Investigating the Relationship between Health and Economic Growth: Empirical Evidence from a Panel of 5 Asian Countries

In this paper, we investigate the relationship between health and economic growth through including investment, exports, imports, and research and development (R&D), for 5 Asian countries using panel unit root, panel cointegration with structural breaks and panel long-run estimator for the period 1974-2007. We model this relationship within the production function framework, and unravel two important results. First, we find that in three variants of the growth model, variables share a long-run relationship; that is, they are cointegrated. Second, we find that in the long-run, while health, investment, exports, and R&D have contributed positively to economic growth, imports have had a statistically significant negative effect while education has had an insignificant effect. We draw important policy implications from these findings.Health; Economic Growth; Panel Unit Root; Panel Cointegration.

Task irrelevant external cues can influence language selection in voluntary object naming: evidence from Hindi-English bilinguals

We examined if external cues such as other agents’ actions can influence the choice of language during voluntary and cued object naming in bilinguals in three experiments. Hindi– English bilinguals first saw a cartoon waving at a color patch. They were then asked to either name a picture in the language of their choice (voluntary block) or to name in the instructed language (cued block). The colors waved at by the cartoon were also the colors used as language cues (Hindi or English). We compared the influence of the cartoon’s choice of color on naming when speakers had to indicate their choice explicitly before naming (Experiment 1) as opposed to when they named directly on seeing the pictures (Experiment 2 and 3). Results showed that participants chose the language indicated by the cartoon greater number of times (Experiment 1 and 3). Speakers also switched significantly to the language primed by the cartoon greater number of times (Experiment 1 and 2). These results suggest that choices leading to voluntary action, as in the case of object naming can be influenced significantly by external non-linguistic cues. Importantly, these symbolic influences can work even when other agents are merely indicating their choices and are not interlocutors in bilingual communicatio

Protein Biochemistry and Expression Regulation of Cadmium/Zinc Pumping ATPases in the Hyperaccumulator Plants Arabidopsis halleri and Noccaea caerulescens

P1B-ATPases are decisive for metal accumulation phenotypes, but mechanisms of their regulation are only partially understood. Here, we studied the Cd/Zn transporting ATPases NcHMA3 and NcHMA4 from Noccaea caerulescens as well as AhHMA3 and AhHMA4 from Arabidopsis halleri. Protein biochemistry was analyzed on HMA4 purified from roots of N. caerulescens in active state. Metal titration of NcHMA4 protein with an electrochromic dye as charge indicator suggested that HMA4 reaches maximal ATPase activity when all internal high-affinity Cd2+ binding sites are occupied. Although HMA4 was reported to be mainly responsible for xylem loading of heavy metals for root to shoot transport, the current study revealed high expression of NcHMA4 in shoots as well. Further, there were additional 20 and 40 kD fragments at replete Zn2+ and toxic Cd2+, but not at deficient Zn2+ concentrations. Altogether, the protein level expression analysis suggested a more multifunctional role of NcHMA4 than previously assumed. Organ-level transcription analysis through quantitative PCR of mRNA in N. caerulescens and A. halleri confirmed the strong shoot expression of both NcHMA4 and AhHMA4. Further, in shoots NcHMA4 was more abundant in 10 μM Zn2+ and AhHMA4 in Zn2+ deficiency. In roots, NcHMA4 was up-regulated in response to deficient Zn2+ when compared to replete Zn2+ and toxic Cd2+ treatment. In both species, HMA3 was much more expressed in shoots than in roots, and HMA3 transcript levels remained rather constant regardless of Zn2+ supply, but were up-regulated by 10 μM Cd2+. Analysis of cellular expression by quantitative mRNA in situ hybridisation showed that in A. halleri, both HMA3 and HMA4 mRNA levels were highest in the mesophyll, while in N. caerulescens they were highest in the bundle sheath of the vein. This is likely related to the different final storage sites for hyperaccumulated metals in both species: epidermis in N. caerulescens, mesophyll in A. halleri

To look or not to look: Subliminal abrupt-onset cues influence constrained free-choice saccades

