Pennhaaning1746

capensis but echolocates at a higher dominant pulse frequency). In the open desert, R. capensis emitted both lower frequency and higher SL pulses giving them longer detection distances than R. capensis in the cluttered fynbos. SL contributed more to differences in detection distances in both R. capensis and R. damarensis than frequency. In a few instances, R. damarensis achieved similar detection distances to desert-inhabiting R. capensis by emitting much higher SLs despite their average SLs being lower. These results suggest that lower frequency echolocation pulses are not a prerequisite for open desert living but may increase detection distance while avoiding energetic costs required for high SLs.Genetic purity is a prerequisite for exploiting the potential of hybrids in cross-pollinated crops, such as sunflower. In this regard DNA-based study was conducted using 110 simple sequence repeat (SSR) markers to check the genetic purity of 23 parents and their 60 hybrids in sunflower. The polymorphism was shown in 92 markers with value 83.63%. The SSR markers ORS-453 and CO-306 showed the highest PIC values of 0.76 and 0.74, respectively. The primer ORS-453 amplified allele size of 310 base pairs (bp) for female parent L6 and 320 bp for L11, while for male parents, T1 and T2 had allele size 350 bp and 340 bp, respectively. The hybrids from these parents showed a similar size of alleles with parents, including hybrids L6×T1 (310 bp and 350 bp), L6×T2 (310 bp and 340 bp), and L11×T2 (320 bp and 340 bp). Similarly, the primer CO-306 amplified allele size 350 bp and 330 bp for female parents L6 and L11, respectively, while, allele size 300 bp and 310 bp for male parents T1 and T2, respectively. The hybrids' allele size was like the parents viz., L6×T1 (350 bp and 300 bp), L6×T2 (350 bp and 310 bp), and L11×T2 (330 bp and 310 bp). All 60 hybrids and their 23 parents were grouped into three main clusters (A, B and C) based upon DARWIN v.6.0 and STRUCTURE v.2.3 Bayesian analyses using genotypic data. Further, each main cluster was divided into two sub-divisions. Each sub-division showed the relatedness of parents and their hybrids, thus authenticating the genetic purity of hybrids. In conclusion, this study provides useful for accurate and effective identification of hybrids, which will help to improve seed genetic purity testing globally.Decline of ovarian function in menopausal women increases metabolic disease risk. Curcuma comosa extract and its major compound, (3R)-1,7-diphenyl-(4E,6E)-4,6-heptadien-3-ol (DPHD), improved estrogen-deficient ovariectomized (OVX) rat metabolic disturbances. However, information on their effects on metabolites is limited. Here, we investigated the impacts of C. comosa ethanol extract and DPHD on 12-week-old OVX rat metabolic disturbances, emphasizing the less hydrophobic metabolites. Metabolomics analysis of OVX rat serum showed a marked increase compared to sham-operated rat (SHAM) in levels of lysophosphatidylcholines (lysoPCs), particularly lysoPC (180) and lysoPC (160), and of arachidonic acid (AA), metabolites associated with inflammation. OVX rat elevated lysoPCs and AA levels reverted to SHAM levels following treatments with C. STC-15 cell line comosa ethanol extract and DPHD. Overall, our studies demonstrate the effect of C. comosa extract in ameliorating the metabolic disturbances caused by ovariectomy, and the elevated levels of bioactive lipid metabolites, lysoPCs and AA, may serve as potential biomarkers of menopausal metabolic disturbances.Salmonella enterica serovar 4,[5],12i-, a monophasic variant of Salmonella Typhimurium lacking the phase 2 flagellin, is one of the common serotypes causing Salmonellosis worldwide. However, information on Salmonella serovar 4,[5],12i- from Guizhou Province has lacked so far. This study aimed to investigate the antimicrobial resistance, the presence of antimicrobial resistance genes and virulence genes, and characterize the MLST genotypes of Salmonella serovar 4,[5],12i- isolates from Guizhou province, China. We collected 363 non-typhoid Salmonella (NTS) isolates of Guizhou from 2013 to 2018. Biochemical identification, serogroups testing, and specific multiplex polymerase chain reaction (mPCR) assay were conducted to identify Salmonella 4,[5],12i- isolates. Isolates were determined the antimicrobial resistance by the micro broth dilution method, detected the presence of antimicrobial resistance genes and virulence genes by PCR, and examined the molecular genotyping by Multilocus sequence typing (MLST). Eight public health strategies in Guizhou.Optoacoustic tomography (OAT) has recently been advanced toward ultrafast volumetric imaging frame rates in the kilohertz range. As a result, excessive data processing and storage capacity requirements are increasingly being imposed on the imaging systems. OAT data commonly exhibit significant sparsity across the spatial, temporal or spectral domains, which facilitated the development of compressed sensing algorithms exploiting various sparse acquisition and under-sampling schemes to reduce data rates. However, performance of compressed sensing critically depends on a priori knowledge on the type of acquired data and/or imaged object, commonly resulting in lack of general applicability and unpredictable image quality. In this work, we report on a fast adaptive OAT data compression framework operating on fully sampled tomographic data. It is based on a wavelet packet transform that maximizes the data compression ratio according to the desired signal energy loss. A dedicated reconstruction method was further developed that efficiently renders images directly from the compressed data. Up to 1000x compression ratios were achieved while providing efficient control over the resulting image quality from arbitrary datasets exhibiting diverse spatial, temporal and spectral characteristics. Our approach enables faster and longer acquisitions and facilitates long-term storage of large OAT datasets.Differentiating types of hematologic malignancies is vital to determine therapeutic strategies for the newly diagnosed patients. Flow cytometry (FC) can be used as diagnostic indicator by