The influence of different intensity of monsoon on typhoon precipitation: a comparative study of typhoons Soudelor and Maria

In this paper, multiple sets of reanalysis datasets are used to analyze the intensity of the Asian summer monsoon from 1979 to 2021. Typhoon Soudelor (No. 1513) and Typhoon Maria (No. 1808) were selected from the weak monsoon year 2015 and the strong monsoon year 2018, both of which were generated in the Northwest Pacific in July and made landfall in South China. The Weather Research and Forecasting Model (WRF) was used to simulate the two typhoons, starting 48 h prior to their landfall. The reasons for the differences in precipitation and how the monsoon affects typhoon precipitation in this process are analyzed from the aspects of monsoon background, ambient weather systems, typhoon thermodynamic structure, and water vapor conditions. The analysis shows that the circulation of Soudelor was stable and maintained for a long time. Despite existing in the background of a weak monsoon, the monsoon flow was able to reach the key area affecting typhoons and inject enough warm and moist flow to affect Soudelor. Combined with the analysis of typhoon structure, the strong water vapor transport of Soudelor and increased low-level convergence were conducive to the formation of typhoon-related rainstorms. The monsoon appeared to provide environmental conditions favorable for typhoon precipitation, resulting in a wide range of precipitation and heavy precipitation. Typhoon Maria developed and changed rapidly, moved rapidly, and the precipitation maintained itself for a relatively short time. The monsoon flow was not transported into Maria, resulting in insufficient water vapor inside Maria, which prevented the strengthening of typhoon precipitation. The precipitation of Maria mainly came from the dynamics of the typhoon itself and was not affected by the monsoon. In addition, this study defines an area on the southwest side of the typhoon moving with the center of the typhoon as the key area affecting typhoons. The characteristics of this area can be simply linked to typhoon precipitation, which can be considered an important research area for future analysis and prediction of typhoon precipitation

Integrative genomic and transcriptomic analysis for pinpointing recurrent alterations of plant homeodomain genes and their clinical significance in breast cancer

A wide range of the epigenetic effectors that regulate chromatin modification, gene expression, genomic stability, and DNA repair contain structurally conserved domains called plant homeodomain (PHD) fingers. Alternations of several PHD finger-containing proteins (PHFs) due to genomic amplification, mutations, deletions, and translocations have been linked directly to various types of cancer. However, little is known about the genomic landscape and the clinical significance of PHFs in breast cancer. Hence, we performed a large-scale genomic and transcriptomic analysis of 98 PHF genes in breast cancer using TCGA and METABRIC datasets and correlated the recurrent alterations with clinicopathological features and survival of patients. Different subtypes of breast cancer had different patterns of copy number and expression for each PHF. We identified a subset of PHF genes that was recurrently altered with high prevalence, including PYGO2 (pygopus family PHD finger 2), ZMYND8 (zinc finger, MYND-type containing 8), ASXL1 (additional sex combs like 1) and CHD3 (chromodomain helicase DNA binding protein 3). Copy number increase and overexpression of ZMYND8 were more prevalent in Luminal B subtypes and were significantly associated with shorter survival of breast cancer patients. ZMYND8 was also involved in a positive feedback circuit of the estrogen receptor (ER) pathway, and the expression of ZMYND8 was repressed by the bromodomain and extra terminal (BET) inhibitor in breast cancer. Our findings suggest a promising avenue for future research—to focus on a subset of PHFs to better understand the molecular mechanisms and to identify therapeutic targets in breast cancer

Invisible Watermarking for Audio Generation Diffusion Models

Diffusion models have gained prominence in the image domain for their capabilities in data generation and transformation, achieving state-of-the-art performance in various tasks in both image and audio domains. In the rapidly evolving field of audio-based machine learning, safeguarding model integrity and establishing data copyright are of paramount importance. This paper presents the first watermarking technique applied to audio diffusion models trained on mel-spectrograms. This offers a novel approach to the aforementioned challenges. Our model excels not only in benign audio generation, but also incorporates an invisible watermarking trigger mechanism for model verification. This watermark trigger serves as a protective layer, enabling the identification of model ownership and ensuring its integrity. Through extensive experiments, we demonstrate that invisible watermark triggers can effectively protect against unauthorized modifications while maintaining high utility in benign audio generation tasks.Comment: This is an invited paper for IEEE TPS, part of the IEEE CIC/CogMI/TPS 2023 conferenc

Dandelion-Inspired, Wind-Dispersed Polymer-Assembly Controlled by Light

Abstract The rise of stimuli-responsive polymers has brought about a wealth of materials for small-scale, wirelessly controlled soft-bodied robots. Thinking beyond conventional robotic mobilities already demonstrated in synthetic systems, such as walking, swimming and jumping, flying in air by dispersal, gliding, or even hovering is a frontier yet to be explored by responsive materials. The demanding requirements for actuator's performance, lightweight, and effective aerodynamic design underlie the grand challenges. Here, a soft matter-based porous structure capable of wind-assisted dispersal and lift-off/landing action under the control of a light beam is reported. The design is inspired by the seed of dandelion, resembling several biomimetic features, i.e., high porosity, lightweight, and separated vortex ring generation under a steady wind flow. Superior to its natural counterparts, this artificial seed is equipped with a soft actuator made of light-responsive liquid crystalline elastomer, which induces reversible opening/closing actions of the bristles upon visible light excitation. This shape-morphing enables manual tuning of terminal velocity, drag coefficient, and wind threshold for dispersal. Optically controlled wind-assisted lift-off and landing actions, and a light-induced local accumulation in descending structures are demonstrated. The results offer novel approaches for wirelessly controlled, miniatured devices that can passively navigate over a large aerial space.publishedVersionPeer reviewe

