Action of drugs on the central nervous system with special reference to acetyl choline

1. Stimulation of the central end of the vagus and injection of acetyl choline in the ventricles of the brain have a similar effect on respiration, namely a marked depression of respiratory movements.2. Atropine and physostigmine do not modify the effect of central vagal stimulation or intraventricular acetyl choline injection.3. In a small proportion of cases an acetyl-cholinelike substance appears in the cerebro- spinal fluid after stimulation of the central end of the vagus when no such substance is present in the normal cerebro-spinal fluid.4. The physiological and physico -chemical properties of the brain extracts of cats and rabbits show a close resemblance to the properties of acetyl choline.5. Various biological tests applied to the brain extracts show that acetyl choline is normally presents in the brain.6. The concentration of acetyl choline is highest i the basal ganglia, and lowest in the cerebellum; !the concentration in the cortex is slightly lower than that in the basal ganglia.7.. The acetyl choline equivalent in the basal ganglia, is about 0.44 y in cats and about 0.14γ in rabbit's.8. It is suggested that the effects of stimulation of the central end of the vagus are due to liberation' of acetyl choline in the central nervous system

Long lead time drought forecasting using lagged climate variables and a stacked long short-term memory model.

Drought forecasting with a long lead time is essential for early warning systems and risk management strategies. The use of machine learning algorithms has been proven to be beneficial in forecasting droughts. However, forecasting at long lead times remains a challenge due to the effects of climate change and the complexities involved in drought assessment. The rise of deep learning techniques can solve this issue, and the present work aims to use a stacked long short-term memory (LSTM) architecture to forecast a commonly used drought measure, namely, the Standard Precipitation Evaporation Index. The model was then applied to the New South Wales region of Australia, with hydrometeorological and climatic variables as predictors. The multivariate interpolated grid of the Climatic Research Unit was used to compute the index at monthly scales, with meteorological variables as predictors. The architecture was trained using data from the period of 1901-2000 and tested on data from the period of 2001-2018. The results were then forecasted at lead times ranging from 1 month to 12 months. The forecasted results were analysed in terms of drought characteristics, such as drought intensity, drought onset, spatial extent and number of drought months, to elucidate how these characteristics improve the understanding of drought forecasting. The drought intensity forecasting capability of the model used two statistical metrics, namely, the coefficient of determination (R2) and root-mean-square error. The variation in the number of drought months was examined using the threat score technique. The results of this study showed that the stacked LSTM model can forecast effectively at short-term and long-term lead times. Such findings will be essential for government agencies and can be further tested to understand the forecasting capability of the presented architecture at shorter temporal scales, which can range from days to weeks

Instability in Rice Production in Gujarat: A Decomposition Analysis

Rice is the most important and extensively grown food crop in India and is the staple food for more than half of the world population. In India, Gujarat ranks 15th in terms of area and production and 9th in productivity (2011). The scope for expanding rice production lies in enhancing productivity. The growth rates of rice area, production and productivity during 1982-83 to 2011-12 were 0.41, 1.25 and 0.83 percent per annum respectively. The growth estimate from last 30 years data shows that negligible increase was recorded in area and production of rice. Presently the yield level of rice in the state is comparatively low from national average need to be increased substantially. The magnitude of instability in area and production of rice has been higher in all the selected districts compared to state. Variability in production has been at a higher rate compared to area and productivity variability in this crop. The area-yield co-variance had a stabilizing effect on reduction of instability in rice production It can be inferred that the wide fluctuation in production of rice crop have been due to the high variability in its productivity. The future development programmes should envisage on increase of yield for bringing stabilization in production of the crop. The area instability also needs to be reduced. This could be reduced by more investment on research for rice production technology in the state

Liquisolid Compacts: A Review

Solubility is a major problem for nearly one third drugs in their development phase. Liquisolid technique is a most promising technique for promoting dissolution by increase in solubility. Liquisolid compact technology is a novel concept for oral drug delivery. Liquisolid compact technology was first described by spireas et.al. (1998). According to the new formulation method of liqui-solid compacts, liquid medications such as solutions or suspensions of water insoluble drugs in suitable nonvolatile liquid vehicles can be converted into acceptably flowing and compressible powders by blending with selected powder excipients

Pathways and challenges of the application of artificial intelligence to geohazards modelling

© 2020 International Association for Gondwana Research The application of artificial intelligence (AI) and machine learning in geohazard modelling has been rapidly growing in recent years, a trend that is observed in several research and application areas thanks to recent advances in AI. As a result, the increasing dependence on data driven studies has made its practical applications towards geohazards (landslides, debris flows, earthquakes, droughts, floods, glacier studies) an interesting prospect. These aforementioned geohazards were responsible for roughly 80% of the economic loss in the past two decades caused by all natural hazards. The present study analyses the various domains of geohazards which have benefited from classical machine learning approaches and highlights the future course of direction in this field. The emergence of deep learning has fulfilled several gaps in: i) classification; ii) seasonal forecasting as well as forecasting at longer lead times; iii) temporal based change detection. Apart from the usual challenges of dataset availability, climate change and anthropogenic activities, this review paper emphasizes that the future studies should focus on consecutive events along with integration of physical models. The recent catastrophe in Japan and Australia makes a compelling argument to focus towards consecutive events. The availability of higher temporal resolution and multi-hazard dataset will prove to be essential, but the key would be to integrate it with physical models which would improve our understanding of the mechanism involved both in single and consecutive hazard scenario. Geohazards would eventually be a data problem, like geosciences, and therefore it is essential to develop models that would be capable of handling large voluminous data. The future works should also revolve towards interpretable models with the hope of providing a reasonable explanation of the results, thereby achieving the ultimate goal of Explainable AI

Short-term spatio-temporal drought forecasting using random forests model at New South Wales, Australia

