algorithms

BACKGROUND: Sharing data from clinical studies can accelerate scientific progress, improve transparency, and increase the potential for innovation and collaboration. However, privacy concerns remain a barrier to data sharing. Certain concerns, such as reidentification risk, can be addressed through the application of anonymization algorithms, whereby data are altered so that it is no longer reasonably related to a person. Yet, such alterations have the potential to influence the data set's statistical properties, such that the privacy-utility trade-off must be considered...

BACKGROUND AND OBJECTIVES: While immigrants to high-income countries have a lower risk of multiple sclerosis (MS) compared with host populations, it is unknown whether this lower risk among immigrants increases over time. Our objective was to evaluate the association between proportion of life spent in Canada and the hazard of incident MS in Canadian immigrants. METHODS: We conducted a population-based retrospective cohort study in Ontario, using linked health administrative databases...

OBJECTIVE: Hyperdense artery sign (HAS) on noncontrast brain computed tomography (CT) indicates an acute thrombus within the cerebral artery. It is a valuable imaging biomarker for diagnosing large-vessel occlusion; however, its identification may be challenging with the presence of vascular calcification. Dual-energy CT virtual noncalcium (VNCa) imaging using a 3-material decomposition algorithm is helpful for differentiating between calcification and hemorrhage. This study aimed to clarify the potential of VNCa imaging for differentiating HAS from vascular calcification...

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is associated with various cognitive, behavioral, and mood symptoms that complicate diagnosis and treatment. The heterogeneity of these symptoms may also vary depending on certain sociodemographic factors. It is therefore important to establish more homogenous symptom profiles in patients with ADHD and determine their association with the patient's sociodemographic makeup. The current study used unsupervised machine learning to identify symptom profiles across various cognitive, behavioral, and mood symptoms in adults with ADHD...

BACKGROUND: Reducing proton treatment time improves patient comfort and decreases the risk of error from intrafractional motion, but must be balanced against clinical goals and treatment plan quality. PURPOSE: To improve the delivery efficiency of spot scanning proton therapy by simultaneously reducing the number of spots and energy layers using the reweighted <mml:math xmlns:mml="https://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msub><mml:mi>l</mml:mi> <mml:mn>1</mml:mn></mml:msub> <mml:annotation>$l_1$</mml:annotation></mml:semantics> </mml:math> regularization method...

Since the 1980s, the paddlewheel effect has been suggested as a mechanism to boost lithium-ion diffusion in inorganic materials via the rotation of rotor-like anion groups. However, it remains unclear whether the paddlewheel effect, defined as large-angle anion group rotations assisting Li hopping, indeed exists; furthermore, the physical mechanism by which the anion-group dynamics affect lithium-ion diffusion has not yet been established. In this work, we differentiate various types of rotational motions of anion groups and develop quaternion-based algorithms to detect, quantify, and relate them to lithium-ion motion in ab initio molecular dynamics simulations...

Hazardous chemical vehicles are specialized vehicles used for transporting flammable gases, medical waste, and liquid chemicals, among other dangerous chemical substances. During their transportation, there are risks of fire, explosion, and leakage of hazardous materials, posing serious threats to human safety and the environment. To mitigate these possible hazards and decrease their probability, this study proposes a lightweight object detection method for hazardous chemical vehicles based on the YOLOv7-tiny model...

BACKGROUND: Patients with gastric cancer respond poorly to immunotherapy. There are still unknowns about the biomarkers associated with immunotherapy sensitivity and their underlying molecular mechanisms. METHODS: Gene expression data for gastric cancer were gathered from TCGA and GEO databases. DEGs associated with immunotherapy response came from ICBatlas. KEGG and GO analyses investigated pathways. Hub genes identification employed multiple machine algorithms...

BACKGROUND: CESC is the second most commonly diagnosed gynecological malignancy. Given the pivotal involvement of metabolism-related genes (MRGs) in the etiology of multiple tumors, our investigation aims to devise a prognostic risk signature rooted in cancer stemness and metabolism. METHODS: The stemness index based on mRNA expression (mRNAsi) of samples from the TCGA dataset was computed using the One-class logistic regression (OCLR) algorithm. Furthermore, potential metabolism-related genes related to mRNAsi were identified through weighted gene co-expression network analysis (WGCNA)...

Treemaps are a powerful tool for representing hierarchical data in a space-efficient manner and are used in various domains, including network security or software development. However, interpreting the topology encoded by nested rectangles can be challenging, particularly compared to tree-structured representations like node-link diagrams or icicle plots. To address this challenge, we introduce TreEducation, a visual education platform designed to improve the visualization literacy skills required for reading treemaps among non-expert users...

