Novel insights into biomarkers of progression in Desmoid tumor

Desmoid tumor (DT) is a rare neoplasm characterized by the proliferation of myofibroblastic cells that infiltrates and invades adjacent tissues. Due to its locally aggressive and recurrent nature, DT often causes local symptoms and can be challenging to manage clinically. Therefore, identifying biomarkers that can predict the progression of DT and guide treatment decisions is critical. This review summarizes several biomarkers that have been implicated in active surveillance (AS) and the prediction of postoperative recurrence and attempts to elucidate their underlying mechanisms. Some of these novel markers could provide prognostic value for clinicians, and ultimately help facilitate optimal and accurate therapeutic decisions for DT

Large anomalous Hall effect in a hexagonal ferromagnetic Fe5Sn3 single crystal

In this paper, we report an experimental observation of the large anomalous Hall effect (AHE) in a hexagonal ferromagnetic Fe5Sn3 single crystal with current along the b axis and a magnetic field normal to the bc plane. The intrinsic contribution of the anomalous Hall conductance sigma_AH^int was approximately 613 {\Omega}-1 cm-1, which was more than 3 times the maximum value in the frustrated kagome magnet Fe3Sn2 and nearly independent of the temperature over a wide range between 5 and 350 K. The analysis results revealed that the large AHE was dominated by a common, intrinsic term, while the extrinsic contribution, i.e., the skew scattering and side jump, turned out to be small. In addition to the large AHE, it was found the types of majority carriers changed at approximately 275 and 30 K, consistent with the critical temperatures of the spin reorientation. These findings suggest that the hexagonal ferromagnetic Fe5Sn3 single crystal is an excellent candidate to use for the study of the topological features in ferromagnets.Comment: accepted as a rapid communication in Phy. Rev.

Escherichia coli infection indicates favorable outcomes in patients with infected pancreatic necrosis

IntroductionInfected pancreatic necrosis (IPN) is a severe complication of acute necrotizing pancreatitis with increasing morbidity. Escherichia coli is the most frequently cultured microorganism in IPN. However, the implications of Escherichia coli infection on the outcomes of patients with IPN remain unclear. Therefore, this study aimed to evaluate the clinical impacts of Escherichia coli infection on IPN.MethodsA prospective database with consecutive patients with IPN between January 2010 and April 2022 at a tertiary hospital was post-hoc analyzed. The clinical and microbiological characteristics, surgical management, and follow-up data of patients with and without Escherichia coli infection were compared.ResultsA total of 294 IPN patients were enrolled in this cohort. Compared with non-Escherichia coli infection cases (n=80, 27.2%), patients with Escherichia coli infection (n=214, 72.8%) were characterized by more frequent polymicrobial infections (77.5% vs. 65.0%, P=0.04) but a lower occurrence of severe acute pancreatitis (SAP) (42.5% vs. 61.7%, P=0.003). In addition, significantly lower mortality (12.5% vs. 30.4%, p=0.002), fewer step-up surgical interventions (73.8% vs. 85.1%, P=0.025), and a lower rate of multiple organ failure (MOF) (25.0% vs. 40.2%, P=0.016) were also observed in patients with Escherichia coli infection. Multivariate analysis of mortality predictors indicated that MOF (odds ratio [OR], 6.197; 95% confidence interval [CI], 2.373–16.187; P<0.001) and hemorrhage (OR, 3.485; 95% CI, 1.623–7.487; P=0.001) were independent predictors associated with higher mortality in patients with IPN. Escherichia coli infection was significantly associated with a lower mortality (OR, 0.302; 95% CI, 0.121–0.751; P= 0.01).ConclusionEscherichia coli infection indicates a favorable prognosis in patients with IPN, although the mechanism needs further investigation

