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Sleep plays a vital role in older adults' health. The Community Aging in Place-Advancing Better Living for Elders (CAPABLE) trial, conducted in Maryland between 2012 and 2016, is a 5-month biobehavioral environmental intervention study to reduce functional disabilities in 300 low-income older adults. Individual and environmental factors impacting sleep were addressed in CAPABLE. This secondary data analysis was to test the preliminary effect of CAPABLE on actigraph-measured sleep, compared with a social engagement control in 73 CAPABLE participants with pretest-posttest actigraph data. Participants in this analysis were aged 75.8±7.5 years; 86.3% of them were females and 84.9% were Black/African Americans. Both CAPABLE intervention and social engagement control improved sleep efficiency and reduced sleep onset latency. The effect of CAPABLE on sleep was comparable to social engagement. These findings underline the importance of promoting physical function and maintaining social activity for sleep in low-income older adults with disabilities.

Patients with Alzheimer's infection (AD) and Parkinson's sickness (PD) regularly have cover in clinical show and cognitive neuropathology proposing that these two illnesses share basic fundamental instruments. Parkinson sickness emerges from diminished dopamine creation in the mind. Patients with these two illnesses often cover in clinical introduction and cerebrum neuropathology proposing that they share basic common fundamental systems. Therefore, it become important two find the presence of common affected brain region of interest. The paper proposes the technique to find out the relation between the common affected brain areas. The work with in carried in-depth analysis of brain functional MRI scans using statistical parametric mapping on both AD, PD patients (n=35), which comprises of 5 healthy participants with average age 83, 5 AD participants with average age 75, Early mild cognitive impairment (EMCI) participants whose average age is 65, Late mild cognitive impairment (EMCI) participants whose average age is 75 and PD participants with average age of 64. Using a two sample t-test the ROI (region of interest) was noted using MarsBar plugin of SPM tool.

Objectives Built environments can affect health, but data in many geographic areas are limited. We used a big data source to create national indicators of neighborhood quality and assess their associations with health. Methods We leveraged computer vision and Google Street View images accessed from December 15, 2017, through July 17, 2018, to detect features of the built environment (presence of a crosswalk, non–single-family home, single-lane roads, and visible utility wires) for 2916 US counties. We used multivariate linear regression models to determine associations between features of the built environment and county-level health outcomes (prevalence of adult obesity, prevalence of diabetes, physical inactivity, frequent physical and mental distress, poor or fair self-rated health, and premature death [in years of potential life lost]). Results Compared with counties with the least number of crosswalks, counties with the most crosswalks were associated with decreases of 1.3%, 2.7%, and 1.3% of adult obesity, physical inactivity, and fair or poor self-rated health, respectively, and 477 fewer years of potential life lost before age 75 (per 100 000 population). The presence of non–single-family homes was associated with lower levels of all health outcomes except for premature death. The presence of single-lane roads was associated with an increase in physical inactivity, frequent physical distress, and fair or poor self-rated health. Visible utility wires were associated with increases in adult obesity, diabetes, physical and mental distress, and fair or poor self-rated health. Conclusions The use of computer vision and big data image sources makes possible national studies of the built environment’s effects on health, producing data and results that may inform national and local decision-making.

Despite profound financial challenges during the COVID-19 pandemic, there is a gap in estimating their effects on mental health and well-being among older adults.The National Health and Aging Trends Study is an ongoing nationally representative cohort study of US older adults. Outcomes included mental health related to COVID-19 (scores averaged across eight items ranging from one to four), sleep quality during COVID-19, loneliness during COVID-19, having time to yourself during COVID-19, and hopefulness during COVID-19. Exposures included income decline during COVID-19 and financial difficulty due to COVID-19. Propensity score weighting produced covariate balance for demographic, socioeconomic, household, health, and well-being characteristics that preceded the pandemic to estimate the average treatment effect. Sampling weights accounted for study design and non-response.In weighted and adjusted analyses (n = 3257), both income decline during COVID-19 and financial difficulty due to COVID-19 were associated with poorer mental health related to COVID-19 (b = -0.159, p 0.001 and b = -0.381, p 0.001, respectively), poorer quality sleep (OR = 0.63, 95% CI: 0.46, 0.86 and OR = 0.42, 95% CI: 0.30, 0.58, respectively), more loneliness (OR = 1.53, 95% CI: 1.16, 2.02 and OR = 2.72, 95% CI: 1.96, 3.77, respectively), and less time to yourself (OR = 0.54, 95% CI: 0.40, 0.72 and OR = 0.37, 95% CI: 0.27, 0.51, respectively) during COVID-19.Pandemic-related financial challenges are associated with worse mental health and well-being regardless of pre-pandemic characteristics, suggesting that they are distinct social determinants of health for older adults. Timely intervention is needed to support older adults experiencing pandemic-related financial challenges.

