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Activation of PKG1α is a compelling strategy for the treatment of cardiovascular diseases. As the main effector of cyclic guanosine monophosphate (cGMP), activation of PKG1α induces smooth muscle relaxation in blood vessels, lowers pulmonary blood pressure, prevents platelet aggregation, and protects against cardiac stress. The development of activators has been mostly limited to cGMP mimetics and synthetic peptides. Described herein is the optimization of a piperidine series of small molecules to yield activators that demonstrate

Motivation The advancement of high-throughput technology characterizes a wide variety of epigenetic modifications and noncoding RNAs across the genome involved in disease pathogenesis via regulating gene expression. The high dimensionality of both epigenetic/noncoding RNA and gene expression data make it challenging to identify the important regulators of genes. Conducting univariate test for each possible regulator–gene pair is subject to serious multiple comparison burden, and direct application of regularization methods to select regulator–gene pairs is computationally infeasible. Applying fast screening to reduce dimension first before regularization is more efficient and stable than applying regularization methods alone. Results We propose a novel screening method based on robust partial correlation to detect epigenetic and noncoding RNA regulators of gene expression over the whole genome, a problem that includes both high-dimensional predictors and high-dimensional responses. Compared to existing screening methods, our method is conceptually innovative that it reduces the dimension of both predictor and response, and screens at both node (regulators or genes) and edge (regulator–gene pairs) levels. We develop data-driven procedures to determine the conditional sets and the optimal screening threshold, and implement a fast iterative algorithm. Simulations and applications to long noncoding RNA and microRNA regulation in Kidney cancer and DNA methylation regulation in Glioblastoma Multiforme illustrate the validity and advantage of our method. Availability and implementation The R package, related source codes and real datasets used in this article are provided at https://github.com/kehongjie/rPCor. Supplementary information Supplementary data are available at Bioinformatics online.

Screening campaigns, especially those aimed at modulating enzyme activity, often rely on measuring substrate→product conversions. Unfortunately, the presence of endogenous substrates and/or products can limit one's ability to measure conversions. As well, coupled detection systems, often used to facilitate optical readouts, are subject to interference. Stable isotope labeled substrates can overcome background contamination and yield a direct readout of enzyme activity. Not only can isotope kinetic assays enable early screening, but they can also be used to follow hit progression in translational (pre)clinical studies. Herein, we consider a case study surrounding lipid biology to exemplify how metabolic flux analyses can connect stages of drug development, caveats are highlighted to ensure reliable data interpretations. For example, when measuring enzyme activity in early biochemical screening it may be enough to quantify the formation of a labeled product. In contrast, cell-based and in vivo studies must account for variable exposure to a labeled substrate (or precursor) which occurs via tracer dilution and/or isotopic exchange. Strategies are discussed to correct for these complications. We believe that measures of metabolic flux can help connect structure-activity relationships with pharmacodynamic mechanisms of action and determine whether mechanistically differentiated biophysical interactions lead to physiologically relevant outcomes. Adoption of this logic may allow research programs to (i) build a critical bridge between primary screening and (pre)clinical development, (ii) elucidate biology in parallel with screening and (iii) suggest a strategy aimed at in vivo biomarker development.

HighlightsThe Sentinel-2 images are reconstructed by the SupReME algorithm to obtain rich spatial features and consistent spectral reflectance.The reconstructed images are more advantageous for LAI estimation than the original images.The PROSAIL coupled RF model is verified to be an effective method for time-series LAI estimation at 10 m spatial resolution.Abstract. Accurate time-series crop leaf area index (LAI) monitoring can provide data support for field management and early yield estimation. The Sentinel-2 satellite has a high spatial, temporal, and spectral resolution, and its unique three red-edge bands provide an ideal data source for LAI estimation. However, the inconsistent spatial resolution of different bands hinders the application potential of Sentinel-2 images. In view of this problem, we focused on mining more information provided by the high spatial resolution bands of Sentinel-2 images using the Super-Resolution for Multispectral Multiresolution Estimation (SupReME) algorithm. Furthermore, The SNAP (Sentinel Application Platform) biophysical processor and the PROSAIL radiation transfer model coupled with Random Forest (RF) model were applied to estimate time-series LAI of maize canopy at 10 m spatial resolution, and the Leaf Area Index Wireless Sensor Network (LAINet) measurements were used for accuracy verification. Finally, the effectiveness of images reconstructed by SupReME and the two inversion methods for time-series LAI estimation were evaluated. The results showed that: (1) the Sentinel-2 images reconstructed by SupReME can improve spatial characteristics while maintaining spectral invariance, and they were more advantageous for LAI estimation than the original images; (2) The SNAP biophysical processor suits a quick large-scale estimation with robustness, while the PROSAIL coupled RF model achieved a higher coefficient of determination (R2) and a lower root mean square error (RMSE) (R2 increased by more than 0.1, RMSE decreased by more than 0.33) for time-series LAI estimation in this specific study area; (3) both inversion methods showed apparent underestimation at the late growth stage. This study verifies the feasibility of obtaining high spatial resolution images using a super-resolution algorithm for LAI inversion and provides the effect of two commonly used inversion methods for time-series LAI estimation at 10 m resolution. Keywords: Leaf area index, PROSAIL model, Random forest, SNAP biophysical processor, SupReME algorithm.

