Search Results (70,363)

Huntington’s disease (HD) is a neurodegenerative disorder caused by the expansion of the CAG repeat in exon 1 of the HTT gene, leading to the formation of a toxic variant of the huntingtin protein. It is a rare but severe hereditary disease for which no effective treatment method has been found yet. The primary therapeutic targets include the mutant protein and the mutant mRNA of HTT. Current clinical trial approaches in gene therapy involve the application of splice modulation, siRNA, or antisense oligonucleotides for RNA-targeted knockdown of HTT. However, these approaches do not take into account the diversity of HTT transcript isoforms in the normal conditions and in HD. In this review, we discuss the features of transcriptional regulation and processing that lead to the formation of various HTT mRNA variants, each of which may uniquely contribute to the progression of the disease. Furthermore, understanding the role of known transcription factors of HTT in pathology may aid in the development of potentially new therapeutic tools based on endogenous regulators. Full article

Alginate oligosaccharides (AOs), derived from alginate degradation, exhibit diverse biological activities and hold significant promise in various fields. The enzymatic preparation of AOs relies on alginate lyases, which offers distinct advantages. In contrast to the conventional use of sodium alginate derived from brown algae as the substrate for the enzymatic preparation of AOs, AO preparation directly from brown algae is more appealing due to its time and energy efficiency. Thus, the identification of potent alginate lyases and cost-effective brown algae substrates is crucial for optimizing AO production. Herein, we identified and characterized an alginate lyase, AlyP18, capable of efficiently decomposing algae, from a marine bacterium Pseudoalteromonas agarivorans A3 based on secretome analysis. AlyP18 is a mesothermal, endo-type and bifunctional alginate lyase with high enzymatic activity. Two brown algae substrates, Laminaria japonica roots and Macrocystis pyrifera, were used for the AO preparation by AlyP18. Upon optimization of AlyP18 hydrolysis parameters, the substrate degradation efficiency and AO production reached 53% and ~32% for L. japonica roots, respectively, and 77% and ~46.5% for M. pyrifera. The generated AOs primarily consisted of dimers to pentamers, with trimers and tetramers being dominant. This study provides an efficient alginate lyase and alternative brown algal feedstock for the bioconversion of high-value AOs from brown algae. Full article

Labor management remains a critical issue in obstetrics, with dystocic labor presenting significant challenges in both management and outcomes. Recent advancements in intrapartum ultrasound have facilitated substantial progress in monitoring labor progression. This paper explores the integration of artificial intelligence (AI) into obstetric care, focusing on the Artificial Intelligence Dystocia Algorithm (AIDA) for assessing spatial dystocia during labor. The AIDA utilizes intrapartum ultrasonography to measure four geometric parameters: the angle of progression, the degree of asynclitism, the head–symphysis distance, and the midline angle. These measurements are analyzed using machine learning techniques to predict delivery outcomes and stratify risk. The AIDA classification system categorizes labor events into five classes, providing a nuanced assessment of labor progression. This approach offers several potential advantages, including objective assessment of fetal position, earlier detection of malpositions, and improved risk stratification, placing labor events within a broader context of labor dystocia and obstetric care and discussing their potential impact on clinical practice. This paper serves as a more comprehensive overview and discussion of the AIDA approach, its implications, perspectives, and future directions. However, challenges such as the technological requirements, training needs, and integration with clinical workflows are also addressed. This study emphasizes the necessity for additional validation across diverse populations and careful consideration of its ethical implications. The AIDA represents a significant advancement in applying AI to intrapartum care, potentially enhancing clinical decision-making and improving outcomes in cases of suspected dystocia. This paper explicates the key methodological approaches underpinning the AIDA, illustrating the integration of artificial intelligence and clinical expertise. The innovative framework presented offers a paradigm for similar endeavors in other medical specialties, potentially catalyzing advancements in AI-assisted healthcare beyond obstetrics. Full article

