Modeling Effects of Rumination on Free Recall Using ACT-R.

Ruminative thinking, characterized by a recurrent focus on negative and self-related thought, is a key cognitive vulnerability marker of depression and, therefore, a key individual difference variable. This study aimed to develop a computational cognitive model of rumination focusing on the organization and retrieval of information in memory, and how these mechanisms differ in individuals prone to rumination and individuals less prone to rumination. Adaptive Control of Thought-Rational (ACT-R) was used to develop a rumination model by adding memory chunks with negative valence to the declarative memory. In addition, their strength of association was increased to simulate recurrent negative focus, thereby making it harder to disengage from. The ACT-R models were validated by comparing them against two empirical datasets containing data from control and depressed participants. Our general and ruminative models were able to recreate the benchmarks of free recall while matching the behavior exhibited by the control and the depressed participants, respectively. Our study shows that it is possible to build a computational theory of rumination that can accurately simulate the differences in free recall dynamics between control and depressed individuals. Such a model could enable a more fine-tuned investigation of underlying cognitive mechanisms of depression and potentially help to improve interventions by allowing them to more specifically target key mechanisms that instigate and maintain depression.

Hepatitis B in Trans-Himalayan Tribal Population: High Prevalence, High Pediatric Burden, Virologic Patterns, and Implications for Public Health Interventions.

Background/Aims: The prevalence of hepatitis B is higher in tribal populations, compared to non-tribal populations in India. Therefore, this study aimed to investigate the risk factors, virological and biochemical profile of patients with hepatitis B in a tribal population. Methods: This study analyzed data collected from a community-based project conducted in Spiti, Himachal Pradesh, from July 2015 to 2017. The study included adults and children inhabiting 40 cluster villages out of 82 villages in the subdivision. The blood samples were collected for liver panel, hepatitis B surface antigen (HBsAg), hepatitis B e antigen (HBeAg), Anti-HBe antibody (anti-HBe Ab) and Hepatitis B virus DNA (HBV-DNA). Results: HBsAg was positive in 23.08% of the population (968/4201), with a prevalence of 13.51% in children under 5 years of age. HBeAg positivity was seen in 22.4% of the participants, while anti-HBe Ab positivity was seen in 59.03% of the participants. HBeAg positive infection, HBeAg positive hepatitis, HBeAg negative hepatitis and HBeAg negative infection were seen in 18.06%, 1.98%, 6.17% and 74.01% of the participants, respectively. HBeAg positivity was highest in 2nd decade (40.83% vs 22% overall). Patients with HBeAg positivity exhibited higher levels of HBV DNA [1960 (IQR: 0-108) IU/ml vs 97.2 (IQR: 0-2090) IU/ml, P < 0.001] and alanine transaminase (ALT) [22.5 (IQR: 16-33) U/L vs 19 (IQR: 14-26) U/L, P = 0.003] levels compared to HBeAg negative patients. Conclusion: This study shows a high prevalence of hepatitis B in tribal population, particularly among children under 5 years of age.

A study of neural artistic style transfer models and architectures for Indian art styles.

Neural Style Transfer (NST) has been a widely researched topic as of late enabling new forms of image manipulation. Here we perform an extensive study on NST algorithms and extend the existing methods with custom modifications for application to Indian art styles. In this paper, we aim to provide a comprehensive analysis of various methods ranging from the seminal work of Gatys et al which demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style, to the state of the art image-to-image translation models which use Generative Adversarial Networks (GANs) to learn the mapping between two domain of images. We observe and infer based on the results produced by the models on which one could be a more suitable approach for Indian art styles, especially Tanjore paintings which are unique compared to the Western art styles. We then propose the method which is more suitable for the domain of Indian Art style along with custom architecture which includes an enhancement and evaluation module. We then present evaluation methods, both qualitative and quantitative which includes our proposed metric, to evaluate the results produced by the model.

Bioactive Compounds, Antioxidant, and Antibacterial Activity Against MDR and Food-Borne Pathogenic Bacteria of Psidium guajava. L Fruit During Ripening.

