Flagellimonas halotolerans sp. nov., a novel bacterium isolated from the South China Sea.

Two Gram-stain-negative strains, designed SYSU M86414T and SYSU M84420, were isolated from marine sediment samples of the South China Sea (Sansha City, Hainan Province, PR China). These strains were aerobic and could grow at pH 6.0-8.0 (optimum, pH 7.0), 4-37 °C (optimum, 28 °C), and in the presence of 0-10 % NaCl (w/v; optimum 3 %). The predominant respiratory menaquinone of strains SYSU M86414T and SYSU M84420 was MK-6. The primary cellular polar lipid was phosphatidylethanolamine. The major cellular fatty acids (>10 %) in both strains were iso-C15 : 0, iso-C15 : 1 G, and iso-C17 : 0 3-OH. The DNA G+C content of strains SYSU M86414T and SYSU M84420 were both 42.10 mol%. Phylogenetic analyses based on 16S rRNA gene sequences and core genes indicated that these novel strains belonged to the genus Flagellimonas and strain SYSU M86414T showed the highest 16S rRNA gene sequence similarity to Flagellimonas marinaquae JCM 11811T (98.83 %), followed by Flagellimonas aurea BC31-1-A7T (98.62 %), while strain SYSU M84420 had highest 16S rRNA gene sequence similarity to F. marinaquae JCM 11811T (98.76 %) and F. aurea BC31-1-A7T (98.55 %). Based on the results of polyphasic analyses, strains SYSU M86414T and SYSU M84420 should be considered to represent a novel species of the genus Flagellimonas, for which the name Flagellimonas halotolerans sp. nov. is proposed. The type strain of the proposed novel isolate is SYSU M86414T (=GDMCC 1.3806T=KCTC 102040T).

Unveiling the Hidden Impact: Hematoma Volumes Unravel Circuit Disruptions in Intracerebral Hemorrhage.

Intracerebral hemorrhage (ICH) imposes a significant burden on patients, and the volume of hematoma plays a crucial role in determining the severity and prognosis of ICH. Although significant recent progress has been made in understanding the cellular and molecular mechanisms of surrounding brain tissue in ICH, our current knowledge regarding the precise impact of hematoma volumes on neural circuit damage remains limited. Here, using a viral tracing technique in a mouse model of striatum ICH, two distinct patterns of injury response were observed in upstream connectivity, characterized by both linear and nonlinear trends in specific brain areas. Notably, even low-volume hematomas had a substantial impact on downstream connectivity. Neurons in the striatum-ICH region exhibited heightened excitability, evidenced by electrophysiological measurements and changes in metabolic markers. Furthermore, a strong linear relationship (R2 = 0.91) was observed between hematoma volumes and NFL damage, suggesting a novel biochemical index for evaluating changes in neural injury. RNA sequencing analysis revealed the activation of the MAPK signaling pathway following hematoma, and the addition of MAPK inhibitor revealed a decrease in neuronal circuit damage, leading to alleviation of motor dysfunction in mice. Taken together, our study highlights the crucial role of hematoma size as a determinant of circuit injury in ICH. These findings have important implications for clinical evaluations and treatment strategies, offering opportunities for precise therapeutic approaches to mitigate the detrimental effects of ICH and improve patient outcomes.

Brunonianines D-F, three new C19-diterpenoid alkaloids from the Delphinium brunonianum, with therapeutic effect on ovarian cancer in vitro and in vivo.

