Plant-based index linked to fall risk in older Chinese adults: cross-sectional evidence from a national cohort.

OBJECTIVES: Epidemiology showed that the falling incidences increased with advanced age, and recent findings found link between nutritional intake and risk of falls. Nevertheless, the relationship between different plant-based diets and the risk of falls in older adults remains unclear. Our investigation aimed to evaluate the correlation between various plant-based diet indices and the occurrence of falls. DESIGN: This study is a cross-sectional and post-hoc analysis from a national cohort study. SETTING AND PARTICIPANTS: We included individuals over 65 years from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) recruited in 2018 with information on falls and dietary assessments, finally 11,044 participants were eligible. MEASUREMENTS: Using food frequency questionnaire (FFQ), we calculated plant-based index scores categorized as unhealthy plant-based index (uPDI) and healthy plant-based index (hPDI). The primary outcome was falls obtained from questionnaire. Statistical analysis was performed utilizing logistic regression model to investigate the relationship between the plant-based diet indices and falls. We also used the subgroup analysis to investigate the interaction of falls and plant-based diet index (PDI) among different status and used the restricted cubic spline (RCS) curves to investigate the connection between the PDI scores and falls risk. RESULTS: Among 11,044 participants included in our study, a total of 2493 fall cases were observed. The logistic regression analysis revealed that the plant-based index related to falls. In the adjusted model, per 10-unit increment of hPDI has a significant decreased risk of falls (odd ratio [OR]: 0.85, 95% confidence interval [CI]: 0.79-0.91, P for trend < 0.001) and per 10-unit increment in uPDI increased the risk of falls (OR: 1.21, 95% CI: 1.13-1.30, P for trend < 0.001). We also revealed an interaction between smoking status and falls among the uPDI group (Pinteraction = 0.012). Finally, we found that with plant-based index scores increased, the odds of falls among hPDI decreased (P for overall < 0.001, P nonlinear = 0.0239), and the odds of falls among uPDI increased (P for overall < 0.001, P nonlinear = 0.0332). CONCLUSION AND IMPLICATIONS: We found significant association between the Plant-based diet index and the risk of falls, highlighting the key role of the consumption of nutritious plant-based foods on the risk of falls, which needed take into account in developing intervention and prevention strategies to decrease falls among older Chinese adults.

Multi-disease X-ray Image Classification of the Chest Based on Global and Local Fusion Adaptive Networks.

BACKGROUND: Chest X-ray image classification for multiple diseases is an important research direction in the field of computer vision and medical image processing. It aims to utilize advanced image processing techniques and deep learning algorithms to automatically analyze and identify X-ray images, determining whether specific pathologies or structural abnormalities exist in the images. OBJECTIVE: We present the MMPDenseNet network designed specifically for chest multi-label disease classification. METHODS: Initially, the network employs the adaptive activation function Meta-ACON to enhance feature representation. Subsequently, the network incorporates a multi-head self-attention mechanism, merging the conventional convolutional neural network with the Transformer, thereby bolstering the ability to extract both local and global features. Ultimately, the network integrates a pyramid squeeze attention module to capture spatial information and enrich the feature space. RESULTS: The concluding experiment yielded an average AUC of 0.898, marking an average accuracy improvement of 0.6% over the baseline model. When compared with the original network, the experimental results highlight that MMPDenseNet considerably elevates the classification accuracy of various chest diseases. CONCLUSION: It can be concluded that the network, thus, holds substantial value for clinical applications.

GMAlignNet: multi-scale lightweight brain tumor image segmentation with enhanced semantic information consistency.

