Single-pixel three-dimensional imaging of the terahertz-wave by complex-field synthesis.

We propose a novel three-dimensional (3D) imaging technique by terahertz (THz) waves. Specifically, we modulate the THz wave using diffusers to produce three different speckle-like illumination patterns. The object is raster scanned by the three illumination patterns to generate three raw images via the single-pixel detection method. Subsequently, we synthesize a complex field using the three raw images. Finally, the retrieved image is calculated using the phase correlation of the complex point spread function. The proposed imaging system is simple and highly cost-effective. Therefore, it is a promising technique that can be adopted for industrial inspection and security screening.

Recording of a holographic cylindrical vector beam converter with a truncated cone prism.

This Letter proposes a holographic cylindrical vector beam converter (HCVBC) design that incorporates a continuously polarization-selective volume hologram circular-grating. A specially designed truncated cone prism is adopted for recording, which is conducted with a single incident, expanded, radially polarized beam. A prototype of this HCVBC was recorded and tested successfully. This design has the advantages of high diffraction efficiency, a narrow band, compactness, and planar configuration; thus, it is especially suitable for low-cost mass production and has high potential for application in related fields.

How Is the Lung Cancer Incidence Rate Associated with Environmental Risks? Machine-Learning-Based Modeling and Benchmarking.

The lung cancer threat has become a critical issue for public health. Research has been devoted to its clinical study but only a few studies have addressed the issue from a holistic perspective that included social, economic, and environmental dimensions. Therefore, in this study, risk factors or features, such as air pollution, tobacco use, socioeconomic status, employment status, marital status, and environment, were comprehensively considered when constructing a predictive model. These risk factors were analyzed and selected using stepwise regression and the variance inflation factor to eliminate the possibility of multicollinearity. To build efficient and informative prediction models of lung cancer incidence rates, several machine learning algorithms with cross-validation were adopted, namely, linear regression, support vector regression, random forest, K-nearest neighbor, and cubist model tree. A case study in Taiwan showed that the cubist model tree with feature selection was the best model with an RMSE of 3.310 and an R-squared of 0.960. Through these predictive models, we also found that apart from smoking, the average NO2 concentration, employment percentage, and number of factories were also important factors that had significant impacts on the incidence of lung cancer. In addition, the random forest model without feature selection and with feature selection could support the interpretation of the most contributing variables. The predictive model proposed in the present study can help to precisely analyze and estimate lung cancer incidence rates so that effective preventative measures can be developed. Furthermore, the risk factors involved in the predictive model can help with the future analysis of lung cancer incidence rates from a holistic perspective.

Analysis of dental amalgam fillings on primary Sjögren's syndrome: A population-based case-control study in Taiwan.

ABSTRACT: Primary Sjören's syndrome (pSS) is an autoimmune disease characterized by the inflammatory infiltrate and progressive dysfunction of salivary glands. Dental amalgam with mercury has been raised the public concerns regarding its purported mercury toxicity from dental amalgam to possible systemic inflammatory and immune reactions.In this study, a nationwide population-based database was employed to investigate the association of amalgam filling (AMF) and the risk of pSS. A retrospective case-control study was sourced from the Taiwanese National Health Insurance Research Database (NHIRD) from 2000 to 2013. Case and control groups were matched by sex, age, urbanization level, monthly income, and comorbidities using the propensity score method with a 1:1 ratio. In this study, 5848 cases and 5848 controls were included.The results demonstrated no statistically significant differences between AMF and pSS (odds ratio [OR]: 0.974, 95% confidence interval [CI] = 0.904-1.049). In addition, pSS was also not associated with AMF for women (OR: 0.743, 95% CI = 0.552-1.000) and men (OR: 1.006, 95% CI = 0.670-1.509), respectively.Taken together, evidence demonstrated that the association of AMF and pSS was inconsistent from this robust register databank.

Down-sampling slim camera using a micro-lens array.

