Sampling Pattern Research Articles

Atomistic simulation of primary microstructure formation in metals during crystallization from the melt.

Additive manufacturing of metallic parts by Selective Laser Melting (SLM) implies high temperature gradients and small volume of the melt bath. These conditions make the process scales close to those available for state-of-the-art massively parallel atomistic simulations. In the paper, the microscopic mechanisms responsible for the formation of primary microstructure during molten metal solidification are investigated using classical molecular dynamics (CMD). The 316L austenitic stainless steel with face centred cubic lattice, which is widely used in industry including SLM applications was chosen as a material for the CMD simulations. It was shown that solidified material inherits substrate defects and catches new ones, which interact with the solidification front thus producing the primary microstructure. Peculiarities of solidification in different crystallographic directions and solidification front interaction with grain boundaries and newly produced defects (mostly twin boundaries) as well as their formation are under study. Resulting microstructures of virtual samples are compared with those of real samples produced by SLM and analysed by the electron backscatter diffraction (EBSD) method. The comparison shows similarities of EBSD and CMD sample patterns and evidences for the capability of the large-scale atomistic simulations to reproduce main features of the microstructures formed in the metallic SLM additive production.

Secondary contact erodes Pleistocene diversification in a wide-ranging freshwater mussel (Quadrula).

The isolated river drainages of eastern North America serve as a natural laboratory to investigate the roles of allopatry and secondary contact in the evolutionary trajectories of recently diverged lineages. Drainage divides facilitate allopatric speciation, but due to their sensitivity to climatic and geomorphological changes, neighboring rivers frequently coalesce, creating recurrent opportunities of isolation and contact throughout the history of aquatic lineages. The freshwater mussel Quadrula quadrula is widely distributed across isolated rivers of eastern North America and possesses high phenotypic and molecular variation across its range. We integrate sequence data from three genomes, including female- and male-inherited mitochondrial markers and thousands of nuclear encoded SNPs with morphology and geography to illuminate the group's divergence history. Across contemporary isolated rivers, we found continuums of molecular and morphological variation, following a pattern of isolation by distance. In contact zones, hybridization was frequent with no apparent fitness consequences, as advanced hybrids were common. Accordingly, we recognize Q. quadrula as a single cohesive species with subspecific variation (Q. quadrula rumphiana). Demographic modeling and divergence dating supported a divergence history characterized by allopatric vicariance followed by secondary contact, likely driven by river rearrangements and Pleistocene glacial cycles. Despite clinal range-wide variation and hybridization in contact zones, the process-based species delimitation tool delimitR, which considers demographic scenarios like secondary contact, supported the delimitation of the maximum number of species tested. As such, when interpreting species delimitation results, we suggest careful consideration of spatial sampling and subsequent geographic patterns of biological variation, particularly for wide-ranging taxa.

INVESTIGATION OF THE PREVALENCE OF ROTAVIRUS INFECTION IN CALVES USING POLYACRYLAMIDE GEL ELECTROPHORESIS

Bovine rotaviruses cause loss of calves and cause great financial losses to breeders. Bovine rotaviruses, which are classified in the Reovirales order, Sedoreoviridae family and Rotavirus genus, are mostly classified as G and P genotypes according to VP7 and VP4 gene regions. In addition, 10 different species (group A-J) have been identified according to genetic and antigenic properties of another major antigen, VP6. Group A rotaviruses are the most common cause of diarrhea in calves, while group B and C infections are also known. For the protection of calves, rotavirus screening should be performed on a herd basis and the infection status of cattle should be revealed. For this purpose, stool samples of 100 calves with diarrhea symptoms in the inventory of XXXXX University, Faculty of Veterinary Medicine, Department of Virology were used. Polyacrylamide gel electrophoresis (PAGE), which allows the examination of segments of the genome, was used to check for the presence of the virus. Nucleic acid extraction was performed on the stool samples before electrophoresis and then extracts were loaded into the prepared polyacrylamide gel and run. The samples were stained with silver nitrate stain, segment patterns were determined and the presence of rotavirus was analyzed. While 27 of the analyzed samples were positive, 5 samples were suspicious and 68 samples were negative. The segment pattern of the positive samples was compatible with group A and all of them were classified in this group. Although they were in the same group, it was determined that the positive samples had 3 different electrophoretypes. As a result, it was determined that rotaviruses still have an important role in the etiology of calf diarrhea. Besides, the detected rotaviruses showed variation, although they were in group A, and breeders in the region should pay attention to control and hygiene measures.

