Explore the vast range of titles available.

The diurnal variation in precipitation is an essential aspect of the hydrological cycle. It is also an effective way to assess the model performance and understand the local climate. However, there is still a lack of detailed evaluation and in-depth analyses of precipitation at the sub-daily scale for kilometer-scale dynamical downscaling simulations. In this study, we evaluated the precipitation outputs from quarter-degree dynamical downscaling modeling (28kmDDM) and kilometer-scale dynamical downscaling modeling (4kmDDM) at the sub-daily scale over the Tibetan Plateau (TP) during the summer of 2014. The advantages and disadvantages of kilometer-scale simulation are well-dissected. Our findings indicated that 4kmDDM is clearly advantageous for capturing daily-scale precipitation features, primarily due to its superior ability to simulate convective precipitation during the day. On the contrary, compared to the station observations, 4kmDDM exhibits a nocturnal underestimation, particularly in the southeastern basins of the TP, where steep topographic relief exists. Compared to 28kmDDM, 4kmDDM simulates more realistic surface air temperatures and abundant precipitable water vapor. However, the formation of low-level clouds in 4kmDDM is limited due to insufficient condensation. Under stable nocturnal stratification, 4kmDDM lacks the potential to trigger more condensation in the middle and high layers without low-level condensation latent heating. Fewer clouds and hydrometeors are the primary factors contributing to nocturnal precipitation underestimation. This study highlights the discrepancies of the kilometer-scale simulation in nocturnal precipitation under stable stratification.

The distribution of water resources in sub-basins across the Western Tibetan Plateau (WTP) is of critical importance due to not only ecological vulnerability resulting from the extremely arid climatology but also the political sensitivities surrounding the international rivers. In this study, we utilize an advanced water vapor tracer (WVT) embedded in the widely used regional climate model—Weather and Research Forecast (WRF), to quantify moisture contributions from four main sources towards precipitation over the WTP region. We also analyze influences on other sub-basins in the TP for comparison purposes. We examine how changes in sea surface temperature (SST) during 2010s compared to 1980s have influenced precipitation patterns and moisture contributions over recent decades. Our findings indicate that terrestrial moisture sources contribute more than oceanic sources towards the endorheic TP region. Recycling processes originating from highlands area are revealed to play a greater role in contributing moisture over WTP compared to those from lowlands areas. Furthermore, our results demonstrate stronger agreements between wetting distribution patterns and distributions of liquid/solid hydrometeors rather than water vapor distribution itself, highlighting condensation/freezing as critical factors. Notably, we observe different responses within Amu Dayra basin compared to the main WTP when subjected to SST changes. This study focuses on delineating distinct roles of terrestrial and oceanic moisture sources in driving precipitation changes over WTP, while specifically emphasizing condensation process’ contribution to inner TP’s precipitation and highlighting moisture transport form oceans’ influence on precipitation patterns over Amu Dayra basin.

Carotid ultrasound requires skilled operators due to small vessel dimensions and high anatomical variability, exacerbating sonographer shortages and diagnostic inconsistencies. Prior automation attempts, including rule-based approaches with manual heuristics and reinforcement learning trained in simulated environments, demonstrate limited generalizability and fail to complete real-world clinical workflows. Here, we present UltraBot, a fully learning-based autonomous carotid ultrasound robot, achieving human-expert-level performance through four innovations: (1) A unified imitation learning framework for acquiring anatomical knowledge and scanning operational skills; (2) A large-scale expert demonstration dataset (247,000 samples, 100 × scale-up), enabling embodied foundation models with strong generalization; (3) A comprehensive scanning protocol ensuring full anatomical coverage for biometric measurement and plaque screening; (4) The clinical-oriented validation showing over 90% success rates, expert-level accuracy, up to 5.5 × higher reproducibility across diverse unseen populations. Overall, we show that large-scale deep learning offers a promising pathway toward autonomous, high-precision ultrasonography in clinical practice. Ultrasound examination significantly relies on manual operation, which has significant downsides. The authors present UltraBot, a carotid ultrasound robot capable of automated scanning, measurement, and plaque screening, and build an embodied foundation model using deep learning for intelligent, high-precision ultrasound.

