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.

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.

Reduced Thalamus Volume May Reflect Nicotine Severity in Young Male Smokers.

Introduction: Nicotine acts as an agonist at presynaptic nicotinic acetylcholine receptors and to facilitate synaptic release of several neurotransmitters including dopamine and glutamate. The thalamus has the highest density of nicotinic acetylcholine receptors in the brain, which may make this area more vulnerable to the addictive effects of nicotine. However, the volume of thalamus abnormalities and the association with smoking behaviors in young smokers remains unknown. Methods: Thirty-six young male smokers and 36 age-, gender- and education-matched nonsmokers participated in the current study. The nicotine dependence severity and cumulative effect were assessed with the Fagerström test for nicotine dependence (FTND) and pack-years. We used subcortical volume analyses method in FreeSurfer to investigate the thalamus volume differences between young smokers and nonsmokers. Correlation analysis was used to investigate the relationship between thalamus volume and smoking behaviors (pack-years and FTND) in young smokers. Results and Conclusions: Relative to nonsmokers, the young smokers showed reduced volume of bilateral thalamus. In addition, the left thalamus volume was correlated with FTND in young smokers. It is hoped that our findings can shed new insights into the neurobiology of young smokers. Implications: In this article, we investigated the changes of thalamus volume in young male smokers compared with nonsmokers. Reduced left thalamus volume was correlated with FTND in young smokers, which may reflect nicotine severity in young male smokers.

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.

Abnormal functional integration across core brain networks in migraine without aura.

Background: As a complex subjective experience, pain processing may be related to functional integration among intrinsic connectivity networks of migraine patients without aura. However, few study focused on the pattern alterations in the intrinsic connectivity networks of migraine patients without aura. Results: Thirty-one migraine patients without aura and 31 age- and education-matched healthy controls participated in this study. After identifying the default mode network, central executive network and salience network as core intrinsic connectivity networks by using independent component analysis, functional connectivity, and effective connectivity during the resting state were used to investigate the abnormalities in intrinsic connectivity network interactions. Migraine patients without aura showed decreased functional connectivity among intrinsic connectivity networks compared with healthy controls. The strength of causal influences from the right frontoinsular cortex to the right anterior cingulate cortex became weaker, and the right frontoinsular cortex to the right medial prefrontal cortex became stronger in migraine patients without aura. Conclusions: These changes suggested that the salience network may play a major role in the pathophysiological features of migraine patients without aura and helped us to synthesize previous findings into an aberrant network dynamical framework.

The left dorsolateral prefrontal cortex and caudate pathway: New evidence for cue-induced craving of smokers.

Although the activation of the prefrontal cortex (PFC) and the striatum had been found in smoking cue induced craving task, whether and how the functional interactions and white matter integrity between these brain regions contribute to craving processing during smoking cue exposure remains unknown. Twenty-five young male smokers and 26 age- and gender-matched nonsmokers participated in the smoking cue-reactivity task. Craving related brain activation was extracted and psychophysiological interactions (PPI) analysis was used to specify the PFC-efferent pathways contributed to smoking cue-induced craving. Diffusion tensor imaging (DTI) and probabilistic tractography was used to explore whether the fiber connectivity strength facilitated functional coupling of the circuit with the smoking cue-induced craving. The PPI analysis revealed the negative functional coupling of the left dorsolateral prefrontal cortex (DLPFC) and the caudate during smoking cue induced craving task, which positively correlated with the craving score. Neither significant activation nor functional connectivity in smoking cue exposure task was detected in nonsmokers. DTI analyses revealed that fiber tract integrity negatively correlated with functional coupling in the DLPFC-caudate pathway and activation of the caudate induced by smoking cue in smokers. Moreover, the relationship between the fiber connectivity integrity of the left DLPFC-caudate and smoking cue induced caudate activation can be fully mediated by functional coupling strength of this circuit in smokers. The present study highlighted the left DLPFC-caudate pathway in smoking cue-induced craving in smokers, which may reflect top-down prefrontal modulation of striatal reward processing in smoking cue induced craving processing. Hum Brain Mapp 38:4644-4656, 2017. © 2017 Wiley Periodicals, Inc.

The implication of frontostriatal circuits in young smokers: A resting-state study.

