Design of an improved graph-based model for real-time anomaly detection in healthcare using hybrid CNN-LSTM and federated learning.

Real-time monitoring and anomaly detection are essential in healthcare to ensure safe conditions for patients and maintain the integrity of medical data samples. The majority of existing systems, despite improvements in healthcare technologies, cannot capture the spatial and temporal patterns of multimodal data simultaneously, process high Volume data in real-time, and ensure the privacy of patients' identity effectively. In this work, we handle these limitations by proposing a complete approach that uses state-of-the-art deep learning and data processing architectures to realize resilient anomaly detection in healthcare systems.

Non-fragile output-feedback synchronization for delayed discrete-time complex-valued neural networks with randomly occurring uncertainties.

This paper is step forward to establish an exponential synchronization criterion for discrete-time complex-valued neural networks (CVNNs) having time-varying delays subject to randomly occurring uncertain weighting parameters, in order to overcome the fluctuation when the output-feedback controller imposes on its dynamics. To achieve this, Jensen's weighted summation inequalities (WSIs) and an extended reciprocal convex matrix inequality (ERCMI) are extended into the domain of complex field. By introducing some augmented vectors, a Lyapunov-Krasovskii functional (LKF) is constructed to attain an improved delay-dependent linear matrix inequalities (LMIs) constraint for the exponential synchronization phenomenon of the desired master-slave neuronal system model.

Exponential State Estimation for Memristor-Based Discrete-Time BAM Neural Networks With Additive Delay Components.

This paper focuses on the dynamical behavior for a class of memristor-based bidirectional associative memory neural networks (BAMNNs) with additive time-varying delays in discrete-time case. The necessity of the proposed problem is to design a proper state estimator such that the dynamics of the corresponding estimation error is exponentially stable with a prescribed decay rate. By constructing an appropriate Lyapunov-Krasovskii functional (LKF) and utilizing Cauchy-Schwartz-based summation inequality, the delay-dependent sufficient conditions for the existence of the desired estimator are derived in the absence of uncertainties which are further extended to available uncertain parameters of the prescribed memristor-based BAMNNs in terms of linear matrix inequalities (LMIs).

A novel approach for increasing transformation efficiency in DH5α cells using silver nanoparticles.

The present study is the first report on the application of silver nanoparticles for efficient bacterial transformation. EC50 value of 100 nm silver nanoparticles against DH5α cells was recorded as 4.49 mg L in toxicity assay.

An Improved Result on Dissipativity and Passivity Analysis of Markovian Jump Stochastic Neural Networks With Two Delay Components.

In this paper, we investigate the dissipativity and passivity of Markovian jump stochastic neural networks involving two additive time-varying delays. Using a Lyapunov-Krasovskii functional with triple and quadruple integral terms, we obtain delay-dependent passivity and dissipativity criteria for the system. Using a generalized Finsler lemma (GFL), a set of slack variables with special structure are introduced to reduce design conservatism.

Dissipativity analysis of stochastic memristor-based recurrent neural networks with discrete and distributed time-varying delays.

In this paper, based on the knowledge of memristor-based recurrent neural networks (MRNNs), the model of the stochastic MRNNs with discrete and distributed delays is established. In real nervous systems and in the implementation of very large-scale integration (VLSI) circuits, noise is unavoidable, which leads to the stochastic model of the MRNNs. In this model, the delay interval is decomposed into two subintervals by using the tuning parameter α such that 0 < α < 1.

Emerging links between type 2 diabetes and Alzheimer's disease.

Type 2 diabetes mellitus and Alzheimer's disease are both associated with increasing age, and each increases the risk of development of the other. Epidemiological, clinical, biochemical and imaging studies have shown that elevated glucose levels and diabetes are associated with cognitive dysfunction, the most prevalent cause of which is Alzheimer's disease. Cross sectional studies have clearly shown such an association, whereas longitudinal studies are equivocal, reflecting the many complex ways in which the two interact.

State estimation for delayed genetic regulatory networks based on passivity theory.

This paper is concerned with the state estimation problem for delayed genetic regulatory networks (GRNs) based on passivity analysis approach. The main purpose of the problem is to design the estimator to approximate the true concentrations of the mRNA and protein through available measurement outputs. Time-varying delays are explicitly assumed to be non-differentiable and constraint on the derivative of the time-varying delay is less than one can be removed.

Association of Polycystic Ovary Syndrome with Anticonvulsant Use: ACase Report.

A 20-year-old woman with epilepsy had resolution of features of polycystic ovary syndrome on discontinuing valproic acid; they reappeared with the newer antiepileptic divalproex sodium.

Clinical association of autoimmune diseases with diabetes mellitus: analysis from southern India.

The adult form of polyglandular autoimmune syndrome (type 2) is associated with disorders of the thyroid, adrenal, and pancreas (type 1 diabetes mellitus). We profile the clinical association of autoimmune diseases among patients from our center in southern India: autoimmune diseases were diagnosed in 1.68% of persons with diabetes mellitus (147/15,523).

Hyperthyroidism in a girl with Down's syndrome.

We report an adolescent girl with Down's syndrome, who presented with hyperthyroidism. Autoimmune thyroid disorders can occur in children with Down's syndrome, hypothyroidism developing more frequently. Hyperthyroidism can also be associated with Down's syndrome, and should not be missed.

Evaluation of osteoporosis in patients with fracture neck of femur using conventional radiography.

Osteoporosis was assessed at the hip (Singh's index) and calcaneus (calcaneal index) using conventional radiography in 80 patients (31 men, 49 women) with fracture neck of femur. Forty four patients had intracapsular fracture neck of femur (ICFN), and 36 extracapsular fracture neck of femur (ECFN). Fractures occurred more with increasing age in both groups, with female preponderance in the older age group.

Hypothyroidism presenting with polycystic ovary syndrome.

During a 30 months period, two women of primary hypothyroidism (2/13; 1.04%) presented with features of polycystic ovary syndrome (PCOS). In hypothyroidism, sex hormone binding globulin levels are decreased; increased conversion of androstenedione to testosterone, and aromatization to estradiol are present, all these being an exaggeration of biochemical changes characteristic of PCOS.