Critical linkages between livestock production, livestock trade and potential spread of human African trypanosomiasis in Uganda:Bioeconomic herd modeling and livestock trade analysis

Background: Tsetse-transmitted human African trypanosomiasis (HAT) remains endemic in Uganda. The chronic form caused by Trypanosoma brucei gambiense (gHAT) is found in north-western Uganda, whereas the acute zoonotic form of the disease, caused by T. b. brucei rhodesiense (rHAT), occurs in the eastern region. Cattle is the major reservoir of rHAT in Uganda. These two forms of HAT are likely to converge resulting in a public health disaster. This study examines the intricate and intrinsic links between cattle herd dynamics, livestock trade and potential risk of spread of rHAT northwards. Methods: A bio-economic cattle herd model was developed to simulate herd dynamics at the farm level. Semi-structured interviews (n = 310), focus group discussions (n = 9) and key informant interviews (n = 9) were used to evaluate livestock markets (n = 9) as part of the cattle supply chain analysis. The cattle market data was used for stochastic risk analysis. Results: Cattle trade in eastern and northern Uganda is dominated by sale of draft and adult male cattle as well as exportation of young male cattle. The study found that the need to import draft cattle at the farm level was to cover deficits because of the herd structure, which is mostly geared towards animal traction. The importation and exportation of draft cattle and disposal of old adult male cattle formed the major basis of livestock movement and could result in the spread of rHAT northwards. The risk of rHAT infected cattle being introduced to northern Uganda from the eastern region via cattle trade was found to be high (i.e. probability of 1). Conclusion: Through deterministic and stochastic modelling of cattle herd and cattle trade dynamics, this study identifies critical links between livestock production and trade as well as potential risk of rHAT spread in eastern and northern Uganda. The findings highlight the need for targeted and routine surveillance and control of zoonotic diseases such as rHAT

Livestock network analysis for rhodesiense human African trypanosomiasis control in Uganda

Background: Infected cattle sourced from districts with established foci for Trypanosoma brucei rhodesiense human African trypanosomiasis (rHAT) migrating to previously unaffected districts, have resulted in a significant expansion of the disease in Uganda. This study explores livestock movement data to describe cattle trade network topology and assess the effects of disease control interventions on the transmission of rHAT infectiousness.Methods: Network analysis was used to generate a cattle trade network with livestock data which was collected from cattle traders (n = 197) and validated using random graph methods. Additionally, the cattle trade network was combined with a susceptible, infected, recovered (SIR) compartmental model to simulate spread of rHAT (Ro 1.287), hence regarded as “slow” pathogen, and evaluate the effects of disease interventions.Results: The cattle trade network exhibited a low clustering coefficient (0.5) with most cattle markets being weakly connected and a few being highly connected. Also, analysis of the cattle movement data revealed a core group comprising of cattle markets from both eastern (rHAT endemic) and northwest regions (rHAT unaffected area). Presence of a core group may result in rHAT spread to unaffected districts and occurrence of super spreader cattle market or markets in case of an outbreak. The key cattle markets that may be targeted for routine rHAT surveillance and control included Namutumba, Soroti, and Molo, all of which were in southeast Uganda. Using effective trypanosomiasis such as integrated cattle injection with trypanocides and spraying can sufficiently slow the spread of rHAT in the network.Conclusion: Cattle trade network analysis indicated a pathway along which T. b. rhodesiense could spread northward from eastern Uganda. Targeted T. b. rhodesiense surveillance and control in eastern Uganda, through enhanced public–private partnerships, would serve to limit its spread

Patents and Industrialisation. An Historical Overview of the British Case, 1624-1907

A Report to the Strategic Advisory Board on Intellectual Property Policy (SABIP), U

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Social factors affecting seasonal variation in bovine trypanosomiasis on the Jos Plateau, Nigeria