The thickness of a camera is proportional to the image distance, although the lens can be replaced by a flat optics, such as a meta lens. However, there is no suitable method to reduce this thickness for low-cost applications. Here we proposed a novel down-sampling slim camera based on a micro-lens array (MLA) and an array sensor. By down-sampling the multiple micro images with a suitable array sensor, an enlarged image directly appears. Since the imaging module only consists of a low-resolution array sensor and an MLA, the thickness of the camera can be reduced to sub-millimeter. The proposed low-cost slim camera is suitable for imaging and sensing of internet-of-things (IoT) in particular. It also has a great application potential in the imaging of non-visible light.

Development of optical depth-sensing technology with a mechanical control lens and diffuser.

A novel, to the best of our knowledge, depth-sensing technology that enables a shallow depth of field was developed by adding a diffuser to the rear end of a mechanical control lens that can capture 2D images. The sensor in the optical depth-sensing system obtains the function curve between the motor step and the focus distance through calibration and imports the measured values into the control program's database. The optical depth-sensing system scans the visible range of an interval, and the Laplacian equation can be applied to confirm whether the interval was in focus by judging the sharpness of the contour of the objects captured in the interval and to define the outline of the objects. Then, the depth information can be obtained by calculating the focus distance based on the motor step during scanning. Finally, the focus images of individual objects are used to calculate the image contours in the depth direction. The focus images of each object are combined to reconstruct a 2.5D model within the sensing range. The optical depth-sensing system is not affected by sunlight or the material of the measured object. Furthermore, the system can be used to obtain color images by using a modified lens. The optical path is simple and does not require complex calculations. Therefore, the proposed system is not easily affected by the environment and exhibits high resolution and calculation speed.

Categorizing SHR and WKY rats by chi2 algorithm and decision tree.

Classifying mental disorder is a big issue in psychology in recent years. This article focuses on offering a relation between decision tree and encoding of fMRI that can simplify the analysis of different mental disorders and has a high ROC over 0.9. Here we encode fMRI information to the power-law distribution with integer elements by the graph theory in which the network is characterized by degrees that measure the number of effective links exceeding the threshold of Pearson correlation among voxels. When the degrees are ranked from low to high, the network equation can be fit by the power-law distribution. Here we use the mentally disordered SHR and WKY rats as samples and employ decision tree from chi2 algorithm to classify different states of mental disorder. This method not only provides the decision tree and encoding, but also enables the construction of a transformation matrix that is capable of connecting different metal disorders. Although the latter attempt is still in its fancy, it may have a contribution to unraveling the mystery of psychological processes.

[Impacts of electroacupuncture on neurological function and protein expressions of apoptosis-related Cyt-C and Caspase-9 in rats with traumatic brain injury].

