Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome-wide single nucleotide polymorphism scan suggests adaptation to urbanization in an important pollinator, the red-tailed bumblebee (Bombus lapidarius L.)

Urbanization is considered a global threat to biodiversity; the growth of cities results in an increase in impervious surfaces, soil and air pollution, fragmentation of natural vegetation and invasion of non-native species, along with numerous environmental changes, including the heat island phenomenon. The combination of these effects constitutes a challenge for both the survival and persistence of many native species, while also imposing altered selective regimes. Here, using 110 314 single nucleotide polymorphisms generated by restriction-site-associated DNA sequencing, we investigated the genome-wide effects of urbanization on putative neutral and adaptive genomic diversity in a major insect pollinator, Bombus lapidarius, collected from nine German cities and nine paired rural sites. Overall, genetic differentiation among sites was low and there was no obvious genome-wide genetic structuring, suggesting the absence of strong effects of urbanization on gene flow. We nevertheless identified several loci under directional selection, a subset of which was associated with urban land use, including the percentage of impervious surface surrounding each sampling site. Overall, our results provide evidence of local adaptation to urbanization in the face of gene flow in a highly mobile insect pollinator.
Accumulation and sublethal effects of triclosan and its transformation product methyl‐triclosan in the earthworm Eisenia andrei exposed to environmental concentrations in an artificial soil

Municipal biosolids are increasingly used as a low‐cost fertilizer in agricultural soil. Biosolids are contaminated by low concentrations (nanograms per gram dry wt range) of a large variety of organic contaminants, such as triclosan. The effect of exposure to low concentrations of organic contaminants on soil biota remains largely undocumented. We evaluated the sublethal effects of triclosan on the earthworm Eisenia andrei using an artificial soil amended with a nominal concentration of triclosan of 50 ng g−1 dry weight soil. Using a 56‐d reproduction test, we monitored the effect of triclosan exposure on adult earthworm survival, growth, and reproduction. The bioaccumulation of triclosan in earthworm tissue (adults and juveniles) and degradation of triclosan were monitored. The genotoxicity of triclosan was evaluated using a comet assay (DNA damage) on adult earthworm coelomocytes. Exposure to a low concentration of triclosan had no significant effects on adult earthworm survival and DNA damage but significantly stimulated growth (p < 0.05) by 2‐fold compared with controls. It also significantly affected E. andrei reproduction parameters (p < 0.05), as evidenced by an increase in the number of cocoons and juveniles and a decrease in the mean dry weight of juveniles. The bioaccumulation of triclosan in earthworms was moderate (bioaccumulation factor ∼2). In biosolid‐borne trials, the bioaccumulation of methyl‐triclosan in earthworm tissues was higher than that of the parent compound triclosan. We conclude that exposure to low concentrations of triclosan in artificial soil can significantly affect the growth and reproductive performance of earthworms (i.e., E. andrei). More research is required with natural soils to assess triclosan bioavailability for earthworms. Environ Toxicol Chem 2018;37:1940–1948. © 2018 SETAC
Prognostic Significance of Indicators of Systemic Inflammatory Responses in Glioblastoma Patients

Background: High-grade gliomas, with glioblastomas as the most frequently observed histologic subtype, are the most common primary brain tumours in adults. It is considered that inflammatory responses play a major role in malignancies, including tumour progression. This study aimed to determine the prognostic significance of the neutrophil to lymphocyte ratio (NLR) and the thrombocyte to lymphocyte ratio (PLR) as indicators of systemic inflammatory response (SIR) in glioblastoma patients. Methods: A total of 90 patients treated for glioblastoma were retrospectively evaluated. Absolute counts were used to generate NLR and PLR. A SIR was considered to be present with an NLR ≥5 and/or PLR ≥150. Results: Median follow-up time was 11.3 months (range: 1-70 months). The 1-year and 2-year overall survival rates were 55.2% and 19.5%, respectively. Univariate analysis showed that there was no correlation between overall survival and gender (p=0.184), comorbid disease (p = 0.30), clinical presentation (p = 0.884), or tumour lateralization (p = 0.159). Multivariate analysis showed that overall survival was significantly correlated with SIR based on NLR (HR: 2.41), and ECOG performance status (HR: 1.53). The prognostic factors that affected survival, other than SIR, were Eastern Cooperative Oncology Group (ECOG) performance status (p = 0.003), and tumour localization (p = 0.006). Conclusion: The present findings confirm that NLR based on peripheral blood counts prior to treatment can be used as a prognostic factor in patients with glioblastoma. Since tumour aggression increases and survival decreases as the NLR value rises, choice of treatment modality is facilitated for glioblastoma patients.
Electrochemical Determination of Melamine on Static Mercury Drop Electrode and on Gold Nano Particle Modified Carbon Paste Electrode