DbPTM 2025 update: comprehensive integration of PTMs and proteomic data for advanced insights into cancer research.

Post-translational modifications (PTMs) are essential for modulating protein function and influencing stability, activity and signaling processes. The dbPTM 2025 update significantly expands the database to include over 2.79 million PTM sites, of which 2.243 million are experimentally validated from 48 databases and over 80 000 research articles. This version integrates proteomic data from 13 cancer types, with a particular focus on phosphoproteomic data and kinase activity profiles, allowing the exploration of personalized phosphorylation patterns in tumor samples. Integrating kinase-substrate phosphorylations with E3 ligase-substrate interactions, dbPTM 2025 provides a detailed map of PTM regulatory networks, offering insights into cancer-specific post-translational regulations. This update also includes advanced search capabilities, enabling users to efficiently query PTM data across species, PTM typesand modified residues. The platform's new features-interactive visualization tools and streamlined data downloads-allow researchers to access and analyze PTM data easily. dbPTM 2025 also enhances functional annotations, regulatory networks and disease associations, broadening its application for cancer research and the study of disease-associated PTMs. Through these enhancements, dbPTM 2025 is a comprehensive, user-friendly resource, facilitating the study of PTMs and their roles in cancer research. The database is now freely accessible at https://biomics.lab.nycu.edu.tw/dbPTM/.

Intraoperative Peripheral Frozen Margin Assessment in Soft Tissue Sarcoma.

Intraoperative peripheral margin sampling in soft tissue sarcoma (STS) is a routine practice among musculoskeletal oncologists. Practice patterns are variable, and evidence to support it is lacking. Rates of peripheral margin sampling at our institution were analyzed in addition to its clinical utility and cost-effectiveness. Peripheral margin sampling patterns at a tertiary sarcoma center were retrospectively evaluated. Concordance between peripheral margins and final pathology was assessed using McNemar's test and κ Coefficient. Clinical outcomes were compared, and a cost-utility analysis was performed. A total of 179 patients were included. 66% had peripheral margins sampled of which 23% had frozen margins analyzed. Ten patients had positive margins (5.5% of all patients; 8.4% in those with margins sampled) and R1 margins on the final tumor specimen were identified in 15 patients (8.4%). There were no R2 resections. Three patients underwent repeat surgical resection (20%). Three patients with R1 resections had negative peripheral margins sampled, suggesting falsely reassuring peripheral margins. Peripheral margin sampling averaged $5000/patient. Routine peripheral margin sampling in STS resection is of questionable utility with added cost. Prospective studies are warranted to determine the optimal approach to surgical margin assessment.

Travel self-containment of new towns in a polycentric city: insights from Shanghai with mobile phone data

ABSTRACT Travel self-containment has been a main issue in new town planning since the mid-twentieth century, but its assessment has long been a challenge, especially in a polycentric city where networked structure complicates travel behavior. Traditional methods, such as census investigations can hardly trace multidirectional commuting patterns of large samples nor disclose non-commuting patterns. This paper fills the void by using billions of passive mobile phone data by which the interval of stay in a particular location and its spatiotemporal regularity can be identified to capture phone users’ travel routes. Shanghai, a polycentric city that evolved under the longstanding development of the ‘One City, Nine Towns’ polycentric urban spatial plan, is selected to assess various types of travel. The results disclose the disparity of the inward, intra, lateral and reverse journeys of commuting and non-commuting routes and reveal how the specificity of new towns, e.g. distance to the central city and metro lines, matters for such disparity.

Bacterial profile-based body fluid identification using a machine learning approach.

