Population dynamics of head-direction neurons during drift and reorientation.

The head direction (HD) system functions as the brain's internal compass1,2, classically formalized as a one-dimensional ring attractor network3,4. In contrast to a globally consistent magnetic compass, the HD system does not have a universal reference frame. Instead, it anchors to local cues, maintaining a stable offset when cues rotate5-8 and drifting in the absence of referents5,8-10. However, questions about the mechanisms that underlie anchoring and drift remain unresolved and are best addressed at the population level. For example, the extent to which the one-dimensional description of population activity holds under conditions of reorientation and drift is unclear. Here we performed population recordings of thalamic HD cells using calcium imaging during controlled rotations of a visual landmark. Across experiments, population activity varied along a second dimension, which we refer to as network gain, especially under circumstances of cue conflict and ambiguity. Activity along this dimension predicted realignment and drift dynamics, including the speed of network realignment. In the dark, network gain maintained a 'memory trace' of the previously displayed landmark. Further experiments demonstrated that the HD network returned to its baseline orientation after brief, but not longer, exposures to a rotated cue. This experience dependence suggests that memory of previous associations between HD neurons and allocentric cues is maintained and influences the internal HD representation. Building on these results, we show that continuous rotation of a visual landmark induced rotation of the HD representation that persisted in darkness, demonstrating experience-dependent recalibration of the HD system. Finally, we propose a computational model to formalize how the neural compass flexibly adapts to changing environmental cues to maintain a reliable representation of HD. These results challenge classical one-dimensional interpretations of the HD system and provide insights into the interactions between this system and the cues to which it anchors.

Dual mutations in the whitefly nicotinic acetylcholine receptor ß1 subunit confer target-site resistance to multiple neonicotinoid insecticides.

Neonicotinoid insecticides, which target insect nicotinic acetylcholine receptors (nAChRs), have been widely and intensively used to control the whitefly, Bemisia tabaci, a highly damaging, globally distributed, crop pest. This has inevitably led to the emergence of populations with resistance to neonicotinoids. However, to date, there have been no reports of target-site resistance involving mutation of B. tabaci nAChR genes. Here we characterize the nAChR subunit gene family of B. tabaci and identify dual mutations (A58T&R79E) in one of these genes (BTß1) that confer resistance to multiple neonicotinoids. Transgenic D. melanogaster, where the native nAChR Dß1 was replaced with BTß1A58T&R79E, were significantly more resistant to neonicotinoids than flies where Dß1 were replaced with the wildtype BTß1 sequence, demonstrating the causal role of the mutations in resistance. The two mutations identified in this study replace two amino acids that are highly conserved in >200 insect species. Three-dimensional modelling suggests a molecular mechanism for this resistance, whereby A58T forms a hydrogen bond with the R79E side chain, which positions its negatively-charged carboxylate group to electrostatically repulse a neonicotinoid at the orthosteric site. Together these findings describe the first case of target-site resistance to neonicotinoids in B. tabaci and provide insight into the molecular determinants of neonicotinoid binding and selectivity.

Neural tuning and representational geometry.

A central goal of neuroscience is to understand the representations formed by brain activity patterns and their connection to behaviour. The classic approach is to investigate how individual neurons encode stimuli and how their tuning determines the fidelity of the neural representation. Tuning analyses often use the Fisher information to characterize the sensitivity of neural responses to small changes of the stimulus. In recent decades, measurements of large populations of neurons have motivated a complementary approach, which focuses on the information available to linear decoders. The decodable information is captured by the geometry of the representational patterns in the multivariate response space. Here we review neural tuning and representational geometry with the goal of clarifying the relationship between them. The tuning induces the geometry, but different sets of tuned neurons can induce the same geometry. The geometry determines the Fisher information, the mutual information and the behavioural performance of an ideal observer in a range of psychophysical tasks. We argue that future studies can benefit from considering both tuning and geometry to understand neural codes and reveal the connections between stimuli, brain activity and behaviour.

