On-chip ultra-fast data acquisition system for optical scanning acoustic microscopy using 0.35um CMOS technology

Optical Scanning Acoustic Microscopy (OSAM) is a non-contacting method of investigating the properties and hidden faults of solid materials. This thesis presents an ultra-fast data acquisition system (DAQ) which samples and digitises the output signal of OSAM. The author's work includes the design of the clock source and the sampler, and integration of the whole system. The clock source is a unique pulse generator based on a 2.624GHz PLL with a Quadrature VCO (QVCO), which is able to generate 4 clock signals in accurate quadrature phase difference. The pulse generator used the 4-phase clocks to provide control pulses to the sampler. The pulses were carefully aligned to the clock edges by digital logic, so that jitters were reduced as much as possible. The required short time delay for the sampler was also provided by the pulse generator, and this was implemented by a smartly-controlled switch box which re-shuffles the 4-phase clocks. The presented sampler is a novel 10.496GSample/s Sub-Sampling Sample-and-Hold Amplifier (SHA). The SHA sampled the input, and transformed its spectrum down to a low-frequency range so that it can be digitised. Charge-domain sampling strategy and double differential switches were both developed in this circuit to significantly improve the sampling speed. The periodicity of the system input was exploited in repetitive sampling to reduce the noise. These designed modules were integrated into a DAQ for a 2x8 sensor array. A pseudo-parallel scanning strategy was presented to minimise the power consumption, and a current-based buffer was applied to deliver the control pulses into the array. The DAQ was implemented on-chip in a low-cost 0.35um standard CMOS process. The measurement results showed that the DAQ successfully achieved a sampling rate more than 10GS/s, with a maximum output resolution of approximately 6 bits

On-chip ultra-fast data acquisition system for optical scanning acoustic microscopy using 0.35um CMOS technology

Optical Scanning Acoustic Microscopy (OSAM) is a non-contacting method of investigating the properties and hidden faults of solid materials. This thesis presents an ultra-fast data acquisition system (DAQ) which samples and digitises the output signal of OSAM. The author's work includes the design of the clock source and the sampler, and integration of the whole system. The clock source is a unique pulse generator based on a 2.624GHz PLL with a Quadrature VCO (QVCO), which is able to generate 4 clock signals in accurate quadrature phase difference. The pulse generator used the 4-phase clocks to provide control pulses to the sampler. The pulses were carefully aligned to the clock edges by digital logic, so that jitters were reduced as much as possible. The required short time delay for the sampler was also provided by the pulse generator, and this was implemented by a smartly-controlled switch box which re-shuffles the 4-phase clocks. The presented sampler is a novel 10.496GSample/s Sub-Sampling Sample-and-Hold Amplifier (SHA). The SHA sampled the input, and transformed its spectrum down to a low-frequency range so that it can be digitised. Charge-domain sampling strategy and double differential switches were both developed in this circuit to significantly improve the sampling speed. The periodicity of the system input was exploited in repetitive sampling to reduce the noise. These designed modules were integrated into a DAQ for a 2x8 sensor array. A pseudo-parallel scanning strategy was presented to minimise the power consumption, and a current-based buffer was applied to deliver the control pulses into the array. The DAQ was implemented on-chip in a low-cost 0.35um standard CMOS process. The measurement results showed that the DAQ successfully achieved a sampling rate more than 10GS/s, with a maximum output resolution of approximately 6 bits

A new machine learning model for predicting severity prognosis in patients with pulmonary embolism: Study protocol from Wenzhou, China