Wireless networks, especially 5G and WiFi networks, have made great strides in increasing network bandwidth and coverage over the past decades. However, the mobility and channel conditions inherent to wireless networks have the potential to impair the performance of traditional Transmission Control Protocol (TCP) congestion control algorithms (CCAs). Google proposed a novel TCP CCA based on Bottleneck Bandwidth and Round-Trip propagation time (BBR), which is capable of achieving high transmission rates and low latency through the estimation of the available bottleneck capacity. Nevertheless, some studies have revealed that BBR exhibits deficiencies in fairness among flows with disparate Round-Trip Times (RTTs) and also displays inter-protocol unfairness. In high-speed wireless networks, ensuring fairness is of paramount importance to guarantee equitable bandwidth allocation among diverse traffic types and to enhance overall network utilization. To address this issue, this paper proposes a BBR–Pacing Gain (BBR–PG) algorithm. By deriving the pacing rate control model, the impact of pacing gain on BBR fairness is revealed. Adjusting the pacing gain according to the RTT can improve BBR’s performance. Simulations and real network experiments have shown that the BBR–PG algorithm retains the throughput advantages of the original BBR algorithm while significantly enhancing fairness. In our simulation experiments, RTT fairness and intra-protocol fairness were improved by 50% and 46%, respectively. Full article

Shield tunneling generates a massive amount of muck, and achieving the on-site reuse of muck is an urgent need in the field of shield tunneling. This study, based on a section of the Xianyang diversion tunnel in a loess stratum, aims to optimize the mix ratios of loess muck grouting materials to meet specific performance requirements. Laboratory tests were conducted to analyze the effects of the bentonite content and water–solid ratio on the properties of grout. The engineering properties, cost, and environmental impact of the optimized loess muck grouting materials were compared with those of traditional grouting materials. Additionally, XRD, SEM, and CT were employed to investigate the solidification mechanism of loess muck grouting materials. The results show that the bleeding rate, setting time, fluidity, and consistency of loess muck grouting materials decreased with increasing bentonite content, while these properties increased as the water–solid ratio rose. The compressive strength reached 0.26 MPa and 1.05 MPa at 3 d and 28 d, respectively. Compared to traditional grouting materials, the economic cost and carbon emissions of loess muck grouting materials were reduced by 49.46% and 37.17%, respectively. As the curing time increased, gel filling and particle agglomeration reduced the number of pores. The dense microstructure is the primary factor for the improvement of strength. Full article

Background/Objectives: Noonan syndrome (NS) is a genetic multisystem disease characterized by distinctive facial features, short stature, chest deformity, and congenital heart defects. NS is caused by gene variants of the RAS/MAPK pathway, with PTPN11 accounting for about 50% of cases. This study aimed to identify PTPN11 pathogenic variants in Mexican patients with NS to enhance our understanding of the disease in this population. Methods: This study included 91 probands and 60 relatives, all of which were clinically evaluated by a geneticist. Sanger sequencing was used to screen the entire PTPN11 gene. Results: Twenty-one previously reported pathogenic variants were identified in 47.3% of the probands. The most frequently occurring were p.Asn308Asp (16.3%) and p.Met504Val (16.3%). Variants p.Tyr279Cys and p.Thr468Met were found exclusively in patients with lentiginosis. Eighty-three percent of patients carried a variant in one of the three exons (3, 8, or 13) where the greatest genetic diversity was observed. Common clinical findings identified in probands included short stature (82%), cardiac anomalies (70.7%), short neck (68.4%), and pectus excavatum (63.2%), although features represented by only one patient each were also detected. Conclusions: This study confirmed the clinical diagnosis of NS in 43 probands and 11 relatives, and further genetic analysis of the remaining 48 probands is required to identify the causal variant. The genetic and clinical variability observed in our cohort was consistent with reports from other populations, underscoring the importance of comprehensive care for all patients. This research provides the most extensive clinical and molecular characterization of NS in Mexican patients, identifying pathogenic variants of PTPN11. Full article