Crystal-Phase Engineering in Heterogeneous Catalysis.

The performance of a chemical reaction is critically dependent on the electronic and/or geometric structures of a material in heterogeneous catalysis. Over the past century, the Sabatier principle has already provided a conceptual framework for optimal catalyst design by adjusting the electronic structure of the catalytic material via a change in composition. Beyond composition, it is essential to recognize that the geometric atomic structures of a catalyst, encompassing terraces, edges, steps, kinks, and corners, have a substantial impact on the activity and selectivity of a chemical reaction. Crystal-phase engineering has the capacity to bring about substantial alterations in the electronic and geometric configurations of a catalyst, enabling control over coordination numbers, morphological features, and the arrangement of surface atoms. Modulating the crystallographic phase is therefore an important strategy for improving the stability, activity, and selectivity of catalytic materials. Nonetheless, a complete understanding of how the performance depends on the crystal phase of a catalyst remains elusive, primarily due to the absence of a molecular-level view of active sites across various crystal phases. In this review, we primarily focus on assessing the dependence of catalytic performance on crystal phases to elucidate the challenges and complexities inherent in heterogeneous catalysis, ultimately aiming for improved catalyst design.

Vaccine Molecule Design Based on Phage Display and Computational Modeling against Rhabdovirus.

Rhabdoviruses with rich species lead a variety of high lethality and rapid transmission diseases to plants and animals around the globe. Vaccination is one of the most effective approaches to prevent and control virus disease. However, the key antigenic epitopes of glycoprotein being used for vaccine development are unclear. In this study, fish-derived Abs are employed for a Micropterus salmoides rhabdovirus (MSRV) vaccine design by phage display and bioinformatics analysis. We constructed an anti-MSRV phage Ab library to screen Abs for glycoprotein segment 2 (G2) (G129-266). Four M13-phage-displayed Abs (Ab-5, Ab-7, Ab-8 and Ab-30) exhibited strong specificity to target Ag, and Ab-7 had the highest affinity with MSRV. Ab-7 (300 µg/ml) significantly increased grass carp ovary cell viability to 83.40% and significantly decreased the titer of MSRV. Molecular docking results showed that the key region of Ag-Ab interaction was located in 10ESQEFTTLTSH20 of G2. G2Ser11 and G2Gln12 were replaced with alanine, respectively, and molecular docking results showed that the Ag-Ab was nonbinding (ΔG > 0). Then, the peptide vaccine KLH-G210-20 was immunized to M. salmoides via i.p. injection. ELISA result showed that the serum Ab potency level increased significantly (p < 0.01). More importantly, the challenge test demonstrated that the peptide vaccine elicited robust protection against MSRV invasion, and the relative percentage survival reached 62.07%. Overall, this study proposed an approach for screening key epitope by combining phage display technology and bioinformatics tools to provide a reliable theoretical reference for the prevention and control of viral diseases.

Emergent neural dynamics and geometry for generalization in a transitive inference task.

Relational cognition-the ability to infer relationships that generalize to novel combinations of objects-is fundamental to human and animal intelligence. Despite this importance, it remains unclear how relational cognition is implemented in the brain due in part to a lack of hypotheses and predictions at the levels of collective neural activity and behavior. Here we discovered, analyzed, and experimentally tested neural networks (NNs) that perform transitive inference (TI), a classic relational task (if A > B and B > C, then A > C). We found NNs that (i) generalized perfectly, despite lacking overt transitive structure prior to training, (ii) generalized when the task required working memory (WM), a capacity thought to be essential to inference in the brain, (iii) emergently expressed behaviors long observed in living subjects, in addition to a novel order-dependent behavior, and (iv) expressed different task solutions yielding alternative behavioral and neural predictions. Further, in a large-scale experiment, we found that human subjects performing WM-based TI showed behavior inconsistent with a class of NNs that characteristically expressed an intuitive task solution. These findings provide neural insights into a classical relational ability, with wider implications for how the brain realizes relational cognition.