The current standard treatment for ovarian cancer consists of surgery to reduce the size of the tumor, followed by treatment with chemotherapeutic drugs, which have major side effects. Therefore, finding a new natural product drug with fewer side effects is a strategy. Delphinium brunonianum (D. brunonianum) is a traditional Tibetan medicine, mainly from southern Tibet, China, whereas the chemical constituents in this plant remain elusive. The major metabolites in the dichloromethane fraction of D. brunonianum were analyzed and purified by HPLC and various column chromatography techniques. Nine diterpenoid alkaloids (1-9) and one amide alkaloid (10) were isolated from D. brunonianum, including three novel C19-type diterpenoid alkaloids (Brunonianines D-F) (1-3). Their structures were elucidated by 1D/2D NMR, HR-ESI-MS and single-crystal X-ray diffraction analyses. All compounds were evaluated for toxicity in four tumor cell lines. Most of the compounds exhibited potent inhibitory effects on Skov-3 cell lines, with IC50 values ranging from 2.57 to 8.05 µM. The western blotting experiment was used to further analyze the expression levels of molecules in the Bax/Bcl-2/Caspase-3 signaling pathway for compound 1. Molecular docking was performed to predict the binding modes of Brunonianine D with target proteins. In vivo experiments were also performed and evaluated in real time by monitoring the size of the Skov-3 tumor. Additionally, tumor H&E staining and the TUNEL assay used to evaluate anti-tumor effects.

Multi-stimulus perception and visualization by an intelligent liquid metal-elastomer architecture.

Multi-stimulus responsive soft materials with integrated functionalities are elementary blocks for building soft intelligent systems, but their rational design remains challenging. Here, we demonstrate an intelligent soft architecture sensitized by magnetized liquid metal droplets that are dispersed in a highly stretchable elastomer network. The supercooled liquid metal droplets serve as microscopic latent heat reservoirs, and their controllable solidification releases localized thermal energy/information flows for enabling programmable visualization and display. This allows the perception of a variety of information-encoded contact (mechanical pressing, stretching, and torsion) and noncontact (magnetic field) stimuli as well as the visualization of dynamic phase transition and stress evolution processes, via thermal and/or thermochromic imaging. The liquid metal-elastomer architecture offers a generic platform for designing soft intelligent sensing, display, and information encryption systems.

Objective and automatic assessment approach for diagnosing attention-deficit/hyperactivity disorder based on skeleton detection and classification analysis in outpatient videos.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is diagnosed in accordance with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria by using subjective observations and information provided by parents and teachers. However, subjective analysis often leads to overdiagnosis or underdiagnosis. There are two types of motor abnormalities in patients with ADHD. First, hyperactivity with fidgeting and restlessness is the major diagnostic criterium for ADHD. Second, developmental coordination disorder characterized by deficits in the acquisition and execution of coordinated motor skills is not the major criterium for ADHD. In this study, a machine learning-based approach was proposed to evaluate and classify 96 patients into ADHD (48 patients, 26 males and 22 females, with mean age: 7y6m) and non-ADHD (48 patients, 26 males and 22 females, with mean age: 7y8m) objectively and automatically by quantifying their movements and evaluating the restlessness scales. METHODS: This approach is mainly based on movement quantization through analysis of variance in patients' skeletons detected in outpatient videos. The patients' skeleton sequence in the video was detected using OpenPose and then characterized using 11 values of feature descriptors. A classification analysis based on six machine learning classifiers was performed to evaluate and compare the discriminating power of different feature combinations. RESULTS: The results revealed that compared with the non-ADHD group, the ADHD group had significantly larger means in all cases of single feature descriptors. The single feature descriptor "thigh angle", with the values of 157.89 ± 32.81 and 15.37 ± 6.62 in ADHD and non-ADHD groups (p < 0.0001), achieved the best result (optimal cutoff, 42.39; accuracy, 91.03%; sensitivity, 90.25%; specificity, 91.86%; and AUC, 94.00%). CONCLUSIONS: The proposed approach can be used to evaluate and classify patients into ADHD and non-ADHD objectively and automatically and can assist physicians in diagnosing ADHD.

Treatment of giant neurofibromas in extremities and trunk wall of neurofibromatosis type 1 patients: a Chinese 12-year single-institution experience.

