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The microstructural evolution, precipitation behavior, and hardness variation of the Ti-V-Mo complex microalloyed steel during isothermal holding following austenite deformation have been investigated using thermal simulation tester (Gleeble-3800), optical microscope (OM), scanning electron microscope (SEM), electron backscatter diffraction (EBSD), transmission electron microscope (TEM), and Vickers hardness tester. The results indicate that the microstructure transforms from austenite to ferrite as the holding time increases from 0 s to 5400 s at 630 °C, with the microstructure primarily comprising martensite and ferrite. The proportion of ferrite continues to increase, while the proportion of martensite decreases. At a holding time of 600 s, the austenite has nearly completed the ferrite transformation (95%), and the average grain size is very fine (3.9 μm). At this point, the (Ti, V, Mo)C particles exhibit the highest density and a smaller size (7.7 nm), with the hardness reaching its maximum value (415 HV). Concurrently, numerous spherical precipitates particles with a size of < 10 nm formed in the ferrite region, conforming to the Baker–Nutting (BN) orientation relationship with respect to the matrix: (100)(Ti, V, Mo)C//(100)α-Fe, [011](Ti, V, Mo)C//[012]α-Fe. As the holding time increases from 30 s to 60 s, the hardness decreases rapidly due to the reduction of martensite and addition of soft-phase ferrite. However, the subsequent slow decrease in hardness is primarily attributed to weakened precipitation strengthening caused by precipitates coarsening and decreased grain refinement strengthening due to the slight increase in ferrite grain size as the holding time increases from 600 s to 5400 s.

In a mixed traffic environment consisting of connected autonomous vehicles (CAVs) and human-driven vehicles (HVs), platooning intensity serves as a critical metric, quantifying the strength of CAV clustering, with inherent ramifications for traffic flow efficiency. While various definitions of platooning intensity are found in existing literature, many fall short in effectively capturing the strength of CAV clustering in mixed traffic. To address the gap, this study models the vehicle stream of mixed traffic on the single-lane road as a binary sequence and proposes the autocorrelation-based platooning intensity (API) metric. Through theoretical analysis, the proposed API is shown to be an effective indicator for measuring the clustering strength of CAVs. The probability distribution of API through fisher transformation is also derived. This study then moves on to formulate the capacity of mixed traffic, taking into account CAV penetration rate, API, and stochastic headway. Numerical verification of the estimated mixed traffic capacity reveals a negligible error (less than 1%) compared to simulated capacity. Marginal analysis confirms the validity of related propositions, notably that stronger CAV clustering does not always improve traffic capacity due to headway stochasticity. The outcome of this study contributes to the understanding of CAV platooning intensity and offers valuable insights for advancing mixed traffic modeling and management.

Abstract Structure-based generative chemistry is essential in computer-aided drug discovery by exploring a vast chemical space to design ligands with high binding affinity for targets. However, traditional in silico methods are limited by computational inefficiency, while machine learning approaches face bottlenecks due to auto-regressive sampling. To address these concerns, we have developed a conditional deep generative model, PMDM, for 3D molecule generation fitting specified targets. PMDM consists of a conditional equivariant diffusion model with both local and global molecular dynamics, enabling PMDM to consider the conditioned protein information to generate molecules efficiently. The comprehensive experiments indicate that PMDM outperforms baseline models across multiple evaluation metrics. To evaluate the applications of PMDM under real drug design scenarios, we conduct lead compound optimization for SARS-CoV-2 main protease (Mpro) and Cyclin-dependent Kinase 2 (CDK2), respectively. The selected lead optimization molecules are synthesized and evaluated for their in-vitro activities against CDK2, displaying improved CDK2 activity.

