bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hunter-gatherers in a howling wilderness: Neoliberal capitalism as a language that speaks itself
Authors: Wallace Rodrick 
Published: July 2019

AbstractThe 'self-referential' character of evolutionary process noted by Goldenfeld and Woese (2010) can be restated in the context of a generalized Darwinian theory applied to economic process through a 'language' model: The underlying inherited and learned culture of the firm, the short-time cognitive response of the firm to patterns of threat and opportunity that is sculpted by that culture, and the embedding socioeconomic environment, are represented as interacting information sources constrained by the asymptotic limit theorems of information theory. If unregulated, the larger, compound, source that characterizes high probability evolutionary paths of this composite then becomes, literally, a self-dynamic language that speaks itself. Such a structure is, for those enmeshed in it, more akin to a primitive hunter-gatherer society at the mercy of internal ecological dynamics than to, say, a neolithic agricultural community in which a highly ordered, deliberately adapted, ecosystem is consciously farmed so as to match its productivity to human needs.
Generating descriptive text from functional brain images
Authors: Pereira Francisco , Detre Greg , Botvinick Matthew 
Published: July 2019

AbstractRecent work has shown that it is possible to take brain images of a subject acquired while they saw a scene and reconstruct an approximation of that scene from the images. Here we show that it is also possible to generate text from brain images. We began with images collected as participants read names of objects (e.g., ``Apartment&apos;). Without accessing information about the object viewed for an individual image, we were able to generate from it a collection of semantically pertinent words (e.g., &quot;door,&quot; &quot;window&quot;). Across images, the sets of words generated overlapped consistently with those contained in articles about the relevant concepts from the online encyclopedia Wikipedia. The technique described, if developed further, could offer an important new tool in building human computer interfaces for use in clinical settings.
Large sets of edit-metric sequence identification tags to facilitate large-scale multiplexing of reads from massively parallel sequencing
Authors: Faircloth Brant , Glenn Travis 
Published: July 2019

Abstract
Background
Massively parallel DNA sequencing technologies provide exponential increases in the amount of data returned from the sequencing process, relative to traditional (Sanger-based) techniques. Use of unique, synthetic oligonucleotides (identifying sequence tags) on each sample enables the deconvolution of samples pooled prior to massively parallel sequencing. To counteract oligonucleotide synthesis and sequencing errors, sequence tags should be drawn from combinations with sufficient differences and with an appropriate error-correcting code over the alphabet [A,C,G,T]. This method ensures that errors within the tags do not cause sequences to be assigned to the wrong sample while also enabling correction and recovery of incorrectly-sequenced or incorrectly-synthesized tags. The set of available tags should be large, allowing sample multiplexing to scale with rapid changes in sequencing platform output. The set of tags should also account for errors possible during both the oligonucleotide synthesis and sequencing processes. Most tags in current use have been designed to maintain a particular Hamming distance: a scheme that ensures the distance between tag sequences is maintained in the presence of substitutions. However, sequence identification tags conforming to the edit-metric are more appropriate: edit-metric sequence tags are robust to insertions, deletions, and substitution errors.

Results
We present edittag, a python package containing several tools to facilitate the design of edit-metric-based sequence identification tags, check existing sets of sequence identification tags for conformance to the edit-metric, and apply sequence identification tags to primers and/or adapters. We use edittag to design several large sets of edit-metric sequence tags ranging from four to 10 nucleotides in length and edit distance three to nine. Finally, we test a set of fusion primers designed with the software developed here, demonstrating high levels of successful amplification.

Conclusions
Researchers using sequence identification tags should consider using edit-metric-based sequence tags in place of the more common, Hamming-distance-based alternatives. Edit-metric sequence tags are robust to insertion, deletion, and substitution errors, thus more robust to all forms of error present during the DNA sequencing process. Edittag facilitates the generation and application of these robust sequence tags.
Understanding Genomic Evolution of Olfactory Receptors through Fractal and Mathematical Morphology
Authors: Hassan Sk. Sarif , Pal Choudhury Pabitra , Dayasagar B. S. , Chakraborty Shantanav , Guha Ranita , Goswami Arunava 
Published: July 2019

AbstractFractals and Mathematical Morphology are immensely used to study many problems in different branches of science and technology including the domain of Biology. There are many more unrevealed facts and figures of genes and genome in Computational Biology. In this paper, our objective is to explore how the evolutionary network is associated among Human, Chimpanzee and Mouse with regards to their genomic information. We are about to explore their genomic evolution through the quantitative measures of fractals and morphology. We have considered olfactory receptors for our case study. These olfactory receptors do function in different species with subtle differences in the structures of DNA sequences. Those subtle differences can be exposed through intricate details of Fractals and Mathematical Morphology.
Kinetic model based on molecular mechanism for action potential
Authors: Xu Lizhong 
Published: July 2019