IntroductionPulmonary embolism (PE) is a common thrombotic disease and potentially deadly cardiovascular disorder. The ratio of clinical misdiagnosis and missed diagnosis of PE is very large because patients with PE are asymptomatic or non-specific.MethodsUsing the clinical data from the First Affiliated Hospital of Wenzhou Medical University (Wenzhou, China), we proposed a swarm intelligence algorithm-based kernel extreme learning machine model (SSACS-KELM) to recognize and discriminate the severity of the PE by patient’s basic information and serum biomarkers. First, an enhanced method (SSACS) is presented by combining the salp swarm algorithm (SSA) with the cuckoo search (CS). Then, the SSACS algorithm is introduced into the KELM classifier to propose the SSACS-KELM model to improve the accuracy and stability of the traditional classifier.ResultsIn the experiments, the benchmark optimization performance of SSACS is confirmed by comparing SSACS with five original classical methods and five high-performance improved algorithms through benchmark function experiments. Then, the overall adaptability and accuracy of the SSACS-KELM model are tested using eight public data sets. Further, to highlight the superiority of SSACS-KELM on PE datasets, this paper conducts comparison experiments with other classical classifiers, swarm intelligence algorithms, and feature selection approaches.DiscussionThe experimental results show that high D-dimer concentration, hypoalbuminemia, and other indicators are important for the diagnosis of PE. The classification results showed that the accuracy of the prediction model was 99.33%. It is expected to be a new and accurate method to distinguish the severity of PE

Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques

IntroductionPulmonary embolism (PE) is a cardiopulmonary condition that can be fatal. PE can lead to sudden cardiovascular collapse and is potentially life-threatening, necessitating risk classification to modify therapy following the diagnosis of PE. We collected clinical characteristics, routine blood data, and arterial blood gas analysis data from all 139 patients.MethodsCombining these data, this paper proposes a PE risk stratified prediction framework based on machine learning technology. An improved algorithm is proposed by adding sobol sequence and black hole mechanism to the cuckoo search algorithm (CS), called SBCS. Based on the coupling of the enhanced algorithm and the kernel extreme learning machine (KELM), a prediction framework is also proposed.ResultsTo confirm the overall performance of SBCS, we run benchmark function experiments in this work. The results demonstrate that SBCS has great convergence accuracy and speed. Then, tests based on seven open data sets are carried out in this study to verify the performance of SBCS on the feature selection problem. To further demonstrate the usefulness and applicability of the SBCS-KELM framework, this paper conducts aided diagnosis experiments on PE data collected from the hospital.DiscussionThe experiment findings show that the indicators chosen, such as syncope, systolic blood pressure (SBP), oxygen saturation (SaO2%), white blood cell (WBC), neutrophil percentage (NEUT%), and others, are crucial for the feature selection approach presented in this study to assess the severity of PE. The classification results reveal that the prediction model’s accuracy is 99.26% and its sensitivity is 98.57%. It is expected to become a new and accurate method to distinguish the severity of PE

Implementation and performances of the IPbus protocol for the JUNO Large-PMT readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. Thanks to the tight requirements on its optical and radio-purity properties, it will be able to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range from tens of keV to hundreds of MeV. A key requirement for the success of the experiment is an unprecedented 3% energy resolution, guaranteed by its large active mass (20 kton) and the use of more than 20,000 20-inch photo-multiplier tubes (PMTs) acquired by high-speed, high-resolution sampling electronics located very close to the PMTs. As the Front-End and Read-Out electronics is expected to continuously run underwater for 30 years, a reliable readout acquisition system capable of handling the timestamped data stream coming from the Large-PMTs and permitting to simultaneously monitor and operate remotely the inaccessible electronics had to be developed. In this contribution, the firmware and hardware implementation of the IPbus based readout protocol will be presented, together with the performances measured on final modules during the mass production of the electronics

Mass testing of the JUNO experiment 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose, large size, liquid scintillator experiment under construction in China. JUNO will perform leading measurements detecting neutrinos from different sources (reactor, terrestrial and astrophysical neutrinos) covering a wide energy range (from 200 keV to several GeV). This paper focuses on the design and development of a test protocol for the 20-inch PMT underwater readout electronics, performed in parallel to the mass production line. In a time period of about ten months, a total number of 6950 electronic boards were tested with an acceptance yield of 99.1%

Validation and integration tests of the JUNO 20-inch PMTs readout electronics

The Jiangmen Underground Neutrino Observatory (JUNO) is a large neutrino detector currently under construction in China. JUNO will be able to study the neutrino mass ordering and to perform leading measurements detecting terrestrial and astrophysical neutrinos in a wide energy range, spanning from 200 keV to several GeV. Given the ambitious physics goals of JUNO, the electronic system has to meet specific tight requirements, and a thorough characterization is required. The present paper describes the tests performed on the readout modules to measure their performances.Comment: 20 pages, 13 figure