Anthocyanins, key quality components of tea, act as an important bridge between plants and the environment due to their function on protecting plants from biotic and abiotic irritants. This study aimed to assess the interactions between anthocyanins metabolism and the environment. Purple (P) and green (G) leaves with different anthocyanin contents were inoculated with tea plant anthracnose. High-throughput metabolomics and 16S microbial diversity sequencing methods were used to screen the anthocyanin fractions of tea plant leaves responsive to anthracnose. The interconnections between metabolites and the resistance of phyllosphere microorganisms to fungal pathogens were then analyzed. The results showed that leaves with high anthocyanin content (0.14% of diseased area ratio) were less impacted by anthracnose infestation than leaves with low anthocyanin (3.12%). The cyanidin content decreased after infection in purple leaves (PR) and increased in green leaves (GR). The relative abundance of Cyanobacteria was suppressed by the significant enrichment of Proteobacteria after anthracnose infection in green leaves. However, there were no significant differences between these two groups of microorganisms in purple leaves. Collinear network analysis revealed a strong correlation between Cyanobacteria and Dihydrosorbinol and between Proteobacteria and cyanidin metabolites. Among them, OTU456 (Bosea) was identified as the key taxonomic group of bacterial communities in the green-infected leaf network. In summary, the anthracnose resistance benefits from cyanidin mediated by proteobacteria in tea plants. These results deepen our understanding of the regulation of secondary metabolism in tea plants and the formation of plant resistance. Full article

The accurate detection of railway tracks from unmanned aerial vehicle (UAV) images is essential for intelligent railway inspection and the development of electronic railway maps. Traditional computer vision algorithms struggle with the complexities of high-precision track extraction due to challenges such as diverse track shapes, varying angles, and complex background information in UAV images. While deep learning neural networks have shown promise in this domain, they still face limitations in precisely extracting track line edges. To address these challenges, this paper introduces an improved NL-LinkNet network, named NL-LinkNet-SSR, designed specifically for railway track detection. The proposed NL-LinkNet-SSR integrates a Sobel edge detection module and a SimAM attention module to enhance the model’s accuracy and robustness. The Sobel edge detection module effectively captures the edge information of track lines, improving the segmentation and extraction of target edges. Meanwhile, the parameter-free SimAM attention module adaptively emphasizes significant features while suppressing irrelevant information, broadening the model’s perceptual field and improving its responsiveness to target areas. Experimental results show that the NL-LinkNet-SSR significantly outperforms the original NL-LinkNet model across multiple key metrics, including a more than 0.022 increase in accuracy, over a 4% improvement in F1-score, and a more than 3.5% rise in mean Intersection over Union (mIoU). These enhancements suggest that the improved NL-LinkNet-SSR offers a more reliable solution for railway track detection, advancing the field of intelligent railway inspection. Full article

Agriculture is a leading cause of biodiversity loss, making the transition to sustainable agroecological practices crucial. Insectivorous bats play a crucial role as biological controllers in regard to agricultural crops, serving as important insect predators. The purpose of this study is to assess bat communities in three distinct habitats, namely the interior of a vineyard, native vegetation, and the transitional edge between them, by analyzing the echolocation patterns of different species. Generalized linear mixed models were used to evaluate the influence of landscape characteristics on bat communities and at the species level, allowing the incorporation of variables at different scales (at 10 m, 100 m, and 1000 m radius) from each sampling site. Our results show that edges enhance bat richness, their general activity, and feeding patterns, and are of particular benefit to certain species: Tadarida brasiliensis, Myotis chiloensis, and Lasiurus varius. Implementing agroecological practices, such as the maintenance of tree hedgerows at the landscape scale, along with native vegetation at the landscape scale, can amplify feeding activity in vineyards, thereby enhancing the provision of ecosystem services in agroecosystems. The edges of vineyards and natural vegetation are crucial for providing habitats for bats and increasing their foraging activity, as well as providing a way to enhance agroecological practices in vineyards to bolster ecosystem services. Full article

