Search

Deep subsurface weathering has traditionally been measured by quantifying variations in weathered material properties through cores and boreholes. However, cores collected from the subsurface necessarily sample a minimal fraction of the subsurface. On the other hand, geophysical surveys can be used to obtain a representative perspective of the subsurface. To better understand the near-surface weathered layers, the paper presents an application of rock physics modelling with the help of field-based seismic refraction measurement. We acquired seismic refraction data in hillslope using 4.5 Hz natural frequency geophone with 5-meter geophone spacing. We estimated the subsurface compressional (P) wave velocity (VP) structure and mapped the seismic profile. The profile demonstrated saprolite thickness varying from 1 to 2 meters. To better identify the weathered layers (saprolite) in this section, we predicted porosity using the velocity-porosity relationship model. The porosity estimated through the model was validated by the porosity of the sample extracted from boreholes. This study shows how low-cost techniques can provide useful information about near-surface complexity. Therefore, from this study, it was concluded that proper identification of near-surface layers requires a combination of complementary field-based measurement and precise lab-based rock physics measurement.

Background The bispectoral index (BIS), a parameter derived from electroencephalogram, has been used to assess the depth of anesthesia. The objectives of this study were to evaluate the effect of BIS monitoring on sevoflurane consumption and recovery profile at the end of anesthesia. After obtaining Institutional Review Board approval and written informed consent, 25 American Society of Anesthesiologists (ASA) physical status classification 1 and 2 patients undergoing breast cancer surgeries who had BIS monitoring in addition to standard ASA monitoring (BIS GROUP) were compared against 25 controls (control group). In the control group, adequate depth of anesthesia was maintained using routine clinical parameters like heart rate (HR), mean arterial pressure (MAP), and minimum alveolar concentration (MAC) of sevoflurane, while in the BIS group, it was maintained by keeping the BIS score between 40 and 60 (mean 50). Data including demographics, sevoflurane consumption, hemodynamic variables, and recovery profile at the end of anesthesia was assessed in terms of time for eye opening (TEO), time for motor response (TMR), time for extubation (TE), and modified Aldrete scoring (MAS). Results The mean sevoflurane consumption was lower (P = 0.019) in the BIS group. TEO (P = 0.001), TMR (P = 0.0001), and TE (0.003) were shorter in the BIS group. Difference in MAS between the 2 groups was not statistically significant (P = 0.085). Conclusions BIS monitoring during anesthesia resulted in significant reduction in the sevoflurane consumption. Patients who had BIS monitoring awoke earlier and had better recovery profile at the end of anesthesia.

Lack of retinoblastoma protein (Rb) causes aggressive intraocular retinal tumors in children. Recently, Rb tumors have been shown to have a distinctly altered metabolic phenotype, such as reduced expression of glycolytic pathway proteins alongside altered pyruvate and fatty acid levels. In this study, we demonstrate that loss of Hexokinase 1(HK1) in tumor cells rewires their metabolism allowing enhanced oxidative phosphorylation-dependent energy production. We show that rescuing HK1 or RB1 in these retinoblastoma cells reduced cancer hallmarks such as proliferation, invasion, spheroid formation and increased their sensitivity to chemotherapy drugs. Induction of HK1 was accompanied by a metabolic shift of the cells to glycolysis and a reduction in mitochondrial mass. Cytoplasmic HK1 bound Liver Kinase B1 (LKB1) and phosphorylated AMP-activated kinase-α (AMPKα Thr172), thereby reducing mitochondria-dependent energy production. We validated these findings in tumor samples from Rb patients compared to age-matched healthy retina. HK1 or RB1 expression in Rb-/- cells led to reduction in their respiratory capacity and glycolytic proton flux. HK1 overexpression reduced tumor burden in an intraocular tumor xenograft model. AMPKα activation by AICAR also enhanced the tumoricidal effects of chemotherapeutic drug topotecan in vivo. Therefore, enhancing HK1 or AMPKα activity can reprogram cancer metabolism and sensitize retinoblastoma tumors to lower doses of existing treatments, a potential therapeutic modality for retinoblastoma.

