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From an industry perspective, the past decade has been a whirlwind of innovation in automotive light detection and ranging (LiDAR). Numerous laser technologies and system solutions have been fiercely competing for market share. However, recent trends suggest a growing convergence on vertical-cavity surface-emitting laser (VCSEL) and antireflective VCSEL (AR-VCSEL) based solutions. This commentary, rooted in the practical realities of the industry, examines the historical trajectory of industrial laser technology for commercial automotive LiDAR. It specifically focuses on the recent applications of VCSEL/AR-VCSEL technologies and their future prospects.

Pine Island Glacier (PIG), the largest glacier in the Amundsen Sea Embayment of West Antarctica, has contributed to over a quarter of the observed sea level rise around Antarctica. In recent years, multiple observations have confirmed its continuous retreat, ice flow acceleration and profound surface melt. Understanding these changes is crucial for accurately monitoring ice mass discharge and future Antarctic contributions to sea level rise. Therefore, it is essential to investigate the complex interactions between these variables to comprehend how they collectively affect the overall stability of the intricate PIG system. In this study, we utilized high-resolution remote sensing data and deep learning method to detect and analyze the spatio-temporal variations of surface melt, ice shelf calving, and ice flow velocity of the PIG from 2015 to 2023. We explored the correlations among these factors to understand their long-term impacts on the glacier's stability. Our findings reveal a retreat of 26.3 km and a mass loss of 1001.6 km2 during 2015–2023. Notably, extensive surface melting was observed, particularly in the 2016/2017 and 2019/2020 melting seasons. Satellite data vividly illustrate prolonged and intense melting periods, correlating with a significant retreat in the glacier's terminus position in 2019/2020. Furthermore, the comprehensive analysis of surface melting and the cumulative retreat of the ice shelf from 2017 to 2020 on the PIG shows a temporal relationship with subsequent significant changes in ice flow velocity, ranging from 10.9 to 12.2 m d−1, with an average acceleration rate of 12%. These empirical findings elucidate the intricate relationship among surface melt, ice flow velocity, and consequential glacier dynamics. A profound understanding of these interrelationships holds paramount importance in glacier dynamic changes and modeling, providing invaluable insights into potential glacier responses to global climate change.

Abstract Full-Heusler alloys with earth-abundant elements exhibit high mechanical strength and favorable electrical transport behavior, but their high intrinsic lattice thermal conductivity limits potential thermoelectric application. Here, the thermoelectric transport properties of Fe-based Full-Heusler Fe2MAl (M = V, Nb, Ta) alloys are comprehensively investigated utilizing density functional theory. The results suggest that Fe2NbAl exhibits exceptionally low lattice thermal conductivity due to low phonon velocities and weakly bound Nb atoms. In Fe2NbAl, the underbonding of the Nb atoms leads large Grüneisen parameters and high anharmonic scattering rates of low-frequency acoustic phonon. Meanwhile, the high band degeneracy and large electrical conductivity lead to a maximum p-type power factor of 255.6 μW·K−2·cm−1 at 900 K. The combination of low lattice thermal conductivity and favorable electrical transport properties leads a maximum p-type dimensionless figure of merit of 1.7. Our work indicates Fe2NbAl, as a low-cost, environmentally friendly, is a potential high-performance p-type thermoelectric material.

Abstract Magnaporthe oryzae is one of the most destructive pathogens that threaten rice production around the world. Previous studies mainly focus on pathogenic mechanism of M. oryzae during infection on rice at leaf stage. However, the pathogenic mechanism of M. oryzae infection on panicle tissue is not well understood. In the present study, we performed RNA sequencing (RNA-seq) to study gene expression patterns of M. oryzae during infection at leaf stage and at panicle stage, respectively. The differentially expressed genes (DEGs) of M. oryzae in the infected leaf and panicle tissues were analyzed. Gene ontology (GO) enrichment analysis of DEGs revealed that M. oryzae genes involved in the biological processes were different at leaf and panicle stages. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEGs indicates that genes related to individual and important pathways may function at different infection stages. In particular, CAZymes carbohydrate esterases (CEs), carbohydrate-binding modules (CBMs), and glycoside hydrolases (GHs) may play important roles during M. oryzae infection on rice leaves, while glycosyltransferases (GTs) and GHs may play important roles during infection at rice panicle stage. Further analysis of effectors (BAS3, BAS113, BAS162, MoCDIP4, and MoHEG13) and their homologous genes suggest that they are involved in host defense suppression. Our findings provide insights into understanding the infection mechanisms of M. oryzae for rice leaf blast and panicle blast disease.

Abstract To alleviate the selenium (Se) stress in fruit trees and improve its accumulation, the effects of exogenous indole-3-acetic acid (IAA) on the growth and Se accumulation of grapevine under Se stress were studied. The application of exogenous IAA increased the biomass of grapevine, and the concentration of exogenous IAA had a regression relationship with the biomass. The root and shoot biomass were the maximum at 60 mg L− 1 IAA, increasing by 15.61% and 23.95%, respectively, compared with the control. Exogenous IAA also increased the photosynthetic pigments and the activities of superoxide dismutase and peroxidase in grapevine. Moreover, exogenous IAA increased the contents of total Se, organic Se, and inorganic Se, and the concentration of exogenous IAA had a regression relationship with the total Se content. The highest contents of root total Se and shoot total Se were accumulated at 90 mg L− 1 IAA, increasing by 29.94% and 55.77% respectively,. In addition, the correlation and path analyses revealed that the carotenoid content and root total Se content were closely associated with the shoot total Se content. Therefore, the application of exogenous IAA can alleviate the stress of Se to grape and promote its uptake and the most effective amount for the uptake of Se is 90 mg L− 1 IAA.

ABSTRACT Aims/Introduction Serum asprosin is expected to become a screening indicator in early‐stage diabetic heart disease. The relationship between serum asprosin and left ventricular diastolic dysfunction (LVDD) was studied in elderly patients with type 2 diabetes mellitus in the community. Materials and Methods A total of 252 elderly patients with type 2 diabetes mellitus were recruited from Zhuoma Community Care Station and Chengbei West Street Community Care Service Center in Changzhi City of Shanxi Province from November 2019 to July 2021. Patients were divided into the LVDD group (n = 195) and the non‐LVDD group (n = 57). The t‐test, Mann–Whitney U test, and χ2 test were used to compare indicators between the LVDD group and the non‐LVDD group. Pearson or Spearman correlation analysis was adopted to evaluate the correlation between serum asprosin and other clinical data. Multivariate logistic regression analysis was applied to analyze the influencing factors on LVDD. Results Compared with patients without LVDD, patients with LVDD had a higher level of low‐density lipoprotein cholesterol (LDL‐C), fasting blood glucose (FPG), and asprosin, but a lower level of early diastolic movement speed (A) to diastolic movement velocity (E) (E/A). Asprosin was positively associated with waist circumference (WC), body mass index (BMI), creatinine, triglycerides (P 355.08 pg/mL [OR (95% CI) is 2.549 (1.275–5.095), P

Abstract The phenomenon of semantic satiation, which refers to the loss of meaning of a word or phrase after being repeated many times, is a well-known psychological phenomenon. However, the microscopic neural computational principles responsible for these mechanisms remain unknown. In this study, we use a deep learning model of continuous coupled neural networks to investigate the mechanism underlying semantic satiation and precisely describe this process with neuronal components. Our results suggest that, from a mesoscopic perspective, semantic satiation may be a bottom-up process. Unlike existing macroscopic psychological studies that suggest that semantic satiation is a top-down process, our simulations use a similar experimental paradigm as classical psychology experiments and observe similar results. Satiation of semantic objectives, similar to the learning process of our network model used for object recognition, relies on continuous learning and switching between objects. The underlying neural coupling strengthens or weakens satiation. Taken together, both neural and network mechanisms play a role in controlling semantic satiation.