Identifying the origins of biological traces is critical for the reconstruction of crime scenes in forensic investigations. Traditional methods for body fluid identification rely on chemical, enzymatic, immunological, and spectroscopic techniques, which can be sample-consuming and depend on simple color-change reactions. However, these methods have limitations when residual samples are insufficient after DNA extraction. This study aimed to develop a method for body fluid identification by leveraging bacterial DNA profiling to overcome the limitations of the conventional approaches. Bacterial profiles were determined by sequencing the hypervariable region of the 16S rRNA gene,using DNA metabarcoding of evidence collected from criminal cases. Amplicon sequence variants (ASVs) were analyzed to identify significant microbial patterns in different body fluid samples. The bacterial profile-based method demonstrated high discriminatory power with a machine learning model trained using the naïve Bayes algorithm, achieving an accuracy of over 98% in classifying samples into one of four body fluid types: blood, saliva, vaginal secretion, and mixture traces of vaginal secretions and semen. Bacterial profiling enhances the accuracy and robustness of body fluid identification in forensic analysis, providing a valuable alternative to traditional methods by utilizing DNA and microbial community data despite the uncontrollable conditions. This approach offers significant improvements in the classification accuracy and practical applicability in forensic investigations.

Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning

BackgroundNon-alcoholic fatty liver disease (NAFLD) and ulcerative colitis (UC) are two common health issues that have gained significant global attention. Previous studies have suggested a possible connection between NAFLD and UC, but the underlying pathophysiology remains unclear. This study investigates common genes, underlying pathogenesis mechanisms, identification of diagnostic markers applicable to both conditions, and exploration of potential therapeutic targets shared by NAFLD and UC.MethodsWe obtained datasets for NAFLD and UC from the GEO database. The DEGs in the GSE89632 dataset of the NAFLD and GSE87466 of the UC dataset were analyzed. WGCNA, a powerful tool for identifying modules of highly correlated genes, was employed for both datasets. The DEGs of NAFLD and UC and the modular genes were then intersected to obtain shared genes. Functional enrichment analysis was conducted on these shared genes. Next, we utilize the STRING database to establish a PPI network. To enhance visualization, we employ Cytoscape software. Subsequently, the Cytohubba algorithm within Cytoscape was used to identify central genes. Diagnostic biomarkers were initially screened using LASSO regression and SVM methods. The diagnostic value of ROC curve analysis was assessed to detect diagnostic genes in both training and validation sets for NAFLD and UC. A nomogram was also developed to evaluate diagnostic efficacy. Additionally, we used the CIBERSORT algorithm to explore immune infiltration patterns in both NAFLD and UC samples. Finally, we investigated the correlation between hub gene expression, diagnostic gene expression, and immune infiltration levels.ResultsWe identified 34 shared genes that were found to be associated with both NAFLD and UC. These genes were subjected to enrichment analysis, which revealed significant enrichment in several pathways, including the IL-17 signaling pathway, Rheumatoid arthritis, and Chagas disease. One optimal candidate gene was selected through LASSO regression and SVM: CCL2. The ROC curve confirmed the presence of CCL2 in both the NAFLD and UC training sets and other validation sets. This finding was further validated using a nomogram in the validation set. Additionally, the expression levels of CCL2 for NAFLD and UC showed a significant correlation with immune cell infiltration.ConclusionThis study identified a gene (CCL2) as a biomarker for NAFLD and UC, which may actively participate in the progression of NAFLD and UC. This discovery holds significant implications for understanding the progression of these diseases and potentially developing more effective diagnostic and treatment strategies.

Synthesis and Characterization of Cu-Co Substituted Strontium Hexaferrite for Magnetic Applications

In this study, the structural and magnetic characterizations of a series of SrCoxCuxFe12-2xO19; 0.0 ≤ x ≤ 0.5 samples, which were produced by the conventional ceramic method, were carried out. The structure was examined by X-ray diffraction (XRD) technique and optical microscopy. A vibrating sample magnetometer (VSM) was used to characterize the ferrimagnetic properties. XRD patterns confirmed the presence of a single phase as Sr-hexaferrite while there were not any peaks of unreacted Fe2O3 phase. The densities varied between 92 – 98 %. XRD patterns of the Sr-hexaferrite samples were slightly modified because of the co-substitution of Cu-Co; however, the magnetic properties changed remarkably. The sample with Cu-Co of x = 0.1 content had the highest saturation magnetization (33.52 emu/g), remanence (19.74 emu/g), and coercivity (4.3 kOe). The synthesized magnetic samples were found to be suitable for the development of attractive ferrimagnetic properties for the current ferrite magnets.

