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With the significant expansion of civil aviation, particularly in the low-altitude economy, there is a significant gap between the escalating demand for airworthiness certification of novel aircraft designs, such as electric vertical take-off and landing (eVTOL) vehicles, and the inefficiency of the current safety assessment process. This gap is partially attributed to safety assessors' limited exposure to these innovative aircraft models in the safety assessment process, necessitating extensive efforts in identifying precedents and their handling strategies. Complicating matters further, pertinent case studies are scattered across diverse, unstandardized digital formats, obliging assessors to navigate voluminous electronic records while concurrently establishing links among fragmented information scattered across multiple files. This study introduces an advanced information integration methodology, comprising a multi-level path-based architecture and a self-updating algorithm. The proposed method not only furnishes safety assessors with pertinent knowledge featuring explicative interconnectedness automatically, but also dynamically enriches this knowledge corpus through operational usage. Additionally, we devise a suite of evaluative criteria to validate the capacity of our method in processing and consolidating relevant safety datasets. Experimental analyses affirm the efficacy of our proposed approach in streamlining and refreshing safety assessment data. The automation of the retrieval of analogous cases, which relieves the reliance on expert knowledge, enhances the efficiency of the overall safety appraisal procedure. Consequently, this research contributes a solution to enhancing the velocity and accuracy of aircraft certification processes. [ABSTRACT FROM AUTHOR]

A large number of raw data collected by satellites are processed by the production chain to obtain a large number of product data, of which the secure exchange and storage is of interest to researchers in the field of remote sensing information science. Authentic, secure data provide a critical foundation for data analysis and decision-making. Traditional centralized cloud computing systems are vulnerable to attack and, once the central server is successfully attacked, all data will be lost. Distributed ledger technology (DLT) is an innovative computer technology that can ensure information security and traceability, is tamper-proof, and can be applied to the field of remote sensing. Although there are many advantages to using DLT in remote sensing applications, there are some obstacles and limitations to its application. Remote sensing data have the characteristics of a large data volume, a spatiotemporal nature, global scale, and so on, and it is difficult to store and interconnect remote sensing data in the blockchain. To address these issues, this paper proposes a trustworthy and decentralized system using blockchain technology. The novelty of this paper is the proposal of a multi-level blockchain architecture in which the system collects remote sensing data and stores them in the Interplanetary File System (IPFS) network; after generating the IPFS hash, the network rehashes the value again and uploads it on the Ethereum chain for public query. The distributed data storage improves data security, supports the secure exchange of information, and improves the efficiency of data management. [ABSTRACT FROM AUTHOR]

Umami peptides enhance the umami taste of food and have good food processing properties, nutritional value, and numerous potential applications. Wet testing for the identification of umami peptides is a time-consuming and expensive process. Here, we report the iUmami-DRLF that uses a logistic regression (LR) method solely based on the deep learning pre-trained neural network feature extraction method, unified representation (UniRep based on multiplicative LSTM), for feature extraction from the peptide sequences. The findings demonstrate that deep learning representation learning significantly enhanced the capability of models in identifying umami peptides and predictive precision solely based on peptide sequence information. The newly validated taste sequences were also used to test the iUmami-DRLF and other predictors, and the result indicates that the iUmami-DRLF has better robustness and accuracy and remains valid at higher probability thresholds. The iUmami-DRLF method can aid further studies on enhancing the umami flavor of food for satisfying the need for an umami-flavored diet. [ABSTRACT FROM AUTHOR]

By integrating biological and bioinformatics data, Turek et al. (2023) [[21]] performed a genome assembly on the B10v3 cucumber genome and presented a well-organized and annotated reference genome of cucumber, contributing to better understanding of the cucumber genome. Using genome-wide classification and functional annotation, Cenci et al. (2022) [[18]] effectively characterized a large gene family across multiple species and established a classification framework for gene functional transfer, enabling a better understanding of the evolutionary history of one gene. In addition, several papers explored functional genomics of plant genomes, such as gene functional annotation and the development of functional genomics tools. Comparative genomics and functional genomics are two basic branches of plant genomics. [Extracted from the article]