BackgroundNow, there are no sensitive biomarkers for improving Alzheimer’s disease (AD) and comorbid Parkinson’s disease (PD). The aim of the present study was to analyze differentially expressed genes (DEGs) in brain tissue from AD and PD patients via bioinformatics analysis, as well as to explore precise diagnostic and therapeutic targets for AD and comorbid PD.MethodsGFE122063 and GSE7621 data sets from GEO in NCBI, were used to screen differentially expressed genes (DEGs) for AD and PD, and identify the intersected genes, respectively. Intersected genes were analyzed by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Then, STRING site and Cytoscape were used to construct a protein–protein interaction (PPI) network, CytoNCA algorithm to analyze and evaluate centrality, Mcode plug-in to analyze module, and Cytohubba to screen key genes. Combined GO-KEGG enrichment analysis with Cytoscape algorithm to screen the key gene in AD complicated with PD. Then, the DEGs for AD and PD were imported into the Association Map (CMap) online platform to screen out the top 10 small molecule drugs, and using molecular docking techniques to evaluate the interactions between small molecule drugs and key genes receptors.ResultsIn total, 231 upregulated genes and 300 downregulated genes were identified. GO analysis revealed that the DEGs were highly enriched in signal transduction, and KEGG analysis revealed that the DEGs were associated with the MAPK and PI3K-Akt signaling pathways. Epidermal growth factor receptor (EGFR) was identified as a potential receptor gene in AD and comorbid PD. EGFR was upregulated in both AD and PD, and the proteins that interact with EGFR were enriched in the Ras/Raf/MAPK and PI3K/Akt signaling pathways. Semagacestat was identified as a drug with therapeutic potential for treating AD complicated with PD. There was a high binding affinity between semagacestat and EGFRNTD, with seven hydrogen bonds and one hydrophobic bond.DiscussionSemagacestat may improve the health of patients with AD complicated with PD through the regulation of the Ras/Raf/MAPK and PI3K/Akt signaling pathways by EGFR, providing evidence supporting the structural modification of semagacestat to develop a more effective drug for treating AD complicated with PD.

Abstract Background The CACNA1S gene encodes the alpha 1 S-subunit of the voltage-gated calcium channel, which is primarily expressed in the skeletal muscle cells. Pathogenic variants of CACNA1S can cause hypokalemic periodic paralysis (HypoPP), malignant hyperthermia susceptibility, and congenital myopathy. We aimed to study the clinical and molecular features of a male child with a CACNA1S variant and depict the molecular sub-regional characteristics of different phenotypes associated with CACNA1S variants. Case presentation We presented a case of HypoPP with recurrent muscle weakness and hypokalemia. Genetic analyses of the family members revealed that the proband had a novel c.497 C > A (p.Ala166Asp) variant of CACNA1S, which was inherited from his father. The diagnosis of HypoPP was established in the proband as he met the consensus diagnostic criteria. The patient and his parents were informed to avoid the classical triggers of HypoPP. The attacks of the patient are prevented by lifestyle changes and nutritional counseling. We also showed the molecular sub-regional location of the variants of CACNA1S which was associated with different phenotypes. Conclusions Our results identified a new variant of CACNA1S and expanded the spectrum of variants associated with HypoPP. Early genetic diagnosis can help avoid diagnostic delays, perform genetic counseling, provide proper treatment, and reduce morbidity and mortality.

Slow transit constipation (STC) is a common and debilitating condition characterized by delayed colonic transit and difficulty in fecal expulsion, significantly impacting patients’ physical and mental wellbeing as well as their overall quality of life. This study investigates the therapeutic potential of Liqi Tongbian Decoction (LTD) in the treatment of STC, especially in cases involving the context of Qi stagnation, through a multifaceted approach involving the modulation of intestinal flora and short-chain fatty acids (SCFAs). We employed a rat model of STC with Qi Stagnation Pattern, established using the “loperamide + tail-clamping provocation method,” to explore the effects of LTD on fecal characteristics, intestinal motility, and colonic pathology. Importantly, LTD exhibited the ability to increase the richness, diversity, and homogeneity of intestinal flora while also modulating the composition of microorganisms. It significantly increased the production of SCFAs, especially butyric acid. Moreover, LTD exerted a substantial influence on the synthesis of serotonin (5-HT) by modulating the expression of tryptophan hydroxylase (TPH) and interacting with the 5-HT4 receptor (5-HT4R), resulting in enhanced colonic motility. Correlation analyses revealed a positive correlation between certain bacterial genera, such as Lachnospiraceae_NK4A136 spp. and Clostridiales spp. and the concentrations of butyric acid and 5-HT. These results suggest a mechanistic link between microbiome composition, SCFAs production, and 5-HT synthesis. These findings highlight the potential of LTD to alleviate STC by facilitating a beneficial interplay among intestinal flora, SCFAs production, and 5-HT-mediated colonic motility, providing novel insights into the management of STC with Qi Stagnation Pattern.