Soft sensor model for endpoint carbon content and temperature in BOF steelmaking based on just-in-time learning with multilayer structure preservation and sparse information enhancement

Ensuring precise prediction of the endpoint carbon content and temperature is paramount in controlling the endpoint of the basic oxygen furnace (BOF) steelmaking process. However, due to the frequent fluctuations in real-time blowing conditions, the conventional just-in-time learning soft sensor approach encounters challenges in effectively anticipating the blowing endpoint. To address these aforementioned issues, this research introduces a novel approach known as the pattern-oriented multilayer structure preservation and sparse information enhancement model (POMLSP-SIE). This approach involves dynamically generating a dataset with the same distribution based on the characteristics of the query sample pattern. It is combined with a multi-pathway dimension reduction model to preserve multi-view spatial geometry information while reducing data dimension. The low-dimensional embedding serves as dictionaries containing various hierarchical structural characteristics. Significant information within these dictionaries is sparsely represented to amplify its influence during the regression prediction process while diminishing the impact of less relevant information. This refinement aims to rectify the problem of static regression models being weak to adapt to changing working conditions, ultimately enhancing prediction performance. The effectiveness of the proposed method is substantiated through an experimental study utilising real converter steelmaking process data, thereby confirming its practical applicability.

Characterization of dark chocolates based on polyphenolic profiles and antioxidant activity.

Dark chocolates were characterized according to geographical origin, cocoa variety, and cocoa content using the methylxanthine and polyphenolic composition and antioxidant activity as the data. The main study objective was to uncover sample patterns and identify possible markers of quality, variety, or origin to deal with authentication or fraud detection issues. In the study, a set of 26 dark chocolates from different varieties (e.g., Criollo, Forastero, and Trinitario) harvested in Africa, America, and Asia was analyzed. The optimized sample treatment consisted of defatting the chocolate (1g of sample with 5mL of cyclohexane for 15min, three times) and then extracting the analytes by sonication with methanol/water 60:40 (v:v) for 15min. The filtered extracts were analyzed by reversed-phase high-performance liquid chromatography with UV and spectrophotometric methods (Folin-Ciocalteu, ferric reducing antioxidant power, and aluminum methods) to determine individual phenolics and overall indexes of antioxidant and flavonoid content. Results from this chocolate set indicated that American samples are richer than African counterparts in alkaloids and phenolics (e.g., 1.7 vs. 1.1mgg-1 caffeine and 14.5 vs. 12.5mgg-1 total flavanols, respectively). Regarding cocoa varieties, Criollo cocoa was richer in bioactive compounds and antioxidant capacity (e.g., 16, 15, and 12mgg-1 total flavanols for Criollo, Forastero, and Trinitario, respectively). These results indicate that the analytes resulted in potential descriptors of varietal or geographical attributes.

Experimental study on multiscale strain localisation based on soil cell element model

The granular and natural characteristics of soil introduce size effects to its deformation and strength properties. Therefore, investigating the phenomenon of strain localisation in soil requires a multi-scale characterisation. This study examined the intrinsic scale patterns in samples with different sizes of reinforcing particles through triaxial compression tests. Additionally, the formation mechanism of microscopic shear bands was investigated using numerical simulation methods. Drawing from the soil cell model theory, the average strain energy release coefficient was introduced to validate the transformation of the overall strain energy of the specimen after reaching the peak stress. This reflects the progressive initiation and competitive process of multiple bands. The results indicate that samples with different sizes and types of reinforcing particles exhibit various failure patterns, including single-type, ‘x’-shaped, ‘v’-shaped, parallel and others. The soil exhibits size effects, with the ratio of intrinsic scale to particle size decreasing as the size of reinforcing particles increases. Prior to the stress peak, non-elastic dissipation energy begins to increase, indicating the initiation of plastic deformation in the soil. Localised strain zones are activated, and after the peak, there is a sharp increase in stress within the shear bands, accompanied by rebound outside the band.

