Determinants of Rural Poverty in Tanzania: Evidence from Mkinga District, Tanga Region

Identification of factors that are strongly linked to poverty is an important aspect in developing successful strategies intended for poverty reduction. This study aimed at assessing the determinants of poverty in Mkinga district in rural Tanzania. Ordinal regression model was used to model events of observing scores of livelihood status in the area of study. The study revealed that nearly 93% of respondents in the area were poor. Gender, size of land the household owns, the size of farm used in farming, Household size and the dependency ratio were found to be related to poverty, hence influencing poverty in the area of study. While the government is responsible in providing proper infrastructural settings, this paper recommends that, people especially women in this area should be empowered to have positive attitudes towards participating into economic activities using resources around them. Keywords: Rural poverty, Economic growth, Poverty determinants, and Mkinga distric

Energy-based Out-of-distribution Detection

Determining whether inputs are out-of-distribution (OOD) is an essential building block for safely deploying machine learning models in the open world. However, previous methods relying on the softmax confidence score suffer from overconfident posterior distributions for OOD data. We propose a unified framework for OOD detection that uses an energy score. We show that energy scores better distinguish in- and out-of-distribution samples than the traditional approach using the softmax scores. Unlike softmax confidence scores, energy scores are theoretically aligned with the probability density of the inputs and are less susceptible to the overconfidence issue. Within this framework, energy can be flexibly used as a scoring function for any pre-trained neural classifier as well as a trainable cost function to shape the energy surface explicitly for OOD detection. On a CIFAR-10 pre-trained WideResNet, using the energy score reduces the average FPR (at TPR 95%) by 18.03% compared to the softmax confidence score. With energy-based training, our method outperforms the state-of-the-art on common benchmarks

Association between Metformin Use and Cancer Stage at Diagnosis among Elderly Medicare Beneficiaries with Preexisting Type 2 Diabetes Mellitus and Incident Prostate Cancer

Objective. To examine the association between metformin use and cancer stage at diagnosis among elderly men with preexisting diabetes mellitus and incident prostate cancer. Methods. This study used a population-based observational cohort of elderly men (≥66 years) with preexisting diabetes and incident prostate cancer between 2008 and 2009 ( = 2, 652). Cancer stage at diagnosis (localized versus advanced) was based on the American Joint Cancer Committee classification. Metformin use and other independent variables were measured during the one year before cancer diagnosis. Logistic regressions with inverse probability treatment weights were used to control for the observed selection bias. Results. A significantly lower percentage of metformin users were diagnosed with advanced prostate cancer as compared to nonusers (4.7% versus 6.7%, \u3c 0.03). After adjusting for the observed selection bias and other independent variables, metformin use was associated with a 32% reduction in the risk of advanced prostate cancer (adjusted odds ratio, AOR: 0.68, 95% confidence interval, CI: 0.48, 0.97). Conclusions. This is the first epidemiological study to support the role of metformin in reducing the risk of advanced prostate cancer. Randomized clinical trials are needed to confirm the causal link between metformin use and prostate cancer diagnosis stage

Bacteria of the Burkholderia cepacia complex are cyanogenic under biofilm and colonial growth conditions

<p>Abstract</p> <p>Background</p> <p>The <it>Burkholderia cepacia </it>complex (Bcc) is a collection of nine genotypically distinct but phenotypically similar species. They show wide ecological diversity and include species that are used for promoting plant growth and bio-control as well species that are opportunistic pathogens of vulnerable patients. Over recent years the Bcc have emerged as problematic pathogens of the CF lung. <it>Pseudomonas aeruginosa </it>is another important CF pathogen. It is able to synthesise hydrogen cyanide (HCN), a potent inhibitor of cellular respiration. We have recently shown that HCN production by <it>P. aeruginosa </it>may have a role in CF pathogenesis. This paper describes an investigation of the ability of bacteria of the Bcc to make HCN.</p> <p>Results</p> <p>The genome of <it>Burkholderia cenocepacia </it>has 3 putative HCN synthase encoding (<it>hcnABC</it>) gene clusters. <it>B. cenocepacia </it>and all 9 species of the Bcc complex tested were able to make cyanide at comparable levels to <it>P. aeruginosa</it>, but only when grown surface attached as colonies or during biofilm growth on glass beads. In contrast to <it>P. aeruginosa </it>and other cyanogenic bacteria, cyanide was not detected during planktonic growth of Bcc strains.</p> <p>Conclusion</p> <p>All species in the Bcc are cyanogenic when grown as surface attached colonies or as biofilms.</p

