Genomic and Phenotypic Analysis of bla KPC-2 Associated Carbapenem Resistance in Klebsiella aerogenes: Insights into Clonal Spread and Resistance Mechanisms Across Hospital Departments in Beijing.

Purpose: This study conducted an phenotypic and whole-genome sequencing analysis with Klebsiella aerogenes to elucidate its clinical epidemiological characteristics, antimicrobial resistance (AMR) phenotype, biofilm formation ability and hemolytic activity testing, AMR genes and phylogenetic relationships, so as to provide a further understanding of the intra-hospital strain transmission. Methods: Samples were collected from a hospital in Beijing between 2020 and 2022. All strains underwent bacterial identification, antimicrobial susceptibility testing (AST) using the VITEK-2 compact system. Biofilm formation ability and hemolytic activity were tested. Second-generation sequencing was applied to all strains, with those carrying the bla KPC gene were selected for third-generation sequencing. Whole-genome analysis identified resistance genes, plasmid types, MLST typing, and phylogenetic relationships. Plasmids were assembled to detect plasmid structures and AMR gene location. Results: Among the 42 K. aerogenes isolates, 21 were carbapenem-resistant K. aerogenes (CRKA). All strains exhibited strong biofilm formation and no hemolytic activity. Most were sourced from sputum (83.3%). CRKA demonstrated extensive resistance to antibiotics, particularly ß-lactamase inhibitors and Cefotetan. This resistance pattern was closely associated with the presence of an IncFII(pHN7A8) plasmid, which carried multiple resistance genes, including bla KPC-2, bla CTX-M-65, bla TEM-1, rmtB and a large number of mobile elements. The majority of CRKA strains clustered within the same branch of the phylogenetic tree, exhibiting minimal single nucleotide polymorphism (0-13 SNPs) differences, and they shared the same sequence type (ST292), resistance genes, and plasmids, originating from different departments, suggesting clonal transmission among the hospital. Conclusion: Our research reveals that the clonal transmission of CRKA occurs across various departments within the hospital. The widespread resistance observed in CRKA, attributed to the presence of bla KPC and ESBLs genes, underscores the need for heightened vigilance to prevent the further dissemination of CRKA within the hospital and, potentially, throughout the wider community.

Inhibition of the ATP synthase increases sensitivity of Escherichia coli carrying mcr-1 to polymyxin B.

Bacterial infections caused by multidrug-resistant (MDR) gram-negative strains carrying the mobile colistin resistance gene mcr-1 are serious threats to world public health due to the lack of effective treatments. Inhibition of the ATP synthase makes bacteria such as Staphylococcus aureus and Klebsiella pneumoniae more sensitive to polymyxin. This provides new strategies for treating infections caused by polymyxins-resistant bacteria carrying mcr-1. Six mcr-1-positive strains were isolated from clinical samples, and all were identified as Escherichia coli. Here we investigated several ATP synthase inhibitors, N,N'-dicyclohexylcarbodiimide (DCCD), resveratrol, and piceatannol, for their antibacterial effects against the mcr-1-positive strains combined with polymyxin B (POL). Checkerboard assay, time-kill assay, biofilm inhibition and eradication assay indicated the significant synergistic effect of ATP synthase inhibitors/POL combination in vitro. Meanwhile, mouse infection model experiment was also performed, showing a 5 log10 reduction of the pathogen after treatment with the resveratrol/POL combination. Moreover, adding adenosine disodium triphosphate (Na2ATP) could inhibit the antibacterial effect of the ATP synthase inhibitors/POL combination. In conclusion, our study confirmed that inhibition of ATP production could increase the susceptibility of bacteria carrying mcr-1 to polymyxins. This provides a new strategy against polymyxins-resistant bacteria infection.

Association of metals and bisphenols exposure with lipid profiles and dyslipidemia in Chinese adults: Independent, combined and interactive effects.

