Dietary Prebiotics and Bioactive Milk Fractions Improve NREM Sleep, Enhance REM Sleep Rebound and Attenuate the Stress-Induced Decrease in Diurnal Temperature and Gut Microbial Alpha Diversity.

Severe, repeated or chronic stress produces negative health outcomes including disruptions of the sleep/wake cycle and gut microbial dysbiosis. Diets rich in prebiotics and glycoproteins impact the gut microbiota and may increase gut microbial species that reduce the impact of stress. This experiment tested the hypothesis that consumption of dietary prebiotics, lactoferrin (Lf) and milk fat globule membrane (MFGM) will reduce the negative physiological impacts of stress. Male F344 rats, postnatal day (PND) 24, received a diet with prebiotics, Lf and MFGM (test) or a calorically matched control diet. Fecal samples were collected on PND 35/70/91 for 16S rRNA sequencing to examine microbial composition and, in a subset of rats; Lactobacillus rhamnosus was measured using selective culture. On PND 59, biotelemetry devices were implanted to record sleep/wake electroencephalographic (EEG). Rats were exposed to an acute stressor (100, 1.5 mA, tail shocks) on PND 87 and recordings continued until PND 94. Test diet, compared to control diet, increased fecal Lactobacillus rhamnosus colony forming units (CFU), facilitated non-rapid eye movement (NREM) sleep consolidation (PND 71/72) and enhanced rapid eye movement (REM) sleep rebound after stressor exposure (PND 87). Rats fed control diet had stress-induced reductions in alpha diversity and diurnal amplitude of temperature, which were attenuated by the test diet (PND 91). Stepwise multiple regression analysis revealed a significant linear relationship between early-life Deferribacteres (PND 35) and longer NREM sleep episodes (PND 71/72). A diet containing prebiotics, Lf and MFGM enhanced sleep quality, which was related to changes in gut bacteria and modulated the impact of stress on sleep, diurnal rhythms and the gut microbiota

Inferring the Origin Locations of Tweets with Quantitative Confidence

Social Internet content plays an increasingly critical role in many domains, including public health, disaster management, and politics. However, its utility is limited by missing geographic information; for example, fewer than 1.6% of Twitter messages (tweets) contain a geotag. We propose a scalable, content-based approach to estimate the location of tweets using a novel yet simple variant of gaussian mixture models. Further, because real-world applications depend on quantified uncertainty for such estimates, we propose novel metrics of accuracy, precision, and calibration, and we evaluate our approach accordingly. Experiments on 13 million global, comprehensively multi-lingual tweets show that our approach yields reliable, well-calibrated results competitive with previous computationally intensive methods. We also show that a relatively small number of training data are required for good estimates (roughly 30,000 tweets) and models are quite time-invariant (effective on tweets many weeks newer than the training set). Finally, we show that toponyms and languages with small geographic footprint provide the most useful location signals.Comment: 14 pages, 6 figures. Version 2: Move mathematics to appendix, 2 new references, various other presentation improvements. Version 3: Various presentation improvements, accepted at ACM CSCW 201

SAW Sensor for Fastener Failure Detection

The proof of concept for using surface acoustic wave (SAW) strain sensors in the detection of aircraft fastener failures is demonstrated. SAW sensors were investigated because they have the potential for the development of passive wireless systems. The SAW devices employed four orthogonal frequency coding (OFC) spread spectrum reflectors in two banks on a high temperature piezoelectric substrate. Three SAW devices were attached to a cantilever panel with removable side stiffeners. Damage in the form of fastener failure was simulated by removal of bolts from the side stiffeners. During testing, three different force conditions were used to simulate static aircraft structural response under loads. The design of the sensor, the panel arrangement and the panel testing results are reported. The results show that the sensors successfully detected single fastener failure at distances up to 54.6 cm from the failure site under loaded conditions

CTMC calculations of electron capture and ionization in collisions of multiply charged ions with elliptical Rydberg atoms

We have performed classical trajectory Monte Carlo (CTMC) studies of electron capture and ionization in multiply charged (Q=8) ion-Rydberg atom collisions at intermediate impact velocities. Impact parallel to the minor and to the major axis, respectively, of the initial Kepler electron ellipse has been investigated. The important role of the initial electron momentum distribution found for singly charged ion impact is strongly disminished for higher projectile charge, while the initial spatial distribution remains important for all values of Q studied.Comment: 3 pages, 5 figure

