Yoga Pose Estimation Using Angle-Based Feature Extraction.

OBJECTIVE: This research addresses the challenges of maintaining proper yoga postures, an issue that has been exacerbated by the COVID-19 pandemic and the subsequent shift to virtual platforms for yoga instruction. This research aims to develop a mechanism for detecting correct yoga poses and providing real-time feedback through the application of computer vision and machine learning (ML) techniques. METHODS AND PROCEDURES: This study utilized computer vision-based pose estimation methods to extract features and calculate yoga pose angles. A variety of models, including extremely randomized trees, logistic regression, random forest, gradient boosting, extreme gradient boosting, and deep neural networks, were trained and tested to classify yoga poses. Our study employed the Yoga-82 dataset, consisting of many yoga pose images downloaded from the web. RESULTS: The results of this study show that the extremely randomized trees model outperformed the other models, achieving the highest prediction accuracy of 91% on the test dataset and 92% in a fivefold cross-validation experiment. Other models like random forest, gradient boosting, extreme gradient boosting, and deep neural networks achieved accuracies of 90%, 89%, 90%, and 85%, respectively, while logistic regression underperformed, having the lowest accuracy. CONCLUSION: This research concludes that the extremely randomized trees model presents superior predictive power for yoga pose recognition. This suggests a valuable avenue for future exploration in this domain. Moreover, the approach has significant potential for implementation on low-powered smartphones with minimal latency, thereby enabling real-time feedback for users practicing yoga at home.

Modeling and optimal control applied to reduce the effects of greenhouse gases emitted from the coal-based power plant in Bangladesh.

The coal-fired power station is believed to be one of the major emitters of air pollutants, particularly carbon dioxide (CO2), which is the main sensitive driver of climate change due to global warming, consequently causing significant intimidation for the Sundarbans, the world's largest mangrove forest and nearby due to high emissions of air pollutants such as Carbon-Di-Oxide (CO2). Here, we used a compartmental mathematical model with 3 compartments to study the dynamics of greenhouse gas emissions, concentration, and uptake, which we can control by installing a chemical reactor system near the power plant and naturally afforesting the regions. The model was built from scratch to study these types of problems. First, we formulated the optimal control problem by connecting two control measurement systems: a chemical reactor system and natural afforestation. For this purpose, Pontryagin's maximum principle is used. The novelty of this work is the investigation of optimal strategies to minimize the impact of gases emitted by Coal based power plants on neighboring regions. More realistic facts such as system damage from excess emissions, most absorbers, and other facts are covered here. The numerical solution obtained illustrates the outcome of the system with initial values and theoretical parameters that best represent reality. By evaluating the performance index scores, and objective function values, we found that both controls (the chemical reactor system and natural afforestation) help minimize air pollution. We then simulated our model with 5 different control strategies to observe its performance in reducing pollutants. Once we determine that two control strategies are equally effective in reducing pollution, let's compare them by looking at the costs associated with each strategy. Therefore, using both control systems (chemical reactor and natural afforestation) with a higher reaction rate, we suggested chemical reactor system control as the best strategy.

Accelerated in vitro oxidative degradation testing of polypropylene surgical mesh.

Polypropylene (PP) surgical mesh had reasonable success in repair of hernia and treatment of stress urinary incontinence (SUI); however, their use for the repair of pelvic organ prolapse (POP) resulted in highly variable results with lifelong complications in some patients. One of several factors that could be associated with mesh-related POP complications is changes in the properties of the implanted surgical mesh due to oxidative degradation of PP in vivo. Currently, there are no standardized in vitro bench testing methods available for assessing the susceptibility to oxidative degradation and estimating long-term in vivo stability of surgical mesh. In this work, we adapted a previously reported automated reactive accelerated aging (aRAA) system, which uses elevated temperatures and high concentrations of hydrogen peroxide (H2 O2 ), for accelerated bench-top oxidative degradation testing of PP surgical mesh. Since H2 O2 is highly unstable at elevated temperatures and for prolonged periods, the aRAA system involves a feedback loop based on electrochemical detection methods to maintain consistent H2 O2 concentration in test solutions. Four PP mesh samples with varying mesh knit designs, filament diameter, weight, and % porosity, were selected for testing using aRAA up to 4 weeks and characterized using thermal analysis, Fourier-transform infrared spectroscopy-attenuated total reflectance (FTIR-ATR) and scanning electron microscopy (SEM). Additionally, the oxidation index (OI) values were calculated based on the FTIR-ATR spectra to estimate the oxidative degradation and oxidation reaction kinetics of PP surgical mesh. The OI values and surface damage in the form of surface flaking, peeling, and formation of transverse cracks increased with aRAA aging time. The aRAA test method introduced here could be used to standardize the assessment of long-term stability of surgical mesh and may also be adopted for accelerated oxidative degradation testing of other polymer-based medical devices.

Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design.

There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure-property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner.

Harnessing Psycho-lingual and Crowd-Sourced Dictionaries for Predicting Taboos in Written Emotional Disclosure in Anonymous Confession Boards.

There have been many efforts in the last decade in the health informatics community to develop systems that can automatically recognize and predict disclosures on social media. However, a majority of such efforts have focused on simple topic prediction or sentiment classification. However, taboo disclosures on social media that people are not comfortable to talk with their friends represent an abstract theme dependent on context and background. Recent research has demonstrated the efficacy of injecting concept into the learning model to improve prediction. We present a vectorization scheme that combines corpus- and lexicon-based approaches for predicting taboo topics from anonymous social media datasets. The proposed vectorization scheme exploits two context-rich lexicons LIWC and Urban Dictionary. Our methodology achieves cross-validation accuracies of up to 78.1% for the supervised learning task on Facebook Confessions dataset, and 70.5% for the transfer learning task on the YikYak dataset. For both the tasks, supervised algorithms trained with features generated by the proposed vectorizer perform better than vanilla t f - i d f representation. This work presents a novel methodology for predicting taboos from anonymous emotional disclosures on confession boards.

Property Prediction of Organic Donor Molecules for Photovoltaic Applications Using Extremely Randomized Trees.

Organic solar cells are an inexpensive, flexible alternative to traditional silicon-based solar cells but disadvantaged by low power conversion efficiency due to empirical design and complex manufacturing processes. This process can be accelerated by generating a comprehensive set of potential candidates. However, this would require a laborious trial and error method of modeling all possible polymer configurations. A machine learning model has the potential to accelerate the process of screening potential donor candidates by associating structural features of the compound using molecular fingerprints with their highest occupied molecular orbital energies. In this paper, extremely randomized tree learning models are employed for the prediction of HOMO values for donor compounds, and a web application is developed.1 The proposed models outperform neural networks trained on molecular fingerprints as well as SMILES, as well as other state-of-the-art architectures such as Chemception and Molecular Graph Convolution on two datasets of varying sizes.

ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition.

Conventional machine learning approaches for predicting material properties from elemental compositions have emphasized the importance of leveraging domain knowledge when designing model inputs. Here, we demonstrate that by using a deep learning approach, we can bypass such manual feature engineering requiring domain knowledge and achieve much better results, even with only a few thousand training samples. We present the design and implementation of a deep neural network model referred to as ElemNet; it automatically captures the physical and chemical interactions and similarities between different elements using artificial intelligence which allows it to predict the materials properties with better accuracy and speed. The speed and best-in-class accuracy of ElemNet enable us to perform a fast and robust screening for new material candidates in a huge combinatorial space; where we predict hundreds of thousands of chemical systems that could contain yet-undiscovered compounds.

Regulation of energy metabolism during early mammalian development: TEAD4 controls mitochondrial transcription.

