Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimate of Genetic Parameters for Temperament and Body Measurements of Beef Cattle

Japanese beef cattle multipliers are characterized by small herd size and an intensive feeding system. As a result, the temperament of cows is one of the major concerns of farmers when they replace and cull cows. Therefore, the temperament of individuals seems to affect the culling (and/or selection) criteria of cows. Genetic analyses of temperament in beef cattle have been reported in the U.S. and Australia1-3). For Japanese Black Cattle, SATO4) reported intermediate to high heritability of temperament using about 200 cows in a univ. station. But, reliable genetic parameters have not yet been reported for the commercial beef cattle population in Japan. The purpose of this study was to estimate the genetic parameters for temperament and then relationship to body measurements on field beef cattle records.
Population Structure and Phenotypic Characterization as a Basis for Conservation and Sustainable Use of Reyna Creole Cattle in Nicaragua

The Reyna Creole cattle in Nicaragua is a local breed originating from the Bos taurus cattle brought to Latin America during the Spanish colonization in the fifteenth century. Reyna Creole cattle are today found in five herds. The primary use of the breed is to produce milk. The breed comprises about 650 purebred female animals. The main objective of the thesis was to characterize the population structure and productivity in terms of milk production and some reproduction traits, and to discuss options for conservation and improvement of the breed. The inbreeding level was 13% on average, primarily due to use of individual bulls for long periods. An effective population size of 28 to 46 animals was estimated, showing that the Reyna Creole cattle breed is endangered, close to critical status. Average birth weight was 27.8 kg, age at first calving 37.4 months and calving interval 14.0 months. Large differences between herds were observed for all traits. Heritabilities were 0.34, zero and 0.20 for the respective trait. Average lactation yield for 534 Reyna cows with 1,750 lactations was 1,319 kg with large variations between herds and time periods. Test-day records on milk yield and fat and protein contents were collected monthly during one year for three Reyna herds and two herds with crossbred cows. Commercial dairy breed crosses had the highest production, and Reyna the lowest, but large differences were noted in management between the herds. The heritability for lactation yield in Reyna cattle was 0.18. Large variation between and within the Reyna herds suggest good opportunities to increase productivity of the Reyna breed by improving management and breeding strategies. It is proposed to keep all females in the herds, develop mating plans and to apply a recording scheme for keeping pedigrees and records on reproduction and milk production traits. Only young bulls, selected from the best cows for milk production and calving interval, are proposed to be used for 1-2 years each to manage inbreeding and keep generation intervals short.
University of Groningen The Sortase A Substrates FnbpA, FnbpB, ClfA and ClfB Antagonize Colony Spreading of Staphylococcus aureus Tsompanidou,

