Vascular Dynamics Aid a Coupled Neurovascular Network Learn Sparse Independent Features: A Computational Model

Cerebral vascular dynamics are generally thought to be controlled by neural activity in a unidirectional fashion. However, both computational modeling and experimental evidence point to the feedback effects of vascular dynamics on neural activity. Vascular feedback in the form of glucose and oxygen controls neuronal ATP, either directly or via the agency of astrocytes, which in turn modulates neural firing. Recently, a detailed model of the neuron-astrocyte-vessel system has shown how vasomotion can modulate neural firing. Similarly, arguing from known cerebrovascular physiology, an approach known as “hemoneural hypothesis” postulates functional modulation of neural activity by vascular feedback. To instantiate this perspective, we present a computational model in which a network of “vascular units” supplies energy to a neural network. The complex dynamics of the vascular network, modeled by a network of oscillators, turns neurons ON and OFF randomly. The informational consequence of such dynamics is explored in the context of an auto-encoder network. In the proposed model, each vascular unit supplies energy to a subset of hidden neurons of an autoencoder network, which constitutes its “projective field.” Neurons that receive adequate energy in a given trial have reduced threshold, and thus are prone to fire. Dynamics of the vascular network are governed by changes in the reconstruction error of the auto-encoder network, interpreted as the neuronal demand. Vascular feedback causes random inactivation of a subset of hidden neurons in every trial. We observe that, under conditions of desynchronized vascular dynamics, the output reconstruction error is low and the feature vectors learnt are sparse and independent. Our earlier modeling study highlighted the link between desynchronized vascular dynamics and efficient energy delivery in skeletal muscle. We now show that desynchronized vascular dynamics leads to efficient training in an auto-encoder neural network

Study on Performance of Different Fodder Crops under Low Cost Green House Hydroponic Fodder Production System

Hydroponics play most significant role in augmenting fodder shortage and helps for dairy production efficiently. A study was conducted to assess the performance and suitability of different crops under low cost green house hydroponic fodder production unit at SHE&CS Krishi Vigyan Kendra, Yagantipalle. Four varieties of cereals grains and four verities of Pulses were tested. One kilogram grain each of the variety was soaked for 12 hours in water for sprouting in air tight condition for 36 hours. The sprouted seed was spread in trays of size 2.5 ft X 1.5ft and kept in the Hydroponic Unit. Automatic sprinkling of water was managed by cyclic timer. Chemical fertilizer was not used. Data on sprouted seed weight and weight of biomass after 5 days was recorded using electronic weighing balance. The high biomass yield after 5days in cereals was recorded in Bajra followed by sorghum, Barley and Maize. Among pulses Pillipesara yielded highest weight followed by Cowpea, Lucerne and Horse gram. Highest plant height among cereals was recorded in Barley and cowpea in pulses. The difference among all the varieties in respect of biomass yield and plant height was found to be significant. Negative correlation was found between plant height and biomass yield

Prevalence of asthma in urban and rural children in Tamil Nadu

Background. There are very few community-based studies on the prevalence of asthma in Indian children. We aimed to estimate the prevalence of asthma in children under 12 years of age and to study possible differences in the prevalence of childhood asthma in urban and rural areas of Tamil Nadu. Methods. A total of 584 children from Chennai and 271 children from 25 villages around Chennai formed the urban and rural groups, respectively. From November 1999 to February 2000, data were collected using a simplified version of the ISAAC questionnaire, which was administered by trained students. Symptoms suggestive of asthma or hyperreactive airways disease in children under 12 years of age were recorded from the selected urban and rural populations by questioning the parents. The results were analysed separately for children 0-5 and 6-12 years of age. Results. Of the 855 children studied, the overall prevalence of breathing difficulty (including asthma) was 18% and the prevalence of ‘diagnosed’ asthma was 5%. Twenty-two per cent of urban and 9% of rural children 6-12 years of age reported breathing difficulty ‘at any time in the past’ (p<0.01). A significantly higher proportion of 6-12-year-old urban children also reported nocturnal dry cough (28.4%v. 18.7%,p<0.05). Urban children reported recent wheeze more often than rural children (92% v. 77%, p=0.01). Conclusions. Symptoms suggestive of asthma were present in 18% of children under 12 years of age. Though the prevalence of diagnosed childhood asthma was about 5% in both urban and rural areas, the prevalence of ‘breathing difficulty’ and nocturnal cough was significantly higher among urban children in the age group of 6-12 years. Children living in urban areas also reported ‘recent wheeze’ more often than rural children. Our data suggest that the actual prevalence of asthma and other ‘wheezy’ illnesses may be higher than that previously documented. Further studies are needed to confirm the difference in prevalence between urban and rural children and also to identify possible causes that could account for the higher urban prevalence of asthma in Tamil Nadu