BACKGROUNDS: Giant neurofibromas occurring in individuals diagnosed with neurofibromatosis type 1 (NF1) often result in considerable disfigurement, functional impairment, and diminished quality of life. Although debulking surgery poses inherent risks of complications, it remains the most efficacious approach to address these issues. The primary objective of this study was to share our surgical experience with giant neurofibromas in the extremities and trunk wall of NF1 patients which may help surgeons to minimize intraoperative bleeding and facilitate tumor excision. METHODS: A retrospective review was conducted at a single center, encompassing 36 NF1 patients with giant neurofibromas in the extremities and trunk wall who underwent debulking surgery from July 2010 to July 2022. RESULTS: Twenty-one male and fifteen female NF1 patients who received one to four surgical interventions were evaluated. The average age at the time of surgery was 17.8 years. The median follow-up time was 52 months. Our findings revealed relatively low rates of complications and recurrence. Notably, patients expressed satisfaction with both the aesthetic and functional results. CONCLUSIONS: Debulking surgery of giant neurofibromas in the extremities and trunk wall of NF1 patients can effectively reduce the tumor burden, leading to improvements in both the appearance and function.

HMGB1 prefers to interact with structural RNAs and regulates rRNA methylation modification and translation in HeLa cells.

BACKGROUND: High-mobility group B1 (HMGB1) is both a DNA binding nuclear factor modulating transcription and a crucial cytokine that mediates the response to both infectious and noninfectious inflammation such as autoimmunity, cancer, trauma, and ischemia reperfusion injury. HMGB1 has been proposed to control ribosome biogenesis, similar as the other members of a class of HMGB proteins. RESULTS: Here, we report that HMGB1 selectively promotes transcription of genes involved in the regulation of transcription, osteoclast differentiation and apoptotic process. Improved RNA immunoprecipitation by UV cross-linking and deep sequencing (iRIP-seq) experiment revealed that HMGB1 selectively bound to mRNAs functioning not only in signal transduction and gene expression, but also in axon guidance, focal adhesion, and extracellular matrix organization. Importantly, HMGB1-bound reads were strongly enriched in specific structured RNAs, including the domain II of 28S rRNA, H/ACA box snoRNAs including snoRNA63 and scaRNAs. RTL-P experiment showed that overexpression of HMGB1 led to a decreased methylation modification of 28S rRNA at position Am2388, Cm2409, and Gm2411. We further showed that HMGB1 overexpression increased ribosome RNA expression levels and enhanced protein synthesis. CONCLUSION: Taken together, our results support a model in which HMGB1 binds to multiple RNA species in human cancer cells, which could at least partially contribute to HMGB1-modulated rRNA modification, protein synthesis function of ribosomes, and differential gene expression including rRNA genes. These findings provide additional mechanistic clues to HMGB1 functions in cancers and cell differentiation.

Iron-driven self-assembly of dopamine into dumbbell-shaped nanozyme for visual and rapid detection of norfloxacin on a smartphone-assisted platform.

Antibiotics in aquatic environments raise health concerns. Therefore, the rapid, on-site, and accurate detection of antibiotic residues is crucial for protecting the environment and human health. Herein, a dumbbell-shaped iron (Fe3+)-dopamine coordination nanozyme (Fe-DCzyme) was developed via an iron-driven self-assembly strategy. It exhibited excellent peroxidase-like activity, which can be quenched by adding l-cysteine to prevent Fe3+/Fe2+ electron transfer but restored by adding norfloxacin. Given the 'On-Off-On' effect of peroxidase-like activity, Fe-DCzyme was used as a colourimetric sensor for norfloxacin detection, and showed a wide linear range from 0.05 to 6.00 µM (R2 = 0.9950) and LOD of 27.0 nM. A portable smartphone-assisted detection platform using Fe-DCzyme was also designed to convert norfloxacin-induced color changes into RGB values as well as to realise the rapid, on-site and quantitative detection of norfloxacin. A good linear relation (0.10-6.00 µM) and high sensitivity (LOD = 79.3 nM) were achieved for the smartphone-assisted Fe-DCzyme detection platform. Its application was verified using norfloxacin spiking methods with satisfactory recoveries (92.66%-119.65%). Therefore, the portable smartphone-assisted Fe-DCzyme detection platform with low cost and easy operation can be used for the rapid, on-site and visual quantitative detection of antibiotic residues in water samples.