Abstract Background Sequencing the mitochondrial genome has been increasingly important for the investigation of primary mitochondrial diseases (PMD) and mitochondrial genetics. To overcome the limitations originating from PCR-based mtDNA enrichment, we set out to develop and evaluate a PCR-independent approach in this study, named Pime-Seq (PCR-independent mtDNA enrichment and next generation Sequencing). Results By using the optimized mtDNA enrichment procedure, the mtDNA reads ratio reached 88.0 ± 7.9% in the sequencing library when applied on human PBMC samples. We found the variants called by Pime-Seq were highly consistent among technical repeats. To evaluate the accuracy and reliability of this method, we compared Pime-Seq with lrPCR based NGS by performing both methods simultaneously on 45 samples, yielding 1677 concordant variants, as well as 146 discordant variants with low-level heteroplasmic fraction, in which Pime-Seq showed higher reliability. Furthermore, we applied Pime-Seq on 4 samples of PMD patients retrospectively, and successfully detected all the pathogenic mtDNA variants. In addition, we performed a prospective study on 192 apparently healthy pregnant women during prenatal screening, in which Pime-Seq identified pathogenic mtDNA variants in 4 samples, providing extra information for better health monitoring in these cases. Conclusions Pime-Seq can obtain highly enriched mtDNA in a PCR-independent manner for high quality and reliable mtDNA deep-sequencing, which provides us an effective and promising tool for detecting mtDNA variants for both clinical and research purposes.

IntroductionExogenous melatonin (MT) can promote horticultural crops growth under stress conditions.MethodsIn this study, the effects of exogenous MT on the accumulation of selenium (Se) in grape were studied under Se stress.Results and discussionUnder Se stress, exogenous MT increased the biomass, content of photosynthetic pigments and antioxidant enzyme activity of grapevines. Compared with Se treatment, MT increased the root biomass, shoot biomass, chlorophyll a content, chlorophyll b content, carotenoids, superoxide dismutase activity, and peroxidase activity by 18.11%, 7.71%, 25.70%, 25.00%, 25.93%, 5.73%, and 9.41%, respectively. Additionally, MT increased the contents of gibberellin, auxin, and MT in grapevines under Se stress, while it decreased the content of abscisic acid. MT increased the contents of total Se, organic Se and inorganic Se in grapevines. Compared with Se treatment, MT increased the contents of total Se in the roots and shoots by 48.82% and 135.66%, respectively. A transcriptome sequencing analysis revealed that MT primarily regulated the cellular, metabolic, and bioregulatory processes of grapevine under Se stress, and the differentially expressed genes (DEGs) were primarily enriched in pathways, such as aminoacyl-tRNA biosynthesis, spliceosome, and flavonoid biosynthesis. These involved nine DEGs and nine metabolic pathways in total. Moreover, a field experiment showed that MT increased the content of Se in grapes and improved their quality. Therefore, MT can alleviate the stress of Se in grapevines and promote their growth and the accumulation of Se.

ABSTRACT Platinum-based chemotherapy failure represents a significant challenge in the management of ovarian cancer (OC) and contributes to disease recurrence and poor prognosis. Recent studies have shed light on the involvement of the gut microbiota in modulating anticancer treatments. However, the precise underlying mechanisms, by which gut microbiota regulates the response to platinum-based therapy, remain unclear. Here, we investigated the role of gut microbiota on the anticancer response of cisplatin and its underlying mechanisms. Our results demonstrate a substantial improvement in the anticancer efficacy of cisplatin following antibiotic-induced perturbation of the gut microbiota in OC-bearing mice. 16S rRNA sequencing showed a pronounced alteration in the composition of the gut microbiome in the cecum contents following exposure to cisplatin. Through metabolomic analysis, we identified distinct metabolic profiles in the antibiotic-treated group, with a notable enrichment of the gut-derived metabolite 3-methylxanthine in antibiotic-treated mice. Next, we employed a strategy combining transcriptome analysis and chemical–protein interaction network databases. We identified metabolites that shared structural similarity with 3-methylxanthine, which interacted with genes enriched in cancer-related pathways. It is identified that 3-methylxanthinesignificantly enhances the effectiveness of cisplatin by promoting apoptosis both in vivo and in vitro. Importantly, through integrative multiomics analyses, we elucidated the mechanistic basis of this enhanced apoptosis, revealing a dopamine receptor D1-dependent pathway mediated by 3-methylxanthine. This study elucidated the mechanism by which gut-derived metabolite 3-methylxanthine mediated cisplatin-induced apoptosis. Our findings highlight the potential translational significance of 3-methylxanthine as a promising adjuvant in conjunction with cisplatin, aiming to improve treatment outcomes for OC patients.IMPORTANCEThe precise correlation between the gut microbiota and the anticancer effect of cisplatin in OC remains inadequately understood. Our investigation has revealed that manipulation of the gut microbiota via the administration of antibiotics amplifies the efficacy of cisplatin through the facilitation of apoptosis in OC-bearing mice. Metabolomic analysis has demonstrated that the cecum content from antibiotic-treated mice exhibits an increase in the levels of 3-methylxanthine, which has been shown to potentially enhance the therapeutic effectiveness of cisplatin by an integrated multiomic analysis. This enhancement appears to be attributable to the promotion of cisplatin-induced apoptosis, with 3-methylxanthine potentially exerting its influence via the dopamine receptor D1-dependent pathway. These findings significantly contribute to our comprehension of the impact of the gut microbiota on the anticancer therapy in OC. Notably, the involvement of 3-methylxanthine suggests its prospective utility as a supplementary component for augmenting treatment outcomes in patients afflicted with ovarian cancer.

Background and objectiveEnlarged perivascular spaces in basal ganglia (BG-EPVS) are considered an imaging marker of cerebral small vessel disease (CSVD), but its pathogenesis and pathophysiological process remain unclear. While decreased cerebral perfusion is linked to other CSVD markers, the relationship between BG-EPVS and cerebral perfusion remains ambiguous. This study aimed to explore this association.MethodsElderly individuals with severe BG-EPVS (n = 77) and age/sex-matched controls (n = 89) underwent head CT perfusion imaging. The cerebral perfusion parameters including mean transit time (MTT), time to maximum (TMAX), cerebral blood flow (CBF), and cerebral blood volume (CBV) were quantitatively measured by symmetric regions of interest plotted in the basal ganglia region. Point-biserial correlation and logistics regression analysis were performed to investigate the association between BG-EPVS and cerebral perfusion.ResultsThere were no significant differences in MTT, TMAX, or CBF between BG-EPVS group and control group. CBV was significantly lower in the BG-EPVS group (p = 0.035). Point-biserial correlation analysis showed a negative correlation between BG-EPVS and CBV (r = −0.198, p = 0.011). BG-EPVS group and control group as the dependent variable, binary logistics regression analysis showed that CBV was not an independent risk factor for severe BG-EPVS (p = 0.448). All enrolled patients were divided into four groups according to the interquartile interval of CBV. The ordered logistic regression analysis showed severe BG-EPVS was an independent risk factor for decreased CBV after adjusting for confounding factors (OR = 2.142, 95%CI: 1.211–3.788, p = 0.009).ConclusionSevere BG-EPVS is an independent risk factor for decreased CBV in the elderly, however, the formation of BG-EPVS is not solely dependent on changes in CBV in this region. This finding provides information about the pathophysiological consequence caused by severe BG-EPVS.

Abstract Multijunction vertical-cavity surface-emitting lasers (VCSELs) have gained popularity in automotive LiDARs, yet achieving a divergence of less than 16° (D86) is difficult for conventional extended cavity designs due to multiple-longitudinal-mode lasing. Our innovation, the antireflective vertical-cavity surface-emitting laser (AR-VCSEL), addresses this challenge by introducing an antireflective light reservoir, where the electric field intensity is substantially higher than the gain region. This reduces the required cavity length for minimal divergence, preserving the single-longitudinal-mode lasing. A 6-junction AR-VCSEL array showcases a halved divergence and tripled brightness compared to its conventional counterpart. Various multijunction AR-VCSEL array designs achieve a divergence range of 8° to 16° (D86). Notably, a 7 μm AR-VCSEL emitter achieves 28.4 mW in single transverse mode lasing. AR-VCSEL stands out among semiconductor lasers, offering a well-balanced power density and brightness, making it a cost-effective solution for long-distance LiDARs. The antireflective cavity concept may inspire diverse applications in photonic devices beyond LiDARs.

