[Construction of an evaluation index system for the capability of comprehensive control of mountain - type zoonotic visceral leishmaniasis based on the One Health concept]

OBJECTIVE

To construct an evaluation index system for the capability of comprehensive control of mountain-type zoonotic visceral leishmaniasis based on the One Health concept, so as to provide insights into the control and elimination of mountain-type zoonotic visceral leishmaniasis using the One Health approach.

METHODS

A preliminary evaluation index system was constructed based on literature review, panel discussions and field surveys. Thirty-three experts were selected from 7 provincial disease control and prevention centers in Beijing Municipality, Hebei Province, Shanxi Province, Henan Province, Sichuan Province, Shaanxi Province and Gansu Province where mountain-type zoonotic visceral leishmaniasis was endemic, and two rounds of expert consultations were conducted to screen the indicators. The positive coefficient, degree of concentration, degree of coordination, and authority of the experts were calculated, and the normalized weights of each index were calculated with the analytic hierarchy process.

RESULTS

The response rates of questionnaires during two rounds of expert consultation were both 100.00% (33/33), and the authority coefficients of the experts were 0.86 and 0.88, respectively. The coefficients of coordination among experts on the rationality, importance, and operability of the indicators were 0.392, 0.437, 0.258, and 0.364, 0.335, 0.263, respectively (all P values < 0.05). Following screening, the final evaluation index system included 3 primary indicators, 17 secondary indicators, and 50 tertiary indicators. The normalized weights of primary indicators "external environment", "internal support" and "comprehensive control" were 16.98%, 38.73% and 44.29%, respectively. Among the secondary indicators of the primary indicator "external environment", the highest weight was seen for natural environment (66.67%), and among the secondary indicators of the primary indicator "internal support", the lowest weight was seen for the scientific research for visceral leishmaniasis control (8.26%), while other indicators had weights of 12.42% to 13.38%. Among the secondary indicators of the primary indicator "comprehensive control", the weight was 16.67% for each indicator.

CONCLUSIONS

An evaluation index system has been constructed for the capability of comprehensive control of mountain-type zoonotic visceral leishmaniasis based on the One Health concept. In addition to assessment of the effect of conventional mountain-type zoonotic visceral leishmaniasis control measures, this index system integrates the importance of top-level design, organizational management, and implementation of control measures, and includes indicators related to multi-sectoral cooperation.

Dietary supplementation of proteases on growth performance, nutrient digestibility, blood characteristics and gut microbiota of growing pigs fed sorghum-based diets

Low-tannin sorghum is an excellent energy source in pig diets. However, sorghum contains several anti-nutritional factors that may have negative effects on nutrient digestibility. The impacts of proteases on growth performance, nutrient digestibility, blood parameters, and gut microbiota of growing pigs fed sorghum-based diets were studied in this study. Ninety-six pigs (20.66 ± 0.65 kg BW) were allocated into three groups (eight pens/group, four pigs/pen): (1) CON (control diet, sorghum-based diet included 66.98% sorghum), (2) PRO1 (CON + 200 mg/kg proteases), (3) PRO2 (CON + 400 mg/kg proteases) for 28 d. No differences were observed in growth performance and apparent total tract digestibility (ATTD) of nutrients between CON and PRO1 groups. Pigs fed PRO2 diet had increased (P < 0.05) BW on d 21 and 28, and increased (P < 0.05) average daily gain during d 14-21 and the overall period compared with pigs fed CON diet. In addition, pigs fed PRO2 diet had improved (P < 0.05) ATTD of gross energy, CP, and DM compared with pigs fed CON and PRO1 diets. Pigs fed PRO2 diet had lower (P < 0.05) plasma globulin (GLB) level and higher (P < 0.05) plasma glucose, albumin (ALB) and immunoglobulin G levels, and ALB/GLB ratio than pigs fed CON and PRO1 diets. Furthermore, pigs fed PRO2 diet had decreased (P < 0.05) the relative abundance of Acidobacteriota at the phylum level and increased (P < 0.05) the relative abundance of Prevotella_9 at the genus level. The linear discriminant analysis effect size analysis also showed that pigs fed PRO2 diet had significantly enriched short-chain fatty acid-producing bacteria, such as Subdoligranulum and Parabacteroides. In conclusion, protease supplementation at 400 mg/kg improved the growth performance of growing pigs fed sorghum-based diets, which may be attributed to the improvement of nutrient digestibility, host metabolism, immune status and associated with the altered gut microbiota profiles.

Impact of COVID-19 infection experience on mental health status of intensive care unit patients' family members: a real-world study

PURPOSE

Family members of patients hospitalized in intensive care unit (ICU) are susceptible to adverse psychological outcomes. However, there is a paucity of studies specifically examining the mental health symptoms in ICU patients' family members with a prior history of coronavirus disease 2019 (COVID-19) infection.

AIM

This study aimed to investigate mental health status and its influencing factors of ICU patients' family members with previous COVID-19 infection experience in China.

DESIGN

Nationwide, cross-sectional cohort of consecutive participants of family members of ICU patients from 10 provinces randomly selected in mainland China conducted between October 2022 and May 2023.

METHODS

The basic information scale, Self-rating depression scale, Self-rating Anxiety Scale, Impact of Event Scale-Revised, Pittsburgh sleep quality index, Perceived Stress Scale, Connor-Davidson resilience scale, Simplified Coping Style Questionnaire were employed to explore mental health status among participants.

RESULTS

A total of 463 participants, comprising 156 individuals in Covid-19 family member cohort (infection group) and 307 individuals in control family member cohort (control group), met inclusion criteria. The infection group exhibited significantly higher incidence of composite mental health symptoms compared to control group (P = 0.017). Multivariable logistic regression analysis revealed that being female, engaging in physical/mental labor, residing in rural areas, and having children were identified as risk factors for the development of depression, anxiety, and post-traumatic stress disorder symptoms, while medical history of surgery was protective factor. A predictive model demonstrated a favorable discriminative ability and excellent calibration.

CONCLUSION

COVID-19 infection experience regarded as new traumatic stressors worsen mental health status of ICU patients' family members.