BACKGROUND: African Animal Trypanosomiasis (AAT) is a widespread disease of livestock in Nigeria and presents a major constraint to rural economic development. The Jos Plateau was considered free from tsetse flies and the trypanosomes they transmit due to its high altitude and this trypanosomiasis free status attracted large numbers of cattle-keeping pastoralists to the area. The Jos Plateau now plays a major role in the national cattle industry in Nigeria, accommodating approximately 7% of the national herd, supporting 300,000 pastoralists and over one million cattle. During the past two decades tsetse flies have invaded the Jos Plateau and animal trypanosomiasis has become a significant problem for livestock keepers. Here we investigate the epidemiology of trypanosomiasis as a re-emerging disease on the Plateau, examining the social factors that influence prevalence and seasonal variation of bovine trypanosomiasis. METHODS: In 2008 a longitudinal two-stage cluster survey was undertaken on the Jos Plateau. Cattle were sampled in the dry, early wet and late wet seasons. Parasite identification was undertaken using species-specific polymerase chain reactions to determine the prevalence and distribution of bovine trypanosomiasis. Participatory rural appraisal was also conducted to determine knowledge, attitudes and practices concerning animal husbandry and disease control. RESULTS: Significant seasonal variation between the dry season and late wet season was recorded across the Jos Plateau, consistent with expected variation in tsetse populations. However, marked seasonal variations were also observed at village level to create 3 distinct groups: Group 1 in which 50% of villages followed the general pattern of low prevalence in the dry season and high prevalence in the wet season; Group 2 in which 16.7% of villages showed no seasonal variation and Group 3 in which 33.3% of villages showed greater disease prevalence in the dry season than in the wet season. CONCLUSIONS: There was high seasonal variation at the village level determined by management as well as climatic factors. The growing influence of management factors on the epidemiology of trypanosomiasis highlights the impact of recent changes in land use and natural resource competition on animal husbandry decisions in the extensive pastoral production system

Lenalidomide in combination with dexamethasone at first relapse in comparison with its use as later salvage therapy in relapsed or refractory multiple myeloma

This subset analysis of data from two phase III studies in patients with relapsed or refractory multiple myeloma (MM) evaluated the benefit of initiating lenalidomide plus dexamethasone at first relapse. Multivariate analysis showed that fewer prior therapies, along with β2-microglobulin (≤2.5 mg/L), predicted a better time to progression (TTP; study end-point) with lenalidomide plus dexamethasone treatment. Patients with one prior therapy showed a significant improvement in benefit after first relapse compared with those who received two or more therapies. Patients with one prior therapy had significantly prolonged median TTP (17.1 vs. 10.6 months; P=0.026) and progression-free survival (14.1 vs. 9.5 months, P=0.047) compared with patients treated in later lines. Overall response rates were higher (66.9% vs. 56.8%, P=0.06), and the complete response plus very good partial response rate was significantly higher in first relapse (39.8% vs. 27.7%, P=0.025). Importantly, overall survival was significantly prolonged for patients treated with lenalidomide plus dexamethasone with one prior therapy, compared with patients treated later in salvage (median of 42.0 vs. 35.8 months, P=0.041), with no differences in toxicity, dose reductions, or discontinuations despite longer treatment. Therefore, lenalidomide plus dexamethasone is both effective and tolerable for second-line MM therapy and the data suggest that the greatest benefit occurs with earlier use

Predicting risk for Alcohol Use Disorder using longitudinal data with multimodal biomarkers and family history: a machine learning study.

Predictive models have succeeded in distinguishing between individuals with Alcohol use Disorder (AUD) and controls. However, predictive models identifying who is prone to develop AUD and the biomarkers indicating a predisposition to AUD are still unclear. Our sample (n = 656) included offspring and non-offspring of European American (EA) and African American (AA) ancestry from the Collaborative Study of the Genetics of Alcoholism (COGA) who were recruited as early as age 12 and were unaffected at first assessment and reassessed years later as AUD (DSM-5) (n = 328) or unaffected (n = 328). Machine learning analysis was performed for 220 EEG measures, 149 alcohol-related single nucleotide polymorphisms (SNPs) from a recent large Genome-wide Association Study (GWAS) of alcohol use/misuse and two family history (mother DSM-5 AUD and father DSM-5 AUD) features using supervised, Linear Support Vector Machine (SVM) classifier to test which features assessed before developing AUD predict those who go on to develop AUD. Age, gender, and ancestry stratified analyses were performed. Results indicate significant and higher accuracy rates for the AA compared with the EA prediction models and a higher model accuracy trend among females compared with males for both ancestries. Combined EEG and SNP features model outperformed models based on only EEG features or only SNP features for both EA and AA samples. This multidimensional superiority was confirmed in a follow-up analysis in the AA age groups (12-15, 16-19, 20-30) and EA age group (16-19). In both ancestry samples, the youngest age group achieved higher accuracy score than the two other older age groups. Maternal AUD increased the model's accuracy in both ancestries' samples. Several discriminative EEG measures and SNPs features were identified, including lower posterior gamma, higher slow wave connectivity (delta, theta, alpha), higher frontal gamma ratio, higher beta correlation in the parietal area, and 5 SNPs: rs4780836, rs2605140, rs11690265, rs692854, and rs13380649. Results highlight the significance of sampling uniformity followed by stratified (e.g., ancestry, gender, developmental period) analysis, and wider selection of features, to generate better prediction scores allowing a more accurate estimation of AUD development