BACKGROUND: Although studies have assessed the association of metals and bisphenols with lipid metabolism, the observed results have been controversial, and limited knowledge exists about the combined and interactive effects of metals and bisphenols exposure on lipid metabolism. METHODS: Plasma metals and serum bisphenols concentrations were evaluated in 888 participants. Multiple linear regression and logistic regression models were conducted to assess individual associations of 18 metals and 3 bisphenols with 5 lipid profiles and dyslipidemia risk, respectively. The dose-response relationships of targeted contaminants with lipid profiles and dyslipidemia risk were captured by applying a restriction cubic spline (RCS) function. The bayesian kernel machine regression (BKMR) model was used to assess the overall effects of metals and bisphenols mixture on lipid profiles and dyslipidemia risk. The interactive effects of targeted contaminants on interested outcomes were explored by constructing an interaction model. RESULTS: Single-contaminant analyses revealed that exposure to iron (Fe), nickel (Ni), copper (Cu), arsenic (As), selenium (Se), strontium (Sr), and tin (Sn) was associated with elevated lipid levels. Cobalt (Co) showed a negative association with high density lipoprotein cholesterol (HDL-C). Bisphenol A (BPA) and bisphenol AF (BPAF) were associated with decreased HDL-C levels, with nonlinear associations observed. Vanadium (V), lead (Pb), and silver (Ag) displayed U-shaped dose-response relationships with most lipid profiles. Multi-contaminant analyses indicated positive trends between contaminants mixture and total cholesterol (TC), triglycerides (TG), low density lipoprotein cholesterol (LDL-C), and non-high-density lipoprotein cholesterol (non-HDL-C). The interaction analyses showed that Se-Fe exhibited synergistic effects on LDL-C and non-HDL-C, and Se-Sn showed a synergistic effect on HDL-C. CONCLUSIONS: Our study suggested that exposure to metals and bisphenols was associated with changes in lipid levels, and demonstrated their combined and interactive effects.

Tumour mutation burden and infiltrating immune cell subtypes influenced the breast cancer prognosis.

Background: Most of its issues are still undecided on the relationship between tumour mutation burden (TMB) and immune-related genes in the breast cancer. This study explores their relationship based on gene mutation and transcription data in The Cancer Genome Atlas (TCGA) database, and the effects of immune cells in TMB and tumour microenvironments on prognosis of breast cancer patients. Methods: Cases were divided into low-TMB and high-TMB subgroups. Differentially expressed immune-related genes were identified in different TMB subgroups, and patient prognosis was predicted by gene function enrichment analysis, invasive immune cells and different clinical pathological features were compared among different TMB subgroups. Results: A total of 986 mutation data from breast cancer patients were obtained. Compared with low-TMB group, the survival period of high-TMB group was relatively longer. A total of 337 differential expression genes were identified in this study. Of these genes, seven differentially expressed immune-related genes were associated with prognosis. In the high-TMB group, activated CD4+ memory T cells and other cells had high expression, the expression ratio of memory B cells and other cells in low-TMB group was high. Conclusions: TMB-related immunological infiltration characteristics showed meaningful value for prognosis prediction for breast cancer patients. Differentially expressed immune-related genes in TMB subgroups provide important information on the survival prediction.

Improve-RRBS: a novel tool to correct the 3' trimming of reduced representation sequencing reads.

Motivation: Reduced Representation Bisulfite Sequencing (RRBS) is a popular approach to determine DNA methylation of the CpG-rich regions of the genome. However, we observed that false positive differentially methylated sites (DMS) are also identified using the standard computational analysis. Results: During RRBS library preparation the MspI digested DNA undergo end-repair by a cytosine at the 3' end of the fragments. After sequencing, Trim Galore cuts these end-repaired nucleotides. However, Trim Galore fails to detect end-repair when it overlaps with the 3' end of the sequencing reads. We found that these non-trimmed cytosines bias methylation calling, thus, can identify DMS erroneously. To circumvent this problem, we developed improve-RRBS, which efficiently identifies and hides these cytosines from methylation calling with a false positive rate of maximum 0.5%. To test improve-RRBS, we investigated four datasets from four laboratories and two different species. We found non-trimmed 3' cytosines in all datasets analyzed and as much as >50% of false positive DMS under certain conditions. By applying improve-RRBS, these DMS completely disappeared from all comparisons. Availability and implementation: Improve-RRBS is a freely available python package https://pypi.org/project/iRRBS/ or https://github.com/fothia/improve-RRBS to be implemented in RRBS pipelines.

