Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of the Type of Pasture on the Meat Characteristics of Chilote Lambs

Chilote sheep are a native breed from Chiloe Archipelago in the southern Chile. They are descendants from sheep originally introduced by the Spaniards in the 1600s, and then evolved in a harsh environment in relative isolation from the continent. There is little information about the quality of the meat of the Chilote lambs (Ch). The objective of this study was to compare the quality of Ch lamb meat with two types of marginal pastures. The two treatments were: 1) Ch lambs, naturalized grassland (n = 13) and 2) Ch lambs, rangeland (n = 11). Rangeland is composed of both grasses and native shrubs. All lambs were kept with their mothers until slaughter at 90 days of age. Instrumental color, shear force, pH levels, and chemical and fatty acid content were analyzed. The pasture type did affect the results, given that Ch lambs fed on naturalized pasture had a lower shear force and higher intramuscular fat levels whereas Ch lambs fed on rangeland pasture showed higher percentages of n-3, n-6 fatty acids and Polyunsaturated fatty acids. However, the concentrations of fatty acids were similar in both groups. The results indicated some evidences that the type of pasture of Chiloe archipelago confers specific differences of quality that could form the basis to generate a premium product.
Efficient identification of DNA hybridization partners in a sequence database

MOTIVATION The specific hybridization of complementary DNA molecules underlies many widely used molecular biology assays, including the polymerase chain reaction and various types of microarray analysis. In order for such an assay to work well, the primer or probe must bind to its intended target, without also binding to additional sequences in the reaction mixture. For any given probe or primer, potential non-specific binding partners can be identified using state-of-the-art models of DNA binding stability. Unfortunately, these models rely on dynamic programming algorithms that are too slow to apply on a genomic scale. RESULTS We present an algorithm that efficiently scans a DNA database for short (approximately 20-30 base) sequences that will bind to a query sequence. We use a filtering approach, in which a series of increasingly stringent filters is applied to a set of candidate k-mers. The k-mers that pass all filters are then located in the sequence database using a precomputed index, and an accurate model of DNA binding stability is applied to the sequence surrounding each of the k-mer occurrences. This approach reduces the time to identify all binding partners for a given DNA sequence in human genomic DNA by approximately three orders of magnitude, from two days for the ENCODE regions to less than one minute for typical queries. Our approach is scalable to large DNA sequences. Our method can scan the human genome for medium strength binding sites to a candidate PCR primer in an average of 34.5 minutes. AVAILABILITY Software implementing the algorithms described here is available at http://noble.gs. washington.edu/proj/dna-binding.
A Model-Free Approach to Infer the Diffusion Network from Event Cascade

Information diffusion through various types of networks, such as social networks and media networks, is a very common phenomenon on the Internet nowadays. In many scenarios, we can track only the time when the information reaches a node. However, the source infecting this node is usually unobserved. Inferring the underlying diffusion network based on cascade data (observed sequence of infected nodes with timestamp) without additional information is an essential and challenging task in information diffusion. Many studies have focused on constructing complex models to infer the underlying diffusion network in a parametric way. However, the diffusion process in the real world is very complex and hard to be captured by a parametric model. Even worse, inferring the parameters of a complex model is impractical under a large data volume. Different from previous works focusing on building models, we propose to interpret the diffusion process from the cascade data directly in a non-parametric way, and design a novel and efficient algorithm named Non-Parametric Distributional Clustering (NPDC). Our algorithm infers the diffusion network according to the statistical difference of the infection time intervals between nodes connected with diffusion edges versus those with no diffusion edges. NPDC is a model-free approach since we do not define any transmission models between nodes in advance. We conduct experiments on synthetic data sets and two large real-world data sets with millions of cascades. Our algorithm achieves substantially higher accuracy of network inference and is orders of magnitude faster compared with the state-of-the-art solutions.
‘My biggest fear was whether or not I would make friends’: working-class students’ reflections on their transition to university in Ireland