OBJECTIVE: To observe the impacts of electroacupuncture (EA) on neurological function, the pathological morphology in brain tissue, apoptosis level and the protein expressions of apoptosis-related cytochrome C (Cyt-C) and cysteine aspartic acid protease-9 (Caspase-9) in the rats with traumatic brain injury (TBI) and explore the potential mechanism of EA in treatment of TBI. METHODS: A total of 70 clean-grade SD mice were randomized into a blank group (8 rats), a sham-operation group (8 rats), a model group (27 rats) and an EA group (27 rats). In terms of interventions of 3, 7 and 14 days, 3 subgroups were divided in the model group and the EA group successively, 9 rats in each subgroup. The modified Feeney free-fall percussion method was adopted to establish TBI models of rats. In the sham-operation group, only the skull was exposed and drilled and no free-fall percussion was exerted. One day after modeling, EA was given in the rats of EA group at "Shuigou" (GV 26), "Baihui" (GV 20) and "Neiguan" (PC 6) and "Zusanli" (ST 36) on the affected side, with intermittent wave, 2 Hz in frequency, once daily, 10 min each time, for 3, 7 and 14 days successively. Separately, on the day 3, 7 and 14 of intervention, the modified neurological severity scale (mNSS) was used to evaluate the degree of neurological function injury in the rats, HE staining and Nissl staining were to observe the pathological and morphological changes in brain tissue, TUNEL method was to observe the level of apoptosis in brain tissue and immunohistochemistry (IHC) method and Western blot were to determine the protein expressions of Cyt-C and Caspase-9 in brain tissue. RESULTS: Compared with the sham-operation group, on the day 3, 7 and 14 of intervention, mNSS scores were increased obviously in the rats of the model group respectively (P<0.01). Compared with the model group, on the day 3, 7 and 14 of intervention, mNSS scores were reduced in the rats of the EA group respectively (P<0.05). On day 3 of intervention, in brain injury region of the rats in the model group and the EA group, gross tissue necrosis, nuclear fragmentation, consolidation and obvious vacuolar changes, reduced Nissl bodies and scattered arrangement were found. On day 7 and 14 of intervention, in the model group and the EA group, the new connective tissue filling and normal cells were visible and Nissl bodies increased. The overall repair and Nissl body quantity in the EA group were better than the model group. Compared with the sham-operation group, on day 3, 7 and 14 of intervention, the numbers of apoptotic cells were increased obviously in the model group (P<0.01) and they were reduced in the EA group as compared with the model group (P<0.05). Compared with the sham-operation group, on day 3, 7 and 14 of intervention, the protein expressions of Cyt-C and Caspase-9 in damaged brain tissue were all increased obviously in the model group (P<0.01) and they were all reduced in the EA group as compared with the model group successively (P<0.05). CONCLUSION: Electroacupuncture remarkably improves the condition in the neurological function injury and reduces apoptosis degree in TBI model rats, which is likely related to the down-regulation of the protein expressions of Cyt-C and Caspase-9 in damaged brain tissue and further to bring the impacts on mitochondria mediated apoptosis process.

Dental Amalgam Fillings and Multiple Sclerosis: A Nationwide Population-Based Case-Control Study in Taiwan.

Multiple sclerosis (MS) is an inflammatory neurological disease characterized by autoimmune-mediated demyelination of the central nervous system. Genetic and environmental factors may contribute to the development of MS. This has not been confirmed yet. Dental amalgam has long been controversial in MS due to its mercury content but the toxicological implications of mercury-containing amalgam fillings (AMF) for MS remain to be elucidated. We conducted a case-control study to investigate the association between AMF and the risk of MS from the Taiwanese National Health Insurance Research Database (NHIRD). Case (n = 612) and control (n = 612) groups were matched by sex, age, urbanization level, monthly income, and Charlson comorbidity index by propensity score matched with a 1:1 ratio from 2000 to 2013. Differences between cases and controls was not statistically significant (OR: 0.82, 95% CI = 0.65-1.05). In subjects stratified by gender, MS was also not associated with AMF for women (OR: 0.743, 95% CI = 0.552-1.000) and men (OR: 1.006, 95% CI = 0.670-1.509), respectively. In summary, this Taiwanese nationwide population-based case-control study did not find an association between MS and AMF.

Association between Dental Amalgam Filling and Essential Tremor: A Nationwide Population-Based Case Control Study in Taiwan.

Essential tremor (ET) is a common neurological disorder and the most common movement disorder. Low-level occupational exposure to mercury vapor is known to be a crucial factor that increases the risk of tremor. Dental amalgam is one of the main sources of mercury in those who possess amalgam restorations. However, the relationship between ET and amalgam filling (AMF) is not quite clear. The purpose of this study was to investigate the association between AMF and the risk of ET using a population-based administrative databank. The data for this study were sourced from the Taiwanese National Health Insurance Research Database (NHIRD). A retrospective case-control study was conducted using this databank from 2000 to 2013. Case and control groups were matched by sex, age, urbanization level, monthly income, and Charlson comorbidity index using the propensity score method with a 1:1 ratio. In this study, 3008 cases and 3008 controls were included. The results from this nationwide population-based case-control study did not indicate any association between ET and AMF in Taiwan. Although the results were not significantly statistical, the findings may be worthy to be valued.

