Do Banks Compete on Non-Price Terms? Evidence from Loan Covenants

We study the interplay between non-price loan terms and competition in credit markets. We exploit a regulatory shock to regulated banks' ability to offer favorable non-price terms, particularly covenant-lite loans. We find that borrowers trade-off increased covenants and lower interest rates from regulated banks, with covenant-lite loans and higher rates from non-banks. This non-price competition alters market structure: less covenant-sensitive borrowers remain with regulated lenders, and financially weaker borrowers switch to shadow banks or leave the leveraged lending market. As a result, banks' market share declines. Our findings on borrower behavior and loan terms align with a stylized equilibrium model

Promoting the learning of modern and contemporary physics in high schools in informal and non-formal contexts

In this paper, we introduce active learning strategies developed by the Educational Division of the Physics Department of the University of Cagliari to promote the learning of modern and contemporary physics (e.g., general relativity, particle physics, cosmology, and related topics) in high schools in informal and nonformal contexts. We discuss their features and potential role in facilitating science and physics instruction by integrating pedagogical theory and education research. We illustrate our theoretical framework and the methodologies we implemented to design specific educational strategies —and the evaluation of their effectiveness— to improve motivation, curiosity, and interest in modern and contemporary physics, as well as bring these topics more extensively to high schools. Finally, examples of the proposed educational activities are presented and their implications in informal and non-formal contexts are discussed

Mediterranean River Buffalo CSN1S1 gene: search for polymorphisms and association studies.

The aim of the present work was to study the variability at CSN1S1 locus of the Italian Mediterranean river buffalo and to investigate possible allele effects on milk yield and its composition. Effects of parity, calving season and month of production were also evaluated. Three SNPs were detected. The first mutation, located at position 89 of 17th exon (c.628C>T), is responsible for the amino acid change (p.Ser178Leu). The other two polymorphisms, detected at the positions 144 (c.882G>A) and 239 (c.977A>G) of 19th exon respectively, are silent (3’ UTR). Associations between the CSN1S1 genotypes and milk production traits were investigated using 4,122 test day records of 503 lactations from 175 buffalo cows. Milk yield, fat and protein percentages were analyzed using a mixed linear model. A significant association between the c.628C>T SNP and the protein percentage was found. In particular, the CC genotype showed an average value of about 0.04% higher than the CT and TT genotypes. The allele substitution effect of the cytosine into the thymine was -0.014, with a quite low (0.3%) protein percentage (PP) contribution on total phenotypic variance. A large dominance effect was detected. Furthermore, a characterization of the CSN1S1 transcripts and a method based on MboI-ACRS-PCR for a rapid genotyping of c.628C>T were provided

Kernel learning for ligand-based virtual screening: discovery of a new PPARγ agonist

Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 We demonstrate the theoretical and practical application of modern kernel-based machine learning methods to ligand-based virtual screening by successful prospective screening for novel agonists of the peroxisome proliferator-activated receptor gamma (PPARgamma) [1]. PPARgamma is a nuclear receptor involved in lipid and glucose metabolism, and related to type-2 diabetes and dyslipidemia. Applied methods included a graph kernel designed for molecular similarity analysis [2], kernel principle component analysis [3], multiple kernel learning [4], and, Gaussian process regression [5]. In the machine learning approach to ligand-based virtual screening, one uses the similarity principle [6] to identify potentially active compounds based on their similarity to known reference ligands. Kernel-based machine learning [7] uses the "kernel trick", a systematic approach to the derivation of non-linear versions of linear algorithms like separating hyperplanes and regression. Prerequisites for kernel learning are similarity measures with the mathematical property of positive semidefiniteness (kernels). The iterative similarity optimal assignment graph kernel (ISOAK) [2] is defined directly on the annotated structure graph, and was designed specifically for the comparison of small molecules. In our virtual screening study, its use improved results, e.g., in principle component analysis-based visualization and Gaussian process regression. Following a thorough retrospective validation using a data set of 176 published PPARgamma agonists [8], we screened a vendor library for novel agonists. Subsequent testing of 15 compounds in a cell-based transactivation assay [9] yielded four active compounds. The most interesting hit, a natural product derivative with cyclobutane scaffold, is a full selective PPARgamma agonist (EC50 = 10 ± 0.2 microM, inactive on PPARalpha and PPARbeta/delta at 10 microM). We demonstrate how the interplay of several modern kernel-based machine learning approaches can successfully improve ligand-based virtual screening results