ACGRHA-Net: Accelerated Multi-Contrast MR Imaging with Adjacency Complementary Graph assisted Residual Hybrid Attention Network

Multi-contrast magnetic resonance (MR) imaging is an advanced technology used in medical diagnosis, but the long acquisition process can lead to patient discomfort and limit its broader application. Shortening acquisition time by undersampling k-space data introduces noticeable aliasing artifacts. To address this, we propose a method that reconstructs multi-contrast MR images from zero-filled data by utilizing a fully-sampled auxiliary contrast MR image as a prior to learn an adjacency complementary graph. This graph is then combined with a residual hybrid attention network, forming the adjacency complementary graph assisted residual hybrid attention network (ACGRHA-Net) for multi-contrast MR image reconstruction. Specifically, the optimal structural similarity is represented by a graph learned from the fully sampled auxiliary image, where the node features and adjacency matrices are designed to precisely capture structural information among different contrast images. This structural similarity enables effective fusion with the target image, improving the detail reconstruction. Additionally, a residual hybrid attention module is designed in parallel with the graph convolution network, allowing it to effectively capture key features and adaptively emphasize these important features in target contrast MR images. This strategy prioritizes crucial information while preserving shallow features, thereby achieving comprehensive feature fusion at deeper levels to enhance multi-contrast MR image reconstruction. Extensive experiments on the different datasets, using various sampling patterns and accelerated factors demonstrate that the proposed method outperforms the current state-of-the-art reconstruction methods.

Visualization experiments and numerical simulation on the growth of fracture height in bedding-rich reservoirs

The development of shale bedding and differences in properties have significant effects on the growth of the fracture height. To investigate the impact mechanism of bedding on the fracture growth height, the visualization fracturing experiments based on polymethylmethacrylate (PMMA) samples were performed to investigate the impact of the injection rates, viscosity and temporary plugging parameters on the growth pattern of hydraulic fractures. Furthermore, the solid–fluid-damage fracturing coupled model considering the vertical distribution of bedding was constructed by the continuous-discontinuous element method, and the impact of key formation parameters and treatment parameters were investigated. The results show that the height growth pattern of PMMA samples was affected by the flow rate and fluid viscosity. The fracture can cross the bedding at high-viscosity fluid. But in low-viscosity fluid the fracture tends to be arrested by the bedding. And the fracture cannot cross the bedding again after In-fracture temporary plugging. The fractures vertical growth pattern mainly includes three types at various stratigraphic parameters and treatment parameters, “工” type fracture, “丰” type fracture, and “I” type fracture, respectively. For vertical stress differentials below 3 MPa or Young's modulus below 20 GPa or injection rates below 1.8 m3/min or the fluid viscosity below 5 mPa·s, the fracture will be limited within the bedding.

Transvenous phrenic nerve stimulation reduces diaphragm injury during controlled mechanical ventilation in a preclinical model of ARDS.

Patients with acute respiratory distress syndrome (ARDS) require periods of deep sedation and mechanical ventilation, leading to diaphragm dysfunction. Our study seeks to determine whether the combination of temporary transvenous diaphragm neurostimulation (TTDN) and mechanical ventilation changes the degree of diaphragm injury and cytokines concentration in a preclinical ARDS model. Moderate ARDS was induced in pigs using oleic acid, followed by ventilation for 12 h post-injury with volume-control at 8 mL/kg, positive end-expiratory pressure (PEEP) 5 cmH2O, respiratory rate and [Formula: see text] set to achieve normal arterial blood gases. Two groups received TTDN: every second breath (MV + TTDN50%, n = 6) or every breath (MV + TTDN100%, n = 6). One group received ventilation only (MV, n = 6). Full-thickness diaphragm and quadricep muscle biopsies were taken at study end. Samples were fixed and stained with hematoxylin and eosin and a point-counting technique was applied to calculate abnormal muscle area fraction. Cytokine concentrations were measured in homogenized tissue using porcine-specific enzyme-linked immunosorbent assay (ELISA) and compared with serum samples. Percentage of abnormal diaphragm tissue was different between MV [8.1% (6.0-8.8)] versus MV + TTDN50% [3.4% (2.1-4.8)], P = 0.010 and MV versus MV + TTDN100% [3.1% (2.5-4.0)], P = 0.005. Percentage of abnormal quadriceps tissue was not different between groups. Cytokine concentration patterns in diaphragm samples were different between all groups (P < 0.001) and the interaction between TTDN application and resultant cytokine concentration pattern was significant (P = 0.025). TTDN, delivered in synchrony with mechanical ventilation, mitigated diaphragm injury, as evidenced by less abnormal tissue in the diaphragm samples, in pigs with oleic acid-induced ARDS and is an exciting tool for lung and diaphragm-protective ventilation.NEW & NOTEWORTHY This study adds to our understanding of applying transvenous diaphragm neurostimulation synchronously with mechanical ventilation by examining its effects on diaphragm muscle injury and cytokine concentration patterns in pigs with acute respiratory distress syndrome (ARDS). We observed that using this therapy for 12 h post lung injury mitigated ventilator-induced diaphragm injury and changed the pattern of cytokine concentration measured in diaphragm tissue. These findings suggest that transvenous diaphragm neurostimulation is an exciting tool for lung and diaphragm protective ventilation.

