Individual Factors Affecting Entrepreneurship in Hispanics

A model of entrepreneurship (Baron & Henry, 2011) is used to understand and explain the factors related to the behaviors of Hispanic entrepreneurs. Testable hypotheses to guide future research are presented

Influence of Raters’ Attributes on Biases Toward Immigrants

Although immigrants offer many benefits for organizations and our society, they continue to experience unfair discrimination, prejudice, and hostility in the employment process. One contributing factor towards the negative perceptions toward immigrants are the raters’ attributes (i.e., decision makers in the workplace). These attributes include their demographic background (e.g., age, gender), differences between raters’ and immigrants’ cultural values, raters’ personality, and raters’ previous contact with immigrants. In order to understand raters’ biases toward immigrants, we used the social cognition framework (Miller & Brewer, 1984) to explain the reasons for these biases, and offered hypotheses to guide future research on the issue

Large-scale Nonlinear Variable Selection via Kernel Random Features

We propose a new method for input variable selection in nonlinear regression. The method is embedded into a kernel regression machine that can model general nonlinear functions, not being a priori limited to additive models. This is the first kernel-based variable selection method applicable to large datasets. It sidesteps the typical poor scaling properties of kernel methods by mapping the inputs into a relatively low-dimensional space of random features. The algorithm discovers the variables relevant for the regression task together with learning the prediction model through learning the appropriate nonlinear random feature maps. We demonstrate the outstanding performance of our method on a set of large-scale synthetic and real datasets.Comment: Final version for proceedings of ECML/PKDD 201

Determining appropriate approaches for using data in feature selection

Feature selection is increasingly important in data analysis and machine learning in big data era. However, how to use the data in feature selection, i.e. using either ALL or PART of a dataset, has become a serious and tricky issue. Whilst the conventional practice of using all the data in feature selection may lead to selection bias, using part of the data may, on the other hand, lead to underestimating the relevant features under some conditions. This paper investigates these two strategies systematically in terms of reliability and effectiveness, and then determines their suitability for datasets with different characteristics. The reliability is measured by the Average Tanimoto Index and the Inter-method Average Tanimoto Index, and the effectiveness is measured by the mean generalisation accuracy of classification. The computational experiments are carried out on ten real-world benchmark datasets and fourteen synthetic datasets. The synthetic datasets are generated with a pre-set number of relevant features and varied numbers of irrelevant features and instances, and added with different levels of noise. The results indicate that the PART approach is more effective in reducing the bias when the size of a dataset is small but starts to lose its advantage as the dataset size increases

Incidence, Reproductive Outcome, and Economic Impact of Reciprocal Translocations in the Domestic Pig

Pigs (Sus scrofa) have vast economic importance, with pork accounting for over 30% of the global meat consumption. Chromosomal abnormalities, and in particular reciprocal translocations (RTs), are an important cause of hypoprolificacy (litter size reduction) in pigs. However, these do not necessarily present with a recognizable phenotype and may cause significant economic losses for breeders when undetected. Here, we present a reappraisal of the incidence of RTs across several European pig herds, using contemporary methodology, as well as an analysis modelling the economic impact of these abnormalities. Molecular cytogenetic investigation was completed by karyotyping and/or multiprobe FISH (fluorescence in situ hybridisation) between 2016–2021, testing 2673 animals. We identified 19 types of chromosome abnormalities, the prevalence of these errors in the database was 9.1%, and the estimated incidence of de novo errors was 0.90%. Financial modelling across different scenarios revealed the potential economic impact of an undetected RT, ranging from £69,802 for an individual affected terminal boar in a commercial farm selling weaned pigs, to £51,215,378 for a genetics company with an undetected RT in a dam line boar used in a nucleus farm. Moreover, the added benefits of screening by FISH instead of karyotyping were estimated, providing a strong case for proactive screening by this approach

Microglia dysfunction caused by the loss of Rhoa disrupts neuronal physiology and leads to neurodegeneration

© 2020 The Author(s). Creative Commons Attribution (CC BY 4.0)Nervous tissue homeostasis requires the regulation of microglia activity. Using conditional gene targeting in mice, we demonstrate that genetic ablation of the small GTPase Rhoa in adult microglia is sufficient to trigger spontaneous microglia activation, producing a neurological phenotype (including synapse and neuron loss, impairment of long-term potentiation [LTP], formation of β-amyloid plaques, and memory deficits). Mechanistically, loss of Rhoa in microglia triggers Src activation and Src-mediated tumor necrosis factor (TNF) production, leading to excitotoxic glutamate secretion. Inhibiting Src in microglia Rhoa-deficient mice attenuates microglia dysregulation and the ensuing neurological phenotype. We also find that the Rhoa/Src signaling pathway is disrupted in microglia of the APP/PS1 mouse model of Alzheimer disease and that low doses of Aβ oligomers trigger microglia neurotoxic polarization through the disruption of Rhoa-to-Src signaling. Overall, our results indicate that disturbing Rho GTPase signaling in microglia can directly cause neurodegeneration.The authors acknowledge the support of the following i3S Scientific Platforms: Animal Facility, Translational Cytometry Unit (TraCy), BioSciences Screening (BS) and Advanced Light Microscopy (ALM), and members of the national infrastructure PPBI-Portuguese Platform of BioImaging (supported by POCI-01–0145-FEDER-022122). FCT Portugal ( PTDC/MED-NEU/31318/2017-031318 ) supported work in the J.B.R. lab. FCT Portugal , PEst ( UID/NEU/04539/2013 ), COMPETE-FEDER ( POCI-01-0145-FEDER-007440 ), Centro 2020 Regional Operational Programme ( CENTRO-01-0145-FEDER-000008 : BrainHealth 2020), and Strategic Project UIDB/04539/2020 and UIDP/04539/2020 (CIBB) supported work in the A.F.A. lab. C.C.P. and R.S. hold employment contracts financed by national funds through FCT (Fundação para a Ciência e a Tecnologia, I.P.) in the context of the program contract described in paragraphs 4, 5, and 6 of article 23 of law no. 57/2016, of August 29th, as amended by law no. 57/2017 of July 19th.info:eu-repo/semantics/publishedVersio