Background The built environment is a structural determinant of health and has been shown to influence health expenditures, behaviors, and outcomes. Traditional methods of assessing built environment characteristics are time-consuming and difficult to combine or compare. Google Street View (GSV) images represent a large, publicly available data source that can be used to create indicators of characteristics of the physical environment with machine learning techniques. The aim of this study is to use GSV images to measure the association of built environment features with health-related behaviors and outcomes at the census tract level. Methods We used computer vision techniques to derive built environment indicators from approximately 31 million GSV images at 7.8 million intersections. Associations between derived indicators and health-related behaviors and outcomes on the census-tract level were assessed using multivariate regression models, controlling for demographic factors and socioeconomic position. Statistical significance was assessed at the α = 0.05 level. Results Single lane roads were associated with increased diabetes and obesity, while non-single-family home buildings were associated with decreased obesity, diabetes and inactivity. Street greenness was associated with decreased prevalence of physical and mental distress, as well as decreased binge drinking, but with increased obesity. Socioeconomic disadvantage was negatively associated with binge drinking prevalence and positively associated with all other health-related behaviors and outcomes. Conclusions Structural determinants of health such as the built environment can influence population health. Our study suggests that higher levels of urban development have mixed effects on health and adds further evidence that socioeconomic distress has adverse impacts on multiple physical and mental health outcomes.

Characteristics of the neighborhood built environment influence health and health behavior. Google Street View (GSV) images may facilitate measures of the neighborhood environment that are meaningful, practical, and adaptable to any geographic boundary. We used GSV images and computer vision to characterize neighborhood environments (green streets, visible utility wires, and dilapidated buildings) and examined cross-sectional associations with chronic health outcomes among patients from the University of California, San Francisco Health system with outpatient visits from 2015 to 2017. Logistic regression models were adjusted for patient age, sex, marital status, race/ethnicity, insurance status, English as preferred language, assignment of a primary care provider, and neighborhood socioeconomic status of the census tract in which the patient resided. Among 214,163 patients residing in California, those living in communities in the highest tertile of green streets had 16–29% lower prevalence of coronary artery disease, hypertension, and diabetes compared to those living in communities in the lowest tertile. Conversely, a higher presence of visible utility wires overhead was associated with 10–26% more coronary artery disease and hypertension, and a higher presence of dilapidated buildings was associated with 12–20% greater prevalence of coronary artery disease, hypertension, and diabetes. GSV images and computer vision models can be used to understand contextual factors influencing patient health outcomes and inform structural and place-based interventions to promote population health.

This study examined whether killings of George Floyd, Ahmaud Arbery, and Breonna Taylor by current or former law enforcement officers in 2020 were followed by shifts in public sentiment toward Black people. Methods: Google searches for the names “Ahmaud Arbery,” “Breonna Taylor,” and “George Floyd” were obtained from the Google Health Application Programming Interface (API). Using the Twitter API, we collected a 1% random sample of publicly available U.S. race-related tweets from November 2019–September 2020 (N = 3,380,616). Sentiment analysis was performed using Support Vector Machines, a supervised machine learning model. A qualitative content analysis was conducted on a random sample of 3,000 tweets to understand themes in discussions of race and racism and inform interpretation of the quantitative trends. Results: The highest rate of Google searches for any of the three names was for George Floyd during the week of May 31 to June 6, the week after his murder. The percent of tweets referencing Black people that were negative decreased by 32% (from 49.33% in November 4–9 to 33.66% in June 1–7) (p, Highlights • Transitory decline in negative Black sentiment following the murder of George Floyd. • Greater public awareness of structural racism and desire for social change. • Though brief, these periods may provide opportunities for sustainable change.