Simple Summary: Pholcus is the most diverse spider genus in Pholcidae, and is widely distributed in the Palaearctic, Indo-Malayan, Afrotropical, and Australasian Regions. Previously, the Pholcus spiders have not been recorded from the Lüliang Mountains of North China. We undertook an expedition there for the first time. Phylogenetic analyses of DNA sequence data from four gene fragments (COI, H3, wnt, 28S) suggested that Pholcus from the Lüliang Mountains were grouped into nine well-supported clades. We adopted an integrative approach, including morphology and four methods of molecular species delimitation (ABGD, GMYC, bPTP, and BPP), to investigate species boundaries. Such analyses identified the nine clades as nine separate species, of which eight are new to science. All of them belong to the P. phungiformes species group. Spiders of the genus Pholcus were collected for the first time during an expedition to the Lüliang Mountains in Shanxi Province, North China. Phylogenetic analyses of DNA sequence data from COI, H3, wnt, and 28S genes allowed us to group them into nine well-supported clades. We used morphology and four methods of molecular species delimitation, namely Automatic Barcode Gap Discovery (ABGD), the Generalized Mixed Yule Coalescent (GMYC), Bayesian Poisson Tree Processes (bPTP), and Bayesian Phylogenetics and Phylogeography (BPP), to investigate species boundaries. These integrative taxonomic analyses identified the nine clades as nine distinct species, comprising Pholcus luya Peng & Zhang, 2013 and eight other species new to science: Pholcus jiaocheng sp. nov., Pholcus linfen sp. nov., Pholcus lishi sp. nov., Pholcus luliang sp. nov., Pholcus wenshui sp. nov., Pholcus xiangfen sp. nov., Pholcus xuanzhong sp. nov., and Pholcus zhongyang sp. nov. The species occur in geographic proximity and show many morphological similarities. All of them belong to the P. phungiformes species group. The records from the Lüliang Mountains represent the westernmost distribution limit of this species group. [ABSTRACT FROM AUTHOR]

Recent advances in single-cell sequencing assays for the transposase-accessibility chromatin (scATAC-seq) technique have provided cell-specific chromatin accessibility landscapes of cis-regulatory elements, providing deeper insights into cellular states and dynamics. However, few research efforts have been dedicated to modeling the relationship between regulatory grammars and single-cell chromatin accessibility and incorporating different analysis scenarios of scATAC-seq data into the general framework. To this end, we propose a unified deep learning framework based on the ProdDep Transformer Encoder, dubbed PROTRAIT, for scATAC-seq data analysis. Specifically motivated by the deep language model, PROTRAIT leverages the ProdDep Transformer Encoder to capture the syntax of transcription factor (TF)-DNA binding motifs from scATAC-seq peaks for predicting single-cell chromatin accessibility and learning single-cell embedding. Based on cell embedding, PROTRAIT annotates cell types using the Louvain algorithm. Furthermore, according to the identified likely noises of raw scATAC-seq data, PROTRAIT denoises these values based on predated chromatin accessibility. In addition, PROTRAIT employs differential accessibility analysis to infer TF activity at single-cell and single-nucleotide resolution. Extensive experiments based on the Buenrostro2018 dataset validate the effeteness of PROTRAIT for chromatin accessibility prediction, cell type annotation, and scATAC-seq data denoising, therein outperforming current approaches in terms of different evaluation metrics. Besides, we confirm the consistency between the inferred TF activity and the literature review. We also demonstrate the scalability of PROTRAIT to analyze datasets containing over one million cells. [ABSTRACT FROM AUTHOR]

Thermophilic proteins have great potential to be utilized as biocatalysts in biotechnology. Machine learning algorithms are gaining increasing use in identifying such enzymes, reducing or even eliminating the need for experimental studies. While most previously used machine learning methods were based on manually designed features, we developed BertThermo, a model using Bidirectional Encoder Representations from Transformers (BERT), as an automatic feature extraction tool. This method combines a variety of machine learning algorithms and feature engineering methods, while relying on single-feature encoding based on the protein sequence alone for model input. BertThermo achieved an accuracy of 96.97% and 97.51% in 5-fold cross-validation and in independent testing, respectively, identifying thermophilic proteins more reliably than any previously described predictive algorithm. Additionally, BertThermo was tested by a balanced dataset, an imbalanced dataset and a dataset with homology sequences, and the results show that BertThermo was with the best robustness as comparied with state-of-the-art methods. The source code of BertThermo is available. [ABSTRACT FROM AUTHOR]

Regulators in gene regulatory networks (GRNs) are crucial for identifying cell states. However, GRN inference based on scRNA-seq data has several problems, including high dimensionality and sparsity, and requires more label data. Therefore, we propose a meta-learning GRN inference framework to identify regulatory factors. Specifically, meta-learning solves the parameter optimization problem caused by high-dimensional sparse data features. In addition, a few-shot solution was used to solve the problem of lack of label data. A structural equation model (SEM) was embedded in the model to identify important regulators. We integrated the parameter optimization strategy into the bi-level optimization to extract the feature consistent with GRN reasoning. This unique design makes our model robust to small-scale data. By studying the GRN inference task, we confirmed that the selected regulators were closely related to gene expression specificity. We further analyzed the GRN inferred to find the important regulators in cell type identification. Extensive experimental results showed that our model effectively captured the regulator in single-cell GRN inference. Finally, the visualization results verified the importance of the selected regulators for cell type recognition. [ABSTRACT FROM AUTHOR]

