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In this study, the authors have designed biocompatible nano-vesicles using graphene oxide (GO) for the release of chlorambucil (CHL) drugs targeting cancerous cells. The GO sheets were first sulfonated and conjugated with folic acid (FA) molecules for controlled release and high loading efficiency of CHL. The chlorambucil (CHL) drug loading onto the functionalized GO surface was performed through π-π stacking and hydrophobic interactions with the aromatic planes of GO. The drug loading and “in vitro” release from the nano-vesicles at different pH were studied. The average particle size, absorption, and loading efficiency (%) of FA-conjugated GO sheets (CHL-GO) were observed to be 300 nm, 58%, and 77%, respectively. The drug release study at different pH (i.e., 7.4 and 5.5) showed a slight deceleration at pH 7.4 over pH 5.5. The amount of drug released was very small at pH 7.4 in the first hour which progressively increased to 24% after 8 h. The rate of drug release was faster at pH 5.5; initially, 16% to 27% in the first 3 h, and finally it reached 73% after 9 h. These observations indicate that the drug is released more rapidly at acidic pH with a larger amount of drug-loading ability. The rate of drug release from the CHL-loaded GO was 25% and 75% after 24 h. The biotoxicity study in terms of % cell viability of CHL-free and CHL-loaded GO against human cervical adenocarcinoma cell line was found to have lower cytotoxicity of CHL-loaded nano-vesicles (IC50 = 18 μM) as compared to CHL-free (IC50 = 8 μM). It is concluded that a high drug-loading efficiency and controlled release with excellent biotoxicity of CHL-GO offers an excellent application in the biomedical field.

Chaos-based image encryption schemes are applied widely for their cryptographic properties. However, chaos and cryptographic relations remain a challenge. The chaotic systems are defined on the set of real numbers and then normalized to a small group of integers in the range 0–255, which affects the security of such cryptosystems. This paper proposes an image encryption system developed using deep learning to realize the secure and efficient transmission of medical images over an insecure network. The non-linearity introduced with deep learning makes the encryption system secure against plaintext attacks. Another limiting factor for applying deep learning in this area is the quality of the recovered image. The application of an appropriate loss function further improves the quality of the recovered image. The loss function employs the structure similarity index metric (SSIM) to train the encryption/decryption network to achieve the desired output. This loss function helped to generate cipher images similar to the target cipher images and recovered images similar to the originals concerning structure, luminance and contrast. The images recovered through the proposed decryption scheme were high-quality, which was further justified by their PSNR values. Security analysis and its results explain that the proposed model provides security against statistical and differential attacks. Comparative analysis justified the robustness of the proposed encryption system.

The majority of inguinal hernia repairs today, open or laparoscopic, are performed with mesh tension-free repair. The introduction of mesh, though beneficial, posed a new set of post-operative problems related with the mesh, and mesh migration or penetration is one of the most unusual ones with considerable morbidity. Mesh migration following laparoscopic repair is rare, and only a handful of cases have been reported in the literature. Here we present the first ever case report of mesh migration and penetration through rectum developing after two years post-operatively. The mesh was removed and the patient was discharged in a stable condition.

