bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genome-based transmission modeling separates imported tuberculosis from recent transmission within an immigrant population
Authors: Ayabina Diepreye , Rønning Janne O , Alfsnes Kristian , Debech Nadia , Brynildsrud Ola B , Arnesen Trude , Norheim Gunnstein , Mengshoel Anne-Torunn , Rykkvin Rikard , Dahle Ulf R , Colijn Caroline , Eldholm Vegard 
Published: December 2017

AbstractBackgroundIn many countries tuberculosis incidence is low and largely shaped by immigrant populations from high-burden countries. This is the case in Norway, where more than 80 per cent of TB cases are found among immigrants from high-incidence countries. A variable latent period, low rates of evolution and structured social networks make separating import from within-border transmission a major conundrum to TB-control efforts in many low-incidence countries.MethodsClinical Mycobacterium tuberculosis isolates belonging to an unusually large genotype cluster associated with people born in the Horn of Africa, have been identified in Norway over the last two decades. We applied modeled transmission based on whole-genome sequence data to estimate infection times for individual patients. By contrasting these estimates with time of arrival in Norway, we estimate on a case-by-case basis whether patients were likely to have been infected before or after arrival.ResultsIndependent import was responsible for the majority of cases, but we estimate that about a quarter of the patients had contracted TB in Norway.ConclusionsThis study illuminates the transmission dynamics within an immigrant community. Our approach is broadly applicable to many settings where TB control programs can benefit from understanding when and where patients acquired tuberculosis.
A clustering neural network model of insect olfaction
Authors: Pehlevan Cengiz , Genkin Alexander , Chklovskii Dmitri B. 
Published: December 2017

AbstractA key step in insect olfaction is the transformation of a dense representation of odors in a small population of neurons - projection neurons (PNs) of the antennal lobe - into a sparse representation in a much larger population of neurons -Kenyon cells (KCs) of the mushroom body. What computational purpose does this transformation serve? We propose that the PN-KC network implements an online clustering algorithm which we derive from the k-means cost function. The vector of PN-KC synaptic weights converging onto a given KC represents the corresponding cluster centroid. KC activities represent attribution indices, i.e. the degree to which a given odor presentation is attributed to each cluster. Remarkably, such clustering view of the PN-KC circuit naturally accounts for several of its salient features. First, attribution indices are nonnegative thus rationalizing rectification in KCs. Second, the constraint on the total sum of attribution indices for each presentation is enforced by a Lagrange multiplier identified with the activity of a single inhibitory interneuron reciprocally connected with KCs. Third, the soft-clustering version of our algorithm reproduces observed sparsity and overcompleteness of the KC representation which may optimize supervised classification downstream.
Rice blast resistance gene Pii is controlled by a pair of NBS-LRR genes Pii-1 and Pii-2
Authors: Takagi Hiroki , Abe Akira , Uemura Aiko , Oikawa Kaori , Utsushi Hiroe , Yaegashi Hiroki , Kikuchi Hideko , Shimizu Motoki , Abe Yoshiko , Kanzaki Hiroyuki , Saito Hiromasa , Terauchi Ryohei , Fujisaki Koki 
Published: December 2017

SUMMARYNucleotide-binding, leucine-rich repeat receptors (NLRs) are conserved cytosolic receptors that recognize pathogen effectors and trigger immunity in plants. Recent studies indicate that NLRs function in pairs. Rice resistance gene Pii has been known to confer resistance against rice blast pathogen Magnaporthe oryzae carrying AVR-Pii. Previously we reported isolation of Pii gene from the rice cultivar Hitomebore (Takagi et al. 2013). To further understand rice components required for Pii-mediated resistance, we screened 5,600 mutant lines of Hitomebore cultivar and identified two mutants that lost Pii resistance without any changes in Pii gene sequence. Application of MutMap-Gap, the whole genome sequencing-based method of mutation identification, to the two mutants revealed that they have mutations in another NLR gene located close to Pii. The F1 plants derived from a cross of the two mutants showed pii phenotype, demonstrating that the newly identified NLR gene is indeed a component of Pii resistance. We thus designate the previously isolated Pii gene as Pii-1 and the newly isolated NLR gene as Pii-2.
Altering brain dynamics with transcranial random noise stimulation
Authors: van der Groen Onno , Wenderoth Nicole , Mattingley Jason B. 
Published: December 2017