An electrochemical method was developed for the determination of melamine (2,4,6 triamino- 1,3,5-triazine) on static mercury drop electrode (SMDE) and on gold nano particle modified carbon paste electrode. Interaction of melamine with Cu2﹢ ion was investigated and the decrease of Cu stripping peak was measured for the determination of melamine using SMDE. Oxidation property of melamine was improved using gold nano particle modified carbon paste electrode. Direct determination of melamine was proposed from the oxidation peak with three sigma detection limit of 0.43 μML﹣1. Recovery in milk powder sample has shown good recovery of melamine using the modified carbon paste electrode.
The effect of melatonin on sedation of children undergoing magnetic resonance imaging.

BACKGROUND Melatonin may induce a natural sleepiness and improve predictability of sedation drugs. We have investigated its clinical value in children sedated for magnetic resonance imaging. METHODS In a stratified randomized double-blind study, 98 children received either melatonin or placebo 10 min before they were sedated with a standard oral regimen. Children >5 and <15 kg received chloral hydrate and those >or=15 and <40 kg had a combination of temazepam with droperidol (T&D). The doses of melatonin were 3 and 6 mg, respectively. One observer recorded the time taken to reach criteria for deep sedation, sedation failure and other sedation-related events. RESULTS In the chloral hydrate group (n=50) 50% were deeply sedated by 31 min after melatonin and 40 min after placebo (P=0.57). There were zero and 1 failures, respectively. The geometric mean time taken to reach deep sedation was 39 min in both subgroups. In the T&D group (n=48) 50% were deeply sedated by 70 min in both subgroups (two failures in each); geometric mean times were 68 and 71 min, respectively (P=0.58). Children closed their eyes slightly earlier after melatonin (respective geometric means 42 vs 48, P=0.17), and took slightly longer to achieve discharge criteria (146 vs 135, P=0.47). CONCLUSION In these doses and clinical conditions, melatonin did not contribute to sedation of children.
Universal Dependency Parsing for Hindi-English Code-Switching

Code-switching is a phenomenon of mixing grammatical structures of two or more languages under varied social constraints. The code-switching data differ so radically from the benchmark corpora used in NLP community that the application of standard technologies to these data degrades their performance sharply. Unlike standard corpora, these data often need to go through additional processes such as language identification, normalization and/or back-transliteration for their efficient processing. In this paper, we investigate these indispensable processes and other problems associated with syntactic parsing of code-switching data and propose methods to mitigate their effects. In particular, we study dependency parsing of code-switching data of Hindi and English multilingual speakers from Twitter. We present a treebank of Hindi-English code-switching tweets under Universal Dependencies scheme and propose a neural stacking model for parsing that efficiently leverages the part-of-speech tag and syntactic tree annotations in the code-switching treebank and the preexisting Hindi and English treebanks. We also present normalization and back-transliteration models with a decoding process tailored for code-switching data. Results show that our neural stacking parser is 1.5% LAS points better than the augmented parsing model and 3.8% LAS points better than the one which uses first-best normalization and/or back-transliteration.
A mean field view of the landscape of two-layer neural networks

