Averaging Rate Scheduler for Decentralized Learning on Heterogeneous Data

State-of-the-art decentralized learning algorithms typically require the data distribution to be Independent and Identically Distributed (IID). However, in practical scenarios, the data distribution across the agents can have significant heterogeneity. In this work, we propose averaging rate scheduling as a simple yet effective way to reduce the impact of heterogeneity in decentralized learning. Our experiments illustrate the superiority of the proposed method (~3% improvement in test accuracy) compared to the conventional approach of employing a constant averaging rate.Comment: 9 pages, 3 figures, 4 tables. arXiv admin note: text overlap with arXiv:2305.0479

To analyze and compare the different spectrum sensing techniques over the primary channels from the study of CRN concept

The motive behind this paper is to utilize the RF Spectrum properly and to reduce the false alarm rate and to employ various techniques for cognitive radio network(CRN). In cognitive radio networks, to eliminate interference form other than primary users to the users who hold primary license of the spectrum, reliable spectrum sensing is needful. In endeavor, where the samples of noise are correlated, the spectrum sensing methods enhance considering impairment by the noise samples which are not dependent will not provide optimum performances. The probabilities of FA and detection of the designed detector in the low signal to noise ratio reign are visualized. The average probabilities are designed over distinct channel gains. Numerical and simulation results determine the better of the designed method over the known energy detection method and local optimum method with analogous convolution. Furthermore, we consider the endeavor where the calculated correlation is different from the ideal correlation and investigate the effect of this correlation mismatch on the probabilities of constant false alarm and detection of the purposed method. On comparing with the conventional techniques feature detection algorithm, cyclostationary approach is more accurate i.e. it can change the computational hazard according to current electromagnetic environment by motility its sampling times and the cyclic frequency step size

CoDeC: Communication-Efficient Decentralized Continual Learning

Training at the edge utilizes continuously evolving data generated at different locations. Privacy concerns prohibit the co-location of this spatially as well as temporally distributed data, deeming it crucial to design training algorithms that enable efficient continual learning over decentralized private data. Decentralized learning allows serverless training with spatially distributed data. A fundamental barrier in such distributed learning is the high bandwidth cost of communicating model updates between agents. Moreover, existing works under this training paradigm are not inherently suitable for learning a temporal sequence of tasks while retaining the previously acquired knowledge. In this work, we propose CoDeC, a novel communication-efficient decentralized continual learning algorithm which addresses these challenges. We mitigate catastrophic forgetting while learning a task sequence in a decentralized learning setup by combining orthogonal gradient projection with gossip averaging across decentralized agents. Further, CoDeC includes a novel lossless communication compression scheme based on the gradient subspaces. We express layer-wise gradients as a linear combination of the basis vectors of these gradient subspaces and communicate the associated coefficients. We theoretically analyze the convergence rate for our algorithm and demonstrate through an extensive set of experiments that CoDeC successfully learns distributed continual tasks with minimal forgetting. The proposed compression scheme results in up to 4.8x reduction in communication costs with iso-performance as the full communication baseline

DEVELOPMENT OF BINARY AND TERNARY COMPLEX OF CEFUROXIME AXETIL WITH CYCLODEXTRIN FOR IMPROVING PHARMACEUTICAL CHARACTERISTICS

