A deep learning dataset for metal multiaxial fatigue life prediction.

Multiaxial fatigue failure of metals, a common issue in industrial production, often leads to significant losses. Recently, many researchers have applied deep learning methods to predict the multiaxial fatigue life of metals, achieving promising results. Due to the high costs of fatigue testing, training data for deep learning is scarce and labor-intensive to collect.

MULTIMODAL MICROSCOPY IMAGE ALIGNMENT USING SPATIAL AND SHAPE INFORMATION AND A BRANCH-AND-BOUND ALGORITHM.

Multimodal microscopy experiments that image the same population of cells under different experimental conditions have become a widely used approach in systems and molecular neuroscience. The main obstacle is to align the different imaging modalities to obtain complementary information about the observed cell population (e.g.

Blind demixing methods for recovering dense neuronal morphology from barcode imaging data.

Cellular barcoding methods offer the exciting possibility of 'infinite-pseudocolor' anatomical reconstruction-i.e., assigning each neuron its own random unique barcoded 'pseudocolor,' and then using these pseudocolors to trace the microanatomy of each neuron.

Deep-Red and Near-Infrared Iridium Complexes with Fine-Tuned Emission Colors by Adjusting Trifluoromethyl Substitution on Cyclometalated Ligands Combined with Matched Ancillary Ligands for Highly Efficient Phosphorescent Organic Light-Emitting Diodes.

Six novel Ir(C^N)(L^X)-type heteroleptic iridium complexes with deep-red and near-infrared region (NIR)-emitting coverage were constructed through the cross matching of various cyclometalating (C^N) and ancillary (LX) ligands. Here, three novel C^N ligands were designed by introducing the electron-withdrawing group CF on the ortho (o-), meta (m-), and para (p-) positions of the phenyl ring in the 1-phenylisoquinoline (piq) group, which were combined with two electron-rich LX ligands (dipba and dipg), respectively, leading to subsequent iridium complexes with gradually changing emission colors from deep red (≈660 nm) to NIR (≈700 nm). Moreover, a series of phosphorescent organic light-emitting diodes (PhOLEDs) were fabricated by employing these phosphors as dopant emitters with two doping concentrations, 5% and 10%, respectively.

BARcode DEmixing through Non-negative Spatial Regression (BarDensr).

Modern spatial transcriptomics methods can target thousands of different types of RNA transcripts in a single slice of tissue. Many biological applications demand a high spatial density of transcripts relative to the imaging resolution, leading to partial mixing of transcript rolonies in many voxels; unfortunately, current analysis methods do not perform robustly in this highly-mixed setting. Here we develop a new analysis approach, BARcode DEmixing through Non-negative Spatial Regression (BarDensr): we start with a generative model of the physical process that leads to the observed image data and then apply sparse convex optimization methods to estimate the underlying (demixed) rolony densities.

Complexity and graded regulation of neuronal cell-type-specific alternative splicing revealed by single-cell RNA sequencing.

The enormous cellular diversity in the mammalian brain, which is highly prototypical and organized in a hierarchical manner, is dictated by cell-type-specific gene-regulatory programs at the molecular level. Although prevalent in the brain, the contribution of alternative splicing (AS) to the molecular diversity across neuronal cell types is just starting to emerge. Here, we systematically investigated AS regulation across over 100 transcriptomically defined neuronal types of the adult mouse cortex using deep single-cell RNA-sequencing data.

Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data.

Background: A fundamental fact in biology states that genes do not operate in isolation, and yet, methods that infer regulatory networks for single cell gene expression data have been slow to emerge. With single cell sequencing methods now becoming accessible, general network inference algorithms that were initially developed for data collected from bulk samples may not be suitable for single cells. Meanwhile, although methods that are specific for single cell data are now emerging, whether they have improved performance over general methods is unknown.

Gelsemine alleviates both neuropathic pain and sleep disturbance in partial sciatic nerve ligation mice.

Aim: Gelsemine, an alkaloid from the Chinese herb Gelsemium elegans (Gardn & Champ) Benth., is effective in mitigating chronic pain in rats. In the present study we investigated whether the alkaloid improved sleep disturbance, the most common comorbid symptoms of chronic pain, in a mouse model of neuropathic pain.

Bee venom phospholipase A2-induced phasic contractions in mouse rectum: independent roles of eicosanoid and gap junction proteins and their loss in experimental colitis.

Various events including digestion and inflammation are regulated by secreted phospholipase A2 (sPLA2) in gastrointestinal tissues, however, the role of sPLA2 on contractile activity has not been elucidated. We investigated the effect of bee venom PLA2 (bvPLA2), which is homologous to the central domain of group III sPLA2, on contractile activity in mouse rectum. The longitudinal preparations of rectum showed rhythmic phasic contractions (RPCs) with varied amplitude and high frequency.

Excretion of berberine and its metabolites in oral administration in rats.

Berberine (BBR) has been confirmed to show extensive bioactivities for the treatments of diabetes and hypercholesterolemia in clinic. However, there are few pharmacokinetic studies to elucidate the excretions of BBR and its metabolites. Our research studied the excretions of BBR and its metabolites in rats after oral administration (200 mg/kg).