Usage of machine learning in agriculture is showing exponential improvement and gateway for interdisciplinary research. The implementation of autonomous techniques in agriculture can help farmers to perceive the disorder in crop yields, including identification of patterns. In the horticulture field, detection of disease for plant protection and crop management perform the significant role. In this paper, the objective is to study and analyze the various disease classification methodologies, advance techniques of machine learning used by different researchers in agriculture application.

Alzheimer’s disease is an irreversible, progressive brain disorder that slowly destroys memory and thinking capabilities and, eventually, the loss of mental function. While most of its symptoms occurs in mid stages, Depression can be considered as one of the symptoms which can be used to identify Alzheimer in early stages. Identifying Depression in Alzheimer’s patient is a difficult task with a single test or questionnaire. This paper proposes a technique to identify depression from PET scan of patients in early stages of Alzheimer’s disease. The observation and in-depth analysis are carried on the activity of the Cingulate Cortex, a less active noted in individuals having the disease of depression. We have taken total 15 participants in this study, which comprises of 6 healthy patient’s participants who were aged between of 19 and 35, 3 Early mild cognitive impairment (EMCI) participants who were between 50 to 70 age group and 6 late life depression (LLD) patients whose age group is between 70 and 80.

The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents&rsquo, risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.

Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter&rsquo, s Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019&ndash, June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment.

Previous studies have demonstrated that there is a high possibility that the presence of certain built environment characteristics can influence health outcomes, especially those related to obesity and physical activity. We examined the associations between select neighborhood built environment indicators (crosswalks, non-single family home buildings, single-lane roads, and visible wires), and health outcomes, including obesity, diabetes, cardiovascular disease, and premature mortality, at the state level. We utilized 31,247,167 images collected from Google Street View to create indicators for neighborhood built environment characteristics using deep learning techniques. Adjusted linear regression models were used to estimate the associations between aggregated built environment indicators and state-level health outcomes. Our results indicated that the presence of a crosswalk was associated with reductions in obesity and premature mortality. Visible wires were associated with increased obesity, decreased physical activity, and increases in premature mortality, diabetes mortality, and cardiovascular mortality (however, these results were not significant). Non-single family homes were associated with decreased diabetes and premature mortality, as well as increased physical activity and park and recreational access. Single-lane roads were associated with increased obesity and decreased park access. The findings of our study demonstrated that built environment features may be associated with a variety of adverse health outcomes.

BACKGROUND Social media platforms such as Twitter can serve as a potential data source for public health research to characterize the social neighborhood environment. Few studies have linked Twitter-derived characteristics to individual-level health outcomes. OBJECTIVE This study aims to assess the association between Twitter-derived social neighborhood characteristics, including happiness, food, and physical activity mentions, with individual cardiometabolic outcomes using a nationally representative sample. METHODS We collected a random 1% of the geotagged tweets from April 2015 to March 2016 using Twitter’s Streaming Application Interface (API). Twitter-derived zip code characteristics on happiness, food, and physical activity were merged to individual outcomes from restricted-use National Health and Nutrition Examination Survey (NHANES) with residential zip codes. Separate regression analyses were performed for each of the neighborhood characteristics using NHANES 2011-2016 and 2007-2016. RESULTS Individuals living in the zip codes with the two highest tertiles of happy tweets reported BMI of 0.65 (95% CI –1.10 to –0.20) and 0.85 kg/m2 (95% CI –1.48 to –0.21) lower than those living in zip codes with the lowest frequency of happy tweets. Happy tweets were also associated with a 6%-8% lower prevalence of hypertension. A higher prevalence of healthy food tweets was linked with an 11% (95% CI 2% to 21%) lower prevalence of obesity. Those living in areas with the highest and medium tertiles of physical activity tweets were associated with a lower prevalence of hypertension by 10% (95% CI 4% to 15%) and 8% (95% CI 2% to 14%), respectively. CONCLUSIONS Twitter-derived social neighborhood characteristics were associated with individual-level obesity and hypertension in a nationally representative sample of US adults. Twitter data could be used for capturing neighborhood sociocultural influences on chronic conditions and may be used as a platform for chronic outcomes prevention.