Survivability Prognosis for Lung Cancer Patients at Different Severity Stages by a Risk Factor-Based Bayesian Network Modeling.

Lung cancer is a major reason of mortalities. Estimating the survivability for this disease has become a key issue to families, hospitals, and countries. A conditional Gaussian Bayesian network model was presented in this study. This model considered 15 risk factors to predict the survivability of a lung cancer patient at 4 severity stages. We surveyed 1075 patients. The presented model is constructed by using the demographic, diagnosed-based, and prior-utilization variables. The proposed model for the survivability prognosis at different four stages performed R2 of 93.57%, 86.83%, 67.22%, and 52.94%, respectively. The model predicted the lung cancer survivability with high accuracy compared with the reported models. Our model also shows that it reached the ceiling of an ideal Bayesian network.

Effect of Comorbidity on Lung Cancer Diagnosis Timing and Mortality: A Nationwide Population-Based Cohort Study in Taiwan.

The effect of comorbidity on lung cancer patients' survival has been widely reported. The aim of this study was to investigate the effects of comorbidity on the establishment of the diagnosis of lung cancer and survival in lung cancer patients in Taiwan by using a nationwide population-based study design. This study collected various comorbidity patients and analyzed data regarding the lung cancer diagnosis and survival during a 16-year follow-up period (1995-2010). In total, 101,776 lung cancer patients were included, comprising 44,770 with and 57,006 without comorbidity. The Kaplan-Meier analyses were used to compare overall survival between lung cancer patients with and without comorbidity. In our cohort, chronic bronchitis patients who developed lung cancer had the lowest overall survival in one (45%), five (28.6%), and ten years (26.2%) since lung cancer diagnosis. Among lung cancer patients with nonpulmonary comorbidities, patients with hypertension had the lowest overall survival in one (47.9%), five (30.5%), and ten (28.2%) years since lung cancer diagnosis. In 2010, patients with and without comorbidity had 14.86 and 9.31 clinical visits, respectively. Lung cancer patients with preexisting comorbidity had higher frequency of physician visits. The presence of comorbid conditions was associated with early diagnosis of lung cancer.

Prism-hologram-prism sandwiched recording method for polarization-selective substrate-mode volume holograms with a large diffraction angle.

We propose a prism-hologram-prism sandwiched recording method for the fabrication of polarization-selective substrate-mode volume holograms with a large diffraction angle. In fabrication, the C-RT20 photopolymer is sandwiched between two 45°-90°-45° prisms and the interference fringes can be easily recorded in the recording material. The experimental results are in good agreement with the theoretical predictions. The proposed method features of a reflection-type recording setup for a transmission element and belongs to a technique of longer-wavelength construction for shorter-wavelength reconstruction. In addition, the method is much easier than the traditional recording method of two incident beam interference and has application potential in holographic photonics.

Decision Tree Approach to the Impact of Parents' Oral Health on Dental Caries Experience in Children: A Cross-Sectional Study.

Decision tree (DT) analysis was applied in this cross-sectional study to investigate caries experience in children by using clinical and microbiological data obtained from parent-child pairs. Thirty pairs of parents and children were recruited from periodontal and pediatric dental clinics. All participants were clinically examined for caries and periodontitis by a calibrated examiner. Cariogenic and periodontopathic bacteria examinations were conducted. The Kendall rank correlation coefficient was used to measure the association between data variables obtained through clinical and microbiological examinations. A classificatory inductive decision tree was generated using the C4.5 algorithm with the top-down approach. The C4.5 DT analysis was applied to classify major influential factors for children dental caries experience. The DT identified parents' periodontal health classification, decayed, missing, filled permanent teeth (DMFT) index, periodontopathic test (PerioCheck) result, and periodontal pocket depth as the classification factors for children caries experience. 13.3% of children were identified with a low decayed, missing, filled primary teeth (dmft) index (dmft < 3) whose parents had a periodontal pocket depth ≤3.7, PerioCheck score >1, DMFT index <13.5, and periodontal classification >2. The DT model for this study sample had an accuracy of 93.33%. Here, parental periodontal status and parents' DMFT were the factors forming the DT for children's caries experience.

