Transurethral En Bloc Resection Versus Standard Resection of Bladder Tumour: A Randomised, Multicentre, Phase 3 Trial.

BACKGROUND: Conventionally, standard resection (SR) is performed by resecting the bladder tumour in a piecemeal manner. En bloc resection of the bladder tumour (ERBT) has been proposed as an alternative technique in treating non-muscle-invasive bladder cancer (NMIBC). OBJECTIVE: To investigate whether ERBT could improve the 1-yr recurrence rate of NMIBC, as compared with SR. DESIGN, SETTING, AND PARTICIPANTS: A multicentre, randomised, phase 3 trial was conducted in Hong Kong. Adults with bladder tumour(s) of ≤3 cm were enrolled from April 2017 to December 2020, and followed up until 1 yr after surgery. INTERVENTION: Patients were randomly assigned to receive either ERBT or SR in a 1:1 ratio. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary outcome was 1-yr recurrence rate. A modified intention-to-treat analysis on patients with histologically confirmed NMIBC was performed. The main secondary outcomes included detrusor muscle sampling rate, operative time, hospital stay, 30-d complications, any residual or upstaging of disease upon second-look transurethral resection, and 1-yr progression rate. RESULTS AND LIMITATIONS: A total of 350 patients underwent randomisation, and 276 patients were histologically confirmed to have NMIBC. At 1 yr, 31 patients in the ERBT group and 46 in the SR group developed recurrence; the Kaplan-Meier estimate of 1-yr recurrence rates were 29% (95% confidence interval, 18-37) in the ERBT group and 38% (95% confidence interval, 28-46) in the SR group (p = 0.007). Upon a subgroup analysis, patients with 1-3 cm tumour, single tumour, Ta disease, or intermediate-risk NMIBC had a significant benefit from ERBT. None of the patients in the ERBT group and three patients in the SR group developed progression to muscle-invasive bladder cancer; the Kaplan-Meier estimates of 1-yr progression rates were 0% in the ERBT group and 2.6% (95% confidence interval, 0-5.5) in the SR group (p = 0.065). The median operative time was 28 min (interquartile range, 20-45) in the ERBT group and 22 min (interquartile range, 15-30) in the SR group (p < 0.001). All other secondary outcomes were similar in the two groups. CONCLUSIONS: In patients with NMIBC of ≤3 cm, ERBT resulted in a significant reduction in the 1-yr recurrence rate when compared with SR (funded by GRF/ECS, RGC, reference no.: 24116518; ClinicalTrials.gov number, NCT02993211). PATIENT SUMMARY: Conventionally, non-muscle-invasive bladder cancer is treated by resecting the bladder tumour in a piecemeal manner. In this study, we found that en bloc resection, that is, removal of the bladder tumour in one piece, could reduce the 1-yr recurrence rate of non-muscle-invasive bladder cancer.

MOSAIC enables in situ saturation mutagenesis of genes and CRISPR prime editing guide RNA optimization in human cells.

CRISPR prime editing offers unprecedented versatility and precision for the installation of genetic edits in situ . Here we describe the development and characterization of the Multiplexing Of Site-specific Alterations for In situ Characterization ( MOSAIC ) method, which leverages a non-viral PCR-based prime editing method to enable rapid installation of thousands of defined edits in pooled fashion. We show that MOSAIC can be applied to perform in situ saturation mutagenesis screens of: (1) the BCR-ABL1 fusion gene, successfully identifying known and potentially new imatinib drug-resistance variants; and (2) the IRF1 untranslated region (UTR), re-confirming non-coding regulatory elements involved in transcriptional initiation. Furthermore, we deployed MOSAIC to enable high-throughput, pooled screening of hundreds of systematically designed prime editing guide RNA ( pegRNA ) constructs for a large series of different genomic loci. This rapid screening of >18,000 pegRNA designs identified optimized pegRNAs for 89 different genomic target modifications and revealed the lack of simple predictive rules for pegRNA design, reinforcing the need for experimental optimization now greatly simplified and enabled by MOSAIC. We envision that MOSAIC will accelerate and facilitate the application of CRISPR prime editing for a wide range of high-throughput screens in human and other cell systems.