Diabetic retinopathy (DR) is an ophthalmological disease that causes damage in the blood vessels of the eye. DR causes clotting, lesions or haemorrhage in the light-sensitive region of the retina. Person suffering from DR face loss of vision due to the formation of exudates or lesions in the retina. The detection of DR is critical to the successful treatment of patients suffering from DR. The retinal fundus images may be used for the detection of abnormalities leading to DR. In this paper, an automated ensemble deep learning model is proposed for the detection and classification of DR. The ensembling of a deep learning model enables better predictions and achieves better performance than any single contributing model. Two deep learning models, namely modified DenseNet101 and ResNeXt, are ensembled for the detection of diabetic retinopathy. The ResNeXt model is an improvement over the existing ResNet models. The model includes a shortcut from the previous block to next block, stacking layers and adapting split–transform–merge strategy. The model has a cardinality parameter that specifies the number of transformations. The DenseNet model gives better feature use efficiency as the dense blocks perform concatenation. The ensembling of these two models is performed using normalization over the classes followed by maximum a posteriori over the class outputs to compute the final class label. The experiments are conducted on two datasets APTOS19 and DIARETDB1. The classifications are carried out for both two classes and five classes. The images are pre-processed using CLAHE method for histogram equalization. The dataset has a high-class imbalance and the images of the non-proliferative type are very low, therefore, GAN-based augmentation technique is used for data augmentation. The results obtained from the proposed method are compared with other existing methods. The comparison shows that the proposed method has higher accuracy, precision and recall for both two classes and five classes. The proposed method has an accuracy of 86.08 for five classes and 96.98% for two classes. The precision and recall for two classes are 0.97. For five classes also, the precision and recall are high, i.e., 0.76 and 0.82, respectively.

Blazars are observed to emit non-thermal radiation across the entire electromagnetic spectrum from the radio to the very-high-energy γ-ray region. The broadband radiation measured from a blazar is dominated by emission from a relativistic plasma jet which is assumed to be powered by a spinning supermassive black hole situated in the central region of the host galaxy. The formation of jets, their mode of energy transport, actual power budget, and connection with the central black hole are among the most fundamental open problems in blazar research. However, the observed broadband spectral energy distribution from blazars is generally explained by a simple one-zone leptonic emission model. The model parameters place constraints on the contributions from the magnetic field, radiation field, and kinetic power of particles to the emission region in the jet. This in turn constrains the minimum power transported by the jet from the central engine. In this work, we explore the potential of machine learning frameworks including linear regression, support vector machine, adaptive boosting, bagging, gradient boosting, and random forests for the estimation of the mass of the supermassive black hole at the center of the host galaxy of blazars using the best-fit emission model parameters derived from the broadband spectral energy distribution modeling in the literature. Our study suggests that the support vector machine, adaptive boosting, bagging, and random forest algorithms can predict black hole masses with reasonably good accuracy.

Abstract The rapid consumption of fossil fuel and increased environmental damage caused by it have given a strong impetus to the growth and development of fuel-efficient vehicles. Hybrid electric vehicles (HEVs) have evolved from their inchoate state and are proving to be a promising solution to the serious existential problem posed to the planet earth. Not only do HEVs provide better fuel economy and lower emissions satisfying environmental legislations, but also they dampen the effect of rising fuel prices on consumers. HEVs combine the drive powers of an internal combustion engine and an electrical machine. The main components of HEVs are energy storage system, motor, bidirectional converter and maximum power point trackers (MPPT, in case of solar-powered HEVs). The performance of HEVs greatly depends on these components and its architecture. This paper presents an extensive review on essential components used in HEVs such as their architectures with advantages and disadvantages, choice of bidirectional converter to obtain high efficiency, combining ultracapacitor with battery to extend the battery life, traction motors’ role and their suitability for a particular application. Inclusion of photovoltaic cell in HEVs is a fairly new concept and has been discussed in detail. Various MPPT techniques used for solar-driven HEVs are also discussed in this paper with their suitability.

We highlight here the fact that the distantly observed luminosity of a spherically symmetric compact star radiating thermal radiation isotropically is higher by a factor of (1+zb)2 compared to the corresponding flat space-time case, where zb is the surface gravitational redshift of the compact star. In particular, we emphasize that if the thermal radiation is indeed emitted isotropically along the respective normal directions at each point, this factor of increment (1+zb)2 remains unchanged even if the compact object would lie within its photon sphere. Since a canonical neutron star has zb≈0.1, the actual X-ray luminosity from the neutron star surface could be ∼20% higher than what would be interpreted by ignoring the general relativistic effects described here. For a static compact object, supported by only isotropic pressure, compactness is limited by the Buchdahl limit zb<2.0. However, for compact objects supported by anisotropic pressure, zb could be even higher (zb<5.211). In addition, in principle, there could be ultra-compact objects having zb≫1. Accordingly, the general relativistic effects described here might be quite important for studies of thermal radiation from some ultra-compact objects.