ABSTRACT Over the last few decades there has been increased recognition that the concept of ‘access’ encompasses not only entry to higher education (HE) but also participation and successful completion. Despite this, very little research has been carried out in Ireland on non-traditional students’ experience of university. This article explores the experiences of 16 students from lower socio-economic backgrounds at one Irish university, with particular reference to peer relationships and their sense of ‘belonging’. The research identified the challenges which these students faced in the first few months, including feelings of isolation and ‘class–cultural discontinuities’. For one student, from the Traveller community, the transition to university was particularly complex, leading to the ‘compartmentalisation’ of different elements of her life. The research also, however, highlights these students’ positive experiences of university life, which often centred on peer relationships and shared interests and aspirations. Overall, the research points to a diverse range of working-class experiences of university life, and the importance of targeted supports in actively encouraging friendship development in the early stages.
Predicting Protein Aggregation during Storage in Lyophilized Solids Using Solid State Amide Hydrogen/Deuterium Exchange with Mass Spectrometric Analysis (ssHDX-MS)

Solid state amide hydrogen/deuterium exchange with mass spectrometric analysis (ssHDX-MS) was used to assess the conformation of myoglobin (Mb) in lyophilized formulations, and the results correlated with the extent of aggregation during storage. Mb was colyophilized with sucrose (1:1 or 1:8 w/w), mannitol (1:1 w/w), or NaCl (1:1 w/w) or in the absence of excipients. Immediately after lyophilization, samples of each formulation were analyzed by ssHDX-MS and Fourier transform infrared spectroscopy (FTIR) to assess Mb conformation, and by dynamic light scattering (DLS) and size exclusion chromatography (SEC) to determine the extent of aggregation. The remaining samples were then placed on stability at 25 °C and 60% RH or 40 °C and 75% RH for up to 1 year, withdrawn at intervals, and analyzed for aggregate content by SEC and DLS. In ssHDX-MS of samples immediately after lyophilization (t = 0), Mb was less deuterated in solids containing sucrose (1:1 and 1:8 w/w) than in those containing mannitol (1:1 w/w), NaCl (1:1 w/w), or Mb alone. Deuterium uptake kinetics and peptide mass envelopes also indicated greater Mb structural perturbation in mannitol, NaCl, or Mb-alone samples at t = 0. The extent of deuterium incorporation and kinetic parameters related to rapidly and slowly exchanging amide pools (Nfast, Nslow), measured at t = 0, were highly correlated with the extent of aggregation on storage as measured by SEC. In contrast, the extent of aggregation was weakly correlated with FTIR band intensity and peak position measured at t = 0. The results support the use of ssHDX-MS as a formulation screening tool in developing lyophilized protein drug products.
The Influence of Preservice Secondary Science Teachers' Beliefs on Their Planning.

Research in teacher education has shifted from an emphasis on teachers' behaviors to a focus on teachers' thinking. The purposes of this paper are to describe categories of beliefs held by preservice secondary science teachers, to describe how the preservice teachers planned lessons, and to discuss the influence of their beliefs on their planning. This paper draws on data from two studies of preservice teacher education. The informants for these studies were five preservice teachers enrolled in secondary science education. Data collection consisted of semi-structured, open-ended interviews, observations, and lesson plans written by the informants. The findings from both studies indicated that preservice teachers shared many common beliefs, possibly reflecting the influence of the education classes. Most teachers placed importance on science processes and valued understanding over memorization of facts. The findings support the contention that beliefs are a dominant factor in influencing teachers' planning and that teacher educators may influence previous teachers' developing beliefs, or reinforce existing beliefs. It was recommended that science educators promote self-reflection among preservice science teachers. (CW)
Online Teacher Education: Exploring the Impact of a Reading and Literacy Program on Student Learning.