Investigation of trends and variability in rainfall patterns is of paramount importance for the effective planning and management of rainfed cropping systems. In this study, a detailed analysis is carried out to understand the changes in rainfall patterns and characteristics at a regional scale on agro-climatic zone basis, in the mid-Mahanadi river basin. Daily rainfall data of India Meteorological Department (IMD) at 0.25° × 0.25° spatial resolution has been analyzed for the period of 1979–2013 on a monthly, seasonal, and annual timescale to investigate variability, trends, and seasonality of rainfall. Results of analysis reveal that there is large spatio-temporal variation in the river basin with a shift in seasonality of rainfall, with increased prolonged dry spells, and significant increase in extreme weather events over the agro-climatic zones (AZ). Most of the agro-climatic zones are found to have significant decreasing trends in the magnitude of rainfall during the non-monsoon season. In particular, agro-climatic zones nearer to the coastal region are more vulnerable to the high rainfall, whereas the North-Eastern-Ghat (AZ67) and Western-Central-Table (AZ68) regions are susceptible to the moisture stress. Further, analysis of groundwater level fluctuation shows a significant decline in groundwater level during the monsoon as well as non-monsoon seasons. The resulted effect of rainfall variability can be seen in terms of an increase in the prolonged dry spells as well as decline in the groundwater level. Moreover, the results of this study indicate a climate change signal at the regional scale that will help the policymakers and water managers to identify the critical agro-climatic zones in the mid-Mahanadi river basin for the formulation of efficient water resources management and planning strategies, particularly for the major crops of the rainfed agriculture regions.

The application of fiber reinforced polymer (FRP) composites to repair reinforcement concrete (RC) structures has emerged as a new and viable choice. However, the understanding of the durability and long-term performance of this combined system still remains elusive. Adopting non-destructive techniques such as acoustic emission (AE) will raise confidence in exploiting the full potential of this material. The objective of the current study is to identify failure mechanisms in CFRP-retrofitted RC beams by applying advanced pattern recognition techniques on the collected AE data. Six RC beams with artificially induced damage repaired with CFRP sheets are tested with flexural loads and monitored with AE sensors. Since damage mechanisms in the retrofitted RC beams are unknown a priori, a pattern recognition methodology is developed. After preprocessing the AE data using the principal component analysis (PCA), the unsupervised k-means clustering method is applied to automatically cluster and separate the AE patterns. The neural networks based on multi-layer perceptron (MLP) or support vector machine (SVM) algorithm are then developed to better understand the trends in the AE data and their association with the observed damage mechanism. Finally, the trained models are used to successfully identify damage modes in other similar samples.

An evidence of a changing climate is already sensed in India where there is a large diversity from region to region and from season to season. The rainfall pattern in the pre-monsoon (March–April–May) season is important as it helps in determining many crop-related activities in many parts of the country. In the present study, an attempt is made to analyze the current trends in pre-monsoon season rainfall and temperatures over 34 meteorological subdivisions in India using the India Meteorological Department observed analysis datasets of rainfall (1951–2013) and temperatures (1981–2013). The results suggest that a significant decreasing trend in the pre-monsoon rainfall at all-India level and the maximum reduction is found in the month of March. From the regional scale analysis, the south peninsular India is found to have an increasing trend, whereas a decreasing trend is observed over Jammu and Kashmir, Himachal Pradesh and northeast parts of the country. The analyses reveal an association of pre-monsoon rainfall over India with precipitation over China and temperature over the South China Sea, which may act as a precursor for the pre-monsoon rainfall that ultimately could affect the Indian summer monsoon rainfall. The increase of rainfall activity over south peninsular India is attributed to increases in the meridional temperature gradient with an increase of specific humidity. The results of the study will be useful for a long-term risk management in various sectors and would aid in adapting new technologies for a sustainable development in the changing climate scenario.

The seasonal scale precipitation amount is an important ingredient in planning most of the agricultural practices (such as a type of crops, and showing and harvesting schedules). India being an agroeconomic country, the seasonal scale prediction of precipitation is directly linked to the socioeconomic growth of the nation. At present, seasonal precipitation prediction at regional scale is a challenging task for the scientific community. In the present study, an attempt is made to develop multi-model dynamical-statistical approach for seasonal precipitation prediction at the regional scale (meteorological subdivisions) over India for four prominent seasons which are winter (from December to February; DJF), pre-monsoon (from March to May; MAM), summer monsoon (from June to September; JJAS), and post-monsoon (from October to December; OND). The present prediction approach is referred as extended range forecast system (ERFS). For this purpose, precipitation predictions from ten general circulation models (GCMs) are used along with the India Meteorological Department (IMD) rainfall analysis data from 1982 to 2008 for evaluation of the performance of the GCMs, bias correction of the model results, and development of the ERFS. An extensive evaluation of the performance of the ERFS is carried out with dependent data (1982–2008) as well as independent predictions for the period 2009–2014. In general, the skill of the ERFS is reasonably better and consistent for all the seasons and different regions over India as compared to the GCMs and their simple mean. The GCM products failed to explain the extreme precipitation years, whereas the bias-corrected GCM mean and the ERFS improved the prediction and well represented the extremes in the hindcast period. The peak intensity, as well as regions of maximum precipitation, is better represented by the ERFS than the individual GCMs. The study highlights the improvement of forecast skill of the ERFS over 34 meteorological subdivisions as well as India as a whole during all the four seasons.