The coal pulverizing system is an important auxiliary system in thermal power generation systems. The working condition of a coal pulverizing system may directly affect the safety and economy of power generation. Prognostics and health management is an effective approach to ensure the reliability of coal pulverizing systems. As the coal pulverizing system is a typical dynamic and nonlinear high-dimensional system, it is difficult to construct accurate mathematical models used for anomaly detection. In this paper, a novel data-driven integrated framework for anomaly detection of the coal pulverizing system is proposed. A neural network model based on gated recurrent unit (GRU) networks, a type of recurrent neural network (RNN), is constructed to describe the temporal characteristics of high-dimensional data and predict the system condition value. Then, aiming at the prediction error, a novel unsupervised clustering algorithm for anomaly detection is proposed. The proposed framework is validated by a real case study from an industrial coal pulverizing system. The results show that the proposed framework can detect the anomaly successfully.

Short tandem repeats (STRs) and single nucleotide polymorphisms (SNPs) have been already used to perform noninvasive prenatal paternity testing from maternal plasma DNA. The frequently used technologies were PCR followed by capillary electrophoresis and SNP typing array, respectively. Here, we developed a noninvasive prenatal paternity testing (NIPAT) based on SNP typing with maternal plasma DNA sequencing. We evaluated the influence factors (minor allele frequency (MAF), the number of total SNP, fetal fraction and effective sequencing depth) and designed three different selective SNP panels in order to verify the performance in clinical cases. Combining targeted deep sequencing of selective SNP and informative bioinformatics pipeline, we calculated the combined paternity index (CPI) of 17 cases to determine paternity. Sequencing-based NIPAT results fully agreed with invasive prenatal paternity test using STR multiplex system. Our study here proved that the maternal plasma DNA sequencing-based technology is feasible and accurate in determining paternity, which may provide an alternative in forensic application in the future.

In this study, different mass fraction (0%, 10%, 20%, 30%) of bamboo flour (BF) was blended with PLA pellets and PBAT masterbatch via extrusion compounding process. The effect of bamboo flour content on the thermal and mechanical properties of the prepared wood-plastic composites (WPCs) arecharacterized by SEM, XRD, and DSC. The results show that the addition of BF did not alter the crystal structure of PLA and PBAT. However, as the bamboo flour content increase to 20% and 30%, agglomeration and uneven distribution are observed for the composites. Additionally, the initial decomposition temperature decreases, and the tensile strength declines.

Copy number variations (CNVs) represent an important type of genetic variation that deeply impact phenotypic polymorphisms and human diseases. The advent of high-throughput sequencing technologies provides an opportunity to revolutionize the discovery of CNVs and to explore their relationship with diseases. However, most of the existing methods depend on sequencing depth and show instability with low sequence coverage. In this study, using low coverage whole-genome sequencing (LCS) we have developed an effective population-scale CNV calling (PSCC) method. In our novel method, two-step correction was used to remove biases caused by local GC content and complex genomic characteristics. We chose a binary segmentation method to locate CNV segments and designed combined statistics tests to ensure the stable performance of the false positive control. The simulation data showed that our PSCC method could achieve 99.7%/100% and 98.6%/100% sensitivity and specificity for over 300 kb CNV calling in the condition of LCS (∼2×) and ultra LCS (∼0.2×), respectively. Finally, we applied this novel method to analyze 34 clinical samples with an average of 2× LCS. In the final results, all the 31 pathogenic CNVs identified by aCGH were successfully detected. In addition, the performance comparison revealed that our method had significant advantages over existing methods using ultra LCS. Our study showed that PSCC can sensitively and reliably detect CNVs using low coverage or even ultra-low coverage data through population-scale sequencing.