Predicting personalized cumulative live birth rate after a complete in vitro fertilization cycle: an analysis of 32,306 treatment cycles in China.

BACKGROUND: The cumulative live birth rate (CLBR) has been regarded as a key measure of in vitro fertilization (IVF) success after a complete treatment cycle. Women undergoing IVF face great psychological pressure and financial burden. A predictive model to estimate CLBR is needed in clinical practice for patient counselling and shaping expectations. METHODS: This retrospective study included 32,306 complete cycles derived from 29,023 couples undergoing IVF treatment from 2014 to 2020 at a university-affiliated fertility center in China. Three predictive models of CLBR were developed based on three phases of a complete cycle: pre-treatment, post-stimulation, and post-treatment. The non-linear relationship was treated with restricted cubic splines. Subjects from 2014 to 2018 were randomly divided into a training set and a test set at a ratio of 7:3 for model derivation and internal validation, while subjects from 2019 to 2020 were used for temporal validation. RESULTS: Predictors of pre-treatment model included female age (non-linear relationship), antral follicle count (non-linear relationship), body mass index, number of previous IVF attempts, number of previous embryo transfer failure, type of infertility, tubal factor, male factor, and scarred uterus. Predictors of post-stimulation model included female age (non-linear relationship), number of oocytes retrieved (non-linear relationship), number of previous IVF attempts, number of previous embryo transfer failure, type of infertility, scarred uterus, stimulation protocol, as well as endometrial thickness, progesterone and luteinizing hormone on trigger day. Predictors of post-treatment model included female age (non-linear relationship), number of oocytes retrieved (non-linear relationship), cumulative Day-3 embryos live-birth capacity (non-linear relationship), number of previous IVF attempts, scarred uterus, stimulation protocol, as well as endometrial thickness, progesterone and luteinizing hormone on trigger day. The C index of the three models were 0.7559, 0.7744, and 0.8270, respectively. All models were well calibrated (p = 0.687, p = 0.468, p = 0.549). In internal validation, the C index of the three models were 0.7422, 0.7722, 0.8234, respectively; and the calibration P values were all greater than 0.05. In temporal validation, the C index were 0.7430, 0.7722, 0.8234 respectively; however, the calibration P values were less than 0.05. CONCLUSIONS: This study provides three IVF models to predict CLBR according to information from different treatment stage, and these models have been converted into an online calculator ( https://h5.eheren.com/hcyc/pc/index.html#/home ). Internal validation and temporal validation verified the good discrimination of the predictive models. However, temporal validation suggested low accuracy of the predictive models, which might be attributed to time-associated amelioration of IVF practice.

Copper induces cytotoxicity in freshwater bivalve Anodonta woodiana hemocytes.

Hemocytes of freshwater bivalves are an important target model for evaluating copper (Cu) toxicity in vitro, with excess Cu causing adverse responses in these organisms. Despite this, the mechanisms underlying cytotoxicity remain poorly understood. The freshwater bivalve Anodonta woodiana, employed as a model organism in freshwater environments, was utilized in this study. Hemocytes of A. woodiana were exposed to various aqueous Cu treatments (0.001, 0.01, 0.1, 1, and 10 mg/L), and a control group (no Cu added) for 3 h to investigate the cytotoxic mechanisms of Cu. The results showed a significant increase in the production of reactive oxygen species in hemocytes of all Cu exposed groups compared to the control (p < 0.05). Remarkably, Cu treatments disrupted the cellular membrane (p < 0.05) but did not induce significant changes in the stability of the lysosomal membrane. Cu targeted the mitochondria, leading to a reduction in mitochondrial membrane potential. Additionally, all Cu treatments significantly increased the degree of DNA damage (p < 0.05). Cellular damage and a significant decline in cell viability were observed when the Cu exposure concentration reached 0.1, 1, and 10 mg/L (p < 0.05). Our study provides new insights into the cytotoxicity mechanisms triggered by Cu in hemocytes of the freshwater bivalve A. woodiana, even under environmentally relevant conditions of 0.01 mg/L exposure.