A batch-service queueing model with a discrete batch Markovian arrival process

Queueing systems with batch service have been investigated extensively during the past decades. However, nearly all the studied models share the common feature that an uncorrelated arrival process is considered, which is unrealistic in several real-life situations. In this paper, we study a discrete-time queueing model, with a server that only initiates service when the amount of customers in system (system content) reaches or exceeds a threshold. Correlation is taken into account by assuming a discrete batch Markovian arrival process (D-BMAP), i.e. the distribution of the number of customer arrivals per slot depends on a background state which is determined by a first-order Markov chain. We deduce the probability generating function of the system content at random slot marks and we examine the influence of correlation in the arrival process on the behavior of the system. We show that correlation merely has a small impact on the threshold that minimizes the mean system content. In addition, we demonstrate that correlation might have a significant influence on the system content and therefore has to be included in the model

Consistent Anisotropic Repulsions for Simple Molecules

We extract atom-atom potentials from the effective spherical potentials that suc cessfully model Hugoniot experiments on molecular fluids, e.g., $O_2$ and $N_2$. In the case of $O_2$ the resulting potentials compare very well with the atom-atom potentials used in studies of solid-state propertie s, while for $N_2$ they are considerably softer at short distances. Ground state (T=0K) and room temperatu re calculations performed with the new $N-N$ potential resolve the previous discrepancy between experimental and theoretical results.Comment: RevTeX, 5 figure

Exploring the Regression Path of Deep Learning Algorithms for Big Data and High-Dimensional Data

Deep learning algorithms have become crucial for handling and extracting insights from big data and high-dimensional data. This paper explores the regression capabilities of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory Networks (LSTMs) in predicting patient outcomes in a hospital setting. By leveraging these advanced algorithms, the study aims to automate feature extraction, thereby reducing the need for manual feature engineering. The models were evaluated using standard regression metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²) score. The results indicate that LSTMs outperform CNNs and RNNs across all metrics, highlighting their superior ability to capture complex temporal patterns in high-dimensional medical data. This study underscores the potential of deep learning algorithms in enhancing predictive accuracy and operational efficiency in healthcare

MicroRNAs in pulmonary arterial remodeling

Pulmonary arterial remodeling is a presently irreversible pathologic hallmark of pulmonary arterial hypertension (PAH). This complex disease involves pathogenic dysregulation of all cell types within the small pulmonary arteries contributing to vascular remodeling leading to intimal lesions, resulting in elevated pulmonary vascular resistance and right heart dysfunction. Mutations within the bone morphogenetic protein receptor 2 gene, leading to dysregulated proliferation of pulmonary artery smooth muscle cells, have been identified as being responsible for heritable PAH. Indeed, the disease is characterized by excessive cellular proliferation and resistance to apoptosis of smooth muscle and endothelial cells. Significant gene dysregulation at the transcriptional and signaling level has been identified. MicroRNAs are small non-coding RNA molecules that negatively regulate gene expression and have the ability to target numerous genes, therefore potentially controlling a host of gene regulatory and signaling pathways. The major role of miRNAs in pulmonary arterial remodeling is still relatively unknown although research data is emerging apace. Modulation of miRNAs represents a possible therapeutic target for altering the remodeling phenotype in the pulmonary vasculature. This review will focus on the role of miRNAs in regulating smooth muscle and endothelial cell phenotypes and their influence on pulmonary remodeling in the setting of PAH

