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Noninvasive detection of human blood components is the dream of human beings and the goal of clinical detection. From the perspective of mathematical analysis, based on the grey analysis system, the principle of spectral chemical quantitative analysis and the solution method of multivariate linear equation, this paper pioneers the spectrum elimination method, and obtains a complete, high-precision, synchronous and noninvasive detection system for a variety of human blood components. The spectral elimination method applies the principle of elimination method in mathematics to the noninvasive quantitative analysis of human blood components by spectral method, reduces the influence of non-target components on the detection of target components, and improves the accuracy of noninvasive quantitative analysis of human blood components. To demonstrate the effectiveness of the method, taking the analysis of the contents of seven blood components (hemoglobin, red blood cell count, neutrophils, lymphocytes, monocytes, eosinophils and basophils) in blood as an example, fourteen models were established by two different methods. From the comparison of modeling results, it can be concluded that when the seven models established by spectral elimination method predict the corresponding seven components of all samples, the predicted correlation coefficients are more than 0.9500. The experimental results show that the spectral elimination method and non-invasive detection system proposed can predict the content of human blood components with high accuracy. This paper studies a high-precision, simultaneous and noninvasive quantitative analysis system of multiple human blood components for the first time, which not only makes great progress in the non-invasive chemical quantitative analysis of human blood components by spectroscopy, but also has great application value for clinical medical treatment and disease diagnosis.

To enhance the detection precision of samples with scattering characteristics by near infrared spectroscopy (NIRS), this study developed a heteromorphic sample pool and established the related 2D light intensity acquisition system, which can simultaneously acquire multi-path exit light adsorption and scattering information of the samples under test. The Intralipid-20% phantom solutions in 34 samples with different concentrations were detected, while one-dimensional (1D) exit light intensity distributions and two-dimensional (2D) exit light intensity distributions on the surface of the samples were analyzed and modeled using partial least squares. In contrast with the prediction results based on the modeling method of 1D exit light intensity distribution, the modeling method of 2D exit light intensity distribution exhibits more favorable results; specifically, correlation coefficient enhanced by 2.48%, while root mean square error reduced by 6.89%. The experimental results demonstrate that using heteromorphic sample pool can effectively achieve NIRS-based detection precision and speed of chemical components in the solutions with scattering characteristics, which can provide important references for high-throughput and high-precision detection of turbid media in analytical chemistry.

For measuring the optical parameters of biological tissues accurately and rapidly, this study built an optical parameters measurement system based on near-infrared (NIR) Diffuse Photon Density Waves (DPDW) technology using frequency sweep modulation. The transmitted swept optical amplitude and phase can be nonlinearly fitted according to the physical diffusive approximation equation then to obtain the absorption coefficient and reduced scattering coefficient of the tissue phantoms, respectively. The experimental results of the 20 Intralipid solutions show that the average absolute errors of μ a and μ s ′ are 0.066 cm−1 and 0.716 cm−1, respectively, and the correlation coefficients between the measured and true values were 0.965 and 0.989, respectively. The experimental results of the 3 solid phantoms show that the average absolute errors of μ a and μ s ′ are 0.1140 cm−1 and 0.0023 cm−1, respectively. Therefore, the DPDW technology with frequency sweep modulation can detect the optical parameters of the tissue phantoms accurately and quickly, and it would be optimized for tissue tomography imaging and blood components noninvasively detection in vivo.

Mesial temporal sclerosis (MTS) is the commonest brain abnormalities in patients with intractable epilepsy. Its diagnosis is usually performed by neuroradiologists based on visual inspection of magnetic resonance imaging (MRI) scans, which is a subjective and time-consuming process with inter-observer variability. In order to expedite the identification of MTS, an automated computer-aided method based on brain MRI characteristics is proposed in this paper. It includes brain segmentation and hippocampus extraction followed by calculating features of both hippocampus and its surrounding cerebrospinal fluid. After that, support vector machines are applied to the generated features to identify patients with MTS from those without MTS. The proposed technique is developed and evaluated on a data set comprising 15 normal controls, 18 left and 18 right MTS patients. Experimental results show that subjects are correctly classified using the proposed classifiers with an accuracy of 0.94 for both left and right MTS detection. Overall, the proposed method could identify MTS in brain MR images and show a promising performance, thus showing its potential clinical utility.