measuring the multi-parameter fluorescent markers on thousands of antibody-bound cells, but the manual interpretation of large scale flow cytometry data has long been a time-consuming and complicated task for hematologists and laboratory professionals. Past studies have led to the development of representation learning algorithms to perform sample-level automatic classification. In this work, we propose a chunking-for-pooling strategy to include large-scale FC data into a supervised deep representation learning procedure for automatic hematologic malignancy classification. The use of discriminatively-trained representation learning strategy and the fixed-size chunking and pooling design are key components of this framework. It improves the discriminative power of the FC sample-level embedding and simultaneously addresses the robustness issue due to an inevitable use of down-sampling in conventional distribution based approaches for deriving FC representation. We evaluated our framework on two datasets. Our framework outperformed other baseline methods and achieved 92.3% unweighted average recall (UAR) for four-class recognition on the UPMC dataset and 85.0% UAR for five-class recognition on the hema.to dataset. We further compared the robustness of our proposed framework with that of the traditional downsampling approach. Analysis of the effects of the chunk size and the error cases revealed further insights about different hematologic malignancy characteristics in the FC data.The objective of this study is to propose MD-VAE a multi-task disentangled variational autoencoders (VAE) for exploring characteristics of latent representations (LR) and exploiting LR for diverse tasks including glucose forecasting, event detection, and temporal clustering. We applied MD-VAE to one virtual continuous glucose monitoring (CGM) data from an FDA-approved Type 1 Diabetes Mellitus simulator (T1DMS) and one publicly available CGM data of real patients for glucose dynamics of Type 1 Diabetes Mellitus. LR captured meaningful information to be exploited for diverse tasks, and was able to differentiate characteristics of sequences with clinical parameters. LR and generative models have received relatively little attention for analyzing CGM data so far. However, as proposed in our study, VAE has the potential to integrate not only current but also future information on glucose dynamics and unexpected events including interactions of devices in the data-driven manner. We expect that our model can provide complementary views on the analysis of CGM data.As different scientific disciplines begin to converge on machine learning for causal inference, we demonstrate the application of machine learning algorithms in the context of longitudinal causal estimation using electronic health records. Our aim is to formulate a marginal structural model for estimating diabetes care provisions in which we envisioned hypothetical (i.e. counterfactual) dynamic treatment regimes using a combination of drug therapies to manage diabetes metformin, sulfonylurea and SGLT-2i. The binary outcome of diabetes care provisions was defined using a composite measure of chronic disease prevention and screening elements [27] including (i) primary care visit, (ii) blood pressure, (iii) weight, (iv) hemoglobin A1c, (v) lipid, (vi) ACR, (vii) eGFR and (viii) statin medication. We used several statistical learning algorithms to describe causal relationships between the prescription of three common classes of diabetes medications and quality of diabetes care using the electronic health records ugh the improvement of diabetes care provisions in primary care.Few-shot class-incremental learning (FSCIL) has two main problems (1) catastrophically forgetting old classes while feature representations drift into new classes, and (2) over-fitting new classes for the few training samples available. Revealed by our analyses, the problems are caused by feature distribution crumbling, which causes class confusion when continuously embedding few samples to a fixed feature space. In this study, we propose a Dynamic Support Network (DSN), which refers to an adaptively updating network with compressive node expansion to 'support' the feature space. In each training session, DSN tentatively expands network nodes to enlarge feature representation capacity for incremental classes. It then dynamically compresses the expanded network by node self-activation to pursue compact feature representation which alleviates over-fitting. Simultaneously, DSN selectively recalls old class distributions during incremental learning process to support feature distributions and avoid confusion between classes. DSN with compressive node expansion and class distribution recalling provides a systematic solution for the problems of catastrophically forgetting and overfitting. Experiments on CUB, CIFAR-100, and miniImage datasets show that DSN significantly improves upon the baseline approach, achieving new state-of-the-arts. The code is publicly available.While convenient in daily life, face recognition technologies also raise privacy concerns for regular users on the social media since they could be used to analyze face images and videos, efficiently and surreptitiously without any security restrictions. In this paper, we investigate the face privacy protection from a technology standpoint based on a new type of customized cloak, which can be applied to all the images of a regular user, to prevent malicious face recognition systems from uncovering their identity. Specifically, we propose a new method, named one person one mask (OPOM), to generate person-specific (class-wise) universal masks by optimizing each training sample in the direction away from the feature subspace of the source identity. To make full use of the limited training images, we investigate several modeling methods, including affine hulls, class centers and convex hulls, to obtain a better description of the feature subspace of source identities. The effectiveness of the proposed method is evaluated on both common and celebrity datasets against black-box face recognition models with different loss functions and network architectures.