Probabilistic Metric Embedding via Metric Labeling

We consider probabilistic embedding of metric spaces into ultra-metrics (or equivalently to a constant factor, into hierarchically separated trees) to minimize the expected distortion of any pairwise distance. Such embeddings have been widely used in network design and online algorithms. Our main result is a polynomial time algorithm that approximates the optimal distortion on any instance to within a constant factor. We achieve this via a novel LP formulation that reduces this problem to a probabilistic version of uniform metric labeling

Data Exchange Markets via Utility Balancing

This paper explores the design of a balanced data-sharing marketplace for entities with heterogeneous datasets and machine learning models that they seek to refine using data from other agents. The goal of the marketplace is to encourage participation for data sharing in the presence of such heterogeneity. Our market design approach for data sharing focuses on interim utility balance, where participants contribute and receive equitable utility from refinement of their models. We present such a market model for which we study computational complexity, solution existence, and approximation algorithms for welfare maximization and core stability. We finally support our theoretical insights with simulations on a mean estimation task inspired by road traffic delay estimation.Comment: To appear in WWW 202

HIV-tuberculosis-associated immune reconstitution inflammatory syndrome is characterized by Toll-like receptor and inflammasome signalling

Patients with HIV-associated tuberculosis (TB) initiating antiretroviral therapy (ART) may develop immune reconstitution inflammatory syndrome (TB-IRIS). No biomarkers for TB-IRIS have been identified and the underlying mechanisms are unclear. Here we perform transcriptomic profiling of the blood samples of patients with HIV-associated TB. We identify differentially abundant transcripts as early as week 0.5 post ART initiation that predict downstream activation of proinflammatory cytokines in patients who progress to TB-IRIS. At the characteristic time of TB-IRIS onset (week 2), the signature is characterized by over-representation of innate immune mediators including TLR signalling and TREM-1 activation of the inflammasome. In keeping with the transcriptional data, concentrations of plasma cytokines and caspase-1/5 are elevated in TB-IRIS. Inhibition of MyD88 adaptor and group 1 caspases reduces secretion of cytokines including IL-1 in TB-IRIS patients. These data provide insight on the pathogenesis of TB-IRIS and may assist the development of specific therapies

Characteristics of predictor sets found using differential prioritization

<p>Abstract</p> <p>Background</p> <p>Feature selection plays an undeniably important role in classification problems involving high dimensional datasets such as microarray datasets. For filter-based feature selection, two well-known criteria used in forming predictor sets are relevance and redundancy. However, there is a third criterion which is at least as important as the other two in affecting the efficacy of the resulting predictor sets. This criterion is the degree of differential prioritization (DDP), which varies the emphases on relevance and redundancy depending on the value of the DDP. Previous empirical works on publicly available microarray datasets have confirmed the effectiveness of the DDP in molecular classification. We now propose to establish the fundamental strengths and merits of the DDP-based feature selection technique. This is to be done through a simulation study which involves vigorous analyses of the characteristics of predictor sets found using different values of the DDP from toy datasets designed to mimic real-life microarray datasets.</p> <p>Results</p> <p>A simulation study employing analytical measures such as the distance between classes before and after transformation using principal component analysis is implemented on toy datasets. From these analyses, the necessity of adjusting the differential prioritization based on the dataset of interest is established. This conclusion is supported by comparisons against both simplistic rank-based selection and state-of-the-art equal-priorities scoring methods, which demonstrates the superiority of the DDP-based feature selection technique. Reapplying similar analyses to real-life multiclass microarray datasets provides further confirmation of our findings and of the significance of the DDP for practical applications.</p> <p>Conclusion</p> <p>The findings have been achieved based on analytical evaluations, not empirical evaluation involving classifiers, thus providing further basis for the usefulness of the DDP and validating the need for unequal priorities on relevance and redundancy during feature selection for microarray datasets, especially highly multiclass datasets.</p

Fast optical investigation of cardiac electrophysiology by parallel detection in multiwell plates

Current techniques for fast characterization of cardiac electrophysiology employ optical technologies to control and monitor action potential features of single cells or cellular monolayers placed in multiwell plates. High-speed investigation capacities are commonly achieved by serially analyzing well after well employing fully automated fluorescence microscopes. Here, we describe an alternative cost-effective optical approach (MULTIPLE) that exploits high-power LED arrays to globally illuminate a culture plate and an sCMOS sensor for parallel detection of the fluorescence coming from multiple wells. MULTIPLE combines optical detection of action potentials using a red-shifted voltage-sensitive fluorescent dye (di-4-ANBDQPQ) with optical stimulation, employing optogenetic actuators, to ensure excitation of cardiomyocytes at constant rates. MULTIPLE was first characterized in terms of interwell uniformity of the illumination intensity and optical detection performance. Then, it was applied for probing action potential features in HL-1 cells (i.e., mouse atrial myocyte-like cells) stably expressing the blue light-activatable cation channel CheRiff. Under proper stimulation conditions, we were able to accurately measure action potential dynamics across a 24-well plate with variability across the whole plate of the order of 10%. The capability of MULTIPLE to detect action potential changes across a 24-well plate was demonstrated employing the selective K(v)11.1 channel blocker (E-4031), in a dose titration experiment. Finally, action potential recordings were performed in spontaneous beating human induced pluripotent stem cell derived cardiomyocytes following pharmacological manipulation of their beating frequency. We believe that the simplicity of the presented optical scheme represents a valid complement to sophisticated and expensive state-of-the-art optical systems for high-throughput cardiac electrophysiological investigations.Cardiolog

