Impaired olfactory identification in dementia-free individuals is associated with the functional abnormality of the precuneus.

OBJECTIVE: Olfactory dysfunction indicates a higher risk of developing dementia. However, the potential structural and functional changes are still largely unknown. METHODS: A total of 236 participants were enrolled, including 45 Alzheimer's disease (AD) individuals and 191dementia-free individuals. Detailed study methods, comprising neuropsychological assessment and olfactory identification test (University of Pennsylvania smell identification test, UPSIT), as well as structural and functional magnetic resonance imaging (MRI) were applied in this research. The dementia-free individuals were divided into two sub-groups based on olfactory score: dementia-free with olfactory dysfunction (DF-OD) sub-group and dementia-free without olfactory dysfunction (DF-NOD) sub-group. The results were analyzed for subsequent intergroup comparisons and correlations. The cognitive assessment was conducted again three years later. RESULTS: (i) At dementia-free stage, there was a positive correlation between olfactory score and cognitive function. (ii) In dementia-free group, the volume of crucial brain structures involved in olfactory recognition and processing (such as amygdala, entorhinal cortex and basal forebrain volumes) are positively associated with olfactory score. (iii) Compared to the DF-NOD group, the DF-OD group showed a significant reduction in olfactory network (ON) function. (iv) Compared to DF-NOD group, there were significant functional connectivity (FC) decline between PCun_L(R)_4_1 in the precuneus of posterior default mode network (pDMN) and the salience network (SN) in DF-OD group, and the FC values decreased with falling olfactory scores. Moreover, in DF-OD group, the noteworthy reduction in FC were observed between PCun_L(R)_4_1 and amygdala, which was a crucial component of ON. (v) The AD conversion rate of DF-OD was 29.41%, while the DF-NOD group was 12.50%. The structural and functional changes in the precuneus were also observed in AD and were more severe. CONCLUSIONS: In addition to the olfactory circuit, the precuneus is a critical structure in the odor identification process, whose abnormal function underlies the olfactory identification impairment of dementia-free individuals.

2.5D transfer deep learning model for segmentation of contrast-enhancing lesions on brain magnetic resonance imaging of multiple sclerosis and neuromyelitis optica spectrum disorder.

Background: Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are the two mimic autoimmune diseases of the central nervous system, which are rare in East Asia. Quantitative detection of contrast-enhancing lesions (CELs) on contrast-enhancing T1-weighted magnetic resonance (MR) images is of great significance for assessing the disease activity of MS and NMOSD. However, it is challenging to develop automatic segmentation algorithms due to the lack of data. In this work, we present an automatic segmentation model of CELs based on Fully Convolutional with Attention DenseNet (FCA-DenseNet) and transfer learning strategy to address the challenge of CEL quantification in small-scale datasets. Methods: A transfer learning approach was employed in this study, whereby pretraining was conducted using 77 MS subjects from the open access datasets (MICCAI 2016, MICCAI 2017, ISBI 2015) for white matter hyperintensity segmentation, followed by fine-tuning using 24 MS and NMOSD subjects from the local dataset for CEL segmentation. The proposed FCA-DenseNet combined the Fully Convolutional DenseNet and Convolutional Block Attention Module in order to improve the learning capability. A 2.5D data slicing strategy was used to process complex 3D MR images. U-Net, ResUNet, TransUNet, and Attention-UNet are used as comparison models to FCA-DenseNet. Dice similarity coefficient (DSC), positive predictive value (PPV), true positive rate (TPR), and volume difference (VD) are used as evaluation metrics to evaluate the performances of different models. Results: FCA-DenseNet outperforms all other models in terms of all evaluation metrics, with a DSC of 0.661±0.187, PPV of 0.719±0.201, TPR of 0.680±0.254, and VD of 0.388±0.334. Transfer learning strategy has achieved success in building segmentation models on a small-scale local dataset where traditional deep learning approaches fail to train effectively. Conclusions: The improved FCA-DenseNet, combined with transfer learning strategy and 2.5D data slicing strategy, has successfully addressed the challenges in constructing deep learning models on small-scale datasets, making it conducive to clinical quantification of brain CELs and diagnosis of MS and NMOSD.