MicroRec: Efficient Recommendation Inference by Hardware and Data Structure Solutions

Deep neural networks are widely used in personalized recommendation systems. Unlike regular DNN inference workloads, recommendation inference is memory-bound due to the many random memory accesses needed to lookup the embedding tables. The inference is also heavily constrained in terms of latency because producing a recommendation for a user must be done in about tens of milliseconds. In this paper, we propose MicroRec, a high-performance inference engine for recommendation systems. MicroRec accelerates recommendation inference by (1) redesigning the data structures involved in the embeddings to reduce the number of lookups needed and (2) taking advantage of the availability of High-Bandwidth Memory (HBM) in FPGA accelerators to tackle the latency by enabling parallel lookups. We have implemented the resulting design on an FPGA board including the embedding lookup step as well as the complete inference process. Compared to the optimized CPU baseline (16 vCPU, AVX2-enabled), MicroRec achieves 13.8~14.7x speedup on embedding lookup alone and 2.5$~5.4x speedup for the entire recommendation inference in terms of throughput. As for latency, CPU-based engines needs milliseconds for inferring a recommendation while MicroRec only takes microseconds, a significant advantage in real-time recommendation systems.Comment: Accepted by MLSys'21 (the 4th Conference on Machine Learning and Systems

Identification of diagnostic hub genes related to neutrophils and infiltrating immune cell alterations in idiopathic pulmonary fibrosis

BackgroundThere is still a lack of specific indicators to diagnose idiopathic pulmonary fibrosis (IPF). And the role of immune responses in IPF is elusive. In this study, we aimed to identify hub genes for diagnosing IPF and to explore the immune microenvironment in IPF.MethodsWe identified differentially expressed genes (DEGs) between IPF and control lung samples using the GEO database. Combining LASSO regression and SVM-RFE machine learning algorithms, we identified hub genes. Their differential expression were further validated in bleomycin-induced pulmonary fibrosis model mice and a meta-GEO cohort consisting of five merged GEO datasets. Then, we used the hub genes to construct a diagnostic model. All GEO datasets met the inclusion criteria, and verification methods, including ROC curve analysis, calibration curve (CC) analysis, decision curve analysis (DCA) and clinical impact curve (CIC) analysis, were performed to validate the reliability of the model. Through the Cell Type Identification by Estimating Relative Subsets of RNA Transcripts algorithm (CIBERSORT), we analyzed the correlations between infiltrating immune cells and hub genes and the changes in diverse infiltrating immune cells in IPF.ResultsA total of 412 DEGs were identified between IPF and healthy control samples, of which 283 were upregulated and 129 were downregulated. Through machine learning, three hub genes (ASPN, SFRP2, SLCO4A1) were screened. We confirmed their differential expression using pulmonary fibrosis model mice evaluated by qPCR, western blotting and immunofluorescence staining and analysis of the meta-GEO cohort. There was a strong correlation between the expression of the three hub genes and neutrophils. Then, we constructed a diagnostic model for diagnosing IPF. The areas under the curve were 1.000 and 0.962 for the training and validation cohorts, respectively. The analysis of other external validation cohorts, as well as the CC analysis, DCA, and CIC analysis, also demonstrated strong agreement. There was also a significant correlation between IPF and infiltrating immune cells. The frequencies of most infiltrating immune cells involved in activating adaptive immune responses were increased in IPF, and a majority of innate immune cells showed reduced frequencies.ConclusionOur study demonstrated that three hub genes (ASPN, SFRP2, SLCO4A1) were associated with neutrophils, and the model constructed with these genes showed good diagnostic value in IPF. There was a significant correlation between IPF and infiltrating immune cells, indicating the potential role of immune regulation in the pathological process of IPF

General synthesis of 2D rare-earth oxide single crystals with tailorable facets

Two-dimensional (2D) rare-earth oxides (REOs) are a large family of materials with various intriguing applications and precise facet control is essential for investigating new properties in the 2D limit. However, a bottleneck remains with regard to obtaining their 2D single crystals with specific facets because of the intrinsic non-layered structure and disparate thermodynamic stability of different facets. Herein, for the first time, we achieve the synthesis of a wide variety of high-quality 2D REO single crystals with tailorable facets via designing a hard-soft-acid-base couple for controlling the 2D nucleation of the predetermined facets and adjusting the growth mode and direction of crystals. Also, the facet-related magnetic properties of 2D REO single crystals were revealed. Our approach provides a foundation for further exploring other facet-dependent properties and various applications of 2D REO, as well as inspiration for the precise growth of other non-layered 2D materials

Vomiting and wasting disease associated with hemagglutinating encephalomyelitis viruses infection in piglets in jilin, china

One coronavirus strain was isolated from brain tissues of ten piglets with evident clinical manifestations of vomiting, diarrhea and dyskinesia in Jilin province in China. Antigenic and genomic characterizations of the virus (isolate PHEV-JLsp09) were based on multiplex PCR and negative staining electron microscopy and sequence analysis of the Hemagglutinin-esterase (HE) gene. These piglets were diagnosed with Porcine hemagglutinating encephalomyelitis virus (PHEV)