Psidium guajava fruits are highly appreciated for their nutrients and bioactive compounds content, which contribute to their antioxidant and antimicrobial capacities. The purpose of this study was to determine bioactive compound (phenolic, flavonoids, and carotenoid contents), antioxidant activity (DPPH, ABTS, ORAC, and FRAP), and antibacterial potential against MDR and food-borne pathogenic strains of Escherichia coli, and Staphylococcus aureus during different stages of fruit ripening.The results elucidated that ripe fruits (methanolic extract) contain the highest total phenolic, flavonoids, and carotenoid contents (417.36 ± 2.63 µg GAE/gm of FW, 711.78 ± 0.70 µg QE/gm of FW and 0.683 ± 0.06 µg/gm of FW) followed by hexane, ethyl acetate, and aqueous. Methanolic extract of the ripe fruits showed the highest antioxidant activity when measured by DPPH (61.55 ± 0.91%), FRAP (31.83 ± 0.98 mM Fe(II)/gm of FW), ORAC (17.19 ± 0.47 mM TE/ gm of FW), and ABTS (41.31 ± 0.99 µmol Trolox/gm of FW) assays. In the antibacterial assay, the ripe stage had the highest antibacterial activity against MDR and food-borne pathogenic strains of Escherichia coli, and Staphylococcus aureus. The methanolic ripe extract was found to possess maximum antibacterial activity ZOI, MIC, and IC50 18.00 ± 1.00 mm, 95.95 ± 0.05%, and 0.58 µg/ml; 15.66 ± 0.57 mm, 94.66 ± 0.19%, and 0.50 µg/ml, respectively, against pathogenic and MDR strains of E. coli and 22.33 ± 0.57 mm, 98.97 ± 0.02%, and 0.26 µg/ml; 20.33 ± 1.15 mm, 96.82 ± 0.14%, and 0.39 µg/ml, respectively, against pathogenic and MDR strains of S. aureus. Considering the bioactive compounds and beneficial effects, these fruit extracts could be promising antibiotic alternatives, avoiding antibiotic overuse and its negative effects on human health and the environment, and can be recommended as a novel functional food.

Positive identity development through community engagement among youth with intellectual and developmental disabilities.

BACKGROUND: Identity development in individuals with disabilities is often negatively impacted by exclusion, marginalisation, and stigma. However, meaningful opportunities for community engagement can serve as one pathway towards establishing positive identity. This pathway is further examined in the present study. METHODS: Researchers used a tiered, multi-method, qualitative methodology consisting of audio diaries, group interviews, and individual interviews with seven youth (ages 16-20) with intellectual and developmental disabilities, recruited through the Special Olympics U.S. Youth Ambassador Program. RESULTS: Participants' identities incorporated disability while simultaneously transcending the social limits of disability. Participants viewed disability as one aspect of their broader identity, shaped by leadership and engagement experiences such as those offered by the Youth Ambassador Program. CONCLUSIONS: Findings have implications for understanding identity development in youth with disabilities, the importance of community engagement and structured leadership opportunities, and the value of tailoring qualitative methodologies to the subject of the research.

Raw Electroencephalogram-Based Cognitive Workload Classification Using Directed and Nondirected Functional Connectivity Analysis and Deep Learning.

With the phenomenal rise in internet-of-things devices, the use of electroencephalogram (EEG) based brain-computer interfaces (BCIs) can empower individuals to control equipment with thoughts. These allow BCI to be used and pave the way for pro-active health management and the development of internet-of-medical-things architecture. However, EEG-based BCIs have low fidelity, high variance, and EEG signals are very noisy. These challenges compel researchers to design algorithms that can process big data in real-time while being robust to temporal variations and other variations in the data. Another issue in designing a passive BCI is the regular change in user's cognitive state (measured through cognitive workload). Though considerable amount of research has been conducted on this front, methods that could withstand high variability in EEG data and still reflect the neuronal dynamics of cognitive state variations are lacking and much needed in literature. In this research, we evaluate the efficacy of a combination of functional connectivity algorithms and state-of-the-art deep learning algorithms for the classification of three different levels of cognitive workload. We acquire 64-channel EEG data from 23 participants executing the n-back task at three different levels; 1-back (low-workload condition), 2-back (medium-workload condition), and 3-back (high-workload condition). We compared two different functional connectivity algorithms, namely phase transfer entropy (PTE) and mutual information (MI). PTE is a directed functional connectivity algorithm, whereas MI is non-directed. Both methods are suitable for extracting functional connectivity matrices in real-time, which could eventually be used for rapid, robust, and efficient classification. For classification, we use the recently proposed BrainNetCNN deep learning model, designed specifically to classify functional connectivity matrices. Results reveal a classification accuracy of 92.81% with MI and BrainNetCNN and a staggering 99.50% with PTE and BrainNetCNN on test data. PTE can yield a higher classification accuracy due to its robustness to linear mixing of the data and its ability to detect functional connectivity across a range of analysis lags.

