Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Capture Methods and Infection Rates for the Tick, Dermacentor andersoni, in Montana

Montana is home to the tick Dermacentor andersoni which serves as a vector for Colorado tick fever. It is important to study ticks in the field to improve capture rates for the purpose of testing infection rates. To determine the most effective way to capture ticks, four separate capturing methods were assembled in two tick prevalent locations. These methods include AAAP pheromones, CO2, drag netting, and AAAP in combination with CO2. I hypothesized that the combination of AAAP and CO2 would yield the best capture results. I implemented a mark-recapture study using fluorescent powder and hypothesized that CO2 and pheromones combined would provide the best population estimates. My findings indicate that drag net sampling and CO2 alone were the best tick attractants which could be due to pheromone amounts high enough to repel ticks rather than attract them. CO2 and drag netting had similar efficacy, whereas AAAP attracted no ticks throughout all trials. I developed an RT-PCR protocol to detect the presence of Colorado tick fever in D. andersoni and hypothesized that infection rates would be within the range of 10-25%. At Woodlake Campground and Dearborn the infection rate of Colorado tick fever was 12%. The infection rate at Beaver Creek drainage was 10%. My results suggest that drag netting, perhaps combined with CO2 , lead to higher capture rates and the most effective means of estimating population density. Also, my results show that the infection rate of ticks was within 10-12% at three sites in Lewis and Clark County.
Lactation performance and milk composition studies in transgenic goats expressing recombinant proteins in the milk

Milk expression of recombinant proteins of pharmaceutical value in transgenic dairy animals is an emerging solution for molecules that cannot be made efficiently using the standard bioreactor platform. However, the expression of large quantities of recombinant proteins in the mammary gland can result in phenotypes with compromised lactation physiology. These negative consequences have been vaguely described in the literature and, in most cases; they have not been studied in depth with view to understanding the mechanisms of mammary disruption by transgene expression. Therefore, using a transgenic herd of goats expressing recombinant human butyrylcholinesterase, the main goals of this research were to: a) study the lactation performance and basic milk parameters; b) assess milk composition; c) explore the integrity of epithelial tight junctions, endoplasmic reticulum stress and accelerated cell death; d) investigate delayed lactogenesis; and e) assess the effects of "compensatory treatments" on transgenic lactation performance. Our findings showed that transgenic lactations were characterized by a slow/delayed start of milk production, a relatively normal milk volume at peak and a premature shutdown of milk production compared to controls. These compromised productivities were associated with a disrupted lipid secretion at the level of the secretory epithelium, and a dramatic raise in the presence of phagocytes in milk that was not associated with mammary infection. Milk composition studies indicated that transgenic goats produce lower quantities of caseins and short chain fatty acids. Through determination of serum album presence and Na:K ratio in milk we established the development of permeable tight junctions as an apparent mechanism of lactation disruption in the transgenic animals. Delayed expression of α-lactalbumin was proposed as a major determinant of the delayed lactogenesis observed in the tra%%%%L'expression des proteines recombinantes de valeur pharmaceutique dans la glande mammaire des animaux transgeniques est une solution emergeante pour les molecules qui ne peuvent pas etre produites efficacement par les bioreacteurs standards. Toutefois, l'expression de grandes quantites de proteines recombinantes dans la glande mammaire peut entrainer des phenotypes avec une physiologie de la lactation compromise. Ces consequences negatives ont ete vaguement decrites dans la litterature et, dans la plupart des cas ; elles n'ont pas ete etudiees en profondeur pour comprendre les mecanismes qui perturbent la glande mammaire suite a l'expression des transgenes. Par consequent, en utilisant un troupeau de chevres transgeniques qui expriment la forme recombinee de la butyrylcholinesterase humaine, les principaux objectifs de cette recherche etaient: a) d'etudier la performance de la production laitiere et les parametres de base du lait; b) d'evaluer la composition du lait; c) d'explorer l'integrite des jonctions serres…
Ingestive behavior of dairy goats fed diets containing increasing levels of neutral detergent fiber and particle size using multivariate analysis

The purpose of this study was to evaluate the effect of interaction between increasing neutral detergent fiber content and particle sizes on ingestive behavior of dairy goats . Forty-eight lactating, multiparous Saanen and Alpine goats, with average milk production of 1.4 ±0.57 kg d -1 , around 60 th ±12 day of lactation were distributed in a 3 x 4 factorial completely randomized design. The diets consisted: three particles sizes (02, 05 or 15 cm) and four levels of neutral detergent fiber (34, 41, 49 or 57% NDFf) from forage (Tifton 85 hay). The ingestive behavior was monitored during 24 hours. A regression and a multivariate time series cluster analysis were performed. No interaction was found (p > 0.05) between treatments. Feeding time was different according to the particle size, having an increasing linear effect. Rumination and idle times were not affected (p > 0.05). The temporal feeding behavior was clustered into two groups according to the profile of particle size of the diet. Rumination peaks were randomly distributed with more intense activity before morning and afternoon meals. The increase in NDFf content in the diet did not change the ingestive behavior. The multivariate cluster analysis in a time series data is useful to interpret animal feeding behavior.
SIMULATION USING R FROM WITHIN EXCEL FOR TEACHING FIRST YEAR BIOLOGISTS

