Canary melon (Cucumis melo L. var. Inodorus) response to lime-amended acid soil in the humid tropical rainforest of Nigeria

Preliminary field experiments were conducted to examine the influences of lime (CaCO3) rate (0, 1, 2, 3, 4, and 5 t ha-1 ) on the production of canary melon (Cucumis melo L. var. Inodorus) on acidic soil of Calabar, Nigeria. Canary melon production is presently limited to the northern part of Nigeria. The southern part of Nigeria has the potential to support its production, but for low soil pH. The experiment was laid out in a randomised complete block design with three replicates. The initial soil pH (1:2.5 H2O), 4.13, was improved to 4.69 (1 t ha-1 ) – 5.93 (5 t ha-1 ). There was no significant difference (p > 0.05) in soil pH increase after 2 t ha-1 of CaCO3. Liming significantly (p ≤ 0.05) increased available P, total N, Ca2+ , Mg2+ , K+ , effective cation exchange capacity, and base saturation of the soil, but reduced exchangeable acidity. Increased lime rates increased (p ≤ 0.05) seedling emergence, leaf (area, area index), vine (length and thickness), and fruit and seed yields. However, fruits sweetness was inconsistent. CaCO3 had significant (p ≤ 0.001) linear relationships and correlations with growth and yield traits of canary melon. Canary melon can be cultivated in Calabar with an application of 2 – 5 t ha-1 of CaCO3

Socio-cultural factors influencing Maasai participation in income generating activities: women case of Arusha Chini and Maboghini wards, Moshi Rural

The study was conducted in Arusha chini and Maboghini wards to determine socio-cultural factors that influence Maasai women participation in income generating activities. Specific objectives of the study were: to identify income generating activities that Maasai women participate; to identify socio-cultural factors influencing Maasai women in income generating activities; to determine gender relations affecting Maasai women participation in income generating activities and to asses the extent of participation of Maasai women in income generating activities. The study comprised 69 women from the two wards who were involved in IGAs and 51 who were not engaged in it. Data were collected using structured questionnaire and analyzed using SPSS software package. Study findings showed that major sources of income among respondents were non-farm activities, farming and salaries. Key activities undertaken were food vending and livestock keeping. The study found that socio-cultural factors influencing Maasai women participation in income generating activities in the study area include age, marital status, education background, household size and lack of decision making on income expenditure. Majority of the respondents involved in IGAs started business with an initial capital of less than 50 000 Tshs. Main sources of initial capital were own capital and family. Inadequate capital, unreliable markets, lack of entrepreneurship skills and family responsibilities were identified as major constraints to women’s IGAs. The study concluded that Maasai women involvement in IGAs contributes low to the wellbeing of most households in the study area. The study recommends the need to create enabling environment that ought to improve lending policy and entrepreneurship skills

Socio-cultural factors influencing Maasai participation in income generating activities: women case of Arusha Chini and Maboghini wards, Moshi Rural

The study was conducted in Arusha chini and Maboghini wards to determine socio-cultural factors that influence Maasai women participation in income generating activities. Specific objectives of the study were: to identify income generating activities that Maasai women participate; to identify socio-cultural factors influencing Maasai women in income generating activities; to determine gender relations affecting Maasai women participation in income generating activities and to asses the extent of participation of Maasai women in income generating activities. The study comprised 69 women from the two wards who were involved in IGAs and 51 who were not engaged in it. Data were collected using structured questionnaire and analyzed using SPSS software package. Study findings showed that major sources of income among respondents were non-farm activities, farming and salaries. Key activities undertaken were food vending and livestock keeping. The study found that socio-cultural factors influencing Maasai women participation in income generating activities in the study area include age, marital status, education background, household size and lack of decision making on income expenditure. Majority of the respondents involved in IGAs started business with an initial capital of less than 50 000 Tshs. Main sources of initial capital were own capital and family. Inadequate capital, unreliable markets, lack of entrepreneurship skills and family responsibilities were identified as major constraints to women’s IGAs. The study concluded that Maasai women involvement in IGAs contributes low to the wellbeing of most households in the study area. The study recommends the need to create enabling environment that ought to improve lending policy and entrepreneurship skills

