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Objective: Mobile health applications (apps) have gained significant popularity and widespread utilization among patients with coronary heart disease (CHD). The objective of this study is to evaluate the effects of mHealth apps on clinical outcomes and health behaviors in patients with CHD. Methods: Databases were searched from inception until December 2023, including Cochrane Library, PubMed, EMBASE, Web of Science, CINAHL, China National Knowledge Infrastructure (CNKI), Chinese BioMedical Literature Service System (SinoMed), Wanfang Data, China Science and Technology Journal Database (VIP), for randomized controlled trials (RCTs) regarding the effectiveness of mHealth apps in patients with CHD. Two researchers conducted a comprehensive review of the literature, extracting relevant data and evaluating each study’s methodological quality separately. The meta-analysis was performed utilizing Review Manager v5.4 software. Results: A total of 34 RCTs were included, with 5,319 participants. The findings demonstrated that using mHealth apps could decrease the incidence of major adverse cardiac events (RR = 0.68, P = 0.03), readmission rate (RR = 0.56, P 0.05). Conclusion: Mobile health apps have the potential to lower the incidence of major adverse cardiac events (MACEs), readmission rates, and blood lipids in patients with CHD. They can also help enhance cardiac function, promote medication adherence, and alleviate symptoms of anxiety and depression. To further corroborate these results, larger-scale, multi-center RCTs with longer follow-up periods are needed.

Industrial Computed Tomography (CT) serves as a pivotal non-destructive testing technique, significantly impacting the research and application domains of automatic defect detection in three-dimensional (3D) CT images. Recent advancements have showcased the exceptional capabilities of Convolutional Neural Networks (CNNs) in addressing image recognition and segmentation tasks. In the context of industrial 3D CT imaging, the paramount challenge lies in the segmentation of microstructural cracksâ task complicated by low grayscale contrast, small sizes, and imbalanced sample distributions. To tackle these complexities, we introduce a novel crack segmentation methodology grounded in multi-task deep convolutional neural networks.Our network architecture is composed of dual modules: a segmentation task module dedicated to crack extraction and a classification task module aimed at enhancing accuracy, with shared feature maps to leverage synergistic benefits. Inspired by the work of Chen et al., we incorporate the Efficient Channel Attention (ECA) block, an attention mechanism designed to refine segmentation accuracy. Empirical evaluations demonstrate that our proposed approach outperforms existing methodologies, showcasing superior performance in crack segmentation tasks. Notably, our network exhibits commendable generalization abilities. Despite being trained exclusively on simulated images, it yields robust and accurate predictions when applied to real-world CT images, indicating its adaptability across varied datasets. This study not only advances the technological frontier in industrial CT image analysis but also furnishes a potent tool and reference for future research endeavors in related fields.

Land and groundwater resources are fundamental pillars of sustainable human development. The negligent abandonment of ammunition and its fragments during range activities can result in severe contamination of range sites, thereby posing a significant risk to both the ecological environment and human health. Nevertheless, numerous uncertainties persist regarding the comprehension of range contaminated sites. In this study, the literature on the range of contaminated sites decommissioned after 2000 was systematically examined to consolidate basic information related to these sites, such as contaminant types, contamination status, and remediation measures. Considerable attention is devoted to investigating the advancement of diverse techniques, such as phytoremediation, chemical leaching, and solidification/stabilization, to remediate polluted areas within decommissioned firing ranges. Among the various types of remediation means, physical remediation and chemical remediation have higher remediation efficiency, but generally have higher costs and are prone to secondary pollution. Bioremediation is low cost and environmentally friendly, but has a long restoration cycle. The choice of remediation method should be based on actual needs. Additionally, this study puts forth prospective avenues for future research. Ultimately, this endeavor aims to attract the interest of scholars toward the remediation of contaminated sites within firing ranges, thereby making a valuable contribution to both human wellbeing and sustainable progress.

The transfection efficiency and stability of the delivery vehicles of plasmid DNA (pDNA) are critical metrics to ensure high-quality and high-yield production of viral vectors. We previously identified that the optimal size of pDNA/poly(ethylenimine) (PEI) transfection particles is 400–500 nm and developed a bottom-up assembly method to construct stable 400-nm pDNA/PEI particles and benchmarked their transfection efficiency in producing lentiviral vectors (LVVs). Here, we report scale-up production protocols for such transfection particles. Using a two-inlet confined impinging jet (CIJ) mixer with a dual syringe pump set-up, we produced a 1-L batch at a flow rate of 100 mL/min, and further scaled up this process with a larger CIJ mixer and a dual peristaltic pump array, allowing for continuous production at a flow rate of 1 L/min without a lot size limit. We demonstrated the scalability of this process with a 5-L lot and validated the quality of these 400-nm transfection particles against the target product profile, including physical properties, shelf and on-bench stability, transfection efficiency, and LVV production yield in both 15-mL bench culture and 2-L bioreactor runs. These results confirm the potential of this particle assembly process as a scalable manufacturing platform for viral vector production.