AbstractThe Hodgkin-Huxley model for action potentials has been widely used but was not built on a microscopic description of the neuronal membrane. Through molecular dynamics simulations, the molecular mechanism of the channel currents is becoming clear. However, the quantitative link between molecular mechanism and action potential remains to be elucidated. Here, a kinetic model for action potential based on the molecular mechanism of the channel currents is proposed. Using it, the experimental observations about action potential are reproduced quantitatively and explained based on molecular mechanism. We find that the accumulation of Na+ ions near exit of the electivity filter is the dominant event to cause the refractory period of the Na+ channel and the types of the channel currents depend on its rate constants. The channel inductance represents the inertia of the channel to retain a certain ion binding state, the channel resistances include ones against state transition and charge transfer.
An uncultured human-associated bacterium model
Authors: Dinis Jorge , Barton David , Ghadiri Jamsheed , Surendar Deepa , Reddy Kavitha , Velasquez Fernando , Chaffee Carol , Lee Wendy , Gavrilova Helen , Ozuna Hazel , Smits Samuel , Ouverney Cleber 
Published: July 2019

AbstractThe Bacteria Domain experienced an explosion of novel lineages identified within the last decade, especially of lineages made entirely of uncultured members. Since numerous cultivable bacteria have been shown to be instrumental in human development, health, and diseases, it is reasonable to speculate that strains from uncultured groups, which comprise nearly 80% of the human gut and 68% of human oral microbial consortia, participate in similar functions. The study of human-associated uncultured bacteria, however, has many practical limitations, such as access to patient samples, unpredictable microbial composition, and low relative abundance, all of which challenge experimental promptness and reproducibility. We propose that uncultured bacteria from environmental sources can serve as a model to better understand the roles their counterparts play in humans. In this study, we illustrate this concept using an environmental TM7 bacterium with &8805;98.5% 16S rDNA gene homology to a group of TM7 bacteria found in both the human oral cavity and skin. Our TM7 model was readily detectable with molecular techniques as viable cells in sludge and in quantities greater than its human-associated relatives. Our approach circumvents difficulties imposed by sampling humans until either a TM7 strain of interest is cultured or an alternative method is proposed.
Word Activation Forces Map Word Networks
Authors: Guo Jun , Guo Hanliang , Wang Zhanyi 
Published: July 2019

AbstractWords associate with each other in a manner of intricate clusters^1-3^. Yet the brain capably encodes the complex relations into workable networks^4-7^ such that the onset of a word in the brain automatically and selectively activates its associates, facilitating language understanding and generation^8-10^. One believes that the activation strength from one word to another forges and accounts for the latent structures of the word networks. This implies that mapping the word networks from brains to computers^11,12^, which is necessary for various purposes^1,2,13-15^, may be achieved through modeling the activation strengths. However, although a lot of investigations on word activation effects have been carried out^8-10,16-20^, modeling the activation strengths remains open. Consequently, huge labor is required to do the mappings^11,12^. Here we show that our found word activation forces, statistically defined by a formula in the same form of the universal gravitation, capture essential information on the word networks, leading to a superior approach to the mappings. The approach compatibly encodes syntactical and semantic information into sparse coding directed networks, comprehensively highlights the features of individual words. We find that based on the directed networks, sensible word clusters and hierarchies can be efficiently discovered. Our striking results strongly suggest that the word activation forces might reveal the encoding of word networks in the brain.
Scaffolder - Software for Reproducible Genome Scaffolding.
Authors: Barton Michael , Barton Hazel 
Published: July 2019

Abstract
Background:
Assembly of short-read sequencing data can result in a fragmented non-contiguous series of genomic sequences. Therefore a common step in a genome project is to join neighboring sequence regions together and fill gaps in the assembly using additional sequences. This scaffolding step, however, is non-trivial and requires manually editing large blocks of nucleotide sequence. Joining these sequences together also hides the source of each region in the final genome sequence. Taken together, these considerations may make reproducing or editing an existing genome build difficult.

Methods:
The software outlined here, “Scaffolder,” is implemented in the Ruby programming language and can be installed via the RubyGems software management system. Genome scaffolds are defined using YAML - a data format, which is both human and machine-readable. Command line binaries and extensive documentation are available.

Results:
This software allows a genome build to be defined in terms of the constituent sequences using a relatively simple syntax to define the scaffold. This syntax further allows unknown regions to be defined, and adds additional sequences to fill gaps in the scaffold. Defining the genome construction in a file makes the scaffolding process reproducible and easier to edit compared with FASTA nucleotide sequence.