CRISPR PERSIST-On enables heritable and fine-tunable human gene activation.

Current technologies for upregulation of endogenous genes use targeted artificial transcriptional activators but stable gene activation requires persistent expression of these synthetic factors. Although general "hit-and-run" strategies exist for inducing long-term silencing of endogenous genes using targeted artificial transcriptional repressors, to our knowledge no equivalent approach for gene activation has been described to date. Here we show stable gene activation can be achieved by harnessing endogenous transcription factors ( EndoTF s) that are normally expressed in human cells. Specifically, EndoTFs can be recruited to activate endogenous human genes of interest by using CRISPR-based gene editing to introduce EndoTF DNA binding motifs into a target gene promoter. This Precision Editing of Regulatory Sequences to Induce Stable Transcription-On ( PERSIST-On ) approach results in stable long-term gene activation, which we show is durable for at least five months. Using a high-throughput CRISPR prime editing pooled screening method, we also show that the magnitude of gene activation can be finely tuned either by using binding sites for different EndoTF or by introducing specific mutations within such sites. Our results delineate a generalizable framework for using PERSIST-On to induce heritable and fine-tunable gene activation in a hit-and-run fashion, thereby enabling a wide range of research and therapeutic applications that require long-term upregulation of a target gene.

Deep Learning Methods for Calibrated Photometric Stereo and Beyond.

Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensity at each pixel. Photometric stereo prevails in superior per-pixel resolution and fine reconstruction details. However, it is a complicated problem because of the non-linear relationship caused by non-Lambertian surface reflectance. Recently, various deep learning methods have shown a powerful ability in the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo methods utilizing orthographic cameras and directional light sources. We first analyze these methods from different perspectives, including input processing, supervision, and network architecture. We summarize the performance of deep learning photometric stereo models on the most widely-used benchmark data set. This demonstrates the advanced performance of deep learning-based photometric stereo methods. Finally, we give suggestions and propose future research trends based on the limitations of existing models.

Landmark Localization from Medical Images with Generative Distribution Prior.

In medical image analysis, anatomical landmarks usually contain strong prior knowledge of their structural information. In this paper, we propose to promote medical landmark localization by modeling the underlying landmark distribution via normalizing flows. Specifically, we introduce the flow-based landmark distribution prior as a learnable objective function into a regression-based landmark localization framework. Moreover, we employ an integral operation to make the mapping from heatmaps to coordinates differentiable to further enhance heatmap-based localization with the learned distribution prior. Our proposed Normalizing Flow-based Distribution Prior (NFDP) employs a straightforward backbone and non-problem-tailored architecture (i.e., ResNet18), which delivers high-fidelity outputs across three X-ray-based landmark localization datasets. Remarkably, the proposed NFDP can do the job with minimal additional computational burden as the normalizing flows module is detached from the framework on inferencing. As compared to existing techniques, our proposed NFDP provides a superior balance between prediction accuracy and inference speed, making it a highly efficient and effective approach. The source code of this paper is available at https://github.com/jacksonhzx95/NFDP.

Active Compliance Smart Control Strategy of Hybrid Mechanism for Bonnet Polishing.

Compliance control strategies have been utilised for the ultraprecision polishing process for many years. Most researchers execute active compliance control strategies by employing impedance control law on a robot development platform. However, these methods are limited by the load capacity, positioning accuracy, and repeatability of polishing mechanisms. Moreover, a sophisticated actuator mounted at the end of the end-effector of robots is difficult to maintain in the polishing scenario. In contrast, a hybrid mechanism for polishing that possesses the advantages of serial and parallel mechanisms can mitigate the above problems, especially when an active compliance control strategy is employed. In this research, a high-frequency-impedance robust force control strategy is proposed. It outputs a position adjustment value directly according to a contact pressure adjustment value. An open architecture control system with customised software is developed to respond to external interrupts during the polishing procedure, implementing the active compliance control strategy on a hybrid mechanism. Through this method, the hybrid mechanism can adapt to the external environment with a given contact pressure automatically instead of relying on estimating the environment stiffness. Experimental results show that the proposed strategy adapts the unknown freeform surface without overshooting and improves the surface quality. The average surface roughness value decreases from 0.057 um to 0.027 um.