ACC Deaminase Produced by PGPR Mitigates the Adverse Effect of Osmotic and Salinity Stresses in Pisum sativum through Modulating the Antioxidants Activities.

Salinity-induced ethylene production and reactive oxygen species (ROS) inhibit agricultural productivity. The plant synthesizes ethylene directly from aminocyclopropane-1-carboxylic acid (ACC). By using ACC as a nitrogen source, bacteria with ACC deaminase (ACCD) inhibit the overproduction of ethylene, thereby maintaining the ROS. The present study investigated the ACCD activity of previously identified rhizobacterial strains in Dworkin and Foster (DF) minimal salt media supplemented with 5 mM ACC (as N-source). Bacterial isolates GKP KS2_7 (Pseudomonas aeruginosa) and MBD 133 (Bacillus subtilis) could degrade ACC into α-ketobutyrate, exhibiting ACCD activity producing more than ~257 nmol of α-ketobutyrate mg protein−1 h−1, and were evaluated for other plant growth-promoting (PGP) traits including indole acetic acid production (>63 µg/mL), phosphate solubilization (>86 µg mL−1), siderophore (>20%) ammonia and exopolysaccharide production. Furthermore, Fourier Transform Infrared analysis also demonstrated α-ketobutyrate liberation from ACC deamination in DF minimal salt media, thereby confirming the ACCD activity. These isolates also showed enhanced tolerance to salinity stress of 3% w/v NaCl in vitro, in addition to facilitating multifarious PGP activities. Seed bacterization by these ACCD-producing bacterial isolates (GKP KS2_7 and MBD 133) revealed a significant decline in stress-stimulated ethylene levels and its associated growth inhibition during seedling germination. They also mitigated the negative effects of salt stress and increased the root-shoot length, fresh and dry weight of root and shoot, root-shoot biomass, total sugar, protein, reducing sugar, chlorophyll content, and antioxidants enzymes in Pisum sativum. As a result, these strains (GKP KS2_7 and MBD 133) might be applied as biofertilizers to counteract the negative effects of soil salinity.

Consequences of Arsenic Contamination on Plants and Mycoremediation-Mediated Arsenic Stress Tolerance for Sustainable Agriculture.

Arsenic contamination in water and soil is becoming a severe problem. It is toxic to the environment and human health. It is usually found in small quantities in rock, soil, air, and water which increase due to natural and anthropogenic activities. Arsenic exposure leads to several diseases such as vascular disease, including stroke, ischemic heart disease, and peripheral vascular disease, and also increases the risk of liver, lungs, kidneys, and bladder tumors. Arsenic leads to oxidative stress that causes an imbalance in the redox system. Mycoremediation approaches can potentially reduce the As level near the contaminated sites and are procuring popularity as being eco-friendly and cost-effective. Many fungi have specific metal-binding metallothionein proteins, which are used for immobilizing the As concentration from the soil, thereby removing the accumulated As in crops. Some fungi also have other mechanisms to reduce the As contamination, such as biosynthesis of glutathione, cell surface precipitation, bioaugmentation, biostimulation, biosorption, bioaccumulation, biovolatilization, methylation, and chelation of As. Arsenic-resistant fungi and recombinant yeast have a significant potential for better elimination of As from contaminated areas. This review discusses the relationship between As exposure, oxidative stress, and signaling pathways. We also explain how to overcome the detrimental effects of As contamination through mycoremediation, unraveling the mechanism of As-induced toxicity.