Yuan-Yuan Liu,1,2 Ren-Feng Zhao,2 Chao Liu,2 Jie Zhou,2 Liu Yang,2 Li Li1 1Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Ministry of Education, Nanning, Guangxi, People’s Republic of China; 2The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, People’s Republic of ChinaCorrespondence: Yuan-Yuan Liu; Li Li Tel +86-13877152206; +86-13878113406Email 43322045@qq.com; yuanyuan_liu2021@163.comObjective: Nomograms are statistics-based predictive tools that integrate predictive factors. Herein, a nomogram was developed and validated to predict the overall survival (OS) in serous ovarian cancer (SOC).Methods: Primary SOC patients with satisfactory cytoreductive surgery, chemotherapy, and OS ≥ 1 month were included in this study. A total of 6957 patients from the Surveillance, Epidemiology, and End Results (SEER) database comprised the training group and 1244 patients comprised the external validation group. The nomogram was structured on Cox models and evaluated in both the training and validation groups using consistency index, area under the receiver operating characteristics curve, calibration plots, and risk subgroup classification. Kaplan–Meier curves were plotted to compare the survival outcomes between subgroups. A decision-curve analysis was used to test the clinical value of the nomogram.Results: Independent factors, including age, tumor grade, and Federation of Gynecology and Obstetrics (FIGO) stage, identified by multivariate analysis in the training cohort, were selected for the nomogram. The consistency indexes for OS were 0.689 in the training cohort and 0.639 in the validation cohort. The calibration curves showed good consistency between predicted and actual 3- and 5-year OS. Significant differences were observed in the survival curves of different risk subgroups. The decision-curve analysis indicated that our nomogram was superior to the American Joint Committee on Cancer (AJCC) staging system.Conclusion: A nomogram was constructed to predict the long-term OS in SOC and verified in Asians. The accurate predictions facilitated personalized treatments and follow-up strategies.Keywords: nomograms, serous ovarian cancer, overall survival, SEER, external verification

Model organisms are instrumental substitutes for human studies to expedite basic, translational, and clinical research. Despite their indispensable role in mechanistic investigation and drug development, molecular congruence of animal models to humans has long been questioned and debated. Little effort has been made for an objective quantification and mechanistic exploration of a model organism’s resemblance to humans in terms of molecular response under disease or drug treatment. We hereby propose a framework, namely Congruence Analysis for Model Organisms (CAMO), for transcriptomic response analysis by developing threshold-free differential expression analysis, quantitative concordance/discordance scores incorporating data variabilities, pathway-centric downstream investigation, knowledge retrieval by text mining, and topological gene module detection for hypothesis generation. Instead of a genome-wide vague and dichotomous answer of “poorly” or “greatly” mimicking humans, CAMO assists researchers to numerically quantify congruence, to dissect true cross-species differences from unwanted biological or cohort variabilities, and to visually identify molecular mechanisms and pathway subnetworks that are best or least mimicked by model organisms, which altogether provides foundations for hypothesis generation and subsequent translational decisions.

Computer audition (CA) has experienced a fast development in the past decades by leveraging advanced signal processing and machine learning techniques. In particular, for its noninvasive and ubiquitous character by nature, CA-based applications in healthcare have increasingly attracted attention in recent years. During the tough time of the global crisis caused by the coronavirus disease 2019 (COVID-19), scientists and engineers in data science have collaborated to think of novel ways in prevention, diagnosis, treatment, tracking, and management of this global pandemic. On the one hand, we have witnessed the power of 5G, Internet of Things, big data, computer vision, and artificial intelligence in applications of epidemiology modeling, drug and/or vaccine finding and designing, fast CT screening, and quarantine management. On the other hand, relevant studies in exploring the capacity of CA are extremely lacking and underestimated. To this end, we propose a novel multitask speech corpus for COVID-19 research usage. We collected 51 confirmed COVID-19 patients' in-the-wild speech data in Wuhan city, China. We define three main tasks in this corpus, i.e., three-category classification tasks for evaluating the physical and/or mental status of patients, i.e., sleep quality, fatigue, and anxiety. The benchmarks are given by using both classic machine learning methods and state-of-the-art deep learning techniques. We believe this study and corpus cannot only facilitate the ongoing research on using data science to fight against COVID-19, but also the monitoring of contagious diseases for general purpose.