KCNQ1 rs2237895 gene polymorphism increases susceptibility to type 2 diabetes mellitus in Asian populations.

BACKGROUND: The association of single nucleotide polymorphism of KCNQ1 gene rs2237895 with type 2 diabetes mellitus (T2DM) is currently controversial. It is unknown whether this association can be gene realized across different populations. AIM: To determine the association of KCNQ1 rs2237895 with T2DM and provide reliable evidence for genetic susceptibility to T2DM. METHODS: We searched PubMed, Embase, Web of Science, Cochrane Library, Medline, Baidu Academic, China National Knowledge Infrastructure, China Biomedical Liter-ature Database, and Wanfang to investigate the association between KCNQ1 gene rs2237895 and the risk of T2DM up to January 12, 2022. Review Manager 5.4 was used to analyze the association of the KCNQ1 gene rs2237895 polymorphism with T2DM and to evaluate the publication bias of the selected literature. RESULTS: Twelve case-control studies (including 11273 cases and 11654 controls) met our inclusion criteria. In the full population, allelic model [odds ratio (OR): 1.19; 95% confidence interval (95%CI): 1.09-1.29; P < 0.0001], recessive model (OR: 1.20; 95%CI: 1.11-1.29; P < 0.0001), dominant model (OR: 1.27. 95%CI: 1.14-1.42; P < 0.0001), and codominant model (OR: 1.36; 95%CI: 1.15-1.60; P = 0.0003) (OR: 1.22; 95%CI: 1.10-1.36; P = 0.0002) indicated that the KCNQ1 gene rs2237895 polymorphism was significantly correlated with susceptibility to T2DM. In stratified analysis, this association was confirmed in Asian populations: allelic model (OR: 1.25; 95%CI: 1.13-1.37; P < 0.0001), recessive model (OR: 1.29; 95%CI: 1.11-1.49; P = 0.0007), dominant model (OR: 1.35; 95%CI: 1.20-1.52; P < 0.0001), codominant model (OR: 1.49; 95%CI: 1.22-1.81; P < 0.0001) (OR: 1.26; 95%CI: 1.16-1.36; P < 0.0001). In non-Asian populations, this association was not significant: Allelic model (OR: 1.06, 95%CI: 0.98-1.14; P = 0.12), recessive model (OR: 1.04; 95%CI: 0.75-1.42; P = 0.83), dominant model (OR: 1.06; 95%CI: 0.98-1.15; P = 0.15), codominant model (OR: 1.08; 95%CI: 0.82-1.42; P = 0.60. OR: 1.15; 95%CI: 0.95-1.39; P = 0.14). CONCLUSION: KCNQ1 gene rs2237895 was significantly associated with susceptibility to T2DM in an Asian population. Carriers of the C allele had a higher risk of T2DM. This association was not significant in non-Asian populations.

Precise Construction of Nitrogen-Enriched Porous Ionic Polymers as Highly Efficient Sulfur Dioxide Adsorbent.

Porous ionic polymers with unique features have exhibited high performance in various applications. However, the fabrication of functional porous ionic polymers with custom functionality and porosity for efficient removal of low-concentration SO2 remains challenging. Herein, a novel nitrogen-enriched porous ionic polymer NH2Py-PIP is prepared featuring high-content nitrogen sites (15.9 wt.%), adequate ionic sites (1.22 mmol g-1), and a hierarchical porous structure. The proposed construction pathway relies on a tailored nitrogen-functionalized cross-linker NH2Py, which effectively introduces abundant functional sites and improves the porosity of porous ionic polymers. NH2Py-PIP with a well-engineered SO2-affinity environment achieves excellent SO2/CO2 selectivity (1165) and high SO2 adsorption capacity (1.13 mmol g-1 at 0.002 bar), as well as enables highly efficient and reversible dynamic separation performance. Modeling studies further elucidate that the nitrogen sites and bromide anions collaboratively promote preferential adsorption of SO2. The unique design in this work provides new insights into constructing functional porous ionic polymers for high-efficiency separations.

