Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Transcriptome and Weighted Gene Co-expression Network Analysis Identify Key Transcription Factors of Rosa chinensis ‘Old Blush’ After Exposure to a Gradual Drought Stress Followed by Recovery

Rose is one of the most fundamental ornamental crops, but its yield and quality are highly limited by drought. The key transcription factors (TFs) and co-expression networks during rose’s response to drought stress and recovery after drought stress are still limited. In this study, the transcriptomes of leaves of 2-year-old cutting seedlings of Rosa chinensis ‘Old Blush’ from three continuous droughted stages (30, 60, 90 days after full watering) and rewatering were analyzed using RNA sequencing. Weighted gene co-expression network analysis (WGCNA) was used to construct a co-expression network, which was associated with the physiological traits of drought response to discovering the hub TFs involved in drought response. More than 45 million high-quality clean reads were generated from the sample and used for comparison with the rose reference genome. A total of 46433 differentially expressed genes (DEGs) were identified. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that drought stress caused significant changes in signal transduction, plant hormones including ABA, auxin, brassinosteroid (BR), cytokinin, ethylene (ET), jasmonic acid (JA) and salicylic acid (SA), primary and secondary metabolism, and a certain degree of recovery after rewatering. Gene co-expression analysis identified 18 modules, in which four modules showed a high degree of correlation with physiological traits. In addition, 42 TFs including members of NACs, WRKYs, MYBs, AP2/ERFs, ARFs, and bHLHs with high connectivity in navajowhite1 and blue modules were screened. This study provides the transcriptome sequencing report of R. chinensis ‘Old Blush’ during drought stress and rewatering process. The study also identifies the response of candidate TFs to drought stress, providing guidelines for improving the drought tolerance of the rose through molecular breeding in the future.
Predicting In-hospital Mortality Using D-Dimer in COVID-19 Patients With Acute Ischemic Stroke

Background: Coronavirus disease 2019 (COVID-19) has been associated with coagulopathy, and D-dimer levels have been used to predict disease severity. However, the role of D-dimer in predicting mortality in COVID-19 patients with acute ischemic stroke (AIS) remains incompletely characterized. Methods: We conducted a retrospective cohort study using the Optum® de-identified COVID-19 Electronic Health Record dataset. Patients were included if they were 18 or older, had been hospitalized within 7 days of confirmed COVID-19 positivity from March 1, 2020 to November 30, 2020. We determined the optimal threshold of D-dimer to predict in-hospital mortality and compared risks of in-hospital mortality between patients with D-dimer levels below and above the cutoff. Risk ratios (RRs) were estimated adjusting for baseline characteristics and clinical variables. Results: Among 15,250 patients hospitalized with COVID-19 positivity, 285 presented with AIS at admission (2%). Patients with AIS were older [70 (60–79) vs. 64 (52–75), p < 0.001] and had greater D-dimer levels at admission [1.42 (0.76–3.96) vs. 0.94 (0.55–1.81) μg/ml FEU, p < 0.001]. Peak D-dimer level was a good predictor of in-hospital mortality among all patients [c-statistic 0.774 (95% CI 0.764–0.784)] and among patients with AIS [c-statistic 0.751 (95% CI 0.691–0.810)]. Among AIS patients, the optimum cutoff was identified at 5.15 μg/ml FEU with 73% sensitivity and 69% specificity. Elevated peak D-dimer level above this cut-off was associated with almost 3 times increased mortality [adjusted RR 2.89 (95% CI 1.87–4.47), p < 0.001]. Conclusions: COVID-19 patients with AIS present with greater D-dimer levels. Thresholds for outcomes prognostication should be higher in this population.
Renmin University of China at TRECVID 2020: Sentence Encoder Assembly for Ad-hoc Video Search