Paddy fields are classified as wetland environments, and they comprise freshwater ecosystems. They are ecologically important habitats and breeding grounds for many aquatic insects, amphibians, and reptiles. However, paddy field ecosystems are constantly threatened by climate change and the indiscriminate use of pesticides. The metabarcode analysis of eDNA (environmental DNA) method is highly effective at accumulating information on many organisms that inhabit paddy field ecosystems. It can indirectly identify the existence of taxa that are no longer found in the target ecosystem due to behavioral characteristics, such as those exhibited by amphibians. In the metabarcoding results of this study, genes of five species of frogs were found, but it was impossible to confirm all of the frogs’ taxa, morphological pictures, and croak sounds. On the other hand, some frog taxa were only found in the metabarcoding analysis. The eDNA of the frogs found only in the metabarcoding analysis is estimated to have been introduced from nearby areas inhabited by frogs rather than the target region. Due to the powerful analytical resolution of eDNA metabarcoding, this eDNA-based paddy field search is expected to help investigate the biodiversity in agricultural ecosystems. Full article

Triosephosphate isomerase (TPI) dysfunction is a critical factor in diverse pathological conditions. Deficiencies in TPI lead to the accumulation of toxic methylglyoxal (MGO), which induces non-enzymatic post-translational modifications, thus compromising protein stability and leading to misfolding. This study investigates how specific TPI mutations (E104D, N16D, and C217K) affect the enzyme’s structural stability when exposed to its substrate glyceraldehyde 3-phosphate (G3P) and MGO. We employed circular dichroism, intrinsic fluorescence, native gel electrophoresis, and Western blotting to assess the structural alterations and aggregation propensity of these TPI mutants. Our findings indicate that these mutations markedly increase TPI’s susceptibility to MGO-induced damage, leading to accelerated loss of enzymatic activity and enhanced protein aggregation. Additionally, we observed the formation of MGO-induced adducts, such as argpyrimidine (ARGp), that contribute to enzyme inactivation and aggregation. Importantly, the application of MGO-scavenging molecules partially mitigated these deleterious effects, highlighting potential therapeutic strategies to counteract MGO-induced damage in TPI-related disorders. Full article

Thin-film composite (TFC) membranes containing various fillers and additives present an effective alternative to conventional dense polymer membranes, which often suffer from low permeance (flux) and the permeability–selectivity tradeoff. Alongside the development and utilization of numerous new polymers over the past few decades, diverse additives such as metal–organic frameworks (MOFs), graphene oxides (GOs), and ionic liquids (ILs) have been integrated into the polymer matrix to enhance performance. However, achieving desirable interfacial compatibility between these additives and the host polymer matrix, particularly in TFC structures, remains a significant challenge. This review discusses recent advancements in TFC membranes for CO₂/N₂ separation, focusing on material structure, polymer–additive interaction, interface and separation properties. Specifically, we examine membranes operating under dry conditions to clearly assess the impact of additives on membrane properties and performance. Additionally, we provide a perspective on future research directions for designing high-performance membrane materials. Full article

Traditional antiviral peptide (AVP) discovery is a time-consuming and expensive process. This study introduces AVP-GPT, a novel deep learning method utilizing transformer-based language models and multimodal architectures specifically designed for AVP design. AVP-GPT demonstrated exceptional efficiency, generating 10,000 unique peptides and identifying potential AVPs within two days on a GPU system. Pre-trained on a respiratory syncytial virus (RSV) dataset, AVP-GPT successfully adapted to influenza A virus (INFVA) and other respiratory viruses. Compared to state-of-the-art models like LSTM and SVM, AVP-GPT achieved significantly lower perplexity (2.09 vs. 16.13) and higher AUC (0.90 vs. 0.82), indicating superior peptide sequence prediction and AVP classification. AVP-GPT generated a diverse set of peptides with excellent novelty and identified candidates with remarkably higher antiviral success rates than conventional design methods. Notably, AVP-GPT generated novel peptides against RSV and INFVA with exceptional potency, including four peptides exhibiting EC50 values around 0.02 uM—the strongest anti-RSV activity reported to date. These findings highlight AVP-GPT’s potential to revolutionize AVP discovery and development, accelerating the creation of novel antiviral drugs. Future studies could explore the application of AVP-GPT to other viral targets and investigate alternative AVP design strategies. Full article