Although the U-shaped architecture, represented by UNet, has become a major network model for brain tumor segmentation, the repeated convolution and sampling operations can easily lead to the loss of crucial information. Additionally, directly fusing features from different levels without distinction can easily result in feature misalignment, affecting segmentation accuracy. On the other hand, traditional convolutional blocks used for feature extraction cannot capture the abundant multi-scale information present in brain tumor images. This paper proposes a multi-scale feature-aligned segmentation model called GMAlignNet that fully utilizes Ghost convolution to solve these problems. Ghost hierarchical decoupled fusion unit and Ghost hierarchical decoupled unit are used instead of standard convolutions in the encoding and decoding paths. This transformation replaces the holistic learning of volume structures by traditional convolutional blocks with multi-level learning on a specific view, facilitating the acquisition of abundant multi-scale contextual information through low-cost operations. Furthermore, a feature alignment unit is proposed that can utilize semantic information flow to guide the recovery of upsampled features. It performs pixel-level semantic information correction on misaligned features due to feature fusion. The proposed method is also employed to optimize three classic networks, namely DMFNet, HDCNet, and 3D UNet, demonstrating its effectiveness in automatic brain tumor segmentation. The proposed network model was applied to the BraTS 2018 dataset, and the results indicate that the proposed GMAlignNet achieved Dice coefficients of 81.65%, 90.07%, and 85.16% for enhancing tumor, whole tumor, and tumor core segmentation, respectively. Moreover, with only 0.29 M parameters and 26.88G FLOPs, it demonstrates better potential in terms of computational efficiency and possesses the advantages of lightweight. Extensive experiments on the BraTS 2018, BraTS 2019, and BraTS 2020 datasets suggest that the proposed model exhibits better potential in handling edge details and contour recognition.

Determinants and Prognoses of Visual-Functional Mismatches After Mechanical Reperfusion in ST-Elevation Myocardial Infarction.

Background: Discordance between the anatomy and physiology of the coronary has important implications for managing patients with stable coronary disease, but its significance in ST-elevation myocardial infarction has not been fully elucidated. Methods: The retrospective study involved patients diagnosed with ST-elevation myocardial infarction (STEMI) who underwent percutaneous coronary intervention (PCI), along with quantitative coronary angiography (QCA) and quantitative flow ratio (QFR) assessments. Patients were stratified into four groups regarding the severity of the culprit vessel, both visually and functionally: concordantly negative (QCA-diameter stenosis [DS] ≤ 50% and QFR > 0.80), mismatch (QCA-DS > 50% and QFR > 0.80), reverse mismatch (QCA-DS ≤ 50% and QFR ≤ 0.80), and concordantly positive (QCA-DS > 50% and QFR ≤ 0.80). Multivariable logistic regression analyses were conducted to identify the clinical factors linked to visual-functional mismatches. Kaplan‒Meier analysis was conducted to estimate the 18-month adverse cardiovascular events (MACE)-free survival between the four groups. Results: The study involved 310 patients, with 68 presenting visual-functional mismatch, and 51 exhibiting reverse mismatch. The mismatch was associated with higher angiography-derived microcirculatory resistance (AMR) (adjusted odds ratio [aOR]=1.016, 95% CI: 1.010-1.022, P<0.001). Reverse mismatch was associated with larger area stenosis (aOR=1.044, 95% CI: 1.004-1.086, P=0.032), lower coronary flow velocity (aOR=0.690, 95% CI: 0.567-0.970, P<0.001) and lower AMR (aOR=0.947, 95% CI: 0.924-0.970, P<0.001). Additionally, the mismatch group showed the worst 18-month MACE-free survival among the four groups (Log rank test p = 0.013). Conclusion: AMR plays a significant role in the occurrence of visual-functional mismatches between QCA-DS and QFR, and the mismatch group showed the worst prognosis.

HCS-Net: Multi-level deformation strategy combined with quadruple attention for image registration.