The monthly prediction of summer monsoon rainfall is very challenging because of its complex and chaotic nature. In this study, a non-linear technique known as Artificial Neural Network (ANN) has been employed on the outputs of Global Climate Models (GCMs) to bring out the vagaries inherent in monthly rainfall prediction. The GCMs that are considered in the study are from the International Research Institute (IRI) (2-tier CCM3v6) and the National Centre for Environmental Prediction (Coupled-CFSv2). The ANN technique is applied on different ensemble members of the individual GCMs to obtain monthly scale prediction over India as a whole and over its spatial grid points. In the present study, a double-cross-validation and simple randomization technique was used to avoid the over-fitting during training process of the ANN model. The performance of the ANN-predicted rainfall from GCMs is judged by analysing the absolute error, box plots, percentile and difference in linear error in probability space. Results suggest that there is significant improvement in prediction skill of these GCMs after applying the ANN technique. The performance analysis reveals that the ANN model is able to capture the year to year variations in monsoon months with fairly good accuracy in extreme years as well. ANN model is also able to simulate the correct signs of rainfall anomalies over different spatial points of the Indian domain .

The Extended Range Forecasts System (ERFS) has been generating monthly and seasonal forecasts on real-time basis throughout the year over India since 2009. India is one of the major rice producer and consumer in South Asia; more than 50% of the Indian population depends on rice as staple food. Rice is mainly grown in kharif season, which contributed 84% of the total annual rice production of the country. Rice cultivation in India is rainfed, which depends largely on rains, so reliability of the rainfall forecast plays a crucial role for planning the kharif rice crop. In the present study, an attempt has been made to test the reliability of seasonal and sub-seasonal ERFS summer monsoon rainfall forecasts for kharif rice yield predictions at Kharagpur, West Bengal by using CERES-Rice (DSSATv4.5) model. These ERFS forecasts are produced as monthly and seasonal mean values and are converted into daily sequences with stochastic weather generators for use with crop growth models. The daily sequences are generated from ERFS seasonal (June–September) and sub-seasonal (July–September, August–September, and September) summer monsoon (June to September) rainfall forecasts which are considered as input in CERES-rice crop simulation model for the crop yield prediction for hindcast (1985–2008) and real-time mode (2009–2015). The yield simulated using India Meteorological Department (IMD) observed daily rainfall data is considered as baseline yield for evaluating the performance of predicted yields using the ERFS forecasts. The findings revealed that the stochastic disaggregation can be used to disaggregate the monthly/seasonal ERFS forecasts into daily sequences. The year to year variability in rice yield at Kharagpur is efficiently predicted by using the ERFS forecast products in hindcast as well as real time, and significant enhancement in the prediction skill is noticed with advancement in the season due to incorporation of observed weather data which reduces uncertainty of yield prediction. The findings also recommend that the normal and above normal yields are predicted well in advance using the ERFS forecasts. The outcomes of this study are useful to farmers for taking appropriate decisions well in advance for climate risk management in rice production during different stages of the crop growing season at Kharagpur.

The objective of the present study was to investigate the inter-annual variation and error structure in the prediction of monthly precipitation through two global coupled models, the National Centers for Environmental Prediction Climate Forecast System version 2 (CFSv2) and the Geophysical Fluid Dynamics Laboratory model. In view of the consistent systematic bias (dry bias during summer monsoon months and wet bias during pre-monsoon months in CFSv2) a requirement to correct the inherent error is inevitable. For this purpose, a few bias correction methods, standardization−reconstruction (Z), quantile−quantile mapping (QQ) and nonlinear transformation (NL_Zi), are explored. The methods are applied to the outputs of the dynamic models and the efficiency is examined through different statistical skill measures. A maximum error reduction is noticed for March, July, September and December. A decreasing tendency for rainfall in July is represented by the raw model and its biased counterpart. The observed probability is noticed to be overestimated (underestimated) corresponding to below normal (above normal) precipitation in the raw model. The varying relationship between monthly precipitation and the NINO3.4 index might be a reason for misleading prediction during extreme years. Among the bias correction methods, NL_Zi showed maximum improvement in terms of predicting the precipitation amount and probability distribution all through the year irrespective of the selection of the coupled model.