Lercanidipine's Antioxidative Effect Prevents Noise-Induced Hearing Loss.

Noise-induced hearing loss (NIHL) is a prevalent form of adult hearing impairment, characterized by oxidative damage to auditory sensory hair cells. Although certain dihydropyridines, the L-type calcium channel blockers, exhibit protective properties against such damage, the ability of third-generation dihydropryidines like lercanidipine to mitigate NIHL remains unclear.We utilized glucose oxidase (GO)-treated OC1 cell lines and cochlear explants to evaluate the protective influence of lercanidipine on hair cells. To further investigate its effectiveness, we exposed noise-stimulated mice in vivo and analyzed their hearing thresholds. Additionally, we assessed the antioxidative capabilities of lercanidipine by examining oxidation-related enzyme expression and levels of oxidative stress markers, including 3-nitrotyrosine (3NT) and 4-hydroxynonenal (4HNE). Our findings demonstrate that lercanidipine significantly reduces the adverse impacts of GO on both OC-1 cell viability (0.3 to 2.5 µM) and outer hair cell (OHC) survival in basal turn cochlear explants (7 µM). These results are associated with increased mRNA expression of antioxidant enzyme genes (HO-1, SOD1/2, and Txnrd1), along with decreased expression of oxidase genes (COX-2, iNOS). Crucially, lercanidipine administration prior to, and following, noise exposure effectively ameliorates NIHL, as evidenced by lowered hearing thresholds and preserved OHC populations in the basal turn, 14 days post-noise stimulation at 110 dB SPL. Moreover, our observations indicate that lercanidipine's antioxidative action persists even three days after simultaneous drug and noise treatments, based on 3-nitrotyrosine and 4-hydroxynonenal immunostaining in the basal turn. Based on these findings, we propose that lercanidipine has the capacity to alleviate NIHL and safeguard OHC survival in the basal turn, potentially via its antioxidative mechanism. These results suggest that lercanidipine holds promise as a clinically viable option for preventing NIHL in affected individuals.

Research on Indoor Environment Prediction of Pig House Based on OTDBO-TCN-GRU Algorithm.

Temperature and humidity, along with concentrations of ammonia and hydrogen sulfide, are critical environmental factors that significantly influence the growth and health of pigs within porcine habitats. The ability to accurately predict these environmental variables in pig houses is pivotal, as it provides crucial decision-making support for the precise and targeted regulation of the internal environmental conditions. This approach ensures an optimal living environment, essential for the well-being and healthy development of the pigs. The existing methodologies for forecasting environmental factors in pig houses are currently hampered by issues of low predictive accuracy and significant fluctuations in environmental conditions. To address these challenges in this study, a hybrid model incorporating the improved dung beetle algorithm (DBO), temporal convolutional networks (TCNs), and gated recurrent units (GRUs) is proposed for the prediction and optimization of environmental factors in pig barns. The model enhances the global search capability of DBO by introducing the Osprey Eagle optimization algorithm (OOA). The hybrid model uses the optimization capability of DBO to initially fit the time-series data of environmental factors, and subsequently combines the long-term dependence capture capability of TCNs and the non-linear sequence processing capability of GRUs to accurately predict the residuals of the DBO fit. In the prediction of ammonia concentration, the OTDBO-TCN-GRU model shows excellent performance with mean absolute error (MAE), mean square error (MSE), and coefficient of determination (R2) of 0.0474, 0.0039, and 0.9871, respectively. Compared with the DBO-TCN-GRU model, OTDBO-TCN-GRU achieves significant reductions of 37.2% and 66.7% in MAE and MSE, respectively, while the R2 value is improved by 2.5%. Compared with the OOA model, the OTDBO-TCN-GRU achieved 48.7% and 74.2% reductions in the MAE and MSE metrics, respectively, while the R2 value improved by 3.6%. In addition, the improved OTDBO-TCN-GRU model has a prediction error of less than 0.3 mg/m3 for environmental gases compared with other algorithms, and has less influence on sudden environmental changes, which shows the robustness and adaptability of the model for environmental prediction. Therefore, the OTDBO-TCN-GRU model, as proposed in this study, optimizes the predictive performance of environmental factor time series and offers substantial decision support for environmental control in pig houses.