Subliminal cues have been shown to capture attention and modulate manual response behaviour but their impact on eye movement behaviour is not well-studied. In two experiments, we examined if subliminal cues influence constrained free-choice saccades and if this influence is under strategic control as a function of task-relevancy of the cues. On each trial, a display containing four filled circles at the centre of each quadrant was shown. A central coloured circle indicated the relevant visual field on each trial (Up or Down in Experiment 1; Left or Right in Experiment 2). Next, abrupt-onset cues were presented for 16 ms at one of the four locations. Participants were then asked to freely choose and make a saccade to one of the two target circles in the relevant visual field. The analysis of the frequency of saccades, saccade endpoint deviation and saccade latency revealed a significant influence of the relevant subliminal cues on saccadic decisions. Latency data showed reduced capture by spatially-irrelevant cues under some conditions. These results indicate that spatial attentional control settings as defined in our study could modulate the influence of subliminal abrupt-onset cues on eye movement behaviour. We situate the findings of this study in the attention-capture debate and discuss the implications for the subliminal cueing literature.  &nbsp

Yield and quality of carrageenan from Kappaphycus alvarezii subjected to different physical and chemical treatments

With a view to findout a suitable method for carrageenan extraction from Kappaphycus alvarezii, a detailed investigation was made on quantitative and qualitative estimation of carrageenan subjected to different physical and chemical treatments. The dried material presoaked in KOH and heated for 5 hours at 900C and precipitated with propanol gave the maximum yield of 59.4 % and viscosity of 25.25 cps. In Ca(OH)2 treatment, the yield was almost similar to that of KOH treatment but the viscosity was very low (9.45 cps). The gel was found to be brown in colour when treated with NaOH and milky white with Ca (OH) 2. KOH gel was thick, translucent with high viscosity. Pretreatments of dried seaweed with NaOH, KOH and Ca (OH) 2 followed by pressure-cooking showed relatively higher yield of carrageenan, but the viscosity, clarity and the texture of the gel were found to be poor. Clarity of gel obtained by KOH- methanol treatment was higher when compared to other treatments. The yield of carrageenan was higher when the extracted material was frozen overnight and thawed, but the gel was brown in colour with less rigidity. The present study shows that treatment with KOH gives better yield and quality gel

Knowledge regarding Malnutrition and Its Prevention – A Study on Slum Dwelling Mothers

Introduction: A large proportion of under-five-year kids are suffering from malnutrition. A study was conducted to assess the knowledge of mothers regarding malnutrition and its prevention and evaluate effectiveness of structured teaching program regarding malnutrition and its prevention in terms of knowledge gain in mothers of under-five children attending a crèche run by an NGO in a slum area of New Delhi.Methodology: Quantitative research approach with one group pre-test, post-test design was used. Tool used for generating necessary data was a structured knowledge questionnaire, after establishing its validity and reliability. Purposive sampling technique was used to select 45 mothers having children under five years of age. The study was conducted at a crèche run by an NGO in a slum area of Delhi.Results: Before administration of the structured teaching program, 18 (40%) mothers had poor knowledge, 15 (33.3%) had average knowledge and 12 (26.7%) had good knowledge about malnutrition and its prevention, while after administration of structured teaching program, 12 (26.7%) had poor knowledge, 21 (46.7%) had average knowledge and 12 (26.7%) had good knowledge about malnutrition and its prevention indicating that the intervention was effective. There was significant relationship between knowledge gain and age, education and monthly family income of mothers. Conclusion: Finding of the study revealed that mothers having children under five years of age had poor knowledge about malnutrition and its prevention. The structured teaching program was an effective tool to enhance the knowledge of mothers

Live Image Colour Segmentation Using Different Methods of ANN

Machine learning is a new dimension of science since last 2 decade which motivates algorithms that can learn from data by building a model, based on inputs and using that to make predications or decisions, rather than following only explicitly programmed instructions. Machine learning is sometimes conflated with data mining, which focuses more on exploratory data analysis. Data mining is the extraction of interesting (non-trivial, implicit, previously unknow and potential useful) patterns of knowledge from huge amount of data In computer vision image segmentation is the process of partitioning a digital image into multiple segments (set of pixels, also known as super-pixels). The goals of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics. After that by considering region co-ordinates it separates all color in different figure