In this paper we summarize our TRECVID 2020 [2] video retrieval experiments. We participated in the Ad-hoc Video Search (AVS) task with a fully deep learning based solution. Our solution is based on a newly developed model, which we term Sentence Encoder Assembly (SEA) [11]. The nov-elty of the SEA model is two-fold. First, di ↵ erent from the prior art that uses only a single common space, SEA supports text-video matching in multiple encoder-speciﬁc common spaces. Such a property prevents the matching from being dominated by a speciﬁc encoder that produces an encoding vector much longer than other encoders. Second, in order to explore complementarities among the individual common spaces, we propose to train SEA by multi-space multi-loss learning. We exploit MSR-VTT and TGIF as training data. For video representation, we use pre-trained ResNet-152 and ResNeXt-101 to extract frame-level features, and C3D to extract segment-level features. Video-level features are obtained by mean pooling. Using SEA alone obtains a mean infAP of 0.236 for the 2020 task. Having SEA pre-trained on the Google’s Conceptual Captions dataset is helpful, obtaining a higher infAP of 0.251. We again ﬁnd late average fusion of distinct models (consisting of SEA and W2VV++ trained in varied settings) beneﬁcial, obtaining the best infAP of 0.269 among our four submissions, and ranked at the second place teamwise.
One-Time Optimization of Advanced T Cell Culture Media Using a Machine Learning Pipeline

The growing application of cell and gene therapies in humans leads to a need for cell type-optimized culture media. Design of Experiments (DoE) is a successful and well known tool for the development and optimization of cell culture media for bioprocessing. When optimizing culture media for primary cells used in cell and gene therapy, traditional DoE approaches that depend on interpretable models will not always provide reliable predictions due to high donor variability. Here we present the implementation of a machine learning pipeline into the DoE-based design of cell culture media to optimize T cell cultures in one experimental step (one-time optimization). We applied a definitive screening design from the DoE toolbox to screen 12 major media components, resulting in 25 (2k + 1) media formulations. T cells purified from a set of four human donors were cultured for 6 days and cell viability on day 3 and cell expansion on day 6 were recorded as response variables. These data were used as a training set in the machine learning pipeline. In the first step, individual models were created for each donor, evaluated and selected for each response variable, resulting in eight final statistical models (R2 > 0.92, RMSE < 1.5). These statistical models were used to predict T cell viability and expansion for 105 random in silico-generated media formulations for each donor in a grid search approach. With the aim of identifying similar formulations in all donors, the 40 best performing media formulations of each response variable were pooled from all donors (n = 320) and subjected to unsupervised clustering using the k-means algorithm. The median of each media component in each cluster was defined as the cluster media formulation. When these formulations were tested in a new set of donor cells, they not only showed a higher T cell expansion than the reference medium, but also precisely matched the average expansion predicted from the donor models of the training set. In summary, we have shown that the introduction of a machine learning pipeline resulted in a one-time optimized T cell culture medium and is advantageous when working with heterogeneous biological material.
Phylogeny of the Varidnaviria Morphogenesis Module: Congruence and Incongruence With the Tree of Life and Viral Taxonomy

Double-stranded DNA viruses of the realm Varidnaviria (formerly PRD1-adenovirus lineage) are characterized by homologous major capsid proteins (MCPs) containing one (kingdom: Helvetiavirae) or two β-barrel domains (kingdom: Bamfordvirae) known as the jelly roll folds. Most of them also share homologous packaging ATPases (pATPases). Remarkably, Varidnaviria infect hosts from the three domains of life, suggesting that these viruses could be very ancient and share a common ancestor. Here, we analyzed the evolutionary history of Varidnaviria based on single and concatenated phylogenies of their MCPs and pATPases. We excluded Adenoviridae from our analysis as their MCPs and pATPases are too divergent. Sphaerolipoviridae, the only family in the kingdom Helvetiavirae, exhibit a complex history: their MCPs are very divergent from those of other Varidnaviria, as expected, but their pATPases groups them with Bamfordvirae. In single and concatenated trees, Bamfordvirae infecting archaea were grouped with those infecting bacteria, in contradiction with the cellular tree of life, whereas those infecting eukaryotes were organized into three monophyletic groups: the Nucleocytoviricota phylum, formerly known as the Nucleo-Cytoplasmic Large DNA Viruses (NCLDVs), Lavidaviridae (virophages) and Polintoviruses. Although our analysis mostly supports the recent classification proposed by the International Committee on Taxonomy of Viruses (ICTV), it also raises questions, such as the validity of the Adenoviridae and Helvetiavirae ranking. Based on our phylogeny, we discuss current hypotheses on the origin and evolution of Varidnaviria and suggest new ones to reconcile the viral and cellular trees.
Substantial and Reproducible Individual Variability in Skeletal Muscle Outcomes in the Cross-Over Designed Planica Bed Rest Program