In order to solve the problem of measurement blindness caused by the lack of detectability analysis in the near-infrared spectroscopy (NIRS), two important parameters including Equivalent Signal Noise Ratio (ESNR) and Overlapping Coefficient (OC) are proposed in this manuscript. According to the proportion of the component absorbance to the total absorbance and the overlap degree between near-infrared spectral curves of the components, the above parameters can achieve quantitative analysis of the concentration of components tested based on NIRS. The research combines the theoretical simulations and ethanol concentration experiments. The quantitative relationship between above two parameters and spectral analysis error is discussed by the partial least squares (PLS) modeling NIRS. The estimated RMSE of ethanol concentration obtained by theoretical analysis of this study was 0.30%, and the actual RMSE of near-infrared spectroscopy was 0.32%. The relative error is 6.67%, and the results are consistent. This study provided an effective and rapid prediction method for the quantitative analysis of NIRS, and optimized the theory of the detectability analysis of NIRS, which is a significant guidance for the quantitative analysis of the concentration measured by NIRS.

The temperature of extracellular fluid is a significant characteristic for electrophysiological experiments of patch clamp. It is important to understood the factors affecting the temperature measurement of an extracellular fluid, such as the effect of the concentration of different ions. In this study, the effect of different ions at different concentrations in a small amount of physiological solution on the measurement accuracy of a temperature model was investigated by near-infrared spectroscopy and chemometrics. The interval partial least squares method was used to select an effective wavelength range and develop calibration models using the spectra in the selected range and temperature values. The results indicate that Ca2+ has the greatest effect on the measurement accuracy of the temperature model of a cell physiological solution, followed by K+, and Na+ has the least effect. Moreover, the results indicate that the model accuracy decreased with the increase in ion concentration. These findings provide a basis for correcting the effects of different ion concentrations in a physiological solution for improving the accuracy of temperature measurements by near-infrared spectroscopy.

Detection of dural hematoma based on multi-channel near-infrared differential absorbance has the advantages of rapid and non-invasive detection. The location and number of detectors around the light source are critical for reducing the pathological characteristics of the prediction model on dural hematoma degree. Therefore, rational selection of detector numbers and their distances from the light source is very important. In this paper, a detector position screening method based on Variable Importance in the Projection (VIP) analysis is proposed. A preliminary modeling based on Partial Least Squares method (PLS) for the prediction of dural position μa was established using light absorbance information from 30 detectors located 2.0–5.0 cm from the light source with a 0.1 cm interval. The mean relative error (MRE) of the dural position μa prediction model was 4.08%. After VIP analysis, the number of detectors was reduced from 30 to 4 and the MRE of the dural position μa prediction was reduced from 4.08% to 2.06% after the reduction in detector numbers. The prediction model after VIP detector screening still showed good prediction of the epidural position μa. This study provided a new approach and important reference on the selection of detector location in near-infrared dural hematoma detection. Keywords: Detector location screening, Epidural hematoma detection, Variable importance in the projection

Key technologies in empty bottle inspection systems are studied in this paper to solve detecting error and poor adaptability problems. Those technologies have two different approaches: the ones in the first group locate and track bottle mouth, bottle bottom and walls while the other group technologies involve defect detecting. Such vision inspection systems are required to perform with high accuracy and adaptability under high speed and mechanical vibration working conditions. This study proposes distinctive algorithms for bottle locating, tracking and defect detecting based on inspection requirements and images of bottle mouth, bottom and walls. On the premise of satisfying the inspecting requirements, the simplicity, ease of implementation and universality of the algorithms are considered to improve the detection speed and the adaptability of the system to different kinds of bottles. Experiments showed that the system proposed in this paper can improve the detection accuracy and speed.