Longevity and graceful ageing is what every elderly yearns for. With advances in better health care, better standards and newer modalities of medical expertise the elderly population is in a phase three of demographic transition and the number of elderly in India are on the rise. With increasing age comes the prospect of multiple morbidities. The current medical practice guidelines often require multiple medications to treat each chronic disease state for optimal clinical benefit and poly-pharmacy is inevitable. The multiple drugs when consumed in excess or in lesser dosage can in turn result in adverse drug reactions. An elders body responds in a different manner from the adult body mechanism especially with respect to drug-drug interactions, reduced organ functional capacities and most importantly medication non compliance due to morbidities of ageing viz. dementia, blindness, etc. Also there is accumulation of toxic products of drug metabolism due to pharmacokinetic and pharmacodynamics changes occurring with advancing age. A Novel substitute for pill burden and poly pharmacy3 is the drug box.3 A simple, cost-effective tool for the elderly, which helps in appropriate storage of medicines having compartments which coincide with the number of days in a week or a month, with divisions as per doses. Few boxes even show the time of the day during which the drug must be taken. Drug dispensers are high tech pill boxes keep the medications locked till the time it needs to be taken, and dispenses it at the right time thus prevents drug over dosage. This is very helpful for the elderly patients who suffer from dementia and those who are on multiple drug therapies. Keywords: Drug box, Novel tool, Pill burden.

Carbon fiber–reinforced polymer (CFRP) composites have been widely used to repair and strength concrete structures. Nevertheless, the durability and long-term performance of FPR-strengthene...

A fairly large amount of existing bridges in the United States are in an urgent need of repairing and upgrading. With the increasing application of fiber-reinforced polymers (FRPs), the FRP...

The electroretinogram (ERG) has proven to be useful in the evaluation and monitoring of patients with posterior uveitis. ERG oscillatory potentials (OPs) are sometimes reduced in many uveitic eyes with otherwise grossly normal ERG responses. This study compares ERG parameters, including OPs, between patients with birdshot chorioretinopathy, other posterior uveitis, and controls. This was a retrospective case–control study. Sixty-four patients seen at a clinical practice had a total of 93 visits during which ERG was performed on both eyes. ERG data from 93 age-matched controls were also collected. Root-mean-squared (RMS) energy of the OPs was calculated using Fourier analysis for 88 patients and 88 age-matched controls for whom complete data were available. Photopic flicker amplitudes, photopic flicker latencies, scotopic b-wave amplitudes, and OP RMS values were compared between patients and controls. Diagnostic performance was assessed using receiver operating characteristic (ROC) curves. The mean ages of patients and controls were 55.9 ± 10.8 (SD) years and 55.1 ± 11.5, respectively. 83% of the patients had a diagnosis of BCR. The mean OP RMS value was significantly different in patients (15.6 µV ± 9.7 µV) versus control eyes (33.0 µV ± 12.7 µV), p

Acoustic emission (AE) monitoring has been identified as one popular technique applicable for damage discrimination in composites. However, because the damage is not directly identified fro...

Background: Diabetes mellitus is a metabolic disorder characterized by hyperglycemia along with biochemical alterations of glucose and lipid peroxidation. It produces free radicals that induce lipid peroxidation which acts as an indicator for oxidative stress in the body. The widely used assay for lipid peroxidation involves measurement of malondialdehyde (MDA). Defensive system in the body consists of antioxidant enzymes which help in scavenging free radicals. Two such antioxidant enzymes are reduced glutathione (GSH) and superoxide dismutase (SOD). In this study MDA, GSH and SOD are assessed in serum and saliva of age- and sex-matched 33 diabetics and nondiabetics. Objective: (1) To estimate the levels of MDA, GSH and SOD in saliva and serum of both diabetics and nondiabetics. (2) To correlate the levels of MDA, GSH and SOD in saliva and serum of both diabetics and nondiabetics. (3) To find if serum levels of MDA, GSH and SOD can be predicted from values of the same in saliva, in both groups. Materials and Methods: Whole unstimulated saliva and venous blood samples obtained after 12 h of overnight fast were transported to the designated laboratory chosen for the study. Supernatants of the centrifuged samples were used for the assays of MDA, GSH and SOD. Results: A significant correlation was obtained between serum and saliva values of MDA and GSH, hence the prediction of serum MDA and GSH was possible from their subsequent saliva values. Although the levels of serum and salivary SOD showed a weak positive correlation, prediction of SOD was not possible. Conclusion: Saliva can be used as a diagnostic tool for the estimation of MDA, GSH and SOD.