Abstract Night-time warming is vital for human production and daily life. Conventional methods like active heaters are energy-intensive, while passive insulating films possess restrictions regarding space consumption and the lack of heat gain. In this work, a nanophotonic-based night-time warming strategy that passively inhibits thermal radiation of objects while actively harnessing that of atmosphere is proposed. By using a photonic-engineered thin film that exhibits high reflectivity (~0.91) in the atmospheric transparent band (8–14 μm) and high absorptivity (~0.7) in the atmospheric radiative band (5–8 and 14–16 μm), temperature rise of 2.1 °C/4.4 °C compared to typical low-e film and broadband absorber is achieved. Moreover, net heat loss as low as 9 W m−2 is experimentally observed, compared to 16 and 39 W m−2 for low-e film and broadband absorber, respectively. This strategy suggests an innovative way for sustainable warming, thus contributes to addressing the challenges of climate change and promoting global carbon neutrality.

Abstract Advanced multispectral detection technologies have emerged as a significant threat to objects, necessitating the use of multiband camouflage. However, achieving effective camouflage and thermal management across the entire infrared spectrum, especially the short-wave infrared (SWIR) band, remains challenging. This paper proposes a multilayer wavelength-selective emitter that achieves effective camouflage across the entire infrared spectrum, including the near-infrared (NIR), SWIR, mid-wave infrared (MWIR), and long-wave infrared (LWIR) bands, as well as the visible (VIS) band. Furthermore, the emitter enables radiative heat dissipation in two non-atmospheric windows (2.5–3 μm and 5–8 μm). The emitter’s properties are characterized by low emittance of 0.270/0.042/0.218 in the SWIR/MWIR/LWIR bands, and low reflectance of 0.129/0.281 in the VIS/NIR bands. Moreover, the high emittance of 0.742/0.473 in the two non-atmospheric windows ensures efficient radiative heat dissipation, which results in a temperature decrement of 14.4 °C compared to the Cr reference at 2000 W m−2 input power density. This work highlights the role of solar radiance in camouflage, and provides a comprehensive guideline for developing multiband camouflage compatible with radiative heat dissipation, from the visible to LWIR.

Abstract With the deepening of human research on deep space exploration, our research on the soft landing methods of landers has gradually deepened. Adding a buffer and energy-absorbing structure to the leg structure of the lander has become an effective design solution. Based on the energy-absorbing structure of the leg of the interstellar lander, this paper studies the appearance characteristics of the predatory feet of the Odontodactylus scyllarus. The predatory feet of the Odontodactylus scyllarus can not only hit the prey highly when preying, but also can easily withstand the huge counter-impact force. The predatory feet structure of the Odontodactylus scyllarus, like a symmetrical cone, shows excellent rigidity and energy absorption capacity. Inspired by this discovery, we used SLM technology to design and manufacture two nickel-titanium samples, which respectively show high elasticity, shape memory, and get better energy absorption capacity. This research provides an effective way to design and manufacture high-mechanical energy-absorbing buffer structures using bionic 3D printing technology and nickel-titanium alloys.

To enhance the accuracy of identifying fresh meat varieties using laser-induced breakdown spectroscopy (LIBS), we utilized the LightGBM model in combination with the Optuna algorithm. The procedure involved flattening fresh meat slices with glass slides and collecting spectral data of the plasma from the surfaces of the fresh meat tissues (pork, beef, and chicken) using LIBS technology. A total of 900 spectra were collected. Initially, we established LightGBM and SVM (support vector machine) models for the collected spectra. Subsequently, we applied information gain and peak extraction algorithms to select the features for each model. We then employed Optuna to optimize the hyperparameters of the LightGBM model, while a 10-fold cross-validation was conducted to determine the optimal parameters for SVM. Ultimately, the LightGBM model achieved higher accuracy, macro-F1, and Cohen’s kappa coefficient (kappa coefficient) values of 0.9370, 0.9364, and 0.9244, respectively, compared to the SVM model’s values of 0.8888, 0.8881, and 0.8666. This study provides a novel method for the rapid classification of fresh meat varieties using LIBS.

Abstract Colorful radiative coolers (CRCs) can be widely applied for energy sustainability especially and meet aesthetic purposes simultaneously. Here, we propose a high-efficiency CRC based on thin film stacks and engineered diffuse reflection unit, which brings out 7.1 °C temperature difference compared with ambient under ~ 700 W·m−2 solar irradiation. Different from analogous schemes, the proposed CRCs produce vivid colors by diffuse reflection and rest of the incident light is specular-reflected without being absorbed. Adopting the structure of TiO2/SiO2 multilayer stack, the nanophotonic radiative cooler shows extra low absorption across the solar radiation waveband. Significant radiative cooling performance can be achieved with the emissivity reaching 95.6% in the atmosphere transparent window (8–13 μm). Moreover, such CRC can be fabricated on flexible substrates, facilitating various applications such as the thermal management of cars or wearables. In conclusion, this work demonstrates a new approach for color display with negligible solar radiation absorption and paves the way for prominent radiative cooling.