Aiming at the melt splashing behavior in the smelting process of an oxygen-enriched side-blowing furnace, the volume of fluid model and the realizable k − ε turbulence model are coupled and simulated. The effects of different operating parameters (injection velocity, immersion depth, liquid level) on splash height are explored, and the simulation results are verified by water model experiments. The results show that the bubbles with residual kinetic energy escape to the slag surface and cause slag splashing. The slag splashing height gradually increases with the increase in injection velocity, and the time-averaged splashing height reaches 1.01 m when the injection speed is 160 m/s. Increasing the immersion depth of the lance, and the slag splashing height gradually decreases. When the immersion depth is 0.12 m, the time-averaged splashing height is 0.85 m. Increasing the liquid level is beneficial to reduce the splash height, when the liquid level is 2.7 m, the splash height reduces to 0.77 m. With the increase in the liquid level, the slag splashing height gradually decreases, and the time-averaged splashing height is 0.77 m when the initial liquid level is 2.7 m. [ABSTRACT FROM AUTHOR]

Furthermore, in terms of protein identification, Xu et al. concentrated on the study of antioxidant protein identification; they proposed a machine learning method, SeqSVM, to predict antioxidant proteins through extracted sequence features [[10]]. The identification of special protein or RNA molecules via computational methods is of great importance in understanding their biological functions and developing new treatments for diseases. Seven papers focus on describing protein function prediction or protein identification, which include the prediction of signal peptides in proteins, protein hydroxylation site prediction, protein-protein interaction (PPI) prediction, and protein identification. [Extracted from the article]

Cell-penetrating peptides (CPPs) are short peptides that can carry cargo into cells. CPPs are widely utilized due to their powerful loading capacity and transduction efficiency. Identifying CPPs is the basis for studying their functions and mechanisms; however, experimental methods to identify CPPs are expensive and time-consuming. Recently, CPP predictors based on machine learning methods have become a research hotspot. Although considerable progress has been made, some challenges remain unresolved. First, most predictors employ a variety of feature descriptors to transform an original sequence into multiview data; however, extant methods ignore the relationships between different views, limiting further performance improvement. Second, most machine learning models are actually black boxes and cannot offer insightful advice. In this paper, a novel Hilbert–Schmidt independence criterion (HSIC)-based multiview TSK fuzzy system is proposed. Compared with other machine learning methods, TSK fuzzy systems have better interpretability, and the introduction of multiview mechanisms provides comprehensive insight into the intrinsic laws of the data. HSIC is utilized here to measure the independence and enhance the complementarity between different views. Notably, the proposed method attained prediction accuracy results of 92.2% and 96.2% for the training and independent test sets, respectively. The empirical results show that our promising approach features greater recognition performance than the state-of-the-art method. [ABSTRACT FROM AUTHOR]

The MADS-box gene family is one of the largest families in plants and plays an important roles in floral development. The MADS-box family includes the SRF-like domain and K-box domain. It is considered that the MADS-box gene family encodes a DNA-binding domain that is generally related to transcription factors, and plays important roles in regulating floral development. Our study identified 211 MADS-box protein sequences in the Zea mays proteome and renamed all the genes based on the gene annotations. All the 211 MADS-box protein sequences were coded by 98 expressed genes. Phylogenetic analysis of the MADS-box genes showed that all the family members were categorized into five subfamilies: MIKC-type, Mα, Mβ, Mγ, and Mδ. Gene duplications are regarded as products of several types of errors during the period of DNA replication and reconstruction; in our study all the 98 MADS-box genes contained 22 pairs of segmentally duplicated events which were distributed on 10 chromosomes. We compared expression data in different tissues from the female spikelet, silk, pericarp aleurone, ear primordium, leaf zone, vegetative meristem, internode, endosperm crown, mature pollen, embryo, root cortex, secondary root, germination kernels, primary root, root elongation zone, and root meristem. According to analysis of gene ontology pathways, we found a total of 41 pathways in which MADS-box genes in maize are involved. All the studies we conducted provided an overview of MADS-box gene family members in maize and showed multiple functions as transcription factors. The related research of MADS-box domains has provided the theoretical basis of MADS-box domains for agricultural applications. [ABSTRACT FROM AUTHOR]

SALT, a new dedicated readout Application Specific Integrated Circuit (ASIC) for the Upstream Tracker, a new silicon detector in the Large Hadron Collider beauty (LHCb) experiment, has been designed and developed. It is a 128-channel chip using an innovative architecture comprising a low-power analogue front-end with fast pulse shaping and a 40 MSps 6-bit Analog-to-Digital Converter (ADC) in each channel, followed by a Digital Signal Processing (DSP) block performing pedestal and Mean Common Mode (MCM) subtraction and zero suppression. The prototypes of SALT were fabricated and tested, confirming the full chip functionality and fulfilling the specifications. A signal-to-noise ratio of about 20 is achieved for a silicon sensor with a 12 pF input capacitance. In this paper, the SALT architecture and measurements of the chip performance are presented.