An Empirical Analysis of Tax Ratios and Tax Efforts for Kenya and Malawi

The study intended to analyse the trends in tax ratio and tax effort differentials between Kenya and Malawi using secondary annual data for the period 1980 to 2015. The data were obtained from both the International Monetary Fund and World Bank data bases. The study was carried out to analyze tax ratios for Kenya and Malawi, estimate the tax effort for each, and identify the factors that accounted for the differences in the tax ratios and tax effort indices in the two countries. The regression models for the two countries were estimated using the ordinary least squares (OLS) method. The results reveal that GDP per capita was explaining changes in tax revenue in Kenya both in the long run and the short run, share of agriculture to GDP, and the share of industry were influencing the tax revenue in Kenya, in the long run. However, the coefficient for the dummy variable for political reform in Kenya has been insignificant. In Malawi, GDP per capita, share of agriculture in GDP, share of industry in GDP, and the dummy variable for political reform were all explaining changes in tax revenue in the long run but not in the short run. In regards to the tax efforts, the study reviles that Malawi was undertaxing while Kenya was overtaxing given the structure of their respective economies. The study recommends the two countries have to work towards optimal level of taxation

Deep learning model for automatic segmentation of lungs and pulmonary metastasis in small animal MR images

Lungs are the most frequent site of metastases growth. The amount and size of pulmonary metastases acquired from MRI imaging data are the important criteria to assess the efficacy of new drugs in preclinical models. While efficient solutions both for MR imaging and the downstream automatic segmentation have been proposed for human patients, both MRI lung imaging and segmentation in preclinical animal models remains challenging due to the physiological motion (respiratory and cardiac movements), to the low amount of protons in this organ and to the particular challenge of precise segmentation of metastases. As a consequence post-mortem analysis is currently required to obtain information on metastatic volume. In this work, we have developed a complete methodological pipeline for automated analysis of lungs and metastases in mice, consisting of an MR sequence for image acquisition and a deep learning method for automatic segmentation of both lungs and metastases. On one hand, we optimized an MR sequence for mouse lung imaging with high contrast for high detection sensitivity. On the other hand we developed DeepMeta, a multiclass U-Net 3+ deep learning model to automatically segment the images. To assess if the proposed deep learning pipeline is able to provide an accurate segmentation of both lungs and pulmonary metastases, we have longitudinally imaged mice with fast- and slow-growing metastasis. Fifty-five balb/c mice were injected with two different derivatives of renal carcinoma cells. Mice were imaged with a SG-bSSFP (self-gated balanced steady state free precession) sequence at different time points after the injection of cancer cells. Both lung and metastases segmentations were manually performed by experts. DeepMeta was trained to perform lung and metastases segmentation based on the resulting ground truth annotations. Volumes of lungs and of pulmonary metastases as well as the number of metastases per mouse were measured on a separate test dataset of MR images. Thanks to the SG method, the 3D bSSFP images of lungs were artifact-free, enabling the downstream detection and serial follow-up of metastases. Moreover, both lungs and metastases segmentation was accurately performed by DeepMeta as soon as they reached the volume of ∼ 0.02 m m 3 . Thus we were able to distinguish two groups of mice in terms of number and volume of pulmonary metastases as well as in terms of the slow versus fast patterns of growth of metastases. We have shown that our methodology combining SG-bSSFP with deep learning, enables processing of the whole animal lungs and is thus a viable alternative to histology alone

DypFISH: Dynamic Patterned FISH to Interrogate RNA and Protein Spatial and Temporal Subcellular Distribution

Advances in single cell RNA sequencing have allowed for the identification and characterization of cellular subtypes based on quantification of the number of transcripts in each cell. However, cells may differ not only in the number of mRNA transcripts that they exhibit, but also in their spatial and temporal distribution, intrinsic to the definition of their cellular state. Here we describe DypFISH, an approach to quantitatively investigate the spatial and temporal subcellular localization of RNA and protein, by combining micropatterning of cells with fluorescence microscopy at high resolution. We introduce a range of analytical techniques for quantitatively interrogating single molecule RNA FISH data in combination with protein immunolabeling over time. Strikingly, our results show that constraining cellular architecture reduces variation in subcellular mRNA and protein distributions, allowing the characterization of their localization and dynamics with high reproducibility. Many tissues contain cells that exist in similar constrained architectures. Thus DypFISH reveals reproducible patterns of clustering, strong correlative influences of mRNA-protein localization on MTOC orientation when they are present and interdependent dynamics globally and at specific subcellular locations which can be extended to physiological systems