To evaluate the individual responses in skeletal muscle outcomes following bed rest, data from three studies (21-day PlanHab; 10-day FemHab and LunHab) were combined. Subjects (n = 35) participated in three cross-over campaigns within each study: normoxic (NBR) and hypoxic bed rest (HBR), and hypoxic ambulation (HAMB; used as control). Individual variability (SDIR) was investigated as √(SDExp2–SDCon2), where SDExp and SDCon are the standard deviations of the change score (i.e., post – pre) in the experimental (NBR and HBR) and the control (HAMB) groups, respectively. Repeatability and moderators of the individual variability were explored. Significant SDIR was detected for knee extension torque, and thigh and calf muscle area, which translated into an individual response ranging from 3 to −17% for knee extension torque, −2 to −12% for calf muscle area, and −1 to −8% for thigh muscle area. Strong correlations were found for changes in NBR vs. HBR (i.e., repeatability) in thigh and calf muscle area (r = 0.65–0.75, P < 0.0001). Change-scores in knee extension torque, and thigh and calf muscle area strongly correlated with baseline values (P < 0.001; r between −0.5 and −0.9). Orthogonal partial least squares regression analysis explored if changes in the investigated variables could predict calf muscle area alterations. This analysis indicated that 43% of the variance in calf muscle area could be attributed to changes in all of the other variables. This is the first study using a validated methodology to report clinically relevant individual variability after bed rest in knee extension torque, calf muscle area, and (to a lower extent) thigh muscle area. Baseline values emerged as a moderator of the individual response, and a global bed rest signature served as a moderately strong predictor of the individual variation in calf muscle area alterations.
AdvAndMal: Adversarial Training for Android Malware Detection and Family Classification

In recent years, Android malware has continued to evolve against detection technologies, becoming more concealed and harmful, making it difficult for existing models to resist adversarial sample attacks. At the current stage, the detection result is no longer the only criterion for evaluating the pros and cons of the model with its algorithms, it is also vital to take the model’s defensive ability against adversarial samples into consideration. In this study, we propose a general framework named AdvAndMal, which consists of a two-layer network for adversarial training to generate adversarial samples and improve the effectiveness of the classifiers in Android malware detection and family classification. The adversarial sample generation layer is composed of a conditional generative adversarial network called pix2pix, which can generate malware variants to extend the classifiers’ training set, and the malware classification layer is trained by RGB image visualized from the sequence of system calls. To evaluate the adversarial training effect of the framework, we propose the robustness coefficient, a symmetric interval i = [−1, 1], and conduct controlled experiments on the dataset to measure the robustness of the overall framework for the adversarial training. Experimental results on 12 families with the largest number of samples in the Drebin dataset show that the accuracy of the overall framework is increased from 0.976 to 0.989, and its robustness coefficient is increased from 0.857 to 0.917, which proves the effectiveness of the adversarial training method.
Optimal Random Packing of Spheres and Extremal Effective Conductivity

A close relation between the optimal packing of spheres in Rd and minimal energy E (effective conductivity) of composites with ideally conducting spherical inclusions is established. The location of inclusions of the optimal-design problem yields the optimal packing of inclusions. The geometrical-packing and physical-conductivity problems are stated in a periodic toroidal d-dimensional space with an arbitrarily fixed number n of nonoverlapping spheres per periodicity cell. Energy E depends on Voronoi tessellation (Delaunay graph) associated with the centers of spheres ak (k=1,2,…,n). All Delaunay graphs are divided into classes of isomorphic periodic graphs. For any fixed n, the number of such classes is finite. Energy E is estimated in the framework of structural approximations and reduced to the study of an elementary function of n variables. The minimum of E over locations of spheres is attained at the optimal packing within a fixed class of graphs. The optimal-packing location is unique within a fixed class up to translations and can be found from linear algebraic equations. Such an approach is useful for random optimal packing where an initial location of balls is randomly chosen; hence, a class of graphs is fixed and can dynamically change following prescribed packing rules. A finite algorithm for any fixed n is constructed to determine the optimal random packing of spheres in Rd.
CMU Informedia at TRECVID 2020: Activity Detection with Dense Spatio-temporal Proposals