AbstractRandom noise can enhance the detectability of weak signals in nonlinear threshold systems, a phenomenon known as stochastic resonance (SR). This concept is not only applicable to single threshold systems but can also be applied to dynamical systems with multiple attractor states, such as observed during the phenomenon of binocular rivalry. Binocular rivalry can be characterized by marginally stable attractor states between which the brain switches in a spontaneous, stochastic manner. The switches are thought to be driven by a combination of neuronal adaptation and noise. Here we used a computational model to predict the effect of noise on perceptual dominance durations when either low-contrast or high-contrast stimuli are presented. Subsequently we compared the model prediction to a series of three experiments where we measured binocular rivalry dynamics when noise (zero-mean Gaussian white noise) was added either to the visual stimulus (Exp. 1) or directly to the visual cortex (Exp. 2 and Exp. 3) by applying transcranial Random Noise Stimulation (tRNS 1mA, 100-640 Hz zero mean Gaussian white noise). We found that adding noise significantly reduced the mixed percept duration (Exp. 1 and Exp. 2). This effect was only present for low-contrast but not for high-contrast visual stimuli which is in line with the model predictions. Our results demonstrate that both central and peripheral noise can influence state-switching dynamics of binocular rivalry under specific conditions (e.g. low visual contrast stimuli), in line with a SR-mechanism.
Biological Self-organisation and Markov blankets
Authors: Palacios Ensor Rafael , Razi Adeel , Parr Thomas , Kirchhoff Michael , Friston Karl 
Published: December 2017

AbstractBiological self-organisation is a process of spontaneous pattern formation; namely the emergence of coherent and stable systemic configurations that distinguish themselves from their environment. This process can occur at various spatial scales: from the microscopic (giving rise to cells) to the macroscopic (the emergence of organisms). Self-organisation at each level is essential to account for the hierarchical organisation of living organisms (organelles within cells, within tissues, within organs, etc.). In this paper, we pursue the idea that Markov blankets – statistical boundaries separating states that are external to a system from its internal states – emerge at every possible level of the description of the (living) system. Through simulations, we show that the concept of a Markov blanket is fundamental in defining biological systems and underwrites the nature and form of interactions between successive levels of hierarchical structure. We demonstrate the validity of our argument using simulations, based on the normative principle of variational free energy minimisation. Specifically, we adopt a top-down approach to provide a proof of concept for the claim that the self-organisation of Markov blankets (and blankets of blankets) underwrites the self-evidencing, autopoietic behaviour of living systems.
Physico-chemical properties of co-formulation of insulin with pramlintide
Authors: da Silva Dayana Cabral , Lima Luís Maurício T. R. 
Published: December 2017

ABSTRACTSince the discovery of amylin its use has been discouraged by the inadequacy of the protocol involving multiple injections in addition to insulin. While a combined fixed-dose formulation is thus highly desirable, it has long been limited due to incompatibility as historically documented. We have investigated the compatibility of regular and fast-acting insulin analogues (Aspart, AspB28, and LisPro, LysB28ProB29) with the amylin analogue pramlintide. Insulin interacts with pramlintide, forming heterodimers as probed by electrospray ionization - ion mobility spectrometry-mass spectrometry. While their interaction is likely to delay the amyloid aggregation of pramlintide in phosphate-buffered solution pH 7.0, they do not prevent aggregation at this condition. At acidic sodium acetate solution pH 5.0, combination of pramlintide and the fast-acting insulin analogues become stable against amyloid aggregation. The co-formulated product at high concentration of both pramlintide (600 μg/mL,150 μM) and LisPro insulin (50 IU/mL, 300 μM) showed also stability against amyloid aggregation. These data indicate a potential for the development of a co-formulation of fast-acting LisPro insulin with pramlintide, which could bring benefits for the combined therapy.AbbreviationsIAPP,islet amyloid polypeptide;ESI-IMS-MS,Electrospray Ionization–Ion Mobility Spectrometry–Mass Spectrometry.
Defining Major Depressive Disorder Cohorts Using the EHR: Multiple Phenotypes Based on ICD-9 Codes and Medication Orders
Authors: Ingram Wendy Marie , Baker Anna M. , Bauer Christopher R. , Brown Jason P. , Goes Fernando S. , Larson Sharon , Zandi Peter P. 
Published: December 2017

