Analyzing the antimicrobial efficacy of the economically important tree Knema linifolia (Roxb.) Warb

Knema linifolia is widely used for fuel wood, fodder and healthcare purposes. This plant treats various diseases in different parts of India, including Assam, Meghalaya, Alipurduar and Darjeeling districts of West Bengal. This study was carried out to determine the bactericidal properties of various parts of K. linifolia aqueous extract. The aqueous extract of the leaves, bark, stem and plant sap were tested against Escherichia coli (gram-negative bacteria) & Staphylococcus aureus (gram-positive bacteria). Among the tested extracts, both the leaf and bark extracts were found to have high bactericidal potential and can kill more than 60% of both bacterial strains with a concentration of 300µg/mL through an agar diffusion test. The MIC (Minimum Inhibitory Concentration) values for the leaf and bark extracts were recorded at ≤1000µg/mL & ≤500µg/mL, respectively. It has also been found that both the bark and leaf extracts contain high tannins, which might be essential for the antibacterial properties of Knema sp. There is currently a lack of proper documentation on using K. linifolia, which makes it challenging to conduct clinical or commercial research to support new uses in modern phototherapy. This study aims to fill this gap and provide significant information that could lead to changes in modern medicine

Pterocarpus angolensis: Botanical, Chemical and Pharmacological Review of an Endangered Medicinal Plant of India

Herbal products for primary health care are gaining huge interests of the people and the various healthcare professionals. This is mainly because of the local availability and cost-effectiveness of plant remedies over expensive modern treatments. Pterocarpus angolensis, a deciduous plant belonging to the family of Fabaceae is mainly found in the tropical regions of Africa. This tree is rich in medicinal properties which are immensely used by the locals in Africa for the treatment of ringworm infections, ulcers, urinary schistosomiasis, skin injury, etc. The extracts of   P. angolensis are treasured in Africa for their effectiveness against many diseases like gonorrhea, mouth diseases, diarrhea, etc. It is reported to have inhibitory activity against various pathogens like Escherichia coli, Staphylococcus aureus, and Salmonella typhimurium because of the high concentration of bioactive compounds like flavonoids, tannins, and other phenolic compounds in the bark and leaves of the tree. Various research papers demonstrated the polar and nonpolar constituents of this plant showing antimicrobial, anti-plasmodial activities against Streptococcus agalactiae, Candida krusei, etc. In India, very few of these plants have been reported to be alive in the Darjeeling district, West Bengal. But, lack of proper documentation or research paper led to negligence related to the importance of this species and it has already been listed in the IUCN Red List of threatened species. The main objective of this review is to spread awareness about the conservation of the plant possessing such remarkable properties. Secondly, to provide an overview of the phytochemical screening of various important medicinal constituents that this plant possesses and this might lead to change in the field of modern medicine

A Dose Finding Study of Weekly and Every-3-Week nab-Paclitaxel Followed by Carboplatin as First-Line Therapy in Patients with Advanced Non-small Cell Lung Cancer

INTRODUCTION: This nonrandomized study aimed to identify the optimal dose of every-3-week (q3w) and weekly nab-paclitaxel plus q3w carboplatin as first-line therapy in patients with advanced non-small cell lung cancer (NSCLC) for a phase 3 trial. METHODS: Previously untreated patients with advanced NSCLC enrolled sequentially into seven cohorts (25 patients/cohort, N = 175). Cohorts 1 to 4 and 5 to 7 received nab-paclitaxel q3w and weekly, respectively. Patients were evaluated for efficacy and safety. RESULTS: The most common treatment-related > or = grade 3 adverse events were neutropenia (60%), neuropathy (19%), fatigue (9%), and thrombocytopenia (29%) (no grade 4 neuropathy or fatigue). A 100 mg/m(2) weekly nab-paclitaxel produced less serious adverse events than other doses/schedules. Response rate (RR) was greater in the weekly versus q3w cohorts (47% vs. 30%). Median progression-free survival (PFS) ranged from 4.8 to 6.9 months, and overall survival (OS) ranged from 8.3 to 15.0 months (all cohorts). Patients receiving 100 mg/m(2) weekly nab-paclitaxel achieved 48% RR with 6.2 and 11.3 months of PFS and OS, respectively. In a retrospective analysis, patients with nonsquamous cell carcinoma receiving weekly nab-paclitaxel had significantly improved RR (59.4% vs. 23.5%, respectively, p = 0.003), and >2 months longer PFS and OS compared with q3w schedule. In patients with squamous cell carcinoma, the q3w schedule significantly increased PFS by 3 months (p = 0.014) and OS by >2 months (no difference in RR) compared with the weekly schedule. CONCLUSION: nab-Paclitaxel plus carboplatin is an effective therapy for advanced NSCLC. Based on favorable efficacy and safety profiles, a phase 3, randomized, multicenter study comparing 100 mg/m(2) weekly nab-paclitaxel plus q3w carboplatin to solvent-based paclitaxel plus carboplatin has enrolled patients

Fabrication of PVA-Silver nanoparticle composite film for elimination of microbial contaminant from effluent