High-throughput phenotypic screening is a key driver for the identification of novel chemical matter in drug discovery for challenging targets, especially for those with an unclear mechanism of pathology. For toxic or gain-of-function proteins, small-molecule suppressors are a targeting/therapeutic strategy that has been successfully applied. As with other high-throughput screens, the screening strategy and proper assays are critical for successfully identifying selective suppressors of the target of interest. We executed a small-molecule suppressor screen to identify compounds that specifically reduce apolipoprotein L1 (APOL1) protein levels, a genetically validated target associated with increased risk of chronic kidney disease. To enable this study, we developed homogeneous time-resolved fluorescence (HTRF) assays to measure intracellular APOL1 and apolipoprotein L2 (APOL2) protein levels and miniaturized them to 1536-well format. The APOL1 HTRF assay served as the primary assay, and the APOL2 and a commercially available p53 HTRF assay were applied as counterscreens. Cell viability was also measured with CellTiter-Glo to assess the cytotoxicity of compounds. From a 310,000-compound screening library, we identified 1490 confirmed primary hits with 12 different profiles. One hundred fifty-three hits selectively reduced APOL1 in 786-O, a renal cell adenocarcinoma cell line. Thirty-one of these selective suppressors also reduced APOL1 levels in conditionally immortalized human podocytes. The activity and specificity of seven resynthesized compounds were validated in both 786-O and podocytes.

A lamellar-structure TC21 titanium alloy was hot-rolled and subsequently annealed at 820, 880 and 940 °C for 1 and 6 h, and the effects of annealing parameters on static globularization and morphology evolution of both α and β phases were studied. The results show that α globularization process is sluggish due to the limited boundary splitting at 820 °C. With increasing temperature to 880 °C, the accelerated boundary splitting and termination migration promote the α globularization. At 820 and 880 °C, the static recovery (SRV) and recrystallization (SRX) induce the grain refinement of interlamellar β phase. However, the excessively high temperature of 940 °C results in the coarsening of α grains due to the assistance of Ostwald ripening, and produces coarse β grains mainly due to the absence of SRX in interlamellar β phases. Conclusively, 880 °C is an appropriate annealing temperature to produce a homogeneous microstructure in which globularized α and refined β grains distribute homogeneously.

Acoustic-based fault detection has a high potential to monitor the health condition of mechanical parts. However, the background noise of an industrial environment may negatively influence the performance of fault detection. Limited attention has been paid to improving the robustness of fault detection against industrial environmental noise. Therefore, we present the Lenze production background-noise (LPBN) real-world dataset and an automated and noise-robust auditory inspection (ARAI) system for the end-of-line inspection of geared motors. An acoustic array is used to acquire data from motors with a minor fault, major fault, or which are healthy. A benchmark is provided to compare the psychoacoustic features with different types of envelope features based on expert knowledge of the gearbox. To the best of our knowledge, we are the first to apply time-varying psychoacoustic features for fault detection. We train a state-of-the-art one-class-classifier, on samples from healthy motors and separate the faulty ones for fault detection using a threshold. The best-performing approaches achieve an area under curve of 0.87 (logarithm envelope), 0.86 (time-varying psychoacoustics), and 0.91 (combination of both)., the final published version at ICASSP 2023 include small additional content as well as some minor revisions

Speech emotion recognition (SER) is the task of recognising human's emotional states from speech. SER is extremely prevalent in helping dialogue systems to truly understand our emotions and become a trustworthy human conversational partner. Due to the lengthy nature of speech, SER also suffers from the lack of abundant labelled data for powerful models like deep neural networks. Pre-trained complex models on large-scale speech datasets have been successfully applied to SER via transfer learning. However, fine-tuning complex models still requires large memory space and results in low inference efficiency. In this paper, we argue achieving a fast yet effective SER is possible with self-distillation, a method of simultaneously fine-tuning a pretrained model and training shallower versions of itself. The benefits of our self-distillation framework are threefold: (1) the adoption of self-distillation method upon the acoustic modality breaks through the limited ground-truth of speech data, and outperforms the existing models' performance on an SER dataset; (2) executing powerful models at different depth can achieve adaptive accuracy-efficiency trade-offs on resource-limited edge devices; (3) a new fine-tuning process rather than training from scratch for self-distillation leads to faster learning time and the state-of-the-art accuracy on data with small quantities of label information., Comment: Submitted to ICASSP 2023

Since the black hole and cosmological horizons in the de Sitter (dS) spacetime with the Reissner–Nordström (RN) black hole are not independent with each other, which is caused by the gravitational effect, the interplay between two horizons should be considered. Based on this, by introducing the interactive entropy the RN–dS spacetime is analogous to a thermodynamic system with various thermodynamic quantities, in which the laws of thermodynamics still hold on. In our work, the thermodynamic properties of the RN–dS spacetime are mapped out in the induced phase space, which are similar to that in AdS black holes. The phase transition of the RN–dS spacetime between the high-potential and the low-potential black hole phases is observed. Compared with an ordinary thermodynamic system, the similar behaviors about the Joule–Thomson expansion and the critical exponents are also checked out. Finally, the scalar curvatures of two existent phases are presented to reveal the underlying microstructure and nature of phase transition in the RN–dS spacetime, which opens a new window to investigate the dS spacetime with black holes from an observational perspective.

