Miracle-dz/UniFAR-FTUs huggingface dataset:
Population Indices Versus Correlated Density Estimates of Black-Footed Ferret Abundance

Abstract Estimating abundance of carnivore populations is problematic because individuals typically are elusive, nocturnal, and dispersed across the landscape. Rare or endangered carnivore populations are even more difficult to estimate because of small sample sizes. Considering behavioral ecology of the target species can drastically improve survey efficiency and effectiveness. Previously, abundance of the black-footed ferret (Mustela nigripes) was monitored by spotlighting and generating indices of relative abundance because reintroduced populations were slow to establish. Indices, however, lack variance estimates and are costly to generate for the black-footed ferret. We therefore used spotlight surveys and live-trapping in conjunction with a robust mark–recapture estimator to improve abundance monitoring for the black-footed ferret, one of North America's most endangered carnivores. We estimated abundance of the black-footed ferret at Shirley Basin, Wyoming, USA, using correlated density estimates and Program MARK. We compared our results to 2 indices of relative abundance, minimum number alive and predicted number of ferrets from litter counts. The correlated density estimate for the black-footed ferret (N̂R = 229; 95% CI = 161–298) was similar to minimum number alive (N̂ = 192) and predicted number of ferrets from litter counts (N̂ = 235). The efficiency and effectiveness of survey methods we used for the black-footed ferret were high by carnivoran standards. Our results suggest that the sampling approach we utilized can be implemented for a fraction of the cost and effort required to generate 2 indices of relative abundance for the black-footed ferret. Although we recommend managers implement a similar survey approach to monitor abundance of reintroduced populations of the black-footed ferret, analysis with sparse data sets will be problematic. Until the black-footed ferret becomes widespread and abundant at a reintroduction site, spotlighting will remain preferable as a means to generate indices of distribution and relative abundance for the black-footed ferret.
Identification and Characterization of the Donkey CSN1S2 I and II cDNAs

The αs2 casein, encoded by the CSN1S2 gene, is one of the three Calcium sensitive caseins present in the milk of ruminants of zootechnical interest and in the milk of Equidae species (horse and donkey). In the present study, we cloned, sequenced and analysed two different donkey CSN1S2 cDNAs that we called CSN1S2 I and CSN1S2 II. The first, which spans over a fragment of 1016 nt, is constituted by 19 exons and encodes for a predicted protein (called αs2-I) of 221 aminoacids; the second, of which we determined the entire sequence (16 exons), encodes for a predicted peptide (called αs2-II) of 168 aminoacids. Alternative splicing and genetic markers are reported for both genes.
Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search

High dimensional black-box optimization has broad applications but remains a challenging problem to solve. Given a set of samples $\{\vx_i, y_i\}$, building a global model (like Bayesian Optimization (BO)) suffers from the curse of dimensionality in the high-dimensional search space, while a greedy search may lead to sub-optimality. By recursively splitting the search space into regions with high/low function values, recent works like LaNAS shows good performance in Neural Architecture Search (NAS), reducing the sample complexity empirically. In this paper, we coin LA-MCTS that extends LaNAS to other domains. Unlike previous approaches, LA-MCTS learns the partition of the search space using a few samples and their function values in an online fashion. While LaNAS uses linear partition and performs uniform sampling in each region, our LA-MCTS adopts a nonlinear decision boundary and learns a local model to pick good candidates. If the nonlinear partition function and the local model fits well with ground-truth black-box function, then good partitions and candidates can be reached with much fewer samples. LA-MCTS serves as a \emph{meta-algorithm} by using existing black-box optimizers (e.g., BO, TuRBO) as its local models, achieving strong performance in general black-box optimization and reinforcement learning benchmarks, in particular for high-dimensional problems.
Role of Egg-Laying Experience in Avoidance of Superparasitism by Fruit Fly Parasitoid Fopius arisanus (Hymenoptera: Braconidae)

Abstract The effect of prior oviposition experience on the avoidance of superparasitism was investigated in the solitary endoparasitoid Fopius arisanus (Sonan) (Hymenoptera: Braconidae), a highly successful biological control agent of tephritid fruit fly pests. Individual female wasps, either naïve or having prior egg-laying experience in unparasitized hosts, were allowed to sequentially visit two coffee berries, each infested with a clutch of host eggs of the Mediterranean fruit fly, Ceratitis capitata (Wiedemann) (Diptera: Tephritidae). One of the two berries had been previously exploited by a conspecific female or the test-female herself. The wasp was allowed to visit the previously exploited or unexploited berry first while either in the presence or absence of a conspecific female. Naïve wasps readily accepted previously parasitized hosts only when they visited the previously exploited fruit first. In contrast, wasps that had experienced prior oviposition, and wasps that visited the unexploited berry first, never accepted previously parasitized hosts, even when the test wasps contained relatively high egg-load or had been exposed to hosts in the presence of conspecific females. These results demonstrate a dramatic effect of egg-laying experience on the avoidance of superparasitism by this parasitoid. We discuss the adaptive value of avoiding superparasitism in relation to the mechanism of intraspecific competition in this species.
Epileptic Seizures Detection Using Deep Learning Techniques: A Review