Optimization-Inspired Learning With Architecture Augmentations and Control Mechanisms for Low-Level Vision

In recent years, there has been a growing interest in combining learnable modules with numerical optimization to solve low-level vision tasks. However, most existing approaches focus on designing specialized schemes to generate image/feature propagation. There is a lack of unified consideration to construct propagative modules, provide theoretical analysis tools, and design effective learning mechanisms. To mitigate the above issues, this paper proposes a unified optimization-inspired learning framework to aggregate Generative, Discriminative, and Corrective (GDC for short) principles with strong generalization for diverse optimization models. Specifically, by introducing a general energy minimization model and formulating its descent direction from different viewpoints (i.e., in a generative manner, based on the discriminative metric and with optimality-based correction), we construct three propagative modules to effectively solve the optimization models with flexible combinations. We design two control mechanisms that provide the non-trivial theoretical guarantees for both fully- and partially-defined optimization formulations. Under the support of theoretical guarantees, we can introduce diverse architecture augmentation strategies such as normalization and search to ensure stable propagation with convergence and seamlessly integrate the suitable modules into the propagation respectively. Extensive experiments across varied low-level vision tasks validate the efficacy and adaptability of GDC.

Butyrate Inhibits Dendritic Cell Activation and Alleviates Periodontitis

Dendritic cells (DCs) can mediate inflammation-related bone resorption that is crucial in the development of periodontitis. Butyrate is a critical by-product of microbes with antibacterial and anti-inflammatory properties. Here, we found that butyrate inhibited the activation of lipopolysaccharide (LPS)-induced DCs and generation of inflammatory cytokines by DCs. Moreover, butyrate regulated glycolysis in LPS-induced DCs via the G-protein-coupled receptor/hypoxia-inducible factor-1α pathway. In addition, butyrate inhibited the maturation of CD11c+MHC-II+ DCs in vivo, suppressing local inflammatory infiltration and ultimately alleviating bone resorption in a periodontitis model. Our results imply that butyrate suppresses the activation of LPS-induced DCs by modulating their metabolism, highlighting its potential as a therapeutic agent for inflammatory diseases.

Impacts of pregnancy and menopause on COVID-19 severity: a systematic review and meta-analysis of 4.6 million women

BACKGROUND

Corona Virus Disease 2019 (COVID-19) pandemic is still a public health emergency of international concern. However, whether pregnancy and menopause impact the severity of COVID-19 remain unclear.

AIM

This study is performed to investigate the truth.

DESIGN

Study appraisal and synthesis follows PRISMA guideline. Meta-analysis is performed in random-effects model.

METHODS

PubMed, Embase, Cochrane database, Central, CINAHL, ClinicalTrials.gov, WHO COVID-19 database and WHO-ICTRP are searched until 28 March 2023.

RESULTS

In total, 57 studies (4 640 275 COVID-19 women) were analyzed. Pregnant women were at a lower risk of severe COVID-19, intensive care unit (ICU) admission and disease mortality compared to those nonpregnant women with comparable comorbidities. In contrast, pregnant women with more prepregnancy comorbidities were at a higher risk of severe COVID-19, ICU admission and invasive mechanical ventilation (IMV). In addition, pregnant women with pregnancy complications had a significantly increased risk of severe COVID-19 and ICU admission. Menopause increased COVID-19 severity, IMV requirement and disease mortality. Hormone replacement therapy inhibited COVID-19 severity in postmenopausal women. Premenopausal and postmenopausal women had a lower chance of severe illness than age-matched men. The impact of pregnancy on COVID-19 severity was significant in Americans and Caucasians, whereas the effect of menopause on COVID-19 severity was only significant in Chinese.

CONCLUSIONS

Pregnancy and menopause are protective and risk factors for severe COVID-19, respectively. The protective role of pregnancy on COVID-19 is minimal and could be counteracted or masked by prepregnancy or pregnancy comorbidities. The administration of estrogen and progesterone may prevent severe COVID-19.

Automated Learning for Deformable Medical Image Registration by Jointly Optimizing Network Architectures and Objective Functions

Deformable image registration plays a critical role in various tasks of medical image analysis. A successful registration algorithm, either derived from conventional energy optimization or deep networks, requires tremendous efforts from computer experts to well design registration energy or to carefully tune network architectures with respect to medical data available for a given registration task/scenario. This paper proposes an automated learning registration algorithm (AutoReg) that cooperatively optimizes both architectures and their corresponding training objectives, enabling non-computer experts to conveniently find off-the-shelf registration algorithms for various registration scenarios. Specifically, we establish a triple-level framework to embrace the searching for both network architectures and objectives with a cooperating optimization. Extensive experiments on multiple volumetric datasets and various registration scenarios demonstrate that AutoReg can automatically learn an optimal deep registration network for given volumes and achieve state-of-the-art performance. The automatically learned network also improves computational efficiency over the mainstream UNet architecture from 0.558 to 0.270 seconds for a volume pair on the same configuration.

Workplace bullying and suicidal ideation and behaviour: a systematic review and meta-analysis

OBJECTIVES

Suicidal ideation and behaviour are potential outcomes of workplace bullying. This review aimed to determine the extent of the association between workplace bullying and suicidal ideation and behaviour.

STUDY DESIGN

The study incorporated a systematic review and meta-analysis.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement was followed to conduct a comprehensive systematic review and meta-analysis. A combination of subject terms and free words was used to search nine electronic databases. Two reviewers independently screened articles and extracted information according to the inclusion criteria. A meta-analysis was performed with averaged weighted correlations across samples using the STATA software (version 16.0) from pooled estimates of the main results from all studies.

RESULTS

In total, 25 articles of high or medium quality were included in the systematic review; 15 of these were included in the meta-analysis. The prevalence of suicidal ideation and behaviour was 18% and 4%, respectively. Individuals who experienced workplace bullying had 2.03-times and 2.67-times higher odds of reporting suicidal ideation and behaviour, respectively, after adjustment for confounding factors. Moderating and mediating factors may help reduce the risk of suicidal ideation and behaviour for individuals experiencing workplace bullying.

CONCLUSION

This study indicated that exposure to workplace bullying significantly increased the risk of suicidal ideation and behaviour.