Sequenceserver: A Modern Graphical User Interface for Custom BLAST Databases

Comparing newly obtained and previously known nucleotide and amino-acid sequences underpins modern biological research. BLAST is a well-established tool for such comparisons but is challenging to use on new data sets. We combined a user-centric design philosophy with sustainable software development approaches to create Sequenceserver, a tool for running BLAST and visually inspecting BLAST results for biological interpretation. Sequenceserver uses simple algorithms to prevent potential analysis errors and provides flexible text-based and visual outputs to support researcher productivity. Our software can be rapidly installed for use by individuals or on shared servers

Bacterial plant biostimulants: A sustainable way towards improving growth, productivity, and health of crops

This review presents a comprehensive and systematic study of the field of bacterial plant biostimulants and considers the fundamental and innovative principles underlying this technology. Plant biostimulants are an important tool for modern agriculture as part of an integrated crop management (ICM) system, helping make agriculture more sustainable and resilient. Plant biostimulants contain substance(s) and/or microorganisms whose function when applied to plants or the rhizosphere is to stimulate natural processes to enhance plant nutrient uptake, nutrient use efficiency, tolerance to abiotic stress, biocontrol, and crop quality. The use of plant biostimulants has gained substantial and significant heed worldwide as an environmentally friendly alternative to sustainable agricultural production. At present, there is an increasing curiosity in industry and researchers about microbial biostimulants, especially bacterial plant biostimulants (BPBs), to improve crop growth and productivity. The BPBs that are based on PGPR (plant growth-promoting rhizobacteria) play plausible roles to promote/stimulate crop plant growth through several mechanisms that include (i) nutrient acquisition by nitrogen (N2) fixation and solubilization of insoluble minerals (P, K, Zn), organic acids and siderophores; (ii) antimicrobial metabolites and various lytic enzymes; (iii) the action of growth regulators and stress-responsive/induced phytohormones; (iv) ameliorating abiotic stress such as drought, high soil salinity, extreme temperatures, oxidative stress, and heavy metals by using different modes of action; and (v) plant defense induction modes. Presented here is a brief review emphasizing the applicability of BPBs as an innovative exertion to fulfill the current food crisis

Risk Factors for Breast Cancer and Expression of Insulin-Like Growth Factor-2 (IGF-2) in Women with Breast Cancer in Wuhan City, China

PURPOSE: The purpose of this study was to explore the risk factors for breast cancer and establish the expression rate of IGF-2 in female patients. METHODS: A case control study with 500 people in case group and 500 people in control group. A self-administered questionnaire was used to investigate risk factors for breast cancer. All cases were interviewed during a household survey. Immune-histochemical method was used to inspect the expression of IGF-2 in different tissues (benign breast lesions, breast cancer and tumor-adjacent tissue). RESULTS: Multivariate adjusted odds ratios and 95% confidence intervals were calculated using unconditional logistic regression. High body mass index (OR = 1.012,95%CI = 1.008-1.016), working attributes (OR = 1.004, 95%CI = 1.002 = 1.006), long menstrual period (OR = 1.007, 95%CI = 1.005-1.009), high parity OR = 1.003, 95%CI = 1.001-1.005) , frequent artificial abortion (OR = 1.004, 95%CI = 1.001-1.005), family history of cancer (OR = 1.003, 95%CI = 1.000-1.005), period of night shift (OR = 1.003, 95%CI = 1.001-1.006), live in high risk environment (OR = 1.005, 95%CI = 1.002-1.008), and family problems (OR = 1.010, 95%CI = 1.005-1.014) were associated with increased risk for breast cancer. In this study, good sleeping status, positive coping strategies, subjective support, and utility degree of social support were associated with reduced risk for breast cancer (OR = 0.998, 0.997, 0.985, 0.998 respectively; 95%CI = 0.996-1.000, 0.994-1.000, 0.980-0.989, 0.996-1.000, respectively). In benign breast lesions, breast cancer and tumor-adjacent tissue, IGF-2 was mainly expressed in the cytoplasm, but its expression rate was different (p<0.05). CONCLUSIONS: The incidence of breast cancer is a common result of multiple factors. IGF-2 is involved in the development of breast cancer, and its expression varies in different tissues (benign breast lesions, breast cancer and tumor-adjacent tissue)