Background Social media platforms such as Twitter can serve as a potential data source for public health research to characterize the social neighborhood environment. Few studies have linked Twitter-derived characteristics to individual-level health outcomes. Objective This study aims to assess the association between Twitter-derived social neighborhood characteristics, including happiness, food, and physical activity mentions, with individual cardiometabolic outcomes using a nationally representative sample. Methods We collected a random 1% of the geotagged tweets from April 2015 to March 2016 using Twitter’s Streaming Application Interface (API). Twitter-derived zip code characteristics on happiness, food, and physical activity were merged to individual outcomes from restricted-use National Health and Nutrition Examination Survey (NHANES) with residential zip codes. Separate regression analyses were performed for each of the neighborhood characteristics using NHANES 2011-2016 and 2007-2016. Results Individuals living in the zip codes with the two highest tertiles of happy tweets reported BMI of 0.65 (95% CI –1.10 to –0.20) and 0.85 kg/m2 (95% CI –1.48 to –0.21) lower than those living in zip codes with the lowest frequency of happy tweets. Happy tweets were also associated with a 6%-8% lower prevalence of hypertension. A higher prevalence of healthy food tweets was linked with an 11% (95% CI 2% to 21%) lower prevalence of obesity. Those living in areas with the highest and medium tertiles of physical activity tweets were associated with a lower prevalence of hypertension by 10% (95% CI 4% to 15%) and 8% (95% CI 2% to 14%), respectively. Conclusions Twitter-derived social neighborhood characteristics were associated with individual-level obesity and hypertension in a nationally representative sample of US adults. Twitter data could be used for capturing neighborhood sociocultural influences on chronic conditions and may be used as a platform for chronic outcomes prevention.

Obesity is a global epidemic affecting millions. Implementation of interventions to curb obesity rates requires timely surveillance. In this study, we estimated sex-specific obesity prevalence using social media, search queries, demographics and built environment variables. We collected 3,817,125 and 1,382,284 geolocated tweets on food and exercise respectively, from Twitter’s streaming API from April 2015 to March 2016. We also obtained searches related to physical activity and diet from Google Search Trends for the same time period. Next, we inferred the gender of Twitter users using machine learning methods and applied mixed-effects state-level linear regression models to estimate obesity prevalence. We observed differences in discussions of physical activity and foods, with males reporting higher intensity physical activities and lower caloric foods across 40 and 48 states, respectively. In addition, counties with the highest percentage of exercise and food tweets had lower male and female obesity prevalence. Lastly, our models separately captured overall male and female spatial trends in obesity prevalence. The average correlation between actual and estimated obesity prevalence was 0.797(95% CI, 0.796, 0.798) and 0.830 (95% CI, 0.830, 0.831) for males and females, respectively. Social media can provide timely community-level data on health information seeking and changes in behaviors, sentiments and norms. Social media data can also be combined with other data types such as, demographics, built environment variables, diet and physical activity indicators from other digital sources (e.g., mobile applications and wearables) to monitor health behaviors at different geographic scales, and to supplement delayed estimates from traditional surveillance systems.

Neighborhood attributes have been shown to influence health, but advances in neighborhood research has been constrained by the lack of neighborhood data for many geographical areas and few neighborhood studies examine features of nonmetropolitan locations. We leveraged a massive source of Google Street View (GSV) images and computer vision to automatically characterize national neighborhood built environments. Using road network data and Google Street View API, from December 15, 2017-May 14, 2018 we retrieved over 16 million GSV images of street intersections across the United States. Computer vision was applied to label each image. We implemented regression models to estimate associations between built environments and county health outcomes, controlling for county-level demographics, economics, and population density. At the county level, greater presence of highways was related to lower chronic diseases and premature mortality. Areas characterized by street view images as ‘rural’ (having limited infrastructure) had higher obesity, diabetes, fair/poor self-rated health, premature mortality, physical distress, physical inactivity and teen birth rates but lower rates of excessive drinking. Analyses at the census tract level for 500 cities revealed similar adverse associations as was seen at the county level for neighborhood indicators of less urban development. Possible mechanisms include the greater abundance of services and facilities found in more developed areas with roads, enabling access to places and resources for promoting health. GSV images represents an underutilized resource for building national data on neighborhoods and examining the influence of built environments on community health outcomes across the United States. Keywords: Neighborhood, Built environment, Google Street View, Computer vision systems, Geographic information system, Health, Rural