First Observation of the β3αp Decay of ^{13}O via β-Delayed Charged-Particle Spectroscopy

The β-delayed proton decay of ^{13}O has previously been studied, but the direct observation of β-delayed 3αp decay has not been reported. Rare 3αp events from the decay of excited states in ^{13}N^{⋆} provide a sensitive probe of cluster configurations in ^{13}N. To measure the low-energy products following β-delayed 3αp decay, the Texas Active Target (TexAT) time projection chamber was employed using the one-at-a-time β-delayed charged-particle spectroscopy technique at the Cyclotron Institute, Texas A&M University. A total of 1.9×10^{5} ^{13}O implantations were made inside the TexAT time projection chamber. A total of 149 3αp events were observed, yielding a β-delayed 3αp branching ratio of 0.078(6)%. Four previously unknown α-decaying excited states were observed in ^{13}N at 11.3, 12.4, 13.1, and 13.7 MeV decaying via the 3α+p channel.

Pharmacokinetics and safety of Padsevonil in healthy Chinese subjects and comparison of two sampling methods for Padsevonil quantification

OBJECTIVE

Padsevonil (PSL) is a novel antiepileptic drug candidate that inhibits seizure activity in both presynaptic and postsynaptic ways. The pharmacokinetic (PK) profiles and volumetric absorptive microsampling (VAMS) application of PSL in the Chinese population are limited. The objectives of this study were to evaluate the PK profile of PSL and its 2 metabolites, the safety of PSL, and compare the PK profile of PSL from samples collected using the VAMS technique with that of conventional venous samples in healthy Chinese subjects.

SUBJECTS AND METHODS

In this randomized, double-blind, placebo-controlled single-dose study, the participants received either 200 mg PSL or placebo. Blood samples for the PK variables were collected using both the traditional venous method and the VAMS Mitra® technique at the scheduled time points. The PK parameters of PSL and 2 metabolites were calculated, and the concentration agreement of VAMS and venous samples were also evaluated.

RESULTS

A total of 14 subjects were enrolled. The concentration-time profile of PSL showed rapid absorption with a median tmax of 1.25 h (range: 0.5 to 3.0), followed by an apparent biphasic disposition. For PSL, the geometric means of AUC(0-t), AUC, Cmax, and t1/2 were 6,573 h*ng/mL, 6,588 h*ng/mL, 1,387 ng/mL, and 5.275 h, respectively. The geometric mean body weight-normalized AUC(0-t), AUC, and Cmax were 5,712 h*ng/mL, 5,725 h*ng/mL, and 1,205 ng/mL, respectively. The AUC(0-t), AUC, Cmax of PSL and metabolites in VAMS-dried blood were all lower than those in plasma. The Passing-Bablok regression showed that the PSL and metabolite concentrations obtained by VAMS analysis were comparable to those obtained by plasma at some time points. The most frequently reported treatment-emergent adverse events (TEAEs) were somnolence and dizziness. There were no serious TEAEs, severe TEAEs, discontinuations due to TEAEs, or deaths reported during this study. No clinically significant laboratory, vital signs, electrocardiograph (ECG), or physical examination results were reported.

CONCLUSIONS

PSL has a favorable PK profile after single-dose oral administration and good safety properties in healthy Chinese volunteers. The regression analysis results of VAMS and plasma indicated that the application of VAMS for therapeutic drug monitoring in novel antiepileptic drug development is promising and needs further validation.

Learning With Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision

Images captured from low-light scenes often suffer from severe degradations, including low visibility, color casts, intensive noises, etc. These factors not only degrade image qualities, but also affect the performance of downstream Low-Light Vision (LLV) applications. A variety of deep networks have been proposed to enhance the visual quality of low-light images. However, they mostly rely on significant architecture engineering and often suffer from the high computational burden. More importantly, it still lacks an efficient paradigm to uniformly handle various tasks in the LLV scenarios. To partially address the above issues, we establish Retinex-inspired Unrolling with Architecture Search (RUAS), a general learning framework, that can address low-light enhancement task, and has the flexibility to handle other challenging downstream vision tasks. Specifically, we first establish a nested optimization formulation, together with an unrolling strategy, to explore underlying principles of a series of LLV tasks. Furthermore, we design a differentiable strategy to cooperatively search specific scene and task architectures for RUAS. Last but not least, we demonstrate how to apply RUAS for both low- and high-level LLV applications (e.g., enhancement, detection and segmentation). Extensive experiments verify the flexibility, effectiveness, and efficiency of RUAS.

Characteristic Mapping for Ellipse Detection Acceleration

It is challenging to characterize the intrinsic geometry of high-degree algebraic curves with lower-degree algebraic curves. The reduction in the curve's degree implies lower computation costs, which is crucial for various practical computer vision systems. In this paper, we develop a characteristic mapping (CM) to recursively degenerate 3n points on a planar curve of nth order to 3(n - 1) points on a curve of (n - 1)th order. The proposed characteristic mapping enables curve grouping on a line, a curve of the lowest order, that preserves the intrinsic geometric properties of a higher-order curve (ellipse). We prove a necessary condition and derive an efficient arc grouping module that finds valid elliptical arc segments by determining whether the mapped three points are colinear, invoking minimal computation. We embed the module into two latest arc-based ellipse detection methods, which reduces their running time by 25% and 50% on average over five widely used data sets. This yields faster detection than the state-of-the-art algorithms while keeping their precision comparable or even higher. Two CM embedded methods also significantly surpass a deep learning method on all evaluation metrics.

Prevalence and risk factors of erectile dysfunction in COVID-19 patients: a systematic review and meta-analysis

PURPOSE

Studies have found that erectile dysfunction (ED) may be a short-term or long-term complication in coronavirus disease 2019 (COVID-19) patients, but no relevant studies have completed a pooled analysis of this claim. The purpose of the review was to comprehensively search the relevant literature, summarize the prevalence of ED in COVID-19 patients, assess risk factors for its development, and explore the effect of the COVID-19 infection on erectile function.

METHODS

Medline, Embase, and the Cochrane Library was performed from database inception until April 14, 2022. Heterogeneity was analyzed by χ² tests and I² was used as a quantitative test of heterogeneity. Subgroup analyses, meta-regression, and sensitivity analyses were used to analyze sources of heterogeneity.