This study presents findings from an investigation of the impact of teachers who graduated from a fully online master’s degree program with training in pedagogy and a content-specialization in elementary reading and literacy (oERL) on reading achievement in a large urban public school system in the northwestern United States. The research team used a non-equivalent group design and matched pairs of teachers based on degree, grade-level taught, and teaching experience to construct the study on three years of student and teacher data. The study consisted of 70 teachers and 3,828 student observations. Hierarchical linear modeling was employed to understand the teachers’ effects on student learning over time. Results indicate there was a significant positive effect of the oERL on student achievement. Broadly, this study is an example of a serious attempt to ascertain the impact of a high demand and fully online program on the community where graduates are employed. More narrowly, these results support the view that a fully online program aimed at training teachers, a rarity in graduate education, can provide opportunities for those teachers to obtain the pedagogical content knowledge that can positively influence instructional effectiveness.
ClassBench: A Packet Classification Benchmark

Packet classification is an enabling technology for next generation network services and often a performance bottleneck in high-performance routers. The performance and capacity of many classification algorithms and devices, including TCAMs, depend upon properties of filter sets and query patterns. Despite the pressing need, no standard performance evaluation tools or filter sets are publicly available. In response to this problem, we present ClassBench, a suite of tools for benchmarking packet classification algorithms and devices. ClassBench includes a filter set generator that produces synthetic filter sets that accurately model the characteristics of real filter sets. Along with varying the size of the filter sets, we provide high-level control over the composition of the filters in the resulting filter set. The tool suite also includes a trace generator that produces a sequence of packet headers to exercise packet classification algorithms with respect to a given filter set. Along with specifying the relative size of the trace, we provide a simple mechanism for controlling locality of reference. While we have already found ClassBench to be very useful in our own research, we seek to eliminate the significant access barriers to realistic test vectors for researchers and initiate a broader discussion to guide the refinement of the tools and codification of a formal benchmarking methodology. (The ClassBench tools are publicly available at the following site: http://www.arl.wustl.edu/~det3/ClassBench/.)
Independent Component Analysis and Projection Pursuit: A Tutorial Introduction

Independent component analysis (ICA) and projection pursuit (PP) are two related techniques for separating mixtures of source signals into their individual components. These rapidly evolving techniques are currently nding applications in speech separation, ERP, EEG, fMRI, and low-level vision. Their power resides in the simple and realistic assumption that diierent physical processes tend to generate statistically independent signals. We provide an account that is intended as an informal introduction, as well as a mathematical and geometric description of the methods.
Combining think-aloud and physiological data to understand video game experiences

Think-aloud protocols are commonly used to evaluate player experiences of video games but suffer from a lack of objectivity and timeliness. On the other hand, quantitative captures of physiological data are effective; providing detailed, unbiased and continuous responses of players, but lack contexts for interpretation. This paper documents how both approaches could be used together in practice by comparing video-cued retrospective think-aloud data and physiological data collected during a video gameplay experiment. We observed that many interesting physiological responses did not feature in participants' think-aloud data, and conversely, reports of interesting experiences were sometimes not observed in the collected physiological data. Through learnings from our experiment, we present some of the challenges when combining these approaches and offer some guidelines as to how qualitative and quantitative data can be used together to gain deeper insights into player experiences.
Mode of action and membrane specificity of the antimicrobial peptide snakin-2

Antimicrobial peptides (AMPs) are a diverse group of short, cationic peptides which are naturally occurring molecules in the first-line defense of most living organisms. They represent promising candidates for the treatment of pathogenic microorganisms. Snakin-2 (SN2) from tomato (Solanum lycopersicum) is stabilized through six intramolecular disulphide bridges; it shows broad-spectrum antimicrobial activity against bacteria and fungi, and it agglomerates single cells prior to killing. In this study, we further characterized SN2 by providing time-kill curves and corresponding growth inhibition analysis of model organisms, such as E. coli or B. subtilis. SN2 was produced recombinantly in E. coli with thioredoxin as fusion protein, which was removed after affinity purification by proteolytic digestion. Furthermore, the target specificity of SN2 was investigated by means of hemolysis and hemagglutination assays; its effect on plant cell membranes of isolated protoplasts was investigated by microscopy. SN2 shows a non-specific pore-forming effect in all tested membranes. We suggest that SN2 could be useful as a preservative agent to protect food, pharmaceuticals, or cosmetics from decomposition by microbes.
Alanine Aminotransferase and Directly Measured Insulin Sensitivity in a Multiethnic Cohort

