A global dataset of terrestrial evapotranspiration and soil moisture dynamics from 1982 to 2020.

Quantifying terrestrial evapotranspiration (ET) and soil moisture dynamics accurately is crucial for understanding the global water cycle and surface energy balance. We present a novel, long-term dataset of global ET and soil moisture derived from the newly developed Simple Terrestrial Hydrosphere model, version 2 (SiTHv2). This ecohydrological model, driven by multi-source satellite observations and hydrometeorological variables from reanalysis data, provides daily global ET-related estimates (e.g., total ET, plant transpiration, soil evaporation, intercepted evaporation) and three-layer soil moisture dynamics at a 0.1° spatial resolution. Validation with in-situ measurements and comparisons with mainstream global ET and soil moisture products demonstrate robust performance of SiTHv2 in both magnitude and temporal dynamics of ET and soil moisture at multiple scales. The comprehensive water path characterization in the SiTHv2 model makes this seamless dataset particularly valuable for studies requiring synchronized water budget and vegetation response to water constraints. With its long-term coverage and high spatiotemporal resolution, the SiTHv2-derived ET and soil moisture product will be suitable to support analyses related to the hydrologic cycle, drought assessment, and ecosystem health.

The Impact of the COVID-19 Pandemic on Orthodontic Adult Patients' Characteristics and Decision on Orthodontic Appliance.

Purpose: There is an overall paucity of data examining the specific details of orthodontic patients' patterns or orthodontic service disruptions possibly influenced by COVID-19 pandemic. Therefore, this study aimed to explore the impact of the COVID-19 pandemic on orthodontic clinic disruption regarding the change in adult patients' characteristics and decisions of orthodontic treatment devices. Patients and Methods: A retrospective sample of 311 patients receiving orthodontic treatment from 2018 to 2022 were collected and divided into two groups: before (n = 167) and during (n = 144) the COVID-19 pandemic. Demographics, dental indices, the index of complexity outcome and need (ICON), and the degree of treatment difficulty were analyzed. Results: There were fewer students among patients during the COVID-19 pandemic than before (24.5% versus 35.9%, P = 0.036). Compared with patients before the pandemic, more patients selected ceramic brackets or Invisalign during the pandemic (P = 0.022). There were higher percentage of class I dental malocclusions among patients during than before the COVID-19 pandemic (P = 0.044). Moreover, the ICON score and the score of the degree of treatment difficulty were both significantly lower for patients during than before the COVID-19 pandemic (63.9±14.0 versus 58.3±15.3, P=0.001 and 7.4±2.6 versus 6.8±2.6, P=0.049, respectively). Conclusion: The COVID-19 pandemic influenced the characteristics and decisions of orthodontic patients. Those who still came to the orthodontic clinic despite the COVID-19 outbreak may have been those with less malocclusion severity and treatment difficulty. Besides, during the time of covid-19 pandemic, more patients chose ceramic bracket and Invisalign as their orthodontic treatment device rather than conventional or self-ligating metal brackets.

Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data.

Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.

Utilize multi-metabolic parameters as determinants for prediction of skeletal muscle mass quality in elderly type2 diabetic Chinese patients.

BACKGROUND: Sarcopenia, an age-related disorder characterized by loss of skeletal muscle mass and function, is recently recognized as a complication in elderly patients with type 2 diabetes mellitus (T2DM). Skeletal muscles play a crucial role in glycemic metabolism, utilizing around 80% of blood glucose. Accordingly, we aimed to explore the relationship between glucose metabolism and muscle mass in T2DM. METHODS: We employed the AWGS 2019 criteria for diagnosing low muscle mass and 1999 World Health Organization (WHO) diabetes diagnostic standards. This study included data of 191 individuals aged 60 and above with T2DM of Shanghai Pudong Hospital from November 2021 to November 2022. Fasting C-peptide (FPCP), fasting plasma glucose (FPG), 2-hour postprandial plasma glucose (PPG) and postprandial 2-hour C-peptide (PPCP), glycated hemoglobin A1c (HbA1c), glycated albumin (GA), serum lipids spectrum, renal and hepatic function, hemoglobin, and hormone were measured. Based on the findings of univariate analysis, logistic regression and receiver operating characteristic (ROC) curves were established. RESULTS: Participants with low muscle mass had significantly lower alanine and aspartate aminotransferase, and both FPCP and PPCP levels (P < 0.05). Compared with those without low muscle mass, low muscle mass group had significantly higher FPG, HbA1c, GA levels (P < 0.05). Body fat (BF, OR = 1.181) was an independent risk factor for low muscle mass. PPCP (OR = 0.497), BMI (OR = 0.548), and female (OR = 0.050) were identified as protective factors for low skeletal muscle. The AUC of BMI was the highest, followed by the PPCP, gender and BF (0.810, 0.675, 0.647, and 0.639, respectively), and the AUC of the combination of the above four parameters reached 0.895. CONCLUSIONS: In this cross-sectional study, BMI, Female, and PPCP associated with T2DM were protective factors for low muscle mass. BF was associated with T2DM and risk factor for low muscle mass.