Case report: A case of novel homozygous LRBA variant induced by chromosomal segmental uniparental disomy - genetic and clinical insights.

Objective: The study aims to report a rare case of a novel homozygous variant in the LRBA gene, originating from uniparental disomy of paternal origin. This case contributes new clinical data to the LRBA gene variant database. Methods: The study details the case of a 2-year-old child diagnosed in May 2023 at our center with a homozygous LRBA gene variant. Detailed clinical data of the patient were collected, including whole-exome sequencing of peripheral blood mononuclear cells, with parental genetic verification. Results: The child presented with recurrent respiratory infections and chronic neutropenia, progressing to pancytopenia. Imaging showed splenomegaly and enlarged lymph nodes in the axillary and abdominal regions. Peripheral blood lymphocyte count revealed reduced B cells and NK cells. Elevated cytokine levels of IFN-α and IFN-r were observed. Whole-exome sequencing revealed a nonsense homozygous variant in the LRBA gene, specifically c.2584C>T (p.Gln862Ter). The father exhibited a heterozygous variant at this locus, while no variant was found in the mother. Sample analysis indicated characteristics of uniparental disomy. According to the guidelines of the American College of Medical Genetics and Genomics (ACMG), this variant is preliminarily classified as "Likely pathogenic". Currently, there are no reports in academic literature regarding this specific variant site. Conclusion: LRBA gene variants can lead to a rare inborn error of immunity disease. The c.2584C>T (p.Gln862Ter) variant in exon 22 of the LRBA gene is a newly identified pathogenic variant, and the homozygous variant caused by uniparental disomy is exceedingly rare. This case represents the second global report of an LRBA gene function loss due to uniparental disomy abnormalities.

GDF1 ameliorates cognitive impairment induced by hearing loss.

Hearing loss is associated with an increased risk of Alzheimer disease (AD). However, the mechanisms of hearing loss promoting the onset of AD are poorly understood. Here we show that hearing loss aggravates cognitive impairment in both wild-type mice and mouse models of AD. Embryonic growth/differentiation factor 1 (GDF1) is downregulated in the hippocampus of deaf mice. Knockdown of GDF1 mimics the detrimental effect of hearing loss on cognition, while overexpression of GDF1 in the hippocampus attenuates the cognitive impairment induced by deafness. Strikingly, overexpression of GDF1 also attenuates cognitive impairment in APP/PS1 transgenic mice. GDF1 activates Akt, which phosphorylates asparagine endopeptidase and inhibits asparagine endopeptidase-induced synaptic degeneration and amyloid-ß production. The expression of GDF1 is downregulated by the transcription factor CCAAT-enhancer binding protein-ß. These findings indicate that hearing loss could promote AD pathological changes by inhibiting the GDF1 signaling pathway; thus, GDF1 may represent a therapeutic target for AD.

ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization.