Vision Transformer (ViT) architectures are becoming increasingly popular and widely employed to tackle computer vision applications. Their main feature is the capacity to extract global information through the self-attention mechanism, outperforming earlier convolutional neural networks. However, ViT deployment and performance have grown steadily with their size, number of trainable parameters, and operations. Furthermore, self-attention's computational and memory cost quadratically increases with the image resolution...

The recently proposed tensor tubal rank has been witnessed to obtain extraordinary success in real-world tensor data completion. However, existing works usually fix the transform orientation along the third mode and may fail to turn multidimensional low-tubal-rank structure into account. To alleviate these bottlenecks, we introduce two unfolding induced tensor nuclear norms (TNNs) for the tensor completion (TC) problem, which naturally extends tensor tubal rank to high-order data. Specifically, we show how multidimensional low-tubal-rank structure can be captured by utilizing a novel balanced unfolding strategy, upon which two TNNs, namely, overlapped TNN (OTNN) and latent TNN (LTNN), are developed...

In the task of multiview multilabel (MVML) classification, each object is described by several heterogeneous view features and annotated with multiple relevant labels. Existing MVML methods usually assume that these heterogeneous features are strictly view-aligned, and they directly conduct cross-view information fusion to train a multilabel prediction model. However, in real-world scenarios, such strict view-aligned requirement can be hardly satisfied due to the recurrent spatiotemporal asynchronism when collecting MVML data, which would cause inaccurate multiview fusion results and degrade the classification performance...

This article is concerned with the secure state estimation problem for artificial neural networks (ANNs) subject to unknown-but-bounded noises, where sensors and the remote estimator are connected via open and bandwidth-limited communication networks. Using the encoding-decoding mechanism (EDM) and the Paillier encryption technique, a novel homomorphic encryption scheme (HES) is introduced, which aims to ensure the secure transmission of measurement information within communication networks that are constrained by bandwidth...

Acute respiratory distress syndrome (ARDS), a progressive status of acute lung injury (ALI), is primarily caused by an immune-mediated inflammatory disorder, which can be an acute pulmonary complication of rheumatoid arthritis (RA). As a chronic inflammatory disease regulated by the immune system, RA is closely associated with the occurrence and progression of respiratory diseases. However, it remains elusive whether there are shared genes between the molecular mechanisms underlying RA and ARDS. The objective of this study is to identify potential shared genes for further clinical drug discovery through integrated analysis of bulk RNA sequencing datasets obtained from the Gene Expression Omnibus database, employing differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA)...

The prediction of suspended sediment load (SSL) within riverine systems is critical to understanding the watershed's hydrology. Therefore, the novelty of our research is developing an interpretable (explainable) model based on deep learning (DL) and Shapley Additive ExPlanations (SHAP) interpretation technique for prediction of SSL in the riverine systems. This paper investigates the abilities of four DL models, including dense deep neural networks (DDNN), long short-term memory (LSTM), gated recurrent unit (GRU), and simple recurrent neural network (RNN) models for the prediction of daily SSL using river discharge and rainfall data at a daily time scale in the Taleghan River watershed, northwestern Tehran, Iran...

Elderly patients infected with severe acute respiratory syndrome coronavirus 2 are at higher risk of severe clinical manifestation, extended hospitalization, and increased mortality. Those patients are more likely to experience persistent symptoms and exacerbate the condition of basic diseases with long COVID-19 syndrome. However, the molecular mechanisms underlying severe COVID-19 in the elderly patients remain unclear. Our study aims to investigate the function of the interaction between disease-characteristic genes and immune cell infiltration in patients with severe COVID-19 infection...

Implantable neural interfaces with the central and peripheral nervous systems are currently used to restore sensory, motor, and cognitive functions in disabled people with very promising results. They have also been used to modulate autonomic activities to treat diseases such as diabetes or hypertension. Here, this study proposes to extend the use of these technologies to (re-)establish the connection between new (transplanted or artificial) organs and the nervous system in order to increase the long-term efficacy and the effective biointegration of these solutions...

Magnetic Resonance Imaging (MRI) plays an important role in neurology, particularly in the precise segmentation of brain tissues. Accurate segmentation is crucial for diagnosing brain injuries and neurodegenerative conditions. We introduce an Enhanced Spatial Fuzzy C-means (esFCM) algorithm for 3D T1 MRI segmentation to three tissues, i.e. White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). The esFCM employs a weighted least square algorithm utilizing the Structural Similarity Index (SSIM) for polynomial bias field correction...

Genetic mapping is the determination of the position and relative genetic distance between genes or molecular markers in the chromosomes of a particular species. The construction of genetic maps uses data from the genotyping of the mapping population. Among the different mapping populations used, two are relatively common: the F2 and recombinant inbred lines (RILs) obtained as a result of the controlled crossing of genetically diverse parental forms (e.g., inbred lines). Also, the dihaploid (DH) population is often used in plants, but obtaining DHs in different crops, including rye, is very difficult or even impossible...