The many roads to psychosis: recent advances in understanding risk and mechanisms.

Schizophrenia is a chronic and severe mental illness which frequently leads to substantial lifelong disability. The past five years have seen major progress in our understanding of the complex genetic architecture of this disorder. Two major barriers to understanding the core biological processes that underlie schizophrenia and developing better interventions are (1) the absence of etiologically defined biomarkers and (2) the clinical and genetic heterogeneity of the disorder. Here, we review recent advances that have led to changes in our understanding of risk factors and mechanisms involved in the development of schizophrenia. In particular, mechanistic and clinically oriented approaches have now converged on a focus on disruptions in early neurodevelopment and synaptic plasticity as being critical for both understanding trajectories and intervening to change them. Translating these new findings into treatments that substantively change the lives of patients is the next major challenge for the field

Poplar from phytoremediation as a renewable energy source:gasification properties and pollution analysis

Biomass gasification is a very efficient process to produce clean energy in the form of a fuel gas (syngas). Hazelnut shells and poplar have good energy production potential and they are abundant in nature. Hazelnut shells have the characteristics of a very good fuel and poplar is among the fastest growing trees; furthermore, poplar demonstrated the capability to absorb organic contaminants (i.e. heavy metals) from the soil in which they are cultivated. However, poplar is not usually used for biomass gasification and its potential is not fully assessed. Here, 3 types of biomass, hazelnut shells (HS), simple poplar (P) and poplar coming from a phytoremediation procedure (PHYP), were chosen as representative samples to be characterized and tested in a steam gasification process carried out on a bench scale fluidized bed gasifier. A comparison is reported on gasification results, such as gas composition, tar production and gas yield for the biomass feedstocks mentioned above. It was concluded that hazelnut shells and poplar (P and PHYP) could be easily gasified in a fluidized bed gasifier, thus producing a good quality gas with low polluting by-products. The PHYP sample showed lower tar content and higher gas yield. It is guessed that Ca and Mg, found in higher quantities in the PHYP sample, could have had a catalytic effect in tar reforming thus producing lower quantity of heavy hydrocarbons

Malaria, Production and Income of the Producers of Coffee and Cocoa: an Analysis from Survey Data in Côte d’Ivoire. Malaria, coffee and cocoa production and income

The sectors of coffee and cocoa represented in Côte d'Ivoire, before the political crisis, approximately 15% of the GDP and 40% of exports. The zones of production of these two cultures are in the forest area which is infected with malaria. The culture of these products is less constraining than that of the food crops such as rice or yam (one does not need to replant each year for example). However, the maintenance of the ground and of the trees and pest management contribute to obtain high yields. In addition, these products allow the producers to obtain monetary income. However, output is not the sole determinant of the level of income: precocity and speed of gathering, by permitting early sale, contribute to get higher income. In addition, food crops such as rice growing, are produced in the area. The objective of this paper is twofold, first, to evaluate the role of malaria on coffee and cocoa productions, second, to assess if the behaviour of rural households facing a liberalisation of the coffee and cocoa chains has an impact on their income. Three functions are thus estimated: production of coffee, production of cocoa and income. Data are taken from a survey carried out on 800 households (21 villages) in 1999 in the forest area of Danané. The main results are the absence of malaria impact on productions and the dominance of individual over collective sale strategies.cocoa, coffee, lowland rice, malaria, sharecropping, Côte d’Ivoire

ENERGY EFFICIENCY AND LIFE CYCLE ANALYSIS OF ORGANIC AND CONVENTIONAL OLIVE GROVES IN THE MESSARA VALLEY, CRETE, GREECE.

Environmental Impacts of agricultural activities have to be assessed in order to address cultural practices and the type of farming that are best suited to avoid the trade-off between the Ecology and the Economy. Furthermore, this study, comparing the environmental impacts with the Life Cycle Analysis (LCA) of Organic and Conventional olive oil production, is proposing to consider the relationship between the Energy Efficiency and the environmental impacts, notably the Climate Change (Global Warming contribution through Greenhouse Gas emission). The LCA is used to take into account the impacts of the production system from the Cradle (input production) to the Farm gate (final farm product) and considers 7 environmental impacts potential categories: Global Warming, Acidification, Eutrophication, Biodiversity, Erosion, Resource depletion, Ground water depletion. The study also assesses the Energy efficiency of both systems. The results show a clear difference between organic and conventional production, namely a two-fold improvement of the energy efficiency in the organic production. Even if the differences are reduced when the results are calculated on the yield rather than the area, the organic methods have a far smaller contribution to Global warming, Eutrophication, Biodiversity loss, Soil loss, Groundwater depletion and Energy use whereas, the Acidification potential is comparable in both cases. The study recommends encouraging some of the cultural practices that are used in the organic farming methods in order to reduce the burden of agriculture on the local and global ecology as well as the natural resources

A Comparative Study of Machine Learning Models for Tabular Data Through Challenge of Monitoring Parkinson's Disease Progression Using Voice Recordings

People with Parkinson's disease must be regularly monitored by their physician to observe how the disease is progressing and potentially adjust treatment plans to mitigate the symptoms. Monitoring the progression of the disease through a voice recording captured by the patient at their own home can make the process faster and less stressful. Using a dataset of voice recordings of 42 people with early-stage Parkinson's disease over a time span of 6 months, we applied multiple machine learning techniques to find a correlation between the voice recording and the patient's motor UPDRS score. We approached this problem using a multitude of both regression and classification techniques. Much of this paper is dedicated to mapping the voice data to motor UPDRS scores using regression techniques in order to obtain a more precise value for unknown instances. Through this comparative study of variant machine learning methods, we realized some old machine learning methods like trees outperform cutting edge deep learning models on numerous tabular datasets.Comment: Accepted at "HIMS'20 - The 6th Int'l Conf on Health Informatics and Medical Systems"; https://americancse.org/events/csce2020/conferences/hims2