OBJECTIVE— The objective of the present analysis was to evaluate the association of alanine aminotransferase (ALT) with directly measured insulin sensitivity (Si) in a large, multiethnic cohort of U.S. adults and to determine whether ALT adds to existing metabolic risk definitions in identifying subjects with insulin resistance. RESEARCH DESIGN AND METHODS— Si was directly measured from frequently sampled intravenous glucose tolerance tests among 999 nondiabetic African-American, Hispanic, and non-Hispanic white subjects aged 40–69 years who were participating in the Insulin Resistance Atherosclerosis Study. Subjects also received an oral glucose tolerance test, and fasting insulin, ALT, and alcohol intake were determined. RESULTS— ALT was associated with Si after adjustment for age, sex, ethnicity, impaired fasting glucose, triglycerides, HDL, blood pressure, and waist (clinical model) (P < 0.0001). The association remained significant after further adjustment for fasting insulin and impaired glucose tolerance (P = 0.004). In logistic regression analysis, elevated ALT (upper quartile) was associated with insulin resistance (lowest quartile of Si) after adjustment for age, sex, and ethnicity (odds ratio 3.0 [95% CI 2.2–4.1]). Elevated ALT was independently associated with insulin resistance when included in models with waist circumference, National Cholesterol Education Program criteria for metabolic syndrome, hypertriglyceridemic waist, elevated triglyceride-to-HDL ratio, or homeostasis model assessment of insulin resistance (HOMA-IR) (all P < 0.01). Finally, the addition of elevated ALT improved classification of insulin resistance by area under the receiver operating characteristic curve criteria for all models except HOMA-IR. CONCLUSIONS— ALT was associated with insulin resistance independently of conventional and more detailed metabolic measures. These findings suggest that the addition of ALT to existing clinically based metabolic risk definitions is an inexpensive way to improve the identification of subjects with insulin resistance.
A Prospective Study of the Association Between Quantity and Variety of Fruit and Vegetable Intake and Incident Type 2 Diabetes

OBJECTIVE The association between quantity of fruit and vegetable (F&V) intake and risk of type 2 diabetes (T2D) is not clear, and the relationship with variety of intake is unknown. The current study examined the association of both quantity and variety of F&V intake and risk of T2D. RESEARCH DESIGN AND METHODS We examined the 11-year incidence of T2D in relation to quantity and variety of fruit, vegetables, and combined F&V intake in a case-cohort study of 3,704 participants (n = 653 diabetes cases) nested within the European Prospective Investigation into Cancer and Nutrition-Norfolk study, who completed 7-day prospective food diaries. Variety of intake was derived from the total number of different items consumed in a 1-week period. Multivariable, Prentice-weighted Cox regression was used to estimate hazard ratios (HRs) and 95% CIs. RESULTS A greater quantity of combined F&V intake was associated with 21% lower hazard of T2D (HR 0.79 [95% CI 0.62–1.00]) comparing extreme tertiles, in adjusted analyses including variety. Separately, quantity of vegetable intake (0.76 [0.60–0.97]), but not fruit, was inversely associated with T2D in adjusted analysis. Greater variety in fruit (0.70 [0.53–0.91]), vegetable (0.77 [0.61–0.98]), and combined F&V (0.61 [0.48–0.78]) intake was associated with a lower hazard of T2D, independent of known confounders and quantity of intake comparing extreme tertiles. CONCLUSIONS These findings suggest that a diet characterized by a greater quantity of vegetables and a greater variety of both F&V intake is associated with a reduced risk of T2D.
Risk factors from childhood to adulthood for bronchial responsiveness at age 32-42 yr.