Development of resistance to chemotherapy in cancer continues to be a major challenge in cancer management. Ferroptosis, a unique type of cell death, is mechanistically and morphologically different from other forms of cell death. Ferroptosis plays a pivotal role in inhibiting tumour growth and has presented new opportunities for treatment of chemotherapy-insensitive tumours in recent years. Emerging studies have suggested that ferroptosis can regulate the therapeutic responses of tumours. Accumulating evidence supports ferroptosis as a potential target for chemotherapy resistance. Pharmacological induction of ferroptosis could reverse drug resistance in tumours. In this review article, we first discuss the key principles of chemotherapeutic resistance in cancer. We then provide a brief overview of the core mechanisms of ferroptosis in cancer chemotherapeutic drug resistance. Finally, we summarise the emerging data that supports the fact that chemotherapy resistance in different types of cancers could be subdued by pharmacologically inducing ferroptosis. This review article suggests that pharmacological induction of ferroptosis by bioactive compounds (ferroptosis inducers) could overcome chemotherapeutic drug resistance. This article also highlights some promising therapeutic avenues that could be used to overcome chemotherapeutic drug resistance in cancer.

Measuring the dependence of k ≥ 3 random variables and drawing inference from such higher-order dependences are scientifically important yet challenging. Motivated here by protein coevolution with multivariate categorical features, we consider an information theoretic measure of higher-order dependence. The proposed collective dependence is a symmetrization of differential interaction information which generalizes the mutual information of a pair of random variables. We show that the collective dependence can be easily estimated and facilitates a test on the dependence of k ≥ 3 random variables. Upon carefully exploring the null space of collective dependence, we devise a Classification-Assisted Large scaLe inference procedure to DEtect significant k-COllective DEpendence among d ≥ k random variables, with the false discovery rate controlled. Finite sample performance of our method is examined via simulations. We apply this method to the multiple protein sequence alignment data to study the residue or position coevolution for two protein families, the elongation factor P family and the zinc knuckle family. We identify novel functional triplets of amino acid residues, whose contributions to the protein function are further investigated. These confirm that the collective dependence does yield additional information important for understanding the protein coevolution compared to the pairwise measures.

PurposeThe coronavirus disease 2019 (COVID-19) has caused a crisis worldwide. Amounts of efforts have been made to prevent and control COVID-19’s transmission, from early screenings to vaccinations and treatments. Recently, due to the spring up of many automatic disease recognition applications based on machine listening techniques, it would be fast and cheap to detect COVID-19 from recordings of cough, a key symptom of COVID-19. To date, knowledge on the acoustic characteristics of COVID-19 cough sounds is limited, but would be essential for structuring effective and robust machine learning models. The present study aims to explore acoustic features for distinguishing COVID-19 positive individuals from COVID-19 negative ones based on their cough sounds.MethodsWith the theory of computational paralinguistics, we analyse the acoustic correlates of COVID-19 cough sounds based on the COMPARE feature set, i. e., a standardised set of 6,373 acoustic higher-level features. Furthermore, we train automatic COVID-19 detection models with machine learning methods and explore the latent features by evaluating the contribution of all features to the COVID-19 status predictions.ResultsThe experimental results demonstrate that a set of acoustic parameters of cough sounds, e. g., statistical functionals of the root mean square energy and Mel-frequency cepstral coefficients, are relevant for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our automatic COVID-19 detection model performs significantly above chance level, i. e., at an unweighted average recall (UAR) of 0.632, on a data set consisting of 1,411 cough samples (COVID-19 positive/negative: 210/1,201).ConclusionsBased on the acoustic correlates analysis on the COMPARE feature set and the feature analysis in the effective COVID-19 detection model, we find that the machine learning method to a certain extent relies on acoustic features showing higher effects in conventional group difference testing.

