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Stability and control analysis of COVID-19 spread in India using SEIR model
In this work, we investigate a mathematical model that depicts the dynamics of COVID-19, with an emphasis on the effectiveness of detection and diagnosis procedures as well as the impact of quarantine measures. Using data from May 1 to May 31, 2020, the current study compares three states: Tamil Nadu, Maharashtra, and Andhra Pradesh. A compartmental model has been developed in order to forecast the pandemic’s trajectory and devise an effective control strategy. The study then examines the dynamic progression of the pandemic by including important epidemiological factors into a modified SEIR (Susceptible, Exposed, Infectious, Recovered) model. Our method is a thorough analysis of the equilibria of the deterministic mathematical model in question. We use rigorous techniques to find these equilibrium points and then conduct a comprehensive investigation of their stability. Furthermore, an optimum control problem is applied to reduce the illness fatality, taking into account both pharmaceutical and nonpharmaceutical intervention options as control functions. With the aid of Pontryagin’s maximal principle, an objective functional has been created and solved in order to minimize the number of infected people and lower the cost of the controls. In terms of the basic reproduction number, the stability of biologically plausible equilibrium points and the qualitative behavior of the model are examined. We found that the disease transmission rate has an effect on reducing the spread of diseases after conducting sensitivity analysis with regard to the basic reproduction number. According to the findings, Tamil Nadu had the lowest reproduction number ((R_0 = 0.0334)) and Maharashtra the highest ((R_0 = 0.2170)), indicating regional differences in the efficacy of public health initiatives. Furthermore, it has been demonstrated that appropriate control strategies, such as vaccination ((M)), can successfully reduce infection levels and improve recovery rates. In our study compared to the other two states, Tamil Nadu is notable for its quick recovery and decrease in infection rates. In our findings are more dependable and applicable when mathematical analysis and numerical simulations are combined, which also helps to provide a more thorough understanding of the dynamics at work in the COVID-19 environment. This research also offers suggestions for how government agencies, health groups, and legislators can lessen the effects of COVID-19 and distribute resources as efficiently as possible . Finally, we conclude by discussing the optimal control strategy to contain the epidemic.
Quantum Zeno Monte Carlo for computing observables
The recent development of logical quantum processors marks a pivotal transition from the noisy intermediate-scale quantum (NISQ) era to the fault-tolerant quantum computing (FTQC) era. These devices have the potential to address classically challenging problems with polynomial computational time using quantum properties. However, they remain susceptible to noise, necessitating noise resilient algorithms. We introduce Quantum Zeno Monte Carlo (QZMC), a classical-quantum hybrid algorithm that demonstrates resilience to device noise and Trotter errors while showing polynomial computational cost for a gapped system. QZMC computes static and dynamic properties without requiring initial state overlap or variational parameters, offering reduced quantum circuit depth.
Beyond CHD7 gene: unveiling genetic diversity in clinically suspected CHARGE syndrome
The Verloes or Hale diagnostic criteria have been applied for diagnosing CHARGE syndrome in suspected patients. This study was conducted to evaluate the diagnostic rate of CHD7 according to these diagnostic criteria in suspected patients and also to investigate other genetic defects in CHD7-negative patients. The clinical findings and the results of genetic testing of CHD7, chromosome microarray, exome sequencing, or genome sequencing of 59 subjects were reviewed. CHD7 pathogenic variants were identified in 78% of 46 subjects who met either the Verloes or Hale diagnostic criteria and in 87% of 38 subjects who met both criteria, whereas no CHD7 variant was detected in 13 subjects who met neither criterion. Among 23 patients without the CHD7 variant, six genetic diseases were identified in 7 patients, including Wolf–Hirschhorn syndrome, 1q21 deletion syndrome, 19q13 microdeletion, and pathogenic variants in PLCB4, TRRAP, and OTX2. Based on these comprehensive analyses, the overall diagnostic rate was 73% for seven different genetic diseases. This study emphasizes the importance of comprehensive clinical and genetic evaluation in patients with clinically suspected CHARGE syndrome, recognizing the overlapping phenotypes in other rare genetic disorders.
Core N-DRC components play a crucial role in embryonic development and postnatal organ development
Motile cilia and flagella are evolutionarily conserved organelles, and their defects cause primary ciliary dyskinesia (PCD), a disorder characterized by systemic organ dysfunction. The nexin-dynein regulatory complex (N-DRC) is a crucial structural component of motile cilia and flagella, present across various species from Chlamydomonas to humans. Defects in N-DRC components lead to multiple PCD symptoms, including sinusitis and male infertility. However, the phenotypic expression of N-DRC defects varies significantly among individuals, and there has been a lack of systematic study of core N-DRC components in mammals. Utilizing Drc1-4 and Drc7 knockout mice, this study systematically reveals the roles and assembly process of core N-DRC components in ependymal cilia, respiratory cilia, and sperm flagella. The findings show that core N-DRC components are crucial for the survival of mice on a purebred genetic background. In mixed genetic background mice, N-DRC defects impair the motility of motile cilia and the stability of flagellar axonemes. Additionally, a novel role of the N-DRC specific component (A-kinase anchoring protein 3) AKAP3 in regulating sperm phosphorylation was discovered. Collectively, our results provide a comprehensive understanding of the core N-DRC components in mammalian cilia and flagella.
TCMEval-SDT: a benchmark dataset for syndrome differentiation thought of traditional Chinese medicine
This paper presents a large publicly available benchmark dataset (TCMEval-SDT) for the thought process involved in syndrome differentiation in traditional Chinese medicine (TCM). The dataset consists of 300 TCM syndrome diagnosis cases sourced from the internet, classical Chinese medical texts, and medical records from hospitals, with metadata adhering to the Findable, Accessible, Interoperable, and Reusable (FAIR) principles. Each case has been annotated and curated by TCM experts and includes medical record ID, clinical data, explanatory summary, TCM syndrome, clinical information, and TCM pathogenesis, to support algorithms or models in emulating the diagnostic process of TCM clinicians. To provide a comprehensive description of the TCM syndrome diagnosis process, we summarize the diagnosis into four steps: (1) clinical information extraction, (2) TCM pathogenesis reasoning, (3) TCM syndrome reasoning, and (4) explanatory summary. We have also established validation criteria to evaluate their ability in TCM clinical diagnosis using this dataset. To facilitate research and evaluation in syndrome diagnosis of TCM, the TCMEval-SDT dataset is made publicly available under the CC-BY 4.0 license.
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