Prevalence and Predicting Factors of Alcohol Use Among School Going Adolescents in a Hilly State of Northern India.

Background: Alcohol use among adolescents is rising globally. This habit starts in adolescence and continues throughout their life. Alcohol addiction is associated with many other risky behaviors. Social environmental interventions will be an effective measure to control the problem of alcohol use. Objectives: The purpose of this study was to estimate the prevalence of alcohol use among adolescents and to investigate the associated risk and protective factors. Methods: A cross-sectional study was carried out among school going adolescents in the hilly state Himachal. A pre-validated, self-administered questionnaire was used for data collection. Results: The prevalence of alcohol use in adolescent was 6.1% (10.7% in males and 0.4% in females). Binary logistic regression model revealed that parent's and peer's drinking behavior significantly predicts an adolescent's drinking behavior. Conclusion: Our research supports the need for an adolescent health program involving school, peers, and family. Life skill education should be given to the adolescents to inculcate the resilience so that they learn to say no to peers who try to pull them into such habits.

Biopolymeric nanoparticles based effective delivery of bioactive compounds toward the sustainable development of anticancerous therapeutics.

Nowadays, effective cancer therapy is a global concern, and recent advances in nanomedicine are crucial. Cancer is one of the major fatal diseases and a leading cause of death globally. Nanotechnology provides rapidly evolving delivery systems in science for treating diseases in a site-specific manner using natural bioactive compounds, which are gaining widespread attention. Nanotechnology combined with bioactives is a very appealing and relatively new area in cancer treatment. Natural bioactive compounds have the potential to be employed as a chemotherapeutic agent in the treatment of cancer, in addition to their nutritional benefits. Alginate, pullulan, cellulose, polylactic acid, chitosan, and other biopolymers have been effectively used in the delivery of therapeutics to a specific site. Because of their biodegradability, biopolymeric nanoparticles (BNPs) have received a lot of attention in the development of new anticancer drug delivery systems. Biopolymer-based nanoparticle systems can be made in a variety of ways. These systems have developed as a cost-effective and environmentally friendly solution to boost treatment efficacy. Effective drug delivery systems with improved availability, increased selectivity, and lower toxicity are needed. Recent research findings and current knowledge on the use of BNPs in the administration of bioactive chemicals in cancer therapy are summarized in this review.

Enhancing object detection in aerial images.

Unmanned Aerial Vehicles have proven to be helpful in domains like defence and agriculture and will play a vital role in implementing smart cities in the upcoming years. Object detection is an essential feature in any such application. This work addresses the challenges of object detection in aerial images like improving the accuracy of small and dense object detection, handling the class-imbalance problem, and using contextual information to boost the performance. We have used a density map-based approach on the drone dataset VisDrone-2019 accompanied with increased receptive field architecture such that it can detect small objects properly. Further, to address the class imbalance problem, we have picked out the images with classes occurring fewer times and augmented them back into the dataset with rotations. Subsequently, we have used RetinaNet with adjusted anchor parameters instead of other conventional detectors to detect aerial imagery objects accurately and efficiently. The performance of the proposed three step pipeline of implementing object detection in aerial images is a significant improvement over the existing methods. Future work may include improvement in the computations of the proposed method, and minimising the effect of perspective distortions and occlusions.

Mechanistic Insights of Plant Growth Promoting Bacteria Mediated Drought and Salt Stress Tolerance in Plants for Sustainable Agriculture.

Climate change has devastating effects on plant growth and yield. During ontogenesis, plants are subjected to a variety of abiotic stresses, including drought and salinity, affecting the crop loss (20-50%) and making them vulnerable in terms of survival. These stresses lead to the excessive production of reactive oxygen species (ROS) that damage nucleic acid, proteins, and lipids. Plant growth-promoting bacteria (PGPB) have remarkable capabilities in combating drought and salinity stress and improving plant growth, which enhances the crop productivity and contributes to food security. PGPB inoculation under abiotic stresses promotes plant growth through several modes of actions, such as the production of phytohormones, 1-aminocyclopropane-1-carboxylic acid deaminase, exopolysaccharide, siderophore, hydrogen cyanide, extracellular polymeric substances, volatile organic compounds, modulate antioxidants defense machinery, and abscisic acid, thereby preventing oxidative stress. These bacteria also provide osmotic balance; maintain ion homeostasis; and induce drought and salt-responsive genes, metabolic reprogramming, provide transcriptional changes in ion transporter genes, etc. Therefore, in this review, we summarize the effects of PGPB on drought and salinity stress to mitigate its detrimental effects. Furthermore, we also discuss the mechanistic insights of PGPB towards drought and salinity stress tolerance for sustainable agriculture.