Staphylococcus aureus is an important human pathogen that is renowned both for its rapid transmission within hospitals and the community, and for the formation of antibiotic resistant biofilms on medical implants. Recently, it was shown that S. aureus is able to spread over wet surfaces. This motility phenomenon is promoted by the surfactant properties of secreted phenol-soluble modulins (PSMs), which are also known to inhibit biofilm formation. The aim of the present studies was to determine whether any cell surface-associated S. aureus proteins have an impact on colony spreading. To this end, we analyzed the spreading capabilities of strains lacking non-essential components of the protein export and sorting machinery. Interestingly, our analyses reveal that the absence of sortase A (SrtA) causes a hyper-spreading phenotype. SrtA is responsible for covalent anchoring of various proteins to the staphylococcal cell wall. Accordingly, we show that the hyper-spreading phenotype of srtA mutant cells is an indirect effect that relates to the sortase substrates FnbpA, FnbpB, ClfA and ClfB. These surface-exposed staphylococcal proteins are known to promote biofilm formation, and cell-cell interactions. The hyper-spreading phenotype of srtA mutant staphylococcal cells was subsequently validated in Staphylococcus epidermidis. We conclude that cell wall-associated factors that promote a sessile lifestyle of S. aureus and S. epidermidis antagonize the colony spreading motility of these bacteria. Citation: Tsompanidou E, Denham EL, Sibbald MJJB, Yang X-m, Seinen J, et al. (2012) The Sortase A Substrates FnbpA, FnbpB, ClfA and ClfB Antagonize Colony Spreading of Staphylococcus aureus. PLoS ONE 7(9): e44646. doi:10.1371/journal.pone. 0044646 Editor: Patrick M. Schlievert, University of Iowa Carver College of Medicine, United States of America Received April 21, 2012; Accepted August 6, 2012; Published September 7, 2012 Copyright: 2012 Tsompanidou et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: E.T. , E.L. D. , M.J.J.B. S, X.Y. , G.B. and J.M. v.D. were in part supported by the CEU projects LSHM-CT-2006-019064, and LSHG-CT-2006-037469, the transnational SysMO initiative through the BACELL SysMO projects 1 and 2, and the Top Institute Pharma project T4-213. E.T. and A.W. F. were supported through the INTERREG-IVa Euregio-project Safeguard MRSAVetMed-net with reference no. 34-INTERREG IV A-II-2-05 = 025. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: J.M. van.dijl01@umcg.nl . These authors contributed equally to this work.
Anthracnose Fruit Rot of Chilli (Capsicum Annum L.): Some Aspects of its Etiology, Epidemiology and Control in Peninsular Malaysia

Anthracnose fruit rot is a serious limiting factor in the production and yield of fresh chillies (Capsicum annuum L.) in Peninsular Malaysia. Isolates obtained from diseased chilli fruits collected in various localities in the country yielded the fungus, Colletotrichum capsici (Syd.) Butler & Bisby. Pathogenicity studies on mature chilli fruits confirmed that C. capsici could produce typical elliptical or oblong straw-coloured lesions on the fruit surface with subsequent development of black acervuli in concentric rings. Comparison of isolates on potato dextrose agar showed that all the isolates were relatively similar in cultural and morphological characteristics. Germination of conidia occurred 3-4 hours after plating on chilli agar medium. Branching of germtubes occurred between 16-24 hour s after germination. The fungus grew over a wide range of hydrogen-ion concentrations (between pH 3.0 and pH 10.0) with optimum between pH 7.0 and pH 9.5. Acervuli formation and development was more evident on alkaline medium than on acidic medium. Optimum temperature for mycelial growth was between 25˚C and 32˚C. The minimum and maximum temperatures for visible growth were l0˚C and 45˚C , respectively.
Automatic Extraction of Agriculture Terms from Domain Text: A Survey of Tools and Techniques

Agriculture is a key component in any country's development. Domain-specific knowledge resources serve to gain insight into the domain. Existing knowledge resources such as AGROVOC and NAL Thesaurus are developed and maintained by the domain experts. Population of terms into these knowledge resources can be automated by using automatic term extraction tools for processing unstructured agricultural text. Automatic term extraction is also a key component in many semantic web applications, such as ontology creation, recommendation systems, sentiment classification, query expansion among others. The primary goal of an automatic term extraction system is to maximize the number of valid terms and minimize the number of invalid terms extracted from the input set of documents. Despite its importance in various applications, the availability of online tools for the said purpose is rather limited. Moreover, the performance of the most popular ones among them varies significantly. As a consequence, selection of the right term extraction tool is perceived as a serious problem for different knowledge-based applications. This paper presents an analysis of three commonly used term extraction tools, viz. RAKE, TerMine, TermRaider and compares their performance in terms of precision and recall, vis-a-vis RENT, a more recent term extractor developed by these authors for agriculture domain.
Dynamic Near Maximum Independent Set with Time Independent of Graph Size