Early mammalian development is crucially dependent on the establishment of oxidative energy metabolism within the trophectoderm (TE) lineage. Unlike the inner cell mass, TE cells enhance ATP production via mitochondrial oxidative phosphorylation (OXPHOS) and this metabolic preference is essential for blastocyst maturation. However, molecular mechanisms that regulate establishment of oxidative energy metabolism in TE cells are incompletely understood. Here, we show that conserved transcription factor TEAD4, which is essential for pre-implantation mammalian development, regulates this process by promoting mitochondrial transcription. In developing mouse TE and TE-derived trophoblast stem cells (TSCs), TEAD4 localizes to mitochondria, binds to mitochondrial DNA (mtDNA) and facilitates its transcription by recruiting mitochondrial RNA polymerase (POLRMT). Loss of TEAD4 impairs recruitment of POLRMT, resulting in reduced expression of mtDNA-encoded electron transport chain components, thereby inhibiting oxidative energy metabolism. Our studies identify a novel TEAD4-dependent molecular mechanism that regulates energy metabolism in the TE lineage to ensure mammalian development.

Genetic redundancy of GATA factors in the extraembryonic trophoblast lineage ensures the progression of preimplantation and postimplantation mammalian development.

GATA transcription factors are implicated in establishing cell fate during mammalian development. In early mammalian embryos, GATA3 is selectively expressed in the extraembryonic trophoblast lineage and regulates gene expression to promote trophoblast fate. However, trophoblast-specific GATA3 function is dispensable for early mammalian development. Here, using dual conditional knockout mice, we show that genetic redundancy of Gata3 with paralog Gata2 in trophoblast progenitors ensures the successful progression of both pre- and postimplantation mammalian development. Stage-specific gene deletion in trophoblasts reveals that loss of both GATA genes, but not either alone, leads to embryonic lethality prior to the onset of their expression within the embryo proper. Using ChIP-seq and RNA-seq analyses, we define the global targets of GATA2/GATA3 and show that they directly regulate a large number of common genes to orchestrate stem versus differentiated trophoblast fate. In trophoblast progenitors, GATA factors directly regulate BMP4, Nodal and Wnt signaling components that promote embryonic-extraembryonic signaling cross-talk, which is essential for the development of the embryo proper. Our study provides genetic evidence that impairment of trophoblast-specific GATA2/GATA3 function could lead to early pregnancy failure.

PKCζ Promotes Breast Cancer Invasion by Regulating Expression of E-cadherin and Zonula Occludens-1 (ZO-1) via NFκB-p65.

Atypical Protein Kinase C zeta (PKCζ) forms Partitioning-defective (PAR) polarity complex for apico-basal distribution of membrane proteins essential to maintain normal cellular junctional complexes and tissue homeostasis. Consistently, tumor suppressive role of PKCζ has been established for multiple human cancers. However, recent studies also indicate pro-oncogenic function of PKCζ without firm understanding of detailed molecular mechanism. Here we report a possible mechanism of oncogenic PKCζ signaling in the context of breast cancer. We observed that depletion of PKCζ promotes epithelial morphology in mesenchymal-like MDA-MB-231 cells. The induction of epithelial morphology is associated with significant upregulation of adherens junction (AJ) protein E-cadherin and tight junction (TJ) protein Zonula Occludens-1 (ZO-1). Functionally, depletion of PKCζ significantly inhibits invasion and metastatic progression. Consistently, we observed higher expression and activation of PKCζ signaling in invasive and metastatic breast cancers compared to non-invasive diseases. Mechanistically, an oncogenic PKCζ- NFκB-p65 signaling node might be involved to suppress E-cadherin and ZO-1 expression and ectopic expression of a constitutively active form of NFκB-p65 (S536E-NFκB-p65) significantly rescues invasive potential of PKCζ-depleted breast cancer cells. Thus, our study discovered a PKCζ - NFκB-p65 signaling pathway might be involved to alter cellular junctional dynamics for breast cancer invasive progression.

PKCλ/ι signaling-a common node for normal cellular development and breast oncogenesis.

We recently demonstrated that PKCλ/ι signaling is an important contributor to breast cancer development. Strikingly, PKCλ/ι signaling is also important to balance self-renewal versus differentiation in pluripotent stem cells and is essential for embryonic development. This commentary highlights some key functions of PKCλ/ι signaling that are integral to both normal development and cancer progression.