Purpose of the study is to analyze long-term climatology, variability and trends in the daily precipitation events ≥ 1 mm during winter season (Dec to Feb, DJF) over northwest India (NWI) and its related 9 subdivisions using a new high resolution (0.25° × 0.25°) data of India Meteorological Department from 1901 to 2013. The daily rainfall (DR) events are categorized into light rain (LR > 1–5 mm), moderate rain (MR 5–10 mm), heavy rain (HR 10–15 mm) and very heavy rain (VHR > 15 mm), where the contribution of VHR event to seasonal winter precipitation is found to be higher than that of other events. Seasonal precipitation increased over Jammu-Kashmir and Uttarakhand due to a significant increase in the frequency of total combined DR events, whereas an insignificant decrease in seasonal precipitation is noticed in other subdivisions. The analysis indicates that the relationship of various DR events and associated precipitation over NWI with NAO/AO is weakening and strengthening with Nin˜o–3.4 region SST/SOI (ENSO).

Fiber-reinforced plastics (FRPs), with their unique material and structural properties, offer many advantages over conventional structural materials such as concrete and steel. However, the...

Upwelling is a dominant mechanism in the Arabian Sea that occurs annually during southwest monsoon summer season. This results in abundance of phytoplankton and zooplankton in the region and has profound influence on the coastal fisheries. During the southwest monsoon, an intense low-level wind jet blows diagonally across the Arabian Sea generating coastal upwelling along the coasts of Somalia, Oman and the southeastern Arabian Sea. In this study, a synergy of different parameters like sea surface winds, chlorophyll (chl-a), sea surface temperature (SST) and sea level anomaly (SLA) retrieved from remote sensing were used to make a more detailed analysis on upwelling features for the summer seasons of the years 1982–2015. From the analysis, it is observed that upwelling in the Arabian Sea is not homogeneous across the basin despite being driven by monsoon winds. During the study period, Ekman transport, SLA and SST anomaly showed positive trend, whereas chlorophyll showed negative trend of varying strengths. Increased Ekman transport has not generated increased productivity indicating the role of other governing mechanisms on the availability of nutrients in the region.

The precipitation during winter (December through February) over India is highly variable in terms of time and space. Maximum precipitation occurs over the Himalaya region, which is important for water resources and agriculture sectors over the region and also for the economy of the country. Therefore, in the present global warming era, the realistic prediction of winter precipitation over India is important for planning and implementing agriculture and water management strategies. The National Centers for Environmental Prediction (NCEP) issued the operational prediction of climatic variables in monthly to seasonal scale since 2004 using their first version of fully coupled global climate model known as Climate Forecast System (CFSv1). In 2011, a new version of CFS (CFSv2) was introduced with the incorporation of significant changes in older version of CFS (CFSv1). The new version of CFS is required to compare in detail with the older version in the context of simulating the winter precipitation over India. Therefore, the current study presents a detailed analysis on the performance of CFSv2 as compared to CFSv1 for the winter precipitation over India. The hindcast runs of both CFS versions from 1982 to 2008 with November initial conditions are used and the model’s precipitation is evaluated with that of India Meteorological Department (IMD). The models simulated wind and geopotential height against the National Center for Atmospheric Research (NCEP–NCAR) reanalysis-2 (NNRP2) and remote response patterns of SST against Extended Reconstructed Sea Surface Temperatures version 3b (ERSSTv3b) are examined for the same period. The analyses of winter precipitation revealed that both the models are able to replicate the patterns of observed climatology; interannual variability and coefficient of variation. However, the magnitude is lesser than IMD observation that can be attributed to the model’s inability to simulate the observed remote response of sea surface temperatures to all India winter precipitation. Of the two, CFSv1 is appreciable in capturing year-to-year variations in observed winter precipitation while CFSv2 failed in simulating the same. CFSv1 has accounted for less mean bias and RMSE errors along with good correlations and index of agreements than CFSv2 for predicting winter precipitation over India. In addition, the CFSv1 is also having a high probability of detection in predicting different categories (normal, excess and deficit) of observed winter precipitation over India.

Skilful prediction of the monthly and seasonal summer monsoon rainfall over India at a smaller spatial scale is a major challenge for the scientific community. The present study is aimed at achieving this objective by hybridising two mathematical techniques, namely synthetic superensemble (SSE) and supervised principal component regression (SPCR) on six state-of-the art Global Climate Models (GCMs). The performance of the mathematical model is evaluated using correlation analysis, the root mean square error, and the Nash–Sutcliffe efficiency index. Results feature reasonable improvement over central India, which is a zone of maximum rainfall activity in the summer monsoon season. The study also highlights improvement in the monthly prediction of rainfall over raw GCMs (15–20% improvement) with exceptional improvement in July. The developed model is also examined for anomalous years of monsoon and it is found that the model is able to capture the signs of anomalies over different gridpoints of the Indian domain.