Use of partial least squares regression to impute SNP genotypes in Italian Cattle breeds

Background The objective of the present study was to test the ability of the partial least squares regression technique to impute genotypes from low density single nucleotide polymorphisms (SNP) panels i.e. 3K or 7K to a high density panel with 50K SNP. No pedigree information was used. Methods Data consisted of 2093 Holstein, 749 Brown Swiss and 479 Simmental bulls genotyped with the Illumina 50K Beadchip. First, a single-breed approach was applied by using only data from Holstein animals. Then, to enlarge the training population, data from the three breeds were combined and a multi-breed analysis was performed. Accuracies of genotypes imputed using the partial least squares regression method were compared with those obtained by using the Beagle software. The impact of genotype imputation on breeding value prediction was evaluated for milk yield, fat content and protein content. Results In the single-breed approach, the accuracy of imputation using partial least squares regression was around 90 and 94% for the 3K and 7K platforms, respectively; corresponding accuracies obtained with Beagle were around 85% and 90%. Moreover, computing time required by the partial least squares regression method was on average around 10 times lower than computing time required by Beagle. Using the partial least squares regression method in the multi-breed resulted in lower imputation accuracies than using single-breed data. The impact of the SNP-genotype imputation on the accuracy of direct genomic breeding values was small. The correlation between estimates of genetic merit obtained by using imputed versus actual genotypes was around 0.96 for the 7K chip. Conclusions Results of the present work suggested that the partial least squares regression imputation method could be useful to impute SNP genotypes when pedigree information is not available

BAFF, APRIL and BAFFR on the pathogenesis of Immunoglobulin-A vasculitis

BAFF, APRIL and BAFF-R are key proteins involved in the development of B-lymphocytes and autoimmunity. Additionally, BAFF, APRIL and BAFFR polymorphisms were associated with immune-mediated conditions, being BAFF GCTGT>A a shared insertion-deletion genetic variant for several autoimmune diseases. Accordingly, we assessed whether BAFF, APRIL and BAFFR represent novel genetic risk factors for Immunoglobulin-A vasculitis (IgAV), a predominantly B-lymphocyte inflammatory condition. BAFF rs374039502, which colocalizes with BAFF GCTGT>A, and two tag variants within APRIL (rs11552708 and rs6608) and BAFFR (rs7290134 and rs77874543) were genotyped in 386 Caucasian IgAV patients and 806 matched healthy controls. No genotypes or alleles differences were observed between IgAV patients and controls when BAFF, APRIL and BAFFR variants were analysed independently. Likewise, no statistically significant differences were found in the genotype and allele frequencies of BAFF, APRIL or BAFFR when IgAV patients were stratified according to the age at disease onset or to the presence/absence of gastrointestinal (GI) or renal manifestations. Similar results were disclosed when APRIL and BAFFR haplotypes were compared between IgAV patients and controls and between IgAV patients stratified according to the clinical characteristics mentioned above. Our results suggest that BAFF, APRIL and BAFFR do not contribute to the genetic network underlying IgAV.Acknowledgements: We are indebted to the patients and healthy controls for their essential collaboration to this study. We also thank the National DNA Bank Repository (Salamanca) for supplying part of the control samples. This study was supported by European Union FEDER funds and `Fondo de Investigaciones Sanitarias´ (grant PI18/00042) from ‘Instituto de Salud Carlos III’ (ISCIII, Health Ministry, Spain). DP-P is a recipient of a Río Hortega programme fellowship from the ISCIII, co-funded by the European Social Fund (ESF, `Investing in your future´) (grant number CM20/00006). SR-M is supported by funds of the RETICS Program (RD16/0012/0009) (ISCIII, co-funded by the European Regional Development Fund (ERDF)). VP-C is supported by a pre-doctoral grant from IDIVAL (PREVAL 18/01). BA-M is a recipient of a `López Albo´ Post-Residency Programme funded by Servicio Cántabro de Salud. LL-G is supported by funds from IDIVAL (INNVAL20/06). OG is staff personnel of Xunta de Galicia (Servizo Galego de Saude (SERGAS)) through a research-staff stabilization contract (ISCIII/SERGAS) and his work is funded by ISCIII and the European Union FEDER fund (grant numbers RD16/0012/0014 (RIER) and PI17/00409). He is beneficiary of project funds from the Research Executive Agency (REA) of the European Union in the framework of MSCA-RISE Action of the H2020 Programme, Project 734899—Olive-Net. RL-M is a recipient of a Miguel Servet type I programme fellowship from the ISCIII, cofunded by ESF (`Investing in your future´) (grant number CP16/00033)