Abstract High performance X-ray detector with ultra-high spatial and temporal resolution are crucial for biomedical imaging. This study reports a dynamic direct-conversion CMOS X-ray detector assembled with screen-printed CsPbBr3, whose mobility-lifetime product is 5.2 × 10−4 cm2 V–1 and X-ray sensitivity is 1.6 × 104 µC Gyair –1 cm–2. Samples larger than 5 cm $$\times$$ × 10 cm can be rapidly imaged by scanning this detector at a speed of 300 frames per second along the vertical and horizontal directions. In comparison to traditional indirect-conversion CMOS X-ray detector, this perovskite CMOS detector offers high spatial resolution (5.0 lp mm−1) X-ray radiographic imaging capability at low radiation dose (260 nGy). Moreover, 3D tomographic images of a biological specimen are also successfully reconstructed. These results highlight the perovskite CMOS detector’s potential in high-resolution, large-area, low-dose dynamic biomedical X-ray and CT imaging, as well as in non-destructive X-ray testing and security scanning.

Abstract Objectives This study aims to decrease the scan time and enhance image quality in pediatric total-body PET imaging by utilizing multimodal artificial intelligence techniques. Methods A total of 270 pediatric patients who underwent total-body PET/CT scans with a uEXPLORER at the Sun Yat-sen University Cancer Center were retrospectively enrolled. 18F-fluorodeoxyglucose (18F-FDG) was administered at a dose of 3.7 MBq/kg with an acquisition time of 600 s. Short-term scan PET images (acquired within 6, 15, 30, 60 and 150 s) were obtained by truncating the list-mode data. A three-dimensional (3D) neural network was developed with a residual network as the basic structure, fusing low-dose CT images as prior information, which were fed to the network at different scales. The short-term PET images and low-dose CT images were processed by the multimodal 3D network to generate full-length, high-dose PET images. The nonlocal means method and the same 3D network without the fused CT information were used as reference methods. The performance of the network model was evaluated by quantitative and qualitative analyses. Results Multimodal artificial intelligence techniques can significantly improve PET image quality. When fused with prior CT information, the anatomical information of the images was enhanced, and 60 s of scan data produced images of quality comparable to that of the full-time data. Conclusion Multimodal artificial intelligence techniques can effectively improve the quality of pediatric total-body PET/CT images acquired using ultrashort scan times. This has the potential to decrease the use of sedation, enhance guardian confidence, and reduce the probability of motion artifacts.

Abstract Objectives Commercialized total-body PET scanners can provide high-quality images due to its ultra-high sensitivity. We compared the dynamic, regular static, and delayed 18F-fluorodeoxyglucose (FDG) scans to detect lesions in oncologic patients on a total-body PET/CT scanner. Materials & methods In all, 45 patients were scanned continuously for the first 60 min, followed by a delayed acquisition. FDG metabolic rate was calculated from dynamic data using full compartmental modeling, whereas regular static and delayed SUV images were obtained approximately 60- and 145-min post-injection, respectively. The retention index was computed from static and delayed measures for all lesions. Pearson’s correlation and Kruskal–Wallis tests were used to compare parameters. Results The number of lesions was largely identical between the three protocols, except MRFDG and delayed images on total-body PET only detected 4 and 2 more lesions, respectively (85 total). FDG metabolic rate (MRFDG) image-derived contrast-to-noise ratio and target-to-background ratio were significantly higher than those from static standardized uptake value (SUV) images (P 0.05). Dynamic protocol did not significantly differentiate between benign and malignant lesions just like regular SUV, delayed SUV, and retention index. Conclusion The potential quantitative advantages of dynamic imaging may not improve lesion detection and differential diagnosis significantly on a total-body PET/CT scanner. The same conclusion applied to delayed imaging. This suggested the added benefits of complex imaging protocols must be weighed against the complex implementation in the future. Clinical relevance Total-body PET/CT was known to significantly improve the PET image quality due to its ultra-high sensitivity. However, whether the dynamic and delay imaging on total-body scanner could show additional clinical benefits is largely unknown. Head-to-head comparison between two protocols is relevant to oncological management.

In order to investigate the pharmacological effect of Paeonia suffruticosa Andr. stamens on chronic non-bacterial prostatitis (CNBP) in rats, the rats with chronic abacterial prostatitis induced by castration combined subcutaneous injection of hormone (estradiol benzoate 0.25 mg·kg−1) were used as models. Forty rats with CNBP were randomly divided into five groups (8 per group): Model group (normal saline), positive group (celecoxib 250 mg·kg−1), various doses of lyophilized powder of Paeonia suffruticosa Andr. stamens groups (100, 200, 400 mg·kg−1). Another group of normal mice treated with normal saline as normal group. The therapeutic effect of Paeonia suffruticosa Andr. stamens on CNBP was evaluated by lecithin body density, prostatic index, cytokines in prostate tissue and the expression levels of TXB2 and 6-K-PGF1α in blood. The results showed that the inflammation of prostate tissue in rats was significantly improved. Compared with model group, the number of white blood cells, prostate weight, prostate index, TXB2 and the content of PGE2 in prostate tissue of rats were significantly decreased in each dosage group of Paeonia suffruticosa Andr. stamens (P0.05). The contents of 6-keto-PGF1α and NO in high dose of Paeonia suffruticosa Andr. stamens were significantly increased (P0.05). This study showed that Paeonia suffruticosa Andr. stamens had therapeutic effect on chronic abacterial prostatitis, which would provide a theoretical basis for further development and application of the Paeonia suffruticosa Andr. stamens.

Abstract Over the past three decades, considerable efforts have been expended on understanding the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathway in leukemia, following the identification of the JAK2V617F mutation in myeloproliferative neoplasms (MPNs). The aim of this review is to summarize the latest progress in our understanding of the involvement of the JAK/STAT signaling pathway in the development of leukemia. We also attempt to provide insights into the current use of JAK/STAT inhibitors in leukemia therapy and explore pertinent clinical trials in this field.

Milk fat globule membrane (MFGM), which contains abundant glycoproteins and phospholipids, exerts beneficial effects on intestinal health and immunomodulation. The aim of this study was to evaluate the protective effects and possible underlying mechanisms of MFGM on cow’s milk allergy (CMA) in a β-lactoglobulin (BLG)-induced allergic mice model. MFGM was supplemented to allergic mice induced by BLG at a dose of 400 mg/kg body weight. Results demonstrated that MFGM alleviated food allergy symptoms, decreased serum levels of lipopolysaccharide, pro-inflammatory cytokines, immunoglobulin (Ig) E, IgG1, and Th2 cytokines including interleukin (IL)-4, while increased serum levels of Th1 cytokines including interferon-γ and regulatory T cells (Tregs) cytokines including IL-10 and transforming growth factor-β. MFGM modulated gut microbiota and enhanced intestinal barrier of BLG-allergic mice, as evidenced by decreased relative abundance of Desulfobacterota, Rikenellaceae, Lachnospiraceae, and Desulfovibrionaceae, while increased relative abundance of Bacteroidetes, Lactobacillaceae and Muribaculaceae, and enhanced expressions of tight junction proteins including Occludin, Claudin-1 and zonula occludens-1. Furthermore, MFGM increased fecal short-chain fatty acids (SCFAs) levels, which elevated G protein-coupled receptor (GPR) 43 and GPR109A expressions. The increased expressions of GPR43 and GPR109A induced CD103+ dendritic cells accumulation and promoted Tregs differentiation in mesenteric lymph node to a certain extent. In summary, MFGM alleviated CMA in a BLG-induced allergic mice model through enhancing intestinal barrier and promoting Tregs differentiation, which may be correlated with SCFAs-mediated activation of GPRs. These findings suggest that MFGM may be useful as a promising functional ingredient against CMA.