BACKGROUND AND OBJECTIVE: Non-rigid image registration plays a significant role in computer-aided diagnosis and surgical navigation for brain diseases. Registration methods that utilize convolutional neural networks (CNNs) have shown excellent accuracy when applied to brain magnetic resonance images (MRI). However, CNNs have limitations in understanding long-range spatial relationships in images, which makes it challenging to incorporate contextual information. And in intricate image registration tasks, it is difficult to achieve a satisfactory dense prediction field, resulting in poor registration performance. METHODS: This paper proposes a multi-level deformable unsupervised registration model that combines Transformer and CNN to achieve non-rigid registration of brain MRI. Firstly, utilizing a dual encoder structure to establish the dependency relationship between the global features of two images and to merge features of varying scales, as well as to preserve the relative spatial position information of feature maps at different scales. Then the proposed multi-level deformation strategy utilizes different deformable fields of varying resolutions generated by the decoding structure to progressively deform the moving image. Ultimately, the proposed quadruple attention module is incorporated into the decoding structure to merge feature information from various directions and emphasize the spatial features in the dominant channels. RESULTS: The experimental results on multiple brain MR datasets demonstrate that the promising network could provide accurate registration and is comparable to state-of-the-art methods. CONCLUSION: The proposed registration model can generate superior deformable fields and achieve more precise registration effects, enhancing the auxiliary role of medical image registration in various fields and advancing the development of computer-aided diagnosis, surgical navigation, and related domains.

Quantitative monitoring ofloxacin in beef by TLC-SERS combined with machine learning analysis.

Ofloxacin is one kind of quinolone antibiotic drugs, the abuse of ofloxacin in livestock and aquaculture may bring bacterial resistance and healthy problem of people. The illegally feeding cattle with ofloxacin will help it keep health, but the sedimentation of ofloxacin could bring problem in food safety. The accurate, simple and instant monitoring ofloxacin from beef by portable sensor was of vital issue in food quality. A simple and reliable method was proposed for instant and quantitative detecting ofloxacin in beef, in which the thin-layer chromatography (TLC) -surface-enhanced Raman scattering (SERS) spectroscopy was in tandem with machine learning analysis base one principal component analysis-back propagation neural network (PCA-BPNN). The TLC plate was composed with diatomite, that was function as the stationary phase to separate ofloxacin from beef. The real beef juice was directly casted onto the diatomite plate for separating and detecting. The directly monitor ofloxacin from beef was achieved and the sensitivity down to 0.01 ppm. The PCA-BPNN was used as reliable model for quantitative predict the concentration of ofloxacin, that shown superior accuracy compared with the traditional model. The results verify that the diatomite plate TLC-SERS combined with machine-learning analysis is an effective, simple and accurate technique for detecting and quantifying antibiotic drug in meat stuff to improve the food safety.

Rapid, convenient, and ultrasensitive point-of-care sensing of histamine from fish: A Portable chromatographic platform based on derivatization reaction.

Food poisoning caused by histamine ingestion is one of the prevalent allergies associated with fish consumption in the world. Reliable detection of histamine from fish by a portable platform was of urgent importance to food safety. A portable technology for on-site monitoring of histamine in tuna was established through combined azo-derivatized thin-layer chromatography (TLC) with surface-enhanced Raman scattering (SERS) spectroscopy. The real tuna meat sample was directly applied onto the portable sensor for the separation of histamine and azo-derivatizing of histamine was reacted on the TLC plate. The colorless histamine was visualized by azo-derivatization after spraying Pauly reagent onto the diatomite TLC plate. The molecule information and concentration of the histamine was measured and calculated by SERS spectra. Diatomite TLC plate was capable of separating histamine with 1.32 × 10-7 M of Au colloid for the SERS enhancement. Accordingly, the limit of detection of histamine from mixture sample could achieve 2.8 × 10-4 ppm. These results indicated that the portable azo-derivatized TLC-SERS sensor not only visualizes the histamine but also improves the intensity of the Raman spectra. The azo-derivatized TLC-SERS sensor could be applied for rapid, convenient, and ultrasensitive point-of-care sensing of histamine in fish.

Enhancement of Local Crowd Location and Count: Multiscale Counting Guided by Head RGB-Mask.