[Titin: structure, isoforms and functional regulation].

Titin, the largest known protein in the body expressed in three isoforms (N2A, N2BA and N2B), is essential for muscle structure, force generation, conduction and regulation. Since the 1950s, muscle contraction mechanisms have been explained by the sliding filament theory involving thin and thick muscle filaments, while the contribution of cytoskeleton in force generation and conduction was ignored. With the discovery of insoluble protein residues and large molecular weight proteins in muscle fibers, the third myofilament, titin, has been identified and attracted a lot of interests. The development of single molecule mechanics and gene sequencing technology further contributed to the extensive studies on the arrangement, structure, elastic properties and components of titin in sarcomere. Therefore, this paper reviews the structure, isforms classification, elastic function and regulatory factors of titin, to provide better understanding of titin.

Two case reports and a systematic review of the literature on adult cerebral cortical encephalitis with anti-myelin oligodendrocyte glycoprotein antibody.

Background and purpose: Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) has gained recognition in recent years as an immune-mediated inflammatory demyelinating disease of the central nervous system. The clinical features and prognosis of MOGAD adult cerebral cortical encephalitis (adult CCE) have not been fully elucidated. This study aims to further characterize the clinical symptoms, magnetic resonance imaging (MRI) findings, and prognosis of CCE with anti-MOG antibody. Methods: We present two adult cases of CCE with anti-MOG antibody and summarize the clinical symptoms, magnetic resonance imaging (MRI) findings, and prognosis of this phenotype as per a completed systematic review of the literature. Results: We found a total of 39 cases of MOGAD adult CCE (36% females; average age of onset of 29 years). Among them, 85% had seizure, 82% had headache, 64% had cortical symptoms, 64% had fever, 54% had changes of consciousness, and 38% had ocular symptoms. All cases demonstrated cerebral cortical T2 fluid-attenuated inversion recovery (FLAIR) lesions on MRI. Of the 25 patients (with seizure or not) who had EEG reports, 76% of patients showed abnormal EEG. Cerebrospinal fluid (CSF) white blood cell count of 90% of patients and CSF total protein of 67% of patients were elevated. In 16 patients with available CSF cytology data, 11 (69%) had abnormal cytology findings with monocytic predominance. In the 15 cases for which MOG antibody IgG was tested in both serum and CSF, 14 (93%) demonstrated a higher positive MOG IgG titer in serum than CSF. The majority of patients were treated with immunosuppressive therapy (97% corticosteroids, 15% mycophenolate mofetil, 13% IVIg, 5% azathioprine, and 5% other). The majority of patients had a favorable prognosis after treatment, as exemplified by improved clinical symptoms and imaging. Two patients relapsed. Conclusions: The clinical presentation and prognosis of adult CCE remain less understood in comparison to more common MOGAD phenotypes. It is important to consider MOGAD as an underlying etiology for adult CCE, as early detection and immunotherapy may improve outcomes.

Local Brain Network Alterations and Olfactory Impairment in Alzheimer's Disease: An fMRI and Graph-Based Study.

Alzheimer's disease (AD) is associated with the abnormal connection of functional networks. Olfactory impairment occurs in early AD; therefore, exploring alterations in olfactory-related regions is useful for early AD diagnosis. We combined the graph theory of local brain network topology with olfactory performance to analyze the differences in AD brain network characteristics. A total of 23 patients with AD and 18 normal controls were recruited for resting-state functional magnetic resonance imaging (fMRI), clinical neuropsychological examinations and the University of Pennsylvania Smell Identification Test (UPSIT). Between-group differences in the topological properties of the local network were compared. Pearson correlations were explored based on differential brain regions and olfactory performance. Statistical analysis revealed a correlation of the degree of cognitive impairment with olfactory recognition function. Local node topological properties were significantly altered in many local brain regions in the AD group. The nodal clustering coefficients of the bilateral temporal pole: middle temporal gyrus (TPOmid), degree centrality of the left insula (INS.L), degree centrality of the right middle temporal gyrus (MTG.R), and betweenness centrality of the left middle temporal gyrus (MTG.L) were related to olfactory performance. Alterations in local topological properties combined with the olfactory impairment can allow early identification of abnormal olfactory-related regions, facilitating early AD screening.