Goods and services provided by businesses can either promote health or represent an additional risk factor. We assessed the association between business pattern indicators and the prevalence of adult obesity, diabetes, physical inactivity, fair or poor health and frequent physical and mental distress. Data on business types were obtained from the 2013 U.S. Census Bureau County Business Patterns. County health data were obtained from the Centers for Disease Control and Prevention Diabetes Interactive Atlas, Behavior Risk Factor Surveillance System and Fatality Analysis Reporting System. We explored the relationship at county level using the global (Ordinary Least Square regression) and local (Geographically Weighted Regression (GWR)) models in 3108 U.S. counties. Density of full service restaurants and fitness centers was associated with a significant decrease in adult obesity, diabetes, fair or poor health, physical inactivity, physical and mental distress. Conversely, density of payday loan centers was associated with an increase in these adverse health outcomes. However, our GWR models revealed substantial geographical variations in these relationships across the U.S. counties. Better understanding of the association between area-level structures and important health outcomes at the local level is important for developing targeted context-specific policy interventions. Full service restaurants and fitness centers may provide places for people to access higher quality food, socialize and exercise. Conversely, payday loans provide an expensive form of short-term credit and this debt may degrade an individual or family's ability to achieve or maintain health. Our study emphasizes the influence of local built environment characteristics on important health outcomes.

Co-cultivation of mutant Penicillium oxalicum SAU(E)-3.510 and Pleurotus ostreatus MTCC 1804 was evaluated for the production of xylanase-laccase mixture under solid-state fermentation (SSF) condition. Growth compatibility between mutant P. oxalicum SAU(E)-3.510 and white rot fungi (P. ostreatus MTCC 1804, Trametes hirsuta MTCC 136 and Pycnoporus sp. MTCC 137) was analyzed by growing them on potato dextrose agar plate. Extracellular enzyme activities were determined spectrophotometrically. Under derived conditions, paired culturing of mutant P. oxalicum SAU(E)-3.510 and P. ostreatus MTCC 1804 resulted in 58% and 33% higher levels of xylanase and laccase production, respectively. A combination of sugarcane bagasse and black gram husk in a ratio of 3:1 was found to be the most ideal solid substrate and support for fungal colonization and enzyme production during co-cultivation. Maximum levels of xylanase (8205.31 ± 168.31 IU g(-1)) and laccase (375.53 ± 34.17 IU g(-1)) during SSF were obtained by using 4 g of solid support with 80% of moisture content. Furthermore, expressions of both xylanase and laccase were characterized during mixed culture by zymogram analysis. Improved levels of xylanase and laccase biosynthesis were achieved by co-culturing the mutant P. oxalicum SAU(E)-3.510 and P. ostreatus MTCC 1804. This may be because of efficient substrate utilization as compared to their respective monocultures in the presence of lignin degradation compounds because of synergistic action of xylanase and laccase. Understanding and developing the process of co-cultivation appears productive for the development of mixed enzyme preparation with tremendous potential for biobleaching.

