Consensus Features Research Articles

MMLmiRLocNet: miRNA Subcellular Localization Prediction based on Multi-view Multi-label Learning for Drug Design.

Identifying subcellular localization of microRNAs (miRNAs) is essential for comprehensive understanding of cellular function and has significant implications for drug design. In the past, several computational methods for miRNA subcellular localization is being used for uncovering multiple facets of RNA function to facilitate the biological applications. Unfortunately, most existing classification methods rely on a single sequencebased view, making the effective fusion of data from multiple heterogeneous networks a primary challenge. Inspired by multi-view multi-label learning strategy, we propose a computational method, named MMLmiRLocNet, for predicting the subcellular localizations of miRNAs. The MMLmiRLocNet predictor extracts multi-perspective sequence representations by analyzing lexical, syntactic, and semantic aspects of biological sequences. Specifically, it integrates lexical attributes derived from k-mer physicochemical profiles, syntactic characteristics obtained via word2vec embeddings, and semantic representations generated by pre-trained feature embeddings. Finally, module for extracting multi-view consensus-level features and specific-level features was constructed to capture consensus and specific features from various perspectives. The full connection networks are utilized as the output module to predict the miRNA subcellular localization. Experimental results suggest that MMLmiRLocNet outperforms existing methods in terms of F1, subACC, and Accuracy, and achieves best performance with the help of multi-view consensus features and specific features extract network.

Joint learning of latent subspace and structured graph for multi-view clustering

Most existing multi-view clustering methods rely solely on subspace clustering or graph-based clustering. Subspace clustering reduces the redundant information in high dimensional data, but it neglects the intrinsic structural dependencies among samples. Graph-based clustering can model the similarity among samples but tends to suffer from redundant information. In this paper, a novel framework jointing subspace learning and structured graph learning for multi-view clustering (SSMC) is proposed, which benefits from the merits of both subspace learning and structured graph learning. SSMC utilizes graph regularized subspace learning to obtain low dimensional consensus features, where the embedded features are ensured to have maximized correlation to reduce the redundant information, and the graph regularization forces embedded features to preserve their sample similarities. Meanwhile, an adaptive structured graph is learned based on the consensus features in the embedded feature space, avoiding the curse of dimensionality in the graph learning procedure. A rank constraint forces the learned graph to have exactly the same number of connected components as the number of clusters, to obtain a more reliable structured graph. Moreover, an effective algorithm is proposed to optimize the SSMC, where the graph regularized subspace learning part and the structured graph learning part are jointly optimized in a mutual reinforcement manner. The experimental results on real-world benchmark datasets show that the SSMC outperforms the state-of-the-arts in multi-view clustering tasks.

Pegcetacoplan Treatment and Consensus Features of Geographic Atrophy Over 24 Months