Background: Cancer is a significant global health problem that continues to cause a high number of deaths worldwide. Traditional cancer treatments often come with risks that can compromise the functionality of vital organs. As a potential alternative to these conventional therapies, Anticancer peptides (ACPs) have garnered attention for their small size, high specificity, and reduced toxicity, making them as a promising option for cancer treatments. Methods: However, the process of identifying effective ACPs through wet-lab screening experiments is time-consuming and requires a lot of labor. To overcome this challenge, a deep ensemble learning method is constructed to predict anticancer peptides (ACPs) in this study. To evaluate the reliability of the framework, four different datasets are used in this study for training and testing. During the training process of the model, integration of feature selection methods, feature dimensionality reduction measures, and optimization of the deep ensemble model are carried out. Finally, we explored the interpretability of features that affected the final prediction results and built a web server platform to facilitate anticancer peptides prediction, which can be used by all researchers for further studies. This web server can be accessed at http://lmylab.online:5001/. Results: The result of this study achieves an accuracy rate of 98.53% and an AUC (Area under Curve) value of 0.9972 on the ACPfel dataset, it has improvements on other datasets as well.

Abnormal Innervation, Demyelination, and Degeneration of Spiral Ganglion Neurons as Well as Disruption of Heminodes are Involved in the Onset of Deafness in Cx26 Null Mice.

GJB2 gene mutations are the most common causes of autosomal recessive non-syndromic hereditary deafness. For individuals suffering from severe to profound GJB2-related deafness, cochlear implants have emerged as the sole remedy for auditory improvement. Some previous studies have highlighted the crucial role of preserving cochlear neural components in achieving favorable outcomes after cochlear implantation. Thus, we generated a conditional knockout mouse model (Cx26-CKO) in which Cx26 was completely deleted in the cochlear supporting cells driven by the Sox2 promoter. The Cx26-CKO mice showed severe hearing loss and massive loss of hair cells and Deiter's cells, which represented the extreme form of human deafness caused by GJB2 gene mutations. In addition, multiple pathological changes in the peripheral auditory nervous system were found, including abnormal innervation, demyelination, and degeneration of spiral ganglion neurons as well as disruption of heminodes in Cx26-CKO mice. These findings provide invaluable insights into the deafness mechanism and the treatment for severe deafness in Cx26-null mice.

Enhanced performance of fiber-based perovskite solar cell achieved by multi-functional high polymer doping strategy.

Toward the realization of efficient, durable, and sustainable fiber-based perovskite solar cells (fb-PSCs), a comprehensive optimization strategy focused on enhancing electron transport layer (ETL), perovskite (PVK) photovoltaic layer, and hole transport layer (HTL) is presented. A champion PCE of 10.66 % with 37.9 % relative enhancement over control has been achieved in the optimized fb-PSC. A significantly improved mechanical resilience and storage durability are also recorded. Decorating the SnO2 ETL with methylammonium lead triiodide (MAPbI3) strengthened the ETL/PVK interfacial integrity, and doping the MAPbI3 layer with the multi-functional polymer of PJ71 remarkably enhanced the PVK layer's crystallization quality, and effectively passivated the grain boundary defects. A CO2 pre-treatment of the spiro-OMeTAD HTL enhanced its hole conductivity. It is the synergetic combination of these methodologies that mutually contributed to the performance boost of the fb-PSC. The phenomenological model based on layer conductance shows that the PVK layer chiefly influences the device's anti-bending ability, followed by the ETL, and HTL the least impact. To further enhance the PCE of fb-PSCs, optimizing the interface and minimizing the stress-induced defects are essential. These measures, coupled with increasing carrier diffusion length and reducing surface recombination, are key to advancing the fb-PSC performance. An encapsulation with polyolefin elastomer substantially reduced the potential lead leakage of the device, and facilitated its eco-friendly application.

The multilevel extensive diversity across the cynomolgus macaque captured by ultra-deep adaptive immune receptor repertoire sequencing.