Dual roles of dopaminergic pathways in olfactory learning and memory in the oriental fruit fly, Bactrocera dorsalis.

Dopamine (DA) is a key regulator of associative learning and memory in both vertebrates and invertebrates, and it is widely believed that DA plays a key role in aversive conditioning in invertebrates. However, the idea that DA is involved only in aversive conditioning has been challenged in recent studies on the fruit fly (Drosophila melanogaster), ants and crabs, suggesting diverse functions of DA modulation on associative plasticity. Here, we present the results of DA modulation in aversive olfactory conditioning with DEET punishment and appetitive olfactory conditioning with sucrose reward in the oriental fruit fly, Bactrocera dorsalis. Injection of DA receptor antagonist fluphenazine or chlorpromazine into these flies led to impaired aversive learning, but had no effect on the appetitive learning. DA receptor antagonists impaired both aversive and appetitive long-term memory retention. Interestingly, the impairment on appetitive memory was rescued not only by DA but also by octopamine (OA). Blocking the OA receptors also impaired the appetitive memory retention, but this impairment could only be rescued by OA, not by DA. Thus, we conclude that in B. dorsalis, OA and DA pathways mediate independently the appetitive and aversive learning, respectively. These two pathways, however, are organized in series in mediating appetitive memory retrieval with DA pathway being at upstream. Thus, OA and DA play dual roles in associative learning and memory retrieval, but their pathways are organized differently in these two cognitive processes - parallel organization for learning acquisition and serial organization for memory retrieval.

Anti-inflammatory and immunoregulatory effects of colistin sulphate on human PBMCs.

In previous studies, CST has been identified as having an immunostimulatory effect on Caenorhabditis elegans and macrophage of rats. Here, we further investigated its immunomodulatory effects on human peripheral blood mononuclear cells (PBMCs). LPS-stimulated PBMCs inflammatory model was established. Flow cytometry was applied to measure phagocytosis of PBMCs. Cytokine mRNA and protein expression levels of LPS-stimulated PBMCs with or without CST were measured by qRT-PCR and ELISA. The transcriptomic profile of CST-treated PBMCs was investigated by RNA-sequencing. Gene Ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) were applied to find potential signalling pathways. PBMCs showed a significant increase in phagocytic activity at 6 h after being incubated with CST at the concentration of 10 µg/mL. In the presence of LPS, CST maintained and promoted the expression of TNF-α and chemokine CCL24. The content of pro-inflammatory cytokines, such as IL-1ß, IL-6 and IFN-γ, which were released from LPS-stimulated PBMCs, was reduced by CST at 6 h. Anti-inflammatory cytokines, such as IL-4, IL-13 and TGF-ß1, were significantly increased by CST at 24 h. A total of 277 differentially expressed immune-related genes (DEIRGs) were detected and cytokine-cytokine receptor interaction was highly enriched. CST presented obvious anti-inflammatory and immunoregulatory effects in LPS-induced PBMCs inflammatory model not only by improving the ability of PBMCs to clear pathogens but also by decreasing pro-inflammatory cytokines and increasing anti-inflammatory cytokines. And the mechanism may be related to cytokine-cytokine receptor interaction.

[Influencing mechanism of stand age to the accumulation of microbial residue carbon in the Pinus masso-niana plantations]. / 林龄对马尾松人工林微生物残体碳积累的影响机制.