Despite widespread availability and consensus on its advantages for detailed imaging of geographic atrophy (GA), spectral-domain optical coherence tomography (SD-OCT) might benefit from automated quantitative OCT analyses in GA diagnosis, monitoring, and reporting of its landmark clinical trials. To analyze the association between pegcetacoplan and consensus GA SD-OCT end points. This was a post hoc analysis of 11 614 SD-OCT volumes from 936 of the 1258 participants in 2 parallel phase 3 studies, the Study to Compare the Efficacy and Safety of Intravitreal APL-2 Therapy With Sham Injections in Patients With Geographic Atrophy (GA) Secondary to Age-Related Macular Degeneration (OAKS) and Study to Compare the Efficacy and Safety of Intravitreal APL-2 Therapy With Sham Injections in Patients With Geographic Atrophy (GA) Secondary to Age-Related Macular Degeneration (DERBY). OAKS and DERBY were 24-month, multicenter, randomized, double-masked, sham-controlled studies conducted from August 2018 to July 2020 among adults with GA with total area 2.5 to 17.5 mm2 on fundus autofluorescence imaging (if multifocal, at least 1 lesion ≥1.25 mm2). This analysis was conducted from September to December 2023. Study participants received pegcetacoplan, 15 mg per 0.1-mL intravitreal injection, monthly or every other month, or sham injection monthly or every other month. The primary end point was the least squares mean change from baseline in area of retinal pigment epithelium and outer retinal atrophy in each of the 3 treatment arms (pegcetacoplan monthly, pegcetacoplan every other month, and pooled sham [sham monthly and sham every other month]) at 24 months. Feature-specific area analysis was conducted by Early Treatment Diabetic Retinopathy Study (ETDRS) regions of interest (ie, foveal, parafoveal, and perifoveal). Among 936 participants, the mean (SD) age was 78.5 (7.22) years, and 570 participants (60.9%) were female. Pegcetacoplan, but not sham treatment, was associated with reduced growth rates of SD-OCT biomarkers for GA for up to 24 months. Reductions vs sham in least squares mean (SE) change from baseline of retinal pigment epithelium and outer retinal atrophy area were detectable at every time point from 3 through 24 months (least squares mean difference vs pooled sham at month 24, pegcetacoplan monthly: -0.86 mm2; 95% CI, -1.15 to -0.57; P < .001; pegcetacoplan every other month: -0.69 mm2; 95% CI, -0.98 to -0.39; P < .001). This association was more pronounced with more frequent dosing (pegcetacoplan monthly vs pegcetacoplan every other month at month 24: -0.17 mm2; 95% CI, -0.43 to 0.08; P = .17). Stronger associations were observed in the parafoveal and perifoveal regions for both pegcetacoplan monthly and pegcetacoplan every other month. These findings offer additional insight into the potential effects of pegcetacoplan on the development of GA, including potential effects on the retinal pigment epithelium and photoreceptors. ClinicalTrials.gov Identifiers: NCT03525600 and NCT03525613.

PnV: An Efficient Parallel Consensus Protocol Integrating Proof and Voting

Consensus protocols, as crucial components of blockchain technology, play a vital role in ensuring data consistency among distributed nodes. However, the existing voting-based and proof-based consensus protocols encounter scalability issues within the blockchain system. Moreover, most consensus protocols are serialized, which further limits their scalability potential. To address this limitation, parallelization methods have been employed in both types of consensus protocols. Surprisingly, however, novel fusion consensus protocols demonstrate superior scalability compared with these two types but lack the utilization of parallelization techniques. In this paper, we present PnV, an efficient parallel fusion protocol integrating proof-based and voting-based consensus features. It enhances the data structure, consensus process, transaction allocation, and timeout handling mechanisms to enable concurrent block generation by multiple nodes within a consensus round. Experimental results demonstrate that PnV exhibits superior efficiency, excellent scalability, and acceptable delay compared with Proof of Vote (PoV) and BFT-SMART. Moreover, at the system level, the performance of the PnV-based blockchain system optimally surpasses that of the FISCO BCOS platform. Our proposed protocol contributes to advancing blockchain technology by providing a more efficient and practical solution for achieving decentralized consensus in distributed systems.

Dual Contrast-Driven Deep Multi-view Clustering.

Consensus representation learning is one of the most popular approaches in the field of multi-view clustering. However, most of the existing methods cannot learn discriminative representations with a clustering-friendly structure since these methods ignore the separation among clusters and the compactness within each cluster. To tackle this issue, we propose a new deep multi-view clustering network with a dual contrastive mechanism to learn clustering-friendly representations. Specifically, our method employs dual contrasting losses: a dynamic cluster diffusion loss to maximize the distance between different clusters and a reliable neighbor-guided positive alignment loss to enhance compactness within each cluster. Our approach includes several key components: view-specific encoders to extract high-level features from each view, and an adaptive feature fusion strategy to obtain consensus representations across multiple views. The dynamic cluster diffusion module ensures inter-cluster separation by maximizing distances between different clusters in the consensus feature space. Simultaneously, the reliable neighbor-guided positive alignment module improves within-cluster compactness through a pseudo-label and nearest neighbor structure-driven contrastive loss. Experimental results on several datasets show that our method can acquire clustering-friendly representations with both good properties of inter-cluster separation and within-cluster compactness, and outperforms the existing state-of-the-art approaches in clustering performance.