Dual Semi-Supervised Learning for Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Neuropsychological Data.

Deep learning has shown impressive diagnostic abilities in Alzheimer's disease (AD) research in recent years. However, although neuropsychological tests play a crucial role in screening AD and mild cognitive impairment (MCI), there is still a lack of deep learning algorithms only using such basic diagnostic methods. This paper proposes a novel semi-supervised method using neuropsychological test scores and scarce labeled data, which introduces difference regularization and consistency regularization with pseudo-labeling. A total of 188 AD, 402 MCI, and 229 normal controls (NC) were enrolled in the study from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We first chose the 15 features most associated with the diagnostic outcome by feature selection among the seven neuropsychological tests. Next, we proposed a dual semi-supervised learning (DSSL) framework that uses two encoders to learn two different feature vectors. The diagnosed 60 and 120 subjects were randomly selected as training labels for the model. The experimental results show that DSSL achieves the best accuracy and stability in classifying AD, MCI, and NC (85.47% accuracy for 60 labels and 88.40% accuracy for 120 labels) compared to other semi-supervised methods. DSSL is an excellent semi-supervised method to provide clinical insight for physicians to diagnose AD and MCI.

Clinical characteristics of late-onset neuromyelitis optica spectrum disorder.

BACKGROUND: Anti-aquaporin-4 (AQP-4) immunoglobulin G (IgG) is a major autoimmune antibody that contributes to the pathogenesis of neuromyelitis optica spectrum disorder (NMOSD). NMOSD often presents as disability, severe sensory impairment, and sleep disorders, which can cause anxiety and depression and further affect the quality of life. The age of onset is a key factor influencing the prognosis of NMOSD. However, this result was based on studies involving only anti-aquaporin-4 (AQP4) immunoglobulin G (IgG)-seropositive NMOSD patients or studies using the 2006 NMOSD diagnosis criteria. Therefore, further study of the age of onset of NMOSD is valuable. This study aimed to describe the clinical and magnetic resonance imaging (MRI) differences between early-onset neuromyelitis optica spectrum disorder (EO-NMOSD) and late-onset (LO)-NMOSD patients. METHODS: Fifty patients were enrolled, their anti-AQP4-IgG titers were measured, and brain and spinal cord MRIs were obtained. Additionally, several questionnaires related to disease severity, anxiety, depression, cognition, sleep, pain, and fatigue were collected. RESULTS: Higher AQP4-IgG seropositivity, higher AQP4-IgG titer, frequency of thoracic myelitis, and white matter hyperintensities (WMH), as well as greater severity of disability, greater severity of sleep disorders, higher anxiety, poorer cognitive function, and higher clinical dementia rating (CDR)-community affairs scores were observed in late-onset (LO)-NMOSD patients than those in early-onset (EO)-NMOSD. AQP4-IgG titer positively correlated with age, annual relapse rate, Expanded Disability Status Scale (EDSS) sensory scores, Activity of Daily Living Scale (ADL) scores, and Pittsburgh Sleep Quality Index (PSQI) scores. The EDSS-sensory scores positively correlated with age, relapse time, Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, PSQI, ADL, and CDR. WMH was positively correlated with age, EDSS-sensory scores, PSQI scores, and CDR scores and negatively correlated with the California Verbal Learning Test scores. CONCLUSION: LO-NMOSD patients have worse prognoses than those of EO-NMOSD patients. Higher AQP4-IgG titers, more WMHs, thoracic myelitis, and severe sensory symptoms are associated with cognition, depression, anxiety, and sleep disorders.