HighlightsThe potential of fusing GF-1 WFV and MODIS data by the ESTARFM algorithm was demonstrated.A better time window selection method for estimating yields was provided.A better vegetation index suitable for yield estimation based on spatiotemporally fused data was identified.The effect of the spatial resolution of remote sensing data on yield estimations was visualized.Abstract. The accurate estimation of crop yields is very important for crop management and food security. Although many methods have been developed based on single remote sensing data sources, advances are still needed to exploit multisource remote sensing data with higher spatial and temporal resolution. More suitable time window selection methods and vegetation indexes, both of which are critical for yield estimations, have not been fully considered. In this article, the Chinese GaoFen-1 Wide Field View (GF-1 WFV) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data were fused by the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to generate time-series data with a high spatial resolution. Then, two time window selection methods involving distinguishing or not distinguishing the growth stages during the monitoring period, and three vegetation indexes, the normalized difference vegetation index (NDVI), two-band enhanced vegetation index (EVI2) and wide dynamic range vegetation index (WDRVI), were intercompared. Furthermore, the yield estimations obtained from two different spatial resolutions of fused data and MODIS data were analyzed. The results indicate that taking the growth stage as the time window unit division basis can allow a better estimation of winter wheat yield; and that WDRVI is more suitable for yield estimations than NDVI or EVI2. This study demonstrates that the spatial resolution has a great influence on yield estimations; further, this study identifies a better time window selection method and vegetation index for improving the accuracy of yield estimations based on a multisource remote sensing data fusion. Keywords: Remote sensing, Spatiotemporal data fusion, Winter wheat, Yield estimation.

An article inMachine Learning with Applications

We present an EO Q-switched Tm:YAP laser constructed with a periodically poled Mg-doped congruent lithium niobate (PPMgCLN) Bragg modulator. A 2-mm-thick PPMgCLN modulator was adopted, and the internal stress generated in the electric poling process was released by annealing. The peak stress-induced refractive index variance for e-wave was deduced to be 2.29 × 10-5 at 1064 nm. When the absorbed pump power and repetition rate were 13.4 W and 1 kHz, respectively, the laser had a Q-switched pulse energy of 2 mJ, a pulse duration of 60 ns, and a peak power of 33.3 kW. By varying the reflection of the output coupler, output wavelengths of between approximately 1985 and approximately 1940 nm can be selected, making this laser a potential tool for pumping mid-IR sources and biomedical applications.

In a transcription-wide association study of nasal epithelium, we identify novel and previously reported susceptibility genes for atopic asthma in children and show that gene co-expression networks differ markedly between children with and without atopic asthma.

Auscultation of the heart is a widely studied technique, which requires precise hearing from practitioners as a means of distinguishing subtle differences in heart-beat rhythm. This technique is popular due to its non-invasive nature, and can be an early diagnosis aid for a range of cardiac conditions. Machine listening approaches can support this process, monitoring continuously and allowing for a representation of both mild and chronic heart conditions. Despite this potential, relevant databases and benchmark studies are scarce. In this paper, we introduce our publicly accessible database, the Heart Sounds Shenzhen Corpus (HSS), which was first released during the recent INTERSPEECH 2018 ComParE Heart Sound sub-challenge. Additionally, we provide a survey of machine learning work in the area of heart sound recognition, as well as a benchmark for HSS utilising standard acoustic features and machine learning models. At best our support vector machine with Log Mel features achieves 49.7% unweighted average recall on a three category task (normal, mild, moderate/severe).

MotivationThe advancement of high-throughput technology characterizes a wide range of epigenetic modifications across the genome involved in disease pathogenesis via regulating gene expression. The high-dimensionality of both epigenetic and gene expression data make it challenging to identify the important epigenetic regulators of genes. Conducting univariate test for each epigenetic-gene pair is subject to serious multiple comparison burden, and direct application of regularization methods to select epigenetic-gene pairs is computationally infeasible. Applying fast screening to reduce dimension first before regularization is more efficient and stable than applying regularization methods alone.ResultsWe propose a novel screening method based on robust partial correlation to detect epigenetic regulators of gene expression over the whole genome, a problem that includes both high-dimensional predictors and high-dimensional responses. Compared to existing screening methods, our method is conceptually innovative that it reduces the dimension of both predictor and response, and screens at both node (epigenetic features or genes) and edge (epigenetic-gene pairs) levels. We develop data-driven procedures to determine the conditional sets and the optimal screening threshold, and implement a fast iterative algorithm. Simulations and two applications to long non-coding RNA and DNA methylation regulation in Kidney cancer and Glioblastoma Multiforme illustrate the validity and advantage of our method.AvailabilityThe R package, related source codes and real data sets used in this paper are provided at https://github.com/kehongjie/rPCor.