Clarifying the accumulation pattern of soil microbial residue carbon and its contribution to soil organic carbon (SOC) across stand age is helpful to understand the mechanism underlying soil carbon cycling. In this study, we analyzed the differences of amino sugar content, physicochemical properties and microbial composition in surface soil (0-10 cm) in young (6 a), middle-aged (13 a), near-mature (29 a), mature (38 a) and over-mature (57 a) Pinus massoniana plantations of subtropical China, quantified the microbial residue carbon content and its contribution to SOC, and discussed the mechanism. The results showed that SOC, total nitrogen, amorphous iron oxide and leucine aminopeptidase contents in the middle-aged plantation were significantly lower than those in the mature plantation. Soil pH and fungal/bacteria in young plantation were significantly higher than those in other age groups. Across the stand age gradient, the ranges of microbial, fungal and bacterial residue carbon were 7.52-14.63, 4.03-8.00 and 3.48-6.63 g·kg-1, respectively. The contents of all the residue carbon were significantly higher in the mature plantation than that of the middle-aged plantation, which were positively affected by soil total nitrogen content. The contribution of microbial, fungal, and bacterial residue carbon to SOC was 59.7%-72.3%, 33.4%-45.6%, and 24.3%-30.8%, respectively. The contribution of fungal residue carbon to SOC in young plantation was significantly higher than that in other age groups, and the contribution of bacterial residue carbon to SOC in middle-aged plantation was significantly higher than that in young and near-mature plantations, both of which were affected by soil inorganic nitrogen. Fungal residue carbon content was 1.2-1.7 times as that of bacterial residue carbon content, and dominated for the accumulation of microbial residue carbon. Results of the partial least squares model showed that stand age, soil environmental factors (such as leucine aminopeptidase, amorphous iron oxide, pH, and total nitrogen), bacterial residue carbon, fungal residue carbon and the contribution of bacterial residue carbon to SOC had total effects on the contribution of fungal residue carbon to SOC (-0.37, -1.16, 0.90, 1.09, and 0.83, respectively). In conclusion, stand age promoted the accumulation of microbial residue carbon but did not increase its contribution to SOC.

Exploring the ecological meanings of temperature sensitivity of ecosystem respiration from different methods.

Temperature sensitivity (Q10) of ecosystem respiration (Re) is a critical parameter for predicting global terrestrial carbon dynamics and its response to climate warming. However, the determination of Q10 has been controversial. In this study, we scrutinized the underpinnings of three mainstream methods to reveal their relationships in estimating Q10 for Re in the Heihe River Basin, northwest China. Specifically, these methods are Q10 estimated from the long-term method (Q10_long), short-term method (Q10_short), and the low-frequency (Q10_lf) and high-frequency (Q10_hf) signals decomposed by the singular spectrum analysis (SSA) method. We found that: 1) Q10_lf and Q10_long are affected by the confounding effects caused by non-temperature factors, and are 1.8 ± 0.3 and 1.7 ± 0.3, respectively. 2) The high-frequency signals of the SSA method and short-term method have consistent roles in removing the confounding effects. Both Q10_short and Q10_hf reflect the actual response of respiration to temperature. 3) Overall, Q10_long has a larger variability (1.7 ± 0.3) across different biomes, whereas Q10_short and Q10_hf show convergence (1.4 ± 0.2 and 1.3 ± 0.1, respectively). These results highlight the fact that Q10 can be overestimated by the long-term method, whereas the short-term method and high-frequency signals decomposed by the SSA method can obtain closer and convergent values after removing the confounding effects driven by non-temperature factors. Therefore, it is recommended to use the Q10 value estimated by the short-term method or high-frequency signals decomposed by the SSA method to predict carbon dynamics and its response to global warming in Earth system models.

Parrot optimizer: Algorithm and applications to medical problems.

Stochastic optimization methods have gained significant prominence as effective techniques in contemporary research, addressing complex optimization challenges efficiently. This paper introduces the Parrot Optimizer (PO), an efficient optimization method inspired by key behaviors observed in trained Pyrrhura Molinae parrots. The study features qualitative analysis and comprehensive experiments to showcase the distinct characteristics of the Parrot Optimizer in handling various optimization problems. Performance evaluation involves benchmarking the proposed PO on 35 functions, encompassing classical cases and problems from the IEEE CEC 2022 test sets, and comparing it with eight popular algorithms. The results vividly highlight the competitive advantages of the PO in terms of its exploratory and exploitative traits. Furthermore, parameter sensitivity experiments explore the adaptability of the proposed PO under varying configurations. The developed PO demonstrates effectiveness and superiority when applied to engineering design problems. To further extend the assessment to real-world applications, we included the application of PO to disease diagnosis and medical image segmentation problems, which are highly relevant and significant in the medical field. In conclusion, the findings substantiate that the PO is a promising and competitive algorithm, surpassing some existing algorithms in the literature. The supplementary files and open source codes of the proposed Parrot Optimizer (PO) is available at https://aliasgharheidari.com/PO.html and https://github.com/junbolian/PO.