GR-PSN: Learning to Estimate Surface Normal and Reconstruct Photometric Stereo Images.

In this paper, we propose a novel method, namely GR-PSN, which learns surface normals from photometric stereo images and generates the photometric images under distant illumination from different lighting directions and surface materials. The framework is composed of two subnetworks, named GeometryNet and ReconstructNet, which are cascaded to perform shape reconstruction and image rendering in an end-to-end manner. ReconstructNet introduces additional supervision for surface-normal recovery, forming a closed-loop structure with GeometryNet. We also encode lighting and surface reflectance in ReconstructNet, to achieve arbitrary rendering. In training, we set up a parallel framework to simultaneously learn two arbitrary materials for an object, providing an additional transform loss. Therefore, our method is trained based on the supervision by three different loss functions, namely the surface-normal loss, reconstruction loss, and transform loss. We alternately input the predicted surface-normal map and the ground-truth into ReconstructNet, to achieve stable training for ReconstructNet. Experiments show that our method can accurately recover the surface normals of an object with an arbitrary number of inputs, and can re-render images of the object with arbitrary surface materials. Extensive experimental results show that our proposed method outperforms those methods based on a single surface recovery network and shows realistic rendering results on 100 different materials. Our code can be found in https://github.com/Kelvin-Ju/GR-PSN.

Characterising personal, household, and community PM2.5 exposure in one urban and two rural communities in China.

BACKGROUND: Cooking and heating in households contribute importantly to air pollution exposure worldwide. However, there is insufficient investigation of measured fine particulate matter (PM2.5) exposure levels, variability, seasonality, and inter-spatial dynamics associated with these behaviours. METHODS: We undertook parallel measurements of personal, household (kitchen and living room), and community PM2.5 in summer (May-September 2017) and winter (November 2017-Janauary 2018) in 477 participants from one urban and two rural communities in China. After stringent data cleaning, there were 67,326-80,980 person-hours (ntotal = 441; nsummer = 384; nwinter = 364; 307 had repeated PM2.5 data in both seasons) of processed data per microenvironment. Age- and sex-adjusted geometric means of PM2.5 were calculated by key participant characteristics, overall and by season. Spearman correlation coefficients between PM2.5 levels across different microenvironments were computed. FINDINGS: Overall, 26.4 % reported use of solid fuel for both cooking and heating. Solid fuel users had 92 % higher personal and kitchen 24-h average PM2.5 exposure than clean fuel users. Similarly, they also had a greater increase (83 % vs 26 %) in personal and household PM2.5 from summer to winter, whereas community levels of PM2.5 were 2-4 times higher in winter across different fuel categories. Compared with clean fuel users, solid fuel users had markedly higher weighted annual average PM2.5 exposure at personal (78.2 [95 % CI 71.6-85.3] µg/m3 vs 41.6 [37.3-46.5] µg/m3), kitchen (102.4 [90.4-116.0] µg/m3 vs 52.3 [44.8-61.2] µg/m3) and living room (62.1 [57.3-67.3] µg/m3 vs 41.0 [37.1-45.3] µg/m3) microenvironments. There was a remarkable diurnal variability in PM2.5 exposure among the participants, with 5-min moving average from 10 µg/m3 to 700-1200 µg/m3 across different microenvironments. Personal PM2.5 was moderately correlated with living room (Spearman r: 0.64-0.66) and kitchen (0.52-0.59) levels, but only weakly correlated with community levels, especially in summer (0.15-0.34) and among solid fuel users (0.11-0.31). CONCLUSION: Solid fuel use for cooking and heating was associated with substantially higher personal and household PM2.5 exposure than clean fuel users. Household PM2.5 appeared a better proxy of personal exposure than community PM2.5.