The effluent contains many harmful microbes which should be eliminated before it is discharged into a water body. Silver nanoparticles (AgNPs) being high-quality significance and have a great impact on this research field as it inhibits microbial proliferation and infection. Therefore, it may use for Bioremediation purposes, our laboratory is fascinated by the production of polymer matrix entrapment silver nanoparticles for in situ bio-remediation purposes. The AgNPs was prepared from sawdust by decoction method. The yellowish solution turns into dark brown colour indicating the formation of AgNPs. A sharp SPR (Surface Plasmon Resonance) band formation in UV-vis spectroscopy scan establishes the formation and stability of silver nanoparticles in an aqueous solution. SEM microphotograph indicated roughly spheroidal structure with (63±3) nm average diameters of newly synthesized AgNp. Polyvinyl alcohol (PVA) is eco-friendly and non-toxic to the environment was chosen for the preparation of polymeric matrix. The non-toxic concentration (1 μg/mL) of AgNp was dispersed into PVA solution followed by cross-linked with maleic acid. PVA- maleic acid is cross-linked by the formation of an ester bond, whereas silver nanoparticles physically entrap into the cross-linked matrix. The silver nanoparticles were released from the matrix nearly after 10 min of swelling of the composite film. In a microbial assay using E. coli agar medium, PVA-AgNp composite film shows the significant killing of microorganisms. Microbial elimination is measured indirectly by pH measurement and dissolved oxygen concentration measurement of the effluent in situ against RO- water, taken as control. The dissolved oxygen concentration from RO water and effluent water was measured on Day “0” followed by treatment and incubation at the BOD chamber. The treatment with PVA-AgNp composite film reduced the BOD Level and increase dissolved oxygen level simultaneously increasing the quality of water

Optimistic Planning for Markov Decision Processes

International audienceThe reinforcement learning community has recently intensified its interest in online planning methods, due to their relative independence on the state space size. However, tight near-optimality guarantees are not yet available for the general case of stochastic Markov decision processes and closed-loop, state-dependent planning policies. We therefore consider an algorithm related to AO* that optimistically explores a tree representation of the space of closed-loop policies, and we analyze the near-optimality of the action it returns after n tree node expansions. While this optimistic planning requires a finite number of actions and possible next states for each transition, its asymptotic performance does not depend directly on these numbers, but only on the subset of nodes that significantly impact near-optimal policies. We characterize this set by introducing a novel measure of problem complexity, called the near-optimality exponent. Specializing the exponent and performance bound for some interesting classes of MDPs illustrates the algorithm works better when there are fewer near-optimal policies and less uniform transition probabilities

Introducing Interpretive Approach of Phenomenological Research Methodology in Environmental Philosophy

Environmental philosophy, needless to say, is going through a transition in the zenith of the Anthropocene. It is high time to carry out engaged philosophy to bring in philosophical understandings in approaching real-world environmental issues for obtaining some novel insights into the human–environment relationship. For the same, I argue, we need to explore some new methodologies that would be capable of offering the opportunity to do engaged philosophy instead of borrowing methodologies from the social sciences. Here, I examine Phenomenological Research Methodology (PRM) for the same. I elaborate on the process of conducting a field study with this methodology. For analyzing narratives, I choose the interpretive stream over the descriptive one. By drawing extensively from the philosophy of phenomenology, I propose a four-step narrative analysis process that can unveil a narrator’s transcendent mode of being. Finally, I share my research experiences while employing PRM in the field and demonstrate how PRM has the potential to sidestep some of the widely held concerns associated with field studies. Along with, I highlight critical reflection of my experiences while employing this methodology, particularly, in the context of India

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Not AvailableData generated from the designed experiments is analyzed under the assumptions that the error distribution of observationsis normal and homogeneous and data do not contain any outlier. If any of these assumptions is violated, the conclusion drawnfrom this analysis may be false. In the present paper various M-estimation procedures are applied to designed experiments.Efficiencies of these procedures are measured in terms of average variance. An example is given to illustrate the fact thatapplication of robust method changed the conclusions drawn with analysis of original data. For computation of M-estimation,SAS codes are written in IML and given as AppendixNot Availabl

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Not AvailableAgriculture in India is highly sensitive to climatic variations particularly to rainfall and temperature; therefore, any change in rainfall and temperature will influence crop yields. An understanding of the spatial and temporal distribution and changing patterns in climatic variables is important for planning and management of natural resources. Time series analysis of climate data can be a very valuable tool to investigate its variability pattern and, maybe, even to predict short- and long-term changes in the series. In this study, the sub-divisional rainfall data of India during the period 1871 to 2016 has been investigated. One of the widely used powerful nonparametric techniques namely wavelet analysis was used to decompose and de-noise the series into time–frequency component in order to study the local as well as global variation over different scales and time epochs. On the decomposed series, autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models were applied and by means of inverse wavelet transform, the prediction of rainfall for different sub-divisions was obtained. To this end, empirical comparison was carried out toward forecast performance of the approaches namely Wavelet-ANN, Wavelet-ARIMA, and ARIMA. It is reported that Wavelet-ANN and Wavelet-ARIMA approach outperforms the usual ARIMA model for forecasting of rainfall for the data under consideration.Not Availabl

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Not AvailableIn recent times, forecasting of agricultural commodity price becomes a major issue. But in the context of forecasting of time series data exhibiting Long-Range Dependence (LRD) becomes more complex with the fractional differencing value. In general, Autoregressive Fractionally Integrated Moving Average (AFRIMA) model is widely used for time-series forecasting having long range dependency. It has been observed that in many cases forecasting performance with ARFIMA model is not satisfactory. Therefore, Multi-scale Autoregressive (MAR) model based on wavelets decomposition can be used as an alternative for time-series forecasting. In the present investigation, MAR model is estimated using wavelet decomposition at level 6. Here, an attempt has been made to improve the forecasting performance of MAR model by inclusion of some extra regressors (modified MAR model). Daily wholesale price data on coconut of Kerala market has been used for the illustration purpose. A comparative study has been made for ARFIMA, MAR and modified MAR model in terms of Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). The empirical study reveals that forecasting ability of modified MAR model outperforms the other two methodologies in terms of lower MSE and RMSE values.Not Availabl