Tax competition and tax evasion in a multi-jurisdictional world

As an alternative to taxation of capital income at the corporate level, countries could instead tax their individual residents on their worldwide capital income. Information exchange on individuals’ foreign investment income is absolutely necessary for this approach to be effective. The second part of this thesis empirically and theoretically analyzes the circumstances under which countries would be willing to share information with each other so that residence-based capital income taxation becomes a viable option.

Tax Competition and Tax Evasion in a Multi-Jurisdictional World.

As an alternative to taxation of capital income at the corporate level, countries could instead tax their individual residents on their worldwide capital income. Information exchange on individuals’ foreign investment income is absolutely necessary for this approach to be effective. The second part of this thesis empirically and theoretically analyzes the circumstances under which countries would be willing to share information with each other so that residence-based capital income taxation becomes a viable option.

Adverse drug reactions reported to a provincial public health sector pharmacovigilance programme in South Africa

Background. There are limited data in South Africa (SA) on adverse drug reaction (ADR) patterns and common causative medicines, outside of HIV and tuberculosis treatment programmes. In SA, Western Cape Province has a pharmacovigilance programme that collects spontaneous reports of suspected ADRs from public sector healthcare facilities.Objectives. To describe reports received by the pharmacovigilance programme over a 4-year period (excluding those ascribed to medicines used to treat HIV and tuberculosis), as well as challenges faced in the implementation of such a system.Methods. Reports of suspected ADRs and deaths possibly related to ADRs received between January 2015 and December 2018 were reviewed. Causality was assessed by a pharmacist, with multidisciplinary team involvement for all deaths and complicated cases. Causality was categorised according to the World Health Organization-Uppsala Monitoring Centre system. Preventability was assessed using Schumock and Thornton criteria. Observations on preventability and challenges faced in the operation of a spontaneous reporting system were also noted.Results. We received 5 346 reports containing 6 023 suspected ADRs. There were 5 486 ADRs confirmed after causality assessment, in 5 103 reports. Cough, angio-oedema, movement disorders and uterine bleeding disorders were the most common ADRs. Enalapril, etonogestrel, amlodipine and hydrochlorothiazide were the most commonly implicated drugs. Seven deaths were reported; 3 of these reports of deaths had confirmed ADRs, and these ADRs were assessed as contributing to the deaths. Approximately 3.8% of commonly reported ADRs were preventable.Conclusions. Enalapril and etonogestrel were responsible for a significant proportion of ADRs reported to this provincial programme. Future work should include quantification of preventability aspects to better inform gaps in healthcare worker knowledge that can be addressed in order to improve patient care

Influence of temperature on solvents production from whey

The influence of temperature on solvent production from whey was investigated by using strains of Clostridium acetobutylicum and butylicum. Higher yields of solvents were observed at 37°C or at 30°C depending on the strain used.Centro de Investigación y Desarrollo en Fermentaciones Industriale

Solvents production from whey supplemented with corn steep and malt sprouts at 30°C and 37°C

Corn steep and malt-sprouts were used to replace yeast extract in solvents production from whey at 30°C and 37°C employing two clostridia producer strains. The results show different temperature and growth factors dependence in the two strains. Yields of solvents between 0.16 and 0.32 (g/g) were obtained.Centro de Investigación y Desarrollo en Fermentaciones Industriale

Early neuromodulation prevents the development of brain and behavioral abnormalities in a rodent model of schizophrenia