Grown organic matter as a fuel raw material resource

An extensive search was made on biomass production from the standpoint of climatic zones, water, nutrients, costs and energy requirements for many species. No exotic species were uncovered that gave hope for a bonanza of biomass production under culture, location, and management markedly different from those of existing agricultural concepts. A simulation analysis of biomass production was carried out for six species using conventional production methods, including their production costs and energy requirements. These estimates were compared with data on food, fiber, and feed production. The alternative possibility of using residues from food, feed, or lumber was evaluated. It was concluded that great doubt must be cast on the feasibility of producing grown organic matter for fuel, in competition with food, feed, or fiber. The feasibility of collecting residues may be nearer, but the competition for the residues for return to the soil or cellulosic production is formidable

Comparative solution equilibrium studies on pentamethylcyclopentadienyl rhodium complexes of 2,2'-bipyridine and ethylenediamine and their interaction with human serum albumin

Complex formation equilibri um processes of the (N,N) donor containing 2,2' - bipyridine (bpy) and ethylenediamine (en) with (η 5 - pentamethylcyclopentadienyl) rhodium(III) were investigated in aqueous solution via pH - potentiome try, 1 H NMR spectroscopy, and UV – Vis spectrophotometry in the absence and presence of chloride ions . The structure of [RhCp*(en)Cl]ClO 4 (Cp* , pentamethylcyclopentadienyl ) was also studied b y single - crystal X - ray diffraction. p K a values of 8.56 and 9.58 we re determined for [RhCp*(bpy)(H 2 O)] 2+ and [RhCp*(en)(H 2 O)] 2+ , respectively resulting in the formation of negligible amount of mixed hydroxido complexes at pH 7.4 . Stability and the H 2 O/Cl ‒ co - ligand exchange constants of bpy and en complexes considerably exceed those of the bidentate O - donor deferiprone. The strong affinity of the bpy and en complexes to chloride ions most probably contribute s to their low antiproliferative effect. Interac tion s between human serum albumin (HSA) and [ RhCp* (H 2 O) 3 ] 2+ , its complexes formed with deferiprone, bpy and en were also monitored by 1 H NMR spectroscopy, ultrafiltration/ UV - Vis and spectrofluorometry. Numerous binding sites ( ≥ 8 ) are available for [RhCp* (H 2 O) 3 ] 2+ ; and the interaction takes place most probably via covalent bonds through the imidazole nitrogen of His . According to the various fluorescen ce studies [RhCp* (H 2 O) 3 ] 2+ binds on sites I and II , and coordination of surface side chain donor atoms of th e protein is also feasible. The binding of the bpy and en complex is weaker and slower compared to that of [RhCp* (H 2 O) 3 ] 2+ , and formation of ternary HSA - RhCp* - ligand adducts was proved. I n the case of the deferiprone complex , the RhCp* fragment is cleaved off when HSA is loaded with low equivalents of the comp ound

Automatic Synonym Discovery with Knowledge Bases

Recognizing entity synonyms from text has become a crucial task in many entity-leveraging applications. However, discovering entity synonyms from domain-specific text corpora (e.g., news articles, scientific papers) is rather challenging. Current systems take an entity name string as input to find out other names that are synonymous, ignoring the fact that often times a name string can refer to multiple entities (e.g., "apple" could refer to both Apple Inc and the fruit apple). Moreover, most existing methods require training data manually created by domain experts to construct supervised-learning systems. In this paper, we study the problem of automatic synonym discovery with knowledge bases, that is, identifying synonyms for knowledge base entities in a given domain-specific corpus. The manually-curated synonyms for each entity stored in a knowledge base not only form a set of name strings to disambiguate the meaning for each other, but also can serve as "distant" supervision to help determine important features for the task. We propose a novel framework, called DPE, to integrate two kinds of mutually-complementing signals for synonym discovery, i.e., distributional features based on corpus-level statistics and textual patterns based on local contexts. In particular, DPE jointly optimizes the two kinds of signals in conjunction with distant supervision, so that they can mutually enhance each other in the training stage. At the inference stage, both signals will be utilized to discover synonyms for the given entities. Experimental results prove the effectiveness of the proposed framework