RESULTS

Our review included 8 studies, 4 of which functioned as a control group. There were 250,606 COVID-19 patients (mean age: 31-47.1 years, sample size: 23-246,990). The control group consisted of 10,844,200 individuals (mean age: 32.76-42.4 years, sample size 75-10,836,663). The prevalence of ED was 33% (95% CI 18-47%, I² = 99.48%) in COVID-19 patients. The prevalence of ED based on the international coding of diseases (ICD-10) was 9% (95% CI 2-19%), which was significantly lower than the prevalence of ED diagnosed based on the International Index of Erectile Function (IIEF-5) (46%, 95% CI 22-71%, I² = 96.72%). The pooling prevalence of ED was 50% (95% CI 34-67%, I² = 81.54%) for articles published in 2021, significantly higher than that for articles published in 2022 (17%, 95% CI 7-30%, I² = 99.55%). The relative risk of developing ED was 2.64 times in COVID-19 patients higher than in non-COVID-19 patients (RR: 2.64, 95% CI 1.01-6.88). The GRADE-pro score showed that the mean incidence of ED events in COVID-19 patients was 1,333/50,606 (2.6%) compared with 52,937/844,200 (0.4%) in controls; the absolute impact of COVID-19 on ED was 656/100,000 (ranging from 4/100,000 to 2352/100,000). Anxiety (OR: 1.13, 95% CI 1.03-1.26, I² = 0.0%) in COVID-19 patients was a risk factor for ED.

CONCLUSION

COVID-19 patients have a high risk and prevalence of ED, mainly driven by anxiety. Attention should be paid to patient's erectile functioning when treating COVID-19.

[Malaria control knowledge and behaviors and their influencing factors among residents in Banlao Township, Cangyuan County, Yunnan Province]

OBJECTIVE

To investigate the awareness of malaria-related knowledge, the use of mosquito nets and their influencing factors among residents in Banlao Township, Cangyuan County, Yunnan Province.

METHODS

In August 2020, 19 settlement sites in Banlao Township, Cangyuan County, Lincang City, Yunnan Province were selected as study areas, and permanent residents at ages of 10 years and older were enrolled for a questionnaire survey, including residents' demographics, family economic status, malaria control knowledge and use of mosquito nets. In addition, the factors affecting the use of mosquito nets in the night prior to the survey were identified using multivariate logistic regression analysis.

RESULTS

A total of 320 questionnaires were allocated, and all were recovered (a 100% recovery rate). There were 316 valid questionnaires, with an effective recovery rate of 98.75%. The 316 respondents included 152 men and 164 women and 250 Chinese respondents and 66 foreign respondents. The awareness of clinical syndromes of malaria was significantly higher among Chinese residents (71.60%) than among foreign residents (50.00%) (χ² = 11.03, P < 0.01), and the proportions of Chinese and foreign residents sleeping under mosquito nets were 46.00% and 69.70% on the night prior to the survey, respectively (χ² = 11.73, P < 0.01). Multivariate logistic regression analysis identified ethnicity group and type of residence as factors affecting the use of mosquito nets in the night prior to the survey.

CONCLUSIONS

The awareness of malaria control knowledge, the coverage and the use of mosquito nets were low among residents in Banlao Township, Cangyuan County, Yunnan Province. Targeted health education is recommended to improve the awareness of malaria control knowledge and self-protection ability. In addition, improving the allocation of long-lasting mosquito nets and health education pertaining to their uses and increasing the proportion of using mosquito nets correctly is needed to prevent re-establishment of imported malaria.

A Parametric Design Method for Engraving Patterns on Thin Shells

Designing thin-shell structures that are diverse, lightweight, and physically viable is a challenging task for traditional heuristic methods. To address this challenge, we present a novel parametric design framework for engraving regular, irregular, and customized patterns on thin-shell structures. Our method optimizes pattern parameters such as size and orientation, to ensure structural stiffness while minimizing material consumption. Our method is unique in that it works directly with shapes and patterns represented by functions, and can engrave patterns through simple function operations. By eliminating the need for remeshing in traditional FEM methods, our method is more computationally efficient in optimizing mechanical properties and can significantly increase the diversity of shell structure design. Quantitative evaluation confirms the convergence of the proposed method. We conduct experiments on regular, irregular, and customized patterns and present 3D printed results to demonstrate the effectiveness of our approach.

Anticeramide Improves Sjögren's Syndrome by Blocking BMP6-Induced Th1

T-cell dysfunction has been shown to play an important role in the pathogenesis of Sjögren's syndrome (SS). In recent studies, the increased expression of BMP6 has been reported to be related to SS. However, the roles that BMP6 plays in immune homeostasis in the development of SS as well as the downstream signals activated by BMP6 remain unclear. In this study, we investigated the effects and molecular mechanisms of BMP6 on naive CD4⁺ T cells, showing that BMP6 could upregulate interferon (IFN)-γ secretion from CD4⁺ T cells through a ceramide/nuclear factor-κB pathway, with no effect on T-cell activation or proliferation. Moreover, an in vivo study showed that anticeramide treatment (myriocin) for an SS animal model (NOD/LtJ mice) could significantly decrease the IFN-γ expression and Th1 frequency in the salivary glands and suppress the inflammation infiltration in salivary glands and maintain the salivary flow rates, both of which reflect SS-like symptoms. This study identifies a promising target that could effectively attenuate the abnormal state of CD4⁺ T cells and reverse the progression of SS.

Learning Deformable Image Registration From Optimization: Perspective, Modules, Bilevel Training and Beyond

Conventional deformable registration methods aim at solving an optimization model carefully designed on image pairs and their computational costs are exceptionally high. In contrast, recent deep learning-based approaches can provide fast deformation estimation. These heuristic network architectures are fully data-driven and thus lack explicit geometric constraints which are indispensable to generate plausible deformations, e.g., topology-preserving. Moreover, these learning-based approaches typically pose hyper-parameter learning as a black-box problem and require considerable computational and human effort to perform many training runs. To tackle the aforementioned problems, we propose a new learning-based framework to optimize a diffeomorphic model via multi-scale propagation. Specifically, we introduce a generic optimization model to formulate diffeomorphic registration and develop a series of learnable architectures to obtain propagative updating in the coarse-to-fine feature space. Further, we propose a new bilevel self-tuned training strategy, allowing efficient search of task-specific hyper-parameters. This training strategy increases the flexibility to various types of data while reduces computational and human burdens. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data. Extensive results demonstrate the state-of-the-art performance of the proposed method with diffeomorphic guarantee and extreme efficiency. We also apply our framework to challenging multi-modal image registration, and investigate how our registration to support the down-streaming tasks for medical image analysis including multi-modal fusion and image segmentation.

Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement

Enhancing the quality of low-light (LOL) images plays a very important role in many image processing and multimedia applications. In recent years, a variety of deep learning techniques have been developed to address this challenging task. A typical framework is to simultaneously estimate the illumination and reflectance, but they disregard the scene-level contextual information encapsulated in feature spaces, causing many unfavorable outcomes, e.g., details loss, color unsaturation, and artifacts. To address these issues, we develop a new context-sensitive decomposition network (CSDNet) architecture to exploit the scene-level contextual dependencies on spatial scales. More concretely, we build a two-stream estimation mechanism including reflectance and illumination estimation network. We design a novel context-sensitive decomposition connection to bridge the two-stream mechanism by incorporating the physical principle. The spatially varying illumination guidance is further constructed for achieving the edge-aware smoothness property of the illumination component. According to different training patterns, we construct CSDNet (paired supervision) and context-sensitive decomposition generative adversarial network (CSDGAN) (unpaired supervision) to fully evaluate our designed architecture. We test our method on seven testing benchmarks [including massachusetts institute of technology (MIT)-Adobe FiveK, LOL, ExDark, and naturalness preserved enhancement (NPE)] to conduct plenty of analytical and evaluated experiments. Thanks to our designed context-sensitive decomposition connection, we successfully realized excellent enhanced results (with sufficient details, vivid colors, and few noises), which fully indicates our superiority against existing state-of-the-art approaches. Finally, considering the practical needs for high efficiency, we develop a lightweight CSDNet (named LiteCSDNet) by reducing the number of channels. Furthermore, by sharing an encoder for these two components, we obtain a more lightweight version (SLiteCSDNet for short). SLiteCSDNet just contains 0.0301M parameters but achieves the almost same performance as CSDNet. Code is available at https://github.com/KarelZhang/CSDNet-CSDGAN.

Seroprevalence of antibodies to classical swine fever virus and porcine reproductive and respiratory syndrome virus in healthy pigs in Hunan Province, China

Classical swine fever (CSF) and porcine reproductive and respiratory syndrome (PRRS) are responsible for major economic losses and represent a threat to the swine industry worldwide. Routine surveillance serology for CSF and PRRS viruses is critical to maintaining the health status of sow farms in Hunan Province, which is one of the top pig production provinces in China. The aim of our study was to investigate the serological statistics of CSF virus (CSFV) and PRRS virus (PRRSV) in Hunan Province. The cohort serum samples were collected from vaccinated and unvaccinated pigs. Our findings showed that the average rates of CSFV and PRRSV antibody seropositivity were 82.2% (95% CI: 80.1-84.3) and 84.8% (95% CI: 82.5-87.1), respectively, in the immunized group and that these rates were higher than those in the unvaccinated group (58.6% for CSFV and 47.8% for PRRSV). Additionally, the level of CSFV antibody in piglet serum declined gradually with age, whereas PRRSV-specific antibody level increased initially (1 to 2 weeks old) and then declined with age (2 to 4 weeks old). In summary, we investigated the difference in CSFV/PRRSV antibody levels among piglets at various weeks old (1 to 4 weeks) to further establish the duration of maternal immunity in piglets. In addition, routine monitoring of CSFV/PRRSV antibodies in immunized pigs was carried out to evaluate the efficacy of vaccination.

Underexposed Image Correction via Hybrid Priors Navigated Deep Propagation

Enhancing visual quality for underexposed images is an extensively concerning task that plays an important role in various areas of multimedia and computer vision. Most existing methods often fail to generate high-quality results with appropriate luminance and abundant details. To address these issues, we develop a novel framework, integrating both knowledge from physical principles and implicit distributions from data to address underexposed image correction. More concretely, we propose a new perspective to formulate this task as an energy-inspired model with advanced hybrid priors. A propagation procedure navigated by the hybrid priors is well designed for simultaneously propagating the reflectance and illumination toward desired results. We conduct extensive experiments to verify the necessity of integrating both underlying principles (i.e., with knowledge) and distributions (i.e., from data) as navigated deep propagation. Plenty of experimental results of underexposed image correction demonstrate that our proposed method performs favorably against the state-of-the-art methods on both subjective and objective assessments. In addition, we execute the task of face detection to further verify the naturalness and practical value of underexposed image correction. What is more, we apply our method to solve single-image haze removal whose experimental results further demonstrate our superiorities.

Efficient Representation and Optimization for TPMS-Based Porous Structures

In this approach, we present an efficient topology and geometry optimization of triply periodic minimal surfaces (TPMS) based porous shell structures, which can be represented, analyzed, optimized and stored directly using functions. The proposed framework is directly executed on functions instead of remeshing (tetrahedral/hexahedral), and this framework substantially improves the controllability and efficiency. Specifically, a valid TPMS-based porous shell structure is first constructed by function expressions. The porous shell permits continuous and smooth changes of geometry (shell thickness) and topology (porous period). The porous structures also inherit several of the advantageous properties of TPMS, such as smoothness, full connectivity (no closed hollows), and high controllability. Then, the problem of filling an object's interior region with porous shell can be formulated into a constraint optimization problem with two control parameter functions. Finally, an efficient topology and geometry optimization scheme is presented to obtain optimized scale-varying porous shell structures. In contrast to traditional heuristic methods for TPMS, our work directly optimize both the topology and geometry of TPMS-based structures. Various experiments have shown that our proposed porous structures have obvious advantages in terms of efficiency and effectiveness.