Fatty acid binding proteins (FABPs) are proteins found in the cytosol that contribute to disorders related to the cardiovascular system, including atherosclerosis and metabolic syndrome. Functionally, FABPs serve as intracellular lipid chaperones, interacting with hydrophobic ligands and mediating their transportation to sites of lipid metabolism. To date, nine unique members of the FABP family (FABP 1–9) have been identified and classified according to the tissue in which they are most highly expressed. In the literature, FABP3 has been shown to be a promising clinical biomarker for coronary and peripheral artery disease. Given the rising incidence of cardiovascular disease and its associated morbidity/mortality, identifying biomarkers for early diagnosis and treatment is critical. In this review, we highlight key discoveries and recent studies on the role of FABP3 in cardiovascular disorders, with a particular focus on its clinical relevance as a biomarker for peripheral artery disease.

MtrA is an essential response regulator (RR) protein in M. tuberculosis, and its activity is modulated after phosphorylation from its sensor kinase MtrB. Interestingly, many regulatory effects of MtrA have been reported to be independent of its phosphorylation, thereby suggesting alternate mechanisms of regulation of the MtrAB two-component system in M. tuberculosis. Here, we show that RR MtrA undergoes non-enzymatic acetylation through acetyl phosphate, modulating its activities independent of its phosphorylation status. Acetylated MtrA shows increased phosphorylation and enhanced interaction with SK MtrB assessed by phosphotransfer assays and FRET analysis. We also observed that acetylated MtrA loses its DNA-binding ability on gene targets that are otherwise enhanced by phosphorylation. More interestingly, acetylation is the dominant post-translational modification, overriding the effect of phosphorylation. Evaluation of the impact of MtrA and its lysine mutant overexpression on the growth of H37Ra bacteria under different conditions along with the infection studies on alveolar epithelial cells further strengthens the importance of acetylated MtrA protein in regulating the growth of M. tuberculosis. Overall, we show that both acetylation and phosphorylation regulate the activities of RR MtrA on different target genomic regions. We propose here that, although phosphorylation-dependent binding of MtrA drives its repressor activity on oriC and rpf, acetylation of MtrA turns this off and facilitates division in mycobacteria. Our findings, thus, reveal a more complex regulatory role of RR proteins in which multiple post-translational modifications regulate the activities at the levels of interaction with SK and the target gene expression.

The aim of this research paper is to find the wear behaviour of Al7075 MMCs. In this investigation, the wear tests on the as-cast and age-hardened specimens were performed on an advanced rotary tribometer. The materials selected for the analysis are Al7075 as a matrix, and the reinforcements are boron carbide. By using stir casting, metal matrix composites are manufactured by adding B4C as a reinforcement particulate in Al7075. The fabricated composites were characterized and the wear behaviour of these composites was carried out on an advanced rotary tribometer. The wt. % of the reinforcements was taken as 6%, 8%, 10%, and 12%. The almost homogeneous blending of reinforcements is shown by the microstructural characterization of Al7075 MMCs. It is observed that due to the rise in weight percentage of the reinforcement to 12% higher hardness is obtained. For 12% of reinforcements, there is an increase in hardness due to the heat treatment than that of the as-cast composites. From this study, it was found that the wear rate is the function of the applied load, microstructure, and volume fraction of the reinforcements. The wear rate was increasing with the sliding velocity.