UNLABELLED Bronchial responsiveness (BR) is an important risk factor for the development and outcome of asthma. This study assessed childhood risk factors for both the severity of BR in adulthood and either improvement or worsening of BR over time. Finally, we studied cross-sectional risk factors of BR in adulthood. Between 1966 and 1969, 119 allergic asthmatic children (5-14 yr of age) were studied. Of these, 101 (85%) subjects were reinvestigated at age 22-32 yr (visit 2), and at age 32-42 yr (visit 3). Spirometry, PC10 histamine, skin tests, blood eosinophils, and serum total IgE were measured and a questionnaire was used. Higher FEV1 values in childhood were associated with less severe BR at age 32-42 yr independent of other potential risk factors. Larger increases in FEV1 values both from visit 1 to 2 and from visit 2 to 3, a longer time interval from visit 1 to 3, and having pets in childhood were associated with less severe BR at age 32-42 yr. The same factors were found to be associated with less deterioration of BR from visit 2 to 3. In nonsmokers a higher IgE level at visit 2 was a risk factor for an increase in BR. At age 32-42 yr, a low level of lung function and the presence of asthma symptoms were associated with more severe BR, and older age and having pets were associated with less severe BR. IgE was related to more severe BR only in nonsmokers. CONCLUSIONS A lower lung function in childhood and less improvement in FEV1 over time were associated with more severe BR in adulthood.
A Music Therapy Program Design for Neuro-Palliative Care

The use of music therapy to support patients and families receiving traditional (oncology-based) palliative care has been well established in research and practice (Bradt & Dileo, 2010). In this study, intervention research methodology (Fraser, Richman, Galinsky & Day, 2009) was used to develop a music therapy intervention manual for the emergent sub-specialty of neuro-palliative care. Two participants (one neuro-palliative care nurse clinician, one music therapist) were interviewed regarding applications of music therapy for patients with end-stage brain tumours, strokes, and progressive neurologic disorders. These findings, combined with the researcher’s own clinical experience, suggest the essential components of a music therapy program for neuro-palliative care, give detailed descriptions of music therapy interventions for neuro-palliative care, and outline patient and family needs and goals which music therapy may address in a neuro-palliative care settings.
A Gaussian radial basis function based feature selection algorithm

Recently Li et al. proposed a parameter selection method for Gaussian radial basis function (GRBF) in support vector machine (SVM). In his paper cosine similarity was calculated between two vectors based on the properties of GRBF kernel function. Li's method can determine an optimal sigma in SVM and thus efficiently improve its performance, yet it is limited by only focusing on a fixed original feature space and may suffer if the space contains some irrelevant and redundant features, especially in a high-dimensional feature space. In this paper, Li's method is extended to a flexible feature space so that feature selection and parameter selection are conducted at the same time. A feature subset and sigma are determined by minimizing the objective function that considers both within-class and between-class cosine similarities. Our experimental results demonstrate that the proposed method has a better performance than Li's method and traditional SVM in terms of classification accuracy.
Chinese-Japanese Machine Translation Exploiting Chinese Characters

The Chinese and Japanese languages share Chinese characters. Since the Chinese characters in Japanese originated from ancient China, many common Chinese characters exist between these two languages. Since Chinese characters contain significant semantic information and common Chinese characters share the same meaning in the two languages, they can be quite useful in Chinese-Japanese machine translation (MT). We therefore propose a method for creating a Chinese character mapping table for Japanese, traditional Chinese, and simplified Chinese, with the aim of constructing a complete resource of common Chinese characters. Furthermore, we point out two main problems in Chinese word segmentation for Chinese-Japanese MT, namely, unknown words and word segmentation granularity, and propose an approach exploiting common Chinese characters to solve these problems. We also propose a statistical method for detecting other semantically equivalent Chinese characters other than the common ones and a method for exploiting shared Chinese characters in phrase alignment. Results of the experiments carried out on a state-of-the-art phrase-based statistical MT system and an example-based MT system show that our proposed approaches can improve MT performance significantly, thereby verifying the effectiveness of shared Chinese characters for Chinese-Japanese MT.