Distilling Privileged Knowledge for Anomalous Event Detection From Weakly Labeled Videos.

Weakly supervised video anomaly detection (WS-VAD) aims to identify the snippets involving anomalous events in long untrimmed videos, with solely video-level binary labels. A typical paradigm among the existing WS-VAD methods is to employ multiple modalities as inputs, e.g., RGB, optical flow, and audio, as they can provide sufficient discriminative clues that are robust to the diverse, complicated real-world scenes. However, such a pipeline has high reliance on the availability of multiple modalities and is computationally expensive and storage demanding in processing long sequences, which limits its use in some applications. To address this dilemma, we propose a privileged knowledge distillation (KD) framework dedicated to the WS-VAD task, which can maintain the benefits of exploiting additional modalities, while avoiding the need for using multimodal data in the inference phase. We argue that the performance of the privileged KD framework mainly depends on two factors: 1) the effectiveness of the multimodal teacher network and 2) the completeness of the useful information transfer. To obtain a reliable teacher network, we propose a cross-modal interactive learning strategy and an anomaly normal discrimination loss, which target learning task-specific cross-modal features and encourage the separability of anomalous and normal representations, respectively. Furthermore, we design both representation-and logits-level distillation loss functions, which force the unimodal student network to distill abundant privileged knowledge from the well-trained multimodal teacher network, in a snippet-to-video fashion. Extensive experimental results on three public benchmarks demonstrate that the proposed privileged KD framework can train a lightweight yet effective detector, for localizing anomaly events under the supervision of video-level annotations.

Long-term ambient air pollution exposure and cardio-respiratory disease in China: findings from a prospective cohort study.

BACKGROUND: Existing evidence on long-term ambient air pollution (AAP) exposure and risk of cardio-respiratory diseases in China is mainly on mortality, and based on area average concentrations from fixed-site monitors for individual exposures. Substantial uncertainty persists, therefore, about the shape and strength of the relationship when assessed using more personalised individual exposure data. We aimed to examine the relationships between AAP exposure and risk of cardio-respiratory diseases using predicted local levels of AAP. METHODS: A prospective study included 50,407 participants aged 30-79 years from Suzhou, China, with concentrations of nitrogen dioxide (NO2), sulphur dioxide (SO2), fine (PM2.5), and inhalable (PM10) particulate matter, ozone (O3) and carbon monoxide (CO) and incident cases of cardiovascular disease (CVD) (n = 2,563) and respiratory disease (n = 1,764) recorded during 2013-2015. Cox regression models with time-dependent covariates were used to estimate adjusted hazard ratios (HRs) for diseases associated with local-level concentrations of AAP exposure, estimated using Bayesian spatio-temporal modelling. RESULTS: The study period of 2013-2015 included a total of 135,199 person-years of follow-up for CVD. There was a positive association of AAP, particularly SO2 and O3, with risk of major cardiovascular and respiratory diseases. Each 10 µg/m3 increase in SO2 was associated with adjusted hazard ratios (HRs) of 1.07 (95% CI: 1.02, 1.12) for CVD, 1.25 (1.08, 1.44) for COPD and 1.12 (1.02, 1.23) for pneumonia. Similarly, each 10 µg/m3 increase in O3 was associated with adjusted HR of 1.02 (1.01, 1.03) for CVD, 1.03 (1.02, 1.05) for all stroke, and 1.04 (1.02, 1.06) for pneumonia. CONCLUSIONS: Among adults in urban China, long-term exposure to ambient air pollution is associated with a higher risk of cardio-respiratory disease.

Association between incense burning and prenatal depressive symptoms: evidence from the Born in Guangzhou Cohort Study.