Plums (Prunus salicina and Prunus domestica) are prevalent in southwestern China, and have attracted interest owing to their delectable taste and exceptional nutritional properties. Therefore, this study aimed to investigate the nutritional and flavor properties of plum to improve its nutritional utilization. Specifically, we determined the soluble sugars, organic acids, and phenolic components in 86 accessions using high-performance liquid chromatography. Notably, glucose, fructose, malic, and quinic acids were the predominant sweetness and acidity in plums, with sucrose contributing more to the sweetness of the flesh than the peel. Moreover, The peel contains 5.5 fold more phenolics than flesh, epicatechin, gallic acid, and proanthocyanidins C1 and B2 were the primary sources of astringency. Correlation and principal component analyses showed eight core factors for plum flavor rating, and a specific rating criterion was established. Conclusively, these findings provide information on the integrated flavor evaluation criteria and for enhancing optimal breeding of plums.

The Thwaites Glacier (TG), accounting for four percent of present-day sea-level rise, has been confirmed with the irreversible retreat and thinning. Consequently, understanding the continuous calving front retreat within the TG is pivotal for accurately monitoring glacier dynamics and future Antarctic impact on global climate change. In this study, based on the novel deep learning method for calving front delineation and the surface melt detection framework, we utilized high-resolution remote sensing data from 2015 to 2023 to analyze continuous calving retreat and the associated influencing factors including surface melt, and ice velocity of the TG. Our findings reveal a retreat of 18.0 km and a mass loss of [Formula: see text]. Satellite data illustrate prolonged and intense melting periods in the 2016/2017 melting season, correlating with a significant retreat in the glacier's terminus position in early 2017. We also demonstrate that the ice flow velocity on the near the pinning point remains consistently slow and stable (from 0.5 to 1.8 m/day) from 2015 to 2023, while the Thwaites West Ice Tongue (TWIT) undergoes pronounced dynamic variations characterized with higher ice velocities and frequent frontal fluctuations (from 11.4 to 15.7 m/day), subsequently accelerating the calving of the TG. We believe a profound understanding of these interrelationships holds paramount importance in glacier dynamic changes and modeling, providing invaluable insights into potential glacier responses to global climate change and the implementation of SDG13 (Climate Action).

ABSTRACTBifidobacterium longum subsp. infantis is a prevalent member of the gut microbiota of breastfed infants. In this study, the effects of human breastmilk-derived B.longum subsp. infantis CCFM1269 on bone formation in developing BALB/c mice were investigated. Newborn female and male mice were assigned to control group (administered saline), CCFM1269 group (administered B. longum subsp. infantis CCFM1269, 1 × 109 CFU/mouse/day) and I5TI group (administered B. longum subsp. infantis I5TI, 1 × 109 CFU/mouse/day) from 1-week-old to 3-, 4- and 5-week old. B. longum subsp. infantis I5TI served as a negative control in this study. The results demonstrated that B. longum subsp. infantis CCFM1269 promoted bone formation in growing mice by modulating the composition of the gut microbiota and metabolites. The expression of genes and proteins in the PI3K/AKT pathway was stimulated by B. longum subsp. infantis CCFM1269 through the GH/IGF-1 axis in growing mice. This finding suggests B. longum subsp. infantis CCFM1269 may be useful for modulating bone metabolism during growth.

Abstract Background As a screening method, inaccuracies in noninvasive prenatal screening (NIPS) exist, which are often attributable to biological factors. One such factor is the history of transplantation. However, there are still limited reports on such NIPS cases. Methods We report an NIPS case of a pregnant woman who had received a stem cell transplant from a male donor. To determine the karyotype in the woman's original cell, we performed chromosome microarray analysis (CMA) on her postnatal blood and oral mucosa. To comprehensively estimate the cell‐free DNA (cfDNA) composition, we further performed standard NIPS procedures on the postnatal plasma. Moreover, we reviewed all published relevant NIPS case reports about pregnant women with transplantation history. Results NIPS showed a low‐risk result for common trisomies with a fetal fraction of 65.80%. CMA on maternal white blood cells showed a nonmosaic male karyotype, while the oral mucosa showed a nonmosaic female karyotype. The proportion of donor's cfDNA in postnatal plasma was 94.73% based on the Y‐chromosome reads ratio. The composition of cfDNA in maternal plasma was estimated as follows: prenatally, 13.60% maternal, 65.80% donor, and 20.60% fetal/placental, whereas postnatally, 5.27% maternal and 94.73% donor. Conclusions This study expanded our understanding of the influence of stem cell transplantation on NIPS, allowing us to optimize NIPS management for these women.

Volatile compounds are important determinants affecting fruit flavor. Previous study has identified a bud mutant of ‘Ehime 38’ (Citrus reticulata) with different volatile profile. However, the volatile changes between WT and MT during fruit development and underlying mechanism remain elusive. In this study, a total of 35 volatile compounds were identified in the pulps of WT and MT at five developmental stages. Both varieties accumulated similar and the highest levels of volatiles at stage S1, and showed a downward trend as the fruit develops. However, the total volatile contents in the pulps of MT were 1.4–2.5 folds higher than those in WT at stages S2-S5, which was mainly due to the increase in the content of d-limonene. Transcriptomic and RT-qPCR analysis revealed that most genes in MEP pathway were positively correlated with the volatile contents, of which DXS1 might mainly contribute to the elevated volatiles accumulation in MT by increasing the flux into the MEP pathway. Moreover, temporal expression analysis indicated that these MEP pathway genes functioned at different developmental stages. This study provided comprehensive volatile metabolomics and transcriptomics characterizations of a citrus mutant during fruit development, which is valuable for fruit flavor improvement in citrus.

Surface melt plays a vital role in impacting the polar mass balance and global sea level rise. Over the past decades, synthetic aperture radar (SAR) imagery has garnered considerable attention due to its capacity to provide high-precision and long-term information. However, the traditional SAR-based large-scale surface melt detection methods utilizing co-orbit normalization predominantly depend on reference images and the precise spatial registration to mitigate geometric distortions arising from diverse incidence angles. Consequently, both the absence of reference imagery and the movement of ice sheets and shelves present challenges to the method. In this study, we address this issue by developing a reference-free deep learning network integrating the Convolutional Block Attention Module (CBAM) into DeepLabv3+ to automatically detect surface melt and establishing the surface melt dataset based on multi-temporal Sentinel-1 SAR imagery, encompassing diverse surface conditions of the Antarctic. Our model achieves an accuracy of 95.67%, surpassing the reference-based method and an advanced deep learning-based approach by 4.23% and 4.67%, respectively. Moreover, compared to 500 m resolution UMelt product and the kilometer-level results obtained from Advanced Scatterometer (ASCAT) and Special Sensor Microwave Imager Sounder (SSMIS), our model demonstrates the capability to accurately capture the small-scale melting patterns of ice shelves with a higher spatial resolution of 40 m. Notably, our findings underscore the dispensability of reference imagery in traditional methods through the formidable information extraction capabilities of deep learning. We finally applied the proposed method to extract and analyze the spatiotemporal characteristics of surface melt on the Larsen C Ice Shelf during the 2019/2020 period. The corresponding code of this study is at https://github.com/Tangyu35/Surface-melt-detection.

Probiotic Bacillus strains can solve the problems of single flavor and long fermentation time of fermented products caused by the lack of certain functional genes and insufficient metabolism ability of fermenter strains (Lactobacillus and Bifidobacterium) at the present stage. There is a lack of systematic evaluation and review of probiotic Bacillus as food fermentation agents. In this paper, it is observed that probiotic Bacillus strains are involved to varying degrees in liquid-state, semi-solid state, and solid-state fermentation and are widely present in solid-state fermented foods. Probiotic Bacillus strains not only produce abundant proteases and lipases, but also effective antifungal lipopeptides and extracellular polymers, thus enhancing the flavor, nutritional value and safety of fermented foods. Bacillus with probiotic qualities is an underutilized group of probiotic food fermentation agents, which give a potential for the development of fermentation technology in the food business and the integration of ancient traditional fermentation techniques.