The UK Diabetic Retinopathy Electronic Medical Record (UK DR EMR) Users Group, Report 2: real-world data for the impact of cataract surgery on diabetic macular oedema

Aim: To assess the rate of ‘treatment-requiring diabetic macular oedema (DMO)’ in eyes for the two  years before and after cataract surgery. Methods: Multicentre national diabetic retinopathy (DR) database study with anonymised data extraction across 19 centres from an electronic medical record system. Inclusion criteria: eyes undergoing cataract surgery in patients with diabetes with no history of DMO prior to study start. The minimum dataset included: age, visual acuity (all time-points), injection episodes, timing of cataract surgery and ETDRS grading of retinopathy and maculopathy. Main outcome measure: rate of developing first episode of treatment-requiring DMO in relation to timing of cataract surgery in the same eye. Results: 4850 eyes met the inclusion criteria. The rate of developing treatment-requiring DMO in this cohort was 2.9% in the year prior to surgery versus 5.3% in the year after surgery (p<0.01). The risk of ‘treatment-requiring DMO’ increased sharply after surgery, peaking in the 3–6 months' period (annualised rates of 5.2%, 6.8%, 5.6% and 4.0% for the 0–3, 3–6, 6–9 and 9–12 months' post-operative time periods respectively). Risk was associated with pre-operative grade of retinopathy: risk of DMO in the first year post-operatively being 1.0% (no DR pre-operatively), 5.4% (mild non-proliferative diabetic retinopathy; NPDR), 10.0% (moderate NPDR), 13.1% (severe NPDR) and 4.9% (PDR) (p<0.01). Conclusions: This large real-world study demonstrates that the rate of developing treatment-requiring DMO increases sharply in the year after cataract surgery for all grades of retinopathy, peaking in the 3–6 months' postoperative period. Patients with moderate and severe NPDR are at particularly high risk

The study on post-operative wound infections at Vizianagaram in Andhra Pradesh, India

Background: In most of the people post-operative wound infections are responsible for major complications such as cost, morbidity, mortality and duration of hospital stay related to surgeries. Objectives of the study were to demonstrate the incidence of post-operative wound infections at MIMS (Maharagah’s Institute of Medical Sciences, Vizianagaram.Methods: A cross sectional study has been carried out to know the incidence of post-operative wound infection The study was conducted in MIMS (Maharagah’s Institute of Medical Sciences). The study population was enrolled after fulfilling the selection criteria from General Surgery 100 patients (both elective and emergency surgeries) were selected using randomized technique.Results: Out of 100 cases in the study 17 were positive for post-operative wound infection and out of which 10 were mild infections, 9 cases were moderate infections and 3 were severe infections including 1 burst abdomen. Coagulase positive Staph aureus was isolated in 10 out 40. Over all infection rate in the study was 13.58%.Conclusions: The study clearly depicted the changing pattern of wound infection toward mixed infection. A larger study with substantial number of patients will confirm the findings of this study

Band gap tuning of amorphous Al oxides by Zr alloying

The optical band gap and electronic structure of amorphous Al-Zr mixed oxides with Zr content ranging from 4.8 to 21.9% were determined using vacuum ultraviolet and X-ray absorption spectroscopy. The light scattering by the nano-porous structure of alumina at low wavelengths was estimated based on the Mie scattering theory. The dependence of the optical band gap of the Al-Zr mixed oxides on the Zr content deviates from linearity and decreases from 7.3 eV for pure anodized Al2O3 to 6.45 eV for Al-Zr mixed oxides with a Zr content of 21.9%. With increasing Zr content, the conduction band minimum changes non-linearly as well. Fitting of the energy band gap values resulted in a bowing parameter of ∼2 eV. The band gap bowing of the mixed oxides is assigned to the presence of the Zr d-electron states localized below the conduction band minimum of anodized Al2O3.</p