Ensemble learning via supervision augmentation for white matter hyperintensity segmentation.

Since the ambiguous boundary of the lesion and inter-observer variability, white matter hyperintensity segmentation annotations are inherently noisy and uncertain. On the other hand, the high capacity of deep neural networks (DNN) enables them to overfit labels with noise and uncertainty, which may lead to biased models with weak generalization ability. This challenge has been addressed by leveraging multiple annotations per image. However, multiple annotations are often not available in a real-world scenario. To mitigate the issue, this paper proposes a supervision augmentation method (SA) and combines it with ensemble learning (SA-EN) to improve the generalization ability of the model. SA can obtain diverse supervision information by estimating the uncertainty of annotation in a real-world scenario that per image have only one ambiguous annotation. Then different base learners in EN are trained with diverse supervision information. The experimental results on two white matter hyperintensity segmentation datasets demonstrate that SA-EN gets the optimal accuracy compared with other state-of-the-art ensemble methods. SA-EN is more effective on small datasets, which is more suitable for medical image segmentation with few annotations. A quantitative study is presented to show the effect of ensemble size and the effectiveness of the ensemble model. Furthermore, SA-EN can capture two types of uncertainty, aleatoric uncertainty modeled in SA and epistemic uncertainty modeled in EN.

Improving segmentation reliability of multi-scanner brain images using a generative adversarial network.

Background: Magnetic resonance (MR) images generated by different scanners generally have inconsistent contrast properties, making it difficult to perform a combined quantitative analysis of images from a range of scanners. In this study, we aimed to develop an automatic brain image segmentation model to provide a more reliable analysis of MR images taken with different scanners. Methods: The spatially localized atlas network tiles-27 (SLANT-27) deep learning model was used to train the automatic segmentation module, based on a multi-center dataset of 1,917 three-dimensional (3D) T1-weighted MR images. Subsequently, a framework called Qbrain, consisting of a new generative adversarial network (GAN) image transfer module and the SLANT-27 segmentation module, was developed. Another 3D T1-weighted MRI interscan dataset of 48 participants who were scanned in 3 MRI scanners (1.5T Siemens Avanto, 3T Siemens Trio Tim, and 3T Philips Ingenia) on the same day was used to train and test the Qbrain model. Volumetric T1-weighted images were processed with Qbrain, SLANT-27, and FreeSurfer (FS). The automatic segmentation reliability across the scanners was assessed using test-retest variability (TRV). Results: The reproducibility of different segmentation methods across scanners showed a consistent trend in the greater reliability and robustness of QBrain compared to SLANT-27 which, in turn, showed greater reliability and robustness compared to FS. Furthermore, when the GAN image transfer module was added, the mean segmentation error of the TRV of the 3T Siemens vs. 1.5T Siemens, the 3T Philips vs. 1.5T Siemens, and the 3T Siemens vs. 3T Philips scanners was reduced by 1.57%, 2.01%, and 0.56%, respectively. In addition, the segmentation model improved intra-scanner variability (0.9-1.67%) compared with that of FS (2.47-4.32%). Conclusions: The newly developed QBrain method combined with GAN image transfer module and a SLANT-27 segmentation module was shown to improve the reliability of whole-brain automatic structural segmentation results across multiple scanners, thus representing a suitable alternative quantitative method of comparative brain tissue analysis for individual patients.

Energy Transfer Assisted Fast X-ray Detection in Direct/Indirect Hybrid Perovskite Wafer.