N-terminal pro B-type natriuretic peptide (NT-proBNP), a cardiac disease biomarker, has been demonstrated to be a strong independent predictor of cardiovascular events in patients without heart failure. Patients with peripheral arterial disease (PAD) are at high risk of cardiovascular events and death. In this study, we investigated levels of NT-proBNP in patients with PAD compared to non-PAD controls. A total of 355 patients were recruited from outpatient clinics at a tertiary care hospital network. Plasma NT-proBNP levels were quantified using protein multiplex. There were 279 patients with both clinical and diagnostic features of PAD and 76 control patients without PAD (non-PAD cohort). Compared with non-PAD patients, median (IQR) NT-proBNP levels in PAD patients were significantly higher (225 ng/L (120-363) vs 285 ng/L (188-425), p- value = 0.001, respectively). Regression analysis demonstrated that NT-proBNP remained significantly higher in patients with PAD relative to non-PAD despite adjusting for age, sex, hypercholesterolemia, smoking and hypertension [odds ratio = 1.28 (1.07-1.54), p-value

The DNA polymorphisms found in clinical strains of Mycobacterium tuberculosis drive altered physiology, virulence, and pathogenesis in them. Although the lineages of these clinical strains can be traced back to common ancestor/s, there exists a plethora of difference between them, compared to those that have evolved in the laboratory. We identify a mutation present in ~80% of clinical strains, which maps in the HATPase domain of the sensor kinase MtrB and alters kinase and phosphatase activities, and affects its physiological role. The changes conferred by the mutation were probed by in-vitro biochemical assays which revealed changes in signaling properties of the sensor kinase. These changes also affect bacterial cell division rates, size and membrane properties. The study highlights the impact of DNA polymorphisms on the pathophysiology of clinical strains and provides insights into underlying mechanisms that drive signal transduction in pathogenic bacteria.

Data normalization is a data preprocessing task and one of the first to be performed during intellectual analysis, particularly in the case of tabular data. The importance of its implementation is determined by the need to reduce the sensitivity of the artificial intelligence model to the values of the features in the dataset to increase the studied model’s adequacy. This paper focuses on the problem of effectively preprocessing data to improve the accuracy of intellectual analysis in the case of performing medical diagnostic tasks. We developed a new two-step method for data normalization of numerical medical datasets. It is based on the possibility of considering both the interdependencies between the features of each observation from the dataset and their absolute values to improve the accuracy when performing medical data mining tasks. We describe and substantiate each step of the algorithmic implementation of the method. We also visualize the results of the proposed method. The proposed method was modeled using six different machine learning methods based on decision trees when performing binary and multiclass classification tasks. We used six real-world, freely available medical datasets with different numbers of vectors, attributes, and classes to conduct experiments. A comparison between the effectiveness of the developed method and that of five existing data normalization methods was carried out. It was experimentally established that the developed method increases the accuracy of the Decision Tree and Extra Trees Classifier by 1–5% in the case of performing the binary classification task and the accuracy of the Bagging, Decision Tree, and Extra Trees Classifier by 1–6% in the case of performing the multiclass classification task. Increasing the accuracy of these classifiers only by using the new data normalization method satisfies all the prerequisites for its application in practice when performing various medical data mining tasks.

The Yamuna river has become one of the most polluted rivers in India as well as in the world because of the high-density population growth and speedy industrialization. The Yamuna river is severely polluted and needs urgent revival. The Yamuna river in Dehradun is polluted due to exceptional tourist activity, poor sewage facilities, and insufficient wastewater management amenities. The measurement of the quality can be done by water quality assessment. In this study, the water quality index has been calculated for the Yamuna river at Dehradun using monthly measurements of 12 physicochemical parameters. Trend forecasting for river water pollution has been performed using different parameters for the years 2020–2024 at Dehradun. The study shows that the values of four parameters namely, Temperature, Total Coliform, TDS, and Hardness are increasing yearly, whereas the values of pH and DO are not rising heavily. The considered physicochemical parameters for the study are TDS, Chlorides, Alkalinity, DO, Temperature, COD, BOD, pH, Magnesium, Hardness, Total Coliform, and Calcium. As per the results and trend analysis, the value of total coliform, temperature, and hardness are rising year by year, which is a matter of concern. The values of the considered physicochemical parameters have been monitored using various monitoring stations installed by the Central Pollution Control Board (CPCB), India.