Far-field superresolution of thermal sources by double homodyne or double array homodyne detection.

We propose two schemes for estimating the separation of two thermal sources via double homodyne and double array homodyne detection considering the joint measurement of conjugate quadratures of the image plane field.By using the Cramér-Rao bound, we demonstrate that the two schemes can estimate the separation well below the Rayleigh limit and have an advantage over direct imaging when the average photon number per source exceeds five.For arbitrary source strengths, double homodyne detection is superior to homodyne detection when the separation is above 25/4 σ/N s , σ is the beam width, Ns is the average photon number per source.A larger separation can be estimated better via double array homodyne detection with the superiority of flexible operation compared with other schemes. High-speed and high-efficiency detection enables the measurement schemes with potential practical applications in fluorescence microscopy, astronomy and quantum imaging.

Pixelwise immersion factor calibration for underwater hyperspectral imaging instruments.

In situ spectral reflectance initially captured at high spatial resolution with underwater hyperspectral imaging (UHI) is effective for classification and quantification in oceanic biogeochemical studies; however, the measured spectral radiance is rarely used as an absolute quantity due to challenges in calibration of UHI instruments. In this paper, a commercial UHI instrument was calibrated for radiometric flat field response and pixelwise immersion effect to support in situ measurement of absolute spectral radiance. The radiometric and immersion factor calibrations of the UHI instrument were evaluated quantitatively through comparative experiments with a spectroradiometer and a spectrometer. Results show that the immersion factor of the center pixel of the tested UHI instrument was 1.763 in pure water at 600 nm, and the averaged difference in immersion factor between the center and edge pixel of the UHI instrument in the visible light band was only 1∼3% across its half angle field of view of 35° in air. The new calibration coefficients were further used to calculate the spectral radiance of transmitted sunlight through ice algae clusters in sea ice measured by the UHI instrument during an Arctic under-ice bio-optical survey.

Reconstruction of Historical Metal Backgrounds in Lacustrine Environments of China Using an Anodonta woodiana "Specimen Bank".

Anodonta woodiana samples from Xidong Water Works and Mashan in Taihu Lake, Yiyang near Dongting Lake, and Taiping Harbor in Gehu Lake preserved in a "specimen bank" established for the "Freshwater Mussel Watch" monitoring program were used to determine the historical metal backgrounds from different waters in the present study. The elements Co, Ni, Mo, Cd, Al, Cr, Mn, Fe, Cu, Zn, As, Ba, and Pb were determined using A. woodiana from four lacustrine sites. The results showed that Al, Cr, Mn, Fe, Cu, Zn, As, Ba, and Pb were all detected, whereas Co, Ni, Mo, and Cd were below the detection limits of 0.0165, 0.0106, 0.0189 and 0.0182 µg kg- 1, respectively. In particular, A. woodiana was noted to be an unusual Mn hyperaccumulator (ranged from 5124.09 to 13015.47 µg g- 1). The results of discriminant analysis showed that the four water samples could be accurately separated. This difference has the potential to infer the background difference of heavy metal pollution in different lacustrine habitats.

Clinical characteristics of patients with autoimmune nodopathy with anti-neurofascin155 antibodies.

