Genetic barcoding systematically comparing genes in del(5q) MDS reveals a central role for CSNK1A1 in clonal expansion

How genetic haploinsufficiency contributes to the clonal dominance of hematopoietic stem cells (HSC) in del(5q) myelodysplastic syndrome (MDS) remains unresolved. Using a genetic barcoding strategy, a systematic comparison was carried out on genes implicated in the pathogenesis of del(5q) MDS in direct competition with each other and wild-type (WT) cells with single clone resolution. Csnk1a1 haploinsufficient HSCs expanded (oligo)clonally and outcompeted all other tested genes and combinations. Csnk1a1-/+ multipotent progenitors showed a pro-proliferative gene signature and HSCs a downregulation of inflammatory signaling/immune response. In validation experiments, Csnk1a1-/+ HSCs outperformed their WT counterparts under a chronic inflammation stimulus, also known to be caused by neighboring genes on chromosome 5. A crucial role for Csnk1a1 haploinsufficiency in the selective advantage of the 5q- HSC is therefore proposed. It is implemented by creation of a unique competitive advantage through increased HSC self-renewal and proliferation capacity, as well as increased fitness under inflammatory stress.

EvoMBN: Evolving Multi-Branch Networks on Myocardial Infarction Diagnosis Using 12-Lead Electrocardiograms

Multi-branch Networks (MBNs) have been successfully applied to myocardial infarction (MI) diagnosis using 12-lead electrocardiograms. However, most existing MBNs share a fixed architecture. The absence of architecture optimization has become a significant obstacle to a more accurate diagnosis for these MBNs. In this paper, an evolving neural network named EvoMBN is proposed for MI diagnosis. It utilizes a genetic algorithm (GA) to automatically learn the optimal MBN architectures. A novel fixed-length encoding method is proposed to represent each architecture. In addition, the crossover, mutation, selection, and fitness evaluation of the GA are defined to ensure the architecture can be optimized through evolutional iterations. A novel Lead Squeeze and Excitation (LSE) block is designed to summarize features from all the branch networks. It consists of a fully-connected layer and an LSE mechanism that assigns weights to different leads. Five-fold inter-patient cross validation experiments on MI detection and localization are performed using the PTB diagnostic database. Moreover, the model architecture learned from the PTB database is transferred to the PTB-XL database without any changes. Compared with existing studies, our EvoMBN shows superior generalization and the efficiency of its flexible architecture is suitable for auxiliary MI diagnosis in real-world.

Potential inhibitors for blocking the interaction of the coronavirus SARS-CoV-2 spike protein and its host cell receptor ACE2

The outbreak of SARS-CoV-2 continues to pose a serious threat to human health and social and economic stability. In this study, we established an anti-coronavirus drug screening platform based on the Homogeneous Time Resolved Fluorescence (HTRF) technology and the interaction between the coronavirus S protein and its host receptor ACE2. This platform is a rapid, sensitive, specific, and high throughput system. With this platform, we screened two compound libraries of 2,864 molecules and identified three potential anti-coronavirus compounds: tannic acid (TA), TS-1276 (anthraquinone), and TS-984 (9-Methoxycanthin-6-one). Our in vitro validation experiments indicated that TS-984 strongly inhibits the interaction of the coronavirus S-protein and the human cell ACE2 receptor. This data suggests that TS-984 is a potent blocker of the interaction between the S-protein and ACE2, which might have the potential to be developed into an effective anti-coronavirus drug.

Synergy of Sentinel-1 and Sentinel-2 Imagery for Early Seasonal Agricultural Crop Mapping

The exploitation of the unprecedented capacity of Sentinel-1 (S1) and Sentinel-2 (S2) data offers new opportunities for crop mapping. In the framework of the SenSAgri project, this work studies the synergy of very high-resolution Sentinel time series to produce accurate early seasonal binary cropland mask and crop type map products. A crop classification processing chain is proposed to address the following: (1) high dimensionality challenges arising from the explosive growth in available satellite observations and (2) the scarcity of training data. The two-fold methodology is based on an S1-S2 classification system combining the so-called soft output predictions of two individually trained classifiers. The performances of the SenSAgri processing chain were assessed over three European test sites characterized by different agricultural systems. A large number of highly diverse and independent data sets were used for validation experiments. The agreement between independent classification algorithms of the Sentinel data was confirmed through different experiments. The presented results assess the interest of decision-level fusion strategies, such as the product of experts. Accurate crop map products were obtained over different countries in the early season with limited training data. The results highlight the benefit of fusion for early crop mapping and the interest of detecting cropland areas before the identification of crop types.