With the speedy development of sequencing technologies, noninvasive prenatal testing (NIPT) has been widely applied in clinical practice for testing for fetal aneuploidy. The cell-free fetal DNA (cffDNA) concentration in maternal plasma is the most critical parameter for this technology because it affects the accuracy of NIPT-based sequencing for fetal trisomies 21, 18 and 13. Several approaches have been developed to calculate the cffDNA fraction of the total cell-free DNA in the maternal plasma. However, most approaches depend on specific single nucleotide polymorphism (SNP) allele information or are restricted to male fetuses. In this study, we present an innovative method to accurately deduce the concentration of the cffDNA fraction using only maternal plasma DNA. SNPs were classified into four maternal-fetal genotype combinations and three boundaries were added to capture effective SNP loci in which the mother was homozygous and the fetus was heterozygous. The median value of the concentration of the fetal DNA fraction was estimated using the effective SNPs. A depth-bias correction was performed using simulated data and corresponding regression equations for adjustments when the depth of the sequencing data was below 100-fold or the cffDNA fraction is less than 10%. Using our approach, the median of the relative bias was 0.4% in 18 maternal plasma samples with a median sequencing depth of 125-fold. There was a significant association (r = 0.935) between our estimations and the estimations inferred from the Y chromosome. Furthermore, this approach could precisely estimate a cffDNA fraction as low as 3%, using only maternal plasma DNA at the targeted region with a sequencing depth of 65-fold. We also used PCR instead of parallel sequencing to calculate the cffDNA fraction. There was a significant association (r = 98.2%) between our estimations and those inferred from the Y chromosome.

Ultra-low coverage sequencing (ULCS) is one of the most promising strategies for sequencing based clinical application. These clinical applications, especially prenatal diagnosis, have a strict requirement of turn-around-time; therefore, the application of ULCS is restricted by current high throughput sequencing platforms. Recently, the emergence of rapid sequencing platforms, such as MiSeq and Ion Proton, brings ULCS strategy into a new era. The comparison of their performance could shed lights on their potential application in large-scale clinic trials. In this study, we performed ULCS (<0.1X coverage) on both MiSeq and Ion Proton platforms for 18 spontaneous abortion fetuses carrying aneuploidy and compared their performance on different levels. Overall basic data and GC bias showed no significant difference between these two platforms. We also found the sex and aneuploidy detection indicated 100% sensitivity and 100% specificity on both platforms. Our study generated essential data from these two rapid sequencing platforms, which provides useful reference for later research and potentially accelerates the clinical applications of ULCS.

The discovery of cell free fetal DNA (cff-DNA) in maternal plasma has brought new insight for noninvasive prenatal diagnosis. Combining with the rapidly developed massively parallel sequencing technology, noninvasive prenatal detection of chromosome aneuploidy and single base variation has been successfully validated. However, few studies discussed the possibility of noninvasive pathogenic CNVs detection. A novel algorithm for noninvasive prenatal detection of fetal pathogenic CNVs was firstly tested in 5 pairs of parents with heterozygote α-thalassemia of Southeast Asian (SEA) deletion using target region capture sequencing for maternal plasma. Capture probes were designed for α-globin (HBA) and β-globin (HBB) gene, as well as 4,525 SNPs selected from 22 automatic chromosomes. Mixed adaptors with 384 different barcodes were employed to construct maternal plasma DNA library for massively parallel sequencing. The signal of fetal CNVs was calculated using the relative copy ratio (RCR) of maternal plasma combined with the analysis of R-score and L-score by comparing with normal control. With mean of 101.93× maternal plasma sequencing depth for the target region, the RCR value combined with further R-score and L-score analysis showed a possible homozygous deletion in the HBA gene region for one fetus, heterozygous deletion for two fetus and normal for the other two fetus, which was consistent with that of invasive prenatal diagnosis. Our study showed the feasibility to detect pathogenic CNVs using target region capture sequencing, which might greatly extend the scope of noninvasive prenatal diagnosis.