Objective: The current research objective is systematic development and characterization of binary and ternary inclusion complexes of cefuroxime axetil with β-cyclodextrin to improve its pharmaceutical characteristics by using the kneading method. Methods: Phase solubility study was carried out using Higuchi and Connors method. Based on its result, binary complexes of cefuroxime axetil with different ratio of β-cyclodextrin were developed and characterized using differential scanning calorimeter (DSC), fourier transform infrared spectroscopy (FT-IR) and X-ray powder diffractometry (XRD). Then, binary complexes were analyzed for in vitro dissolution testing. The ternary complexes were developed using different ratio of PVP K-30 as a ternary component and evaluated for in vitro dissolution testing and in vitro taste masking. Results: Binary complex of cefuroxime axetil with β-cyclodextrin (1:1) showed better drug release than pure drug. During the development of the ternary complex, β-cyclodextrin (1:1) and 1% w/v PVP K-30 as a ternary agent resulted in an optimized ternary complex. The DSC, FT-IR and XRD studies clearly revealed the formation of binary and ternary complexes. The ternary complex showed better drug release of>85% within 30 min. in comparison to binary complex. The in vitro taste-masking study revealed the taste masking efficiency of the ternary complex of cefuroxime with β-cyclodextrin. Conclusion: The developed binary and ternary complex of cefuroxime axetil based on β-cyclodextrin with PVP K-30 showed improved in vitro dissolution rate and taste masking in comparison to pure drug. The drug release was better in ternary complexes. The present research work successfully shows the utility of binary and ternary complexes for improving pharmaceutical characteristics of cefuroxime axetil

Bronchiectasis in India:results from the European Multicentre Bronchiectasis Audit and Research Collaboration (EMBARC) and Respiratory Research Network of India Registry

BACKGROUND: Bronchiectasis is a common but neglected chronic lung disease. Most epidemiological data are limited to cohorts from Europe and the USA, with few data from low-income and middle-income countries. We therefore aimed to describe the characteristics, severity of disease, microbiology, and treatment of patients with bronchiectasis in India. METHODS: The Indian bronchiectasis registry is a multicentre, prospective, observational cohort study. Adult patients ( 6518 years) with CT-confirmed bronchiectasis were enrolled from 31 centres across India. Patients with bronchiectasis due to cystic fibrosis or traction bronchiectasis associated with another respiratory disorder were excluded. Data were collected at baseline (recruitment) with follow-up visits taking place once per year. Comprehensive clinical data were collected through the European Multicentre Bronchiectasis Audit and Research Collaboration registry platform. Underlying aetiology of bronchiectasis, as well as treatment and risk factors for bronchiectasis were analysed in the Indian bronchiectasis registry. Comparisons of demographics were made with published European and US registries, and quality of care was benchmarked against the 2017 European Respiratory Society guidelines. FINDINGS: From June 1, 2015, to Sept 1, 2017, 2195 patients were enrolled. Marked differences were observed between India, Europe, and the USA. Patients in India were younger (median age 56 years [IQR 41-66] vs the European and US registries; p<0\ub70001]) and more likely to be men (1249 [56\ub79%] of 2195). Previous tuberculosis (780 [35\ub75%] of 2195) was the most frequent underlying cause of bronchiectasis and Pseudomonas aeruginosa was the most common organism in sputum culture (301 [13\ub77%]) in India. Risk factors for exacerbations included being of the male sex (adjusted incidence rate ratio 1\ub717, 95% CI 1\ub703-1\ub732; p=0\ub7015), P aeruginosa infection (1\ub729, 1\ub710-1\ub750; p=0\ub7001), a history of pulmonary tuberculosis (1\ub720, 1\ub707-1\ub734; p=0\ub7002), modified Medical Research Council Dyspnoea score (1\ub732, 1\ub725-1\ub739; p<0\ub70001), daily sputum production (1\ub716, 1\ub703-1\ub730; p=0\ub7013), and radiological severity of disease (1\ub703, 1\ub701-1\ub704; p<0\ub70001). Low adherence to guideline-recommended care was observed; only 388 patients were tested for allergic bronchopulmonary aspergillosis and 82 patients had been tested for immunoglobulins. INTERPRETATION: Patients with bronchiectasis in India have more severe disease and have distinct characteristics from those reported in other countries. This study provides a benchmark to improve quality of care for patients with bronchiectasis in India. FUNDING: EU/European Federation of Pharmaceutical Industries and Associations Innovative Medicines Initiative inhaled Antibiotics in Bronchiectasis and Cystic Fibrosis Consortium, European Respiratory Society, and the British Lung Foundation

Extracting relevant predictors of the severity of mental illnesses from clinical information using regularisation regression models