Maximum Independent Set ({MaxIS}) problem is a fundamental problem in graph theory, which is NP-hard. Since the underlying graphs are always changing in numerous applications, computing a {MaxIS} over dynamic graphs has received increasing attention in recent years. Due to the intractability to compute an exact MaxIS or its good approximation over dynamic graphs, this paper studies the problem to maintain a high-quality independent set, which is an approximation of the MaxIS, over dynamic graphs. A framework based on swap operations for resolving this problem is presented and two concrete update algorithms based on one-swappable vertices and two-swappble vertex pairs are designed. Both algorithms can compute high-quality independent sets over dynamic graphs with $O(\Delta^3)$ time for general graphs and with $O(1)$ time for bounded-degree graphs. Moreover, the lower bound of the size of the solution maintained by our algorithms is derived if there is no swappable vertex in it. Then the algorithms are extended under \textit{semi-external} setting to maintained a high-quality independent set with limited memory consumption. Extensive experiments are conducted over real graphs to confirm the effectiveness and efficiency of the proposed methods.
Two Modes of Carbonaceous Dust Alignment

Radiative torques (RATs) or mechanical torques acting on irregular grains can induce the alignment of dust grains in respect to the alignment axis (AA), which can be either the direction of the magnetic field or the direction of the radiation. We show that carbonaceous grains can be aligned with their axes both parallel and perpendicular to the AA, and we explore the conditions where the particular mode of alignment takes place. We identify a new process of alignment of charged carbonaceous grains in a turbulent, magnetized interstellar medium with respect to an electric field. This field acts on grains accelerated in a turbulent medium and gyrorotating about a magnetic field. The electric field can also arise from the temporal variations of the magnetic field strength in turbulent, compressible media. The direction of the electric field is perpendicular to the magnetic field, and the carbonaceous grains precess in the electric field because of their electric moments. If this precession is faster than Larmor precession in the magnetic field, the alignment of such grains is with their long axes parallel to the magnetic field. We explore the parameter space for which the new mechanism aligns grains with long axes parallel to the magnetic field. We compare this mechanism with another process that provides the same type of alignment, namely, the RAT alignment of grains with insufficiently fast internal relaxation. We describe the conditions for which the particular mode of carbonaceous grain alignment is realized and discuss what information can be obtained by measuring the resulting polarization.
Interplay of Rearrangements, Strain, and Local Structure during Avalanche Propagation

Jammed soft disks exhibit avalanches of particle rearrangements under quasistatic shear. We follow the avalanches using steepest descent to decompose them into individual localized rearrangements. We characterize the local structural environment of each particle by a machine-learned quantity, softness, designed to be highly correlated with rearrangements, and analyze the interplay between softness, rearrangements and strain. Our findings form the foundation of an augmented elastoplastic model that includes local structure.
Fast and stable randomized low-rank matrix approximation

Randomized SVD has become an extremely successful approach for efficiently computing a low-rank approximation of matrices. In particular the paper by Halko, Martinsson, and Tropp (SIREV 2011) contains extensive analysis, and has made it a very popular method. The typical complexity for a rank-$r$ approximation of $m\times n$ matrices is $O(mn\log n+(m+n)r^2)$ for dense matrices. The classical Nystr{o}m method is much faster, but applicable only to positive semidefinite matrices. This work studies a generalization of Nystr{o}m method applicable to general matrices, and shows that (i) it has near-optimal approximation quality comparable to competing methods, (ii) the computational cost is the near-optimal $O(mn\log n+r^3)$ for dense matrices, with small hidden constants, and (iii) crucially, it can be implemented in a numerically stable fashion despite the presence of an ill-conditioned pseudoinverse. Numerical experiments illustrate that generalized Nystr{o}m can significantly outperform state-of-the-art methods, especially when $r\gg 1$, achieving up to a 10-fold speedup. The method is also well suited to updating and downdating the matrix.
Optical and Near-infrared Observations of the Nearby SN Ia 2017cbv