Mixed enzyme preparation having both xylanase and laccase activity was evaluated for its bleach enhancing ability of mixed wood pulp. The enzyme was produced through co-cultivation of mutant Penicillium oxalicum SAU(E)-3.510 and Pleurotus ostreatus MTCC 1804 under solid-state fermentation. Bleaching of pulp with mixed enzyme had resulted into a notable decrease in kappa number and increased brightness as compared to xylanase alone. Analysis of bleaching conditions had denoted that 8 IU g(-1) of mixed enzyme preparation (xylanase/laccase, 22:1) had led into maximal removal of lignin from pulp when bleaching was performed at 10% pulp consistency (55 degrees C, pH 9.0) for 3 h. An overall improvement of 21%, 8%, 3%, and 5% respectively in kappa number, brightness, yellowness, and viscosity of pulp was achieved under derived bleaching conditions. Process of enzymatic bleaching was further ascertained by analyzing the changes occurring in polysaccharide and lignin by HPLC and FTIR. The UV absorption spectrum of the compounds released during enzymatic treatment had denoted a characteristic peak at 280 nm, indicating the presence of lignin in released coloring matter. The changes in fiber morphology following enzymatic delignification were studied by scanning electron microscopy.

The use of congress grass ( Parthenium sp.) and water hyacinth ( Eichhornia crassipes ) as low cost raw materials for xylanase production from mutant Penicillium oxalicum SAU E -3.510 in submerged fermentation was investigated. For development of mutant from wild type P. oxalicum SA-8 ITCC 6024, a strategy of mixed mutagenesis was followed using UV-irradiation and ethidium bromide, which had resulted into 1.87 fold increases in the activity of the enzyme. For enzyme production, the fungus was cultivated in mineral medium containing congress grass as carbon source. Considerably higher levels of production (475.2 ± 6.0 IU ml −1 ) were achieved in media containing congress grass, although it was slightly less than that was obtained (488.5 ± 6.5 IU ml −1 ) in presence of commercial oat spelt xylan. This fact confirms the feasibility of using this low cost non-food resource as an alternative carbon source to save costs of the enzyme production process. Maximum xylanase activity was reported at 55 °C with its stability at 80 °C for 2 h. The highest activity of xylanase at pH 9.0 and its stability at similar pH for 24 h denote the alkalitolerant nature of enzyme.

An environmentally sound biobleaching to get high quality paper pulp from mixed wood pulp was attempted employing laccase from Aspergillus fumigatus VkJ2.4.5 for lignin removal. Laccase treatment was performed in the presence of a mediator N-hydroxybenzotriazole (HBT, 1.5% w/w), resulting into notably higher level of delignification of the pulp. Enzyme at 10 Ug−1 of pulp at 50°C, pH 6.0, for 2 h with a pulp consistency of 10% was found suitable for enabling maximum decrease in the kappa number. The kappa number and yellowness decreased by 14 and 4% whereas ISO brightness improved by 7%. The presence of a characteristic peak at 280 nm indicated the presence of lignin in the effluent during biobleaching. Analysis of FTIR spectra of residual lignin revealed characteristic modifications following enzymatic bleaching by laccase mediator system (LMS). Variations in morphology and crystallinity of pulp were evaluated by scanning electron microscopy and X-ray diffraction analysis.

Lactic acid production by Rhizopus oryzae RBU2-10 immobilized in loofa sponge was evaluated. Shape and texture of loofa sponge, which was obtained from the mature dried fruit of Luffa cylindrica , remained intact after its treatment with buffers of varying pH and following its repeated autoclaving for up to four cycles (121 °C, 20 min per cycle). The medium having four pieces of loofa sponge (1.008 cm 3 ) per 100 ml medium and inoculated with 3 × 10 6 spores ml −1 resulted maximum production (80.75 g l −1 ) of lactic acid in 48 h of fermentation. Repeated batch fermentation for lactic acid production could be carried out for 10 cycles. Remarkably higher levels of productivity (1.66–1.84 g l −1 h −1 ) was obtained during first five cycles of fermentation with a maximum productivity (1.84 g l −1 h −1 ) obtained during third cycle of fermentation.

The membrane localization and activation of cytoskeletal protein talin are key steps to initiate the integrin transmembrane receptors' activation, which mediates many cellular adhesive responses such as cell migration, spreading and proliferation. RIAM, a membrane anchor and small GTPase RAP1 effector, is known to bind to the C-terminal rod domain of talin (talin-R) and promote localizations of talin to the membrane. Through systematic mapping analysis, we find that RIAM also binds to the N-terminal head of talin (talin-H), a crucial domain involved in binding and activating integrins. We show that the RIAM binding to talin-H sterically occludes the binding of a talin-R domain that otherwise masks the integrin-binding site on talin-H. We further provide functional evidence that such RIAM-mediated steric unmasking of talin triggers integrin activation. Our findings thus uncover a novel role for RIAM in conformational regulation of talin during integrin activation and cell adhesion.

