AR Spectral Analysis Technique for Human PPG,ECG and EEG Signals

In this study, Fast Fourier transform (FFT) and autoregressive (AR) methods were selected for processing the photoplethysmogram (PPG), electrocardiogram (ECG), electroencephalogram (EEG) signals recorded in order to examine the effects of pulsed electromagnetic field (PEMF) at extremely low frequency (ELF) upon the human electrophysiological signal behavior. The parameters in the autoregressive (AR) method were found by using the least squares method. The power spectra of the PPG, ECG, and EEG signals were obtained by using these spectral analysis techniques. These power spectra were then used to compare the applied methods in terms of their frequency resolution and the effects in extraction of the features representing the PPG, ECG, and EEG signals. Some conclusions were drawn concerning the efficiency of the FFT and least squares AR methods as feature extraction methods used for representing the signals under study.     

Least‐squares estimation of GARCH(1,1) models with heavy‐tailed errors

GARCH(1,1) models are widely used for modelling processes with time‐varying volatility. These include financial time series, which can be particularly heavy tailed. In this paper, we propose a novel log‐transform‐based least‐squares approach to the estimation of GARCH(1,1) models. Within this approach, the scale of the estimated volatility is dependent on an unknown tuning constant. By means of a backtesting exercise on both real and simulated data, we show that knowledge of the tuning constant is not crucial for Value at Risk prediction. However, this does not apply to many other applications where correct identification of the volatility scale is required. In order to overcome this difficulty, we propose two alternative two‐stage least‐squares estimators and we derive their asymptotic properties under very mild moment conditions for the errors. In particular, we establish the consistency and asymptotic normality at the standard convergence rate of for our estimators. Their finite sample properties are assessed by means of an extensive simulation study.     

Design of a multiple kernel learning algorithm for LS-SVM by convex programming

As a kernel based method, the performance of least squares support vector machine (LS-SVM) depends on the selection of the kernel as well as the regularization parameter (Duan, Keerthi, & Poo, 2003). Cross-validation is efficient in selecting a single kernel and the regularization parameter; however, it suffers from heavy computational cost and is not flexible to deal with multiple kernels. In this paper, we address the issue of multiple kernel learning for LS-SVM by formulating it as semidefinite programming (SDP). Furthermore, we show that the regularization parameter can be optimized in a unified framework with the kernel, which leads to an automatic process for model selection. Extensive experimental validations are performed and analyzed.     

A direct ROI quantification method for inherent PVE correction: accuracy assessment in striatal SPECT measurements

Purpose The clinical potential of striatal imaging with dopamine transporter (DAT) SPECT tracers is hampered by the limited capability to recover activity concentration ratios due to partial volume effects (PVE). We evaluated the accuracy of a least squares method that allows retrieval of activity in regions of interest directly from projections (LS-ROI). Methods An Alderson striatal phantom was filled with striatal to background ratios of 6:1, 9:1 and 28:1; the striatal and background ROIs were drawn on a coregistered X-ray CT of the phantom. The activity ratios of these ROIs were derived both with the LS-ROI method and with conventional SPECT EM reconstruction (EM-SPECT). Moreover, the two methods were compared in seven patients with motor symptoms who were examined with N-3-fluoropropyl-2-β-carboxymethoxy-3-β-(4-iodophenyl) (FP-CIT) SPECT, calculating the binding potential (BP). Results In the phantom study, the activity ratios obtained with EM-SPECT were 3.5, 5.3 and 17.0, respectively, whereas the LS-ROI method resulted in ratios of 6.2, 9.0 and 27.3, respectively. With the LS-ROI method, the BP in the seven patients was approximately 60% higher than with EM-SPECT; a linear correlation between the LS-ROI and the EM estimates was found (r = 0.98, p = 0.03). Conclusion The LS-ROI PVE correction capability is mainly due to the fact that the ill-conditioning of the LS-ROI approach is lower than that of the EM-SPECT one. The LS-ROI seems to be feasible and accurate in the examination of the dopaminergic system. This approach can be fruitful in monitoring of disease progression and in clinical trials of dopaminergic drugs.     

基于快速定点独立分量分析算法的母胎心电信号分离

研究快速定点独立分量分析方法在母胎心电信号分离中的应用。采用此算法,在胎儿心电信号与母体心电信号可以视为相互独立的信号源的前提下,对来源于同一孕妇的观测信号进行独立分量分离。快速定点独立分量算法可以有效地分离出单个独立分量,得到的胎儿心电信号(FECG)较理想。采用独立分量分析方法,实现母胎心电信号分离,是一种值得尝试的信号处理方法。     

Identification of environmentally biodegradable scaffolds for the benign design of quinolones and related substances