ABSTRACTBackgroundMajor Depressive Disorder (MDD) is one of the most common mental illnesses and a leading cause of disability worldwide. Electronic Health Records (EHR) allow researchers to conduct unprecedented large-scale observational studies investigating MDD, its disease development and its interaction with other health outcomes. While there exist methods to classify patients as clear cases or controls, given specific data requirements, there are presently no simple, generalizable, and validated methods to classify an entire patient population into varying groups of depression likelihood and severity.MethodsWe have tested a simple, pragmatic electronic phenotype algorithm that classifies patients into one of five mutually exclusive, ordinal groups, varying in depression phenotype. Using data from an integrated health system on 278,026 patients from a 10-year study period we have tested the convergent validity of these constructs using measures of external validation, including patterns of psychiatric prescriptions, symptom severity, indicators of suicidality, comorbidity, mortality, health care utilization, and polygenic risk scores for MDD.ResultsWe found consistent patterns of increasing morbidity and/or adverse outcomes across the five groups, providing evidence for convergent validity.LimitationsThe study population is from a single rural integrated health system which is predominantly white, possibly limiting its generalizability.ConclusionOur study provides initial evidence that a simple algorithm, generalizable to most EHR data sets, provides categories with meaningful face and convergent validity that can be used for stratification of an entire patient population.
Detection and evaluation of heating load of building by machine learning
Authors: Swhli Khaled Mohamed Himair , Jovic Srdjan , Arsic Nebojša , Spalevic Petar 
Published: December 2017

Purpose
This paper aims to explore detection of heating load of building by machine learning. Detection of heating load of building is very important in design of buildings due to efficient energy consumption.

Design/methodology/approach
In this study, detection of heating load of building based on effects of dry-bulb temperature, dew-point temperature, radiation, diffuse radiation and wind speed was analyzed. Machine learning approach was implemented for such a purpose.

Findings
The obtained results could be useful for future planning of heating load of buildings. Because the heating load of building is a very nonlinear phenomenon, it is suitable to use machine learning approach to avoid the nonlinearity of the system.

Originality/value
The obtained results could be used effectively in detection of heating load of buildings.
Regulation of Nod factor biosynthesis by alternative NodD proteins at distinct stages of symbiosis provides additional compatibility scrutiny
Authors: Kelly Simon , Sullivan John T. , Kawaharada Yasuyuki , Radutoiu Simona , Ronson Clive W. , Stougaard Jens 
Published: December 2017

SummaryThe Lotus japonicus symbiont Mesorhizobium loti R7A encodes two copies of nodD and here we identify striking differences in Nod factor biosynthesis gene induction by NodD1 and NodD2 both in vitro and in planta. We demonstrate that induction of Nod factor biosynthesis genes is preferentially controlled by NodD1 and NodD2 at specific stages of symbiotic infection. NodD2 is primarily responsible for induction in the rhizosphere and within nodules, while NodD1 is primarily responsible for induction within root hair infection threads. nodD1 and nodD2 mutants showed significant symbiotic phenotypes and competition studies establish that nodD1 and nodD2 mutants were severely outcompeted by wild‐type R7A, indicating that both proteins are required for proficient symbiotic infection. These results suggest preferential activation of NodD1 and NodD2 by different inducing compounds produced at defined stages of symbiotic infection. We identified Lotus chalcone isomerase CHI4 as a root hair induced candidate involved in the biosynthesis of an inducer compound that may be preferentially recognized by NodD1 within root hair infection threads. We propose an alternative explanation for the function of multiple copies of nodD that provides the host plant with another level of compatibility scrutiny at the stage of infection thread development.
Antibiotic resistome in a large‐scale healthy human gut microbiota deciphered by metagenomic and network analyses
Authors: Feng Jie , Li Bing , Jiang Xiaotao , Yang Ying , Wells George F. , Zhang Tong , Li Xiaoyan 
Published: December 2017