Isolation and Identification of Alkaloid Genes from the Biomass of Fritillaria taipaiensis P.Y. Li

Background/Objectives: Fritillaria taipaiensis P.Y. Li is a valuable traditional Chinese medicinal herb that utilizes bulbs as medicine, which contain multiple alkaloids. Biomass, as a sustainable resource, has promising applications in energy, environmental, and biomedical fields. Recently, the biosynthesis and regulatory mechanisms of the main biomass components of biomass have become a prominent research topic. Methods: In this article, we explored the differences in the heterosteroidal alkaloid components of F. taipaiensis biomass using liquid chromatography–mass spectrometry and high-throughput transcriptome sequencing. Results: The experimental results demonstrated significant differences in the eight types of heterosteroidal alkaloid components among the biomass of F. taipaiensis, including peimisine, imperialine, peimine, peiminine, ebeinone, ebeiedine, ebeiedinone, and forticine. Transcriptomic analysis revealed substantial significant differences in gene expression patterns in the various samples. Three catalytic enzyme-coding genes, 3-hydroxy-3-methylglutaryl coenzyme A synthase (HMGS), 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR), and terpene synthase (TPS), were speculated to contribute to the regulation of the differential accumulation of alkaloid synthesis in F. taipaiensis bulbs. A strong positive correlation was observed between the transcriptional level of the TPS gene and the alkaloid content of F. taipaiensis biomass, suggesting that TPS may be a key gene in the biosynthesis pathway of alkaloids. This finding can be used for subsequent gene function verification and molecular regulatory network analysis. Conclusions: This work provides fundamental data and novel insights for the subsequent research on alkaloid biosynthesis in F. taipaiensis.

Curious Choices: Infants' moment-to-moment information sampling is driven by their exploration history

Infants explore the world around them based on their intrinsically motivated curiosity. However, the cognitive mechanisms underlying such curiosity-driven exploratory behaviour remain largely unknown. Here, infants could freely explore two novel categories, triggering a new exemplar from a category by fixating on either of the two associated areas on a computer screen. This gaze-contingent design enabled us to distinguish between exploration – switching from one category to another – and exploitation – consecutively triggering exemplars from the same category. Data from 10 to 12-month-old infants (N = 68) indicated that moment-to-moment sampling choices were non-random but guided by the infants' exploration history. Self-generated sequences grouped into three clusters of brief yet explorative, longer exploitative, and overall more balanced sampling patterns. Bayesian hierarchical binomial regression models indicated that across sequence patterns, infants' longer trigger time, shorter looking time, and more gaze-shifting were associated with trial-by-trial decisions to disengage from exploiting one category and making an exploratory switch, especially after consecutively viewed stimuli of high similarity. These findings offer novel insights into infants' curiosity-driven exploration and pave the way for future investigations, also regarding individual differences.