High-resolution spatial distribution maps of GDP are essential for accurately analyzing economic development, industrial layout, and urbanization processes. However, the currently accessible GDP gridded datasets are limited in number and resolution. Furthermore, high-resolution GDP mapping remains a challenge due to the complex sectoral structure of GDP, which encompasses agriculture, industry, and services. Meanwhile, multi-source data with high spatial resolution can effectively reflect the level of regional economic development. Therefore, we propose a multi-scale fusion residual network (Res-FuseNet) designed to estimate the GDP grid density by integrating remote sensing and POI data. Specifically, Res-FuseNet extracts multi-scale features of remote sensing and POI data relevant to different sectors. It constructs a joint representation of multi-source data through a fusion mechanism and accurately estimates GDP grid density for three sectors using residual connections. Subsequently, the high-resolution GDP grid data are obtained by correcting and overlaying grid density for each sector using county-level statistical GDP data. The 100-meter gridded GDP map of the urban agglomeration in the middle reaches of the Yangtze River in 2020 was successfully generated using this method. The experimental results confirm that Res-FuseNet outperforms machine learning models and baseline model significantly in training across different sectors and at the town-level. The R2 values for the three sectors are 0.69, 0.91, and 0.99, respectively, while the town-level evaluation results also exhibit high accuracy (R2=0.75). Res-FuseNet provides an innovative high-resolution mapping method, and the generated high-resolution GDP grid data reveal the distribution characteristics of different sector structures and fine-scale economic disparities within cities, offering robust support for sustainable development.

To satisfy the requirements of large workspace, high capability, and low inertia demanded by the high precision and high stiffness aerospace equipment, serial manipulator with insufficient intersection joints is proposed in this article based on finite and instantaneous screw theory. Due to the specific topology structure with no intersecting joints at both ends, the explicit solution of forward and inverse kinematics for the serial manipulator with insufficient intersection joints proposed in this article is extremely difficult. Therefore, an analytical method based on finite and instantaneous screw theory is proposed for this serial manipulator, and the explicit kinematic solution is derived in this article. According to particular calculation principle of finite and instantaneous screw theory, the finite motion of this serial manipulator is described. Based on this description, the product of exponentials formula of the kinematics of the serial manipulator with insufficient intersection joints is established on the basic of finite and instantaneous screw theory for the first time, and a novel subproblem is arose during the decomposition of product of exponentials formula. Regarding this issue, an innovative approach to this subproblem is proposed by introducing the new Paden–Kahan subproblem with elimination, and the explicit solution for the inverse kinematics is derived. Finally, the numerical examples are simulated, and the correctness of the method proposed in this article is verified.

Sonar sound datasets are of significant importance in the domains of underwater surveillance and marine research as they enable experts to discern intricate patterns within the depths of the water. Nevertheless, the task of classifying sonar sound datasets continues to pose significant challenges. In this study, we present a novel approach aimed at enhancing the precision and efficacy of sonar sound dataset classification. The integration of deep long-short-term memory (DLSTM) and convolutional neural networks (CNNs) models is employed in order to capitalize on their respective advantages while also utilizing distinctive feature engineering techniques to achieve the most favorable outcomes. Although DLSTM networks have demonstrated effectiveness in tasks involving sequence classification, attaining their optimal performance necessitates careful adjustment of hyperparameters. While traditional methods such as grid and random search are effective, they frequently encounter challenges related to computational inefficiencies. This study aims to investigate the unexplored capabilities of the fuzzy slime mould optimizer (FUZ-SMO) in the context of LSTM hyperparameter tuning, with the objective of addressing the existing research gap in this area. Drawing inspiration from the adaptive behavior exhibited by slime moulds, the FUZ-SMO proposes a novel approach to optimization. The amalgamated model, which combines CNN, LSTM, fuzzy, and SMO, exhibits a notable improvement in classification accuracy, outperforming conventional LSTM architectures by a margin of 2.142%. This model not only demonstrates accelerated convergence milestones but also displays significant resilience against overfitting tendencies.

The solute carrier family 12 (SLC12) of cation-chloride cotransporters (CCCs) comprises potassium chloride cotransporters (KCCs, e.g. KCC1, KCC2, KCC3, and KCC4)-mediated Cl− extrusion, and sodium potassium chloride cotransporters (N[K]CCs, NKCC1, NKCC2, and NCC)-mediated Cl− loading. The CCCs play vital roles in cell volume regulation and ion homeostasis. Gain-of-function or loss-of-function of these ion transporters can cause diseases in many tissues. In recent years, there have been considerable advances in our understanding of CCCs' control mechanisms in cell volume regulations, with many techniques developed in studying the functions and activities of CCCs. Classic approaches to directly measure CCC activity involve assays that measure the transport of potassium substitutes through the CCCs. These techniques include the ammonium pulse technique, radioactive or nonradioactive rubidium ion uptake-assay, and thallium ion-uptake assay. CCCs' activity can also be indirectly observed by measuring γ-aminobutyric acid (GABA) activity with patch-clamp electrophysiology and intracellular chloride concentration with sensitive microelectrodes, radiotracer 36Cl−, and fluorescent dyes. Other techniques include directly looking at kinase regulatory sites phosphorylation, flame photometry, 22Na+ uptake assay, structural biology, molecular modeling, and high-throughput drug screening. This review summarizes the role of CCCs in genetic disorders and cell volume regulation, current methods applied in studying CCCs biology, and compounds developed that directly or indirectly target the CCCs for disease treatments.

Abstract Background Optimizing patient selection for neoadjuvant chemotherapy in patients with breast cancer remains an unmet clinical need. Quantitative features from medical imaging were reported to be predictive of treatment responses. However, the biologic meaning of these latent features is poorly understood, preventing the clinical use of such noninvasive imaging markers. The study aimed to develop a deep learning signature (DLS) from pretreatment magnetic resonance imaging (MRI) for predicting responses to neoadjuvant chemotherapy in patients with breast cancer and to further investigate the biologic meaning of the DLS by identifying its underlying pathways using paired MRI and proteomic sequencing data. Methods MRI‐based DLS was constructed (radiogenomic training dataset, n = 105) and validated (radiogenomic validation dataset, n = 26) for the prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy. Proteomic sequencing revealed biological functions facilitating pCR (n = 139). Their associations with DLS were uncovered by radiogenomic analysis. Results The DLS achieved a prediction accuracy of 0.923 with an AUC of 0.958, higher than the performance of the model trained by transfer learning. Cellular membrane formation, endocytosis, insulin‐like growth factor binding, protein localization to membranes, and cytoskeleton‐dependent trafficking were differentially regulated in patients showing pCR. Oncogenic signaling pathways, features correlated with human phenotypes, and features correlated with general biological processes were significantly correlated with DLS in both training and validation dataset (p.adj

Abstract Digital public goods (DPGs), if implemented with effective policies, can facilitate the realization of the United Nations Sustainable Development Goals (SDGs). However, there are ongoing deliberations on how to define DPGs and assure that society can extract the maximum benefit from the growing number of digital resources. The International Research Center of Big Data for Sustainable Development Goals (CBAS) sees DPGs as an important mechanism to facilitate information-driven policy and decision-making processes for the SDGs. This article presents the results of a CBAS survey of 51 respondents from around the world spanning multiple scientific fields, who shared their expert opinions on DPGs and their thoughts about challenges related to their practical implementation in supporting the SDGs. Based on the survey results, the paper presents core principles in a proposed strategy, including establishment of international standards, adherence to open science and open data principles, and scalability in monitoring SDG indicators. A community-driven strategy to develop DPGs is proposed to accelerate DPG production in service of the SDGs while adhering to the core principles identified in the survey.