Significance Multilayer neural networks have proven extremely successful in a variety of tasks, from image classification to robotics. However, the reasons for this practical success and its precise domain of applicability are unknown. Learning a neural network from data requires solving a complex optimization problem with millions of variables. This is done by stochastic gradient descent (SGD) algorithms. We study the case of two-layer networks and derive a compact description of the SGD dynamics in terms of a limiting partial differential equation. Among other consequences, this shows that SGD dynamics does not become more complex when the network size increases. Multilayer neural networks are among the most powerful models in machine learning, yet the fundamental reasons for this success defy mathematical understanding. Learning a neural network requires optimizing a nonconvex high-dimensional objective (risk function), a problem that is usually attacked using stochastic gradient descent (SGD). Does SGD converge to a global optimum of the risk or only to a local optimum? In the former case, does this happen because local minima are absent or because SGD somehow avoids them? In the latter, why do local minima reached by SGD have good generalization properties? In this paper, we consider a simple case, namely two-layer neural networks, and prove that—in a suitable scaling limit—SGD dynamics is captured by a certain nonlinear partial differential equation (PDE) that we call distributional dynamics (DD). We then consider several specific examples and show how DD can be used to prove convergence of SGD to networks with nearly ideal generalization error. This description allows for “averaging out” some of the complexities of the landscape of neural networks and can be used to prove a general convergence result for noisy SGD.
EFFECTS OF SEWAGE EFFLUENT IRRIGATION ON THE CHEMICAL COMPONENTS AND MECHANICAL PROPERTIES OF

This study was carried out at the beginning of 2006 to investigate the effect of sewage effluent on the chemical and mechanical properties of the (Melia azedarach L) chinaberry wood as well as to study the differences between juvenile and mature wood in those properties. Six trees (9-years old) were chosen from Melia azedarach L grown in southwest Alexandria city (New Borg El-Arab). The small clear specimens from juvenile and mature wood were prepared and tested according to British Standard Specification. Three mechanical tests were done namely static bending, compression parallel to grain and Janka hardness. Based on extractive-free and the oven-dry weight, cellulose, hemicellulose, lignin, and ash contents were determined according to the standard methods. The results indicated that using sewage effluent in irrigation significantly increased cellulose, lignin, ash and extractives content of wood. The decreased in hemicellulose content for the trees irrigated with sewage effluent compared to tap water was not significant. With exception for ash content, the effects of sewage effluent irrigation on the chemical constituents of wood are quit low. The highest effect of sewage effluent on chemical components was obtained with ash content. Mature wood had significantly higher average cellulose, hemicellulose and extractives contents than juvenile wood. However, lignin and ash contents of juvenile wood were significantly higher than mature wood. Sewage effluent significantly increased wood specific gravity of
Climate Policy Under Cooperation and Competition between Regions with Spatial Heat Transport

We build a novel stochastic dynamic regional integrated assessment model (IAM) of the climate and economic system including a number of important climate science elements that are missing in most IAMs. These elements are spatial heat transport from the Equator to the Poles, sea level rise, permafrost thaw and tipping points. We study optimal policies under cooperation and various degrees of competition between regions. Our results suggest that when the elements of climate science which are accounted for in this paper are ignored, important policy variables such as the social cost of carbon and adaptation could be seriously biased.
Biochemical Characterization of the Active Anti-Hepatitis C Virus Metabolites of 2,6-Diaminopurine Ribonucleoside Prodrug Compared to Sofosbuvir and BMS-986094