Evaluation of 2D Tantalum Carbide MXene for Room to Mid‐temperature Thermoelectric Applications

AbstractTantalum carbide MXene (Ta4C3Tx) were synthesized via HF etching of the Al intermediate layer from the parental tantalum aluminium carbide MAX phase (Ta4AlC3). The structural and vibrational studies confirm the formation of MXene from the MAX phase without disturbing the hexagonal crystal structure. The synthesized samples were analysed using XRD to understand the phase structure. In and out of plane vibrational properties were examined by Raman spectrometer. XPS confirms the successful HF etching of Al in MXene phase and all other elemental configurations. HR‐SEM and HR‐TEM revile the exfoliated layered structure of the MXene and hexagon diffraction pattern of the tantalum carbide MXene sample with increased d‐spacing. The TGA analysis demonstrated the thermal stability of the as‐synthesized post measured compounds. The synthesized tantalum carbide MXene shows stable thermoelectric properties over six thermal cycles. The temperature dependent transport properties were measured from 303 K to 803 K. Ta4C3Tx MXene shows a maximum Seebeck coefficient of 13.8 μV/K and a power factor of 1.88 μW/mK2 with the low lattice thermal conductivity of 5.42 W/mK at 803 K. In this present investigation, Tantalum carbide MXene demonstrated a decent thermoelectric property with high thermal cycling stability.

A Multi-Hierarchical Complementary Feature Interaction Network for Accelerated Multi-Modal MR Imaging

Magnetic resonance (MR) imaging is widely used in the clinical field due to its non-invasiveness, but the long scanning time is still a bottleneck for its popularization. Using the complementary information between multi-modal imaging to accelerate imaging provides a novel and effective MR fast imaging solution. However, previous technologies mostly use simple fusion methods and fail to fully utilize their potential sharable knowledge. In this study, we introduced a novel multi-hierarchical complementary feature interaction network (MHCFIN) to realize joint reconstruction of multi-modal MR images with undersampled data and thus accelerate multi-modal imaging. Firstly, multiple attention mechanisms are integrated with a dual-branch encoder–decoder network to represent shared features and complementary features of different modalities. In the decoding stage, the multi-modal feature interaction module (MMFIM) acts as a bridge between the two branches, realizing complementary knowledge transfer between different modalities through cross-level fusion. The single-modal feature fusion module (SMFFM) carries out multi-scale feature representation and optimization of the single modality, preserving better anatomical details. Extensive experiments are conducted under different sampling patterns and acceleration factors. The results show that this proposed method achieves obvious improvement compared with existing state-of-the-art reconstruction methods in both visual quality and quantity.

Integrating machine learning and bioinformatics approaches for identifying novel diagnostic gene biomarkers in colorectal cancer.

This study aimed to identify diagnostic gene biomarkers for colorectal cancer (CRC) by analyzing differentially expressed genes (DEGs) in tumor and adjacent normal samples across five colon cancer gene-expression profiles (GSE10950, GSE25070, GSE41328, GSE74602, GSE142279) from the Gene Expression Omnibus (GEO) database. Intersecting identified DEGs with the module with the highest correlation to gene expression patterns of tumor samples in the gene co-expression network analysis revealed 283 overlapped genes. Centrality measures were calculated for these genes in the reconstructed STRING protein-protein interaction network. Applying LASSO logistic regression, eleven genes were ultimately recognized as candidate diagnostic genes. Among these genes, the area under the receiver operating characteristic curve (AUROC) values for nine genes (CDC25B, CDK4, IQGAP3, MMP1, MMP7, SLC7A5, TEAD4, TRIB3, and UHRF1) surpassed the threshold of 0.92 in both the training and validation sets. We evaluated the diagnostic performance of these genes with four machine learning algorithms: random forest (RF), support vector machines (SVM), artificial neural network (ANN), and gradient boosting machine (GBM). In the testing dataset (GSE21815 and GSE106582), the AUROC scores were greater than 0.95 for all of the machine learning algorithms, indicating the high diagnostic performance of the nine genes. Besides, these nine genes are also significantly correlated to twelve immune cells, namely Mast cells activated, Macrophages M0, M1, and M2, Neutrophils, T cells CD4 memory activated, T cells follicular helper, T cells CD8, T cells CD4 memory resting, B cells memory, Plasma cells, and Mast cells resting (P < 0.05). These results strongly suggest that all of the nine genes have the potential to serve as reliable diagnostic biomarkers for CRC.