Since the COronaVIrus Disease 2019 (COVID-19) outbreak, developing a digital diagnostic tool to detect COVID-19 from respiratory sounds with computer audition has become an essential topic due to its advantages of being swift, low-cost, and eco-friendly. However, prior studies mainly focused on small-scale COVID-19 datasets. To build a robust model, the large-scale multi-sound FluSense dataset is utilised to help detect COVID-19 from cough sounds in this study. Due to the gap between FluSense and the COVID-19-related datasets consisting of cough only, the transfer learning framework (namely CovNet) is proposed and applied rather than simply augmenting the training data with FluSense. The CovNet contains (i) a parameter transferring strategy and (ii) an embedding incorporation strategy. Specifically, to validate the CovNet's effectiveness, it is used to transfer knowledge from FluSense to COUGHVID, a large-scale cough sound database of COVID-19 negative and COVID-19 positive individuals. The trained model on FluSense and COUGHVID is further applied under the CovNet to another two small-scale cough datasets for COVID-19 detection, the COVID-19 cough sub-challenge (CCS) database in the INTERSPEECH Computational Paralinguistics challengE (ComParE) challenge and the DiCOVA Track-1 database. By training four simple convolutional neural networks (CNNs) in the transfer learning framework, our approach achieves an absolute improvement of 3.57% over the baseline of DiCOVA Track-1 validation of the area under the receiver operating characteristic curve (ROC AUC) and an absolute improvement of 1.73% over the baseline of ComParE CCS test unweighted average recall (UAR). Copyright © 2022 Chang, Jing, Ren and Schuller.

A Quantum Self-Attention Network (QSAN) that can be achieved on near-term quantum devices is investigated. First, the theoretical basis of QSAN, a linearized and reversible Quantum Self-Attention Mechanism (QSAM) including Quantum Logic Similarity (QLS) and Quantum Bit Self-Attention Score Matrix (QBSASM), is explored to solve the storage problem of Self-Attention Mechanism (SAM) due to quadratic complexity. More importantly, QLS uses logical operations instead of inner product operations to enable QSAN to be fully deployed on quantum computers and meanwhile saves quantum bits by avoiding numerical operations, and QBSASM is a by-product generated with the evolution of QSAN, reflecting the output attention distribution in the form of a density matrix. Then, the framework and the quantum circuit of QSAN are designed with 9 execution steps and 5 special functional sub-modules, which can acquire QBSASM effectively in the intermediate process, as well as compressing the number of measurements. In addition, a quantum coordinate prototype is proposed to describe the mathematical connection between the control and output bits in order to realize programming and model optimization conveniently. Finally, a miniaturized experiment is implemented and it demonstrates that QSAN can be trained faster in the presence of quantum natural gradient descent method, as well as produce quantum characteristic attention distribution QBSASM. QSAN has great potential to be embedded in classical or quantum machine learning frameworks to lay the foundation for quantum enhanced Natural Language Processing (NLP).

Coughs sounds have shown promising as a potential marker for distinguishing COVID individuals from non-COVID ones. In this paper, we propose an attention-based ensemble learning approach to learn complementary representations from cough samples. Unlike most traditional schemes such as mere maxing or averaging, the proposed approach fairly considers the contribution of the representation generated by each single model. The attention mechanism is further investigated at the feature level and the decision level. Evaluated on the Track-1 test set of the DiCOVA challenge 2021, the experimental results demonstrate that the proposed feature-level attention-based ensemble learning achieves the best performance (Area Under Curve, AUC: 77.96%), resulting in an 8.05% improvement over the challenge baseline.

Yaozhou kiln is well known for the celadon firing technique, with its long period of development dating back from the Tang Dynasty to the Yuan Dynasty. It is possible to reveal its progress track and firing history from the different celadon belonging to different dynasties. In this study, the L*a*b* chromaticity values of celadon shards were measured using a whiteness meter, and the bubble size was determined by a hand-held microscope. Moreover, energy dispersive X-ray fluorescence was used to determine the chemical composition of the celadon bodies and glazes from different dynasties. Multivariate statistical analysis demonstrated that the Yaozhou celadon properties were typical for northern “high-aluminum and low-silicon” calcium glaze, and the characteristics of celadon glazes were similar for each dynasty. Furthermore, the celadon shards unearthed from the Xi Dajie of Xi'an were more in line with celadon features of the Five Dynasty than of the Tang Dynasty.

Non-linear interaction between the electromagnetic fields (EMF) are occurred when vacuum polarization in quantum electrodynamics (QED) happens. The field of non-linear electrodynamics which may be resulting from this interaction could have important effects on black hole physics. In this work, we investigate the relationship between the observable quantity, the shadow radius and the first-order phase transition for the non-linear charged AdS black hole in the frame of the Einstein-power-Yang-Mills (EPYM) gravity. Through the analysis, we find with a certain condition there exist the non-monotonic behaviors between the shadow radius, the horizon radius, and temperature (or pressure). And from the viewpoint of the shadow radius, the phase transition temperature is higher than that from the viewpoint of the horizon radius with the same condition. These indicate that the shadow radius can be regarded a probe to reveal the thermodynamic phase transition information of black holes. When the system is undergoing the phase transition in two cases of the different non-linear YM charge parameter values: $\gamma=1,~1.5$, the thermal profiles of the coexistent big and small black hole phases with the temperature are presented. Furthermore, the effects of non-linear YM charge parameter on the shadow radius and the thermal profile are also investigated., Comment: arXiv admin note: substantial text overlap with arXiv:2204.01007; text overlap with arXiv:1507.04217 by other authors