The detection of cerebral blood flow (CBF) has important clinical value for the diagnosis and treatment of ischemic cerebrovascular disease. The chronic changes of regional cerebral blood flow (rCBF) need long-term detection in specific cerebrovascular diseases such as Alzheimer's disease. In this paper, the optimal source-detector separation (SDS) in optical heterodyne detection combined with interferometric diffusing wave spectroscopy (OHD-iDWS) was studied. Changing the thickness of the skull and the absorption coefficient of the scalp layer, the Polarized Monte Carlo (PMC) method was used to analyze the influence of the average penetration depth and the number of polarized photons on SDS. In addition, the dynamic phantom experiments were used to evaluate the performance of the OHD-iDWS system. In this study, the system further explored the optimal SDS by distinguishing different flow rates, which provided an optimal method for SDS within 20 mm. Using PMC simulation and dynamic phantom experiments to find out the optimal SDS of rCBF sensing, it provides a theoretical basis for more effective detection of rCBF.

Objective A novel blood pressure of blood loss (BPBL) estimation method with multi-parameter fusion based on stacked auto-encoder neural networks (SAE) is proposed in this work that aims to realize non-invasive continuous monitoring of BPBL. Methods Our approach combined PTT, R-peak to R-peak interval (RRI), peak-foot values (PFV) and peak values (PV), extracted from electrocardiogram (ECG) and photoplethysmo- gram (PPG) for the estimation of BPBL. We used these parameters to establish a PPG-PTT and an RRI-PTT model, then employed the SAE method to get the calculation model between systolic blood pressure (SBP) diastolic blood pressure (DBP) and the characteristic parameters. Results The animal experimental results based on five pigs demonstrated that the RRI-PTT model estimated the BPBL more accuratly and less error compared with the PPG-PTT model (the correlation between estimated SBP & DBP and actual SBP & DBP were 0.9954 and 0.9963, and the root mean square error for SBP & DBP were 2.56 and 2.57 mmHg). Conclusion The PFV, PV, and RRI extracted in this work were correlated to BPBL, which can enhance the accuracy of BPBL estimation. In addition, the experimental results showed that the SAE method played a pivotal role in the non-invasive estimation of BPBL. Significance The estimation method proposed in this study can innovate and expand the research work of non-invasive BP and BPBL, and provide a feasible practice for the non-invasive prediction of BPBL in the future.

Photothermal therapy (PTT) has drawn tremendous attention because of its high therapeutic efficiency in targeting cells while minimizing the damage to normal tissues and organs. Tungsten oxide (W18O49, WO) plays a pivotal role in PTT development and its use in PTT systems has been extensively studied. However, it is difficult to control morphology of WO through conventional hydrothermal method. Which make its related researches have been limited up to now. In this study, we describe the construction and effects on tumor of a novel nanoplatform based on WO and indocyanine green (ICG) loaded in mesoporous silica nanoparticles (MSN) for dual-modal PTT and near-infrared imaging. (WO+ICG)@MSN could efficiently control WO shape without the need of surface modification due to its water-soluble of MSN. (WO+ICG)@MSN produced a PTT synergistic effect under irradiation of a single 808nm near-infrared (NIR) laser. Notably, an enhanced lethal effect of the 808nm laser triggering dual-modal therapy on B16 tumor cells was observed. The in vivo animal experiments showed that (WO+ICG)@MSN induced an effective solid tumor reduction under 808nm NIR light irradiation, revealing the potential of these nanocomposites as a NIR-mediated dual-modal therapeutic platform for cancer treatment.