ABSTRACT Ribonucleoside analog inhibitors (rNAI) target the hepatitis C virus (HCV) RNA-dependent RNA polymerase nonstructural protein 5B (NS5B) and cause RNA chain termination. Here, we expand our studies on β-d-2′-C-methyl-2,6-diaminopurine-ribonucleotide (DAPN) phosphoramidate prodrug 1 (PD1) as a novel investigational inhibitor of HCV. DAPN-PD1 is metabolized intracellularly into two distinct bioactive nucleoside triphosphate (TP) analogs. The first metabolite, 2′-C-methyl-GTP, is a well-characterized inhibitor of NS5B polymerase, whereas the second metabolite, 2′-C-methyl-DAPN-TP, behaves as an adenosine base analog. In vitro assays suggest that both metabolites are inhibitors of NS5B-mediated RNA polymerization. Additional factors, such as rNAI-TP incorporation efficiencies, intracellular rNAI-TP levels, and competition with natural ribonucleotides, were examined in order to further characterize the potential role of each nucleotide metabolite in vivo. Finally, we found that although both 2′-C-methyl-GTP and 2′-C-methyl-DAPN-TP were weak substrates for human mitochondrial RNA (mtRNA) polymerase (POLRMT) in vitro, DAPN-PD1 did not cause off-target inhibition of mtRNA transcription in Huh-7 cells. In contrast, administration of BMS-986094, which also generates 2′-C-methyl-GTP and previously has been associated with toxicity in humans, caused detectable inhibition of mtRNA transcription. Metabolism of BMS-986094 in Huh-7 cells leads to 87-fold higher levels of intracellular 2′-C-methyl-GTP than DAPN-PD1. Collectively, our data characterize DAPN-PD1 as a novel and potent antiviral agent that combines the delivery of two active metabolites.
Does global longitudinal speckle-tracking strain predict left ventricular remodeling in patients with myocardial infarction? a systematic review

Introduction: Left ventricular remodeling is a relatively prevalent complication of acute myocardial infarction (AMI), and it is associated with higher rates of medical issues and mortality. Left ventricle ejection fraction (LVEF) and wall motion score index (WMSI) are unable to detect accurately minor lesions following AMI. Global longitudinal strain (GLS), which is obtained through 2D-speckle tracking echocardiography (2D-STE), provides an angle-dependent measurement by which the infarcted area can be assessed as a means of identifying potential dysfunction. The main objective of this study was to evaluate whether GLS could adequately predict LV remodeling in AMI patients. Methods: The MEDLINE database from database inception to May 6th, 2015, was searched for relevant keywords and the reference lists of systematic reviews and eligible studies were also screened. All studies involving patients with their first reported case of AMI were examined for GLS by 2D-STE and were evaluated for LV remodeling at a three-month follow-up point. Four English-language prospective cohort studies were eligible for inclusion in this study.Result: A total of 291 AMI patients (mean age=57.92 years) were investigated across four different studies. The main finding of this study was that the most reliable and consistent measurement for the purposes of predicting LV remodeling in AMI patients is GLS obtained at the time of discharge, especially in STEMI patients.Discussion: In addition to their poor reproducibility, inability to stratify risks, and inter-observer variability, compensatory hyperkinesis of intact myocytes and myocardial stunning after an AMI are among the main reasons why LVEF and WMSI may not be the most effective predictors of LV remodeling in AMI.Conclusion: GLS obtained by 2D-STE at the time of discharge could be used as a reliable predictor of LV remodeling in AMI patients.
Modeling Compound TCP Over WiFi for IoT

Compound TCP will play a central role in future home WiFi networks supporting Internet of Things (IoT) applications. Compound TCP was designed to be fair but can manifest throughput unfairness in infrastructure-based IEEE 802.11 networks when devices at different locations experience different wireless channel quality. In this paper, we develop a comprehensive analytical model for compound TCP over WiFi. Our model captures the flow and congestion control dynamics of multiple competing long-lived compound TCP connections as well as the medium access control layer dynamics (i.e., contention, collisions, and retransmissions) that arise from different signal-to-noise ratios (SNRs) perceived by the devices. Our model provides accurate estimates for TCP packet loss probabilities and steady-state throughputs for IoT devices with different SNRs. More importantly, we propose a simple adaptive control algorithm to achieve better fairness without compromising the aggregate throughput of the system. The proposed real-time algorithm monitors the access point queue, drives the system dynamics to the desired operating point which mitigates the adverse impacts of SNR differences, and accommodates the sporadically transmitting IoT sensors in the system.
From Fixed-X to Random-X Regression: Bias-Variance Decompositions, Covariance Penalties, and Prediction Error Estimation