Effect of brief interventions to promote behavior change on clinical outcomes of selected non-communicable diseases: The World Health Organization (WHO) Package of Essential Non-communicable disease (PEN) Interventions for primary health care settings - study protocol of a quasi-experimental study.

BACKGROUND: The World Health Organization designed a minimum set of interventions, the World Health Organization Package of Essential Noncommunicable disease interventions (WHO PEN), for detection, prevention, treatment, and care of Non-communicable diseases (NCDs) in resource constraint settings. This intervention study examines the effectiveness of the integration of components of WHO PEN protocols on improved clinical outcomes among patients of cardiovascular disease and diabetes mellitus in urban and rural primary health care settings. METHODS: In this quasi-experimental study (pre-test post-test control group design), trained non-physician health workers will provide behavior change interventions regarding four major NCD risk factors, i.e., tobacco use, excessive alcohol intake, physical inactivity, an unhealthy diet; using 'Brief Advice' to the NCD patients enrolled in the experimental arm. The health centers in the control arm will provide the usual care to all the NCD patients. The intervention will last for six months, and the two groups will be followed up at two months, four months, and six months since enrolment in the study. RESULTS: The primary outcome is improved mean blood pressure levels and the proportion of patients with controlled blood pressure levels. The secondary outcomes assess medication adherence, self-reported reductions in tobacco and alcohol intake, consumption of a heart-healthy diet, and regular physical activity. DISCUSSION: This intervention trial will provide evidence for the utility of individual-level behavioral interventions for adequate management of NCDs. TRIAL REGISTRATION: Clinical Trial Registry of India: CTRI/2018/12/016707.

ACC deaminase producing plant growth promoting rhizobacteria enhance salinity stress tolerance in Pisum sativum.

Salinity stress is one of the most serious environmental stresses which limit plant growth, development and productivity. In this study, we screened 25 bacterial isolates based on the biochemical activity of ACC deaminase. Two potent PGPR namely Bacillus marisflavi (CHR JH 203) and Bacillus cereus (BST YS1_42) having the highest ACC deaminase (ACCD) activity were selected for further analyses such as polymerase chain reaction (PCR), salt tolerance assay, expression analysis, antioxidant assay, etc. The structural gene for ACCD activity was further confirmed by PCR showing the amplicon size ~ 800 bp. The acdS positive isolates exhibited optimum growth at 3% w/v (NaCl), indicating its ability to survive and thrive in induced saline soil. Inoculation of acdS + strain on pea plants was found to be efficient and ameliorated the induced NaCl-stress by enhancing the various parameters like plant-biomass, carbohydrates, reducing sugars, protein, chlorophylls, phenol, flavonoids content and increasing antioxidants enzymes levels in plants. Moreover, the expression of ROS scavenging genes (PsSOD, PsCAT, PsPOX, PsNOS, PsAPX, PsChla/bBP), defense genes and cell rescue genes (PsPRP, PsMAPK, PsFDH) were analyzed. Inoculated plants exhibited a higher gene expression level and salt tolerance under 1%NaCl concentration. Thus, our results indicate that CHR JH 203 and BST YS1_42 strain showed the highest plant growth-promoting attributes could be used as bio-inoculants for crops under saline stress in the field towards sustainable crop development. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-021-03047-5.

Plant-Growth-Promoting Rhizobacteria Emerging as an Effective Bioinoculant to Improve the Growth, Production, and Stress Tolerance of Vegetable Crops.