Exploratory study on the metabolic similarity of denitrifying carbon sources.

Mixed carbon sources have been developed for denitrification to eliminate the "carbon dependency" problem of single carbon. The metabolic correlation between different carbon sources is significant as guidance for the development of novel mixed carbon sources. In this study, to explore the metabolic similarity of denitrifying carbon sources, we selected alcohols (methanol, ethanol, and glycerol) and saccharide carbon sources (glucose, sucrose, and starch). Batch denitrification experiments revealed that methanol-acclimated sludge improved the denitrification rate of both methanol (14.42 mg-N/gMLVSS*h) and ethanol (9.65 mg-N/gMLVSS*h), whereas ethanol-acclimated sludge improved the denitrification rate of both methanol (7.80 mg-N/gMLVSS*h) and ethanol (22.23 mg-N/gMLVSS*h). In addition, the glucose-acclimated sludge and sucrose-acclimated sludge possibly improved the denitrification rate of glucose and sucrose, and the glycerol-acclimated sludge improved the denitrification rate of volatile fatty acids (VFAs), alcohols, and saccharide carbon sources. Functional gene analysis revealed that methanol, ethanol, and glycerol exhibited active alcohol oxidation and glyoxylate metabolism, and glycerol, glucose, and sucrose exhibited active glycolysis metabolism. This indicated that the similarity in the denitrification metabolism of these carbon sources was based on functional gene similarity, and glycerol-acclimated sludge exhibited the most diverse metabolism, which ensured its good denitrification effect with other carbon sources.

Efficacy and safety of omadacycline for treating complicated skin and soft tissue infections: a meta-analysis of randomized controlled trials.

OBJECTIVE: In the present study, we aimed to compare the clinical efficacy and safety of omadacycline (OMC) with its comparators for the treatment of complicated skin and soft tissue infections (cSSTIs) in adult patients. METHODS: Randomized controlled trials (RCTs) evaluating OMC for cSSTIs were searched in databases of PubMed, Embase, Cochrane, Web of Science, and Clinical Trial, up to July 2022. The primary outcomes were clinical efficacy and microbiological response, with secondary outcome was safety. RESULTS: Four RCTs consisting of 1,757 patients were included, with linezolid (LZD) as a comparator drug. For clinical efficacy, OMC was not inferior to LZD in the modified intent-to-treat (MITT) (OR: 1.24, 95% Cl: [0.93, 1.66], P = 0.15) and clinically evaluable (CE) populations (OR: 1.92, 95% Cl: [0.94, 3.92], P = 0.07). For microbiological response, OMC was numerically higher than LZD in the microbiologically evaluable (ME) (OR: 1.74, 95% Cl: [0.81, 3.74], P = 0.16) and microbiological MITT (micro-MITT) populations (OR: 1.27, 95% Cl: [0.92, 1.76], P = 0.14). No significant difference was found in subpopulations of monomicrobial or polymicrobial mixed infection populations. The mortality and adverse event rates were similar between OMC and LZD. CONCLUSIONS: OMC was as good as LZD in terms of clinical efficacy and microbiological response, and has similar safety issues in treating cSSTIs. OMC might be a promising option for treating cSSTIs in adult patients.

Alkadiyne-Pyrene Conjugated Frameworks with Surface Exclusion Effect for Ultrafast Seawater Desalination.