Signatures of selection and environmental adaptation across the goat genome post-domestication

Background: Since goat was domesticated 10,000 years ago, many factors have contributed to the differentiation of goat breeds and these are classified mainly into two types: (i) adaptation to different breeding systems and/or purposes and (ii) adaptation to different environments. As a result, approximately 600 goat breeds have developed worldwide; they differ considerably from one another in terms of phenotypic characteristics and are adapted to a wide range of climatic conditions. In this work, we analyzed the AdaptMap goat dataset, which is composed of data from more than 3000 animals collected worldwide and genotyped with the CaprineSNP50 BeadChip. These animals were partitioned into groups based on geographical area, production uses, available records on solid coat color and environmental variables including the sampling geographical coordinates, to investigate the role of natural and/or artificial selection in shaping the genome of goat breeds. Results: Several signatures of selection on different chromosomal regions were detected across the different breeds, sub-geographical clusters, phenotypic and climatic groups. These regions contain genes that are involved in important biological processes, such as milk-, meat- or fiber-related production, coat color, glucose pathway, oxidative stress response, size, and circadian clock differences. Our results confirm previous findings in other species on adaptation to extreme environments and human purposes and provide new genes that could explain some of the differences between goat breeds according to their geographical distribution and adaptation to different environments. Conclusions: These analyses of signatures of selection provide a comprehensive first picture of the global domestication process and adaptation of goat breeds and highlight possible genes that may have contributed to the differentiation of this species worldwide

Genome-wide SNP profiling of worldwide goat populations reveals strong partitioning of diversity and highlights post-domestication migration routes

Background: Goat populations that are characterized within the AdaptMap project cover a large part of the worldwide distribution of this species and provide the opportunity to assess their diversity at a global scale. We analysed genome-wide 50 K single nucleotide polymorphism (SNP) data from 144 populations to describe the global patterns of molecular variation, compare them to those observed in other livestock species, and identify the drivers that led to the current distribution of goats. Results: A high degree of genetic variability exists among the goat populations studied. Our results highlight a strong partitioning of molecular diversity between and within continents. Three major gene pools correspond to goats from Europe, Africa and West Asia. Dissection of sub-structures disclosed regional gene pools, which reflect the main post-domestication migration routes. We also identified several exchanges, mainly in African populations, and which often involve admixed and cosmopolitan breeds. Extensive gene flow has taken place within specific areas (e.g., south Europe, Morocco and Mali-Burkina Faso-Nigeria), whereas elsewhere isolation due to geographical barriers (e.g., seas or mountains) or human management has decreased local gene flows. Conclusions: After domestication in the Fertile Crescent in the early Neolithic era (ca. 12,000 YBP), domestic goats that already carried differentiated gene pools spread to Europe, Africa and Asia. The spread of these populations determined the major genomic background of the continental populations, which currently have a more marked subdivision than that observed in other ruminant livestock species. Subsequently, further diversification occurred at the regional level due to geographical and reproductive isolation, which was accompanied by additional migrations and/or importations, the traces of which are still detectable today. The effects of breed formation were clearly detected, particularly in Central and North Europe. Overall, our results highlight a remarkable diversity that occurs at the global scale and is locally partitioned and often affected by introgression from cosmopolitan breeds. These findings support the importance of long-term preservation of goat diversity, and provide a useful framework for investigating adaptive introgression, directing genetic improvement and choosing breeding targets