SummaryThe human gut microbiota is an important reservoir of antibiotic resistance genes (ARGs). A metagenomic approach and network analysis were used to establish a comprehensive antibiotic resistome catalog and to obtain co‐occurrence patterns between ARGs and microbial taxa in fecal samples from 180 healthy individuals from 11 different countries. In total, 507 ARG subtypes belonging to 20 ARG types were detected with abundances ranging from 7.12 × 10−7 to 2.72 × 10−1 copy of ARG/copy of 16S‐rRNA gene. Tetracycline, multidrug, macrolide‐lincosamide‐streptogramin, bacitracin, vancomycin, beta‐lactam and aminoglycoside resistance genes were the top seven most abundant ARG types. The multidrug ABC transporter, aadE, bacA, acrB, tetM, tetW, vanR and vanS were shared by all 180 individuals, suggesting their common occurrence in the human gut. Compared to populations from the other 10 countries, the Chinese population harboured the most abundant ARGs. Moreover, LEfSe analysis suggested that the MLS resistance type and its subtype ‘ermF’ were representative ARGs of the Chinese population. Antibiotic inactivation, antibiotic target alteration and antibiotic efflux were the dominant resistance mechanism categories in all populations. Procrustes analysis revealed that microbial phylogeny structured the antibiotic resistome. Co‐occurrence patterns obtained via network analysis implied that 12 species might be potential hosts of 58 ARG subtypes.
Are Robots Taking Our Jobs?
Authors: Borland Jeff , Coelli Michael 
Published: December 2017

AbstractThis article assesses the effect of computer‐based technologies on employment in Australia. We find that: (i) the total amount of work available has not decreased following the introduction of computer‐based technologies; and (ii) the pace of structural change and job turnover in the labour market has not accelerated with the increasing application of computer‐based technologies. A review of recent studies that claim computer‐based technologies may be about to cause widespread job destruction establishes several major flaws with these predictions. Our suggested explanation for why techno‐phobia has such a grip on popular imagination is a human bias to believe that ‘we live in special times’.
Estimating the Cost Savings of Preventive Dental Services Delivered to Medicaid‐Enrolled Children in Six Southeastern States
Authors: Lee Ilbin , Monahan Sean , Serban Nicoleta , Griffin Paul M. , Tomar Scott L. 
Published: December 2017

ObjectiveTo quantify the impact of multiyear utilization of preventive dental services on downstream dental care utilization and expenditures for children.Data Sources/Study SettingWe followed 0.93 million Medicaid‐enrolled children who were 3–6 years old in 2005 from 2005 to 2011. We used Medicaid claims data of Alabama, Georgia, Mississippi, North Carolina, South Carolina, and Texas.Study DesignWe clustered each state's study population into four groups based on utilization of topical fluoride and dental sealants before caries‐related treatment using machine learning algorithms. We evaluated utilization rates and expenditures across the four groups and quantified cost savings of preventive care for different levels of penetration.Data Extraction MethodWe extracted all dental‐related claims using CDT codes.Principal FindingsIn all states, Medicaid expenditures were much lower for children who received topical fluoride and dental sealants before caries development than for all other children, with a per‐member per‐year difference ranging from $88 for Alabama to $156 for Mississippi.ConclusionsThe cost savings from topical fluoride and sealants across the six states ranged from $1.1M/year in Mississippi to $12.9M/year in Texas at a 10 percent penetration level. Preventive dental care for children not only improves oral health outcomes but is also cost saving.
STREET NETWORK STRUCTURE AND CRIME RISK: AN AGENT‐BASED INVESTIGATION OF THE ENCOUNTER AND ENCLOSURE HYPOTHESES
Authors: BIRKS DANIEL , DAVIES TOBY 
Published: December 2017