Background: According to the latest guidelines on chronic inflammatory demyelinating polyradiculoneuropathy (CIDP), patients with CIDP with anti-neurofascin 155 (NF155) antibodies are referred to as autoimmune nodopathy (AN), an autoimmune disorder distinct from CIDP. We aimed to compare the clinical data of patients with AN with anti-NF155 antibodies with those of anti-NF155 antibodies-negative patients with CIDP, and to summarize the clinical characteristics of patients with AN with anti-NF155 antibodies. Methods: Nine patients with AN with anti-NF155 antibodies and 28 serologically negative patients with CIDP were included in this study. Diagnosis was made according to the diagnostic criteria in the European Academy of Neurology (EAN)/Peripheral Nerve Society (PNS) guidelines on CIDP published in 2021. Demographics, clinical manifestations, electrophysiological examination, cerebrospinal fluid (CSF) tests, and response to treatment were retrospectively analyzed. Results: Compared with serologically negative patients with CIDP, those patients with AN with anti-NF155 antibodies were younger (p=0.007), had a younger onset age (p=0.009), more frequent ataxia (p=0.019), higher CSF protein levels (p=0.001), and more frequent axon damage in electrophysiology (p=0.025). The main characteristics of patients with AN with anti-NF155 antibodies include younger age and onset age, limb weakness, sensory disturbance, ataxia, multiple motor-sensory peripheral neuropathies with demyelination and axonal damage on electrophysiological examination, markedly elevated CSF protein levels, and varying degrees of response to immunotherapy. Conclusions: Patients with AN with anti-NF155 antibodies differed from serologically negative patients with CIDP in terms of clinical characteristics. When AN is suspected, testing for antibodies associated with the nodes of Ranvier is essential for early diagnosis and to guide treatment.

Gut microbial alterations in arginine metabolism determine bone mechanical adaptation.

Although mechanical loading is essential for maintaining bone health and combating osteoporosis, its practical application is limited to a large extent by the high variability in bone mechanoresponsiveness. Here, we found that gut microbial depletion promoted a significant reduction in skeletal adaptation to mechanical loading. Among experimental mice, we observed differences between those with high and low responses to exercise with respect to the gut microbial composition, in which the differential abundance of Lachnospiraceae contributed to the differences in bone mechanoresponsiveness. Microbial production of L-citrulline and its conversion into L-arginine were identified as key regulators of bone mechanoadaptation, and administration of these metabolites enhanced bone mechanoresponsiveness in normal, aged, and ovariectomized mice. Mechanistically, L-arginine-mediated enhancement of bone mechanoadaptation was primarily attributable to the activation of a nitric-oxide-calcium positive feedback loop in osteocytes. This study identifies a promising anti-osteoporotic strategy for maximizing mechanical loading-induced skeletal benefits via the microbiota-metabolite axis.

The nutritional roles of zinc for immune system and COVID-19 patients.

Zinc (Zn) is a vital micronutrient that strengthens the immune system, aids cellular activities, and treats infectious diseases. A deficiency in Zn can lead to an imbalance in the immune system. This imbalance is particularly evident in severe deficiency cases, where there is a high susceptibility to various viral infections, including COVID-19 caused by SARS-CoV-2. This review article examines the nutritional roles of Zn in human health, the maintenance of Zn concentration, and Zn uptake. As Zn is an essential trace element that plays a critical role in the immune system and is necessary for immune cell function and cell signaling, the roles of Zn in the human immune system, immune cells, interleukins, and its role in SARS-CoV-2 infection are further discussed. In summary, this review paper encapsulates the nutritional role of Zn in the human immune system, with the hope of providing specific insights into Zn research.

An optimal method for diagnosing heart disease using combination of grasshopper evalutionary algorithm and support vector machines.

Due to the importance of accurate diagnosis and prompt treatment of this condition, the medical world is searching for a solution for its early detection and efficient treatment. Heart disease is one of the leading causes of death in modern society. With the development of computer science today, this issue can be resolved using computers. Data mining is one of the solutions for diagnosing this illness. One of the cutting-edge disciplines, data mining, can aid in better decision-making in many areas of medicine, including disease diagnosis and treatment. In order to improve diagnosis accuracy, a combination method using the evolutionary algorithms locust and support vector machine has been tested in this study. Use should be made of heart disease. Because of the hybrid nature of this approach, normalization is actually carried out in three steps: first, by using pre-processing operations to remove unknown and outlier data from the data set; second, by using the locust evolutionary algorithm to choose the best features from the available features; and third, by classifying the data set using a support vector machine. The accuracy criterion for the proposed method compared to Niobizin methods, neural networks, and J48 trees improved by 18 %, 30 %, and 24 %, respectively, after implementing it on the data set and comparing it with other algorithms used in the field of heart disease diagnosis.