Structure Sensitivity of Metal Catalysts Revealed by Interpretable Machine Learning and First-Principles Calculations.

The nature of the active sites and their structure sensitivity are the keys to rational design of efficient catalysts but have been debated for almost one century in heterogeneous catalysis. Though the Brønsted-Evans-Polanyi (BEP) relationship along with linear scaling relation has long been used to study the reactivity, explicit geometry, and composition properties are absent in this relationship, a fact that prevents its exploration in structure sensitivity of supported catalysts. In this work, based on interpretable multitask symbolic regression and a comprehensive first-principles data set, we discovered a structure descriptor, the topological under-coordinated number mediated by number of valence electrons and the lattice constant, to successfully address the structure sensitivity of metal catalysts. The database used for training, testing, and transferability investigation includes bond-breaking barriers of 20 distinct chemical bonds over 10 transition metals, two metal crystallographic phases, and 17 different facets. The resulting 2D descriptor composing the structure term and the reaction energy term shows great accuracy to predict the reaction barriers and generalizability over the data set with diverse chemical bonds in symmetry, bond order, and steric hindrance. The theory is physical and concise, providing a constructive strategy not only to understand the structure sensitivity but also to decipher the entangled geometric and electronic effects of metal catalysts. The insights revealed are valuable for the rational design of the site-specific metal catalysts.

Application of the TaqMan ARMS-PCR Approach for Genotyping Drug-Induced Hearing Loss Using Dried Blood Samples.

A single nucleotide variant in mitochondrial DNA (mtDNA) 1555A>G is associated with drug-induced hearing loss. For the 1555A>G mutation site, 1555A wild-type and 1555G mutant-type plasmids were constructed, respectively. In this study, a PCR method based on the TaqMan amplification refractory mutation system was proposed to detect mtDNA 1555A>G. A common upstream primer, a common TaqMan probe, and two downstream allele-specific primers with mismatched bases were designed. One-step amplification and detection of the wild-type and mutant type at the 1555 site were realized for the deafness-related gene through two reactions. Based on this detection method, the minimum detection limit of the wild-type and mutant type detection systems for plasmids was 50 copies/µL. The minimum sensitivity for the detection of nucleic acids in real dried blood spot (DBS) samples was 0.1 ng/µL. In the normal DBS DNA sample, the detection limit of the mutation abundance reached 0.78%. The specificity of the detection method was 100%, and the coefficient of variation was less than 3.36%. This approach was validated using clinical DNA extracted from 113 DBS samples of newborns. Additionally, it showed 100% agreement with bi-directional Sanger sequencing. It can be used as an optional method for the clinical detection of deafness-related genes.

Combining Phage Display Technology with In Silico-Designed Epitope Vaccine to Elicit Robust Antibody Responses against Emerging Pathogen Tilapia Lake Virus.