We propose an action recognition system for surveillance scenarios, which wins TRECVID 2020 [1] Activities in Extended Video (ActEV 2 ) Challenge with a large advantage of 23.8% ahead the runner up system. Our system develops a dense spatial-temporal proposal generation model which collaborates with the state-of-the-art action classiﬁers. The proposed system utilizes multiple state-of-the-art modules and is trained on VIRAT Dataset with only released annotations. In this paper, we demonstrate the architecture and algorithms with technique details of the winner system.
Low-Profile Compact Microstrip Magnetic Dipole Antenna With Large Beamwidth and Broad Bandwidth for Vehicular Applications

In this paper, low-profile compact microstrip magnetic dipole antennas with large beamwidth and broad bandwidth are proposed for vehicular applications. At first, by introducing a strip on the bottom layer of microstrip magnetic dipole antenna, the electric current sources are introduced for the enhancement of beamwidth together with the magnetic current source. The operation principle of an equivalent complementary source model is then revealed to provide a physical insight. As seen, the half-power beamwidth (HPBW) can be controllable and effectively enlarged by the electric length of the planar magnetic dipole antenna. Next, by using a stub crossing on the strip, the impedance bandwidth is significantly improved comparing with the unloaded counterpart. After that, the antenna size is miniaturized by etching a pair of slots to extend the length of current path. Experimental results indicate that, under a very low profile of 0. 016λ0 and 0. 032λ0, the antenna prototypes can still achieve an impedance bandwidth of 14.5% and 20.8%, and a maximum HPBW of 99° (E-plane)/180° (H-plane) and 112° (E-plane)/159° (H-plane). It is also demonstrated that when the antenna size is reduced to 0. 442λ0×0. 284λ0, the impedance bandwidth up to 14.9% and the maximum HPBW of 93° (E-plane)/231° (H-plane) can be guaranteed with a low profile about 0. 032λ0. Comparing with other design approaches, the proposed designs feature a very low profile structure, and superior beamwidth and bandwidth properties, as well as simple configuration and compact size.
Changes of Fatty Acids Composition of Lipids by the Change of the Polishing Rate of Rice Grains

昭和46年産,ニホンバレを精米歩合80, 75, 70および60%の各々に搗精し,それぞれの白米から,ソックスレー抽出器を用い,エーテルで粗脂肪を,さらにTaylor法によって結合脂質を得,それらの構成脂肪酸組成をしらべ,次のような結果を得た. (1) 粗脂肪含量は精米歩合が低くなるにつれて減少するが,結合脂質は精米歩合に関係なく0. 6%とほぼ一定であった. (2) 粗脂肪はトリグリセライドが主体であるが,精米歩合が低くなるにつれて,モノグリセライドや遊離脂肪酸の比率が増加した. (3) それらの構成脂肪酸はC16:0, C18:2, C18:1酸が主体であるが,精米歩合が低くなるにつれて飽和酸の全体に占める割合が増加した. (4) これは,量的に多いトリグリセライドにおいて顕著にみられた. 一方,結合脂質ではC16:0酸の比率が高く,脂肪酸組成の精米歩合による変化は小さかった.
Impact of BNT162b2 Vaccination and Isolation on SARS-CoV-2 Transmission in Israeli Households: An Observational Study