Ice shelves play an essential role in the dynamics of the Antarctic ice sheet. The surface meltwater is important, as it can irreversibly weaken ice shelves by exerting additional hydrostatic pressure. Therefore, high-resolution snowmelt products are urgently needed to accurately analyze melting patterns of ice shelves and further estimate mass loss. In this study, a new high-resolution (40 m) snowmelt dataset over all of the Antarctic Peninsula ice shelves larger than 100 km$^2$ was developed based on the modified snowmelt detection framework by using a co-orbit normalization method. The dataset provides detailed snowmelt information on each ice shelf, including the image coverage, melting area and melting ratio every 5 days. The melting patterns of three typical ice shelves (George VI, Wilkins and Larsen C Ice Shelves) and the spatio-temporal melting distribution of the Antarctic Peninsula (AP) were further analyzed. The snowmelt information indicates that both the extent and duration of snowmelt have been increasing in the Antarctic Peninsula from 2015 to 2021, and we found that the snowmelt on the Antarctic Peninsula showed a spatial pattern of significantly intense snowmelt on the western side. We believe this study will provide essential data for ice shelf investigation to support other fields of polar research.

The distribution network can continue to operate for a short time once a single-phase grounding fault arises, but if the defect is not monitored and corrected right away, it might cause serious harm. However, the bulk of fault monitoring algorithms struggle to identify faults when applied to complex distribution networks with a tree structure. In this article, a fault online monitoring method based on power line communication equipment is proposed. The method identifies the fault by monitoring the change of channel frequency response (CFR) of the distribution network in real-time, locates the fault branch by comparing the change amplitude of CFR of different receiving devices, calculates the impedance amplitude of the fault branch, and combines the relation between impedance and distance to further realize the accurate location of the fault point. A large number of simulations show that the method is accurate and stable, and can effectively monitor both high and low-resistance ground faults. Under the simulation conditions without considering signal interference, the fault branch detection accuracy reaches 100%, and the average error of fault distance localization is 19.38 m (1.98%). Compared with other methods, this method is highly applicable, with good real-time performance and high positioning accuracy.

Abstract Pathogenic mutations in mitochondrial DNA cause severe and often lethal multi-system symptoms in primary mitochondrial defects. However, effective therapies for these defects are still lacking. Strategies such as employing mitochondrially targeted restriction enzymes or programmable nucleases to shift the ratio of heteroplasmic mutations and allotopic expression of mitochondrial protein-coding genes have limitations in treating mitochondrial homoplasmic mutations, especially in non-coding genes. Here, we conduct a proof of concept study applying a screened DdCBE pair to correct the homoplasmic m.A4300G mutation in induced pluripotent stem cells derived from a patient with hypertrophic cardiomyopathy. We achieve efficient G4300A correction with limited off-target editing, and successfully restore mitochondrial function in corrected induced pluripotent stem cell clones. Our study demonstrates the feasibility of using DdCBE to treat primary mitochondrial defects caused by homoplasmic pathogenic mitochondrial DNA mutations.

Objective To evaluate predictive factors affecting the short-term efficacy of PD-1 inhibitors in non-small cell lung cancer (NSCLC) and to construct a prediction model. Methods From October 2019 to November 2021, 221 patients with advanced NSCLC who met the inclusion criteria and were treated with PD-1 inhibitors were prospectively enrolled. Patients who were enrolled before May 1st, 2021 were included inthe modeling group (n=149), whereas those who enrolled thereafter were included in the validation group (n=72). The general clinical data of patients, information of the four TCM diagnoses were collected, and TCM syndrome elements were identified. R software version 4.0.4 was used in constructing a nomogram clinical prediction model of objective response rate. The predictive ability and discrimination of the model were evaluated and externally validated by using a validation group. Results After two to four cycles of PD-1 inhibitor therapy in 221 patients, the overall objective response rate was 44.80%. Multivariate logistic regression analysis of the modeling group showed that the TPS score (OR=0.261, P=0.001), number of treatment lines (OR=3.749, P=0.002), treatment mode (OR=2.796, P=0.019), qi deficiency disease syndrome elements (OR=2.296, P=0.043), and syndrome elements of yin deficiency disease (OR=3.228, P=0.005) were the independent predictors of the short-term efficacy of PD-1 inhibitors. Based on the above five independent predictors, a nomogram prediction model for the short-term efficacy of PD-1 inhibitors was constructed. The AUC values of the modeling and validation groups were 0.8317 and 0.7535, respectively. The calibration curves of the two groups showed good agreement between the predicted and true values. The mean absolute errors were 0.053 and 0.039, indicating that the model has good predictive performance. Conclusion The nomogram model constructed on the basis of the syndrome elements of Qi-deficiency disease and Yin-deficiency syndrome of TCM, as well as TPS score, number of treatment lines and treatment mode, is a stable and effective tool for predicting the short-term efficacy of PD-1 inhibitors in advanced non-small cell lung cancer.

Abstract Background Dynamic positron emission tomography (PET) images are useful in clinical practice because they can be used to calculate the metabolic parameters (K i ) of tissues using graphical methods (such as Patlak plots). K i is more stable than the standard uptake value and has a good reference value for clinical diagnosis. However, the long scanning time required for obtaining dynamic PET images, usually an hour, makes this method less useful in some ways. There is a tradeoff between the scan durations and the signal-to-noise ratios (SNRs) of K i images. The purpose of our study is to obtain approximately the same image as that produced by scanning for one hour in just half an hour, improving the SNRs of images obtained by scanning for 30 min and reducing the necessary 1-h scanning time for acquiring dynamic PET images. Methods In this paper, we use U-Net as a feature extractor to obtain feature vectors with a priori knowledge about the image structure of interest and then utilize a parameter generator to obtain five parameters for a two-tissue, three-compartment model and generate a time activity curve (TAC), which will become close to the original 1-h TAC through training. The above-generated dynamic PET image finally obtains the K i parameter image. Results A quantitative analysis showed that the network-generated K i parameter maps improved the structural similarity index measure and peak SNR by averages of 2.27% and 7.04%, respectively, and decreased the root mean square error (RMSE) by 16.3% compared to those generated with a scan time of 30 min. Conclusions The proposed method is feasible, and satisfactory PET quantification accuracy can be achieved using the proposed deep learning method. Further clinical validation is needed before implementing this approach in routine clinical applications.

Abstract DddA-derived cytosine base editors (DdCBEs) greatly facilitated the basic and therapeutic research of mitochondrial DNA mutation diseases. Here we devise a saturated spacer library and successfully identify seven DddA homologs by performing high-throughput sequencing based screen. DddAs of Streptomyces sp. BK438 and Lachnospiraceae bacterium sunii NSJ-8 display high deaminase activity with a strong GC context preference, and DddA of Ruminococcus sp. AF17-6 is highly compatible to AC context. We also find that different split sites result in wide divergence on off-target activity and context preference of DdCBEs derived from these DddA homologs. Additionally, we demonstrate the orthogonality between DddA and DddIA, and successfully minimize the nuclear off-target editing by co-expressing corresponding nuclear-localized DddIA. The current study presents a comprehensive and unbiased strategy for screening and characterizing dsDNA cytidine deaminases, and expands the toolbox for mtDNA editing, providing additional insights for optimizing dsDNA base editors.

Abstract Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consuming procedure. Determining the tumor types directly from whole-slide images (WSIs) is of great value for glioma diagnosis. This study presents an integrated diagnosis model for automatic classification of diffuse gliomas from annotation-free standard WSIs. Our model is developed on a training cohort (n = 1362) and a validation cohort (n = 340), and tested on an internal testing cohort (n = 289) and two external cohorts (n = 305 and 328, respectively). The model can learn imaging features containing both pathological morphology and underlying biological clues to achieve the integrated diagnosis. Our model achieves high performance with area under receiver operator curve all above 0.90 in classifying major tumor types, in identifying tumor grades within type, and especially in distinguishing tumor genotypes with shared histological features. This integrated diagnosis model has the potential to be used in clinical scenarios for automated and unbiased classification of adult-type diffuse gliomas.