Antigen epitope identification is a critical step in the vaccine development process and is a momentous cornerstone for the development of safe and efficient epitope vaccines. In particular, vaccine design is difficult when the function of the protein encoded by the pathogen is unknown. The genome of Tilapia lake virus (TiLV), an emerging virus from fish, encodes protein functions that have not been elucidated, resulting in a lag and uncertainty in vaccine development. Here, we propose a feasible strategy for emerging viral disease epitope vaccine development using TiLV. We determined the targets of specific antibodies in serum from a TiLV survivor by panning a Ph.D.-12 phage library, and we identified a mimotope, TYTTRMHITLPI, referred to as Pep3, which provided protection against TiLV after prime-boost vaccination; its immune protection rate was 57.6%. Based on amino acid sequence alignment and structure analysis of the target protein from TiLV, we further identified a protective antigenic site (399TYTTRNEDFLPT410) which is located on TiLV segment 1 (S1). The epitope vaccine with keyhole limpet hemocyanin (KLH-S1399-410) corresponding to the mimotope induced the tilapia to produce a durable and effective antibody response after immunization, and the antibody depletion test confirmed that the specific antibody against S1399-410 was necessary to neutralize TiLV. Surprisingly, the challenge studies in tilapia demonstrated that the epitope vaccine elicited a robust protective response against TiLV challenge, and the survival rate reached 81.8%. In conclusion, this study revealed a concept for screening antigen epitopes of emerging viral diseases, providing promising approaches for development and evaluation of protective epitope vaccines against viral diseases. IMPORTANCE Antigen epitope determination is an important cornerstone for developing efficient vaccines. In this study, we attempted to explore a novel approach for epitope discovery of TiLV, which is a new virus in fish. We investigated the immunogenicity and protective efficacy of all antigenic sites (mimotopes) identified in serum of primary TiLV survivors by using a Ph.D.-12 phage library. We also recognized and identified the natural epitope of TiLV by bioinformatics, evaluated the immunogenicity and protective effect of this antigenic site by immunization, and revealed 2 amino acid residues that play important roles in this epitope. Both Pep3 and S1399-410 (a natural epitope identified by Pep3) elicited antibody titers in tilapia, but S1399-410 was more prominent. Antibody depletion studies showed that anti-S1399-410-specific antibodies were essential for neutralizing TiLV. Our study demonstrated a model for combining experimental and computational screens to identify antigen epitopes, which is attractive for epitope-based vaccine development.

CProMG: controllable protein-oriented molecule generation with desired binding affinity and drug-like properties.

MOTIVATION: Deep learning-based molecule generation becomes a new paradigm of de novo molecule design since it enables fast and directional exploration in the vast chemical space. However, it is still an open issue to generate molecules, which bind to specific proteins with high-binding affinities while owning desired drug-like physicochemical properties. RESULTS: To address these issues, we elaborate a novel framework for controllable protein-oriented molecule generation, named CProMG, which contains a 3D protein embedding module, a dual-view protein encoder, a molecule embedding module, and a novel drug-like molecule decoder. Based on fusing the hierarchical views of proteins, it enhances the representation of protein binding pockets significantly by associating amino acid residues with their comprising atoms. Through jointly embedding molecule sequences, their drug-like properties, and binding affinities w.r.t. proteins, it autoregressively generates novel molecules having specific properties in a controllable manner by measuring the proximity of molecule tokens to protein residues and atoms. The comparison with state-of-the-art deep generative methods demonstrates the superiority of our CProMG. Furthermore, the progressive control of properties demonstrates the effectiveness of CProMG when controlling binding affinity and drug-like properties. After that, the ablation studies reveal how its crucial components contribute to the model respectively, including hierarchical protein views, Laplacian position encoding as well as property control. Last, a case study w.r.t. protein illustrates the novelty of CProMG and the ability to capture crucial interactions between protein pockets and molecules. It's anticipated that this work can boost de novo molecule design. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this article are freely available at https://github.com/lijianing0902/CProMG.

Electronic Modulation in Homonuclear Dual-Atomic Catalysts for Enhanced CO2 Electroreduction.

Homonuclear dual-atomic catalysts showcase unique electronic modulation due to their dual metal centres, providing new direction in development of efficient catalysts for CO2 electroreduction. This article highlights a few cutting-edge homonuclear dual-atomic catalysts, focusing on their inherent advantages in efficient and selective CO2 electroreduction, to spotlight the potential application of dual-atomic catalysts in CO2 electroreduction.

Methylomonas defluvii sp. nov., a type I methane-oxidizing bacterium from a secondary sedimentation tank of a wastewater treatment plant.