The notion that schizophrenia is a neurodevelopmental disorder in which neuropathologies evolve gradually over the developmental course indicates a potential therapeutic window during which pathophysiological processes may be modified to halt disease progression or reduce its severity. Here we used a neurodevelopmental maternal immune stimulation (MIS) rat model of schizophrenia to test whether early targeted modulatory intervention would affect schizophrenia’s neurodevelopmental course. We applied deep brain stimulation (DBS) or sham stimulation to the medial prefrontal cortex (mPFC) of adolescent MIS rats and respective controls, and investigated its behavioral, biochemical, brain-structural and -metabolic effects in adulthood. We found that mPFC-DBS successfully prevented the emergence of deficits in sensorimotor gating, attentional selectivity and executive function in adulthood, as well as the enlargement of lateral ventricle volumes and mal-development of dopaminergic and serotonergic transmission. These data suggest that the mPFC may be a valuable target for effective preventive treatments. This may have significant translational value, suggesting that targeting the mPFC before the onset of psychosis via less invasive neuromodulation approaches may be a viable preventive strategy.We thank Renate Winter, Doris Zschaber and Roselies Pickert for excellent technical assistance. This research was conducted under the EraNet Neuron framework (DBS_F20rat) and supported by the BMBF, Germany (B01EW1103, 01EE1403A), Fundación Mapfre, Comunidad de Madrid and the Ministry of Economy and Competitiveness ISCIII-FIS grants (PI14/00860, CPII/00005) co-financed by ERDF (FEDER) Funds from the European Commission, ‘A way of making Europe’, Spain (PI14/00860, CPII/00005, MV1500002), the CSO-MOH, Israel (3-8580) and the Canadian Institutes of Health Research, Canada (CIHR, 110068), and co-financed by the DFG, Germany (WI 2140/1-1/2; WI 2140/2-1).Publicad

Renal dysfunction by baseline CD4 cell count in a cohort of adults starting antiretroviral treatment regardless of CD4 count in the HIV Prevention Trials Network 071 [HPTN 071; Population Effect of Antiretroviral Therapy to Reduce HIV Transmission (PopART)] study in South Africa.

OBJECTIVES: Renal dysfunction is a significant cause of morbidity and mortality among HIV-positive individuals. This study evaluated renal dysfunction in a cohort of adults who started antiretroviral treatment (ART) regardless of CD4 count at three Department of Health (DOH) clinics included in the HIV Prevention Trials Network 071 (HPTN 071) Population Effect of Antiretroviral Therapy to Reduce HIV Transmission (PopART) trial. METHODS: A retrospective cohort analysis of routine data for HIV-positive individuals starting ART between January 2014 and November 2015 was completed. Incident renal dysfunction was defined as an estimated glomerular filtration rate (eEGFR)  500 cells/μL [adjusted odds ratio (aOR) 0.29; 95% confidence interval (CI) 0.11-0.80], 351-500 cells/μL (aOR 0.22; 95% CI 0.08-0.59) and 201-350 (aOR 0.48; 95% CI: 0.24-0.97) compared with baseline CD4 counts  200 cells/μL. Strategies that use baseline characteristics, such as age, to identify individuals at high risk of renal dysfunction on ART for enhanced eGFR monitoring may be effective and should be the subject of future research

Gene prediction in metagenomic fragments: A large scale machine learning approach

<p>Abstract</p> <p>Background</p> <p>Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions.</p> <p>Results</p> <p>We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability.</p> <p>Conclusion</p> <p>Large scale machine learning methods are well-suited for gene prediction in metagenomic DNA fragments. In particular, the combination of linear discriminants and neural networks is promising and should be considered for integration into metagenomic analysis pipelines. The data sets can be downloaded from the URL provided (see Availability and requirements section).</p

Achievements and new knowledge unraveled by metagenomic approaches

Metagenomics has paved the way for cultivation-independent assessment and exploitation of microbial communities present in complex ecosystems. In recent years, significant progress has been made in this research area. A major breakthrough was the improvement and development of high-throughput next-generation sequencing technologies. The application of these technologies resulted in the generation of large datasets derived from various environments such as soil and ocean water. The analyses of these datasets opened a window into the enormous phylogenetic and metabolic diversity of microbial communities living in a variety of ecosystems. In this way, structure, functions, and interactions of microbial communities were elucidated. Metagenomics has proven to be a powerful tool for the recovery of novel biomolecules. In most cases, functional metagenomics comprising construction and screening of complex metagenomic DNA libraries has been applied to isolate new enzymes and drugs of industrial importance. For this purpose, several novel and improved screening strategies that allow efficient screening of large collections of clones harboring metagenomes have been introduced