Transcranial alternating current stimulation (tACS) is a type of noninvasive brain stimulation technique that has been shown to modulate motor, cognitive and memory function. Direct electrophysiological evidence of an interaction between tACS and the auditory cortex excitability has rarely been reported. Different stimulation parameters and areas of tACS may have different influence on the regulatory results. In this study, 11-Hz tACS was applied to the auditory cortex of 12 subjects with normal hearing in order to explore its effects on the auditory steady-state response (ASSR). The results indicate that tACS has an inhibitory effect on 40-Hz ASSR. In addition, EEG source analysis shows that 11-Hz tACS may enhance the activity of the middle temporal gyrus under both sham and real conditions, while the estimated source activity of the posterior cingulate gyrus may be reduced under real condition. The results reveal that tACS applied to the temporal lobe of humans will make the 40-Hz ASSR a tendency to decrease, and help improve the understanding of modulation of tACS-induced auditory cortex excitability changes in humans.

The measurement accuracy of turbid media is difficult to improve due to the co-existence of absorption and scattering effects. In this report, the diffuse disc of transmission surface was obtained by using the Monte Carlo simulation model based on wedge sample, and the radial distance of each diffuse disc was extracted to study the influence of different optical parameters on the diffuse disc properties. Subsequently, the partial least square regression (PLS) analysis model was established to analyze the relationship between multi-dimensional radial distance and the concentration of Intralipid-20%. Compared with multi-dimensional spectroscopy fusion (MDSF) and conventional two-dimensional light intensity method, multidimensional radial distance (MDRD) method increased the average correlation coefficient of prediction set by 3.5% and decreased the average mean square error by 28.6%. This study proposes the high-throughput and non-invasive method to analysis the turbid media using Monte Carlo simulation model based wedge sample.

In order to detect the components of a turbid medium, this paper proposes a glucose concentration reconstruction method based on a stacked auto-encoder (SAE) deep neural network. Analysis of different optical properties is performed using multiple diffused reflection spectroscopy and multiple source-detector separation (SDS). In this experiment, a 20% intralipid solution was used to prepare 4%, 5% and 10% intralipid solutions as the research object. At a certain glucose concentration, thirty source-detector separations of diffused reflection spectral signals within the 0.47–4.095 mm range (0.125 mm interval) from the incident position were detected, and a SAE deep neural network was used for modeling and predicting glucose concentration under multiple spectra. The root mean square error of prediction (RMSEP) of the SAE deep neural network using the 4% and 5% intralipid solutions decreased by approximately 26.42% compared with the results using only the 4% intralipid solutions by partial least squares regression (PLSR) method. Moreover, the RMSEP of the SAE deep neural network using the 4% and 5% intralipid solutions decreased by approximately 34.25% compared with the PLSR method using the 5% intralipid solution sample. These results show that the reconstruction accuracy of the SAE deep neural network is higher than the traditional PLSR method, which proves that the SAE neural network is highly suitable for prediction of turbid medium concentration.

Focal cortical dysplasia (FCD) is one of the commonest epileptogenic lesions, and is related to malformations of the cortical development. The findings on magnetic resonance (MR) images are important for the diagnosis and surgical planning of FCD. In this paper, an automated detection technique for FCD is proposed using MR images and deep learning. The input MR image is first preprocessed to correct the bias field, normalize intensities, align with a standard atlas, and strip the non-brain tissues. All cortical patches are then extracted on each axial slice, and these patches are classified into FCD and non-FCD using a deep convolutional neural network (CNN) with five convolutional layers, a max pooling layer, and two fully-connected layers. Finally, the false and missed classifications are corrected in the post-processing stage. The technique is evaluated using images of 10 patients with FCD and 20 controls. The proposed CNN shows a superior performance in classifying cortical image patches compared with multiple CNN architectures. For the system-level evaluation, nine of the ten FCD images are successfully detected, and 85% of the non-FCD images are correctly identified. Overall, this CNN based technique could learn optimal cortical (texture and symmetric) features automatically, and improve the FCD detection.