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.
Computing Conceptual Distances between Breast Cancer Screening Guidelines: An Implementation of a Near-Peer Epistemic Model of Medical Disagreement

Using natural language processing tools, we investigate the differences of recommendations in medical guidelines for the same decision problem -- breast cancer screening. We show that these differences arise from knowledge brought to the problem by different medical societies, as reflected in the conceptual vocabularies used by the different groups of authors.The computational models we build and analyze agree with the near-peer epistemic model of expert disagreement proposed by Garbayo. Even though the article is a case study focused on one set of guidelines, the proposed methodology is broadly applicable. In addition to proposing a novel graph-based similarity model for comparing collections of documents, we perform an extensive analysis of the model performance. In a series of a few dozen experiments, in three broad categories, we show, at a very high statistical significance level of 3-4 standard deviations for our best models, that the high similarity between expert annotated model and our concept based, automatically created, computational models is not accidental. Our best model achieves roughly 70% similarity. We also describe possible extensions of this work.
THE INTERNATIONAL EXPANSION OF CHINA’S SMALL AND MEDIUM-SIZED FAMILY BUSINESS. STATUS TODAY AND FUTURE OUTLOOK

Purpose: The paper critically analyses the relevant literature on the international expansion of China’s small and medium-sized family enterprises with the aim of highlighting the main topics analysed by scholars and identifying areas for future research. Design: The paper reviews the works on the international growth of China’s SMFEs published in selected peer-reviewed English-language journals vis-à-vis what has been published on Chinese MNEs and Western SMFEs. It does this by, first, studying the literature at both firm and industry levels and, second, by analysing the specific characteristics of small business from China along with the particularities of the Chinese business environment. Findings: The paper highlights the need to deepen the understanding of the process leading to SMFEs’ expansion beyond China’s borders to conclude with the identification of areas for future research.
Long range dark matter self-interactions and plasma instabilities

So far, the observed effects of dark matter are compatible with it having purely gravitational interactions. However, in many models, dark matter has additional interactions with itself, with the Standard Model, and/or with additional hidden sector states. In this paper, we discuss models in which dark matter interacts through a light vector mediator, giving rise to a long-ranged force between dark matter particles. Coherent scattering effects through this force can lead to the exponential growth of small perturbations, in analogy to electromagnetic plasma instabilities. These instabilities can be significant at couplings many orders of magnitude below those for which the usual particle-by-particle constraints on dark matter self-interactions apply. While this possibility has been noted in the literature, we provide the first systematic study of such instabilities, including the case where the mediator has finite mass. The latter is relevant for models of kinetically mixed 'dark photon' mediators, which represent an important target for proposed dark matter detection experiments. Our analyses are of the growth of small perturbations, so do not immediately provide observational constraints on dark matter models—however, they do motivate further study of large regions of parameter space.
On Linear Identifiability of Learned Representations

Identifiability is a desirable property of a statistical model: it implies that the true model parameters may be estimated to any desired precision, given sufficient computational resources and data. We study identifiability in the context of representation learning: discovering nonlinear data representations that are optimal with respect to some downstream task. When parameterized as deep neural networks, such representation functions typically lack identifiability in parameter space, because they are overparameterized by design. In this paper, building on recent advances in nonlinear ICA, we aim to rehabilitate identifiability by showing that a large family of discriminative models are in fact identifiable in function space, up to a linear indeterminacy. Many models for representation learning in a wide variety of domains have been identifiable in this sense, including text, images and audio, state-of-the-art at time of publication. We derive sufficient conditions for linear identifiability and provide empirical support for the result on both simulated and real-world data.
Bidirectional Encoder Representations from Transformers (BERT): A sentiment analysis odyssey

The purpose of the study is to investigate the relative effectiveness of four different sentiment analysis techniques: (1) unsupervised lexicon-based model using Sent WordNet; (2) traditional supervised machine learning model using logistic regression; (3) supervised deep learning model using Long Short-Term Memory (LSTM); and, (4) advanced supervised deep learning models using Bidirectional Encoder Representations from Transformers (BERT). We use publicly available labeled corpora of 50,000 movie reviews originally posted on internet movie database (IMDB) for analysis using Sent WordNet lexicon, logistic regression, LSTM, and BERT. The first three models were run on CPU based system whereas BERT was run on GPU based system. The sentiment classification performance was evaluated based on accuracy, precision, recall, and F1 score. The study puts forth two key insights: (1) relative efficacy of four highly advanced and widely used sentiment analysis techniques; (2) undisputed superiority of pre-trained advanced supervised deep learning BERT model in sentiment analysis from text data. This study provides professionals in analytics industry and academicians working on text analysis key insight regarding comparative classification performance evaluation of key sentiment analysis techniques, including the recently developed BERT. This is the first research endeavor to compare the advanced pre-trained supervised deep learning model of BERT vis-a-vis other sentiment analysis models of LSTM, logistic regression, and Sent WordNet.
Formation of Soy Protein Isolate Cold‐set Gels: Protein and Salt Effects

