Effect of climato-environmental parameters on chlorophyll a concentration in the lower Ganga basin, India / Efecto de los parámetros climáticos ambientales sobre la concentración de clorofila a en la cuenca baja del Ganges, India

Introduction: Chlorophyll a concentration proxies the phytoplankton biomass which directly involves in signifying the production functions of aquatic ecosystem. Thus, it is imperative to understand their spatio-temporal kinetics in lotic environment with reference to regional climatic variabilities in the tropical inland waters. Objective: In-situ studies were conducted to examine the changes in phytoplankton biomass in lower Ganga basin as influenced by various environmental parameters under regional climatic variability during 2014-2016. Methods: Firstly, the most key influential environmental parameters on riverine Chl-a concentration were determined. Then the direct cascading effect of changing climatic variables on key environmental parameters were derived through modeling and quantified probable changes in mean Chl-a concentration in the lower stretch of river. Results: Only five environmental parameters namely water temperature, total dissolved solid, salinity, total alkalinity and pH were key factors influencing Chl-a (Multiple R2: 0.638, P < 0.05). Present estimates indicate that if the present rate of regional climatic variability over the last 3 decades (mean air temperature + 0.24 °C, total annual rainfall -196.3 mm) remain consistent over the next three decades (2015-2045), an increase in mean Chl-a by + 170 µgL-1 may likely be expected grossly reaching about 475.94 µg L-1 by the year 2045 or more. Conclusions: The present study is first such comprehending a gross hint towards the probable ecosystem response with an alternative model based methodology in data-deficient situations. Subsequently, the output would also be of great benefit for increase water governance and developing strategy protocol for sustainable water management for greater ecosystem services.

Introducción: La concentración de clorofila a representa la biomasa de fitoplancton la cual influye directamente en las funciones de producción de los ecosistemas acuáticos. Por lo tanto, es imperativo comprender su cinética espacio-temporal en el ambiente lótico con respecto a las variabilidades climáticas regionales en las aguas continentales tropicales. Objetivo: Se realizaron estudios in situ para examinar la influencia de varios parámetros ambientales en la biomasa del fitoplancton en la cuenca baja del Ganges durante 2014-2016. Métodos: En primer lugar, se determinaron los parámetros ambientales más influyentes en la concentración de Chl-a fluvial. Luego, el efecto directo en cascada de las variables climáticas sobre los parámetros ambientales clave, mediante el modelado y los cambios en la concentración media de Chl-a en el tramo inferior del río. Resultados: Solo cinco parámetros ambientales, entre ellos, temperatura del agua, sólidos disueltos totales, salinidad, alcalinidad total y pH, fueron factores clave que influyeron en la Chl-a (R2 múltiple: 0.638, P < 0.05). Las estimaciones actuales indican que si la tasa actual de variabilidad climática regional durante las últimas 3 décadas (temperatura media del aire + 0.24 °C, precipitación total anual -196.3 mm) permanece constante durante las próximas tres décadas (2015-2045), se presente un aumento en el promedio de la Chl-a en +170 µgL-1 y alcance aproximadamente 475.94 µgL-1 para el 2045 o más. Conclusiones: Este estudio presenta una metodología basada en modelos alternativos en situaciones de escasez de datos, la información generada también podría contribuir a mejorar la gobernanza del agua y a desarrollar un protocolo para la gestión sostenible del agua y de esta manera mejorar los servicios ecosistémicos.

Mutational analysis of selected high-grade malignancies in a premenopausal gynecologic cancer population: a potential for targeted therapies?

Background: In 2017, there will be 107,000 cases of gynecologic cancer diagnosed in the US with an overall survival of around 70%-most occurring in post-menopausal individuals. In this study, we have examined a younger (≤ 40 years of age) subpopulation of these women with high grade/ high stage gynecologic malignancies, attempting to identify unique genetic abnormalities or combinations thereof through tissue block specimens. This information was then analyzed in light of known target therapies to see if genetic analysis in this setting would yield significant therapeutic advantage. Methods: We retrospectively evaluated patients with high grade/high stage gynecologic cancers (≤ 40 years of age), examined the presence and status of 400 oncogenes and tumors suppressor genes from Formalin-fixed, Paraffin-embedded (FFPE) tissue and functionally classified mutations by SIFT and Polyphen. Results: Twenty women were identified and stratified into positive and negative outcomes. No demographic, clinicopathologic or treatment factors were significant between these groups. Of the 400 genes evaluated, twelve mutations were significant between the groups, six with targeted therapies. Mutations associated with negative outcomes within histologies/locations were evaluated: ERBB3 in epithelial (ovarian), ALK/GPR124/KMT2D in neuroendocrine (ovarian/endometrial), ROS1/EGFR, ROS1/ERBB3/KMT2D/NIRK1 and GPR124 in sarcoma. All negative outcomes were void of mutations in APC/ABL2. A predictive model for negative outcomes in our cohort was developed from these data: AKAP9-/MBD1-/APC-/ABL2- with a mutation load of > 20.5. Conclusions: Unique multi-gene and mutational outcome correlations were identified in our cohort. Resulting complex mutational profiles in distinctly aggressive gynecologic cancers suggested potential for novel therapeutic treatment. Future larger scale studies will be needed to correlate the genotypic and phenotypic features identified here (AU)