AMatFormer: Efficient Feature Matching via Anchor Matching Transformer

Learning based feature matching methods have been commonly studied in recent years. The core issue for learning feature matching is to how to learn (1) discriminative representations for feature points (or regions) within each intra-image and (2) consensus representations for feature points across inter-images. Recently, self- and cross-attention models have been exploited to address this issue. However, in many scenes, features are coming with large-scale, redundant and outliers contaminated. Previous self-/cross-attention models generally conduct message passing on all primal features which thus lead to redundant learning and high computational cost. To mitigate limitations, inspired by recent seed matching methods, in this paper, we propose a novel efficient Anchor Matching Transformer (AMatFormer) for the feature matching problem. AMatFormer has two main aspects: First, it mainly conducts self-/cross-attention on some anchor features and leverages these anchor features as message bottleneck to learn the representations for all primal features. Thus, it can be implemented efficiently and compactly. Second, AMatFormer adopts a shared FFN module to further embed the features of two images into the common domain and thus learn the consensus feature representations for the matching problem. Experiments on several benchmarks demonstrate the effectiveness and efficiency of the proposed AMatFormer matching approach.

Adaptive Annotation Correlation Based Multi-Annotation Learning for Calibrated Medical Image Segmentation.

Medical image segmentation is a fundamental task in many clinical applications, yet current automated segmentation methods rely heavily on manual annotations, which are inherently subjective and prone to annotation bias. Recently, modeling annotator preference has garnered great interest, and several methods have been proposed in the past two years. However, the existing methods completely ignore the potential correlation between annotations, such as complementary and discriminative information. In this work, the Adaptive annotation CorrelaTion based multI-annOtation LearNing (ACTION) method is proposed for calibrated medical image segmentation. ACTION employs consensus feature learning and dynamic adaptive weighting to leverage complementary information across annotations and emphasize discriminative information within each annotation based on their correlations, respectively. Meanwhile, memory accumulation-replay is proposed to accumulate the prior knowledge and integrate it into the model to enable the model to accommodate the multi-annotation setting. Two medical image benchmarks with different modalities are utilized to evaluate the performance of ACTION, and extensive experimental results demonstrate that it achieves superior performance compared to several state-of-the-art methods.

Method for finding subsets of consensus features in predicting the effectiveness of rehabilitation of patients after COVID-19

Coronavirus infection causes long-term post-Covid syndrome, which determines the need for medical rehabilitation. The use of modern machine learning technologies to predict the effectiveness of rehabilitation can personalize the process of providing assistance.Aim: To create a method for constructing a model to predict the effectiveness of patients rehabilitation who have suffered COVID-19.Material and Methods. The study included 64 patients admitted for inpatient rehabilitation after COVID-19. The average age was 56.92±9.29 years. To obtain information about the patients' health status, a physical examination, six-minute walk test (SHT), clinical and biochemical blood tests, and spirometry were performed in order to obtain information about the health status of the patients. The collected data were anonymized and transformed into a data set for the classification task, the output label of which was a binary attribute indicating the presence or absence of an improvement in the six-minute walk test result by at least 15 %. The proposed method for determining a consistent subset of features was tested by ReliefF, χ2-squared filters and the minimum redundancy and maximum information content algorithm; the binary genetic algorithm NSGA2 was used as a wrapper. The construction of preliminary machine learning models on the found subsets of features using the linear support vector machine was carried out.Results. In the process of testing the proposed method in the task of predicting the effectiveness of rehabilitation of patients after COVID-19, a subset of signs was identified that made it possible to achieve the maximum value of the concordance coefficient. The found set includes the following characteristics: gender, concomitant diseases, shortness of breath, cough, complaints about the gastrointestinal tract, six-minute walk test result, D-dimer level, assessment of shortness of breath according to the Borg scale.Conclusion. The proposed method allowed identifying the signs that are of the greatest importance for predicting the effectiveness of patients’ rehabilitation who have had COVID-19.