Investigation of a novel TBC1D24 variation causing autosomal dominant non-syndromic hearing loss

Abstract Hearing loss is considered one of the most common sensory neurological defects, with approximately 60% of cases attributed to genetic factors. Human pathogenic variants in the TBC1D24 gene are associated with various clinical phenotypes, including dominant nonsyndromic hearing loss DFNA65, characterized by progressive hearing loss after the development of language. This study provides an in-depth analysis of the causative gene and mutations in a family with hereditary deafness. We recruited a three-generation family with autosomal dominant nonsyndromic hearing loss (ADNSHL) and conducted detailed medical histories and relevant examinations. Next-generation sequencing (NGS) was used to identify genetic variants in the proband, which were then validated using Sanger sequencing. Multiple computational software tools were employed to predict the impact of the variant on the function and structure of the TBC1D24 protein. A series of bioinformatics tools were applied to determine the conservation characteristics of the sequence, establish a three-dimensional structural model, and investigate changes in molecular dynamics. A detailed genotype and phenotype analysis were carried out. The family exhibited autosomal dominant, progressive, postlingual, and nonsyndromic sensorineural hearing loss. A novel heterozygous variant, c.1459C>T (p.His487Tyr), in the TBC1D24 gene was identified and confirmed to be associated with the hearing loss phenotype in this family. Conservation analysis revealed high conservation of the amino acid affected by this variant across different species. The mutant protein showed alterations in thermodynamic stability, elasticity, and conformational dynamics. Molecular dynamics simulations indicated changes in RMSD, RMSF, Rg, and SASA of the mutant structure. We computed the onset age of non-syndromic hearing loss associated with mutations in the TBC1D24 gene and identified variations in the hearing progression time and annual threshold deterioration across different frequencies. The identification of a new variant associated with rare autosomal dominant nonsyndromic hereditary hearing loss in this family broadens the range of mutations in the TBC1D24 gene. This variant has the potential to influence the interaction between the TLDc domain and TBC domain, thereby affecting the protein’s biological function

Oxymatrine Attenuates Aβ1-42-Induced Neurotoxicity in Primary Neuronal Cells and Memory Impairment in Rats

Aβ1-42-induced oxidative stress causes the death of neuronal cells, which is involved in the development of Alzheimer’s disease (AD). Oxymatrine (OMT) inhibits oxidative stress. In this study, we investigated the effect of OMT on Aβ1-42-induced neurotoxicity in vivo and in vitro. In the Morris water maze test, OMT significantly decreased the escape latency and increased the number of platform crossings. In vitro, OMT markedly increased cell viability and superoxide dismutase activity. Moreover, OMT decreased the lactate dehydrogenase leakage, malondialdehyde content, and reactive oxygen species in a dose-dependent manner. OMT upregulated the ratio of Bcl-2/Bax and downregulated the level of caspase-3. Furthermore, OMT inhibited the activation of MAPK (ERK 1/2, JNK) and NF-κB. In summary, OMT may be a potential compound for the treatment of AD.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

Synthesis and Promotion of the Osteoblast Proliferation Effect of Morroniside Derivatives

Sambucus williamsii Hance has been used in fractures for thousands of years, but research on its active components, such as morroniside, until now had not been carried out. In this study, morroniside was taken as the leading compound, and fourteen derivatives were synthesized. The promotion of osteoblast proliferation effect of the derivatives was evaluated on MC3T3-E1 cells. Five derivatives (2, 3, 4, 5, and 14) showed a good proliferation effect on MC3T3-E1 cells, and their promoted expression effects on OC (Osteocalcin) and ALP (Alkaline phosphatase) in MC3T3-E1 cells were measured. Compound 3 was shown to have the strongest proliferation effect (EC50 = 14.78 ± 1.17 μg/mL) and to significantly promote the expression of OC and ALP