In this paper we describe the efforts that we are making to engage and impart statistical understanding to a large group of first year degree-level biologists. By using an online assessment system we have confirmed the extent of large areas of statistical misunderstandings. In this paper we describe our strategy for breaking down these statistical understandings using software that is free and can be run within an Excel worksheet.
Familiarity increases processing speed in the visual system

Familiarity with a stimulus leads to an attenuated neural response to the stimulus. Alongside this attenuation, recent studies have also observed a truncation of stimulus-evoked activity for familiar visual input. One proposed function of this truncation is to rapidly put neurons in a state of readiness to respond to new input. Here, we examined this hypothesis by presenting human participants with target stimuli that were embedded in rapid streams of familiar or novel distractor stimuli at different speeds of presentation, while recording brain activity using magnetoencephalography (MEG) and measuring behavioral performance. We investigated the temporal and spatial dynamics of signal truncation and whether this phenomenon bears relationship to participants’ ability to categorize target items within a visual stream. Behaviorally, target categorization performance was markedly better when the target was embedded within familiar distractors, and this benefit became more pronounced with increasing speed of presentation. Familiar distractors showed a truncation of neural activity in the visual system, and this truncation was strongest for the fastest presentation speeds. Moreover, neural processing of the target was stronger when it was preceded by familiar distractors. Taken together, these findings suggest that truncation of neural responses for familiar items may result in stronger processing of relevant target information, resulting in superior perceptual performance. Significance statement The visual response to familiar input is attenuated more rapidly than for novel input. Here we find that this truncation of the neural response for familiar input is strongest for very fast image presentations. We also find a tentative function for this truncation: the neural response to a target image that is embedded within distractors is much greater when the distractors are familiar than when they are novel. Similarly, target categorization performance is much better when the target is embedded within familiar distractors, and this advantage is most obvious for very fast image presentations. This suggests that neural truncation helps to rapidly put neurons in a state of readiness to respond to new input.
Distributed Particle Filtering in Networks of Agents

This thesis is focused on distributed particle filter (DPF) algorithms performing decentralized sequential state estimation in networks of agents. The individual agents are equipped with sensing, computation, and communication capabilities. Applications of networks of agents are manifold and include environmental and agricultural monitoring, target tracking, pollution source localization, healthcare monitoring, chemical plume tracking, and surveillance. Performing distributed (collaborative) estimation of certain states of the environment from the measurements obtained by the agents is an essential task in many applications. Particle filters (PFs) are a modern approach to sequential state estimation that offers superior performance in nonlinear and non-Gaussian systems. A distributed (decentralized) implementation of PFs in networks of agents is complicated by the fact that the measurements are dispersed among the agents. Diffusing the locally available information throughout the network is thus an essential component of DPF algorithms. In this thesis, we group the existing DPF algorithms into three main classes, which we call fusion center-based, statistics dissemination-based, and measurement dissemination-based DPFs. Subclasses of the statistics dissemination-based class include leader agent-based and consensus-based DPFs. We discuss the main features and properties of the DPF algorithms belonging to the various (sub)classes. Then, we propose a novel leader agent-based DPF with a “look-ahead” proposal density adaptation that significantly reduces the estimation error. In this DPF, Gaussian mixture representations of partial posteriors are propagated along an aggregation chain of agents. We also propose several consensus-based DPFs that employ the novel “likelihood consensus” method for a distributed approximate calculation of the global (all-agents) likelihood function. In these DPFs, each agent executes a local PF that calculates a particle representation of the global posterior. To reduce the amount of communications and the latency, we propose a modification of the likelihood consensus that requires only a single consensus iteration per PF recursion. Finally, we develop a distributed, consensus-based proposal adaptation scheme that yields a performance improvement or allows a reduction of the number of particles. Simulation results for a target tracking problem demonstrate the excellent performance of the proposed DPFs, which is often very close to the performance of a centralized PF.
The Effect of Rusa unicolor Antler Deer Extracts from East Kalimantan in Bone Turnover Cell Models.