Deep transfer learning of global spectra for local soil carbon monitoring

There is global interest in spectroscopy and the development of large and diverse soil spectral libraries (SSL) to model soil organic carbon (SOC) and monitor, report, and verify (MRV) its changes. The reason is that increasing SOC can improve food production and mitigate climate change. However, ‘global’ modelling of SOC with such diverse and hyperdimensional SSLs do not generalise well locally, e.g. at a field scale. To address this challenge, we propose deep transfer learning (DTL) to leverage useful information from large-scale SSLs to assist local modelling. We used one global, three country-specific SSLs and data from three local sites with DTL to improve the modelling and localise the SOC estimates in individual fields or farms in each country. With DTL, we transferred instances from the SSLs, representations from one-dimensional convolutional neural networks (1D-CNNs) trained on the SSLs, and both instances and representations to improve local modelling. Transferring instances effectively used information from the global SSL to most accurately estimate SOC in each site, reducing the root mean square error (RMSE) by 25.8% on average compared with local modelling. Our results highlight the effectiveness of DTL and the value of diverse, global SSLs for accurate local SOC predictions. Applying DTL with a global SSL one could estimate SOC anywhere in the world more accurately, rapidly, and cost-effectively, enabling MRV protocols to monitor SOC changes

Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century

Spectroscopic measurements of soil samples are reliable because they are highly repeatable and reproducible. They characterise the samples' mineral-organic composition. Estimates of concentrations of soil constituents are inevitably less precise than estimates obtained conventionally by chemical analysis. But the cost of each spectroscopic estimate is at most one-tenth of the cost of a chemical determination. Spectroscopy is cost-effective when we need many data, despite the costs and errors of calibration. Soil spectroscopists understand the risks of over-fitting models to highly dimensional multivariate spectra and have command of the mathematical and statistical methods to avoid them. Machine learning has fast become an algorithmic alternative to statistical analysis for estimating concentrations of soil constituents from reflectance spectra. As with any modelling, we need judicious implementation of machine learning as it also carries the risk of over-fitting predictions to irrelevant elements of the spectra. To use the methods confidently, we need to validate the outcomes with appropriately sampled, independent data sets. Not all machine learning should be considered 'black boxes'. Their interpretability depends on the algorithm, and some are highly interpretable and explainable. Some are difficult to interpret because of complex transformations or their huge and complicated network of parameters. But there is rapidly advancing research on explainable machine learning, and these methods are finding applications in soil science and spectroscopy. In many parts of the world, soil and environmental scientists recognise the merits of soil spectroscopy. They are building spectral libraries on which they can draw to localise the modelling and derive soil information for new projects within their domains. We hope our article gives readers a more balanced and optimistic perspective of soil spectroscopy and its future. Highlights Spectroscopy is reliable because it is a highly repeatable and reproducible analytical technique. Spectra are calibrated to estimate concentrations of soil properties with known error. Spectroscopy is cost-effective for estimating soil properties. Machine learning is becoming ever more powerful for extracting accurate information from spectra, and methods for interpreting the models exist. Large libraries of soil spectra provide information that can be used locally to aid estimates from new samples

Genome-Wide Association Study in East Asians Identifies Novel Susceptibility Loci for Breast Cancer

Genetic factors play an important role in the etiology of both sporadic and familial breast cancer. We aimed to discover novel genetic susceptibility loci for breast cancer. We conducted a four-stage genome-wide association study (GWAS) in 19,091 cases and 20,606 controls of East-Asian descent including Chinese, Korean, and Japanese women. After analyzing 690,947 SNPs in 2,918 cases and 2,324 controls, we evaluated 5,365 SNPs for replication in 3,972 cases and 3,852 controls. Ninety-four SNPs were further evaluated in 5,203 cases and 5,138 controls, and finally the top 22 SNPs were investigated in up to 17,423 additional subjects (7,489 cases and 9,934 controls). SNP rs9485372, near the TGF-β activated kinase (TAB2) gene in chromosome 6q25.1, showed a consistent association with breast cancer risk across all four stages, with a P-value of 3.8×10−12 in the combined analysis of all samples. Adjusted odds ratios (95% confidence intervals) were 0.89 (0.85–0.94) and 0.80 (0.75–0.86) for the A/G and A/A genotypes, respectively, compared with the genotype G/G. SNP rs9383951 (P = 1.9×10−6 from the combined analysis of all samples), located in intron 5 of the ESR1 gene, and SNP rs7107217 (P = 4.6×10−7), located at 11q24.3, also showed a consistent association in each of the four stages. This study provides strong evidence for a novel breast cancer susceptibility locus represented by rs9485372, near the TAB2 gene (6q25.1), and identifies two possible susceptibility loci located in the ESR1 gene and 11q24.3, respectively

Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes

Genome-wide association studies (GWAS) have identified more than 200 common genetic variants independently associated with colorectal cancer (CRC) risk, but the causal variants and target genes are mostly unknown. We sought to fine-map all known CRC risk loci using GWAS data from 100,204 cases and 154,587 controls of East Asian and European ancestry. Our stepwise conditional analyses revealed 238 independent association signals of CRC risk, each with a set of credible causal variants (CCVs), of which 28 signals had a single CCV. Our cis-eQTL/mQTL and colocalization analyses using colorectal tissue-specific transcriptome and methylome data separately from 1299 and 321 individuals, along with functional genomic investigation, uncovered 136 putative CRC susceptibility genes, including 56 genes not previously reported. Analyses of single-cell RNA-seq data from colorectal tissues revealed 17 putative CRC susceptibility genes with distinct expression patterns in specific cell types. Analyses of whole exome sequencing data provided additional support for several target genes identified in this study as CRC susceptibility genes. Enrichment analyses of the 136 genes uncover pathways not previously linked to CRC risk. Our study substantially expanded association signals for CRC and provided additional insight into the biological mechanisms underlying CRC development

Design and Development of a 1-Degree-of-Freedom Leg Exoskeleton for Rehabilitation

Current leg exoskeletons for rehabilitation are mostly heavy and bulky, which limits their applications in clinical settings. Moreover, portable leg exoskeletons driven by built-in batteries have limited working hours due to low energy efficiency. This thesis was aimed to develop a compact and portable leg exoskeleton with a long lasting battery life. The exoskeleton adopted a planar 1-DOF linkage for compactness and clutched-spring mechanisms for energy efficiency

Traffic accidents of autonomous vehicles based on knowledge mapping: A review

As a characteristic representative of the new generation of vehicles, autonomous driving is expected to improve people's driving experience. The study on traffic accident of autonomous vehicles (AVs) helps provide suggestions for autonomous driving safety from multiple disciplines and perspectives, and provide support for formulating traffic accident treatment schemes. Knowledge mapping, as a cutting-edge research method in bibliometrics, scientifically and objectively displays the relevant research status using visual means. This paper uses CiteSpace 6.1.r3 to analyze 5068 related literature on the Web of Science database from 1991 to 2022 and finds out major thematic clusters, important documents and representative journals according to citation frequency. The results show that research on traffic accidents involving AVs focuses on accident preventing technologies, including how to avoid collisions, track lane-position, and enhance vehicle-to-everything (V2X) communication. This paper extracts the mean research topics and key points involved in the field and illustrates journals related to AVs and traffic accidents, which provides guidance for subsequent researchers to carry out in-depth research and contribute their papers. Popular journals are in disciplines of mathematics, systems, computer, economics, and social science. This paper also suggests scholars to consider the aspects of scene reconstruction, cause analysis, and injury of vulnerable road users, so as to investigate traffic accidents and put forward effective treatment schemes to reduce AV accidents, and ultimately improve road safety

A preliminary study of calcium channel-associated mRNA and miRNA networks in post-traumatic epileptic rats

Abstract The calcium channels are the main pathogenesis and therapeutic target for post-traumatic epilepsy (PTE). However, differentially expressed miRNAs (DEMs) and mRNAs associated with calcium channels in PTE and their interactions are poorly understood. We produced a PTE model in rats and conducted RNA-seq in PTE rats. Gene annotation was used to verify differentially expressed mRNAs related to calcium channels. RNAhybrid, PITA, and Miranda prediction were used to build the miRNA–mRNA pairs. Furthermore, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used for the functional enrichment analysis of DEMs. The quantification changes of mRNA and miRNA were verified by RT-qPCR. There were 431 identified differentially expressed genes (DEGs) in PTE rats compared with the sham group, of which five mRNAs and 7 miRNAs were related to calcium channels. The miRNA–mRNA network suggested a negative correlation between 11 pairs of miRNA–mRNA involved in the p53 signaling pathway, HIF-1 signaling pathway. RT-qPCR verified three upregulated mRNAs in PTE rats, associated with 7 DEMs negatively related to them, respectively. This study has revealed the changes in miRNA–mRNA pairs associated with calcium channels in PTE, which might contribute to the further interpretation of potential underlying molecular mechanisms of PTE and the discovery of promising diagnostics