The influence of pr The influence of pr The influence of pr The influence of pr The influence of protein and calcium concentr increasing protein concentration and decreasing CaCl 2 concentration. The elastic modulus (G concentration. The elastic modulus (G concentration. The elastic modulus (G concentration. The elastic modulus (G concentration. The elastic modulus (G) increased with ) increased with ) increased with ) increased with ) increased with protein and calcium chloride concentrations. Microscopy revealed an increase in the diameters of aggregates protein and calcium chloride concentrations. Microscopy revealed an increase in the diameters of aggregates protein and calcium chloride concentrations. Microscopy revealed an increase in the diameters of aggregates protein and calcium chloride concentrations. Microscopy revealed an increase in the diameters of aggregates protein and calcium chloride concentrations. Microscopy revealed an increase in the diameters of aggregates and pores as CaCl and pores as CaCl and pores as CaCl and pores as CaCl and pores as CaCl 2 concentration increased and as protein concentration decreased. Cold-set gels with a broad concentration increased and as protein concentration decreased. Cold-set gels with a broad concentration increased and as protein concentration decreased. Cold-set gels with a broad concentration increased and as protein concentration decreased. Cold-set gels with a broad concentration increased and as protein concentration decreased. Cold-set gels with a broad range of characteristics can be obtained from soy protein. range of characteristics can be obtained from soy protein. range of characteristics can be obtained from soy protein. range of characteristics can be obtained from soy protein. range of characteristics can be obtained from soy protein.
Health Hazards, Risk factors and Safety Practices in Construction Sites –A Review Study

Construction workers face several health hazards and risk in construction sites. Paying minimum attention to construction site safety may directly associate with loss of productivity, accidents and health problems among workers. This review attempts to identify the health hazards and risk factors in construction site, and causes for lacking safety practices in construction sites. In addition, the differences in safety practices in both developed and developing countries and methods for improving construction site safety are discussed. Health hazards among construction workers are several types: physical health hazards, chemical health hazards and biological health hazards. Effects of some health hazards are chronic while some are acute. Hazardous substances were identified as the most hazardous risk factor in construction sites. Mostly reported risk factors in construction sites are , physical hazards (i.e., noise, heat, humidity, solar radiation, radiation from nuclear power plants), and skin sanitizers, irritants (i.e., bitumen, acids, alkalis, cement). Lack of awareness about site safety or dislike to wear Personal Protective Equipment (PPE) was identified as the one of main causes for scarcity of safety practices in construction site. In addition, loss of productivity after lunch, lack of training facilities and lack of effective labour training (possibly due to the transient nature of the construction workforce) were identified as other possible causes of poor safety practices in construction sites.
NMR analyses of compatible solutes in a halotolerant Brevibacterium sp.

Summary: Internal solutes in a halotolerant Brevibacterium sp. (strain JCM 6894) were analysed by 1H NMR spectroscopy. At the lower osmolarity of complex medium, the main compatible solutes in the cells were glutamate, glycine betaine and hydroxyproline. At higher external salt concentrations, ectoine (1,4,5,6-tetrahydro-2-methyl-4-pyrimidinecarboxylic acid) levels increased and this became the predominant compatible solute at 1.5 M NaCl, both ectoine and hydroxyectoine became dominant, as was also the case in complex medium. Additional compatible solutes detected were γ-aminobutyrate and proline, neither of which were detected in cells grown in complex medium. Regardless of the medium composition and incubation time, ectoine levels were high at high osmolarity, indicating that the halotolerant nature of Brevibacterium sp. is closely related to its accumulation of ectoine. From its accumulation of compatible solutes, we suggest that Brevibacterium sp. JCM 6894 is a different species from other members of the genus Brevibacterium.
Cells Activates Senescence and Increases Susceptibility to Cell-Mediated Cytotoxicity