Objectives Osteoporosis is a condition characterized by skeletal degradation of osseous tissue resulting in an escalated chance of broken bones. Traditionally, Rusa unicolor horn from East Kalimantan is used to treat many diseases, including conditions associated with bone turnover. The aim of the present research was to analyze the effects of 70% ethanol and aqueous extracts of R. unicolor antler's horn from East Kalimantan on nitric oxide inhibition, osteoblast differentiation, and mineralization related to bone turnover. Materials and Methods Nitric oxide inhibition of the extracts in lipopolysaccharide-stimulated RAW 264.7 macrophages was evaluated by Griess reagent, while the effects of extracts on osteoblast differentiations were measured by alkaline phosphatase in p-nitrophenyl phosphate. Their effects on mineralization was determined using alizarin red staining. Results The 70% ethanol and aqueous extracts inhibited cell inflammation (40% and 80%, respectively) and stimulated osteoblast differentiation (65% and 52%, respectively). In the mineralization test, the aqueous extract showed an effect two times higher than that of 70% ethanol extract. Conclusion The extracts can effectively degrade inflammatory marker expression and preserve osteoblast functions.
Evaluation for Morphological and Biochemical Traits Related to Quality Biomass Production among MS Based Forage Sorghum Hybrids

Livestock is a major component of Indian agriculture. With the increase in livestock population during past few years there is huge gap in demand and supply of green as well as dry fodder, hence forage crops improvement programme needs to be strengthened. Keeping this in view hybrid evaluation experiments were conducted during kharif 2013 and 2014 at Research area of forage farm at CCS HAU, Hisar and material was sown in randomized block design to estimate overall performance of hybrids for two successive years. Fifteen MS based hybrids were developed for two successive years and then evaluated for two years for various agronomic traits that affect the biomass production directly or indirectly. Out of these hybrids HH 602, HH 561, HH 529 and HH 619 showed higher green and dry fodder yield for two successive years as compared to best check SSG 59-3 (GFY: 737.6 q/ha and DFY: 137.7 q/ha). As far as their quality is concerned they have low HCN than toxic limit (200 micro g/g) crude protein more than 8% and in vitro dry matter digestibility more than 50% in HH 529 and HH 619. Thus, on the basis of this evaluation programme we can say that these hybrids are promising for green fodder productivity. They may also help to fill the gap between demand and supply of green as well as dry fodder for livestock industry.
A Low-Cost Lightweight Random Number Generator Implementation

Abstract In this paper, a true random number generator based on operational amplifiers is proposed. The circuit consists of an operational amplifier with a positive feedback. It is a Schmitt trigger without any applied input signal. The circuit gives a positive (bit 1) or negative (bit 0) saturated output voltage depending on the polarity of the differential input governed by the thermal noise voltage of the resistors. There is no need to amplify the noise voltage. The circuit response is analyzed with TINA-TI tool. The circuit is implemented using IC LM741 operational amplifier chip as well as on the on-chip operational amplifiers of the MSP430 microcontroller. Due to low data-rate, a small volume of data should be generated. These random data can be used as a seed value for a pseudo random number generator. The data passed NIST test. The generator can be implemented on WISP RFID tags, which have built-in MSP430 microcontrollers.
Environment rapidly upregulates serotonin 2A receptor expression via immediate early gene Egr3

Serotonin 2A receptors (5-HT2ARs) mediate the effects of hallucinogenic drugs and antipsychotic medications, and are reduced in schizophrenia patients’ brains. However, how 5-HT2AR expression is regulated remains poorly understood. We show that 5-HT2ARs are rapidly upregulated in the mouse frontal cortex (FC) in response to an environmental stimulus, sleep deprivation. This induction requires the immediate early gene transcription factor early growth response 3 (Egr3). Further, EGR3 binds to the Htr2a promoter in the FC in vivo, and drives reporter construct expression in vitro via two Htr2a promoter binding sites. These findings suggest that EGR3 directly regulates FC Htr2a expression in response to physiologic stimuli, providing a mechanism by which environment rapidly alters levels of a brain receptor that mediates symptoms, and treatment, of mental illness.
Learning to Detect Objects in Images via a Sparse, Part-Based Representation

We study the problem of detecting objects in still, gray-scale images. Our primary focus is the development of a learning- based approach to the problem that makes use of a sparse, part-based representation. A vocabulary of distinctive object parts is automatically constructed from a set of sample images of the object class of interest; images are then represented using parts from this vocabulary, together with spatial relations observed among the parts. Based on this representation, a learning algorithm is used to automatically learn to detect instances of the object class in new images. The approach can be applied to any object with distinguishable parts in a relatively fixed spatial configuration; it is evaluated here on difficult sets of real-world images containing side views of cars, and is seen to successfully detect objects in varying conditions amidst background clutter and mild occlusion. In evaluating object detection approaches, several important methodological issues arise that have not been satisfactorily addressed in previous work. A secondary focus of this paper is to highlight these issues and to develop rigorous evaluation standards for the object detection problem. A critical evaluation of our approach under the proposed standards is presented.
Integrating telemetry data into spatial capture-recapture modifies inferences on multi-scale resource selection