Robust Temporal Link Prediction in Dynamic Complex Networks via Stable Gated Models With Reinforcement Learning.

Temporal link prediction is one of the most important tasks for predicting time-varying links by capturing dynamics within complex networks. However, it suffers from difficulties such as vulnerability to adversarial attacks and inadaptation to distinct evolutionary patterns. In this article, we propose a robust temporal link prediction architecture via stable gated models with reinforcement learning (SAGE-RL) consisting of a state encoding network (SEN) and a self-adaptive policy network (SPN). The former is utilized to capture network dynamics, while the latter helps the former adapt to distinct evolutionary patterns across various time periods. Within the SEN, a novel stable gate is introduced to ensure multiple spatiotemporal dependency paths and defend against adversarial attacks. An SPN is proposed to select different SEN instances by approximating the optimal action function, thereby adapting to various evolutionary patterns to learn the robust temporal and structural features from dynamic complex networks. It is proven that SAGE-LR with integral Lipschitz graph convolution is stable to relative perturbations in dynamic complex networks. With the aid of extensive experiments on five real-world graph benchmarks, SAGE-LR is shown to substantially outperform current state-of-the-art approaches in terms of precision and stability of temporal link prediction and ability to successfully defend against various attacks. We also implement the temporal link prediction in shipping transaction networks, which forecast effectively its potential transaction risks.

Artificial intelligence enhances whole-slide interpretation of PD-L1 CPS in triple-negative breast cancer: A multi-institutional ring study.

BACKGROUND AND AIMS: Evaluation of the programmed cell death ligand-1 (PD-L1) combined positive score (CPS) is vital to predict the efficacy of the immunotherapy in triple-negative breast cancer (TNBC), but pathologists show substantial variability in the consistency and accuracy of the interpretation. It is of great importance to establish an objective and effective method which is highly repeatable. METHODS: We proposed a model in a deep learning-based framework, which at the patch level incorporated cell analysis and tissue region analysis, followed by the whole-slide level fusion of patch results. Three rounds of ring studies (RSs) were conducted. Twenty-one pathologists of different levels from four institutions evaluated the PD-L1 CPS in TNBC specimens as continuous scores by visual assessment and our artificial intelligence (AI)-assisted method. RESULTS: In the visual assessment, the interpretation results of PD-L1 (Dako 22C3) CPS by different levels of pathologists have significant differences and showed weak consistency. Using AI-assisted interpretation, there were no significant differences between all pathologists (P = 0.43), and the intraclass correlation coefficient (ICC) value was increased from 0.618 [95% confidence interval (CI) = 0.524-0.719] to 0.931 (95% CI = 0.902-0.955). The accuracy of interpretation result is further improved to 0.919 (95% CI = 0.886-0.947). Acceptance of AI results by junior pathologists was the highest among all levels, and 80% of the AI results were accepted overall. CONCLUSION: With the help of the AI-assisted diagnostic method, different levels of pathologists achieved excellent consistency and repeatability in the interpretation of PD-L1 (Dako 22C3) CPS. Our AI-assisted diagnostic approach was proved to strengthen the consistency and repeatability in clinical practice.

Enrichment of Nutmeg Essential Oil from Oil-in-Water Emulsions with PAN-Based Membranes.