Selection of effective manufacturing conditions for directed energy deposition process using machine learning methods

In the directed energy deposition (DED) process, significant empirical testing is required to select the optimal process parameters. In this study, single-track experiments were conducted using laser power and scan speed as parameters in the DED process for titanium alloys. The results of the experiment confirmed that the deposited surface color appeared differently depending on the process parameters. Cross-sectional view, hardness, microstructure, and component analyses were performed according to the color data, and a color suitable for additive manufacturing was selected. Random forest (RF) and support vector machine multi-classification models were constructed by collecting surface color data from a titanium alloy deposited on a single track; the accuracies of the multi-classification models were compared. Validation experiments were performed under conditions that each model predicted differently. According to the results of the validation experiments, the RF multi-classification model was the most accurate.

INVESTIGATION OF MECHANICAL PROPERTIES OF PIPES WITH BUTT CURVED LAP JOINTS SUBJECTED TO TORSION

In this study, the mechanical properties of steel pipe joints bonded with an acrylic adhesive under torsion were investigated by considering different overlap lengths and curvilinear radii. For this purpose, St-37 galvanized steel pipes with ØD = 21.3 mm outer and Ød = 15.9 mm inner diameters were modeled in three dimensions by creating 8, 10, 12 mm overlapping lengths in male and female form and 30, 40 and 50 mm curvilinear radii on the forehead regions. Numerical analyzes of the created models were performed in the ANSYS Workbench program, and samples with an overlap length of 10 mm were prepared on the CNC (Computer Numerical Control) bench to verify the numerical analysis. The prepared male and female specimens were joined using an acrylic-based DP810 structural adhesive resistant to humid environments, curable at room temperature, and validation experiments were carried out. After the study, obtained numerical and experimental data showed that pipe lap joints subjected to torsional moment with the same lap length but different curvilinear radius have been observed to vary significantly in the amount of load they carry. In addition, as the overlap length increased, the amount of load they carried increased. Also, in this study was compared by performing stress analyzes according to the overlap length and radii of curvature.

Bioinformatic Analyses and Experimental Verification Reveal that High FSTL3 Expression Promotes EMT via Fibronectin-1/α5β1 Interaction in Colorectal Cancer

Background: Colorectal cancer (CRC) is a typical cancer prevalent worldwide. Despite the conventional treatments, CRC has a poor prognosis due to relapse and metastasis. Moreover, there is a dearth of sensitive biomarkers for predicting prognosis in CRC.Methods: This study used a bioinformatics approach combining validation experiments to examine the value of follistatin-like 3 (FSTL3) as a prognostic predictor and therapeutic target in CRC.Results:FSTL3 was remarkably upregulated in the CRC samples. FSTL3 overexpression was significantly associated with a poor prognosis. FSTL3 was found to activate the epithelial-mesenchymal transition by promoting the binding of FN1 to α5β1. FSTL3 expression was also positively correlated with the abundance of the potent immunosuppressors, M2 macrophages.Conclusion:FSTL3 overexpression affects CRC prognosis and thus, FSTL3 can be a prognostic biomarker and therapeutic target with potential applications in CRC.

Demonstration of subsurface passive acoustic monitoring (SPAM) to survey for and estimate populations of imperiled underwater-calling frogs

The management and recovery of threatened and endangered amphibians on Department of Defense (DoD) lands relies on an understanding of their distribution and abundance. Fortunately, most anuran species can be surveyed acoustically using vocalizations during the breeding season. This work demonstrated the use of subsurface passive acoustic monitoring (SPAM) to survey for rare underwater-calling, at-risk anuran species on DoD installations. We evaluated the performance of SPAM relative to traditional passive acoustic monitoring (PAM) (microphone) and human manual calling survey (MCS) methods. Results showed that SPAM outperformed PAM and MCS in validation experiments where calls were generated underwater; SPAM was less successful than PAM and MCS in the field demonstration. Most leopard frog calls were apparently produced in air despite previous reports of extensive underwater-calling behavior. This project highlights how acoustic information can help address a data gap in the ecology of at-risk species, which can help refine future survey methodology and management efforts. Ultimately, the utility of SPAM for underwater-calling species will depend on the focal species, the landscape where it occurs, and technological considerations available to the surveyor. SPAM is more expensive than traditional methods but, in some situations, may be the only way to effectively detect species.