The activation of heterodimeric (α/β) integrin transmembrane receptors by cytosolic protein talin is crucial for regulating diverse cell-adhesion-dependent processes, including blood coagulation, tissue remodeling, and cancer metastasis. This process is triggered by the coincident binding of N-terminal FERM (four-point-one-protein/ezrin/radixin/moesin) domain of talin (talin-FERM) to the inner membrane surface and integrin β cytoplasmic tail, but how these binding events are spatiotemporally regulated remains obscure. Here we report the crystal structure of a dormant talin, revealing how a C-terminal talin rod segment (talin-RS) self-masks a key integrin-binding site on talin-FERM via a large interface. Unexpectedly, the structure also reveals a distinct negatively charged surface on talin-RS that electrostatically hinders the talin-FERM binding to the membrane. Such a dual inhibitory topology for talin is consistent with the biochemical and functional data, but differs significantly from a previous model. We show that upon enrichment with phosphotidylinositol-4,5-bisphosphate (PIP2) – a known talin activator, membrane strongly attracts a positively charged surface on talin-FERM and simultaneously repels the negatively charged surface on talin-RS. Such an electrostatic “pull-push” process promotes the relief of the dual inhibition of talin-FERM, which differs from the classic “steric clash” model for conventional PIP2-induced FERM domain activation. These data therefore unravel a new type of membrane-dependent FERM domain regulation and illustrate how it mediates the talin on/off switches to regulate integrin transmembrane signaling and cell adhesion.

In solid-state fermentation, among various solid supports evaluated, banana peel was found to be an ideal support and resulted into higher levels of laccase (6281.4 ± 63.60 U l(-1)) along with notable levels of manganese peroxidase production (1339.0 ± 131.23 U l(-1)) by Aspergillus fumigatus VkJ2.4.5. Maximum levels of laccase was achieved under derived conditions consisting of 80% of moisture level, 6 days of incubation period, 6% inoculum level, and an aeration level of 2.5 l min(-1). A column-tray bioreactor was designed to scale up and economize the enzyme production in three successive cycles of fermentation using the same fungal biomass. Thermal and pH stability profiles revealed that enzyme was stable up to 50°C and at varying pH range from 5-9 for up to 2 h. The apparent molecular weight of laccase was found to be 34 ± 1 kDa. MALDI-TOF/TOF analysis of the protein showed significant homology with maximum identity of 67% to other laccases reported in database.

A novel chitin deacetylase (CDA) producing strain Penicillium oxalicum ITCC 6965 was isolated from residual materials of sea food processing industries. Strain following mutagenesis using ethidium bromide (EtBr) and microwave irradiation had resulted into a mutant P. oxalicum SAE(M)-51 having improved levels of chitin deacetylase (210.71 ± 1.65 Ul(-1)) as compared to the wild type strain (108.26 ± 1.98 Ul(-1)). Maximum enzyme production was achieved in submerged fermentation following 144 hours of incubation with notably improved productivity of 1.46 ± 0.82 Ul(-1) h(-1) as compared to the wild type strain (0.75 ± 0.53 Ul(-1)h(-1)). Scanning electron micrographs of mutant and wild type strains had revealed distinct morphological features. Evaluation of kinetic parameters viz. Q(s), Q(p), Y(p/x), Y(p/s), q(p), q(s) had denoted that strain P. oxalicum SAE(M)-51 is a hyper producer of chitin deacetylase. Glucose as compared to chitin or colloidal chitin had resulted in increased levels of enzyme production. However, replacement of glucose with chitinous substrates had prolonged the duration for enzyme production. The mutant strain had two pH optima that is 6.0 and 8.0 and had an optimum temperature of 30 °C for growth and enzyme production.