Many micropollutants, such as pharmaceuticals and other chemicals, are present globally in the aquatic environment. During effluent treatment and in the environment, transformations result in new chemicals of often unknown structure, fate, and toxicity. The design of chemicals, that can be fully mineralized or broken down into non-hazardous fragments, is considered a green chemistry approach avoiding such problems from the very beginning (“benign by design”). N-heterocycles are central lead scaffolds for many important chemicals and pharmaceuticals such as quinolines, isoquinolines, quinolones, fluoroquinolones, naphthyridones, and quinazolinones. Understanding their environmental biodegradability is mandatory for the design of greener derivatives. While the biodegradability of simple quinolines has already been reported in the literature, information on more complex azaarenes and other N-heterocycles is rather scarce.The goal of this study was to investigate the ready biodegradability of several N-heterocycles to identify biodegradable lead scaffolds. LC-HRMS studies were performed to identify possible metabolites. Out of the 84 tested substances, only 14 were readily biodegradable in either the closed bottle test (OECD 301D) or the manometric respiratory test (OECD 301F). Hydroxylation at the C2 position increased the biodegradation level of the quinolines generally and tolerated even fluorine in the molecule. Moreover, 4-oxo-1,4-dihydroquinoline-3-carboxylic acid has been tested as readily biodegradable. It is an important bioactive lead scaffold with many different applications, i.e., in antibiotics. All other quinolones containing the β-keto-carboxylic acid moiety were persistent, including their bioisosteres. The identified biodegradable scaffolds can be used to design new environmentally biodegradable molecules following green fragment-based design.     

Nomogram预测模型在针灸治疗卵巢功能不全妊娠结局中的价值

目的 探讨基于最小绝对收缩和选择算子（LASSO）-Cox回归构建的Nomogram预测模型在针灸治疗卵巢功能不全（POI）妊娠结局中的价值。方法 选取2019年1月至2021年1月在湖北省第三人民医院接受针灸治疗的POI病人137例，收集相关资料，采用LASSO回归与Cox回归相结合的方法筛选针灸对POI病人妊娠结局的主要影响因素，采用R软件（R3.6.3版）构建预测针灸对POI病人妊娠结局的Nomogram预测模型，采用受试者操作特征（ROC）曲线与校准曲线评估模型的预测效能。结果 妊娠组与未妊娠组在月经情况、基础卵泡刺激素（FSH）水平、FSH与黄体生成素（LH）比值、基础AFC、行针次数、留针时间、针具方面比较，差异有统计学意义（P<0.05）。构建多因素Cox回归模型结果显示，月经情况、基础FSH水平、FSH与LH比值、基础窦卵泡计数（AFC）、行针次数为针灸对POI病人妊娠结局的独立影响因素。ROC曲线显示，6个月妊娠概率和12个月妊娠概率曲线下面积分别为0.78[95%CI:(0.69,0.88)]和0.74[95%CI:(0.63,0.85)]，校准曲线显示该预测...     

Difference in acute and chronic stage ischemic stroke metabolic markers with controls

BackgroundAcute Ischemic Stroke (AIS), a major cause of disability, was previously associated with multiple metabolomic changes, but many findings were contradictory. Case-control and longitudinal study designs could have played a role in that. To clarify metabolomic changes, we performed a simultaneous comparison of ischemic stroke metabolome in acute, chronic stages of stroke and controls.MethodsThrough the nuclear magnetic resonance (NMR) platform, we evaluated 271 serum metabolites from a cohort of 297 AIS patients in acute and chronic stages and 159 controls. We used Sparse Partial Least Squares-Discriminant analysis (sPLS-DA) to evaluate group disparity; multivariate regression to compare metabolome in acute, chronic stages of stroke and controls; and mixed regression to compare metabolome acute and chronic stages of stroke. We applied false discovery rate (FDR) to our calculations.ResultsThe sPLS-DA revealed separation of the metabolome in acute, chronic stages of stroke and controls. Regression analysis identified 38 altered metabolites. Ketones, branched-chain amino acids (BCAAs), energy, and inflammatory compounds were mostly elevated, while alanine and glutamine were decreased in the acute stage. These metabolites declined/increased in the chronic stage, often to the same levels as in controls. Levels of fatty acids, phosphatidylcholines, phosphoglycerides, and sphingomyelins did not change between acute and chronic stages, but were different comparing to controls.ConclusionOur pilot study identified metabolites associated with acute stage of ischemic stroke and those that are altered in stroke patients comparing to controls regardless of stroke acuity. Future investigation in a larger independent cohort is needed to validate these findings.     

Diagnostic and monitoring applications using near infrared (NIR) spectroscopy in cancer and other diseases

Early cancer diagnosis plays a critical role in improving treatment outcomes and increasing survival rates for certain cancers. NIR spectroscopy offers a rapid and cost-effective approach to evaluate the optical properties of tissues at the microvessel level and provides valuable molecular insights. The integration of NIR spectroscopy with advanced data-driven algorithms in portable instruments has made it a cutting-edge technology for medical applications. NIR spectroscopy is a simple, non-invasive and affordable analytical tool that complements expensive imaging modalities such as functional magnetic resonance imaging, positron emission tomography and computed tomography. By examining tissue absorption, scattering, and concentrations of oxygen, water, and lipids, NIR spectroscopy can reveal inherent differences between tumor and normal tissue, often revealing specific patterns that help stratify disease. In addition, the ability of NIR spectroscopy to assess tumor blood flow, oxygenation, and oxygen metabolism provides a key paradigm for its application in cancer diagnosis. This review evaluates the effectiveness of NIR spectroscopy in the detection and characterization of disease, particularly in cancer, with or without the incorporation of chemometrics and machine learning algorithms. The report highlights the potential of NIR spectroscopy technology to significantly improve discrimination between benign and malignant tumors and accurately predict treatment outcomes. In addition, as more medical applications are studied in large patient cohorts, consistent advances in clinical implementation can be expected, making NIR spectroscopy a valuable adjunct technology for cancer therapy management. Ultimately, the integration of NIR spectroscopy into cancer diagnostics promises to improve prognosis by providing critical new insights into cancer patterns and physiology.