We report a systematic review of moderators of CBT efficacy for pediatric OCD relative to other treatments. CENTRAL, MEDLINE, EMBASE, CINAHL, and PsycINFO were searched for RCTs reporting on effect moderation for CBT outcomes. Five studies (N = 365) examined 17 variables with three significant moderators identified. Compared to pill-placebo, CBT monotherapy was not effective for children with a family history of OCD but was for those without a family history. For children with a family history, CBT plus sertraline efficacy was attenuated but remained significant. For children with tics, CBT but not sertraline remained superior to pill-placebo. For non-responders to initial treatment with CBT, continuing CBT was inferior to commencing sertraline for those with tics but was not different for those without tics. A supplementary review identified older age, symptom and impairment severity, co-morbidity and family accommodation as consistent predictors of a poorer outcome to CBT. Current evidence for moderation effects is post-hoc, from single RCTs, has small Ns and requires replication. The review identifies family history of OCD and the presence of tics as factors requiring further examination in properly conducted trials and about which clinicians need to show care in their treatment recommendations.

Kerala, a maritime state of India is bestowed with abundant rainfall which is about three times the national average. This study is conducted to have a better understanding of rainfall variability and trend at regional level for this state during the last 100 years. It is found that the rainfall variation in northern and southern regions of Kerala is large and the deviation is on different timescales. There is a shifting of rainfall mean and variability during the seasons. The trend analysis on rainfall data over the last 100 years reveals that there is a significant (99%) decreasing trend in most of the regions of Kerala especially in the month of January, July and November. The annual and seasonal trends of rainfall in most regions of Kerala are also found to be decreasing significantly. This decreasing trend may be related to global anomalies as a result of anthropogenic green house gas (GHG) emissions due to increased fossil fuel use, land-use change due to urbanisation and deforestation, proliferation in transportation associated atmospheric pollutants. We have also conducted a study of the seasonality index (SI) and found that only one district in the northern region (Kasaragod) has seasonality index of more than 1 and that the distribution of monthly rainfall in this district is mostly attributed to 1 or 2 months. In rest of the districts, the rainfall is markedly seasonal. The trend in SI reveals that the rainfall distribution in these districts has become asymmetric with changes in rainfall distribution.

The increased use of carbon fiber reinforced polymer (CFRP) in retrofitting reinforced concrete (RC) members has led to the need to develop non-destructive techniques that can monitor and characterize the unique damage mechanisms exhibited by such structural systems. This paper presented the damage characterization results of six CFRP retrofitted RC beam specimens tested in the laboratory and monitored using acoustic emission (AE). The focus of this study was to continuously monitor the change in AE parameters and analyze them both qualitatively and quantitatively, when brittle failure modes such as debonding occur in these beams. Although deterioration of structural integrity was traceable and can be quantified by monitoring the AE data, individual failure mode characteristics could not be identified due to the complexity of the system failure modes. In all, AE was an effective non-destructive monitoring tool that can trace the failure progression in RC beams retrofitted with CFRP. It would be advantageous to isolate signals originating from the CFRP and concrete, leading to a more clear understanding of the progression of the brittle damage mechanism involved in such a structural system. For practical applications, future studies should focus on spectral analysis of AE data from broadband sensors and automated pattern recognition tools to classify and better correlate AE parameters to failure modes observed.

P. R. Tiwari, S. C. Kar, U. C. Mohanty, S. Kumari, P. sinha, A. Nair, and S. Dey, 'Skill of precipitation prediction with GCMs over north India during winter season', International Journal of Climatology, Vol. 34 (12): 3440-3455, October 2014, doi: 10.1002/joc.3921. © 2017 Royal Meteorological Society, published by Wiley Online Library.

Bromelain (BRM) is a defense protein present in the fruit and stem of pineapple (Ananas comosus) and it is grouped as a cysteine protease enzyme with diversified medicinal uses. Based on its therapeutic applications, bromelain has got sufficient attention in pharmaceutical industries. In the present study, the full coding gene of bromelain in pineapple stem (1,093 bp) was amplified by RT-PCR. The PCR product was cloned, sequenced, and characterized. The sequence analysis of the gene revealed the single nucleotide polymorphism and its phylogenetic relatedness. The peptide sequence deduced from the gene showed the amino acid variations, physicochemical properties and secondary and tertiary structural features of the protein. The full BRM gene was transformed to prokaryotic vector pET32b and expressed in Escherichia coli BL21 DE3pLysS host cells successfully. The identity of the recombinant bromelain (rBRM) protein was confirmed by Western blot analysis using anti-BRM-rabbit IgG antibody. The activity of recombinant bromelain compared with purified native bromelain was determined by protease assay. The inhibitory effect of rBRM compared with native BRM in the growth of Gram-positive and Gram-negative strains of Streptococcus agalactiae and Escherichia coli O111 was evident from the antibacterial sensitivity test. To the best of our knowledge, this is the first report showing the bactericidal property of rBRM expressed in a prokaryotic system.