Conclusions:
Scaffolder is easy-to-use genome scaffolding software. This tool promotes reproducibility and continuous development in a genome project. Scaffolder can be found at http://next.gs.
Metabolic Activity of Human Chorionic Gonadotropin (hCG) on Glycemia and Leptinemia in Experimental Animals Fed a Cafeteria Diet
Authors: Belluscio Daniel , Lago Nestor , Klimovsky Ezequiel , Berdeal Maria , Federico Andrea 
Published: July 2019

Abstract
Objectives:
To elucidate the relationship of hCG administration to glycemia, Non Esterified Fatty Acids (NEFA), leptin and adiponectin levels on experimental animals previously submitted to a cafeteria diet, and then to a Low Calorie Diet (LCD). Design: Forty-one rats were selected (21 females, 20 males) and divided into seven (0-6) groups. Animals from groups 1 to 6 were fed a &quot;cafeteria diet&quot; with a mean energy content of 10% protein, 30% carbohydrate and 60% fat. Animals from group 0 were fed the standard laboratory diet. After the fattening period, animals from groups 1 to 6 were submitted to a restricted diet consisting of one-third the average daily intake for rats. hCG was administered for five weeks according to a specific protocol. The effects of hCG treatment were evaluated using analysis of variance (ANOVA).

Results:
These assessments were compared: (1) glycemia, adiponectins, leptins and non-esterified fatty acids (NEFA); (2) weight; (3) formulation effect; and (4) dose effect. Differences in leptins were observed between the Control group and Injectable A (p=0.026), Intrarectal Suspension A (p=0.20), Intrarectal Suspension B (p&lt;0.001), and Intrarectal Suspension C (p&lt;0001) groups. In all cases, the average values were higher for the control group. Significant differences were found in the groups treated with Injectable B, Intrarectal Suspension B (p=0.025) and Intrarectal Suspension C (p=0.037). Groups receiving Intrarectal Suspension B or C showed significantly lower mean leptin values. Differences in glycemia were detected between the Control group and Intrarectal Suspension A (p=0.021) and Intrarectal Suspension B (p=0.020) groups. Groups treated with Intrarectal Suspension A or B showed lower mean blood glucose values.

Conclusions:
Results show the activity of hCG (both urinary and recombinant) on glycemia and leptins levels in experimental animals in different formulations.hCG administration significantly decreased blood sugar and leptin levels, whereas adiponectins were only relatively sensitive to hCG.
Sensory adaptation in the light of information and energy
Authors: Rasumov Nikon , Niven Jeremy , Baker Michael , Laughlin Simon 
Published: July 2019

AbstractSignal processing in neurons is constrained by internal noise and energy consumption. Neural systems often invest energy in amplifying signals to protect them against internal noise. There is no general expression for the trade-off between energy and information, because it is strongly dependent on the properties of signal, noise and underlying mechanism. We present the first analysis of this energy-information trade-off in a specific system, the R1-6 photoreceptors of the blowfly, Calliphora vicina. These photoreceptors adapt to light level by adjusting their transduction gain (number of light-gated channels opened per photon) and potassium conductance. By combining experimental measures of photoreceptor impedances, transfer functions and signal quality in a basic membrane model we discovered that photoreceptors do not adapt to maximise their sensitivity. Instead, they use a lower sensitivity at which they capture 99% of the information from naturalistic stimuli. This strategy is efficient because it avoids the wasteful amplification of noisy inputs and reduces energy consumption.
Expression cartography of human tissues using self organizing maps
Authors: Wirth Henry , Binder Hans 
Published: July 2019

Abstract
Background:
The availability of parallel, high-throughput microarray and sequencing experiments poses a challenge how to best arrange and to analyze the obtained heap of multidimensional data in a concerted way. Self organizing maps (SOM), a machine learning method, enables the parallel sample- and gene-centered view on the data combined with strong visualization and second-level analysis capabilities. The paper addresses aspects of the method with practical impact in the context of expression analysis of complex data sets.

Results:
The method was applied to generate a SOM characterizing the whole genome expression profiles of 67 healthy human tissues selected from ten tissue categories (adipose, endocrine, homeostasis, digestion, exocrine, epithelium, sexual reproduction, muscle, immune system and nervous tissues). SOM mapping reduces the dimension of expression data from ten thousands of genes to a few thousands of metagenes where each metagene acts as representative of a minicluster of co-regulated single genes. Tissue-specific and common properties shared between groups of tissues emerge as a handful of localized spots in the tissue maps collecting groups of co-regulated and co-expressed metagenes. The functional context of the spots was discovered using overrepresentation analysis with respect to pre-defined gene sets of known functional impact. We found that tissue related spots typically contain enriched populations of gene sets well corresponding to molecular processes in the respective tissues. Analysis techniques normally used at the gene-level such as two-way hierarchical clustering provide a better signal-to-noise ratio and a better representativeness of the method if applied to the metagenes. Metagene-based clustering analyses aggregate the tissues into essentially three clusters containing nervous, immune system and the remaining tissues.