At present, when specific pathogen free(SPF) egg culture of viruses, a lot of manpower is needed to detect the fertility of chicken embryos, distinguish the three states of live, dead and weak, and then treat them separately. Moreover, due to personnel fatigue and other reasons, it is easy to cause misjudgment and omission, especially weak embryo is most prone to wrong judgment, resulting in resource waste. In this paper, a method based on PhotoPlethysmoGraphy(PPG) and convolutional neural network(CNN) is presented to determine the fertility of chicken embryo automatically, quickly and accurately, and the detection of weak embryo is proposed for the first time. In the experiment, 5000 samples are used as the training set and 1000 samples as the test set. After preprocessing, data are inputted into the independently designed convolutional neural network model to obtain classification results. The accuracy of chicken embryo classification reached 97.3 percent, including 100 percent live embryo, 98.33 percent dead embryo and 92.67 percent weak embryo. The experiment results show that the method of chicken embryo fertility detection based on PPG and convolutional neural network has high application value in the process of vaccine production.

In order to improve the accuracy of the detection of complex solution components in wedge-shaped sample pool, this paper obtained multi-dimensional spectroscopy (MDS) of multi-path non-linear features including samples by multi-position and multi-wavelength detection methods. And this research constructed a visualized light intensity distribution image by multi-dimensional spectroscopy fusion (MDSF) method. The detection of 46 samples of India Ink and Intralipd-20% mixed phantoms was carried out, and the visualized light intensity distribution image was analyzed and trained by partial least squares (PLS) method, and compared to training method that the raw data without the MDSF (Non-MDSF). In this study, the multi-dimensional spectral fusion modeling method is used to increase the correlation coefficient of the model by 1.29%, and the mean square error is reduced by 3.07%. The results show that the use of MDSF method to analyze the composition of complex solutions, the composition of the solution with scattering characteristics is fast and accurate, which is of great significance for the field of complex solution detection and analysis.

The accuracy of component detection of turbid media can be difficult to improve due to the mutual influence of scattering and absorption in light attenuation. In this study, a heteromorphic sample pool was introduced containing turbid media with India Ink and Intralipid-20% fat emulsion, which increases the scattering information of the non-circumferential symmetric hyperspectral image of the turbid media. A gray level co-occurrence matrix (GLCM) was used to extract textural features from the hyperspectral images. Subsequently, the textural features were correlated with the concentrations of Intralipid-20% by means of partial least squares regression, and it was compared with the frequently used analysis of two-dimensional exit light intensity. Experimental results show that textural feature modeling is superior to conventional light intensity modeling with a correlation coefficient of prediction (Rp) = 0.9831 and a root-mean-square error of prediction (RMSEP) = 0.0631% in the prediction set. This study provides a potentially viable method for detecting the components of turbid media quantitatively in analytical chemistry.

The human folate receptor (hFR) type gamma gene is driven by a TATA-less promoter that uses a canonical Sp1 element for basal transcription. Using nuclear extract from 293 (human embryonic) cells, we mapped a second (non-canonical) Sp1 element to which Sp1 bound with a comparable affinity and which overlaps a functional ets binding site (EBS). Mutagenesis experiments revealed that the binding of ets to the EBS activates the promoter synergistically with Sp1 bound at the downstream site; however, binding of Sp1 to the EBS does not contribute to promoter activity. A further increase in Sp1 by inducible expression in recombinant 293 cells resulted in a small but significant decrease in the hFR-gamma promoter activity, but the decrease was abolished when the EBS was deleted from the promoter. In 293 cells, which do not express hFR-gamma, the Sp1 level was relatively high whereas in the hFR-gamma-positive HL60 leukemia cells, the Sp1 level was low and the EBS predominantly bound an ets protein. To account for the above observations, we propose a model in which when the Sp1 level is low, ets out competes Sp1 for binding to the EBS and synergistically enhances the hFR-gamma promoter activity by interacting with Sp1 bound at the canonical site whereas at higher levels, Sp1 represses the promoter by competitively inhibiting the binding of ets. As a partial extension of this model to the regulation of other ets activated genes, we show that Sp1 can predictably bind to a variety of ets elements including those responsive to Ets1 and Spi.1/Pu.1. A dual concentration-dependent action of Sp1 as an activator or a repressor offers a potential mechanism contributing to tissue-specific regulation of ets-dependent genes by Sp1.