To determine whether electronic health record (EHR) access influences the number of laboratory and imaging tests ordered, which is a frequently cited mechanism for EHR-enabled cost savings.We analyzed data on non-federally employed office-based physicians from the 2008 to 2012 Electronic Health Medical Records Survey, a supplement to the National Ambulatory Medical Care Survey.We estimated logistic regressions to determine the relationship between EHR utilization and the volume of laboratory and imaging tests ordered in our study population, controlling for age, sex, race, clinic type, payer type, health status, comorbidities, and new patients.Physicians who actively used an EHR system ordered more complete blood count (CBC) tests than physicians who did not (odds ratio [OR], 1.34; P.001), even after adjusting for patient demographics, health status, and case mix. EHR-using physicians also ordered more computerized tomography scans (OR, 1.41; P.001) and x-rays (OR, 1.39; P.001); the difference for magnetic resonance imaging scans was not significant (OR, 1.08; P = .449). Subgroup analysis highlighted differences in ordering among various patient cohorts.Using the most recent available nationally representative data, excluding federal and Veterans Affairs' hospitals, we found that physicians with EHR access ordered more tests than their non-EHR counterparts, thus contradicting a common rationale for EHR implementation. We argue that EHR use may actually increase healthcare expenditures by facilitating the ease of ordering tests. Whether these extra tests carry clinical utility requires further analysis.

To study whether the ERG and other clinical findings help to distinguish between advanced hydroxychloroquine (HCQ) retinopathy and pericentral or diffuse retinitis pigmentosa (RP) with similar fundus appearance. We conducted a retrospective analysis of patients with advanced HCQ retinopathy (n = 11), pericentral RP (n = 8) and diffuse RP (n = 8). Pericentral RP was defined as having limited fundus damage and relatively normal flicker ERG time-to-peak. Diffuse RP had typical loss of the rod ERG and flicker timing delay. All patients showed reduced amplitude of the ISCEV responses in the full-field electroretinogram (ERG). Aspects of history, visual field results, fundus appearance, fundus autofluorescence and ocular coherence tomography were also compared. Relative to pericentral RP, patients with HCQ toxicity showed delayed flicker ERG time-to-peak and lower ERG amplitudes, particularly combined rod–cone responses. Relative to diffuse RP, most HCQ toxicity patients had some preserved rod ERG response, and there was no obvious predilection for rod over cone damage. In addition, patients with HCQ toxicity usually lacked markers of long-standing degeneration such as bone spicule figures or severe loss of peripheral field. History of familial disease and long-standing night blindness were specific to RP. While the early signs of HCQ damage are typically regional in the posterior pole, advanced disease is characteristically diffuse (unlike pericentral RP). This is appropriate for a systemic toxin, as is the finding that rods and cones were both affected in the ERG to a similar degree (unlike genetic rod–cone dystrophies). For patients with severe HCQ exposure and some of our discriminatory findings, and no family history or prior night blindness, HCQ toxicity is a sufficient diagnosis without invoking a second rare disease (Occam’s razor).

In this study the predictability of northeast monsoon (Oct–Nov–Dec) rainfall over peninsular India by eight general circulation model (GCM) outputs was analyzed. These GCM outputs (forecasts for the whole season issued in September) were compared with high-resolution observed gridded rainfall data obtained from the India Meteorological Department for the period 1982–2010. Rainfall, interannual variability (IAV), correlation coefficients, and index of agreement were examined for the outputs of eight GCMs and compared with observation. It was found that the models are able to reproduce rainfall and IAV to different extents. The predictive power of GCMs was also judged by determining the signal-to-noise ratio and the external error variance; it was noted that the predictive power of the models was usually very low. To examine dominant modes of interannual variability, empirical orthogonal function (EOF) analysis was also conducted. EOF analysis of the models revealed they were capable of representing the observed precipitation variability to some extent. The teleconnection between the sea surface temperature (SST) and northeast monsoon rainfall was also investigated and results suggest that during OND the SST over the equatorial Indian Ocean, the Bay of Bengal, the central Pacific Ocean (over Nino3 region), and the north and south Atlantic Ocean enhances northeast monsoon rainfall. This observed phenomenon is only predicted by the CCM3v6 model.

We developed a questionnaire to assess the knowledge and practice of nursing staff, patient care assistants, phlebotomists, and laboratory technicians regarding blood culture collection and contamination at 2 tertiary care academic centers. Our study showed there were several gaps in knowledge and practice regarding obtaining blood cultures.