This study used polyacrylonitrile (PAN) and heat-treated polyacrylonitrile (H-PAN) membranes to enrich nutmeg essential oils, which have more complex compositions compared with common oils. The oil rejection rate of the H-PAN membrane was higher than that of the PAN membrane for different oil concentrations of nutmeg essential oil-in-water emulsions. After heat treatment, the H-PAN membrane showed a smaller pore size, narrower pore size distribution, a rougher surface, higher hydrophilicity, and higher oleophobicity. According to the GC-MS results, the similarities of the essential oils enriched by the PAN and H-PAN membranes to those obtained by steam distillation (SD) were 0.988 and 0.990, respectively. In addition, these two membranes also exhibited higher essential oil rejection for Bupleuri Radix, Magnolia Officinalis Cortex, Caryophylli Flos, and Cinnamomi Cortex essential oil-in-water emulsions. This work could provide a reference for membrane technology for the non-destructive separation of oil with complex components from oil-in-water emulsions.

Preparation of Mesophase Pitch with Fine-Flow Texture from Ethylene Tar/Naphthalene by Catalytic Synthesis for High-Thermal-Conductivity Carbon Fibers.

Mesophase pitch is usually prepared by radical polymerization or catalytic polymerization from coal tar, petroleum, and aromatic compounds, and the catalytic synthesis of mesophase pitch from pure aromatic compounds is more controllable in the preparation of high-quality mesophase pitch. However, the corrosive and highly toxic nature of the catalyst has limited the further development of this method. In this study, mesophase pitch was synthetized using ethylene tar and naphthalene as raw materials and boron trifluoride diethyl etherate as a catalyst. The effect of the catalytic reaction on the structure and properties of the mesophase pitch was investigated. The results show that naphthalene plays an important role in the mesophase content and reaction pressure (from above 6 MPa to 2.35 MPa). Mesophase pitch with fine-flow texture can be prepared by introducing more methylene groups, naphthenic structures, and aliphatic hydrocarbons during synthesis. Carbon fibers prepared from mesophase pitch show a split structure, and the thermal conductivity is 730 W/(m·K). This work provides theoretical support for lower toxicity and causticity and for reaction-controlled technology for the synthesis of high-purity mesophase pitch.

Self-distillation framework for document-level relation extraction in low-resource environments.

The objective of document-level relation extraction is to retrieve the relations existing between entities within a document. Currently, deep learning methods have demonstrated superior performance in document-level relation extraction tasks. However, to enhance the model's performance, various methods directly introduce additional modules into the backbone model, which often increases the number of parameters in the overall model. Consequently, deploying these deep models in resource-limited environments presents a challenge. In this article, we introduce a self-distillation framework for document-level relational extraction. We partition the document-level relation extraction model into two distinct modules, namely, the entity embedding representation module and the entity pair embedding representation module. Subsequently, we apply separate distillation techniques to each module to reduce the model's size. In order to evaluate the proposed framework's performance, two benchmark datasets for document-level relation extraction, namely GDA and DocRED are used in this study. The results demonstrate that our model effectively enhances performance and significantly reduces the model's size.

Efficient generation of cloned cats with altered coat colour by editing of the KIT gene.

Coat colour largely determines the market demand for several cat breeds. The KIT proto-oncogene (KIT) gene is a key gene controlling melanoblast differentiation and melanogenesis. KIT mutations usually cause varied changes in coat colour in mammalian species. In this study, we used a pair of single-guide RNAs (sgRNAs) to delete exon 17 of KIT in somatic cells isolated from two different Chinese Li Hua feline foetuses. Edited cells were used as donor nuclei for somatic cell nuclear transfer (SCNT) to generate cloned embryos presenting an average cleavage rate exceeding 85%, and an average blastocyst formation rate exceeding 9.5%. 131 cloned embryos were transplanted into four surrogates, and all surrogates carried their pregnancies to term, and delivered 4.58% (6/131) alive cloned kittens, with 1.53% (2/131) being KIT-edited heterozygotes (KITD17/+). The KITD17/+ cats presented an obvious darkness reduction in the mackerel tabby coat. Immunohistochemical analysis (IHC) of skin tissues indicated impaired proliferation and differentiation of melanoblasts caused by the lack of exon17 in feline KIT. To our knowledge, this is the first report on coat colour modification of cats through gene editing. The findings could facilitate further understanding of the regulatory role of KIT on feline coat colour and provide a basis for the breeding of cats with commercially desired coat colour.