Mitochondrial DNA diversity of the eastern black rhinoceros (Diceros bicornis michaeli) in Tanzania: implications for future conservation

No abstract available

Interrogating RNA and protein spatial subcellular distribution in smFISH data with DypFISH

International audienceAdvances in single-cell RNA sequencing have allowed for the identification of cellular subtypes on the basis of quantification of the number of transcripts in each cell. However, cells might also differ in the spatial distribution of molecules, including RNAs. Here, we present DypFISH, an approach to quantitatively investigate the subcellular localization of RNA and protein. We introduce a range of analytical techniques to interrogate single-molecule RNA fluorescence in situ hybridization (smFISH) data in combination with protein immunolabeling. DypFISH is suited to study patterns of clustering of molecules, the association of mRNA-protein subcellular localization with microtubule organizing center orientation, and interdependence of mRNA-protein spatial distributions. We showcase how our analytical tools can achieve biological insights by utilizing cell micropatterning to constrain cellular architecture, which leads to reduction in subcellular mRNA distribution variation, allowing for the characterization of their localization patterns. Furthermore, we show that our method can be applied to physiological systems such as skeletal muscle fibers

Études de santé

La crise du Covid-19 a exacerbé et mis au grand jour les difficultés du système de santé français, notamment sur le volet de la gestion des ressources humaines : pénuries de personnel, épuisement professionnel, rémunérations insuffisantes… Beaucoup de territoires connaissaient déjà ces maux : les déserts médicaux ne datent pas d’hier, pas plus que les problèmes de coordination des soins pour la prise en charge des maladies chroniques ou les lacunes en termes d’accompagnement au grand âge. Tous ces problèmes ne viennent pas de la formation et tous ne trouvent pas leur solution dans les réformes conduites dans ce domaine, mais la façon dont l’appareil de formation délivre les connaissances et fabrique les spécialités, son organisation (entre les universités, les CHU, les facultés, les instituts… et les territoires), les politiques en matière d’admission et de flux d’étudiants (les fameux numerus clausus et quotas), tout cela façonne le système de santé, influe sur les possibilités de coopération et les conflits et modèle le rapport à l’innovation et la distribution des ressources sur le terrain. Si la santé est un bien commun, la réforme des études est l’affaire de tous. Ce livre, qui donne la parole aux acteurs (enseignants, professionnels de santé, chercheurs, étudiants et acteurs des politiques), place les réformes actuelles des études de santé à la portée du plus grand nombre

Comparative genomics of protoploid Saccharomycetaceae

Our knowledge of yeast genomes remains largely dominated by the extensive studies on Saccharomyces cerevisiae and the consequences of its ancestral duplication, leaving the evolution of the entire class of hemiascomycetes only partly explored. We concentrate here on five species of Saccharomycetaceae, a large subdivision of hemiascomycetes, that we call “protoploid” because they diverged from the S. cerevisiae lineage prior to its genome duplication. We determined the complete genome sequences of three of these species: Kluyveromyces (Lachancea) thermotolerans and Saccharomyces (Lachancea) kluyveri (two members of the newly described Lachancea clade), and Zygosaccharomyces rouxii. We included in our comparisons the previously available sequences of Kluyveromyces lactis and Ashbya (Eremothecium) gossypii. Despite their broad evolutionary range and significant individual variations in each lineage, the five protoploid Saccharomycetaceae share a core repertoire of approximately 3300 protein families and a high degree of conserved synteny. Synteny blocks were used to define gene orthology and to infer ancestors. Far from representing minimal genomes without redundancy, the five protoploid yeasts contain numerous copies of paralogous genes, either dispersed or in tandem arrays, that, altogether, constitute a third of each genome. Ancient, conserved paralogs as well as novel, lineage-specific paralogs were identified