A novel approach for code smell detection : an empirical study

Code smells detection helps in improving understandability and maintainability of software while reducing the chances of system failure. In this study, six machine learning algorithms have been applied to predict code smells. For this purpose, four code smell datasets (God-class, Data-class, Feature-envy, and Long-method) are considered which are generated from 74 open-source systems. To evaluate the performance of machine learning algorithms on these code smell datasets, 10-fold cross validation technique is applied that predicts the model by partitioning the original dataset into a training set to train the model and test set to evaluate it. Two feature selection techniques are applied to enhance our prediction accuracy. The Chi-squared and Wrapper-based feature selection techniques are used to improve the accuracy of total six machine learning methods by choosing the top metrics in each dataset. Results obtained by applying these two feature selection techniques are compared. To improve the accuracy of these algorithms, grid search-based parameter optimization technique is applied. In this study, 100% accuracy was obtained for the Long-method dataset by using the Logistic Regression algorithm with all features while the worst performance 95.20 % was obtained by Naive Bayes algorithm for the Long-method dataset using the chi-square feature selection technique.publishedVersio

Post COVID-19 Symptoms: A Neglected Domain

Background: COVID -19 is the most important public health problem of recent time. Most people who have COVID-19 recovers completely within a few weeks but some people continue to have symptoms after initial recovery. Objective: To assess the prevalence of Post COVID symptoms, to assess requirement of treatment and to make recommendation for Post COVID care. Methods: Present cross sectional study was done among patients who recovered from COVID-19 in Meerut district. Mobile numbers of COVID patients were obtained from records, Total 100 randomly selected patients were contacted using google form and information regarding post covid symptoms in between 6 weeks to 12 weeks after recovery from COVID was obtained. Result: 87%patients developed one or more post covid symptoms. Weakness was reported to be most common problem (55%), followed by body ache (26%) and neuropsychiatric symptoms such as difficulty in concentration and insomnia (22%). Every fifth patient reported that symptoms persisted for more than 1 month. Though most of the respondents classified their symptoms as mild and moderate (52.5% and 37.9% respectively), 47% of the symptomatic patients have to take some treatment for these symptoms. Conclusion: Post COVID symptoms are common but usually less severe . Some form of treatment was required to deal with problem. Almost one in five patients reported that symptoms persisted for more than one month. The results highlight the need for post Covid care for COVID recovered patients

Long non-coding RNAs as pan-cancer master gene regulators of associated protein-coding genes: a systems biology approach

Despite years of research, we are still unraveling crucial stages of gene expression regulation in cancer. On the basis of major biological hallmarks, we hypothesized that there must be a uniform gene expression pattern and regulation across cancer types. Among non-coding genes, long non-coding RNAs (lncRNAs) are emerging as key gene regulators playing powerful roles in cancer. Using TCGA RNAseq data, we analyzed coding (mRNA) and non-coding (lncRNA) gene expression across 15 and 9 common cancer types, respectively. 70 significantly differentially expressed genes common to all 15 cancer types were enlisted. Correlating with protein expression levels from Human Protein Atlas, we observed 34 positively correlated gene sets which are enriched in gene expression, transcription from RNA Pol-II, regulation of transcription and mitotic cell cycle biological processes. Further, 24 lncRNAs were among common significantly differentially expressed non-coding genes. Using guilt-by-association method, we predicted lncRNAs to be involved in same biological processes. Combining RNA-RNA interaction prediction and transcription regulatory networks, we identified E2F1, FOXM1 and PVT1 regulatory path as recurring pan-cancer regulatory entity. PVT1 is predicted to interact with SYNE1 at 3′-UTR; DNAJC9, RNPS1 at 5′-UTR and ATXN2L, ALAD, FOXM1 and IRAK1 at CDS sites. The key findings are that through E2F1, FOXM1 and PVT1 regulatory axis and possible interactions with different coding genes, PVT1 may be playing a prominent role in pan-cancer development and progression