Deep generative modeling of annotated bacterial biofilm images.

Biofilms are critical for understanding environmental processes, developing biotechnology applications, and progressing in medical treatments of various infections. Nowadays, a key limiting factor for biofilm analysis is the difficulty in obtaining large datasets with fully annotated images. This study introduces a versatile approach for creating synthetic datasets of annotated biofilm images with employing deep generative modeling techniques, including VAEs, GANs, diffusion models, and CycleGAN.

Cognitive dysfunction and disturbed daily activities of people with dementia impact the psychological stress in family caregivers depending on their anxiety and depression severity.

Care for individuals with dementia, predominantly provided by family members, was associated with significant physical and emotional stress. Existing guidelines for supporting caregivers were frequently based on limited evidence. A cross-sectional clinical study involving 104 pairs of caregivers and individuals with dementia was conducted.

Build-a-Bio-Strip: An Online Platform for Rapid Toxicity Assessment in Chemical Synthesis.

The increasing need to understand and control the environmental impact of chemical processes has revealed the challenge in efficient evaluation of toxicity of the vast number of chemical compounds and their varying effects on biological systems. In this study, we introduce "Build-a-bio-Strip", a novel online service designed to carry out a quick initial analysis of the toxic impact of chemical processes. This platform enables users to automatically generate toxicity characteristics of chemical reactions using their own data on cytotoxicity or median lethal doses of the substances involved or computational predictions based on SMILES strings.

Recognition of Molecular Structure of Phosphonium Salts from the Visual Appearance of Material with Deep Learning Can Reveal Subtle Homologs.

Determining molecular structures is foundational in chemistry and biology. The notion of discerning molecular structures simply from the visual appearance of a material remained almost unthinkable until the advent of machine learning. This paper introduces a pioneering approach bridging the visual appearance of materials (both at the micro- and nanostructural levels) with traditional chemical structure analysis methods.

Determining the orderliness of carbon materials with nanoparticle imaging and explainable machine learning.

Carbon materials have paramount importance in various fields of materials science, from electronic devices to industrial catalysts. The properties of these materials are strongly related to the distribution of defects-irregularities in electron density on their surfaces. Different materials have various distributions and quantities of these defects, which can be imaged using a procedure that involves depositing palladium nanoparticles.

Maternal Sleeping Problems Before and After Childbirth - A Systematic Review.

The perinatal and postpartum period is of great significance for women due to physiological changes, shifts in circadian rhythms, social setting, and psychological well-being, all of which affect the quality and quantity of their sleep. A mixed-studies systematic review was undertaken to enhance our understanding of sleep disturbances and mood disorders in women in late pregnancy and the postpartum period, their connection with breastfeeding, and the assessment of interventions for sleep disturbance. Three electronic databases (PUBMED, EMBASE and Google Scholar) were searched for qualitative, observational, and mixed-method studies from the year 2016 to June 2023.

Quantitative Determination of Active Species Transforming the R-NHC Coupling Process under Catalytic Conditions.

Palladium complexes with -heterocyclic carbenes (Pd/NHC) serve as prominent precatalysts in numerous Pd-catalyzed organic reactions. While the evolution of Pd/NHC complexes, which involves the cleavage of the Pd-C(NHC) bond via reductive elimination and dissociation, is acknowledged to influence the catalysis mechanism and the performance of the catalytic systems, conventional analytic techniques [such as NMR, IR, UV-vis, gas chromatography-mass spectrometry (GC-MS), and high-performance liquid chromatography (HPLC)] frequently fail to quantitatively monitor the transformations of Pd/NHC complexes at catalyst concentrations typical of real-world conditions (below approximately 1 mol %). In this study, for the first time, we show the viability of using electrospray ionization mass spectrometry (ESI-MS).

Autonomous chemical research with large language models.

Transformer-based large language models are making significant strides in various fields, such as natural language processing, biology, chemistry and computer programming. Here, we show the development and capabilities of Coscientist, an artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation. Coscientist showcases its potential for accelerating research across six diverse tasks, including the successful reaction optimization of palladium-catalysed cross-couplings, while exhibiting advanced capabilities for (semi-)autonomous experimental design and execution.

Mental health and sleep disturbances among Ukrainian refugees in the context of Russian-Ukrainian war: A preliminary result from online-survey.

The ongoing Russian-Ukrainian conflict has caused a significant displacement of individuals, with an estimated five million Ukrainians seeking temporary refuge in other countries. We conducted a survey among Ukrainians in various countries worldwide using a convenience sample and analyzed 144 responses. The participants were divided into two groups: group 1, consisting of 92 Ukrainian refugees who were forced to leave their country due to the conflict, and group 2, comprising 52 individuals who did not change their place of residence.