The present experiment was carried out with different floral preservatives to find out their efficacy on the POD and CAT enzyme activity on petals during vase life period of cut tuberose (Polianthes tuberosa L.) flower spikes in cv. “Single” and cv. “Double”. Among the floral preservatives tried the CAT activity was observed to be the minimum in 4% sucrose treatment whereas, the maximum in 25 ppm cobalt chloride treatment. GA3 100ppm and 25ppm cobalt chloride treated spikes recorded the highest POD activity whereas, 4% sucrose treatment recorded the lowest POD activity in the cut flower spikes. Lower activity of peroxide and catalase at senescence were associated with a longer vase life.

A novel neuro fuzzy classifier Hybrid Kohonen Fuzzy C-Means-σ (HKFCM-σ) is proposed in this paper. The proposed classifier is a hybridization of Kohonen Clustering Network (KCN) with FCM-σ clustering algorithm. The network architecture of HKFCM-σ is similar to simple KCN network having only two layers, i.e., input and output layer. However, the selection of winner neuron is done based on FCM-σ algorithm. Thus, embedding the features of both, a neural network and a fuzzy clustering algorithm in the classifier. This hybridization results in a more efficient, less complex and faster classifier for classifying satellite images. HKFCM-σ is used to identify the flooding that occurred in Kashmir area in September 2014. The HKFCM-σ classifier is applied on pre and post flooding Landsat 8 OLI images of Kashmir to detect the areas that were flooded due to the heavy rainfalls of September, 2014. The classifier is trained using the mean values of the various spectral indices like NDVI, NDWI, NDBI and first component of Principal Component Analysis. The error matrix was computed to test the performance of the method. The method yields high producer’s accuracy, consumer’s accuracy and kappa coefficient value indicating that the proposed classifier is highly effective and efficient.

The special issue aims to cover the latest research topics in designing and deploying visual information analytics and management techniques using deep learning. It is intended to serve as a platform to researchers who want to present research in deep learning. The special issue focuses explicitly on deep learning and its application in visual computing and signal processing. It emphasizes on the extent to which Deep Learning can help specialists in understanding and analyzing complex images and signals. The field of Visual Information Analytics and Management is considered in its broadest sense and covers both digital and analog aspects. This involves development of techniques for image analysis, understanding and restoration. Deep learning techniques are effective for visual analytics. Deep learning is a fast growing area and is gaining impetus for application in various fields. Therefore, in this special issue, the objective is to publish articles related to deep learning in various problems of visual information analytics and management.

A PVC coated fabric bag was evaluated for on-farm storage of wheat for 8 months without fumigation. It was compared with fumigated samples stored in a metal bin. Temperature, relative humidity (RH) and carbon dioxide (CO2) of the bag and surroundings were monitored throughout the storage period. The interstitial temperature, RH and CO2 in bag were in the range of 15.2–37.7°C, 28.5–40.6% and 406–5764 ppm respectively. After 2 months of storage, no significant difference was observed between the quality parameters of samples stored in both structures; however, at the end of 8 months of storage, thousand kernel weight (57.8 g), mold count (39.8×101 CFU/g), moisture content (10.7%), besatz (5.3%) and bored grain (1.6%) were significantly higher in the bag samples compared to the control samples, whereas germination percentage was reduced to 76.7% from 96.7% in bag. Nevertheless, the quality of grain was within the acceptable consumable quality limit as per codex standards due to bored grains less than 1.5% for 6 months in bag.

Their optical clarity as larvae and embryos, small size, and high fecundity make zebrafish ideal for whole animal high throughput screening. A high-throughput drug discovery platform (HTP) has been built to perform fully automated screens of compound libraries with zebrafish embryos. A Tg(kdrl:EGFP) line, marking endothelial cell cytoplasm, was used in this work to help develop protocols and functional algorithms for the system, with the intent of screening for angiogenesis inhibitors. Indirubin 3' Monoxime (I3M), a known angiogenesis inhibitor, was used at various concentrations to validate the protocols. Consistent with previous studies, a dose dependant inhibitory effect of I3M on angiogenesis was confirmed. The methods and protocols developed here could significantly increase the throughput of drug screens, while limiting human errors. These methods are expected to facilitate the discovery of novel anti-angiogenesis compounds and can be adapted for many other applications in which samples have a good fluorescent signal.