Regulation of mitochondrial function and cellular energy metabolism by protein kinase C-λ/ι: a novel mode of balancing pluripotency.

Pluripotent stem cells (PSCs) contain functionally immature mitochondria and rely upon high rates of glycolysis for their energy requirements. Thus, altered mitochondrial function and promotion of aerobic glycolysis are key to maintain and induce pluripotency. However, signaling mechanisms that regulate mitochondrial function and reprogram metabolic preferences in self-renewing versus differentiated PSC populations are poorly understood. Here, using murine embryonic stem cells (ESCs) as a model system, we demonstrate that atypical protein kinase C isoform, PKC lambda/iota (PKCλ/ι), is a key regulator of mitochondrial function in ESCs. Depletion of PKCλ/ι in ESCs maintains their pluripotent state as evident from germline offsprings. Interestingly, loss of PKCλ/ι in ESCs leads to impairment in mitochondrial maturation, organization, and a metabolic shift toward glycolysis under differentiating condition. Our mechanistic analyses indicate that a PKCλ/ι-hypoxia-inducible factor 1α-PGC1α axis regulates mitochondrial respiration and balances pluripotency in ESCs. We propose that PKCλ/ι could be a crucial regulator of mitochondrial function and energy metabolism in stem cells and other cellular contexts.

The breast cancer susceptibility genes (BRCA) in breast and ovarian cancers.

The Breast Cancer Susceptibility Genes, BRCA1 and BRCA2, are the dynamic regulators of genomic integrity. Inherited mutations in these genes are associated with the development of cancer in multiple organs including the breast and ovary. Mutations of BRCA1/2 genes greatly increase lifetime risk to develop breast and ovarian cancer and these mutations are frequently observed in hereditary breast and ovarian cancers. In addition, misregulation and altered expressions of BRCA1/2 proteins potentiate sporadic forms of breast cancer. In particular, both genes contribute to DNA repair and transcriptional regulation in response to DNA damage. Thus, deficiencies of BRCA1/2 functions lead to the accumulation of genetic alterations and ultimately influence the development of cancer. Studies since identification of both BRCA1 and BRCA2 have provided strong evidences for their tumor suppressor activities specifically for breast and ovarian cancer and this article aims to review the current state of knowledge regarding the BRCAs and associated cancer risk.

Inhibition of protein kinase C signaling maintains rat embryonic stem cell pluripotency.

Embryonic stem cell (ESC) pluripotency is orchestrated by distinct signaling pathways that are often targeted to maintain ESC self-renewal or their differentiation to other lineages. We showed earlier that inhibition of PKC signaling maintains pluripotency in mouse ESCs. Therefore, in this study, we investigated the importance of protein kinase C signaling in the context of rat ESC (rESC) pluripotency. Here we show that inhibition of PKC signaling is an efficient strategy to establish and maintain pluripotent rESCs and to facilitate reprogramming of rat embryonic fibroblasts to rat induced pluripotent stem cells. The complete developmental potential of rESCs was confirmed with viable chimeras and germ line transmission. Our molecular analyses indicated that inhibition of a PKCζ-NF-κB-microRNA-21/microRNA-29 regulatory axis contributes to the maintenance of rESC self-renewal. In addition, PKC inhibition maintains ESC-specific epigenetic modifications at the chromatin domains of pluripotency genes and, thereby, maintains their expression. Our results indicate a conserved function of PKC signaling in balancing self-renewal versus differentiation of both mouse and rat ESCs and indicate that targeting PKC signaling might be an efficient strategy to establish ESCs from other mammalian species.

EED and KDM6B coordinate the first mammalian cell lineage commitment to ensure embryo implantation.