Estimating population size and resource selection functions (RSFs) are common approaches in applied ecology for addressing wildlife conservation and management objectives. Traditionally such approaches have been undertaken separately with different sources of data. Spatial capture-recapture (SCR) provides a framework for jointly estimating density and multi-scale resource selection, and data integration techniques provide opportunities for improving inferences from SCR models. Here we illustrate an application of integrated SCR-RSF modeling to a population of American marten (Martes americana) in alpine forests of northern New England. Spatial encounter data from camera traps were combined with telemetry locations from radio-collared individuals to examine how density and space use varied with spatial environmental features. We compared multi-model inferences between the integrated SCR-RSF model with telemetry and a standard SCR model with no telemetry. The integrated SCR-RSF model supported more complex relationships with spatial variation in third-order resource selection (i.e., individual space use), including selection for areas with shorter distances to mixed coniferous forest and rugged terrain. Both models indicated increased second-order selection (i.e., density) for areas close to mixed coniferous forest, while the integrated SCR-RSF model had a lower effect size due to modulation from spatial variability in space use. Our application of the integrated SCR-RSF model illustrates the improved inferences from spatial encounter data that can be achieved from integrating auxiliary telemetry data. Integrated modeling allows ecologists to join empirical data to ecological theory using a robust quantitative framework to better address conservation and management objectives.
A method of generating translations of unseen n‐grams by using proportional analogy

In recent years, statistical machine translation has gained much attention. The phrase‐based statistical machine translation model has made significant advancement in translation quality over the word‐based model. In this paper, we attempt to apply the technique of proportional analogy to statistical machine translation systems. We propose a novel approach to apply proportional analogy to generate translations of unseen n‐grams from the phrase table for phrase‐based statistical machine translation. Experiments are conducted with two datasets of different sizes. We also investigate two methods to integrate n‐grams translations produced by proportional analogy into the state‐of‐the‐art statistical machine translation system, Moses.1 The experimental results show that unseen n‐grams translations generated using the technique of proportional analogy are rewarding for statistical machine translation systems with small datasets. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Variations in the trnHpsbA region of Mucuna pruriens L. (DC.) varieties of India: an insight on intraspecific diversity

Mucuna pruriens is a nutritionally and pharmaceutically important underutilized legume belonging to genus Mucuna of Leguminosae family. Extensive intraspecific diversity and adverse morphological features among the members have led to taxonomical confusions in the species. A total of 12 Mucuna pruriens accessions including its two botanical varieties, var. pruriens and var. utilis with six accessions each were analyzed to characterize the intraspecific diversity among them using the three barcode markers viz. ITS2, matK and trnHpsbA. Extensive intraspecific sequence diversity was observed in the trnHpsbA region, where as ITS2 and matK exhibited significant conserved regions. Such intraspecific sequence diversity is a limitation in adapting trnHpsbA as barcode as it may lead to misinterpretations, but in the present study an attempt has been made to extrapolate this diversity to explain within the species variations among Mucuna pruriens genotypes. Intraspecific sequence variations exhibited by trnHpsbA sequences were 83% in correlation with morphological features. The between groups mean distance of var. pruriens and var. utilis was found to be 0.346. Results of this study will complement with the existing molecular, morphological, ytological and biochemical markers towards Velvet bean improvement programs.
The issue of error sensitivity in neural networks

The problem of sensitivity to errors in artificial neural networks is discussed here in behavioral terms, i.e. considering an abstract model of the network and the errors that can affect a neuron's computation. Feed-forward multi-layered networks are considered; the performance taken into account with respect to error sensitivity is their classification capacity. The final aim is evaluation of the probability that a single neuron's error will affect both its own classification capacity and the whole network's classification capacity. A geometrical representation of the neural computation is adopted as the basis for such evaluation. Probability of error propagation is evaluated with respect to the single neuron's output as well as to the complete network's output. The information derived as used to evaluate, for a specific digital network architecture, the must critical sections of the implementation as far as reliability is concerned and thus to point out candidates for ad-hoc fault-tolerance policies. <<ETX>>
Critical thinking in a synchronous online discourse: the pedagogical role of instant messaging in higher education

The adoption of Instant Messaging (IM) technology as an educational tool in higher educational practice is beginning to emerge, and concerns remain about the effectiveness of this technology in learning environments. This thesis reports on an exploratory study that used a mixed method approach to investigate the use of IM for promoting critical thinking in a synchronised online discussion, and the role of time as a variable for enhancing critical thinking skills. Two models for operationalising critical thinking in online environments were adopted for analysis and coding purposes. Results indicate that high-level critical thinking skills (e.g. Integration, Assessment, and Inference) were evident in generated discussions. We also found evidence of a positive correlation between time and critical thinking, further validating the potential role of IM in higher education. We discuss the implications of these findings and propose a planned integration strategy and structured discussion activities as part of that strategy.