The critical roles of frontostriatal circuits had been revealed in addiction. With regard to young smokers, the implication of frontostriatal circuits resting-state functional connectivity (RSFC) in smoking behaviors and cognitive control deficits remains unclear. In this study, the volume of striatum subsets, i.e., caudate, putamen, and nucleus accumbens, and corresponding RSFC differences were investigated between young smokers (n1 = 60) and nonsmokers (n2 = 60), which were then correlated with cigarette smoking measures, such as pack_years-cumulative effect of smoking, Fagerström Test for Nicotine Dependence (FTND)-severity of nicotine addiction, Questionnaire on Smoking Urges (QSU)-craving state, and Stroop task performances. Additionally, mediation analysis was carried out to test whether the frontostriatal RSFC mediates the relationship between striatum morphometry and cognitive control behaviors in young smokers when applicable. We revealed increased volume of right caudate and reduced RSFC between caudate and dorsolateral prefrontal cortex (DLPFC), orbitofrontal cortex in young smokers. Significant positive correlation between right caudate volume and QSU as well as negative correlation between anterior cingulate cortex-right caudate RSFC and FTND were detected in young smokers. More importantly, DLPFC-caudate RSFC strength mediated the relationship between caudate volume and incongruent errors during Stroop task in young smokers. Our results demonstrated that young smokers showed abnormal interactions within frontostriatal circuits, which were associated with smoking behaviors and cognitive control impairments. It is hoped that our study focusing on frontostriatal circuits could provide new insights into the neural correlates and potential novel therapeutic targets for treatment of young smokers. Hum Brain Mapp 37:2013-2026, 2016. © 2016 Wiley Periodicals, Inc.

White matter integrity in young smokers: a tract-based spatial statistics study.

Previous diffusion tensor imaging (DTI) studies revealed contradictory effects of smoking on fractional anisotropy (FA). Multiple DTI-derived indices may help to deduce the pathophysiological type of white matter (WM) changes and provide more specific biomarkers of WM neuropathology in the whole brain of young smokers. Twenty-three young smokers and 22 age-, education- and gender-matched healthy non-smoking controls participated in this study. Tract-based spatial statistics was employed to investigate the WM microstructure in young smokers by integrating multiple indices, including FA, mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD). Compared with healthy non-smoking controls, young smokers showed significantly increased FA with increased AD and decreased RD in several brain regions, while no difference in MD was observed. Specifically, the overlapped WM regions with increased FA, increased AD and decreased RD were found in the right posterior limb of the internal capsule, the right external capsule and the right superior corona radiata. Additionally, average FA and RD values in the WM regions mentioned earlier were significantly correlated with pack-years and Fagerström Test for Nicotine Dependence, while no correlation in AD was found. The WM tracts with increased FA may be more associated with RD, rather than AD in young smokers. We suggested that WM properties of several fibres in young smokers may be the biomarker as the cumulative effect and severity of nicotine dependence.

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.

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.

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.

[Research and implementation of the TLS network transport security technology based on DICOM standard].

With the development of medical information, Picture Archiving and Communications System (PACS), Hospital Information System/Radiology Information System(HIS/RIS) and other medical information management system become popular and developed, and interoperability between these systems becomes more frequent. So, these enclosed systems will be open and regionalized by means of network, and this is inevitable. If the trend becomes true, the security of information transmission may be the first problem to be solved. Based on the need for network security, we investigated the Digital Imaging and Communications in Medicine (DICOM) Standard and Transport Layer Security (TLS) Protocol, and implemented the TLS transmission of the DICOM medical information with OpenSSL toolkit and DCMTK toolkit.

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.

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.

[A study on decreasing the instrument detection limit of atomic fluorescence spectrometry (AFS-930) for Hg].

In the present study, the detection limit of atomic fluorescence spectrometry (AFS-930) was decreased to 2 ng x L(-1) (n=6) based on several optimizing modifications, including that the sub-high voltage of photomultiplier tube and the current of hollow-cathode lamp were elevated to 280 V and to 30 mA, respectively, and the height of atomization cell was set as 10 mm; In addition, the concentration of KBH4 was decreased to 0.5% (KOH 0.2%). With the optimized parameters, a good standard curve of Hg concentration versus intensity of fluorescence (If) could been obtained readily, after that, a 4-ng x L(-1)-Hg water samples was measured accurately with a little relative standard deviation (RSD) of <5%, while for approximately 2-ng x L(-1)-Hg waters the RSD varied within a wide range of 10.9%-27.2%, likely due to the absorption of Hg by polyethylene vessels used in this study and/or due to the contamination by analysis grade reagents used in this study. By using low-absorption polytetrafluoroethylene (PTFE) materials and the guaranteed reagents, the instrument detection limit was further decreased to 1 ng x L(-1) (n=10).