Billions of populations are suffering from the supply-demand imbalance of clean water, resulting in a global sustainability crisis. Membrane desalination is a promising method to produce fresh water from saline waters. However, conventional membranes often encounter challenges related to low water permeation, negatively impacting energy efficiency and water productivity. Herein, we achieve ultrafast desalination over the newly developed alkadiyne-pyrene conjugated frameworks membrane supported on a porous copper hollow fiber. With membrane distillation, the membrane exhibits nearly complete NaCl rejection (>99.9%) and ultrahigh fluxes (∼500 L m-2 h-1) from the seawater salinity-level NaCl solutions, which surpass the commercial polymeric membranes with at least 1 order of magnitude higher permeability. Experimental and theoretical investigations suggest that the large aspect ratio of membrane pores and the high evaporation area contribute to the high flux, and the graphene-like hydrophobic surface of conjugated frameworks exhibits complete salt exclusion. The simulations also confirm that the intraplanar pores of frameworks are impermeable for water and ions.

A Cross-Scale Transformer and Triple-View Attention Based Domain-Rectified Transfer Learning for EEG Classification in RSVP Tasks.

Rapid serial visual presentation (RSVP)-based brain-computer interface (BCI) is a promising target detection technique by using electroencephalogram (EEG) signals. However, existing deep learning approaches seldom considered dependencies of multi-scale temporal features and discriminative multi-view spectral features simultaneously, which limits the representation learning ability of the model and undermine the EEG classification performance. In addition, recent transfer learning-based methods generally failed to obtain transferable cross-subject invariant representations and commonly ignore the individual-specific information, leading to the poor cross-subject transfer performance. In response to these limitations, we propose a cross-scale Transformer and triple-view attention based domain-rectified transfer learning (CST-TVA-DRTL) for the RSVP classification. Specially, we first develop a cross-scale Transformer (CST) to extract multi-scale temporal features and exploit the dependencies of different scales features. Then, a triple-view attention (TVA) is designed to capture spectral features from triple views of multi-channel time-frequency images. Finally, a domain-rectified transfer learning (DRTL) framework is proposed to simultaneously obtain transferable domain-invariant representations and untransferable domain-specific representations, then utilize domain-specific information to rectify domain-invariant representations to adapt to target data. Experimental results on two public RSVP datasets suggests that our CST-TVA-DRTL outperforms the state-of-the-art methods in the RSVP classification task. The source code of our model is publicly available in https://github.com/ljbuaa/CST_TVA_DRTL.

Umbilical cord cysts: Classification, diagnosis, prognosis, and pregnancy recommendations.

Umbilical cord cysts (UCCs)-cysts located in the umbilical cord that are derived from an abnormal embryonic development process-are typically an incidental discovery during prenatal ultrasound. It can be described as either a pseudocyst or a true UCC, which results from focal edema or degeneration of Wharton jelly or the remnants of embryonic development, respectively. Due to the relative rarity of the UCC, the clinical guidance of UCCs is not yet available. Herein, the aim of this paper is to discuss the classification, diagnosis, prognosis, and clinical management of UCCs through a literature review, in order to improve the understanding of UCCs among clinical obstetricians and pediatricians.

A Multi-Graph Cross-Attention-Based Region-Aware Feature Fusion Network Using Multi-Template for Brain Disorder Diagnosis.

Functional connectivity (FC) networks based on resting-state functional magnetic imaging (rs-fMRI) are reliable and sensitive for brain disorder diagnosis. However, most existing methods are limited by using a single template, which may be insufficient to reveal complex brain connectivities. Furthermore, these methods usually neglect the complementary information between static and dynamic brain networks, and the functional divergence among different brain regions, leading to suboptimal diagnosis performance. To address these limitations, we propose a novel multi-graph cross-attention based region-aware feature fusion network (MGCA-RAFFNet) by using multi-template for brain disorder diagnosis. Specifically, we first employ multi-template to parcellate the brain space into different regions of interest (ROIs). Then, a multi-graph cross-attention network (MGCAN), including static and dynamic graph convolutions, is developed to explore the deep features contained in multi-template data, which can effectively analyze complex interaction patterns of brain networks for each template, and further adopt a dual-view cross-attention (DVCA) to acquire complementary information. Finally, to efficiently fuse multiple static-dynamic features, we design a region-aware feature fusion network (RAFFNet), which is beneficial to improve the feature discrimination by considering the underlying relations among static-dynamic features in different brain regions. Our proposed method is evaluated on both public ADNI-2 and ABIDE-I datasets for diagnosing mild cognitive impairment (MCI) and autism spectrum disorder (ASD). Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art methods. Our source code is available at https://github.com/mylbuaa/MGCA-RAFFNet.