Conclusions:
The global view on the behavior of a few well-defined modules of correlated and differentially expressed genes is more intuitive and more informative than the separate discovery of the expression levels of hundreds or thousands of individual genes. The metagene approach is less sensitive to a priori selection of genes. It can detect a coordinated expression pattern whose components would not pass single-gene significance thresholds and it is able to extract context-dependent patterns of gene expression in complex data sets.
More damn lies about data access
Authors: Axton Myles 
Published: July 2019

AbstractMore data than we can handle is no excuse to give up our efforts to promote data access, but it may make us think about new ways to make it sustainable.[This draft was written in the hope that participants of the Sage Congress will write an Nature Genetics Editorial in the manner of Tom Sawyer’s white fence (Twain M. 1876). All contributions received by April 10th 2011 will be attributed.]
Hypothesis: Is the Omega-3 PUFA Content of Milk Related to Type 1 Diabetes?
Authors: Arbesman Harvey , Arbesman Marian 
Published: July 2019

AbstractThe incidence of Type 1 Diabetes (T1D) has been increasing over the past 50 years. The geographical variation in the incidence of T1D varies 400 fold from a low rate in China to a very high incidence in Finland, Sardinia and Yemenite Jews in Israel. The reason behind these epidemiologic findings is unclear at this time and further elucidating the factors related to this intriguing epidemiology of T1D can help us better understand certain risk factors, the pathogenesis of the disease, and help reduce the incidence of T1D. We hypothesize that the variation of the omega-3 polyunsaturated fatty acid (PUFA) content of cow’s milk and human breast milk may play a very important role in the development of Type 1 Diabetes in genetically predisposed individuals and may also be an important confounder in many previously published and ongoing studies. Interestingly, the amount of omega 3 fatty acids in cow’s milk and human breast milk has been shown to vary widely depending on the diet of the cows and nursing mothers. This hypothesis could possibly help explain the conflicting results from various studies regarding cow’s milk exposure, breastfeeding and T1D and may also help explain some of the findings related to other environmental risk factors for the development of T1D through the beneficial effects of omega-3 fatty acids on this autoimmune disease.
Up-expression of GAD65 in the Amygdala of the Rat Model of Chroric Immobilization Stress with Elevated Blood Glucose
Authors: Li Xiao-Jing , Zhang Wei , Liang Bing , Zheng Xiang , Zhou Xue 
Published: July 2019

AbstractIt is believed that the hypothalamus is the regulating center for blood glucose levels, but how chronic stress leads to hyperglycemia is not known. In this experiment, we use the chronic immobilization stressed rat as a model, and found that only rats with increased expression of GAD65 in the amygdala have an elevated level of blood glucose. Considering there are fiber tracks between the amygdala and hypothalamus, including GABAergic projections, this result suggests that the changes in GAD65 expression in the amygdala may correlate with the changes in blood glucose levels, and point to the importance of the the amygdala in blood glucose regulation.
Wikis in scholarly publishing
Authors: Mietchen Daniel , Hagedorn Gregor , Förstner Konrad , Kubke M Fabiana , Koltzenburg Claudia , Hahnel Mark , Penev Lyubomir 
Published: July 2019

AbstractScientific research is a process concerned with the creation, collective accumulation, contextualization, updating and maintenance of knowledge. Wikis provide an environment that allows to collectively accumulate, contextualize, update and maintain knowledge in a coherent and transparent fashion. Here, we examine the potential of wikis as platforms for scholarly publishing. In the hope to stimulate further discussion, the article itself was drafted on "Species-ID":http://species-id.net/w/index.php?title=Wikis_in_scholarly_publishing&oldid=3815 - a wiki that hosts a prototype for wiki-based scholarly publishing - where it can be updated, expanded or otherwise improved.
Counting absolute number of molecules using unique molecular identifiers
Authors: Kivioja Teemu , Vähärautio Anna , Karlsson Kasper , Bonke Martin , Linnarsson Sten , Taipale Jussi 
Published: July 2019

AbstractAdvances in molecular biology have made it easy to identify different DNA or RNA species and to copy them. Identification of nucleic acid species can be accomplished by reading the DNA sequence; currently millions of molecules can be sequenced in a single day using massively parallel sequencing. Efficient copying of DNA-molecules of arbitrary sequence was made possible by molecular cloning, and the polymerase chain reaction. Differences in the relative abundance of a large number of different sequences between two or more samples can in turn be measured using microarray hybridization and/or tag sequencing. However, determining the relative abundance of two different species and/or the absolute number of molecules present in a single sample has proven much more challenging. This is because it is hard to detect individual molecules without copying them, and even harder to make defined number of copies of molecules. We show here that this limitation can be overcome by using unique molecular identifiers (umis), which make each molecule in the sample distinct.