G-Aligner: a graph-based feature alignment method for untargeted LC–MS-based metabolomics

BackgroundLiquid chromatography–mass spectrometry is widely used in untargeted metabolomics for composition profiling. In multi-run analysis scenarios, features of each run are aligned into consensus features by feature alignment algorithms to observe the intensity variations across runs. However, most of the existing feature alignment methods focus more on accurate retention time correction, while underestimating the importance of feature matching. None of the existing methods can comprehensively consider feature correspondences among all runs and achieve optimal matching.ResultsTo comprehensively analyze feature correspondences among runs, we propose G-Aligner, a graph-based feature alignment method for untargeted LC–MS data. In the feature matching stage, G-Aligner treats features and potential correspondences as nodes and edges in a multipartite graph, considers the multi-run feature matching problem an unbalanced multidimensional assignment problem, and provides three combinatorial optimization algorithms to find optimal matching solutions. In comparison with the feature alignment methods in OpenMS, MZmine2 and XCMS on three public metabolomics benchmark datasets, G-Aligner achieved the best feature alignment performance on all the three datasets with up to 9.8% and 26.6% increase in accurately aligned features and analytes, and helped all comparison software obtain more accurate results on their self-extracted features by integrating G-Aligner to their analysis workflow. G-Aligner is open-source and freely available at https://github.com/CSi-Studio/G-Aligner under a permissive license. Benchmark datasets, manual annotation results, evaluation methods and results are available at https://doi.org/10.5281/zenodo.8313034ConclusionsIn this study, we proposed G-Aligner to improve feature matching accuracy for untargeted metabolomics LC–MS data. G-Aligner comprehensively considered potential feature correspondences between all runs, converting the feature matching problem as a multidimensional assignment problem (MAP). In evaluations on three public metabolomics benchmark datasets, G-Aligner achieved the highest alignment accuracy on manual annotated and popular software extracted features, proving the effectiveness and robustness of the algorithm.

Global and cross-modal feature aggregation for multi-omics data classification and application on drug response prediction

With rapid development of single-cell multi-modal sequencing technologies, more and more multi-omics data come into being and provide a unique opportunity for the identification of distinct cell types at the single-cell level. Therefore, it is important to integrate different modalities which are with high-dimensional features for boosting final multi-omics data classification performance. However, existing multi-omics data classification methods mainly focus on exploiting the complementary information of different modalities, while ignoring the learning confidence and cross-modal sample relationship during information fusion. In this paper, we propose a multi-omics data classification network via global and cross-modal feature aggregation, referred to as GCFANet. On one hand, considering that a large number of feature dimensions in different modalities could not contribute to final classification performance but disturb the discriminability of different samples, we propose a feature confidence learning mechanism to suppress some redundant features, as well as enhancing the expression of discriminative feature dimensions in each modality. On the other hand, in order to capture the inherent sample structure information implied in each modality, we design a graph convolutional network branch to learn the corresponding structure preserved feature representation. Then the modal-specific feature representations are concatenated and input to a transformer induced global and cross-modal feature aggregation module for learning consensus feature representation from different modalities. In addition, the consensus feature representation used for final classification is enhanced via a view-specific consistency preserved contrastive learning strategy. Extensive experiments on four multi-omics datasets are conducted to demonstrate the efficacy of the proposed GCFANet.

Hybrid feature ranking and classifier aggregation based on multi-criteria decision-making

This study introduces an ensemble methodology, namely, hybrid feature ranking and classifier aggregation (HyFraCa), to integrate ensemble feature selection and ensemble classification in a composite framework. The proposed HyFraCa is embedded in a multi-criteria decision-making (MCDM)-based scheme for feature ranking and classifier weighting, with an effective aggregation rule that yields a consensus feature ranking from ensembles of heterogeneous classifiers and feature selectors. Experimental evaluations on 20 public UCI datasets demonstrated the superiority of HyFraCa in producing a more accurate and generalizable classification compared with state-of-the-art benchmark ensemble methods. HyFraCa also provides robust and reliable consensus feature rankings, which are favorable for real-world classification problems in which feature interpretability is emphasized.