Metal halide perovskite scintillators encounter unprecedented opportunities in indirect ionizing radiation detection due to their high quantum yields. However, the long scintillation lifetime of microseconds upon irradiation, known as the afterglow phenomenon, obviously limits their fast development. Here, a new type of hybrid X-ray detector wafer combining direct methylamine lead iodide (MAPbI3 ) semiconductor and indirect zero-dimensional cesium copper iodide (Cs3 Cu2 I5 ) scintillator through low-cost fast tableting processes is reported. Due to the fast energy transfer from Cs3 Cu2 I5 to MAPbI3 , the device response time to X-rays is dramatically reduced by nearly 30 times to 36.6 ns, which enables fast X-ray detection capability by a large area detector arrays within 1 s. Moreover, Cs3 Cu2 I5 exists at the grain boundaries of MAPbI3 crystals, and blocks the paths of mobile ions of perovskite, leading to the lowest detectable dose rate of hybrid X-ray detector is thus reduced by 1.5 times compared with control MAPbI3 direct-type semiconductor, and 10 times compared with the Cs3 Cu2 I5 indirect-type scintillator. The direct/indirect hybrid wafer also exhibits improved operation stability at ambient conditions without any encapsulation. This new kind of hybrid X-ray detectors provides strong competitiveness by combining the advantages of both direct perovskite semiconductors and indirect perovskite scintillators for next-generation products.

Effect of Tai Chi combined with Kinesio taping on posture control of football players with FAI: protocol for a randomized controlled trial.

BACKGROUND: Functional ankle instability (FAI) of college football players is an important risk factor affecting their training and competition. Physical therapy and appropriate sports intervention can improve the stability of FAI patients. Previous studies have shown that Tai Chi (TC) and Kinesio taping (KT) can improve the posture control ability of FAI patients. However, whether Tai Chi combined with Kinesio taping effect patch can be used as an effective exercise for rehabilitation of college football players with FAI is not yet proven. METHODS/DESIGN: Fifty-three FAI college football players were randomly assigned to 3 groups: TC+KT (n = 20); TC+KTp (placebo Kinesio taping, KTp, placebo) (n = 17), and KT (n = 16). The TC+KT group received TC and KT functional correction technical intervention, the TC+KTp group received TC and placebo KT technical intervention, and the KT group received KT functional correction technical intervention. Each of the three groups received 30 min each time, 3 times a week, for a total of 6 weeks of intervention training. Star Excursion Balance Test (SEBT) and UniPedal Stance Test (UST) at baseline (before), 4 weeks after intervention (middle), and 6 weeks after intervention (after) and Toe Touch Test (TTT) were evaluated. DISCUSSION: For the first time in this trial, the impact will be evaluated. If the results are the same as expected, they will provide evidence that Tai Chi combined with Kinesio taping sticking intervention can promote the posture control of college football players with FAI. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR1900027253 . Registered on 6 November 2019.

[Effects of alternate-day modified fasting combined exercise on fat reducing and the FNDC5/Irisin-UCP1 pathway].

