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Interpreting natural language is an increasingly important task in computer algorithms due to the growing availability of unstructured textual data. Natural Language Processing (NLP) applications rely on semantic networks for structured knowledge representation. The fundamental properties of semantic networks must be taken into account when designing NLP algorithms, yet they remain to be structurally investigated. We study the properties of semantic networks from ConceptNet, defined by 7 semantic relations from 11 different languages. We find that semantic networks have universal basic properties: they are sparse, highly clustered, and many exhibit power-law degree distributions. Our findings show that the majority of the considered networks are scale-free. Some networks exhibit language-specific properties determined by grammatical rules, for example networks from highly inflected languages, such as e.g. Latin, German, French and Spanish, show peaks in the degree distribution that deviate from a power law. We find that depending on the semantic relation type and the language, the link formation in semantic networks is guided by different principles. In some networks the connections are similarity-based, while in others the connections are more complementarity-based. Finally, we demonstrate how knowledge of similarity and complementarity in semantic networks can improve NLP algorithms in missing link inference. [ABSTRACT FROM AUTHOR]

Transcutaneous electrical nerve stimulator (TENS) has been demonstrated to be beneficial in glycemic control in animal models, but its application in humans has not been well studied. We randomly assigned 160 patients with type 2 diabetes on oral antidiabetic drugs 1:1 to the TENS study device (n = 81) and placebo (n = 79). 147 (92%) randomized participants (mean [SD] age 59 [10] years, 92 men [58%], mean [SD] baseline HbA1c level 8.1% [0.6%]) completed the trial. At week 20, HbA1c decreased from 8.1% to 7.9% in the TENS group (− 0.2% [95% CI − 0.4% to − 0.1%]) and from 8.1% to 7.8% in the placebo group (− 0.3% [95% CI − 0.5% to − 0.2%]) (P = 0.821). Glycemic variability, measured as mean amplitude of glycemic excursion (MAGE) at week 20 were significantly different in the TENS group vs. the placebo group (66 mg/dL [95% CI 58, 73] vs. 79 mg/dL [95% CI 72, 87]) (P = 0.009). Our study provides the clinical evidence for the first time in humans that TENS does not demonstrate a statistically significant HbA1c reduction. However, it is a safe complementary therapy to improve MAGE in patients with type 2 diabetes. [ABSTRACT FROM AUTHOR]

Cancers are immunologically heterogeneous. A range of immunotherapies target abnormal tumor immunity via different mechanisms of actions (MOAs), particularly various tumor-infiltrate leukocytes (TILs). We modeled loss of function (LOF) in four common anti-PD-1 antibody-responsive syngeneic tumors, MC38, Hepa1-6, CT-26 and EMT-6, by systematical depleting a series of TIL lineages to explore the mechanisms of tumor immunity and treatment. CD8+-T-cells, CD4+-T-cells, Treg, NK cells and macrophages were individually depleted through either direct administration of anti-marker antibodies/reagents or using DTR (diphtheria toxin receptor) knock-in mice, for some syngeneic tumors, where specific subsets were depleted following diphtheria toxin (DT) administration. These LOF experiments revealed distinctive intrinsic tumor immunity and thus different MOAs in their responses to anti-PD-1 antibody among different syngeneic tumors. Specifically, the intrinsic tumor immunity and the associated anti-PD-1 MOA were predominately driven by CD8+ cytotoxic TILs (CTL) in all syngeneic tumors, excluding Hepa1-6 where CD4+ Teff TILs played a key role. TIL-Treg also played a critical role in supporting tumor growth in all four syngeneic models as well as M2-macrophages. Pathway analysis using pharmacodynamic readouts of immuno-genomics and proteomics on MC38 and Hepa1-6 also revealed defined, but distinctive, immune pathways of activation and suppression between the two, closely associated with the efficacy and consistent with TIL-pharmacodynamic readouts. Understanding tumor immune-pathogenesis and treatment MOAs in the different syngeneic animal models, not only assists the selection of the right model for evaluating new immunotherapy of a given MOA, but also can potentially help to understand the potential disease mechanisms and strategize optimal immune-therapies in patients. [ABSTRACT FROM AUTHOR]

Rhynchaenus maculosus is an emerging insect pest with an increasingly serious tendency. Lack of biology information results in the bottleneck of integrated management of this pest. To facilitate an available design of integrated pest management strategy, biology of R. maculosus, including voltinism, life cycle, distribution, and damage has been investigated. Results reveal that R. maculosus is oligophagous and distributes in Heilongjiang, Jilin, and Liaoning provinces, China. This pest produces one generation per year (univoltinism) and overwinters as adults in leaf litter. From mid-April to late-April, active overwintering adults emerge from overwintering sites. The next generation of adult R. maculosus appears from mid-May to early June until mid-August to early September when the beetles move into the overwintering places. The entire time span of adult occurrence ranges from 315.6 ± 3.6 to 336.4 ± 3.2 days (Mean ± SD). Larvae undergo 3 instars with a total duration of 20 to 23 days. R. maculosus larvae feed on Q. wutaishanica and Q. mongolica without host-specific preference between the two host species, but do not harm Q. acutissim. Three species of larval parasites were collected and identified as Braconidae sp., Eulophidae sp., and Ceraphronidae sp. Biological information of R. maculosus provides essential insights for design and implementation of integrated management of this pest. [ABSTRACT FROM AUTHOR]