This study aimed to examine the association of incense burning alone, a source of indoor air pollution, and jointly with passive smoking, with prenatal depressive symptoms. Information on incense exposure and depressive symptoms were collected at both early and late pregnancy using questionnaires in the Born in Guangzhou Cohort Study. Mixed-effects logistic regression models were used to assess the associations of incense exposure separately, and together with passive smoking, with prenatal depressive symptoms. Compared to the non-users, pregnant women with household incense burning had higher odds of depressive symptoms (odds ratio (OR), 1.17, 95% CI, 1.06, 1.28). Compared with non-users, women who occasionally (OR, 1.22, 95% CI, 1.09, 1.36) and frequently (1.51, 95% CI, 1.26, 1.80) smelled incense had higher odds of prenatal depressive symptoms. Higher duration of incense smelling was associated with higher odds of prenatal depressive symptoms compared with non-users. There was no strong evidence for an interaction of frequency of incense smelling and passive smoking in prenatal depressive symptoms. Prenatal exposure to incense burning was associated with higher odds of having depressive symptoms during pregnancy, and there is no evidence for interaction with concurrent exposure to passive smoking.

Timing of infant formula introduction in relation to BMI and overweight at ages 1 and 3 years: the Born in Guangzhou Cohort Study (BIGCS).

Mounting evidence suggests that the first few months of life are critical for the development of obesity. The relationships between the timing of solid food introduction and the risk of childhood obesity have been examined previously; however, evidence for the association of timing of infant formula introduction remains scarce. This study aimed to examine whether the timing of infant formula introduction is associated with growth z-scores and overweight at ages 1 and 3 years. This study included 5733 full-term (≥ 37 gestational weeks) and normal birth weight (≥ 2500 and < 4000 g) children in the Born in Guangzhou Cohort Study, a prospective cohort study with data collected at 6 weeks, 6, 12 and 36 months. Compared with infant formula introduction at 0-3 months, introduction at 4-6 months was associated with the lower BMI, weight-for-age and weight-for-length z-scores at 1 and 3 years old. Also, introduction at 4-6 months was associated with the lower odds of at-risk of overweight at age 1 (adjusted OR 0·72, 95 % CI 0·55, 0·94) and 3 years (adjusted OR 0·50, 95 % CI 0·30, 0·85). Introduction at 4-6 months also decreased the odds of overweight at age 1 year (adjusted OR 0·42, 95 % CI 0·21, 0·84) but not at age 3 years. Based on our findings, compared with introduction within the first 3 months, introduction at 4-6 months has a reduction on later high BMI risk and at-risk of overweight. However, these results need to be replicated in other well-designed studies before more firm recommendations can be made.

Awakening Undergraduate Nursing Students' Critical Awareness About Men's Health, Health Literacy and Nursing Practice.

BACKGROUND: This article reports an evaluative replication study, including a workshop inspired by Paulo Freire's critical pedagogy. Purpose: Assess how the nursing students' participation in critiquing Canadian empirical evidence on men's health literacy provokes new perceptions; explore students' intentions of incorporating the aforementioned contents into their professional practice; and test students' skills to formulate a hypothetical short action plan about men's health literacy. METHODS: A qualitative evaluation study inspired by the qualitative program evaluation approach. The setting was a university-based Canadian undergraduate nursing program located in a major metropolitan city. Seventeen undergraduate students (representing 3.65% of year 4 student population) composed the sample. The educational intervention was two workshops (6 h duration; February 2017) including a lecture about men's health literacy with video presentations, class discussions and group work using Freire's method of reflection and discussion for awareness awakening. Hypothetical health literacy promotion was the key outcome. All interactions were digitally audiorecorded, verbatim transcribed and submitted to thematic analysis having as themes: Perspectives of awareness and knowledge expansion, and New personal-professional assets. RESULTS: Students were able to relate new knowledge with their own experiences in the classroom or in the practicum. Application of new knowledge was related to students' social circles and reported familiar health matters. Cultural and community life shaped knowledge expansion and references to men's behaviors. CONCLUSIONS: Mobilization of personal knowledge awoke students' awareness about gaps in the nursing curriculum and the paucity of experiences in clinical placements relating to men's health literacy.

Backbone amides are determinants of Cl- selectivity in CLC ion channels.