Activating mutations in BRAF and NRAS oncogenes in human melanomas are mutually exclusive. This finding has suggested an epistatic relationship but is consistent even with synthetic lethality. To evaluate the latter possibility, a mutated NRAS Q61R oncogene was expressed, under a constitutive or a doxycycline-regulated promoter, in a metastatic melanoma clone (clone 21) harboring an activated BRAF V600E oncogene. After the first 10 to 12 in vitro passages, the constitutive NRAS Q61R transfectant displayed progressive accumulation in G0-G1 phase of the cell cycle and stained for the senescenceassociated B-galactosidase activity (SA-B-Gal). Inducible expression of NRAS Q61R , by the Tet-Off system, in clone 21cells (21NRAS 61ON ) led to overactivation of the RAS/RAF/mitogenactivated protein kinase signaling pathway and, after the 10th in vitro passage, led to promotion of senescence. This was documented by reduced proliferation, flattened cell morphology, reduced growth in Matrigel, positive staining for SA-BGal, and expression of AMP-activated protein kinase and of the cell cycle inhibitor p21 waf1/Cip1 . These effects were detected neither in 21cells with silenced NRAS Q61R (21NRAS 61OFF ) nor in cells transfected with an inducible wild-type NRAS gene (21NRAS WTON ). In addition, when compared with parental 21 cells, or with 21NRAS 61OFF , 21NRAS 61ON and constitutive NRAS Q61R transfectants cells showed increased susceptibility to cytotoxicity by both HLA class I antigen–restricted and nonspecific T cells and up-regulation of several MHC class I antigen processing machinery components. These results suggest a relationship of synthetic lethality between NRAS and BRAF oncogenes, leading to selection against ‘‘doublemutant’’ cells. (Cancer Res 2006; 66(13): 6503-11)
The composite X-ray spectra of radio-loud and radio-quiet SDSS quasars

We present a study of the X-ray emission for a sample of radio-detected quasars constructed from the cross-matches between SDSS, FIRST catalogs and XMM-Newton archives. A sample of radio-quiet SDSS quasars without FIRST radio detection is also assembled for comparison. We construct the optical and X-ray composite spectra normalized at rest frame 4215 Å (or 2200 Å) for both radio-loud quasars (RLQs) and radio-quiet quasars (RQQs) at z ≤ 3.2, with matched X-ray completeness of 19%, redshift and optical luminosity. While the optical composite spectrum of RLQs is similar to that of RQQs, we find that RLQs have a higher X-ray composite spectrum than RQQs, consistent with previous studies in the literature. By dividing the radio-detected quasars into radio loudness bins, we find the X-ray composite spectra are generally higher with increasing radio loudness. Moreover, a significant correlation is found between the optical-to-X-ray spectral index and radio loudness, and there is a unified multi-correlation between the radio and X-ray luminosities and radio loudness in radio-detected quasars. These results could be possibly explained with the corona-jet model, in which the corona and jet are directly related.
Compartmental model with loss of immunity: analysis and parameters estimation for Covid-19

The outbreak of Covid-19 led the world to an unprecedent health and economical crisis. In an attempt to responde to this emergency researchers worldwide are intensively studying the Covid-19 pandemic dynamics. In this work, a SIRSi compartmental model is proposed, which is a modification of the known classical SIR model. The proposed SIRSi model considers differences in the immunization within a population, and the possibility of unreported or asymptomatic cases. The model is adjusted to three major cities of Sao Paulo State, in Brazil, namely, Sao Paulo, Santos and Campinas, providing estimates on the duration and peaks of the outbreak.
OXIDATION DESENSITIZES ACTOMYOSIN TO PYROPHOSPHATE- INDUCED DISSOCIATION BUT FACILITATES MYOSIN CROSS- LINKING BY MICROBIAL TRANSGLUTAMINASE

Structural changes of myosin are a major consequence of muscle protein oxidation. This study tested the hypothesis that oxidative structural modification of myosin in myofibrils by oxidants could change the dissociation of actomyosin by pyrophosphate (PP) and the aggregation of myosin by microbial transglutaminase (TG). Myofibrils or actomyosin prepared from pork Longissimus lumborum (LL) were exposed to hydroxyl radicals (•OH) generated under meat processing conditions at 4°C up to 12 h. Time-dependent protein oxidation was evidenced by the progressive formation of carbonyls, loss of sulfhydryls, increased surface hydrophobicity, and decreased myosin K + -ATPase activity. The protein side-chain and conformational changes led to a weaker binding of PP to myosin and a slow response of the intrinsic viscosity of oxidized actomyosin to PP-Mg 2+ , indicating suppressed dissociation of actomyosin due to oxidation. However, such structural modifications (unfolding) promoted TG-catalyzed myosin cross-linking which was indicated by SDS-PAGE and the amine content analysis. In conclusion, under mild oxidative conditions, •OH-modified actomyosin was less susceptible to PP dissociation, but the myosin moiety became more readily accessible to TG cross-linking. Such information may help meat processors decide appropriate ingredient formulations based on the meat source to maximize the product quality.