Microstructure and mechanical properties of dissimilar NiTi and 304 stainless steel joints produced by ultrasonic welding

Superelastic NiTi alloy and 304 stainless steel (304 SS) were joined with a Cu interlayer by ultrasonic spot welding (USW) using different welding energy inputs. The surface morphology, interfacial microstructure, mechanical properties, and fracture mechanisms of the dissimilar NiTi/304 SS USWed joints were studied. The results showed that the surface oxidation intensified with increasing ultrasonic welding energy due to mutual rubbing between tools and sheets. The weld interface microstructure exhibited voids or unbonded zones at low energy inputs, while an intimate contact was established at the joining interface when applying a higher energy input of 750 J. With increasing energy input to 750 J, the weld interface shows two interfaces due to the behavior of plastic flow of Cu interlayer. The lap-shear load of the joints first increased, achieving a maximum value of ∼690 N at an energy input of 750 J, and then decreased with further increase in welding energy. Interfacial failure was observed at NiTi/Cu interface at all energy inputs, and no intermetallic compounds were found on the fracture surfaces of both the NiTi/Cu and Cu/304 SS interfaces.

Clinical study of the clinical characteristics and prognosis of 1219 cases of endometrial cancer with lymph node metastasis

To analyze the clinical characteristics and prognosis of endometrial cancer patients with lymph node metastasis to provide a reference for lymphadenectomy in endometrial cancer. The data used in this study were extracted from a tertiary hospital in Guangxi, China based on the hospital information system. 1219 patients with endometrial malignancy who were treated in our hospital. The lymph node metastasis rate was 9.8%. The metastasis rate of the abdominal aorta + pelvic lymph nodes (56.7%) was significantly higher than that of the pelvic (24.2%) or para-aortic (19.2%) lymph nodes alone. The proportion of postmenopausal patients with lymph node metastasis was higher than that of premenopausal patients. The proportion of patients with lymph node metastasis with vaginal and uterus involvement, HPV detection, Thinprep Cytologic Testresults, CRP level <10 ug/mL, G3 tumor grade, postoperative pathology indicating cervical invasion, lymphovascular invasion, and muscular infiltration depth > 1/2 was higher than that of patients without lymph node metastasis. The proportion of endometrial cancer patients with lymph node metastasis with CA125 ≥ 35 U/ml was higher than that of those with CA125 < 35 U/ml. The lymph node-positive rate is related to tissue differentiation, lymphangitic infiltration, cervical invasion, muscle infiltration depth > 1/2, and CA125 level. The metastasis rate of pelvic and para-aortic lymph nodes is higher than that of pelvic lymph nodes or para-aortic lymph nodes alone. There was no statistically significant difference in the overall survival rate among the three groups.

[An optimized protocol of meniscus cell extraction for single-cell RNA sequencing]

OBJECTIVE

To optimize the protocol of meniscus cell extraction to enhance the efficiency of cell suspension preparation and maintain a high cell viability for single-cell RNA sequencing.

METHODS

We compared the efficiency of the routine cell extraction methods (short-time digestion and long-time digestion) and the optimized protocol for obtaining meniscus cell suspensions by evaluating the cell number obtained and the cell viability. Single-cell RNA sequencing datasets were analyzed to evaluate the stability of the cell suspension prepared using the optimized protocol. The reliability of the optimized protocol was assessed by comparing the single-cell RNA sequencing dataset obtained by the optimized protocol with published single-cell RNA sequencing datasets of the meniscus.

RESULTS

The optimized protocol harvested a greater number of cells (over 1×10⁵) than the routine protocols. The cell suspension prepared with the optimized protocol showed a cell viability higher than 80%, the highest among the 3 methods. Analysis of single-cell RNA sequencing datasets showed that the ratio of the mitochondrial genes was below 20% in over 80% of the cells. CD34⁺ cells, MCAM⁺ cells and COL1A1⁺ cells were identified in the datasets. Comparison with the publish datasets showed that the optimized protocol was capable of harvesting COL3A1⁺, COL1A1⁺, MYLK⁺, BMP2⁺, CD93⁺ and CDK1⁺ cells.

CONCLUSION

Single-cell suspension prepared from the meniscus can be stably obtained using the optimized protocol for single-cell RNA sequencing using the 10× Genomics platform.

[Surface modification of titanium implant with hBMP-2/hIGF-1 for promoting biocompatibility and osteogenesis]

OBJECTIVE

To prepare the human bone morphogenetic protein-2(hBMP-2)/human insulin-like growth factor-1(hIGF-1)coating titanium(Ti)and assess its performance as a dental implant material.

METHODS

hBMP-2 and hIGF-1 were coated to the smooth surface of a Ti plate, and its efficacy for promoting bone formation and bone integration was compared with a pristine Ti plate.The surface characteristics of the metal samples were evaluated using scanning electron microscope (SEM) and by contact angle measurement.MG63 cells were seeded on the surface of the Ti plates, and MTT assay and alizarin red staining was used to examine the cell proliferation and formation of calcified nodules, respectively.Alkaline phosphatase (ALP)secretion of the cells was examined with ELISA, and cellular expressions of osteocalcin and osteopontin were detected with Western blotting for assessing osteogenesis.

RESULTS

SEM examination showed that the surface of Ti with hBMP-2 and hIGF-1 coating presented with a radial pattern resembling snowflakes.The contact angles of non-coated Ti, hBMP-2-coated Ti, hIGF-1-coated, and hBMP-2/-hIGF-1-coated Ti samples were 83.2°, 54°, 56° and 54°, respectively.Compared with the non-coated Ti plate, the surface-modified Ti samples showed a significantly smaller contact angle (P=0.032, 0.029, and 0.028), indicating a good hydrophilicity of the samples.MTT assay showed that MG63 cells grew well on the surface of the coated Ti plates.The hBMP-2/IGF-1 coating significantly induced cellular secretion of ALP(P=0.021, 0.014)and obviously promoted osteogenesis of MG63 cells (P < 0.05).Western blotting results showed that hBMP-2/IGF-1 coating significantly enhanced the expressions of osteocalcin and osteopontin in the seeded cells (P < 0.05).

CONCLUSION

hBMP-2 and hIGF-1 coating of Ti material can promote osteogenesis of the cells seeded on its surface to improve the performance of such Ti material as dental implants.