© 2020 by the authors. Droughts can cause significant damage to agriculture and water resources, leading to severe economic losses and loss of life. One of the most important aspect is to develop effective tools to forecast drought events that could be helpful in mitigation strategies. The understanding of droughts has become more challenging because of the effect of climate change, urbanization and water management; therefore, the present study aims to forecast droughts by determining an appropriate index and analyzing its changes, using climate variables. The work was conducted in three different phases, first being the determination of Standard Precipitation Evaporation Index (SPEI), using global climatic dataset of Climate Research Unit (CRU) from 1901-2018. The indices are calculated at different monthly intervals which could depict short-term or long-term changes, and the index value represents different drought classes, ranging from extremely dry to extremely wet. However, the present study was focused only on forecasting at short-term scales for New SouthWales (NSW) region of Australia and was conducted at two different time scales, one month and three months. The second phase involved dividing the data into three sample sizes, training (1901-2010), testing (2011-2015) and validation (2016-2018). Finally, a machine learning approach, Random Forest (RF), was used to train and test the data, using various climatic variables, e.g., rainfall, potential evapotranspiration, cloud cover, vapor pressure and temperature (maximum, minimum and mean). The final phase was to analyze the performance of the model based on statistical metrics and drought classes. Regarding this, the performance of the testing period was conducted by using statistical metrics, Coefficient of Determination (R2) and Root-Mean-Square-Error (RMSE) method. The performance of the model showed a considerably higher value of R2 for both the time scales. However, statistical metrics analyzes the variation between the predicted and observed index values, and it does not consider the drought classes. Therefore, the variation in predicted and observed SPEI values were analyzed based on different drought classes, which were validated by using the Receiver Operating Characteristic (ROC)-based Area under the Curve (AUC) approach. The results reveal that the classification of drought classes during the validation period had an AUC of 0.82 for SPEI 1 case and 0.84 for SPEI 3 case. The study depicts that the Random Forest model can perform both regression and classification analysis for drought studies in NSW. The work also suggests that the performance of any model for drought forecasting should not be limited only through statistical metrics, but also by examining the variation in terms of drought characteristics

Temporal hydrological drought index forecasting for New South Wales, Australia using machine learning approaches

© 2020 by the authors. Droughts can cause significant damage to agriculture and water resources leading to severe economic losses. One of the most important aspects of drought management is to develop useful tools to forecast drought events, which could be helpful in mitigation strategies. The recent global trends in drought events reveal that climate change would be a dominant factor in influencing such events. The present study aims to understand this effect for the New South Wales (NSW) region of Australia, which has suffered from several droughts in recent decades. The understanding of the drought is usually carried out using a drought index, therefore the Standard Precipitation Evaporation Index (SPEI) was chosen as it uses both rainfall and temperature parameters in its calculation and has proven to better reflect drought. The drought index was calculated at various time scales (1, 3, 6, and 12 months) using a Climate Research Unit (CRU) dataset. The study focused on predicting the temporal aspect of the drought index using 13 different variables, of which eight were climatic drivers and sea surface temperature indices, and the remainder were various meteorological variables. The models used for forecasting were an artificial neural network (ANN) and support vector regression (SVR). The model was trained from 1901-2010 and tested for nine years (2011-2018), using three different performance metric scores (coecient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The results indicate that ANN was better than SVR in predicting temporal drought trends, with the highest R2 value of 0.86 for the former compared to 0.75 for the latter. The study also reveals that sea surface temperatures and the climatic index (Pacific Decadal Oscillation) do not have a significant effect on the temporal drought aspect. The present work can be considered as a first step, wherein we only study the temporal trends, towards the use of climatological variables and drought incidences for the NSW region

A new synthesis of thiophenes and thiapyrans. Part V: 6:7-Benzothionaphthene and 9-chloronaphtho-(1':8'-bc)-thiapyran

This article does not have an abstract

Naphthoquinone series - Part II: Bazanquinone vat dyes from 2:3-dichloro-1:4-naphthoquinone: part II

This article does not have an abstract

Effects of Pranayam Breathing on Respiratory Pressures and Sympathovagal Balance of Patients with Chronic Airflow Limitation and in Control Subjects

Objectives: The objective of this study was to compare the effects of Pranayam breathing on respiratory muscle strength measured as maximum expiratory and inspiratory pressures (MEP and MIP) and relevant spirometry parameters in patients with chronic obstructive pulmonary disease (COPD) and in control subjects, and on the sympatho-vagal balance in both the groups. Methods: The research was performed in the Clinical Physiology Department, Sultan Qaboos University Hospital, Oman. Eleven patients (mean age 43.91 ± 20.56 yr; mean BMI 21.9 ± 5.5 kg/m2) and 6 controls (43.5 ± 14.6yr; 25.4 ± 3.2 kg/m2) learnt and practised Pranayam. Their respiratory and cardiovascular parameters were recorded. Their respiratory “well being” was noted as a visual analogue score (VAS). The respiratory parameters were expressed as a percentage change of predicted values. Results: Patients’ respiratory parameters were significantly lower than those of controls. Patients’ maximum respiratory pressures did not improve after Pranayam; however, they showed significant improvement in VAS 5.4 ± 2.4 to 7.2 ± 1.2 (P < 0.03). Controls showed significant increase in MIP after Pranayam exercises. There were no changes in other spirometry indices. Controls showed significant increase in their systolic blood pressure and stroke index after exercise. The vago-sympathetic balance shifted towards sympathetic in both patients and controls after exercise. Conclusion: The improvement in MIP in controls indicated the positive effect of Pranayam exercise; however, it may not be an adequately stressful exercise to produce changes in the respiratory parameters of COPD patients. The increase in VAS in patients suggested improvement in respiratory distress and quality of life.