OBJECTIVE: The effects of Alternate-day modified fasting combined exercise on fat mass, muscle mass, and serum Irisin, FNDC5 and UCP1 proteins were investigated in rats with 4 weeks of aerobic exercise and modified alternate-day fasting intervention. METHODS: Thirty-two healthy 8-week-old SPF male SD rats were randomly divided into control group, exercise group, alternate-day modified fasting and alternate-day modified fasting combined with exercise group, 8 rats in each group. The exercise group performed treadmill exercise with moderate exercise intensity(60 min/d,5 d/w), the alternate-day modified fasting group alternated between fasting and free feeding every other day, and fed 25% basal energy feed on fasting days, and the alternate-day modified fasting combined exercise group received two combined interventions. After 4 weeks of intervention, the body fat rate of rats was measured by apical blood sampling and abdominal aortic blood sampling, and the serum was preserved and centrifuged, and the wet weights of bilateral gastrocnemius, bilateral perirenal fat and brown fat at the scapula were weighed, and samples were collected for paraffin sectioning and HE staining, and the cell areas were counted; serum Irisin levels were measured by ELISA, and FNDC5 protein expression in gastrocnemius and UCP1 protein expression in adipose tissue were detected by Western blot. RESULTS: After 4 weeks of intervention, compared with the Con group, energy intake, body weight and body fat were decreased significantly in the Exer, ADMF and ADMF-Exer groups (P＜0.05), the wet weight/body weight and adipocyte area of white fat were reduced significantly (P＜0.01), and there was no significant difference in scapular fat wet weight/body weight (P＞0.05). Compared with the Con group, the gastrocnemius wet weight/body weight in the ADMF group was reduced significantly (P＜0.05), while that in the ADMF-Exer group was increased significantly (P＜0.05), the muscle cross-sectional areas in the Exer group and the ADMF-Exer group were increased (P＜0.05), and the content of gastrocnemius FNDC5 protein, serum Irisin level and expression of adipose UCP1 protein in the ADMF-Exer group were increased significantly (P＜0.05). After 4 weeks of intervention, energy intake was reduced significantly in both ADMF and ADMF-Exer groups (P＜0.01) and body weight of ADMF-Exer group was decreased (P＜0.05) compared with the Exer group. Compared with the Exer group, there were no significant differences in body fat content, white fat wet weight/body weight and scapular fat wet weight/body weight between ADMF group and ADMF-Exer group (P＞0.05), and adipocyte area in ADMF-Exer group was reduced significantly (P＜0.05). Compared with the Exer group, the gastrocnemius muscle wet weight/body weight was reduced significantly in the ADMF group (P＜0.05), and the expression of FNDC5 protein, serum Irisin and adipose UCP1 protein in the ADMF-Exer group were increased significantly compared with the Exer group (P＜0.05). CONCLUSION: Alternate-day modified fasting combined with exercise intervention can effectively control body weight and reduce body fat in rats, and the mechanism may be through the FNDC5/Irisin-UCP1 pathway to induce browning of white adipose tissue and increase thermogenesis of brown fat.

Oriented 2D Perovskite Wafers for Anisotropic X-ray Detection through a Fast Tableting Strategy.

2D perovskite single crystals have emerged as excellent optoelectronic materials owing to their unique anisotropic properties. However, growing large 2D perovskite single crystals remains challenging and time-consuming. Here, a new composition of lead-free 2D perovskite-4-fluorophenethylammonium bismuth iodide [(F-PEA)3 BiI6 ] is reported. An oriented bulk 2D wafer with a large area of 1.33 cm2 is obtained by tableting disordered 2D perovskite powders, resulting in anisotropic resistivities of 5 × 1010 and 2 × 1011  Ω cm in the lateral and vertical directions, respectively. Trivalent Bi3+ ions are employed to achieve a stronger ionic bonding energy with I- ions, which intrinsically suppress the ion-migration effect. Thus, the oriented wafer presents good capabilities in both charge collection and ion-migration suppression under a large applied bias along the out-of-plane direction, making it suitable for low-dosage X-ray detection. The large-area wafer shows a sensitive response to hard X-rays operated at a tube voltage of 120 kVp with the lowest detectable dose rate of 30 nGy s-1 . Thus, the fast tableting process is a facile and effective strategy to synthesize large-area, oriented 2D wafers, showing excellent X-ray detection performance and operational stability that are comparable to those of 2D perovskite single crystals.

SOX2 Regulates lncRNA CCAT1/MicroRNA-185-3p/FOXP3 Axis to Affect the Proliferation and Self-Renewal of Cervical Cancer Stem Cells.