Background Massive vaccination rollouts against SARS-CoV-2 infections have facilitated the easing of control measures in countries like Israel. While several studies have characterized the effectiveness of vaccines against severe forms of COVID-19 or SARS-CoV-2 infection, estimates of their impact on transmissibility remain limited. Here, we evaluated the role of vaccination and isolation on SARS-CoV-2 transmission within Israeli households. Methods From December 2020 to April 2021, confirmed cases were identified among healthcare workers of the Sheba Medical Centre and their family members. Households were recruited and followed up with repeated PCR for a minimum of ten days after case confirmation. Symptoms and vaccination information were collected at the end of follow-up. We developed a data augmentation Bayesian framework to ascertain how age, isolation and BNT162b2 vaccination with more than 7 days after the 2nd dose impacted household transmission of SARS-CoV-2. Findings 210 households with 215 index cases were enrolled. 269 out of 687 (39%) household contacts developed a SARS-CoV-2 infection. Of those, 170 (63%) developed symptoms. Children below 12 years old were less susceptible than adults/teenagers (Relative Risk RR=0.50, 95% Credible Interval CI 0.32-0.79). Vaccination reduced the risk of infection among adults/teenagers (RR=0.19, 95% CI 0.07-0.40). Isolation reduced the risk of infection of unvaccinated adult/teenager (RR=0.11, 95% CI 0.05-0.19) and child contacts (RR=0.16, 95% CI 0.07-0.31) compared to unvaccinated adults/teenagers that did not isolate. Infectivity was significantly reduced in vaccinated cases (RR=0.22, 95% CI 0.06-0.70). Interpretation Within households, vaccination reduces both the risk of infection and of transmission if infected. When contacts were not vaccinated, isolation also led to important reductions in the risk of transmission. Vaccinated contacts might reduce their risk of infection if they isolate, although this requires confirmation with additional data. Funding Sheba Medical Center.
Government’s roles impacts the entrepreneurial orientation of rice farmers

Farmers’ entrepreneurial orientation needs to be improved to maximize the agricultural sector. The government’s role is needed in optimizing existing facilities in the agroecosystem to make it easier for farmers to do farming. The purpose of this study was to analyze the effect of agroecosystems on the entrepreneurial orientation of rice farmers. A total of 200 respondents were taken by purposive sampling. Then analyzed with the PLS-Structured Equation Model. The results showed that the role of government policy had a significant positive effect on the entrepreneurial orientation of farmers, where it had a stronger effect on irrigated rice fields. Mainly, government support for the provision of agricultural tools and machinery (ALSNTN) has been proven to improve the entrepreneurial orientation of farmers, while institutional support for rainfed rice is the thing that most influences the entrepreneurial orientation of rain-fed rice farmers.
Molecular detection of tungro virus on mekongga and inpari 4 rice varieties in West Papua

Tungro disease is one of the important diseases in rice plants. One of the efforts to reduce the spread of tungro virus is to ensure the presence of the virus in the field. This is the first step to prevent the spread and control of tungro disease, especially in West Papua. One detection technique that can be done is molecular detection through PCR techniques. Rice samples detected were Mekongga and Inpari varieties taken from rice plants in West Papua. Total DNA of RTBV, one of the viruses that cause tungro disease, was extracted and amplified using DAF primers (5-GGAATTCCGGCCCTCAaA AACCTAGAAG-3) and DAR (5-GGGGGTACCCCCCTC CGATTTCCCATGTATG-3). The PCR RTBV results showed that the positive samples were infected with Rice tungro bacilliform virus (RTBV). This is indicated by amplification of DNA measuring ± 1400 bp which is the target size of the DAF and DAR primers. The results of this study are preliminary information that can be used as a basis for tungro control and recommendations for future cropping.
Monitoring of the hydrological regime of the Saratov reservoir using the MNDWI index

The article analyzes the possibilities of using Earth remote sensing (ERS) indices for monitoring the state of the environment. The possibility of using remote sensing indices for monitoring and forecasting the state of water bodies and watercourses, their hydrological characteristics was assessed. Within Samara region, the possibility of using the Modified Normalized Difference Water Index (MNDWI) to assess the water saturation of the Saratov reservoir in the Vasilievsky Islands area was considered. This territory was chosen due to the fact that it is sufficiently indicative for assessing changes in the hydrological regime of the reservoir. The processing of multispectral images taken by the Sentinel-2 satellite was carried out on the basis of green and short-wavelength spectral channels. The area of the water surface was calculated based on the number of binary pixels of the processed images. In accordance with the calculations, it was revealed that the area of the Saratov reservoir in the Vasilievsky Islands area has increased. At the same time, it was noted that the area of the water surface of small water bodies that have no connection with the reservoir has increased. The results obtained showed a high efficiency of monitoring changes in the hydrological regime of the reservoir when using the MNDWI.