Background: In crowded crowd images, traditional detection models often have the problems of inaccurate multiscale target count and low recall rate. Methods: In order to solve the above two problems, this paper proposes an MLP-CNN model, which combined with FPN feature pyramid can fuse the feature map of low-resolution and high-resolution semantic information with less computation and can effectively solve the problem of inaccurate head count of multiscale people. MLP-CNN "mid-term" fusion model can effectively fuse the features of RGB head image and RGB-Mask image. With the help of head RGB-Mask annotation and adaptive Gaussian kernel regression, the enhanced density map can be generated, which can effectively solve the problem of low recall of head detection. Results: MLP-CNN model was applied in ShanghaiTech and UCF_ CC_ 50 and UCF-QNRF. The test results show that the error of the method proposed in this paper has been significantly improved, and the recall rate can reach 79.91%. Conclusion: MLP-CNN model not only improves the accuracy of population counting in density map regression, but also improves the detection rate of multiscale population head targets.

Exploiting Unreduced Gametes for Improving Ornamental Plants.

The formation of gametes with somatic chromosome number or unreduced gametes (2n gametes) is an important process involved in the origin of polyploid plants in nature. Unreduced gametes are the result of meiotic mutations occurring during micro- and mega-sporogenesis. 2n gametes have been identified or artificially induced in a large number of plant species. Breeding of plants through 2n gametes can be advantageous because it combines genetic effects of polyploidy with meiotic recombination and sexual hybridization to produce tremendous genetic variation and heterosis. 2n gametes also occur in ornamental plants, but the potential of using 2n gametes in ornamental plant breeding has not been extensively exploited. Ornamental plants are primarily produced for their esthetic appearance and novelty, not for food and yield, and they can be readily propagated through vegetative means. Triploids, tetraploids, and plants with even higher ploidy levels produced through 2n gametes can be propagated through tissue culture to fix their phenotypes, thus leading to the development of new cultivars. In this review article, we intend to discuss the mechanisms underlying the formation of 2n gametes, techniques for 2n gamete identification, methods for enhancing 2n gamete formation, and the current status in the use of 2n gametes for development of novel ornamental plants. We believe that polyploidy breeding through 2n gametes represents a viable way of developing new cultivars, new species, and even new genera of ornamental plants.

Right arcuate fasciculus and left uncinate fasciculus abnormalities in young smoker.

Previous diffusion tensor imaging (DTI) studies had investigated the white matter (WM) integrity abnormalities in smokers. Exposure to nicotine disrupts neurodevelopment during adolescence, possibly by disrupting the trophic effects of acetylcholine. However, little is known about the diffusion parameters of specific fibre bundles at multiple locations in young smokers. Thirty-seven young smokers and 29 age-, education- and gender-matched healthy non-smokers participated in this study. Automated Fibre Quantification (AFQ) was employed to investigate the WM microstructure in young smokers by integrating multiple indices. Diffusion parameters, that is, fractional anisotropy (FA), axial diffusion (AD), radial diffusion (RD) and mean diffusion (MD), were calculated at 100 points along the length of 18 major brain tracts. The relationships between neuroimaging differences and smoking behaviours were explored, including Fagerström Test of Nicotine Dependence (FTND) and pack-years. Compared with non-smokers, young smokers showed significantly increased FA, AD and decreased RD in the left uncinate fasciculus (UF) and right thalamic radiation (TR), increased AD, RD and decreased FA in the right arcuate fasciculus (Arc). Correlation analyses revealed that FA values of the left UF and RD values of the right Arc were negatively correlated with FTND score in smokers and FA values of the right Arc were positively correlated with FTND scores. Positive correlation was observed between AD values of the left UF and pack-years in smokers. The findings enhanced our understanding of the potential effect of adolescent smoking on WM microstructure.

Abnormal functional connectivity of the salience network in insomnia.