An aerobic methanotroph was isolated from a secondary sedimentation tank of a wastewater treatment plant and designated strain OY6T. Cells of OY6T were Gram-stain-negative, pink-pigmented, motile rods and contained an intracytoplasmic membrane structure typical of type I methanotrophs. OY6T could grow at a pH range of 4.5-7.5 (optimum pH 6.5) and at temperatures ranging from 20 °C to 37 °C (optimum 30 °C). The major cellular fatty acids were C14 : 0, C16 : 1ω7c/C16 : 1ω6c and C16 : 1ω5c; the predominant respiratory quinone was MQ-8. The genome size was 5.41 Mbp with a DNA G+C content of 51.7 mol%. OY6T represents a member of the family Methylococcaceae of the class Gammaproteobacteria and displayed 95.74-99.64 % 16S rRNA gene sequence similarity to the type strains of species of the genus Methylomonas. Whole-genome comparisons based on average nucleotide identity (ANI) and digital DNA-DNA hybridisation (dDDH) confirmed that OY6T should be classified as representing a novel species. The most closely related type strain was Methylomonas fluvii EbBT, with 16S rRNA gene sequence similarity, ANI by blast (ANIb), ANI by MUMmer (ANIm) and dDDH values of 99.64, 90.46, 91.92 and 44.5 %, respectively. OY6T possessed genes encoding both the particulate methane monooxygenase enzyme and the soluble methane monooxygenase enzyme. It grew only on methane or methanol as carbon sources. On the basis of phenotypic, genetic and phylogenetic data, strain OY6T represents a novel species within the genus Methylomonas for which the name Methylomonas defluvii sp. nov. is proposed, with strain OY6T (=GDMCC 1.4114T=KCTC 8159T=LMG 33371T) as the type strain.

Karst fissures mitigate the negative effects of drought on plant growth and photosynthetic physiology.

Hard limestone substrates, which are extensively distributed, are believed to exacerbate drought and increase the difficulty of restoration in vulnerable karst regions. Fissures in such substrates may alleviate the negative effect of drought on plants, but the underlying mechanisms remain poorly understood. In a two-way factorial block design, the growth and photosynthesis of 2-year-old Phoebe zhennan seedlings were investigated in two water availabilities (high versus low) and three stimulated fissure habitat groups (soil, soil-filled fissure and non-soil-filled fissure). Moreover, the fissure treatments included both small and big fissures. Compared to the soil group, the non-soil-filled fissure group had decreased the total biomass, root biomass, total root length, and the root length of fine roots in the soil layer at both water availabilities, but increased net photosynthetic rate (Pn) and retained stable water use efficiency (WUE) at low water availability. However, there were no significant differences between the soil-filled fissure group and soil group in the biomass accumulation and allocation as well as Pn. Nevertheless, the SF group decreased the root distribution in total and in the soil layer, and also increased WUE at low water availability. Across all treatments, fissure size had no effect on plant growth or photosynthesis. Karst fissures filled with soil can alleviate drought impacts on plant root growth, which involves adjusting root distribution strategies and increasing water use efficiency. These results suggest that rock fissures can be involved in long-term plant responses to drought stress and vegetation restoration in rocky mountain environments under global climate change.

Constructing Metal(II)-Sulfate Site Catalysts toward Low Overpotential Carbon Dioxide Electroreduction to Fuel Chemicals.

Precise regulation of the active site structure is an important means to enhance the activity and selectivity of catalysts in CO2 electroreduction. Here, we creatively introduce anionic groups, which can not only stabilize metal sites with strong coordination ability but also have rich interactions with protons at active sites to modify the electronic structure and proton transfer process of catalysts. This strategy helps to convert CO2 into fuel chemicals at low overpotentials. As a typical example, a composite catalyst, CuO/Cu-NSO4/CN, with highly dispersed Cu(II)-SO4 sites has been reported, in which CO2 electroreduction to formate occurs at a low overpotential with a high Faradaic efficiency (-0.5 V vs. RHE, FEformate=87.4 %). Pure HCOOH is produced with an energy conversion efficiency of 44.3 % at a cell voltage of 2.8 V. Theoretical modeling demonstrates that sulfate promotes CO2 transformation into a carboxyl intermediate followed by HCOOH generation, whose mechanism is significantly different from that of the traditional process via a formate intermediate for HCOOH production.