Structural damage identification has been one of the key applications in the field of Structural Health Monitoring (SHM). With the development of technology and the growth of demand, the method of identifying damage anomalies in plate structures is increasingly being developed in pursuit of accuracy and high efficiency. Principal Component Analysis (PCA) has always been effective in damage identification in SHM, but because of its sensitivity to outliers and low robustness, it does not work well for complex damage or data. The effect is not satisfactory. This paper introduces the Robust Principal Component Analysis (RPCA) model framework for the characteristics of PCA that are too sensitive to the outliers or noise in the data and combines it with Lamb to achieve the damage recognition of wavefield images, which greatly improves the robustness and reliability. To further improve the real-time monitoring efficiency and reduce the error, this paper proposes a non-convex approximate RPCA (NCA-RPCA) algorithm model. The algorithm uses a non-convex rank approximation function to approximate the rank of the matrix, a non-convex penalty function to approximate the norm to ensure the uniqueness of the sparse solution, and an alternating direction multiplier method to solve the problem, which is more efficient. Comparison and analysis with various algorithms through simulation and experiments show that the algorithm in this paper improves the real-time monitoring efficiency by about ten times, the error is also greatly reduced, and it can restore the original data at a lower rank level to achieve more effective damage identification in the field of SHM.

Older adults with chronic illness, as well as their primary caregivers in multigenerational families, may experience a complex interplay of factors that affect their quality of life (QOL). However, this interplay is not yet well-characterized for Chinese multigenerational families in particular. In this study, we analyzed how family resilience and social support affect the QOL of both older adults and caregivers in multigenerational Chinese families specifically. We enrolled 258 pairs of older adults with chronic illness and their primary caregivers in a multicenter cross-sectional study conducted in southern China in December 2021. Using the Actor-Partner Interdependence Model (APIM), we then examined the correlation between family resilience, social support, and QOL in dyadic analysis and found that QOL, family resilience, and social support for primary caregivers were better than those of older adults with chronic illness (t = 3.66–16.3, p＜0.01). These factors were found to be positively correlated (r = 0.22–0.60, p＜0.05), except for the family resilience of primary caregivers and the QOL of older adults with chronic illness (r = −0.14, p = 0.04). Additionally, actor effect results showed that when a dyadic member has high family resilience and objective social support, they tend to have a better QOL (β = 0.5–1.48, P

ABSTRACTLasiodiplodia theobromae, as one of the causative agents associated with Chinese hickory trunk cankers, has caused huge economic losses to the Chinese hickory industry. Although the biological characteristics of this pathogen and the occurrence pattern of this disease have been well studied, few studies have addressed the related mechanisms due to the poor molecular and genetic study basis of this fungus. In this study, we sequenced and assembled L. theobromae strain LTTK16-3, isolated from a Chinese hickory tree (cultivar of Linan) in Linan, Zhejiang province, China. Phylogenetic analysis and comparative genomics analysis presented crucial cues in the prediction of LTTK16-3, which shared similar regulatory mechanisms of transcription, DNA replication, and DNA damage response with the other four Chinese hickory trunk canker-associated Botryosphaeria strains including, Botryosphaeria dothidea, Botryosphaeria fabicerciana, Botryosphaeria qingyuanensis, and Botryosphaeria corticis. Moreover, it contained 18 strain-specific protein clusters (not conserved in the other L. theobromae strains, AM2As and CITRA15), with potential roles in specific host-pathogen interactions during the Chinese hickory infection. Additionally, an efficient system for L. theobromae protoplast preparation and polyethylene glycol (PEG) -mediated genetic transformation was firstly established as the foundation for its future mechanisms study. Collectively, the high-quality genome data and the efficient transformation system of L. theobromae here set up the possibility of targeted molecular improvements for Chinese hickory canker control.IMPORTANCEFungi with disparate genomic features are physiologically diverse, possessing species-specific survival strategies and environmental adaptation mechanisms. The high-quality genome data and related molecular genetic studies are the basis for revealing the mechanisms behind the physiological traits that are responsible for their environmental fitness. In this study, we sequenced and assembled the LTTK16-3 strain, the genome of Lasiodiplodia theobromae first obtained from a diseased Chinese hickory tree (cultivar of Linan) in Linan, Zhejiang province, China. Further phylogenetic analysis and comparative genomics analysis provide crucial cues in the prediction of the proteins with potential roles in specific host-pathogen interactions during the Chinese hickory infection. An efficient PEG-mediated genetic transformation system of L. theobromae was established as the foundation for the future mechanisms exploration. The above genetic information and tools set up valuable clues to study L. theobromae pathogenesis and assist in Chinese hickory canker control.

Verticillium wilt, caused by Verticillium dahliae, poses a severe threat to cotton growth and yield as a soil-borne disease. The ABI five binding protein (AFP) family, characterized by alkaline leucine zip-like transcription factors, responds to ABA signals, and its expression is induced by ABA as well as various biological and abiotic stresses. In this study, we identified 23 members of the four cotton AFP families, revealing high conservation in both gene and protein structures. Collinearity analysis indicated a closer relationship between GhAFPs and AtAFPs than rice. Cis-element analysis of the cotton AFP promoters unveiled elements associated with plant hormones, abscisic acid, abiotic stress, and light response. To unravel the role of the cotton AFP family in verticillium wilt resistance, we pinpointed a highly expressed gene, GhAFP8, from a resistant variety by monitoring expression levels pre- and post-inoculation for each family gene. Subcellular localization confirmed nuclear localization for GhAFP8. Overexpression of GhAFP8 in tobacco demonstrated enhanced resistance to Verticillium dahliae compared to wild-type plants. Further functional exploration utilizing the VIGs system in cotton revealed that silencing the GhAFP8 gene compromised cotton's resistance to Verticillium dahliae. Transcriptome analysis 72 hours post-inoculation in TRV::00 plants and TRV::GhAFP8-silenced plants showed that GhAFP8 silencing primarily impacts the abscisic acid-activated signaling pathway, MAPK signaling pathway, plant hormone signal transduction, and the activation of plant-pathogen interaction pathway. Additionally, GhAFP8 exhibited responsiveness to signals from jasmonic acid (JA), abscisic acid (ABA), and salicylic acid (SA). In summary, we demonstrated the active role of the GhAFP8 gene in conferring resistance to Verticillium dahliae, positioning it as a promising candidate for cotton breeding against verticillium wilt.

Kiwifruit, a nutrient-dense fruit, has become increasingly popular with consumers in recent decades. However, kiwifruit trees are prone to stunted growth after a few years of planting, called early tree decline. In this study, melatonin (MT), pollen polysaccharide (SF), 14-hydroxyed brassinosteroid (14-HBR) were applied alone or in combination to investigate their influence on plant growth, nutrition absorption and rhizosphere bacterial abundance in kiwifruit seedlings. The results revealed that MT, SF and 14-HBR alone treatments significantly increased leaf chlorophyll content, photosynthetic capacity and activities of dismutase and catalase compared with the control. Among them, MT treatment significantly increased the dry root biomass by 35.7%, while MT+14-HBR treatment significant enhanced the dry shoot biomass by 36.9%. Furthermore, both MT and MT+14-HBR treatments markedly improved the activities of invertase, urease, protease and phosphatase in soil, as well as the abundance of Proteobacteria and Acidobacteria in rhizosphere microorganisms based on 16S rDNA sequencing. In addition, MT treatment improved the content of available K and organic matter in soil, and increased the uptake of P, K and Fe by seedlings. In summary, 14-HBR and MT combined had the best effect on promoting rhizosphere bacterial distribution, nutrient absorption and plant growth. These findings may provide valuable guidance for solving growth weakness problem in kiwifruit cultivation.