Supernova (SN) 2017cbv in NGC 5643 is one of a handful of Type Ia supernovae (SNe Ia) reported to have excess blue emission at early times. This paper presents extensive BVRIYJHKs-band light curves of SN 2017cbv, covering the phase from −16 to +125 days relative to B-band maximum light. The SN 2017cbv reached a B-band maximum of 11.710 ± 0.006 mag, with a postmaximum magnitude decline of Δm15(B) = 0.990 ± 0.013 mag. The SN suffered no host reddening based on Phillips intrinsic color, the Lira–Phillips relation, and the CMAGIC diagram. By employing the CMAGIC distance modulus μ = 30.58 ± 0.05 mag and assuming H0 = 72 km s−1 Mpc−1, we found that 0.73 M⊙ 56Ni was synthesized during the explosion of SN 2017cbv, which is consistent with estimates using reddening- and distance-free methods via the phases of the secondary maximum of the near-IR- (NIR-) band light curves. We also present 14 NIR spectra from −18 to +49 days relative to the B-band maximum light, providing constraints on the amount of swept-up hydrogen from the companion star in the context of the single degenerate progenitor scenario. No Paβ emission feature was detected from our postmaximum NIR spectra, placing a hydrogen mass upper limit of 0.1 M⊙. The overall optical/NIR photometric and NIR spectral evolution of SN 2017cbv is similar to that of a normal SN Ia, even though its early evolution is marked by a flux excess not seen in most other well-observed normal SNe Ia. We also compare the exquisite light curves of SN 2017cbv with some Mch delayed detonation models and sub-Mch double detonation models.
Feature Adaptation of Pre-Trained Language Models across Languages and Domains for Text Classification

Adapting pre-trained language models (PrLMs) (e.g., BERT) to new domains has gained much attention recently. Instead of fine-tuning PrLMs as done in most previous work, we investigate how to adapt the features of PrLMs to new domains without fine-tuning. We explore unsupervised domain adaptation (UDA) in this paper. With the features from PrLMs, we adapt the models trained with labeled data from the source domain to the unlabeled target domain. Self-training is widely used for UDA which predicts pseudo labels on the target domain data for training. However, the predicted pseudo labels inevitably include noise, which will negatively affect training a robust model. To improve the robustness of self-training, in this paper we present class-aware feature self-distillation (CFd) to learn discriminative features from PrLMs, in which PrLM features are self-distilled into a feature adaptation module and the features from the same class are more tightly clustered. We further extend CFd to a cross-language setting, in which language discrepancy is studied. Experiments on two monolingual and multilingual Amazon review datasets show that CFd can consistently improve the performance of self-training in cross-domain and cross-language settings.
Development of Record and Management Software for GPS/Loran Measurements

In this paper, a software implementation that records Global Positioning System (GPS) and long-range navigation (Loran) measurement data output from an integrated GPS/Loran receiver and organizes them based on time is proposed. The purpose of the developed software is to collect measurements from multiple Loran transmitter chains for performance analysis of navigation methods using Loran, and to organize the data based on time to make it easy to use them. In addition, GPS measurements are also collected and managed as ground truth data for performance analysis. The implemented software consists of three modules: recording, classification, and conversion. The recording module records raw text data streamed from the receiver, and the classification module classifies the recorded text data according to the message format. The conversion module parses the classified text data, sorts GPS and Loran measurements based on timestamp, and outputs them according to the software platform of the user to analyze the measurements. Each module of the software runs automatically without user intervention. The functionality of the implemented software was verified using GPS and Loran measurements collected over 24 h from an actual integrated GPS/Loran receiver.
Structure Aware Negative Sampling in Knowledge Graphs

Learning low-dimensional representations for entities and relations in knowledge graphs using contrastive estimation represents a scalable and effective method for inferring connectivity patterns. A crucial aspect of contrastive learning approaches is the choice of corruption distribution that generates hard negative samples, which force the embedding model to learn discriminative representations and find critical characteristics of observed data. While earlier methods either employ too simple corruption distributions, i.e. uniform, yielding easy uninformative negatives or sophisticated adversarial distributions with challenging optimization schemes, they do not explicitly incorporate known graph structure resulting in suboptimal negatives. In this paper, we propose Structure Aware Negative Sampling (SANS), an inexpensive negative sampling strategy that utilizes the rich graph structure by selecting negative samples from a node's k-hop neighborhood. Empirically, we demonstrate that SANS finds semantically meaningful negatives and is competitive with SOTA approaches while requires no additional parameters nor difficult adversarial optimization.
3D Object Localization Using 2D Estimates for Computer Vision Applications