Vegetable cultivation is a promising economic activity, and vegetable consumption is important for human health due to the high nutritional content of vegetables. Vegetables are rich in vitamins, minerals, dietary fiber, and several phytochemical compounds. However, the production of vegetables is insufficient to meet the demand of the ever-increasing population. Plant-growth-promoting rhizobacteria (PGPR) facilitate the growth and production of vegetable crops by acquiring nutrients, producing phytohormones, and protecting them from various detrimental effects. In this review, we highlight well-developed and cutting-edge findings focusing on the role of a PGPR-based bioinoculant formulation in enhancing vegetable crop production. We also discuss the role of PGPR in promoting vegetable crop growth and resisting the adverse effects arising from various abiotic (drought, salinity, heat, heavy metals) and biotic (fungi, bacteria, nematodes, and insect pests) stresses.

Subject-Specific Cognitive Workload Classification Using EEG-Based Functional Connectivity and Deep Learning.

Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging techniques have long been used for estimating cognitive workload. Given the portability, cost-effectiveness and high time-resolution of EEG as compared to fMRI and other neuroimaging modalities, an efficient method of estimating an individual's workload using EEG is of paramount importance. Multiple cognitive, psychiatric and behavioral phenotypes have already been known to be linked with "functional connectivity", i.e., correlations between different brain regions. In this work, we explored the possibility of using different model-free functional connectivity metrics along with deep learning in order to efficiently classify the cognitive workload of the participants. To this end, 64-channel EEG data of 19 participants were collected while they were doing the traditional n-back task. These data (after pre-processing) were used to extract the functional connectivity features, namely Phase Transfer Entropy (PTE), Mutual Information (MI) and Phase Locking Value (PLV). These three were chosen to do a comprehensive comparison of directed and non-directed model-free functional connectivity metrics (allows faster computations). Using these features, three deep learning classifiers, namely CNN, LSTM and Conv-LSTM were used for classifying the cognitive workload as low (1-back), medium (2-back) or high (3-back). With the high inter-subject variability in EEG and cognitive workload and recent research highlighting that EEG-based functional connectivity metrics are subject-specific, subject-specific classifiers were used. Results show the state-of-the-art multi-class classification accuracy with the combination of MI with CNN at 80.87%, followed by the combination of PLV with CNN (at 75.88%) and MI with LSTM (at 71.87%). The highest subject specific performance was achieved by the combinations of PLV with Conv-LSTM, and PLV with CNN with an accuracy of 97.92%, followed by the combination of MI with CNN (at 95.83%) and MI with Conv-LSTM (at 93.75%). The results highlight the efficacy of the combination of EEG-based model-free functional connectivity metrics and deep learning in order to classify cognitive workload. The work can further be extended to explore the possibility of classifying cognitive workload in real-time, dynamic and complex real-world scenarios.

Osteoporosis is under recognized and undertreated in adult spinal deformity patients.

BACKGROUND: Adult spinal deformity (ASD) patients may have osteoporosis, predisposing them to an increased risk for surgical complications. Prior studies have demonstrated that treating osteoporosis improves surgical outcomes. In this study we determine the prevalence of osteoporosis in ASD patients undergoing long spinal fusions and the rate at which osteoporosis is treated. METHODS: ASD patients who frequented either of two major academic medical centers from 2010 through 2019 were studied. All study participants were at least 40 years of age and endured a spinal fusion of at least seven vertebral levels. Medical records were explored for a diagnosis of osteoporosis via ICD-10 code and, if present, whether pharmacological treatment was prescribed. T-tests and chi-squared analyses were used to determine statistical significance. RESULTS: Three hundred ninety-nine patients matched the study's inclusion criteria. Among this group, 131 patients (32.8%) had been diagnosed with osteoporosis prior to surgery. With a mean age of 66.4 years, osteoporotic patients were on average three years older than non-osteoporotic (P=0.002) and more likely to be female (74.8% vs. 61.9%; P=0.01). At the time of surgery, 34.4% of osteoporotic patients were receiving pharmacological treatment. Although not statistically significant, women were more likely to receive medical treatment than men (P=0.07). CONCLUSIONS: The prevalence of osteoporosis in ASD patients undergoing a long spinal fusion is substantially higher than that of the general population. Surgeons should have a low threshold for bone density testing in ASD patients. With only about one-third of osteoporotic patients treated, there is a classic "missed opportunity" in this population.