Abstract Background Based on their anatomical location, rostral projections of nuclei are classified as ascending circuits, while caudal projections are classified as descending circuits. Upper brainstem neurons participate in complex information processing and specific sub-populations preferentially project to participating ascending or descending circuits. Cholinergic neurons in the upper brainstem have extensive collateralizations in both ascending and descending circuits; however, their single-cell projection patterns remain unclear because of the lack of comprehensive characterization of individual neurons. Results By combining fluorescent micro-optical sectional tomography with sparse labeling, we acquired a high-resolution whole-brain dataset of pontine-tegmental cholinergic neurons (PTCNs) and reconstructed their detailed morphology using semi-automatic reconstruction methods. As the main source of acetylcholine in some subcortical areas, individual PTCNs had abundant axons with lengths up to 60 cm and 5000 terminals and innervated multiple brain regions from the spinal cord to the cortex in both hemispheres. Based on various collaterals in the ascending and descending circuits, individual PTCNs were grouped into four subtypes. The morphology of cholinergic neurons in the pedunculopontine nucleus was more divergent, whereas the laterodorsal tegmental nucleus neurons contained richer axonal branches and dendrites. In the ascending circuits, individual PTCNs innervated the thalamus in three different patterns and projected to the cortex via two separate pathways. Moreover, PTCNs targeting the ventral tegmental area and substantia nigra had abundant collaterals in the pontine reticular nuclei, and these two circuits contributed oppositely to locomotion. Conclusions Our results suggest that individual PTCNs have abundant axons, and most project to various collaterals in the ascending and descending circuits simultaneously. They target regions with multiple patterns, such as the thalamus and cortex. These results provide a detailed organizational characterization of cholinergic neurons to understand the connexional logic of the upper brainstem.

Despite recent progress, machine learning methods remain inadequate in modeling the natural protein-protein interaction (PPI) hierarchy for PPI prediction. Here, the authors present a double-viewed hierarchical graph learning model, HIGH-PPI, to predict PPIs and extrapolate the molecular details involved.

The pontomesencephalic tegmentum, comprising the pedunculopontine nucleus and laterodorsal tegmental nucleus, is involved in various functions via complex connections; however, the organizational structure of these circuits in the whole brain is not entirely clear. Here, combining viral tracing with fluorescent micro-optical sectional tomography, we comprehensively investigated the input and output circuits of two cholinergic subregions in a continuous whole-brain dataset. We found that these nuclei receive abundant input with similar spatial distributions but with different quantitative measures and acquire similar neuromodulatory afferents from the ascending reticular activation system. Meanwhile, these cholinergic nuclei project to similar targeting areas throughout multiple brain regions and have different spatial preferences in 3D. Moreover, some cholinergic connections are unidirectional, including projections from the pedunculopontine nucleus and laterodorsal tegmental nucleus to the ventral posterior complex of the thalamus, and have different impacts on locomotion and anxiety. These results reveal the integrated cholinergic connectome of the midbrain, thus improving the present understanding of the organizational structure of the pontine-tegmental cholinergic system from its anatomical structure to its functional modulation.

The glutamatergic and GABAergic neurons in the ventral tegmental area (VTA) and substantia nigra pars compacta (SNc) mediated diverse brain functions. However, their whole-brain neural connectivity has not been comprehensively mapped. Here we used the virus tracers to characterize the whole-brain inputs and outputs of glutamatergic and GABAergic neurons in VTA and SNc. We found that these neurons received similar inputs from upstream brain regions, but some quantitative differences were also observed. Neocortex and dorsal striatum provided a greater share of input to VTA glutamatergic neurons. Periaqueductal gray and lateral hypothalamic area preferentially innervated VTA GABAergic neurons. Specifically, superior colliculus provided the largest input to SNc glutamatergic neurons. Compared to input patterns, the output patterns of glutamatergic and GABAergic neurons in the VTA and SNc showed significant preference to different brain regions. Our results laid the anatomical foundation for understanding the functions of cell-type-specific neurons in VTA and SNc.