The drought during the months of June to September (JJAS) results in significant deficiency in the annual rainfall and affects the hydrological planning, disaster management, and the agriculture sector of India. Advance information on drought characteristics over the space may help in risk assessment over the country. This issue motivated the present study which deals with the prediction of drought during JJAS through standardized precipitation index (SPI) using nine general circulation models (GCM) product. Among these GCMs, three are the atmospheric and six are atmosphere–ocean coupled models. The performance of these GCM’s predicted SPI is examined against the observed SPI for the time period of 1982–2010. After a rigorous analysis, it can be concluded that the skill of prediction by GCM is not satisfactory, whereas the ability of the coupled models is better than the atmospheric models. An attempt has been made to improve the accuracy of predicted SPI using two different multi-model ensemble (MME) schemes, viz., arithmetic mean and weighted mean using singular value decomposition-based multiple linear regressions (SVD-MLR) of GCMs. It is found that among these MME techniques, SVD-MLR-based MME has more skill as compared to simple MME as well as individual GCMs.

In this paper, changes in the long and short spells of different rain intensities are statistically analyzed using daily gridded rainfall data prepared by the India Meteorological Department for the period 1951–2008. In order to study regional changes, analyses have been conducted over nine selected agro-meteorological (agro-met) divisions, five homogeneous zones, and also over the whole of India. Rain events of different intensities with continuous rainfall of more than or equal to 4 days are classified here as long spells. Those with less than 4 days are termed as short spells. Those results which are statistically significant at 95% confidence level are discussed in this paper. Trend analysis shows that during the summer monsoon months of June to September, short spell rain events with heavy intensity have increased over India as a whole. On the other hand, long spell rain events with moderate and low intensities have decreased in numbers. Results further show that the contributions of long spell moderate and short spell low-intensity rain events to the total rainfall have decreased whereas the contributions of short spell heavy and moderate-intensity rain events to the total seasonal rainfall have increased. Percentage changes in various categories of long and short spells in the decade 1991–2000 compared with the earlier decade 1951–1960, highlight the maximum increase in heavy-intensity short spell category and decrease in moderate-intensity long spell category in India as a whole and in most of the homogeneous zones and agro-met divisions. The changes in different types of rain events differ in the six homogeneous zones and nine selected agro-met divisions. However, in three homogeneous zones and three agro-met divisions, the short spell heavy-intensity rain events dominate as in the entire country. There are also changes observed in the monthly occurrences of above categories of rain events during the 4 months of summer monsoon. Such results with details of changes in rain categories in different parts of India have important implications in agriculture sector in the country.

Background and Aims: Immunomodulatory consequences of allogeneic blood transfusion may outweigh the advantages of improved oxygen delivery and tissue perfusion, especially in patients with cancer. In colorectal cancer, there is evidence of cancer relapse in patients who received transfusion. This retrospective analysis was undertaken to evaluate the relationship between perioperative blood transfusion and cancer recurrence in patients undergoing oncosurgery. Methods: In this case-control study, we retrospectively analysed the case sheets of 194 patients who had perioperative transfusion and underwent cancer surgery from March to December 2013(Study group, Cases). They were compared with controls matched for cancer site and TNM staging who did not receive perioperative transfusions (Control Group, Controls). We intended to find out if the Study group had any increased risk of cancer relapse compared with the controls. Records from the institute cancer registry were analysed in 2018 to give a follow-up period of 5 years. Continuous variables were analysed using Student's T test and Mann Whitney U test for normally distributed and skewed data respectively. For Categorical data Fisher's exact test and Chi square test were applied. The risk for recurrence was estimated using odds ratio. Results: The recurrence rate in cases and controls was 53.09% and 19.59% respectively and the odds ratio, 4.647 (CI: 2.954, 7.309). In Cases, significant relapse was noted for carcinomas of ovary, colorectal, bladder, larynx, head of pancreas and liver. Conclusion: In surgical oncology patients, ABT is associated with greater rate of recurrence.

This paper gives a brief review of the acoustic emission technique and its applications to bridge health monitoring. Emphasis is given to the discussion of available techniques of AE data processing, both qualitative and quantitative. An assessment of the statistical quantitative analysis technique, intensity analysis, is illustrated through two case studies. This technique of damage quantification is applied to AE data collected from two genres of bridges in Louisiana: a prestressed concrete slab-on-girder bridge and a steel bridge with a concrete deck. Although there were limitations concerning the number and type of sensors used, much information was collected and useful inferences were made that may help better diagnose the health of bridges monitored in the future using this technique.