A Prognosis Tool Based on Fuzzy Anthropometric and Questionnaire Data for Obstructive Sleep Apnea Severity.

Obstructive sleep apnea (OSA) are linked to the augmented risk of morbidity and mortality. Although polysomnography is considered a well-established method for diagnosing OSA, it suffers the weakness of time consuming and labor intensive, and requires doctors and attending personnel to conduct an overnight evaluation in sleep laboratories with dedicated systems. This study aims at proposing an efficient diagnosis approach for OSA on the basis of anthropometric and questionnaire data. The proposed approach integrates fuzzy set theory and decision tree to predict OSA patterns. A total of 3343 subjects who were referred for clinical suspicion of OSA (eventually 2869 confirmed with OSA and 474 otherwise) were collected, and then classified by the degree of severity. According to an assessment of experiment results on g-means, our proposed method outperforms other methods such as linear regression, decision tree, back propagation neural network, support vector machine, and learning vector quantization. The proposed method is highly viable and capable of detecting the severity of OSA. It can assist doctors in pre-diagnosis of OSA before running the formal PSG test, thereby enabling the more effective use of medical resources.

Diagnosis of Brain Metastases from Lung Cancer Using a Modified Electromagnetism like Mechanism Algorithm.

Brain metastases are commonly found in patients that are diagnosed with primary malignancy on their lung. Lung cancer patients with brain metastasis tend to have a poor survivability, which is less than 6 months in median. Therefore, an early and effective detection system for such disease is needed to help prolong the patients' survivability and improved their quality of life. A modified electromagnetism-like mechanism (EM) algorithm, MEM-SVM, is proposed by combining EM algorithm with support vector machine (SVM) as the classifier and opposite sign test (OST) as the local search technique. The proposed method is applied to 44 UCI and IDA datasets, and 5 cancers microarray datasets as preliminary experiment. In addition, this method is tested on 4 lung cancer microarray public dataset. Further, we tested our method on a nationwide dataset of brain metastasis from lung cancer (BMLC) in Taiwan. Since the nature of real medical dataset to be highly imbalanced, the synthetic minority over-sampling technique (SMOTE) is utilized to handle this problem. The proposed method is compared against another 8 popular benchmark classifiers and feature selection methods. The performance evaluation is based on the accuracy and Kappa index. For the 44 UCI and IDA datasets and 5 cancer microarray datasets, a non-parametric statistical test confirmed that MEM-SVM outperformed the other methods. For the 4 lung cancer public microarray datasets, MEM-SVM still achieved the highest mean value for accuracy and Kappa index. Due to the imbalanced property on the real case of BMLC dataset, all methods achieve good accuracy without significance difference among the methods. However, on the balanced BMLC dataset, MEM-SVM appears to be the best method with higher accuracy and Kappa index. We successfully developed MEM-SVM to predict the occurrence of brain metastasis from lung cancer with the combination of SMOTE technique to handle the class imbalance properties. The results confirmed that MEM-SVM has good diagnosis power and can be applied as an alternative diagnosis tool in with other medical tests for the early detection of brain metastasis from lung cancer.

A hybrid classifier combining Borderline-SMOTE with AIRS algorithm for estimating brain metastasis from lung cancer: a case study in Taiwan.