RWP-RKs represent a small family of transcription factors (TFs) that are unique to plants and function particularly under conditions of nitrogen starvation. These RWP-RKs have been classified in two sub-families, NLPs (NIN-like proteins) and RKDs (RWP-RK domain proteins). NLPs regulate tissue-specific expression of genes involved in nitrogen use efficiency (NUE) and RKDs regulate expression of genes involved in gametogenesis/embryogenesis. During the present study, using in silico approach, 37 wheat RWP-RK genes were identified, which included 18 TaNLPs (2865 to 7340 bp with 4/5 exons), distributed on 15 chromosomes from 5 homoeologous groups (with two genes each on 4B,4D and 5A) and 19 TaRKDs (1064 to 5768 bp with 1 to 6 exons) distributed on 12 chromosomes from 4 homoeologous groups (except groups 1, 4 and 5); 2-3 splice variants were also available in 9 of the 37 genes. Sixteen (16) of these genes also carried 24 SSRs (simple sequence repeats), while 11 genes had targets for 13 different miRNAs. At the protein level, MD simulation analysis suggested their interaction with nitrate-ions. Significant differences were observed in the expression of only two (TaNLP1 and TaNLP2) of the nine representative genes that were used for in silico expression analysis under varying levels of N at post-anthesis stage (data for other genes was not available for in silico expression analysis). Differences in expression were also observed during qRT-PCR, when expression of four representative genes (TaNLP2, TaNLP7, TaRKD6 and TaRKD9) was examined in roots and shoots of seedlings (under different conditions of N supply) in two contrasting genotypes which differed in NUE (C306 with low NUE and HUW468 with high NUE). These four genes for qRT-PCR were selected on the basis of previous literature, level of homology and the level of expression (in silico study). In particular, the TaNLP7 gene showed significant up-regulation in the roots and shoots of HUW468 (with higher NUE) during N-starvation; this gene has already been characterized in Arabidopsis and tobacco, and is known to be involved in nitrate-signal transduction pathway.

Background: The health care providers need to be aware of solving psychodermatological disorders by a multidisciplinary team approach. Objectives: This study was carried out to find the prevalence and correlates of unrecognised depression linked with common skin morbidities among attendees in a teaching hospital dermatology outpatients department. Methods: This was as institution based cross-sectional study conducted during October and November 2016 at Indira Gandhi Institute of Medical Sciences, Patna, India among 356 consecutive consenting adults with common skin diseases attending dermatology outpatient department fulfilling inclusion criteria using Patient Health Questionnaire (PHQ-9). Results: Among 356 participants aged 18 years and above having one of six most commonly diagnosed disease depression was present among 204 (57.3%), mostly mild depression 84 (41.18%) followed by moderate (23.53%) moderately severe (21.57%) and severe (13.72%). Depression among various dermatological ailments was mostly noted with Tinea infection (66%) and least among Vitiligo and Acnae cases (20% each). In statistical analysis, probability of having significantly increased risk of depression was found among females, illiterates and less educated, perusing household works, from larger families, having lower personal income, suffering for more than 13 months and suffering from itching skin disease, receiving continuous treatment, and having co-morbidities. Conclusions: Magnitude of depression among patients suffering from dermatological conditions was alarmingly high and was influenced by the various risk factors.