A Variable Clock Underlies Internally Generated Hippocampal Sequences.

Humans have the ability to store and retrieve memories with various degrees of specificity, and recent advances in reinforcement learning have identified benefits to learning when past experience is represented at different levels of temporal abstraction. How this flexibility might be implemented in the brain remains unclear. We analyzed the temporal organization of male rat hippocampal population spiking to identify potential substrates for temporally flexible representations. We examined activity both during locomotion and during memory-associated population events known as sharp-wave ripples (SWRs). We found that spiking during SWRs is rhythmically organized with higher event-to-event variability than spiking during locomotion-associated population events. Decoding analyses using clusterless methods further indicate that a similar spatial experience can be replayed in multiple SWRs, each time with a different rhythmic structure whose periodicity is sampled from a log-normal distribution. This variability increases with experience despite the decline in SWR rates that occurs as environments become more familiar. We hypothesize that the variability in temporal organization of hippocampal spiking provides a mechanism for storing experiences with various degrees of specificity.SIGNIFICANCE STATEMENT One of the most remarkable properties of memory is its flexibility: the brain can retrieve stored representations at varying levels of detail where, for example, we can begin with a memory of an entire extended event and then zoom in on a particular episode. The neural mechanisms that support this flexibility are not understood. Here we show that hippocampal sharp-wave ripples, which mark the times of memory replay and are important for memory storage, have a highly variable temporal structure that is well suited to support the storage of memories at different levels of detail.

Atomic-Scale Visualization of Heterolytic H2 Dissociation and COx Hydrogenation on ZnO under Ambient Conditions.

Studying catalytic hydrogenation reactions on oxide surfaces at the atomic scale has been challenging because of the typical occurrence of these processes at ambient or elevated pressures, rendering them less accessible to atomic-scale techniques. Here, we report an atomic-scale study on H2 dissociation and the hydrogenation of CO and CO2 on ZnO using ambient pressure scanning tunneling microscopy, ambient pressure X-ray photoelectron spectroscopy, and density functional theory (DFT) calculations. We directly visualized the heterolytic dissociation of H2 on ZnO(101̅0) under ambient pressure and found that dissociation reaction does not require the assistance of surface defects. The presence of CO or CO2 on ZnO at 300 K does not impede the availability of surface sites for H2 dissociation; instead, CO can even enhance the stability of coadsorbed hydride species, thereby facilitating their dissociative adsorption. Our results show that hydride is the active species for hydrogenation, while hydroxyl cannot hydrogenate CO/CO2 on ZnO. Both AP studies and DFT calculations showed that the hydrogenation of CO2 on ZnO is thermodynamically and kinetically more favorable compared to that of CO hydrogenation. Our results point toward a two-step mechanism for CO hydrogenation, involving initial oxidation to CO2 at step sites on ZnO followed by reaction with hydride to form formate. These findings provide molecular insights into the hydrogenation of CO/CO2 on ZnO and deepen our understanding of syngas conversion and oxide catalysis in general.

GCN5 regulates ZBTB16 through acetylation, mediates osteogenic differentiation, and affects orthodontic tooth movement.