Schizophrenia and disruption of circadian rhythms: An overview of genetic, metabolic and clinical signs.

A molecular clock in the suprachiasmatic nucleus of the anterior hypothalamus, which is entrained by the dark-light cycle and controls the sleep-wake cycle, regulates circadian rhythms. The risk of developing mental disorders, such as schizophrenia, has long been linked to sleep abnormalities. Additionally, a common aspect of mental disorders is sleep disturbance, which has a direct impact on the intensity of the symptoms and the quality of life of the patient.

Time-Resolved Formation and Operation Maps of Pd Catalysts Suggest a Key Role of Single Atom Centers in Cross-Coupling.

An approach to the spatially localized characterization of supported catalysts over a reaction course is proposed. It consists of a combination of scanning, transmission, and high-resolution scanning transmission electron microscopy to determine metal particles from arrays of surface nanoparticles to individual nanoparticles and individual atoms. The study of the evolution of specific metal catalyst particles at different scale levels over time, particularly before and after the cross-coupling catalytic reaction, made it possible to approach the concept of 4D catalysis-tracking the positions of catalytic centers in space (3D) over time (+1D).

Drug reprofiling history and potential therapies against Parkinson's disease.

Given the high whittling down rates, high costs, and moderate pace of new medication, revelation, and improvement, repurposing "old" drugs to treat typical and uncommon illnesses is progressively becoming an appealing proposition. Drug repurposing is the way toward utilizing existing medications in treating diseases other than the purposes they were initially designed for. Faced with scientific and economic challenges, the prospect of discovering new medication indications is enticing to the pharmaceutical sector.

Automated Recognition of Nanoparticles in Electron Microscopy Images of Nanoscale Palladium Catalysts.

Automated computational analysis of nanoparticles is the key approach urgently required to achieve further progress in catalysis, the development of new nanoscale materials, and applications. Analysis of nanoscale objects on the surface relies heavily on scanning electron microscopy (SEM) as the experimental analytic method, allowing direct observation of nanoscale structures and morphology. One of the important examples of such objects is palladium on carbon catalysts, allowing access to various chemical reactions in laboratories and industry.

Fully Automated Unconstrained Analysis of High-Resolution Mass Spectrometry Data with Machine Learning.

Mass spectrometry (MS) is a convenient, highly sensitive, and reliable method for the analysis of complex mixtures, which is vital for materials science, life sciences fields such as metabolomics and proteomics, and mechanistic research in chemistry. Although it is one of the most powerful methods for individual compound detection, complete signal assignment in complex mixtures is still a great challenge. The unconstrained formula-generating algorithm, covering the entire spectra and revealing components, is a "dream tool" for researchers.

Melatonergic Receptors (Mt1/Mt2) as a Potential Additional Target of Novel Drugs for Depression.

A complex pathogenesis involving several physiological systems is theorized to underline the development of depressive disorders. Depression is accompanied by circadian regulation disruption and interaction with the functioning of both central and peripheral oscillators. Many aspects of melatonin function unite these systems.

Integration of thermal imaging and neural networks for mechanical strength analysis and fracture prediction in 3D-printed plastic parts.

Additive manufacturing demonstrates tremendous progress and is expected to play an important role in the creation of construction materials and final products. Contactless (remote) mechanical testing of the materials and 3D printed parts is a critical limitation since the amount of collected data and corresponding structure/strength correlations need to be acquired. In this work, an efficient approach for coupling mechanical tests with thermographic analysis is described.

Cognitive and affective disturbances in patients with Parkinson's disease: Perspectives for classifying of motor/neuropsychiatric subtypes.

Parkinson's disease (PD) is a neurological disorder, related to rigidity, bradykinesia, and resting tremors, among other motor symptoms. It is noticed in the increasing frequency of neuropsychiatric disorders, which may be also caused by non-motor symptoms of PD. Treatment of PD is usually based on the classification of motor subtypes; however, it remains unclear whether motor subtypes have differences in the severity of psychiatric symptoms.

Toward Totally Defined Nanocatalysis: Deep Learning Reveals the Extraordinary Activity of Single Pd/C Particles.

Homogeneous catalysis is typically considered "well-defined" from the standpoint of catalyst structure unambiguity. In contrast, heterogeneous nanocatalysis often falls into the realm of "poorly defined" systems. Supported catalysts are difficult to characterize due to their heterogeneity, variety of morphologies, and large size at the nanoscale.