Mental disorders are common non-communicable diseases whose occurrence rises at epidemic rates globally. The determination of the severity of a mental illness has important clinical implications and it serves as a prognostic factor for effective intervention planning and management. This paper aims to identify the relevant predictors of the severity of mental illnesses (measured by psychiatric rating scales) from a wide range of clinical variables consisting of information on both laboratory test results and psychiatric factors . The laboratory test results collectively indicate the measurements of 23 components derived from vital signs and blood tests results for the evaluation of the complete blood count. The 8 psychiatric factors known to affect the severity of mental illnesses are considered, viz. the family history, course and onset of an illness, etc. Retrospective data of 78 patients diagnosed with mental and behavioural disorders were collected from the Lady Hardinge Medical College & Smt. S.K, Hospital in New Delhi, India. The observations missing in the data are imputed using the non-parametric random forest algorithm. The multicollinearity is detected based on the variance inflation factor. Owing to the presence of multicollinearity, regularisation techniques such as ridge regression and extensions of the least absolute shrinkage and selection operator (LASSO), viz. adaptive and group LASSO are used for fitting the regression model. Optimal tuning parameter λ is obtained through 13-fold cross-validation. It was observed that the coefficients of the quantitative predictors extracted by the adaptive LASSO and the group of predictors extracted by the group LASSO were comparable to the coefficients obtained through ridge regression

Structure−Activity Relationships Towards the Identification of High-Potency Selective Human Toll-Like Receptor-7 Agonist

Toll-like receptors (TLRs) act as the “sentinel” of the immune system to link innate immune responses with adaptive immunity. TLR7 agonists are highly immunostimulatory and can be exploited as powerful vaccine adjuvants. A structure-activity relationship study was conducted on the TLR7-active imidazoquinoline (IMDQ) scaffold, starting with 1-benzyl-2-butyl-1H-imidazo[4,5-c]quinolin-4-amine (BBIQ) as a lead structure. A systematic exploration of electron withdrawing as well as electron donating substituents at the para-position of benzyl group at N-1 position of IMDQ scaffold led to the identification of a highly active para-hydroxymethyl IMDQ analogue with an EC50 value of 0.23 µM for human TLR7 with marginal activity for human TLR8, thereby indicating it as a TLR7-specific agonist that was 37-fold more potent than imiquimod. Its bio-steric para-aminomethyl analogue was a dual TLR7 and TLR8 agonist. Molecular modelling was performed which revealed the TLR8 activity of the IMDQ scaffold to be associated with the presence of amino functionality in the benzyl group. TLR7-biased activity was driven by the forming of multiple H-bonds with TLR7 which not formed when the IMDQ scaffold compounds were docked with TLR8. Finally, the role of the IMDQ scaffold agonists as vaccine adjuvants was tested with a Covid-19 vaccine in mice, which showed that TLR7 activity even in the absence of TLR8 activity was sufficient for potentiation of anti-spike protein antibody production, suggesting that TLR7 specific agonists may make suitable vaccine adjuvants

A De novo Peptide from a High Throughput Peptide Library Blocks Myosin A -MTIP Complex Formation in Plasmodium falciparum

Apicomplexan parasites, through their motor machinery, produce the required propulsive force critical for host cell-entry. The conserved components of this so-called glideosome machinery are myosin A and myosin A Tail Interacting Protein (MTIP). MTIP tethers myosin A to the inner membrane complex of the parasite through 20 amino acid-long C-terminal end of myosin A that makes direct contacts with MTIP, allowing the invasion of Plasmodium falciparum in erythrocytes. Here, we discovered through screening a peptide library, a de-novo peptide ZA1 that binds the myosin A tail domain. We demonstrated that ZA1 bound strongly to myosin A tail and was able to disrupt the native myosin A tail MTIP complex both in vitro and in vivo. We then showed that a shortened peptide derived from ZA1, named ZA1S, was able to bind myosin A and block parasite invasion. Overall, our study identified a novel anti-malarial peptide that could be used in combination with other antimalarials for blocking the invasion of Plasmodium falciparum