AbstractStreet networks shape day‐to‐day activities in complex ways, dictating where, when, and in what contexts potential victims, offenders, and crime preventers interact with one another. Identifying generalizable principles of such influence offers considerable utility to theorists, policy makers, and practitioners. Unfortunately, key difficulties associated with the observation of these interactions, and control of the settings within which they take place, limit traditional empirical approaches that aim to uncover mechanisms linking street network structure with crime risk. By drawing on parallel advances in the formal analyses of street networks and the computational modeling of crime events interactions, we present a theoretically informed and empirically validated agent‐based model of residential burglary that permits investigation of the relationship between street network structure and crime commission and prevention through guardianship. Through the use of this model, we explore the validity of competing theoretical accounts of street network permeability and crime risk—the encounter (eyes on the street) and enclosure (defensible space) hypotheses. The results of our analyses provide support for both hypotheses, but in doing so, they reveal that the relationship between street network permeability and crime is likely nonlinear. We discuss the ramifications of these findings for both criminological theory and crime prevention practice.
Biofilm recruitment under nanofiltration conditions: the influence of resident biofilm structural parameters on planktonic cell invasion
Authors: Habimana Olivier , Casey Eoin 
Published: December 2017

SummaryIt is now generally accepted that biofouling is inevitable in pressure‐driven membrane processes for water purification. A large number of published articles describe the development of novel membranes in an effort to address biofouling in such systems. It is reasonable to assume that such membranes, even those with antimicrobial properties, when applied in industrial‐scale systems will experience some degree of biofouling. In such a scenario, an understanding of the fate of planktonic cells, such as those entering with the feed water, has important implications with respect to contact killing particularly for membranes with antimicrobial properties. This study thus sought to investigate the fate of planktonic cells in a model nanofiltration biofouling system. Here, the interaction between auto‐fluorescent Pseudomonas putida planktonic cells and 7‐day‐old Pseudomonas fluorescens resident biofilms was studied under permeate flux conditions in a nanofiltration cross flow system. We demonstrate that biofilm cell recruitment during nanofiltration is affected by distinctive biofilm structural parameters such as biofilm depth.
ddrage: A data set generator to evaluate ddRADseq analysis software
Authors: Timm Henning , Weigand Hannah , Weiss Martina , Leese Florian , Rahmann Sven 
Published: December 2017

AbstractHigh‐throughput sequencing makes it possible to evaluate thousands of genetic markers across genomes and populations. Reduced‐representation sequencing approaches, like double‐digest restriction site‐associated DNA sequencing (ddRADseq), are frequently applied to screen for genetic variation. In particular in nonmodel organisms where whole‐genome sequencing is not yet feasible, ddRADseq has become popular as it allows genomewide assessment of variation patterns even in the absence of other genomic resources. However, while many tools are available for the analysis of ddRADseq data, few options exist to simulate ddRADseq data in order to evaluate the accuracy of downstream tools. The available tools either focus on the optimization of ddRAD experiment design or do not provide the information necessary for a detailed evaluation of different ddRAD analysis tools. For this task, a ground truth, that is, the underlying information of all effects in the data set, is required. Therefore, we here present ddrage, the ddRAD Data Set Generator, that allows both developers and users to evaluate their ddRAD analysis software. ddrage allows the user to adjust many parameters such as coverage and rates of mutations, sequencing errors or allelic dropouts, in order to generate a realistic simulated ddRADseq data set for given experimental scenarios and organisms. The simulated reads can be easily processed with available analysis software such as stacks or pyrad and evaluated against the underlying parameters used to generate the data to gauge the impact of different parameter values used during downstream data processing.
A gene encoding a DUF523 domain protein is involved in the conversion of 2‐thiouracil into uracil
Authors: Aučynaitė Agota , Rutkienė Rasa , Gasparavičiūtė Renata , Meškys Rolandas , Urbonavičius Jaunius 
Published: December 2017

SummaryModified nucleotides are present in many RNA species in all Domains of Life. While the biosynthetic pathways of such nucleotides are well studied, much less is known about the degradation of RNAs and the return to the metabolism of modified nucleotides, their respective nucleosides or heterocyclic bases. Using an E. coli uracil auxotroph, we screened the metagenomic libraries for genes, which would allow the conversion of 2‐thiouracil to uracil and thereby lead to the growth on a defined synthetic medium. We show that a gene encoding a protein consisting of previously uncharacterized Domain of Unknown Function 523 (DUF523) is responsible for such phenotype. We have purified this recombinant protein and demonstrated that it contains a FeS cluster. The substitution of cysteines, which have been predicted to form such clusters, with alanines abolished the growth phenotype. We conclude that DUF523 is involved in the conversion of 2‐thiouracil into uracil in vivo.