Water content of summer maize hybrids grown in China is too high at harvesting stage, which limits the development of grain mechanical harvesting technology. Spraying the desiccant can regulate physiological process of crop grain filling and reduce water content at harvest. We explored the effects of spraying the desiccant on the dehydration process, grain moisture, and grain quality of summer maize hybrids differing in maturity. Spraying the desiccants reduced dry matter accumulation in different organs of maize, with strongest reduction of middle-late maturity hybrids. Dry matter transfer to the grains of the plants and the harvest index was improved, but with no changes of grain quality. The dehydration rate of grains was positively correlated with the rate of dehydration in diffe-rent organs. The dehydration rate of grains after spraying the desiccants was significantly positively correlated with the rate of dehydration of stems and sheaths. With no negative effects on yield, spraying the desiccant increased the total dehydration rate, shortened the time from flowering to physiological maturity, and increased the time from physiological maturity to harvest, which was beneficial to the further reduction of grain moisture in the later stage. The possibility of grain mechanical harvesting was increased. The economic benefits of spraying the desiccants on mechanical grain harvest of summer maize hybrids differing in maturity were not significantly different from those of ear mechanical harvesting. The economic benefits of middle-late maturity hybrids were higher than those of early maturity hybrids. Spraying desiccant may improve the possibility of grain mechanical harvesting.目前,我国种植的夏玉米品种收获时籽粒含水率过高,限制了玉米机械粒收技术的发展。喷施脱水剂可以调控作物籽粒灌浆生理过程,降低收获时的籽粒含水率。本试验研究了喷施脱水剂对不同熟期夏玉米品种脱水过程、收获期籽粒含水率和籽粒品质的调控作用。结果表明: 喷施脱水剂减少了玉米各器官的干物质积累量,促进了植株向籽粒中的干物质转移,提高了收获指数,而且对籽粒品质没有显著影响。相关性分析显示,籽粒脱水速率与各器官脱水速率呈正相关,喷施脱水剂后籽粒脱水速率与茎鞘脱水速率呈极显著正相关。喷施脱水剂在产量没有显著降低的前提下提高了总脱水速率,缩短了开花期至生理成熟期的时间,增加了生理成熟期到收获的时间,有利于后期籽粒含水率的进一步降低,为玉米机械粒收提供了更大的可能性。不同熟期夏玉米品种喷施脱水剂进行机械粒收的经济效益与机械穗收相比没有显著差异,中晚熟品种的经济效益高于早熟品种。因此,收获前合理喷施脱水剂可以作为玉米机收籽粒的一种可行性配套技术。.

With the COVID-19 pandemic, several research teams have reported successful advances in automated recognition of COVID-19 by voice. Resulting voice-based screening tools for COVID-19 could support large-scale testing efforts. While capabilities of machines on this task are progressing, we approach the so far unexplored aspect whether human raters can distinguish COVID-19 positive and negative tested speakers from voice samples, and compare their performance to a machine learning baseline. To account for the challenging symptom similarity between COVID-19 and other respiratory diseases, we use a carefully balanced dataset of voice samples, in which COVID-19 positive and negative tested speakers are matched by their symptoms alongside COVID-19 negative speakers without symptoms. Both human raters and the machine struggle to reliably identify COVID-19 positive speakers in our dataset. These results indicate that particular attention should be paid to the distribution of symptoms across all speakers of a dataset when assessing the capabilities of existing systems. The identification of acoustic aspects of COVID-19-related symptom manifestations might be the key for a reliable voice-based COVID-19 detection in the future by both trained human raters and machine learning models. Copyright ©2021 ISCA.

Recent advances in next-generation sequencing technology have yielded huge amounts of transcriptomic data. The discreteness and the high dimensions of RNA-seq data have posed great challenges in biological network analysis. Although estimation theories for high-dimensional modified Poisson-type graphical models have been proposed for the network analysis of count-valued data, the statistical inference of these models is still largely unknown. We herein propose a two-step procedure in both edgewise and global statistical inference of these modified Poisson-type graphical models using a cutting-edge generalized low-dimensional projection approach for bias correction. Extensive simulations and a real example with ground truth illustrate asymptotic normality of edgewise inference and more accurate inferential results in multiple testing compared to the sole estimation and the inferential method under normal assumption. Furthermore, the application of our method to novel RNA-seq data of childhood atopic asthma in Puerto Ricans demonstrates more biologically meaningful results compared to the sole estimation and the inferential methods based on Gaussian and nonparanormal graphical models.