In the process of orthodontic tooth movement (OTM), periodontal ligament fibroblasts (PDLFs) must undergo osteogenic differentiation. OTM increased the expression of Zinc finger and BTB domain-containing 16 (ZBTB16), which is implicated in osteogenic differentiation. Our goal was to investigate the mechanism of PDLF osteogenic differentiation mediated by ZBTB16. The OTM rat model was established, and PDLFs were isolated and exposed to mechanical force. Hematoxylin-eosin staining, Alizarin Red staining, immunofluorescence, and immunohistochemistry were carried out. The alkaline phosphatase (ALP) activity was measured. Dual-luciferase reporter gene assay and chromatin immunoprecipitation assay were conducted. In OTM models, ZBTB16 was significantly expressed. Additionally, there was an uneven distribution of PDLFs in the OTM group, as well as an increase in fibroblasts and inflammatory infiltration. ZBTB16 interference hindered PDLF osteogenic differentiation and decreased Wnt and ß-catenin levels. Meanwhile, ZBTB16 activated the Wnt/ß-catenin pathway. ZBTB16 also enhanced the expression of the osteogenic molecules osterix, osteocalcin (OCN), osteopontin (OPN), and bone sialo protein (BSP) at mRNA and protein levels. The interactions between Wnt1 and ZBTB16, as well as GCN5 and ZBTB16, were also verified. The adeno-associated virus-shZBTB16 injection also proved to inhibit osteogenic differentiation and reduce tooth movement distance in in vivo tests. ZBTB16 was up-regulated in OTM. Through acetylation modification of ZBTB16, GCN5 regulated the Wnt/ß-catenin signaling pathway and further mediated PDLF osteogenic differentiation.

Comparative transcriptome analysis reveals candidate genes related to the sex differentiation of Schisandra chinensis.

Schisandra chinensis is a monoecious plant with unisex flowers. The fruit of S. chinensis is of high medical with economic value. The yield of S. chinensis fruit is related to the ratio of its female and male flowers. However, there is little research on its floral development and sex differentiation. To elucidate the possible mechanism for the sex differentiation of S. chinensis, we collected 18 samples of female and male flowers from three developmental stages and performed a comparative RNA-seq analysis aimed at identifying differentially expressed genes (DEGs) that may be related to sex differentiation. The results showed 936, 7179, and 6890 differentially expressed genes between female and male flowers at three developmental stages, respectively, and 466 candidate genes may play roles in sex differentiation. KEGG analysis showed genes involved in the flavonoid biosynthesis pathway and DNA replication pathway were essential for the development of female flowers. 51 MADS-box genes and 10 YABBY genes were identified in S. chinensis. The DEGs analysis indicated that MADS-box and YABBY genes were strongly related to the sex determination of S. chinensis. RT-qPCR confirmed the RNA-seq results of 20 differentially expressed genes, including three male-biased genes and 17 female-biased genes. A possible regulatory model of sex differentiation in S. chinensis was proposed according to our results. This study helps reveal the sex-differentiation mechanism of S. chinensis and lays the foundation for regulating the male-female ratio of S. chinensis in the future.

A novel method for determining postmortem interval based on the metabolomics of multiple organs combined with ensemble learning techniques.

Determining postmortem interval (PMI) is one of the most challenging and essential endeavors in forensic science. Developments in PMI estimation can take advantage of machine learning techniques. Currently, applying an algorithm to obtain information on multiple organs and conducting joint analysis to accurately estimate PMI are still in the early stages. This study aimed to establish a multi-organ stacking model that estimates PMI by analyzing differential compounds of four organs in rats. In a total of 140 rats, skeletal muscle, liver, lung, and kidney tissue samples were collected at each time point after death. Ultra-performance liquid chromatography coupled with high-resolution mass spectrometry was used to determine the compound profiles of the samples. The original data were preprocessed using multivariate statistical analysis to determine discriminant compounds. In addition, three interrelated and increasingly complex patterns (single organ optimal model, single organ stacking model, multi-organ stacking model) were established to estimate PMI. The accuracy and generalized area under the receiver operating characteristic curve of the multi-organ stacking model were the highest at 93% and 0.96, respectively. Only 1 of the 14 external validation samples was misclassified by the multi-organ stacking model. The results demonstrate that the application of the multi-organ combination to the stacking algorithm is a potential forensic tool for the accurate estimation of PMI.