The comber is very important in the spinning for high quality yarns, in which the nipper mechanism (NM) determines the quality of the yarn it combs. This paper is to connect topology and multi-body dynamics to reveal the principle of impact motion of the nipper mechanism. Firstly, the working process of the NM is analyzed and corresponding kinematic models of work sub-phases are derived. Subsequently, the transition from the work phase of the jaw closed to the opened is studied. The research results show that during the transformation process, the mechanism presented another work phase with multiple impacts, which is the main reason why the NM could not stably clamp the cotton clump and thus affect the quality of the yarn combed by the comber. According to the relative coordinate method, the dynamic model of the NM is set up. Combining the classical collision theory and the restitution coefficient equation, the acquisition of the impact impulse generated at the jaw and the subsequent motion of the mechanism are decided. Finally, the NM in the E62 comber is taken as an example to verify the correctness of the established dynamic model, and the influences of different input speeds, restitution coefficients, and stiffness coefficients of spring on the jaw’s impact are studied. This has certain theoretical value for improving the speed and efficiency of the comber.

Receiving inspection plays a crucial role in ensuring construction quality after the completion of engineering projects. Traditional inspection measurement methods, such as manual observation means and optical equipment measurement methods, have limitations in terms of the number of measurement sites and the range of measurements. These traditional methods fail to provide accurate curve parameters and continuous spatial morphology information for large-span curved bridge structures. This paper proposes a reverse model measurement method to address this issue. The reverse model is built based on point cloud data acquired by 3D laser scanning technology. Finally, take the Taizicheng No. 1 Bridge as an example, the validity of the proposed method is verified.

The unique characteristics, such as high magnetic moment, high Curie temperature, large magnetic crystalline anisotropy, and low cost, make the τ-phase MnAl a promising candidate as the market alternatives which could fill the gap between the rare earth magnets and ferrite magnets. Because at low temperatures the metal will become more brittle, the cryomilling technology may allow the τ-phase MnAl to be broken quickly and introduce few crystal defects, and then obtain better permanent magnetic properties. In this study, the cryomilling technology was used to grind the τ-phase MnAl, and the structural and magnetic properties of the obtained powder sample were investigated. It was found that compared to room-temperature ball milling, the morphology of cryomilled powders is granular and the powder agglomeration is dramatically suppressed at a low temperature, and as a result the larger particles tend to be broken down into smaller ones rather than being agglomerated to larger thin layers. The grain size D reduced continuously from 145 nm to 36 nm when the sample was milled for 120 minutes. The coercivity increased with increasing the cryo-milling time, while the saturation magnetization decreased. The maximum coercivity of up to 4.9 kOe was obtained by cryo-milling for 100 minutes. By the neutron diffraction analysis, it was confirmed that the decrease of the saturation magnetization with increasing milling time is mainly due to the migration of Mn atoms from 1a site to the 1d site and the decrease of Mn atomic magnetic moment.

In order to address the issues pertaining to the subjective nature and limited precision associated with selecting feature points in the point cloud of a large steel truss structure, this study proposes a batch automatic extraction approach for identifying key feature information, including boundaries, corner points, and bolt holes of large steel truss components. This method relies on the nested application of established processing algorithms such as Euclidean clusters, regional growth clusters, and random sampling consensus. In addition, a novel approach is suggested for validating the precision of feature information extraction through the utilization of standard theoretical models. The results of the experimental and large-scale lower chord tests demonstrate that our approach is not dependent on specialized software, exhibits excellent efficiency, and possesses an acceptable degree of automation. The findings of this study can provide accurate data support for reverse modeling, virtual trial assembly, and dimensional inspection of steel truss components.

Background: Previous studies have reported that transcription factor forkhead box protein 3 (FOXP3) polymorphisms are correlated with the progress of some cancers, but the relationships between the FOXP3 polymorphisms and hepatocellular carcinoma (HCC) risk remain unclear. Method: Genotypes were detected in156 hepatitis B virus (HBV)-HCC patients, 109 HBV-liver cirrhosis (LC) patients, 125 chronic hepatitis B (CHB) patients, and 188 healthy controls. The FOXP3 rs3761547 and rs3761548 polymorphisms were genotyped by polymerase chain reaction (PCR) combined with restriction fragment length polymorphism, and the rs2232365 polymorphism was genotyped using PCR with sequence-specific primers. Results: We did not obtain any significant results with the FOXP3 rs3761547, rs3761548, and rs2232365 polymorphisms in groups of patients compared to healthy controls (all p > 0.05), no matter the overall group or subgroup. Conclusions: Our findings suggest that the FOXP3 polymorphisms at rs3761547, rs3761548, and rs2232365 were not related to HBV-HCC risk in the Chinese population.

The eastern waters of Taiwan have been lacking baseline and research data for several years. This study was initiated by Kuroshio Ocean Education Foundation (KOEF) in collaboration with the Turumoan whale-watching company since 1998, collecting long-term ecological data for cetaceans in the inshore of Hualien, eastern Taiwan. This dataset includes 10,675 records of cetacean sightings from June 1998 to December 2021. Collection of cetacean sighting records was paused for one year in 2001 due to budgetary reasons. All of the sighting records were collected by whale-watching boat guides that were trained by KOEF. Following a standardised protocol, guides used a handheld GPS device and cetacean sighting record sheets to document information about the cetacean species identification, location, time, number of individuals, the presence of mother-calf pairs and mixed-species groups and other states of each sighting during a whale-watching tour. The collection of citizen-science data during this period has significantly advanced Taiwan's cetacean baseline data in the study area. Additionally, we make data available to the public in the form of citizen-science, making a substantial contribution to the advancement of ocean scientific research. We have published the dataset on Global Biodiversity Information Facility, allowing users around the world to download the dataset.This is currently the largest dataset of cetacean sighting records in Taiwan (last updated on 2023-09-05). We have also recorded several species on the International Union for Conservation of Nature (IUCN) Red List categorised as "Data Deficient" during our survey inshore of Hualien, eastern Taiwan, including Ginkgo-toothed beaked whales (Mesoplodon ginkgodens Nishiwaki & Kamiya, 1958), Omura's whales (Balaenoptera omurai Wada, Oishi & Yamada, 2003) and killer whales (Orcinus orca (Linnaeus, 1758)). There are also sperm whales (Physeter macrocephalus Linnaeus, 1758), categorised as "Vulnerable" in the IUCN Red List and false killer whales (Pseudorca crassidens (Owen, 1846)), categorised as "Near Threatened". This study is also the first and only long-term study that has documented cetaceans in the study area.

Abstract Background Extracellular vesicles (EVs) from human umbilical cord mesenchymal stem cells (hUMSCs) are widely considered to be the best mediators for cell-free therapy. An understanding of their composition, especially RNA, is particularly important for the safe and precise application of EVs. Up to date, the knowledge of their RNA components is limited to NGS sequencing and cannot provide a comprehensive transcriptomic landscape, especially the long and full-length transcripts. Our study first focused on the transcriptomic profile of hUMSC-EVs based on nanopore sequencing. Methods In this study, different EV subtypes (exosomes and microvesicles) derived from hUMSCs were isolated and identified by density gradient centrifugation. Subsequently, the realistic long transcriptomic profile in different subtypes of hUMSC-EVs was systematically compared by nanopore sequencing and bioinformatic analysis. Results Abundant transcript variants were identified in EVs by nanopore sequencing, 69.34% of which transcripts were fragmented. A series of full-length and long transcripts was also observed and showed a significantly higher proportion of intact or near-complete transcripts in exosomes than that in microvesicles derived from hUMSCs. Although the composition of RNA biotypes transported by different EV subtypes was similar, the distribution of transcripts and genes revealed the inter-heterogeneity and intra-stability between exosomes and microvesicles. Further, 85 different expressed transcripts (56 genes) and 7 fusion genes were identified. Pathway enrichment analysis showed that upregulated-expressed genes in microvesicles were mainly enriched in multiple neurodegenerative diseases, while upregulated-expressed genes in exosomes were mainly enriched in neutrophil extracellular trap formation, suggesting different functional tendencies of EV subtypes. Conclusions This study provides a novel understanding of different types of hUMSC-EVs, which not only suggests different transcriptome sorting mechanisms between exosomes and microvesicles, but also shows that different EV subtypes from the same source have different physiological functions, suggesting distinct clinical application prospects.