Abnormal Effective Connectivity of the Anterior Forebrain Regions in Disorders of Consciousness / 神经科学通报·英文版

A number of studies have indicated that disorders of consciousness result from multifocal injuries as well as from the impaired functional and anatomical connectivity between various anterior forebrain regions. However, the specific causal mechanism linking these regions remains unclear. In this study, we used spectral dynamic causal modeling to assess how the effective connections (ECs) between various regions differ between individuals. Next, we used connectome-based predictive modeling to evaluate the performance of the ECs in predicting the clinical scores of DOC patients. We found increased ECs from the striatum to the globus pallidus as well as from the globus pallidus to the posterior cingulate cortex, and decreased ECs from the globus pallidus to the thalamus and from the medial prefrontal cortex to the striatum in DOC patients as compared to healthy controls. Prediction of the patients' outcome was effective using the negative ECs as features. In summary, the present study highlights a key role of the thalamo-basal ganglia-cortical loop in DOCs and supports the anterior forebrain mesocircuit hypothesis. Furthermore, EC could be potentially used to assess the consciousness level.

Effects of components on and predictive modeling of microemulsion phase behavior in nonionic systems.

This study aimed to investigate phase behaviors, to study effects of cosolvent addition on size of microemulsion regions and to propose modified logistic regression which could describe microemulsion regions in nonionic systems. The systems composed of rice bran oil (RBO) or isopropyl palmitate (IPP), various ratios of sorbitan monooleate (SMO) and polyoxyethylene 20 sorbitan monooleate (PSMO) mixtures, water and isopropyl alcohol (IPA) or propylene glycol (PG) were studied for their microemulsion regions obtained on the phase diagrams. Concept of modified logistic regression was used to predict probability of microemulsion formation and size of microemulsion regions in the systems. It was found that both oil and cosolvent types affected on microemulsion formation. A system composed of IPP, 2:1 water:IPA, and 1:1 SMO:PSMO could provide the largest microemulsion region. However, the purposed modified logistic regression could be used consistently for only one system of the total four systems due to the faceted shape of microemulsion-zone.

Estimativa de estro em vacas leiteiras utilizando métodos quantitativos preditivos / Dairy cows estrus estimation using predictive and quantitative methods

O Brasil é o sexto maior produtor de leite do mundo, sendo que essa produção cresce a uma taxa anual 4 por cento superior aos demais países produtores. Parte desse aumento na produção de leite deve-se ao uso de diversas tecnologias desenvolvidas para o setor, principalmente, aquelas relacionadas à genética e ao manejo do rebanho. A detecção acurada do cio em vacas é um fator limitante na eficiência reprodutiva do rebanho leiteiro, sendo considerada uma das principais deficiências na reprodução bovina. Falha na identificação do estro com eficiência ocasiona perdas para o produtor. Métodos quantitativos preditivos, baseados em dados históricos e conhecimento especialista, permitem, a partir de uma base de dados organizada, a predição de padrões com baixa percentagem de erro. Este trabalho comparou a precisão das técnicas de estimativa de estro para vacas da raça Holandesa alojadas em galpão freestall, utilizando métodos quantitativos preditivos, por meio da interposição dos pontos intermediários provenientes de série histórica do rebanho. Uma base de regras foi formulada sendo que os valores dos pesos de cada afirmação pertencem a um intervalo de zero a um, e esses limites foram utilizados para gerar a função de pertinência Fuzzy, cuja saída era a predição de estro. Na etapa seguinte, foi aplicada a técnica de Data mining utilizando os parâmetros de movimentação, produção de leite, dias de lactação e comportamento de monta, sendo gerada uma árvore de decisão para analisar os parâmetros mais significativos na previsão de estro em vacas leiteiras. Os resultados indicaram que a presença de estro pode ser detectada com maior precisão usando a observação de movimentação das vacas (87 por cento, erro estimado 4 por cento) ou o comportamento de monta (78 por cento, erro estimado 11 por cento).

Brazil is the sixth world’s larger milk producer, increasing its production at an annual rate of 4 percent above other producer countries. Part of this raise in milk production was due to the use of several technologies that have being developed for the sector, mainly those related to genetics and herd management. Accurate estrus detection in dairy cows is a limiting factor in the reproduction efficiency of dairy cattle, and it has been considered the most important deficiency in the field of reproduction. Failing to detect estrus efficiently may cause losses for the producer. Quantitative predictive methods based on historical data and specialist knowledge may allow, from an organized data base, the prediction of estrus pattern with lower error. This research compared the precision of the estrus prediction techniques for freestall confined Holstein dairy cows using quantitative predictive methods, through the interpolation of intermediate points of historical herd data set. A base of rules was formulated and the values of weight for each statement is within the interval of 0 to 1; and these limits were used to generate a function of pertinence fuzzy that had as output the estrus prediction. In the following stage Data mining technique was applied using the parameters of movement rate, milk production, days of lactation and mounting behavior, and a decision tree was built for analyzing the most significant parameters for predicting estrus in dairy cows. The results indicate that the prediction of estrus incidence may be achieved either using the association of cow’s movement (87 percent, with estimated error of 4 percent) or the observation of mounting behavior (78 percent, with estimated error of 11 percent).