It has been presented the role of long non-coding RNAs (lncRNAs) in cervical cancer (CC). We aim to discuss the effect of sex-determining region Y-box 2 (SOX2)/lncRNA colon cancer-associated transcript-1 (CCAT1)/microRNA-185-3p (miR-185-3p)/forkhead box protein 3 (FOXP3) on the proliferation and self-renewal ability of CC stem cells. MiR-185-3p, SOX2, CCAT1 and FOXP3 expressions were tested in CC tissues and cells. The relationship between SOX2/CCAT1 expression and clinicopathological features in CC patients was verified. Loss- and gain-of-function investigations were conducted in CD44+HeLa cells to discuss biological functions and self-renewal capacity. Finally, the relationships among SOX2, CCAT1, FOXP3 and miR-185-3p were verified. miR-185-3p expression was decreased, while SOX2, CCAT1 and FOXP3 expressions were increased in CC tissues and cells. SOX2 and CCAT1 expressions were linked to tumor size, lymph node metastasis and international federation of gynecology and obstetrics stage of CC. Down-regulating SOX2 or CCAT1 and up-regulating miR-185-3p resulted in inhibition of proliferation, invasion, migration and cell sphere number as well as apoptosis acceleration of CD44+HeLa cells. SOX2 could bind to CCAT1 which affected miR-185-3p expression, and FOXP3 was targeted by miR-185-3p.

Fine-control-valve of halide perovskite single crystal quality for high performance X-ray detection.

Halide perovskite single crystals (HPSCs) provide a unique platform to study the optoelectronic properties of such emerging semiconductor materials, while the temperature induced crystal growth method often has an increased solute integration speed and/or unavoidable solute consumption, resulting in a soaring or slumping crystal growth rate of HPSCs. Here, we developed a universal and facile solvent-volatilization-limited-growth (SVG) strategy to finely control the crystal growth rate by the fine-control-valve for high quality crystal grown through solution processes. The grown HPSCs by SVG method exhibited a record low trap density of 2.8 × 108 cm-3 and a high charge carrier mobility-lifetime product (µτ product) of 0.021 cm2/V, indicating the excellent crystal quality. The crystal surface defects were further passivated by oxygen suppliers as Lewis base, which led to a reduction of surface leakage current by two times when using for low dose rate X-ray detection. Such HPSC X-ray detector displayed a high sensitivity of 1274 µC/(Gyair cm2) with a lowest detectable dose rate of 0.56 µGyair/s under 120 keV hard X-ray. Further applications including alloy composition analysis and metal flaw detection by HPSC detectors were also demonstrated, which not only shows the bright future for product quality inspection and non-destructive materials analysis, but also paves the way for growing high quality single crystals and fabricating polycrystalline films.

Gene regulation by long non-coding RNAs and its biological functions.

Evidence accumulated over the past decade shows that long non-coding RNAs (lncRNAs) are widely expressed and have key roles in gene regulation. Recent studies have begun to unravel how the biogenesis of lncRNAs is distinct from that of mRNAs and is linked with their specific subcellular localizations and functions. Depending on their localization and their specific interactions with DNA, RNA and proteins, lncRNAs can modulate chromatin function, regulate the assembly and function of membraneless nuclear bodies, alter the stability and translation of cytoplasmic mRNAs and interfere with signalling pathways. Many of these functions ultimately affect gene expression in diverse biological and physiopathological contexts, such as in neuronal disorders, immune responses and cancer. Tissue-specific and condition-specific expression patterns suggest that lncRNAs are potential biomarkers and provide a rationale to target them clinically. In this Review, we discuss the mechanisms of lncRNA biogenesis, localization and functions in transcriptional, post-transcriptional and other modes of gene regulation, and their potential therapeutic applications.

Cancer genotypes prediction and associations analysis from imaging phenotypes: a survey on radiogenomics.

In this paper, we present a survey on the progress of radiogenomics research, which predicts cancer genotypes from imaging phenotypes and investigates the associations between them. First, we present an overview of the popular technology modalities for obtaining diagnostic medical images. Second, we summarize recently used methodologies for radiogenomics analysis, including statistical analysis, radiomics and deep learning. And then, we give a survey on the recent research based on several types of cancers. Finally, we discuss these studies and propose possible future research directions. In conclusion, we have identified strong correlations between cancer genotypes and imaging phenotypes. In addition, with the rapid growth of medical data, deep learning models show great application potential for radiogenomics.