Computer vision based applications have received notable attention globally due to the interaction with the physical world. In this paper, a novel method for object localization based on camera calibration and pose estimation is discussed. The 3-dimensional (3D) coordinates are computed by taking multiple 2-dimensional (2D) images from different view that meets the requirement in camera calibration process. Several number of steps are involved in camera calibration including estimation of intrinsic and extrinsic parameters for the removal of lens distortion, estimation of object’s size and camera location. Besides, a technique to estimate the 3D pose using 2D images is proposed and the results of camera parameters and localization are applied for the 3D reconstruction. The hardware implementation of the proposed approach is implemented on HP core i5 with the MATLAB support packages and experimental results are validated for both camera calibration and pose estimation.
Adapting BERT for Word Sense Disambiguation with Gloss Selection Objective and Example Sentences

Domain adaptation or transfer learning using pre-trained language models such as BERT has proven to be an effective approach for many natural language processing tasks. In this work, we propose to formulate word sense disambiguation as a relevance ranking task, and fine-tune BERT on sequence-pair ranking task to select the most probable sense definition given a context sentence and a list of candidate sense definitions. We also introduce a data augmentation technique for WSD using existing example sentences from WordNet. Using the proposed training objective and data augmentation technique, our models are able to achieve state-of-the-art results on the English all-words benchmark datasets.
Graph Sparsification with Generative Adversarial Network

Graph sparsification aims to reduce the number of edges of a network while maintaining its accuracy for given tasks. In this study, we propose a novel method called GSGAN, which is able to sparsify networks for community detection tasks. GSGAN is able to capture those relationships that are not shown in the original graph but are relatively important, and creating artificial edges to reflect these relationships and further increase the effectiveness of the community detection task. We adopt GAN as the learning model and guide the generator to produce random walks that are able to capture the structure of a network. Specifically, during the training phase, in addition to judging the authenticity of the random walk, discriminator also considers the relationship between nodes at the same time. We design a reward function to guide the generator creating random walks that contain useful hidden relation information. These random walks are then combined to form a new social network that is efficient and effective for community detection. Experiments with real-world networks demonstrate that the proposed GSGAN is much more effective than the baselines, and GSGAN can be applied and helpful to various clustering algorithms of community detection.
A tale of two sites – II. Inferring the properties of minihalo-hosted galaxies with upcoming 21-cm interferometers

The first generation of galaxies is expected to form in minihalos, accreting gas through ${\rm H}_2$ cooling, and possessing unique properties. Although unlikely to be directly detected in UV/infrared surveys, the radiation from these molecular-cooling galaxies (MCGs) could leave an imprint in the 21-cm signal from the Cosmic Dawn. Here we quantify their detectability with upcoming radio interferometers. We generate mock 21-cm power spectra using a model for both MCGs as well as more massive, atomic-cooling galaxies (AGCs), allowing both populations to have different properties and scaling relations. The galaxy parameters are chosen so as to be consistent with: (i) high-redshift UV luminosity functions; (ii) the upper limit on the neutral fraction from QSO spectra; (iii) the Thomson scattering optical depth to the CMB; and (iv) the timing of the recent putative EDGES detection. The latter implies a significant contribution of MCGs to the Cosmic Dawn, if confirmed to be cosmological. We then perform Bayesian inference on two models including and ignoring MCG contributions. Comparing their Bayesian evidences, we find a strong preference for the model including MCGs, despite the fact that it has more free parameters. This suggests that if MCGs indeed play a significant role in the Cosmic Dawn, it should be possible to infer their properties from upcoming 21-cm power spectra. Our study illustrates how these observations can discriminate among uncertain galaxy formation models with varying complexities, by maximizing the Bayesian evidence.