ABSTRACT In statistical prediction, classical approaches for model selection and model evaluation based on covariance penalties are still widely used. Most of the literature on this topic is based on what we call the “Fixed-X” assumption, where covariate values are assumed to be nonrandom. By contrast, it is often more reasonable to take a “Random-X” view, where the covariate values are independently drawn for both training and prediction. To study the applicability of covariance penalties in this setting, we propose a decomposition of Random-X prediction error in which the randomness in the covariates contributes to both the bias and variance components. This decomposition is general, but we concentrate on the fundamental case of ordinary least-squares (OLS) regression. We prove that in this setting the move from Fixed-X to Random-X prediction results in an increase in both bias and variance. When the covariates are normally distributed and the linear model is unbiased, all terms in this decomposition are explicitly computable, which yields an extension of Mallows’ Cp that we call RCp. RCp also holds asymptotically for certain classes of nonnormal covariates. When the noise variance is unknown, plugging in the usual unbiased estimate leads to an approach that we call , which is closely related to Sp, and generalized cross-validation (GCV). For excess bias, we propose an estimate based on the “shortcut-formula” for ordinary cross-validation (OCV), resulting in an approach we call RCp+. Theoretical arguments and numerical simulations suggest that RCp+ is typically superior to OCV, though the difference is small. We further examine the Random-X error of other popular estimators. The surprising result we get for ridge regression is that, in the heavily regularized regime, Random-X variance is smaller than Fixed-X variance, which can lead to smaller overall Random-X error. Supplementary materials for this article are available online.
Pretreated Glehnia littoralis Extract Prevents Neuronal Death Following Transient Global Cerebral Ischemia through Increases of Superoxide Dismutase 1 and Brain-derived Neurotrophic Factor Expressions in the Gerbil Hippocampal Cornu Ammonis 1 Area

Background: Glehnia littoralis, as a traditional herbal medicine to heal various health ailments in East Asia, displays various therapeutic properties including antioxidant effects. However, neuroprotective effects of G. littoralis against cerebral ischemic insults have not yet been addressed. Therefore, in this study, we first examined its neuroprotective effects in the hippocampus using a gerbil model of transient global cerebral ischemia (TGCI). Methods: Gerbils were subjected to TGCI for 5 min. G. littoralis extract (GLE; 100 and 200 mg/kg) was administrated orally once daily for 7 days before ischemic surgery. Neuroprotection was examined by neuronal nuclear antigen immunohistochemistry and Fluoro-Jade B histofluorescence staining. Gliosis was observed by immunohistochemistry for glial fibrillary acidic protein and ionized calcium-binding adapter molecule 1. For neuroprotective mechanisms, immunohistochemistry for superoxide dismutase (SOD) 1 and brain-derived neurotrophic factor (BDNF) was done. Results: Pretreatment with 200 mg/kg of GLE protected pyramidal neurons in the cornu ammonis 1 (CA1) area from ischemic insult area (F = 29.770, P < 0.05) and significantly inhibited activations of astrocytes (F = 22.959, P < 0.05) and microglia (F = 44.135, P < 0.05) in the ischemic CA1 area. In addition, pretreatment with GLE significantly increased expressions of SOD1 (F = 28.561, P < 0.05) and BDNF (F = 55.298, P < 0.05) in CA1 pyramidal neurons of the sham- and ischemia-operated groups. Conclusions: Our findings indicate that pretreatment with GLE can protect neurons from ischemic insults, and we suggest that its neuroprotective mechanism may be closely associated with increases of SOD1 and BDNF expressions as well as attenuation of glial activation.
Helical vortices generated by flapping wings of bumblebees

High resolution direct numerical simulations of rotating and flapping bumblebee wings are presented and their aerodynamics is studied focusing on the role of leading edge vortices and the associated helicity production. We first study the flow generated by only one rotating bumblebee wing in circular motion with 45◦ angle of attack. We then consider a model bumblebee flying in a numerical wind tunnel, which is tethered and has rigid wings flapping with a prescribed generic motion. The inflow condition of the wind varies from laminar to strongly turbulent regimes. Massively parallel simulations show that inflow turbulence does not significantly alter the wings’ leading edge vortex, which enhances lift production. Finally, we focus on studying the helicity of the generated vortices and analyze their contribution at different scales using orthogonal wavelets.