The main target of retrosynthesis is to recursively decompose desired molecules into available building blocks. Existing template-based retrosynthesis methods follow a template selection stereotype and suffer from limited training templates, which prevents them from discovering novel reactions. To overcome this limitation, we propose an innovative retrosynthesis prediction framework that can compose novel templates beyond training templates. As far as we know, this is the first method that uses machine learning to compose reaction templates for retrosynthesis prediction. Besides, we propose an effective reactant candidate scoring model that can capture atom-level transformations, which helps our method outperform previous methods on the USPTO-50K dataset. Experimental results show that our method can produce novel templates for 15 USPTO-50K test reactions that are not covered by training templates. We have released our source implementation.

BACKGROUND: Immunotherapy using dendritic cell (DC) vaccine has the potential to overcome the bottleneck of cancer therapy. METHODS: We engineered Lewis lung cancer cells (LLCs) and bone marrow–derived DCs to express tumor-associated antigen (TAA) ovalbumin (OVA) via lentiviral vector plasmid encoding OVA gene. We then tested the antitumor effect of modified DCs both in vitro and in vivo. RESULTS: The results demonstrated that in vitro modified DCs could dramatically enhance T-cell proliferation (P

Paraffin embedding is widely used in microscopic imaging for preparing biological specimens. However, owing to significant fluorescence quenching during the embedding process, it is not compatible with fluorescent-labeling techniques, such as transgenic and viral labeling using green fluorescent protein (GFP). Here, we investigate the quenching mechanism and optimize the embedding process to improve the preservation of fluorescence intensity. The results show that dehydration is the main reason for fluorescence quenching during paraffin embedding, caused by the full denaturation of GFP molecules in ethyl alcohol. To evaluate fluorescent and morphological preservation, we modified the embedding process using tertiary butanol (TBA) instead of ethyl alcohol. Fluorescence intensity following TBA dehydration increased 12.08-fold of that observed in the traditional method. We obtained uniform fluorescence maintenance throughout the whole mouse brain, while the continuous apical dendrites, spines, and axon terminals were shown evenly within the cortex, hippocampus, and the amygdala. Moreover, we embedded a whole rat brain labeled with AAV in the prelimbic cortex (Prl). With the axon terminals in different areas, such as the caudate putamen, thalamus, and pyramidal tract, the results showed a continuous tract of Prl neurons throughout the whole brain. This method was also suitable for tdTomota labeled samples. These findings indicate that this modified embedding method could be compatible with GFP and provides a potential turning point for applications in the fluorescent labeling of samples.

Resin embedding has been widely used for precise imaging of fluorescently labeled biological samples with optical and electron microscopy. The low preservation rate of fluorescence, especially for red fluorescent proteins, has limited the application of resin embedding in multifluorescent protein-labeled samples. Here, we optimized the embedding method to retain the intensity of multiple fluorescent proteins during resin embedding. By reducing the polymerization temperature from 50 to 35°C and adding a fluorescent protein protection reagent during the embedding process, we successfully increased the fluorescence preservation rate by nearly twofold for red fluorescent proteins, including tdTomato, mCherry, and DsRed. Meanwhile, the background fluorescence decreased significantly in the optimized embedding method. This method is suitable not only for red fluorescent protein-labeled samples but also for blue (BFP) and green fluorescent protein (GFP)-labeled samples. We embedded brains labeled with BFP, DsRed, and GFP via AAV and rabies virus and acquired the distribution of input neurons to different cortical areas. With GFP/tdTomato double-labeled samples in resin, we obtained the cholinergic projectome of the pedunculopontine tegmental nucleus (PPTg) and the distribution of cholinergic neurons at single-neuron resolution in the whole brain simultaneously. Input cholinergic terminals from the PPTg were found to innervate the cholinergic soma and fiber in the neocortex, basal forebrain and brainstem, indicating that local cholinergic neurons received long-range cholinergic modulation from the midbrain. Our optimized method is useful for embedding multicolor fluorescent protein-labeled samples to acquire multidimensional structural information on neural circuits at single-neuron resolution in the whole brain.

In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively. Moreover, NRLMF assigns higher importance levels to positive observations (i.e., the observed interacting drug-target pairs) than negative observations (i.e., the unknown pairs). Because the positive observations are already experimentally verified, they are usually more trustworthy. Furthermore, the local structure of the drug-target interaction data has also been exploited via neighborhood regularization to achieve better prediction accuracy. We conducted extensive experiments over four benchmark datasets, and NRLMF demonstrated its effectiveness compared with five state-of-the-art approaches.