Identification of dynamic model frequencies is a critical step in bridge health monitoring. This paper assesses the performance of bridge modal frequency identification based on 50-Hz GNSS displacements of the Sanchaji bridge at Changsha. In the experiment, the Chebyshev filter is used to remove the long-period static component caused by the static displacement and multipath effect, and the ensemble empirical mode decomposition method is used to reduce the influence of random noise on the positioning results. Compared with the results of finite element modeling and accelerometer, using the high-rate GNSS displacement information can accurately identify the first three modal frequencies of Sanchaji bridge, and the difference is less than 5%.

BackgroundAlthough frustration is a common emotional reaction during playing games, an excessive level of frustration can harm users’ experiences, discouraging them from undertaking further game interactions. The automatic detection of players’ frustration enables the development of adaptive systems, which through a real-time difficulty adjustment, would adapt the game to the user’s specific needs; thus, maximising players experience and guaranteeing the game success. To this end, we present our speech-based approach for the automatic detection of frustration during game interactions, a specific task still under-explored in research.MethodThe experiments were performed on the Multimodal Game Frustration Database (MGFD), an audiovisual dataset—collected within the Wizard-of-Oz framework—specially tailored to investigate verbal and facial expressions of frustration during game interactions. We explored the performance of a variety of acoustic feature sets, including Mel-Spectrograms and Mel-Frequency Cepstral Coefficients (MFCCs), as well as the low dimensional knowledge-based acoustic feature set eGeMAPS. Due to the always increasing improvements achieved by the use of Convolutional Neural Networks (CNNs) in speech recognition tasks, unlike the MGFD baseline—based on Long Short-Term Memory (LSTM) architecture and Support Vector Machine (SVM) classifier—in the present work we take into consideration typically used CNNs, including ResNets, VGG, and AlexNet. Furthermore, given the still open debate on the shallow vs deep networks suitability, we also examine the performance of two of the latest deep CNNs, i. e., WideResNets and EfficientNet.ResultsOur best result, achieved with WideResNets and Mel-Spectrogram features, increases the system performance from 58.8 % Unweighted Average Recall (UAR) to 93.1 % UAR for speech-based automatic frustration recognition.

Lung fibrosis, or the scarring of the lung, is a devastating disease with huge unmet medical need. There are limited treatment options and its prognosis is worse than most types of cancer. We previously discovered that MK-0429 is an equipotent pan-inhibitor of all αv integrins that reduces proteinuria and kidney fibrosis in a preclinical model. In the present study, we further demonstrated that MK-0429 significantly inhibits fibrosis progression in a bleomycin-induced lung injury model. In search of newer integrin inhibitors for fibrosis, we characterized monoclonal antibodies discovered using Adimab’s yeast display platform. We identified several potent neutralizing integrin antibodies with unique human and mouse cross-reactivity. Among these, Ab-31 blocked the binding of multiple αv integrins to their ligands with IC50s comparable to those of MK-0429. Furthermore, both MK-0429 and Ab-31 suppressed integrin-mediated cell adhesion and latent TGFβ activation. In IPF patient lung fibroblasts, TGFβ treatment induced profound αSMA expression in phenotypic imaging assays and Ab-31 demonstrated superior in vitro activity at inhibiting αSMA expression, suggesting that the integrin antibody is able to modulate TGFβ action though mechanisms beyond the inhibition of latent TGFβ activation. Together, our results highlight the potential to develop newer integrin therapeutics for the treatment of fibrotic lung diseases.One Sentence Summarytargeting integrin in lung fibrosis

Light shortage in the canopy of summer maize resulted from the decrease of solar radiation and the increase of planting density in Huanghuaihai region could reduce maize yield. In order to explore the effects of phytase Q9 on leaf senescence characteristics of summer maize, three sha-ding treatments with summer maize hybrid 'Denghai 605' (DH605) were conducted, including shading at flowering to maturity stage (S黄淮海夏季太阳辐射量降低以及种植密度增加引起了夏玉米冠层光照不足,产量大幅降低。本试验选用夏玉米品种‘登海605'(DH605)为试验材料,设置3个遮阴处理:花粒期遮阴(S

A paper in INTERSPEECH 2020.

A paper in INTERSPEECH 2020

The COVID-19 outbreak was announced as a global pandemic by the World Health Organisation in March 2020 and has affected a growing number of people in the past few weeks In this context, advanced artificial intelligence techniques are brought to the fore in responding to fight against and reduce the impact of this global health crisis In this study, we focus on developing some potential use-cases of intelligent speech analysis for COVID-19 diagnosed patients In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety For this purpose, two established acoustic feature sets and support vector machines are utilised Our experiments show that an average accuracy of 69 obtained estimating the severity of illness, which is derived from the number of days in hospitalisation We hope that this study can foster an extremely fast, low-cost, and convenient way to automatically detect the COVID-19 disease © 2020 ISCA