The salience network plays an important role in detecting stimuli related to behavior and integrating neural processes. The aim of this study was to investigate changes in functional connectivity of the salience network in insomnia patients. Independent component analysis combined with a dual regression approach was used to examine functional connectivity differences in the salience network between patients with insomnia (n = 33) and healthy controls (n = 33). Pearson correlation analysis was used to analyze the relationship between differences in functional connectivity and the clinical characteristics of insomnia patients. Compared to healthy controls, insomnia patients showed increased functional connectivity in the dorsal anterior cingulate cortex within the salience network, as well as greater connectivity between the salience network and other brain regions including the dorsolateral prefrontal cortex, superior frontal gyrus, sensorimotor area and brain stem. The correlation analysis showed that increased functional connectivity between the salience network and left dorsolateral prefrontal cortex was positively correlated with Pittsburgh Sleep Quality Index score. Increased functional connectivity between salience network and several brain regions may be related to hyperarousal in insomnia patients. The connectivity between salience network and dorsolateral prefrontal cortex may potentially be used as a neuroimaging biomarker of sleep quality.

A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning.

Lung cancer has one of the highest mortalities of all cancers. According to the National Lung Screening Trial, patients who underwent low-dose computed tomography (CT) scanning once a year for 3 years showed a 20% decline in lung cancer mortality. To further improve the survival rate of lung cancer patients, computer-aided diagnosis (CAD) technology shows great potential. In this paper, we summarize existing CAD approaches applying deep learning to CT scan data for pre-processing, lung segmentation, false positive reduction, lung nodule detection, segmentation, classification and retrieval. Selected papers are drawn from academic journals and conferences up to November 2020. We discuss the development of deep learning, describe several important aspects of lung nodule CAD systems and assess the performance of the selected studies on various datasets, which include LIDC-IDRI, LUNA16, LIDC, DSB2017, NLST, TianChi, and ELCAP. Overall, in the detection studies reviewed, the sensitivity of these techniques is found to range from 61.61% to 98.10%, and the value of the FPs per scan is between 0.125 and 32. In the selected classification studies, the accuracy ranges from 75.01% to 97.58%. The precision of the selected retrieval studies is between 71.43% and 87.29%. Based on performance, deep learning based CAD technologies for detection and classification of pulmonary nodules achieve satisfactory results. However, there are still many challenges and limitations remaining including over-fitting, lack of interpretability and insufficient annotated data. This review helps researchers and radiologists to better understand CAD technology for pulmonary nodule detection, segmentation, classification and retrieval. We summarize the performance of current techniques, consider the challenges, and propose directions for future high-impact research.

Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography.

OBJECTIVE: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. METHODS: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. RESULTS: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. CONCLUSION: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

Incorporating patient-reported outcomes in dose-finding clinical trials.

Oncology dose-finding clinical trials determine the maximum tolerated dose (MTD) based on toxicity outcomes captured by clinicians. With the availability of more rigorous instruments for measuring toxicity directly from patients, there is a growing interest to incorporate patient-reported outcomes (PRO) in clinical trials to inform patient tolerability. This is particularly important for dose-finding trials to ensure the identification of a well-tolerated dose. In this paper, we propose three extensions of the continual reassessment method (CRM), termed PRO-CRMs, that incorporate both clinician and patient outcomes. The first method is a marginal modeling approach whereby clinician and patient toxicity outcomes are modeled separately. The other two methods impose a constraint using a joint outcome defined based on both clinician and patient toxicities and model them either jointly or marginally. Simulation studies show that while all three PRO-CRMs select well-tolerated doses based on clinician's and patient's perspectives, the methods using a joint outcome perform better and have similar performance. We also show that the proposed PRO-CRMs are consistent under robust model assumptions.

Automatic lung nodule detection using multi-scale dot nodule-enhancement filter and weighted support vector machines in chest computed tomography.