Automated Georectification, Mosaicking and 3D Point Cloud Generation Using UAV-Based Hyperspectral Imagery Observed by Line Scanner Imaging Sensors

Hyperspectral sensors mounted on unmanned aerial vehicles (UAV) offer the prospect of high-resolution multi-temporal spectral analysis for a range of remote-sensing applications. However, although accurate onboard navigation sensors track the moment-to-moment pose of the UAV in flight, geometric distortions are introduced into the scanned data sets. Consequently, considerable time-consuming (user/manual) post-processing rectification effort is generally required to retrieve geometrically accurate mosaics of the hyperspectral data cubes. Moreover, due to the line-scan nature of many hyperspectral sensors and their intrinsic inability to exploit structure from motion (SfM), only 2D mosaics are generally created. To address this, we propose a fast, automated and computationally robust georectification and mosaicking technique that generates 3D hyperspectral point clouds. The technique first morphologically and geometrically examines (and, if possible, repairs) poorly constructed individual hyperspectral cubes before aligning these cubes into swaths. The luminance of each individual cube is estimated and normalised, prior to being integrated into a swath of images. The hyperspectral swaths are co-registered to a targeted element of a luminance-normalised orthomosaic obtained using a standard red–green–blue (RGB) camera and SfM. To avoid computationally intensive image processing operations such as 2D convolutions, key elements of the orthomosaic are identified using pixel masks, pixel index manipulation and nearest neighbour searches. Maximally stable extremal regions (MSER) and speeded-up robust feature (SURF) extraction are then combined with maximum likelihood sample consensus (MLESAC) feature matching to generate the best geometric transformation model for each swath. This geometrically transforms and merges individual pushbroom scanlines into a single spatially continuous hyperspectral mosaic; and this georectified 2D hyperspectral mosaic is then converted into a 3D hyperspectral point cloud by aligning the hyperspectral mosaic with the RGB point cloud used to create the orthomosaic obtained using SfM. A high spatial accuracy is demonstrated. Hyperspectral mosaics with a 5 cm spatial resolution were mosaicked with root mean square positional accuracies of 0.42 m. The technique was tested on five scenes comprising two types of landscape. The entire process, which is coded in MATLAB, takes around twenty minutes to process data sets covering around 30 Ha at a 5 cm resolution on a laptop with 32 GB RAM and an Intel® Core i7-8850H CPU running at 2.60 GHz.

The Reliability of Expert Diagnosis of Childhood Apraxia of Speech.

The current standard for clinical diagnosis of childhood apraxia of speech (CAS) is expert clinician judgment. The psychometric properties of this standard are not well understood; however, they are important for improving clinical diagnosis. The purpose of this study is to determine the extent to which experts agree on the clinical diagnosis of CAS using two cohorts of children with mixed speech sound disorders (SSDs). Speech samples of children with SSDs were obtained from previous and ongoing research from video recordings of children aged 3-8 years (n = 36) and audio recordings of children aged 8-17 years (n = 56). A total of 23 expert, English-speaking clinicians were recruited internationally. Three of these experts rated each speech sample to provide a description of the observed features and a diagnosis. Intrarater reliability was acceptable at 85% agreement. Interrater reliability on the presence or absence of CAS among experts was poor both as a categorical diagnosis (κ = .187, 95% confidence interval [CI] [0.089, 0.286]) and on a continuous "likelihood of CAS" scale (0-100; intraclass correlation = .183, 95% CI [.037, .347]). Reliability was similar across the video-recorded and audio-only samples. There was greater agreement on other diagnoses (such as articulation disorder) than on the diagnosis of CAS, although these too did not meet the predetermined standard. Likelihood of CAS was greater in children who presented with more American Speech-Language-Hearing Association CAS consensus features. Different expert raters had different thresholds for applying the diagnosis of CAS. If expert clinician judgment is to be used for diagnosis of CAS or other SSDs, further standardization and calibration is needed to increase interrater reliability. Diagnosis may require operationalized checklists or reliable measures that operate along a diagnostic continuum. https://doi.org/10.23641/asha.23949105.

Placing steroid hormones within the human ABCC3 transporter reveals a compatible amphiphilic substrate-binding pocket.