Classifying imbalanced data in medical informatics is challenging. Motivated by this issue, this study develops a classifier approach denoted as BSMAIRS. This approach combines borderline synthetic minority oversampling technique (BSM) and artificial immune recognition system (AIRS) as global optimization searcher with the nearest neighbor algorithm used as a local classifier. Eight electronic medical datasets collected from University of California, Irvine (UCI) machine learning repository were used to evaluate the effectiveness and to justify the performance of the proposed BSMAIRS. Comparisons with several well-known classifiers were conducted based on accuracy, sensitivity, specificity, and G-mean. Statistical results concluded that BSMAIRS can be used as an efficient method to handle imbalanced class problems. To further confirm its performance, BSMAIRS was applied to real imbalanced medical data of lung cancer metastasis to the brain that were collected from National Health Insurance Research Database, Taiwan. This application can function as a supplementary tool for doctors in the early diagnosis of brain metastasis from lung cancer.

An improved electromagnetism-like mechanism algorithm and its application to the prediction of diabetes mellitus.

Recently, the use of artificial intelligence based data mining techniques for massive medical data classification and diagnosis has gained its popularity, whereas the effectiveness and efficiency by feature selection is worthy to further investigate. In this paper, we presents a novel method for feature selection with the use of opposite sign test (OST) as a local search for the electromagnetism-like mechanism (EM) algorithm, denoted as improved electromagnetism-like mechanism (IEM) algorithm. Nearest neighbor algorithm is served as a classifier for the wrapper method. The proposed IEM algorithm is compared with nine popular feature selection and classification methods. Forty-six datasets from the UCI repository and eight gene expression microarray datasets are collected for comprehensive evaluation. Non-parametric statistical tests are conducted to justify the performance of the methods in terms of classification accuracy and Kappa index. The results confirm that the proposed IEM method is superior to the common state-of-art methods. Furthermore, we apply IEM to predict the occurrence of Type 2 diabetes mellitus (DM) after a gestational DM. Our research helps identify the risk factors for this disease; accordingly accurate diagnosis and prognosis can be achieved to reduce the morbidity and mortality rate caused by DM.

Use of oximetry as a screening tool for obstructive sleep apnea: a case study in Taiwan.

Obstructive sleep apnea (OSA) is a relatively common disease in the general population. Patients with OSA have a high risk of various comorbid medical diseases. Polysomnography (PSG) is the current gold standard for diagnosing OSA but is time consuming and expensive. This study aims to identify a sensitive screening parameter that can be used by clinicians to determine the time of referral for PSG examination in Taiwan. Eighty-seven patients, including 67 males and 20 females, were included in this study. We divided the patients into two groups: training data (n = 58) and testing group (n = 29). Pearson χ(2) test was used to perform bivariate analysis, and a decision tree was used to build a model. The decision model selected the frequency of desaturation > 4% per hour (DI4) as the indicator of OSA influence. The testing data accuracy of the C4.5 decision tree was 82.80%. External data were also used to validate the model reliability. The accuracy of the external data was 95.96%. Approximately one-third of patients with DI4 between 11 and 33 suffered from OSA. This population requires further diagnosis. Oximetry is an important and widely available screening method in Taiwan. This study proposes the need for PSG referral if DI4 is between 11 and 33.

Heterodyne moiré surface profilometry.

In this study, a novel moiré fringe analysis technique is proposed for measuring the surface profile of an object. After applying a relative displacement between two gratings at a constant velocity, every pixel of CMOS camera can capture a heterodyne moiré signal. The precise phase distribution of the moiré fringes can be extracted using a one-dimensional fast Fourier transform (FFT) analysis on every pixel, simultaneously filtering the harmonic noise of the moiré fringes. Finally, the surface profile of the tested objected can be generated by substituting the phase distribution into the relevant equation. The findings demonstrate the feasibility of this measuring method, and the measurement error was approximately 4.3 µm. The proposed method exhibits the merits of the Talbot effect, projection moiré method, FFT analysis, and heterodyne interferometry.