The extent to which AIRRs differ among and within individuals remains elusive. Via ultra-deep repertoire sequencing of 22 and 25 tissues in three cynomolgus macaques, respectively, we identified 84 and 114 novel IGHV and TRBV alleles, confirming 72 (85.71%) and 100 (87.72%) of them. The heterogeneous V gene usage patterns were influenced, in turn, by genetics, isotype (for BCRs only), tissue group, and tissue. A higher proportion of intragroup shared clones in the intestinal tissues than those in other tissues suggests a close intra-intestinal adaptive immunity network. Significantly higher mutation burdens in the public clones and the inter-tissue shared IgM and IgD clones indicate that they might target the shared antigens. This study reveals the extensive heterogeneity of the AIRRs at various levels and has broad fundamental and clinical implications. The data generated here will serve as an invaluable resource for future studies on adaptive immunity in health and diseases.

Predicting the therapeutic response to valproic acid in childhood absence epilepsy through electroencephalogram analysis using machine learning.

Childhood absence epilepsy (CAE) is a common type of idiopathic generalized epilepsy, manifesting as daily multiple absence seizures. Although seizures in most patients can be adequately controlled with first-line antiseizure medication (ASM), approximately 25 % of patients respond poorly to first-line ASM. In addition, an accurate method for predicting first-line medication responsiveness is lacking. We used the quantitative electroencephalogram (QEEG) features of patients with CAE along with machine learning to predict the therapeutic effects of valproic acid in this population. We enrolled 25 patients with CAE from multiple medical centers. Twelve patients who required additional medication for seizure control or who were shifted to another ASM and 13 patients who achieved seizure freedom with valproic acid within 6 months served as the nonresponder and responder groups. Using machine learning, we analyzed the interictal background EEG data without epileptiform discharge before ASM. The following features were analyzed: EEG frequency bands, Hjorth parameters, detrended fluctuation analysis, Higuchi fractal dimension, Lempel-Ziv complexity (LZC), Petrosian fractal dimension, and sample entropy (SE). We applied leave-one-out cross-validation with support vector machine, K-nearest neighbor (KNN), random forest, decision tree, Ada boost, and extreme gradient boosting, and we tested the performance of these models. The responders had significantly higher alpha band power and lower delta band power than the nonresponders. The Hjorth mobility, LZC, and SE values in the temporal, parietal, and occipital lobes were higher in the responders than in the nonresponders. Hjorth complexity was higher in the nonresponders than in the responders in almost all the brain regions, except for the leads FP1 and FP2. Using KNN classification with theta band power in the temporal lobe yielded optimal performance, with sensitivity of 92.31 %, specificity of 76.92 %, accuracy of 84.62 %, and area under the curve of 88.46 %.We used various EEG features along with machine learning to accurately predict whether patients with CAE would respond to valproic acid. Our method could provide valuable assistance for pediatric neurologists in selecting suitable ASM.

LeGO-LOAM-FN: An Improved Simultaneous Localization and Mapping Method Fusing LeGO-LOAM, Faster_GICP and NDT in Complex Orchard Environments.

To solve the problem of cumulative errors when robots build maps in complex orchard environments due to their large scene size, similar features, and unstable motion, this study proposes a loopback registration algorithm based on the fusion of Faster Generalized Iterative Closest Point (Faster_GICP) and Normal Distributions Transform (NDT). First, the algorithm creates a K-Dimensional tree (KD-Tree) structure to eliminate the dynamic obstacle point clouds. Then, the method uses a two-step point filter to reduce the number of feature points of the current frame used for matching and the number of data used for optimization. It also calculates the matching degree of normal distribution probability by meshing the point cloud, and optimizes the precision registration using the Hessian matrix method. In the complex orchard environment with multiple loopback events, the root mean square error and standard deviation of the trajectory of the LeGO-LOAM-FN algorithm are 0.45 m and 0.26 m which are 67% and 73% higher than those of the loopback registration algorithm in the Lightweight and Ground-Optimized LiDAR Odometry and Mapping on Variable Terrain (LeGO-LOAM), respectively. The study proves that this method effectively reduces the influence of the cumulative error, and provides technical support for intelligent operation in the orchard environment.