The human ABC transporter ABCC3 (also known as MRP3) transports a wide spectrum of substrates, including endogenous metabolites and exogenous drugs. Accordingly, it participates in multiple physiological processes and is involved in diverse human diseases such as intrahepatic cholestasis of pregnancy, which is caused by the intracellular accumulation of bile acids and estrogens. Here, we report three cryogenic electron microscopy structures of ABCC3: in the apo-form and in complexed forms bound to either the conjugated sex hormones β-estradiol 17-(β-D-glucuronide) and dehydroepiandrosterone sulfate. For both hormones, the steroid nuclei that superimpose against each other occupy the hydrophobic center of the transport cavity, whereas the two conjugation groups are separated and fixed by the hydrophilic patches in two transmembrane domains. Structural analysis combined with site-directed mutagenesis and ATPase activity assays revealed that ABCC3 possesses an amphiphilic substrate-binding pocket able to hold either conjugated hormone in an asymmetric pattern. These data build on consensus features of the substrate-binding pocket of MRPs and provide a structural platform for the rational design of inhibitors.

Molecular Simulations of Conformational Transitions within the Insulin Receptor Kinase Reveal Consensus Features in a Multistep Activation Pathway.

Modulating the transitions between active and inactive conformations of protein kinases is the primary means of regulating their catalytic activity, achieved by phosphorylation of the activation loop (A-loop). To elucidate the mechanism of this conformational activation, we applied the string method to determine the conformational transition path of insulin receptor kinase between the active and inactive conformations and the corresponding free-energy profiles with and without A-loop phosphorylation. The conformational change was found to proceed in three sequential steps: first, the flipping of the DFG motif of the active site; second, rotation of the A-loop; finally, the inward movement of the αC helix. The main energetic bottleneck corresponds to the conformational change in the A-loop, while changes in the DFG motif and αC helix occur before and after A-loop conformational change, respectively. In accordance with this, two intermediate states are identified, the first state just after the DFG flipping and the second state after the A-loop rotation. These intermediates exhibit structural features characteristic of the corresponding inactive and active conformations of other protein kinases. To understand the impact of A-loop phosphorylation on kinase conformation, the free energies of A-loop phosphorylation were determined at several states along the conformational transition path using the free-energy perturbation simulations. The calculated free energies reveal that while the unphosphorylated kinase interconverts between the inactive and active conformations, A-loop phosphorylation restricts access to the inactive conformation, thereby increasing the active conformation population. Overall, this study suggests a consensus mechanism of conformational activation between different protein kinases.

ResVAE ensemble: Unsupervised identification of gene sets in multi-modal single-cell sequencing data using deep ensembles.

Feature identification and manual inspection is currently still an integral part of biological data analysis in single-cell sequencing. Features such as expressed genes and open chromatin status are selectively studied in specific contexts, cell states or experimental conditions. While conventional analysis methods construct a relatively static view on gene candidates, artificial neural networks have been used to model their interactions after hierarchical gene regulatory networks. However, it is challenging to identify consistent features in this modeling process due to the inherently stochastic nature of these methods. Therefore, we propose using ensembles of autoencoders and subsequent rank aggregation to extract consensus features in a less biased manner. Here, we performed sequencing data analyses of different modalities either independently or simultaneously as well as with other analysis tools. Our resVAE ensemble method can successfully complement and find additional unbiased biological insights with minimal data processing or feature selection steps while giving a measurement of confidence, especially for models using stochastic or approximation algorithms. In addition, our method can also work with overlapping clustering identity assignment suitable for transitionary cell types or cell fates in comparison to most conventional tools.

Self-regulation of the nuclear pore complex enables clogging-free crowded transport

Nuclear pore complexes (NPCs) are the main conduits for macromolecular transport into and out of the nucleus of eukaryotic cells. The central component of the NPC transport mechanism is an assembly of intrinsically disordered proteins (IDPs) that fills the NPC channel. The channel interior is further crowded by large numbers of simultaneously translocating cargo-carrying and free transport proteins. How the NPC can efficiently, rapidly, and selectively transport varied cargoes in such crowded conditions remains ill understood. Past experimental results suggest that the NPC is surprisingly resistant to clogging and that transport may even become faster and more efficient as the concentration of transport protein increases. To understand the mechanisms behind these puzzling observations, we construct a computational model of the NPC comprising only a minimal set of commonly accepted consensus features. This model qualitatively reproduces the previous experimental results and identifies self-regulating mechanisms that relieve crowding. We show that some of the crowding-alleviating mechanisms-such as preventing saturation of the bulk flux-are "robust" and rely on very general properties of crowded dynamics in confined channels, pertaining to a broad class of selective transport nanopores. By contrast, the counterintuitive ability of the NPC to leverage crowding to achieve more efficient single-molecule translocation is "fine-tuned" and relies on the particular spatial architecture of the IDP assembly in the NPC channel.