Chloride homeostasis is regulated in all cellular compartments. CLC-type channels selectively transport Cl- across biological membranes. It is proposed that side-chains of pore-lining residues determine Cl- selectivity in CLC-type channels, but their spatial orientation and contributions to selectivity are not conserved. This suggests a possible role for mainchain amides in selectivity. We use nonsense suppression to insert α-hydroxy acids at pore-lining positions in two CLC-type channels, CLC-0 and bCLC-k, thus exchanging peptide-bond amides with ester-bond oxygens which are incapable of hydrogen-bonding. Backbone substitutions functionally degrade inter-anion discrimination in a site-specific manner. The presence of a pore-occupying glutamate side chain modulates these effects. Molecular dynamics simulations show backbone amides determine ion energetics within the bCLC-k pore and how insertion of an α-hydroxy acid alters selectivity. We propose that backbone-ion interactions are determinants of Cl- specificity in CLC channels in a mechanism reminiscent of that described for K+ channels.

Holistic-Guided Disentangled Learning With Cross-Video Semantics Mining for Concurrent First-Person and Third-Person Activity Recognition.

The popularity of wearable devices has increased the demands for the research on first-person activity recognition. However, most of the current first-person activity datasets are built based on the assumption that only the human-object interaction (HOI) activities, performed by the camera-wearer, are captured in the field of view. Since humans live in complicated scenarios, in addition to the first-person activities, it is likely that third-person activities performed by other people also appear. Analyzing and recognizing these two types of activities simultaneously occurring in a scene is important for the camera-wearer to understand the surrounding environments. To facilitate the research on concurrent first-and third-person activity recognition (CFT-AR), we first created a new activity dataset, namely PolyU concurrent first-and third-person (CFT) Daily, which exhibits distinct properties and challenges, compared with previous activity datasets. Since temporal asynchronism and appearance gap usually exist between the first-and third-person activities, it is crucial to learn robust representations from all the activity-related spatio-temporal positions. Thus, we explore both holistic scene-level and local instance-level (person-level) features to provide comprehensive and discriminative patterns for recognizing both first-and third-person activities. On the one hand, the holistic scene-level features are extracted by a 3-D convolutional neural network, which is trained to mine shared and sample-unique semantics between video pairs, via two well-designed attention-based modules and a self-knowledge distillation (SKD) strategy. On the other hand, we further leverage the extracted holistic features to guide the learning of instance-level features in a disentangled fashion, which aims to discover both spatially conspicuous patterns and temporally varied, yet critical, cues. Experimental results on the PolyU CFT Daily dataset validate that our method achieves the state-of-the-art performance.

A systematic review and meta-analysis to assess the relationship between sleep duration/quality, mental toughness and resilience amongst healthy individuals.

The majority of sleep research has focused on deleterious health outcomes, with little attention to positive sequels. A systematic review of the literature regarding sleep duration and/or sleep quality in relation to mental toughness and resilience amongst non-clinical, healthy populations was completed. Eight databases and selected sources for grey literature were searched from their inception to April 2021. A total of 1925 unique records (1898 from the database search and 27 from grey sources) were identified and screened against the pre-set inclusion and exclusion criteria. Of these, 68 studies were eligible and 63 were included in the meta-analysis. Pooled results indicated a weak, positive correlation between sleep duration and resilience (r = 0.11, p < 0.001), and sleep quality (r = 0.27, p < 0.001). The pooled correlation was slightly attenuated for prospective studies pertaining to sleep quality and resilience (r = 0.18, p < 0.001). We found evidence of high publication bias for studies that explored the relationship between sleep quality and resilience. Sleep and resilience are positively correlated but additional research is needed to verify the direct relationship through carefully designed, prospective studies that capture both subjective and objective sleep estimates. For a more comprehensive understanding, complementary reviews that explore the sleep-resilience association are needed for clinical populations, and those who have suffered extreme hardship.

MGF6mARice: prediction of DNA N6-methyladenine sites in rice by exploiting molecular graph feature and residual block.