Novelty Detection and Online Learning for Chunk Data Streams

Datastream analysis aims at extracting discriminative information for classification from continuously incoming samples. It is extremely challenging to detect novel data while incrementally updating the model efficiently and stably, especially for high-dimensional and/or large-scale data streams. This paper proposes an efficient framework for novelty detection and incremental learning for unlabeled chunk data streams. First, an accurate factorization-free kernel discriminative analysis (FKDA-X) is put forward through solving a linear system in the kernel space. FKDA-X produces a Reproducing Kernel Hilbert Space (RKHS), in which unlabeled chunk data can be detected and classified by multiple known-classes in a single decision model with a deterministic classification boundary. Moreover, based on FKDA-X, two optimal methods FKDA-CX and FKDA-C are proposed. FKDA-CX uses the micro-cluster centers of original data as the input to achieve excellent performance in novelty detection. FKDA-C and incremental FKDA-C (IFKDA-C) using the class centers of original data as their input have extremely fast speed in online learning. Theoretical analysis and experimental validation on under-sampled and large-scale real-world datasets demonstrate that the proposed algorithms make it possible to learn unlabeled chunk data streams with significantly lower computational costs and comparable accuracies than the state-of-the-art approaches.

Location-Aware and Regularization-Adaptive Correlation Filters for Robust Visual Tracking

Correlation filter (CF) has recently been widely used for visual tracking. The estimation of the search window and the filter-learning strategies is the key component of the CF trackers. Nevertheless, prevalent CF models separately address these issues in heuristic manners. The commonly used CF models directly set the estimated location in the previous frame as the search center for the current one. Moreover, these models usually rely on simple and fixed regularization for filter learning, and thus, their performance is compromised by the search window size and optimization heuristics. To break these limits, this article proposes a location-aware and regularization-adaptive CF (LRCF) for robust visual tracking. LRCF establishes a novel bilevel optimization model to address simultaneously the location-estimation and filter-training problems. We prove that our bilevel formulation can successfully obtain a globally converged CF and the corresponding object location in a collaborative manner. Moreover, based on the LRCF framework, we design two trackers named LRCF-S and LRCF-SA and a series of comparisons to prove the flexibility and effectiveness of the LRCF framework. Extensive experiments on different challenging benchmark data sets demonstrate that our LRCF trackers perform favorably against the state-of-the-art methods in practice.

A Bilevel Integrated Model With Data-Driven Layer Ensemble for Multi-Modality Image Fusion

Image fusion plays a critical role in a variety of vision and learning applications. Current fusion approaches are designed to characterize source images, focusing on a certain type of fusion task while limited in a wide scenario. Moreover, other fusion strategies (i.e., weighted averaging, choose-max) cannot undertake the challenging fusion tasks, which furthermore leads to undesirable artifacts facilely emerged in their fused results. In this paper, we propose a generic image fusion method with a bilevel optimization paradigm, targeting on multi-modality image fusion tasks. Corresponding alternation optimization is conducted on certain components decoupled from source images. Via adaptive integration weight maps, we are able to get the flexible fusion strategy across multi-modality images. We successfully applied it to three types of image fusion tasks, including infrared and visible, computed tomography and magnetic resonance imaging, and magnetic resonance imaging and single-photon emission computed tomography image fusion. Results highlight the performance and versatility of our approach from both quantitative and qualitative aspects.

A Deep Framework Assembling Principled Modules for CS-MRI: Unrolling Perspective, Convergence Behaviors, and Practical Modeling

Compressed Sensing Magnetic Resonance Imaging (CS-MRI) significantly accelerates MR acquisition at a sampling rate much lower than the Nyquist criterion. A major challenge for CS-MRI lies in solving the severely ill-posed inverse problem to reconstruct aliasing-free MR images from the sparse k -space data. Conventional methods typically optimize an energy function, producing restoration of high quality, but their iterative numerical solvers unavoidably bring extremely large time consumption. Recent deep techniques provide fast restoration by either learning direct prediction to final reconstruction or plugging learned modules into the energy optimizer. Nevertheless, these data-driven predictors cannot guarantee the reconstruction following principled constraints underlying the domain knowledge so that the reliability of their reconstruction process is questionable. In this paper, we propose a deep framework assembling principled modules for CS-MRI that fuses learning strategy with the iterative solver of a conventional reconstruction energy. This framework embeds an optimal condition checking mechanism, fostering efficient and reliable reconstruction. We also apply the framework to three practical tasks, i.e., complex-valued data reconstruction, parallel imaging and reconstruction with Rician noise. Extensive experiments on both benchmark and manufacturer-testing images demonstrate that the proposed method reliably converges to the optimal solution more efficiently and accurately than the state-of-the-art in various scenarios.

On the Convergence of Learning-Based Iterative Methods for Nonconvex Inverse Problems

Numerous tasks at the core of statistics, learning and vision areas are specific cases of ill-posed inverse problems. Recently, learning-based (e.g., deep) iterative methods have been empirically shown to be useful for these problems. Nevertheless, integrating learnable structures into iterations is still a laborious process, which can only be guided by intuitions or empirical insights. Moreover, there is a lack of rigorous analysis about the convergence behaviors of these reimplemented iterations, and thus the significance of such methods is a little bit vague. This paper moves beyond these limits and proposes Flexible Iterative Modularization Algorithm (FIMA), a generic and provable paradigm for nonconvex inverse problems. Our theoretical analysis reveals that FIMA allows us to generate globally convergent trajectories for learning-based iterative methods. Meanwhile, the devised scheduling policies on flexible modules should also be beneficial for classical numerical methods in the nonconvex scenario. Extensive experiments on real applications verify the superiority of FIMA.

LncRNA INHBA-AS1 promotes cell growth, migration, and invasion of oral squamous cell carcinoma by sponging miR-143-3p

Since this article has been suspected of research misconduct and the corresponding authors did not respond to our request to prove originality of data and figures, "LncRNA INHBA-AS1 promotes cell growth, migration, and invasion of oral squamous cell carcinoma by sponging miR-143-3p, by W.-Q. Ma, J. Chen, W. Fang, X.-Q. Yang, A. Zhu, D. Zhang, H.-L. Zhong, B. Yang, Z. Luo, published in Eur Rev Med Pharmacol Sci 2020; 24 (4): 1821-1828-DOI: 10.26355/eurrev_202002_20360-PMID: 32141551" has been withdrawn. The Publisher apologizes for any inconvenience this may cause. https://www.europeanreview.org/article/20360.