A novel CAD scheme for automated lung nodule detection is proposed to assist radiologists with the detection of lung cancer on CT scans. The proposed scheme is composed of four major steps: (1) lung volume segmentation, (2) nodule candidate extraction and grouping, (3) false positives reduction for the non-vessel tree group, and (4) classification for the vessel tree group. Lung segmentation is performed first. Then, 3D labeling technology is used to divide nodule candidates into two groups. For the non-vessel tree group, nodule candidates are classified as true nodules at the false positive reduction stage if the candidates survive the rule-based classifier and are not screened out by the dot filter. For the vessel tree group, nodule candidates are extracted using dot filter. Next, RSFS feature selection is used to select the most discriminating features for classification. Finally, WSVM with an undersampling approach is adopted to discriminate true nodules from vessel bifurcations in vessel tree group. The proposed method was evaluated on 154 thin-slice scans with 204 nodules in the LIDC database. The performance of the proposed CAD scheme yielded a high sensitivity (87.81%) while maintaining a low false rate (1.057 FPs/scan). The experimental results indicate the performance of our method may be better than the existing methods.

12-h abstinence-induced functional connectivity density changes and craving in young smokers: a resting-state study.

Studying the neural correlates of craving to smoke is of great importance to improve treatment outcomes in smoking addiction. According to previous studies, the critical roles of striatum and frontal brain regions had been revealed in addiction. However, few studies focused on the hub of brain regions in the 12 h abstinence induced craving in young smokers. Thirty-one young male smokers were enrolled in the present study. A within-subject experiment design was carried out to compare functional connectivity density between 12-h smoking abstinence and smoking satiety conditions during resting state in young adult smokers by using functional connectivity density mapping (FCDM). Then, the functional connectivity density changes during smoking abstinence versus satiety were further used to examine correlations with abstinence-induced changes in subjective craving. We found young adult smokers in abstinence state (vs satiety) had higher local functional connectivity density (lFCD) and global functional connectivity density (gFCD) in brain regions including striatal subregions (i.e., bilateral caudate and putamen), frontal regions (i.e., anterior cingulate cortex (ACC) and orbital frontal cortex (OFC)) and bilateral insula. We also found higher lFCD during smoking abstinence (vs satiety) in bilateral thalamus. Additionally, the lFCD changes of the left ACC, bilateral caudate and right OFC were positively correlated with the changes in craving induced by abstinence (i.e., abstinence minus satiety) in young adult smokers. The present findings improve the understanding of the effects of acute smoking abstinence on the hubs of brain gray matter in the abstinence-induces craving and may contribute new insights into the neural mechanism of abstinence-induced craving in young smokers in smoking addiction.

Yulangsan polysaccharide inhibits 4T1 breast cancer cell proliferation and induces apoptosis in vitro and in vivo.

Yulangsan polysaccharide (YLSPS) is derived from the root of Millettia pulchra (Benth.) Kurz var. Recent studies have postulated YLSPS as a regimen for cancer treatment. However, the underlying mechanism anti-breast cancer is still poorly unknown. The aim of this study was to examine the suppressive and apoptosis effect of YLSPS on the growth of breast cancer cell 4T1 and its possible underlying mechanism. In this study, breast cancer cell 4T1 viability and apoptosis were assessed by CCK-8 and flow cytometry, relative quantitative real-time PCR and western blot after treated with drug-serum of YLSPS. Furthermore, therapy experiments were conducted using a Balb/c mouse transplanted tumor model of breast cancer. The number of apoptotic cells and microvascular density (MVD) in the tumor tissues were assessed by TUNEL and CD34 immunostaining. Immunohistochemical assays and ELISA were used to detect the expression of VEGF, Bcl-2, Bax and Caspase-3 in the tissues. The in vitro studies showed that the drug-serum of YLSPS significantly inhibition of proliferation and effectively induced apoptosis of 4T1 cells. Oral administration of YLSPS in the breast cancer models significantly reduced the tumor volume and weight. The enhanced antitumor efficacy was associated with decreased angiogenesis, an enhanced antioxidant capacity, an increased induction of apoptosis and an inhibition of lung metastasis. These findings indicate that YLSPS significantly inhibited mouse breast cancer growth in vitro and in vivo. These data suggest that YLSPS may serve as a potential therapeutic agent for breast cancer.

Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs.

OBJECTIVE: A novel computer-aided detection (CAD) scheme for lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy is proposed to assist radiologists by providing a second opinion on accurate lung nodule detection, which is a crucial step in early diagnosis of lung cancer. METHOD: A 3D deep convolutional neural network (CNN) with multi-scale prediction was used to detect lung nodules after the lungs were segmented from chest CT scans, with a comprehensive method utilized. Compared with a 2D CNN, a 3D CNN can utilize richer spatial 3D contextual information and generate more discriminative features after being trained with 3D samples to fully represent lung nodules. Furthermore, a multi-scale lung nodule prediction strategy, including multi-scale cube prediction and cube clustering, is also proposed to detect extremely small nodules. RESULT: The proposed method was evaluated on 888 thin-slice scans with 1186 nodules in the LUNA16 database. All results were obtained via 10-fold cross-validation. Three options of the proposed scheme are provided for selection according to the actual needs. The sensitivity of the proposed scheme with the primary option reached 87.94% and 92.93% at one and four false positives per scan, respectively. Meanwhile, the competition performance metric (CPM) score is very satisfying (0.7967). CONCLUSION: The experimental results demonstrate the outstanding detection performance of the proposed nodule detection scheme. In addition, the proposed scheme can be extended to other medical image recognition fields.

Effects of Chinese Cooking Methods on the Content and Speciation of Selenium in Selenium Bio-Fortified Cereals and Soybeans.

Cereals and soybeans are the main food sources for the majority of Chinese. This study evaluated the effects of four common cooking methods including steaming, boiling, frying, and milking on selenium (Se) content and speciation in seven selenium bio-fortified cereals and soybeans samples. The Se concentrations in the selected samples ranged from 0.91 to 110.8 mg/kg and selenomethionine (SeMet) was detected to be the main Se species. Total Se loss was less than 8.1% during the processes of cooking except milking, while 49.1% of the total Se was lost in milking soybean for soy milk due to high level of Se in residuals. It was estimated that about 13.5, 24.0, 3.1, and 46.9% of SeMet were lost during the processes of steaming, boiling, frying, and milking, respectively. Meanwhile, selenocystine (SeCys2) and methylselenocysteine (SeMeCys) were lost completely from the boiled cereals. Hence, steaming and frying were recommended to cook Se-biofortified cereals in order to minimize the loss of Se.

Altered interhemispheric resting-state functional connectivity in young male smokers.

With the help of advanced neuroimaging approaches, previous studies revealed structural and functional brain changes in smokers compared with healthy non-smokers. Homotopic resting-state functional connectivity between the corresponding regions in cerebral hemispheres may help us to deduce the changes of functional coordination in the whole brain of young male smokers. Functional homotopy reflects an essential aspect of brain function and communication between the left and right cerebral hemispheres, which is important for the integrity of brain function. However, few studies used voxel mirrored homotopic connectivity (VMHC) method to investigate the changes of homotopic connectivity in young male smokers. Twenty-seven young male smokers and 27 matched healthy male non-smokers were recruited in our study. Compared with healthy male non-smokers, young male smokers showed decreased VMHC values in the insula and putamen, and increased VMHC values in the prefrontal cortex. Correlation analysis demonstrated that there were significant positive correlations between the average VMHC values of the prefrontal cortex and pack-years in young male smokers. In addition, significant negative correlation was found between the average VMHC values in the insula and pack-years. Our results revealed the disrupted homotopic resting-state functional connectivity in young male smokers. The novel findings may extend our understanding of smoking.