DNA N6-methyladenine (6mA) is produced by the N6 position of the adenine being methylated, which occurs at the molecular level, and is involved in numerous vital biological processes in the rice genome. Given the shortcomings of biological experiments, researchers have developed many computational methods to predict 6mA sites and achieved good performance. However, the existing methods do not consider the occurrence mechanism of 6mA to extract features from the molecular structure. In this paper, a novel deep learning method is proposed by devising DNA molecular graph feature and residual block structure for 6mA sites prediction in rice, named MGF6mARice. Firstly, the DNA sequence is changed into a simplified molecular input line entry system (SMILES) format, which reflects chemical molecular structure. Secondly, for the molecular structure data, we construct the DNA molecular graph feature based on the principle of graph convolutional network. Then, the residual block is designed to extract higher level, distinguishable features from molecular graph features. Finally, the prediction module is used to obtain the result of whether it is a 6mA site. By means of 10-fold cross-validation, MGF6mARice outperforms the state-of-the-art approaches. Multiple experiments have shown that the molecular graph feature and residual block can promote the performance of MGF6mARice in 6mA prediction. To the best of our knowledge, it is the first time to derive a feature of DNA sequence by considering the chemical molecular structure. We hope that MGF6mARice will be helpful for researchers to analyze 6mA sites in rice.

Joint Spine Segmentation and Noise Removal From Ultrasound Volume Projection Images With Selective Feature Sharing.

Volume Projection Imaging from ultrasound data is a promising technique to visualize spine features and diagnose Adolescent Idiopathic Scoliosis. In this paper, we present a novel multi-task framework to reduce the scan noise in volume projection images and to segment different spine features simultaneously, which provides an appealing alternative for intelligent scoliosis assessment in clinical applications. Our proposed framework consists of two streams: i) A noise removal stream based on generative adversarial networks, which aims to achieve effective scan noise removal in a weakly-supervised manner, i.e., without paired noisy-clean samples for learning; ii) A spine segmentation stream, which aims to predict accurate bone masks. To establish the interaction between these two tasks, we propose a selective feature-sharing strategy to transfer only the beneficial features, while filtering out the useless or harmful information. We evaluate our proposed framework on both scan noise removal and spine segmentation tasks. The experimental results demonstrate that our proposed method achieves promising performance on both tasks, which provides an appealing approach to facilitating clinical diagnosis.

The prevalence of psychological consequences of COVID-19: A systematic review and meta-analysis of observational studies.

A systematic review and a meta-analysis were conducted to examine the overall prevalence of psychological health outcomes during COVID-19. Seven databases were systematically searched to include studies reporting on at least one psychological outcome. The pooled prevalence of primary psychological outcomes was 26% (95%CI: 21-32). Pooled prevalence for symptoms of PTSD was 33% (0-86), anxiety 28% (21-36), stress 27% (14-43), and depression 22% (13-33). The prevalence of psychological outcomes was similar in healthcare workers and in the general population (34% [24-44] and 33% [27-40] respectively). High prevalence figures support the importance of ensuring adequate provision of resources for mental health.

PIP2-dependent coupling of voltage sensor and pore domains in Kv7.2 channel.

Phosphatidylinositol-4,5-bisphosphate (PIP2) is a signaling lipid which regulates voltage-gated Kv7/KCNQ potassium channels. Altered PIP2 sensitivity of neuronal Kv7.2 channel is involved in KCNQ2 epileptic encephalopathy. However, the molecular action of PIP2 on Kv7.2 gating remains largely elusive. Here, we use molecular dynamics simulations and electrophysiology to characterize PIP2 binding sites in a human Kv7.2 channel. In the closed state, PIP2 localizes to the periphery of the voltage-sensing domain (VSD). In the open state, PIP2 binds to 4 distinct interfaces formed by the cytoplasmic ends of the VSD, the gate, intracellular helices A and B and their linkers. PIP2 binding induces bilayer-interacting conformation of helices A and B and the correlated motion of the VSD and the pore domain, whereas charge-neutralizing mutations block this coupling and reduce PIP2 sensitivity of Kv7.2 channels by disrupting PIP2 binding. These findings reveal the allosteric role of PIP2 in Kv7.2 channel activation.