Gender and age differences in the association between sleep characteristics and fasting glucose levels in Chinese adults

AIM

The present study examined the associations between night-time sleep duration, midday napping duration and bedtime, and fasting glucose levels, and whether or not such associations are dependent on gender and age.

METHODS

This study was a cross-sectional analysis of 172,901 adults aged≥40 years living in mainland China. Sleep duration was obtained by self-reports of bedtime at night, waking-up time the next morning and average napping duration at midday. Fasting plasma glucose (FPG)≥7.0mmol/L was defined as hyperglycaemia. Independent associations between night-time sleep duration, midday naptime duration and bedtime with hyperglycaemia were evaluated using regression models.

RESULTS

Compared with night-time sleep durations of 6-7.9h, both short (<6h) and long (≥8h) night-time sleep durations were significantly associated with an increased risk of hyperglycaemia in women [odds ratio (OR): 1.12, 95% confidence interval (CI): 1.01-1.29 and OR: 1.14, 95% CI: 1.08-1.21, respectively], and revealed a U-shaped distribution of risk in women and no significant association in men. Long midday nap durations (≥1h) were significantly but weakly associated with hyperglycaemia (OR: 1.04, 95% CI: 1.01-1.09) compared with no napping without interactions from gender or age, whereas the association between bedtime and fasting glucose levels did vary according to gender and age.

CONCLUSION

Night-time sleep duration, midday napping duration and bedtime were all independently associated with the risk of hyperglycaemia, and some of the associations between these sleep characteristics and hyperglycaemia were gender- and age-dependent.

Crystallographic and calorimetric analysis on Pleurotus ostreatus lectin and its sugar complexes - promiscuous binding driven by geometry

Carbohydrate recognition is established as a property of lectins and implicated in many functions including immunity and defense against pathogens. Many lectins are characterized and proposed for various applications owing to the above said recognition. The crystal structure of a lectin from Pleurotus ostreatus has been determined and shown to be calcium dependent. The overall structure is a tandem repeat of two β-jelly roll domains, a new fold for lectins. The calcium dependence of sugar binding is analyzed in-detail through isothermal titration calorimetry. The serendipitous observation of malonate and glycerol, the intentional N-Acetyl-D-galactosamine, D-Galactose and L-Rhamnose binding to Pleurotus ostreatus lectin by Ca²⁺ coordination revealed that the binding site is promiscuous. Among these sugars, Rhamnose binding found to be thermodynamically most favourable. In all these structures, a vicinal diol motif, one at axial and the other at equatorial positions could be established as a specific requirement for binding. Interestingly, when compared with other calcium mediated lectin structures; this geometric requirement is found conserved. This observation could lead to the conclusion that lectins are not 'molecule specific' but 'geometry specific' so that any molecule not necessarily a sugar may be recognized by this lectin if the geometry exists.

Knowledge-Driven Deep Unrolling for Robust Image Layer Separation

Single-image layer separation targets to decompose the observed image into two independent components in terms of different application demands. It is known that many vision and multimedia applications can be (re)formulated as a separation problem. Due to the fundamentally ill-posed natural of these separations, existing methods are inclined to investigate model priors on the separated components elaborately. Nevertheless, it is knotty to optimize the cost function with complicated model regularizations. Effectiveness is greatly conceded by the settled iteration mechanism, and the adaption cannot be guaranteed due to the poor data fitting. What is more, for a universal framework, the most taxing point is that one type of visual cue cannot be shared with different tasks. To partly overcome the weaknesses mentioned earlier, we delve into a generic optimization unrolling technique to incorporate deep architectures into iterations for adaptive image layer separation. First, we propose a general energy model with implicit priors, which is based on maximum a posterior, and employ the extensively accepted alternating direction method of multiplier to determine our elementary iteration mechanism. By unrolling with one general residual architecture prior and one task-specific prior, we attain a straightforward, flexible, and data-dependent image separation framework successfully. We apply our method to four different tasks, including single-image-rain streak removal, high-dynamic-range tone mapping, low-light image enhancement, and single-image reflection removal. Extensive experiments demonstrate that the proposed method is applicable to multiple tasks and outperforms the state of the arts by a large margin qualitatively and quantitatively.

Involvement of nitrogen in storage root growth and related gene expression in sweet potato (Ipomoea batatas)

Nitrogen (N) could affect storage root growth and development of sweet potato. To manage external N concentration fluctuations, plants have developed a wide range of strategies, such as growth changes and gene expression. Five sweet potato cultivars were used to analyse the functions of N in regulating storage root growth. Growth responses and physiological indicators were measured to determine the physiological changes regulated by different N concentrations. Expression profiles of related genes were analysed via microarray hybridization data and qRT-PCR analysis to reveal the molecular mechanisms of storage root growth regulated by different N concentrations. The growth responses and physiological indicators of the five cultivars were changed by N concentration. The root fresh weight of two of the sweet potato cultivars, SS19 and GS87, was higher under low N concentrations compared with the other cultivars. SS19 and GS87 were found to be having greater tolerance to low N concentration. The expression of N metabolism and storage root growth related genes was regulated by N concentration in sweet potato. These results reveal that N significantly regulated storage root growth. SS19 and GS87 were more tolerant to low N concentration and produced greater storage root yield (at 30 days). Furthermore, several N response genes were involved in both N metabolism and storage root growth.

Quantum Rotation Sensing with Dual Sagnac Interferometers in an Atom-Optical Waveguide

We describe a Sagnac interferometer suitable for rotation sensing, implemented using an atomic Bose-Einstein condensate confined in a harmonic magnetic trap. The atom wave packets are split and recombined by standing-wave Bragg lasers, and the trapping potential steers the packets along circular trajectories with a radius of 0.2 mm. Two conjugate interferometers are implemented simultaneously to provide common-mode rejection of noise and to isolate the rotation signal. With interference visibilities of about 50%, we achieve a rotation sensitivity comparable to Earth's rate in about 10 min of operation. Gyroscope operation was demonstrated by rotating the optical table on which the experiment was performed.