Climate Change Impacts on Fishing in Coastal Rural of Tanzania

Millions of people around the world depend on fisheries and aquaculture directly or indirectly for their food security, livelihoods and poverty reduction. However, ocean’s ability to meet their needs is in grave danger. Fish is a crucial source of livelihood for fishermen in the coastal rural regions of Tanzania but climate change has caused a major threat to the same. As ocean temperatures rise, many fish species are being driven into deeper waters or toward the planet's poles. The shifting conditions, meanwhile, are inviting historically anomalous breeds into new ranges with unpredictable results. The precise degree to which these phenomena are contributing to Tanzania's current plight is difficult to quantify but ample evidence suggests such changes are already affecting fisheries across the country. Climate-related impacts are occurring across regions of Tanzania and primary sectors of its economy are more vulnerable. Keywords: Climatic Change, Fishing, Climatic Change Adaption, Tanzania

Mapping the Human Plasma Proteome by SCX-LC-IMS-MS

The advent of on-line multidimensional liquid chromatography-mass spectrometry has significantly impacted proteomic analyses of complex biological fluids such as plasma. However, there is general agreement that additional advances to enhance the peak capacity of such platforms are required to enhance the accuracy and coverage of proteome maps of such fluids. Here, we describe the combination of strong-cation-exchange and reversed-phase liquid chromatographies with ion mobility and mass spectrometry as a means of characterizing the complex mixture of proteins associated with the human plasma proteome. The increase in separation capacity associated with inclusion of the ion mobility separation leads to generation of one of the most extensive proteome maps to date. The map is generated by analyzing plasma samples of five healthy humans; we report a preliminary identification of 9087 proteins from 37,842 unique peptide assignments. An analysis of expected false-positive rates leads to a high-confidence identification of 2928 proteins. The results are catalogued in a fashion that includes positions and intensities of assigned features observed in the datasets as well as pertinent identification information such as protein accession number, mass, and homology score/confidence indicators. Comparisons of the assigned features reported here with other datasets shows substantial agreement with respect to the first several hundred entries; there is far less agreement associated with detection of lower abundance components

DeepH&M: Estimating single-CpG hydroxymethylation and methylation levels from enrichment and restriction enzyme sequencing methods

Increased appreciation of 5-hydroxymethylcytosine (5hmC) as a stable epigenetic mark, which defines cell identity and disease progress, has engendered a need for cost-effective, but high-resolution, 5hmC mapping technology. Current enrichment-based technologies provide cheap but low-resolution and relative enrichment of 5hmC levels, while single-base resolution methods can be prohibitively expensive to scale up to large experiments. To address this problem, we developed a deep learning-based method, DeepH&M, which integrates enrichment and restriction enzyme sequencing methods to simultaneously estimate absolute hydroxymethylation and methylation levels at single-CpG resolution. Using 7-week-old mouse cerebellum data for training the DeepH&M model, we demonstrated that the 5hmC and 5mC levels predicted by DeepH&M were in high concordance with whole-genome bisulfite-based approaches. The DeepH&M model can be applied to 7-week-old frontal cortex and 79-week-old cerebellum, revealing the robust generalizability of this method to other tissues from various biological time points

Fabrication and characterization of nanotemplated carbon monolithic material

A novel hierarchical nanotemplated carbon monolithic rod (NTCM) was prepared using a novel facile nanotemplating approach. The NTCM was obtained using C60-fullerene modified silica gels as hard templates, which were embedded in a phenolic resin containing a metal catalyst for localized graphitization, followed by bulk carbonization, and template and catalyst removal. TEM, SEM, and BET measurements revealed that NTCM possessed an integrated open hierarchical porous structure, with a trimodal pore distribution. This porous material also possessed a high mesopore volume and narrow mesopore size distribution. During the course of carbonization, the C60 conjugated to aminated silica was partly decomposed, leading to the formation of micropores. The Raman signature of NTCM was very similar to that of multiwalled carbon nanotubes as exemplified by three major peaks as commonly observed for other carbon materials, i.e., the sp3 and sp2 carbon phases coexisted in the sample. Surface area measurements were obtained using both nitrogen adsorption/desorption isotherms (BET) and with a methylene blue binding assay, with BET results showing the NTCM material possessed an average specific surface area of 435 m2 g−1, compared to an area of 372 m2 g−1 obtained using the methylene blue assay. Electrochemical studies using NTCM modified glassy carbon or boron doped diamond (BDD) electrodes displayed quasi-reversible oxidation/reduction with ferricyanide. In addition, the BDD electrode modified with NTCM was able to detect hydrogen peroxide with a detection limit of below 300 nM, whereas the pristine BDD electrode was not responsive to this target compound