The first mammalian cell lineage commitment is the formation of the trophectoderm (TE) and the inner cell mass (ICM) lineages during preimplantation development. Proper development of the TE and ICM lineages is dependent upon establishment of specific transcriptional programs. However, the epigenetic mechanisms that functionally contribute to establish TE- and ICM-specific transcriptional programs are poorly understood. Here, we show that proper development of the TE and ICM lineages is coordinated via combinatorial regulation of embryonic ectoderm development (EED) and lysine-specific demethylase 6B (KDM6B). During blastocyst formation, the relative levels of EED and KDM6B expression determine altered polycomb repressor 2 (PRC2) complex recruitment and incorporation of the repressive histone H3 lysine 27 trimethylation (H3K27Me3) mark at the chromatin domains of TE-specific master regulators CDX2 and GATA3, leading to their activation in the TE lineage and repression in the ICM lineage. Furthermore, ectopic gain of EED along with depletion of KDM6B in preimplantation mouse embryos abrogates CDX2 and GATA3 expression in the nascent TE lineage. The loss of CDX2 and GATA3 in the nascent TE lineage results in improper TE development, leading to failure in embryo implantation to the uterus. Our study delineates a novel epigenetic mechanism that orchestrates proper development of the first mammalian cell lineages.

Gastroprotective effect of beta3 adrenoreceptor agonists ZD 7114 and CGP 12177A in rats.

The effects of beta(3) adrenergic receptor agonists, (S)-4-[2-hydroxy-3-phenoxy-propylamino-ethoxy]-N-(2-methoxyethyl)-phenoxyacetamide (ZD 7114) and (+/-)-4-(3-t-butylamino-2-hydroxypropoxy)benzimidazol-2-one (CGP 12177A), were studied on aspirin plus pylorus ligation-induced gastric ulcers, gastric mucosal blood flow and gastric motility in rats. Pretreatment with ZD 7114 (3 mg kg(-1), p.o.) and CGP 12177A (3.5 mg kg(-1), p.o.) resulted in significant reduction in the incidences of gastric ulceration in aspirin plus pylorus ligated rats and results were comparable with the cimetidine treated group. Ulcer index was significantly reduced by ZD 7114 (0.71+/-0.05, P<0.05) and CGP 12177A (1.15+/-0.27, P<0.05) when compared with the control group (4.47+/-0.38). Further, significant increase in total carbohydrates to protein content ratio (mucin activity) was also observed. However, they did not alter the acid secretory parameters such as total acidity, total acid output and pepsin activity. Effects of ZD 7114 and CGP 12177A on gastric mucosal blood flow were studied using neutral red clearance method. Both the treatments showed significant increase in gastric mucosal blood flow (GV/Bt) as compared to control group. Effect on gastric motility was evaluated by estimation of phenol red concentration in rat stomach. Significantly higher concentrations of phenol red in the stomach were observed in ZD 7114 and CGP 12177A treated rats. Both ZD 7114 and CGP 12177A showed significant gastroprotective effect in the present study. The mechanism of this effect may be attributed to enhancement of gastric mucosal blood flow, reduction in gastric motility and strengthening of gastric mucosal barrier.

Effect of SR 58611A, a beta-3 receptor agonist, against experimental gastro-duodenal ulcers.

The present study was designed to study the effect of SR 58611A, a selective beta 3-adrenoceptor agonist against gastric ulcers: pylorus ligation, water immersion plus restraint stress (WIRS), ethanol, aspirin-induced and on cysteamine-induced duodenal ulcers, in rats. SR 58611A (10 mg/kg, p.o.) was found to be effective in attenuating gastric ulceration and the results were comparable with those from standard cimetidine-treated group. Apart from reducing ulcer index, SR 58611A significantly decreased total acidity and thereby exhibited antisecretory activity in pylorus ligation model. SR 58611A showed significant reduction in ulcer index alongwith significant rise in the gastric wall mucus content in WIRS model. Further it showed significant cytoprotective activity against ethanol insult, that was evident from significant reduction in ulcer index. It showed significant reduction in gastric ulceration in aspirin-treated rats. The drug was found to be ineffective in inhibiting the cysteamine-induced duodenal ulcers as evident from the ulcer index and total lesion area parameters. It is concluded that SR 586111A possesses significant gastroprotective activity. This activity could be attributed to the inhibition of gastric acidity, increase in gastric wall mucus content and the reversal of gastric microvascular injury resulting into protection of the vascular integrity.