Objective. To evaluate the relationship between TGFβ signaling and endothelial lncRNA expression. Methods. Human umbilical vein endothelial cell (HUVECs) lncRNAs and mRNAs were profiled with the Arraystar Human lncRNA Expression Microarray V3.0 after 24 hours of exposure to TGFβ1 (10 ng/mL). Results. Of the 30,584 lncRNAs screened, 2,051 were significantly upregulated and 2,393 were appreciably downregulated (P

Angiogenesis, the formation of new blood vessels from pre-existing ones is a biological process that ensures an adequate blood flow is maintained to provide the cells with a sufficient supply of nutrients and oxygen within the body. Numerous soluble growth factors and inhibitors, cytokines, proteases as well as extracellular matrix proteins and adhesion molecules stringently regulate the multi-factorial process of angiogenesis. The properties and interactions of key angiogenic molecules such as vascular endothelial growth factors (VEGFs), fibroblast growth factors (FGFs) and angiopoietins have been investigated in great detail with respect to their molecular impact on angiogenesis. Since the discovery of angiogenic growth factors, much research has been focused on their biological actions and their potential use as therapeutic targets for angiogenic or anti-angiogenic strategies in a context-dependent manner depending on the pathologies. It is generally accepted that these factors play an indispensable role in angiogenesis. However, it is becoming increasingly evident that this is not their only role and it is likely that the angiogenic factors have important functions in a wider range of biological and pathological processes. The additional roles played by these molecules in numerous pathologies and biological processes beyond angiogenesis are discussed in this review.

In the new millennium and with the advent of high yielding varieties of paddy, the use of various pesticides, some of which may be in minute quantities are highly toxic to aquatic life. Fish culture, therefore, is not more compatible with paddy farming wherever the latest high yielding varieties of paddy are cultivated. Champaran (Bihar) is known to be an area of high quality yielding rice and are even exported to several countries. There are numerous swampy and derelict lentic water bodies in this area. In recent years. however, attempts are being made in the fish culture to utilize the vast swampy and derelict water area for immediate use for the benefit of the poor, without getting involved in to costly reclamation process. Despite of poor and unorganized capture fishery, the percent yield was estimated to be good. The fishes like Channa, Heteropneust, Anabus, Clarius and even some Carps are being cultured in the paddy fields as well as in the water bodies of this area. By virtue of their hardy nature and air breathing habits in many cases, they are excellent material not only for utilizing swampy and derelict water bodies but also in paddy fields as they permit high stocking density and respond to supplementary feeding. Their production potential is by and large directly proportional to inputs and intensity of operational management. Despite of good inputs of these commercial fishes, the potential toxic compounds in various chemicals used for agricultural and other domestic purposes get distributed by a variety of means and accumulate concentrations in the soil and water. These accumulated organic compounds lower the DO level and does not support fish life. Fish species are far from being biochemical inert. As a matter of facts several pesticides are known to induce microsomal enzyme systems in the liver of fish. Physiology, biochemistry and pathology has enabled us to assess the toxicity of the water body with fish and in these circumstances, fish can be used as an indicator of water quality.

We evaluate differences in clear-sky upwelling shortwave radiation reaching the top of the atmosphere in response to increasing the albedo of roof surfaces in an area of India with moderately high aerosol loading. Treated (painted white) and untreated (unpainted) roofs on two buildings in northeast India were analyzed on five cloudless days using radiometric imagery from the IKONOS satellite. Comparison of a radiative transfer model (RRTMG) and radiometric satellite observations shows good agreement ( R ^2 = 0.927). Results show a mean increase of ∼50 W m ^−2 outgoing at the top of the atmosphere for each 0.1 increase of the albedo at the time of the observations and a strong dependence on atmospheric transmissivity.

Extracellular matrix (ECM)-based implantable neural electrodes (NEs) were achieved using a microfabrication strategy on natural-substrate-based organic materials. The ECM-based design minimized the introduction of non-natural products into the brain. Further, it rendered the implants sufficiently rigid for penetration into the target brain region and allowed them subsequently to soften to match the elastic modulus of brain tissue upon exposure to physiological conditions, thereby reducing inflammatory strain fields in the tissue. Preliminary studies suggested that ECM-NEs produce a reduced inflammatory response compared with inorganic rigid and flexible approaches. In vivo intracortical recordings from the rat motor cortex illustrate one mode of use for these ECM-NEs., Competing Interests: COMPETING INTERESTS The authors declare no competing financial interest.