Ancient forest plants possess cytotoxic properties causing liver cancer HepG2 cell apoptosis.

Here, we collected 154 plant species in China ancient forests looking for novel efficient bioactive compounds for cancer treatments. We found 600 bioactive phyto-chemicals that induce apoptosis of liver cancer cell in vitro. First, we screen the plant extract's in vitro cytotoxicity inhibition of cancer cell growth using in vitro HepG2 cell lines and MTT cytotoxicity. The results from these initial MTT in vitro cytotoxicity tests show that the most efficient plants towards hepatoma cytoxicity is Cephalotaxus sinensis, mint bush (Elsholtzia stauntonii) and winged spindle tree (Euonymus alatus). We then used in cell-counting kit-8 (CCK-8) to further understand in vivo tumor growth using nude mice and GC-MS and LC-QTOF-MS to analyze the composition of compounds in the extracts. Extracted chemically active molecules analyzed by network pharmacology showed inhibition on the growth of liver cancer cells by acting on multiple gene targets, which is different from the currently used traditional drugs acting on only one target of liver cancer cells. Extracts from Cephalotaxus sinensis, mint bush (Elsholtzia stauntonii) and winged spindle tree (Euonymus alatus) induce apoptosis in hepatoma cancer cell line HepG2 with a killing rate of more than 83% and a tumor size decrease by 62-67% and a killing rate of only 6% of normal hepatocyte LO2. This study highlight efficient candidate species for cancer treatment providing a basis for future development of novel plant-based drugs to help meeting several of the UN SDGs and planetary health.

SIRT6 deficiency causes ovarian hypoplasia by affecting Plod1-related collagen formation.

SIRT6 is a key member of the mammalian sirtuin family of conserved nicotinamide adenine dinucleotide (NAD+ )-dependent deacetylases. Previous studies have shown that SIRT6 can regulate metabolism, DNA damage repair and aging. Ovarian aging process usually share similar mechanisms with general aging, which is characterized by decreases in both numbers of ovarian follicles and the quality of oocytes. It is reported that the expression level of SIRT6 was significantly decreased in the ovaries of aged mice, and the level of SIRT6 was positively correlated with ovarian reserve, indicating that SIRT6 may be potential markers of ovarian aging. However, its biological roles in follicular development are still unclear. Here, we explored the effect of SIRT6 on follicular development and found that ovarian development was interrupted in SIRT6 knockout (KO) mice, leading to disruptions of puberty and the estrus cycle, significant decreases in numbers of secondary and antral follicles, and decreased collagen in the ovarian stroma. Plod1, a lysyl hydroxylase that is vital for collagen crosslinking and deposition, was decreased at both the mRNA and protein levels in SIRT6-deficient ovaries and granulosa cells (GCs). Additionally, we found abnormal estrogen levels in both SIRT6 KO mice and SIRT6 KD GCs, accompanied by decreases in the levels of the estrogen biosynthesis genes Cyp11a1, Cyp19a1, Mgarp, and increases in the levels of TNF-α and NF-κB. These results confirmed the effect of SIRT6 on follicular development and revealed a possible molecular mechanism for SIRT6 involvement in follicular development via effects on estrogen biosynthesis and collagen formation.

Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats.

Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants and manufactured substances conferring toxicity to human health. The present study investigated whether pyrene, a type of PAH, harms rats. Our research provides an effective feature selection strategy for the animal dataset from Wenzhou Medical University's Experimental Animal Center to thoroughly examine the impacts of PAH toxicity on rat features. Initially, we devised a high-performance optimization method (SCBA) and added the Sobol sequence, vertical crossover and horizontal crossover mechanisms to the bat algorithm (BA). The SCBA-KELM model, which combines SCBA with the kernel extreme learning machine model (KELM), has excellent accuracy and high stability for selecting features. Benchmark function tests are then used in this research to verify the overall optimization performance of SCBA. In this paper, the feature selection performance of SCBA-KELM is verified using various comparative experiments. According to the results, the features of the genes PXR, CAR, CYP2B1/2 and CYP1A1/2 have the most impact on rats. The SCBA-KELM model's classification performance for the gene dataset was 100%, and the model's precision value for the public dataset was around 96%, as determined by the classification index. In conclusion, the model utilized in this research is anticipated to be a reliable and valuable approach for toxicological classification and assessment.