenges due to their complex anatomy and potential for severe neurological impairment. Traditional imaging methods often lack the detailed visualization required for precise surgical planning. This study aimed to develop high-resolution 3D models of the C1 and C2 vertebrae to perform a comprehensive morphometric analysis, identify gender differences, and assess bilateral symmetry to enhance surgical accuracy.

Risk Assessment for Preeclampsia in the Preconception Period Based on Maternal Clinical History via Machine Learning Methods.

: This study was aimed to identify the most effective machine learning (ML) algorithm for predicting preeclampsia based on sociodemographic and obstetric factors during the preconception period. : Data from pregnant women admitted to the obstetric clinic during their first trimester were analyzed, focusing on maternal age, body mass index (BMI), smoking status, history of diabetes mellitus, gestational diabetes mellitus, and mean arterial pressure. The women were grouped by whether they had a preeclampsia diagnosis and by whether they had one or two live births.

The Association Between Periodontal Inflamed Surface Area (PISA), Inflammatory Biomarkers, and Mitochondrial DNA Copy Number.

: Periodontitis is an inflammatory disease induced by bacteria in dental plaque that can activate the host's immune-inflammatory response and invade the bloodstream. We hypothesized that a higher periodontal inflamed surface area (PISA) is associated with higher levels of inflammatory biomarkers, lower levels of antioxidants, and mitochondrial DNA copy number (mtDNAcn). : Using periodontal parameters, we calculated the PISA score, measured the levels of inflammatory biomarkers and antioxidants in the serum, and took buccal swabs for mtDNA and nuclear DNA (nDNA) extraction.

Munsell Soil Colour Prediction from the Soil and Soil Colour Book Using Patching Method and Deep Learning Techniques.

Soil colour is a key indicator of soil health and the associated properties. In agriculture, soil colour provides farmers and advises with a visual guide to interpret soil functions and performance. Munsell colour charts have been used to determine soil colour for many years, but the process is fallible, as it depends on the user's perception.

Detection Method for Bolt Loosening Based on Summation Coefficient of Absolute Spectrum Ratio.

A bolt loosening detection method based on the summation coefficient of the absolute spectrum ratio technique is proposed to address the prevalent issue of bolt loosening in mechanical connections. This proposed method involves initially collecting vibration and rotation speed signals of the motor bolt connection structure, acquiring the baseline spectrum curve of a healthy structure and the spectrum curves of non-healthy structures under different degrees of bolt looseness through chirp Fourier transform (CFT). Subsequently, the spectrum ratio curves between healthy and non-healthy structures are calculated for different degrees of bolt loosening, and then the Summation Coefficient of Absolute Spectrum Ratio (SCASR) is defined to indicate the looseness.