Machine Vision Based Fish Cutting Point Prediction for Target Weight

Food processing companies pursue the distribution of ingredients that were packaged according to a certain weight. Particularly, foods like fish are highly demanded and supplied. However, despite the high quantity of fish to be supplied, most seafood processing companies have yet to install automation equipment. Such absence of automation equipment for seafood processing incurs a considerable cost regarding labor force, economy, and time. Moreover, workers responsible for fish processing are exposed to risks because fish processing tasks require the use of dangerous tools, such as power saws or knives. To solve these problems observed in the fish processing field, this study proposed a fish cutting point prediction method based on AI machine vision and target weight. The proposed method performs three-dimensional (3D) modeling of a fish’s form based on image processing techniques and partitioned random sample consensus (RANSAC) and extracts 3D feature information. Then, it generates a neural network model for predicting fish cutting points according to the target weight by performing machine learning of the extracted 3D feature information and measured weight information. This study allows for the direct cutting of fish based on cutting points predicted by the proposed method. Subsequently, we compared the measured weight of the cut pieces with the target weight. The comparison result verified that the proposed method showed a mean error rate of approximately 3%.

Diagnostic gene signatures and aberrant pathway activation based on m6A methylation regulators in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic autoimmune disease (AD) characterized by persistent synovial inflammation, bone erosion and progressive joint destruction. This research aimed to elucidate the potential roles and molecular mechanisms of N6-methyladenosine (m6A) methylation regulators in RA. An array of tissues from 233 RA and 126 control samples was profiled and integrated for mRNA expression analysis. Following quality control and normalization, the cohort was split into training and validation sets. Five distinct machine learning feature selection methods were applied to the training set and validated in validation sets. Among the six models, the LASSO_λ-1se model not only performed better in the validation sets but also exhibited more stringent performance. Two m6A methylation regulators were identified as significant biomarkers by consensus feature selection from all four methods. IGF2BP3 and YTHDC2, which are differentially expressed in patients with RA and controls, were used to predict RA diagnosis with high accuracy. In addition, IGF2BP3 showed higher importance, which can regulate the G2/M transition to promote RA-FLS proliferation and affect M1 macrophage polarization. This consensus of multiple machine learning approaches identified two m6A methylation regulators that could distinguish patients with RA from controls. These m6A methylation regulators and their target genes may provide insight into RA pathogenesis and reveal novel disease regulators and putative drug targets.

Singular value decomposition of protein sequences as a method to visualize sequence and residue space.

Singular value decomposition (SVD) of multiple sequence alignments (MSAs) is an important and rigorous method to identify subgroups of sequences within the MSA, and to extract consensus and covariance sequence features that define the alignment and distinguish the subgroups. This information can be correlated to structure, function, stability, and taxonomy. However, the mathematics of SVD is unfamiliar to many in the field of protein science. Here, we attempt to present an intuitive yet comprehensive description of SVD analysis of MSAs. We begin by describing the underlying mathematics of SVD in a way that is both rigorous and accessible. Next, we use SVD to analyze sequences generated with a simplified model in which the extent of sequence conservation and covariance between different positions is controlled, to show how conservation and covariance produce features in the decomposed coordinate system. We then use SVD to analyze alignments of two protein families, the homeodomain and the Ras superfamilies. Both families show clear evidence of sequence clustering when projected into singular value space. We use k-means clustering to group MSA sequences into specific clusters, show how the residues that distinguish these clusters can be identified, and show how these clusters can be related to taxonomy and function. We end by providing a description a set of Python scripts that can be used for SVD analysis of MSAs, displaying results, and identifying and analyzing sequence clusters. These scripts are freely available on GitHub.