In order to develop cost-effective photocatalytic devices, a nanorod array-structured carbon quantum dot/zinc oxide (CQDs/ZnO) composite was synthesized on a steel wire mesh substrate using waste rice noodle (WRN) as the raw material. The incorporation of CQDs derived from WRN played a crucial role in controlling the morphology of the ZnO-based array. By optimizing the loading of CQDs, the CQDs/ZnO composite achieved a regular hexagonal prism nanorod array distribution and exhibited highly efficient photocatalytic degradation of various organic pollutants. For instance, in the case of methylene blue, the CQDs/ZnO composite demonstrated a remarkable degradation rate of 99.4% within 90 min, with a high degradation rate constant of 0.033 min−1. Moreover, the composite material could be recycled and reused for five photocatalytic cycles without a significant decrease in its degradation performance, surpassing the performance of the pure ZnO array. Furthermore, the resulting CQDs/ZnO composite array exhibited effective photocatalytic degradation for other organic dyes such as malachite green, methyl violet, basic fuchsin, and rhodamine B. Additionally, this composite material was successfully applied to real water purification, achieving a high mineralization efficiency of 48% and a low leaching rate of Zn of 1.2% in a river water sample after a 90-minute photocatalytic process. The introduction of CQDs derived from WRN into the ZnO array led to efficient electron-hole pair separation, enabling more photogenerated electrons to reduce O2 and more photogenerated holes to oxidize H2O. This resulted in enhanced radical generation and improved photocatalytic degradation of organic pollutants, showcasing the superior performance of the CQDs/ZnO composite array.

Abstract Background Immunoglobulin A nephropathy (IgAN) is a complex autoimmune disease, and the exact pathogenesis remains to be elucidated. This study aimed to explore genes underlying the pathogenesis of IgAN. Methods We conducted the summary data-based Mendelian randomization (SMR) analysis and performed functional mapping and annotation using FUMA to explore genetic loci that are potentially involved in the pathogenies of IgAN. Both analyses used summarized data of a recent genome-wide association study (GWAS) on IgANs, which included 477,784 Europeans (15,587 cases and 462,197 controls) and 175,359 East Asians (71 cases and 175,288 controls). We performed SMR analysis using Consortium for the Architecture of Gene Expression (CAGE) expression quantitative trait loci (eQTL) data and replicated the analysis using Genotype-Tissue Expression (GTEx) eQTL data. Results Using the CAGE eQTL data, our SMR analysis identified 32 probes tagging 25 unique genes whose expression were pleiotropically associated with IgAN, with the top three probes being ILMN_2150787 (tagging HLA-C, P SMR= 2.10 × 10–18), ILMN_1682717 (tagging IER3, P SMR= 1.07 × 10–16) and ILMN_1661439 (tagging FLOT1, P SMR=1.16 × 10–14). Using GTEx eQTL data, our SMR analysis identified 24 probes tagging 24 unique genes whose expressions were pleiotropically associated with IgAN, with the top three probes being ENSG00000271581.1 (tagging XXbac-BPG248L24.12, P SMR= 1.44 × 10–10), ENSG00000186470.9 (tagging BTN3A2, P SMR= 2.28 × 10–10), and ENSG00000224389.4 (tagging C4B, P SMR= 1.23 × 10 –9). FUMA analysis identified 3 independent, significant and lead SNPs, 2 genomic risk loci and 39 genes that are potentially involved in the pathogenesis of IgAN. Conclusion We identified many genetic variants/loci that are potentially involved in the pathogenesis of IgAN. More studies are needed to elucidate the exact mechanisms of the identified genetic variants/loci in the etiology of IgAN.

Lipid nanoparticles (LNPs) are effective vehicles to deliver mRNA vaccines and therapeutics but assessing the mRNA packaging characteristics in LNPs is challenging. Here, the authors report that mRNA and lipid contents in LNP formulations can be quantitatively examined by multi-laser cylindrical illumination confocal spectroscopy at the single-nanoparticle level.

Abstract Dyskinesia is one of the most disabling motor complications in Parkinson’s Disease (PD). Sleep is crucial to keep neural circuit homeostasis, and PD patients often suffer from sleep disturbance. However, few prospective studies have been conducted to investigate the association of sleep quality with dyskinesia in PD. The objective of the current study is to investigate the association between sleep quality and dyskinesia and build a prediction model for dyskinesia in PD. We prospectively followed a group of PD patients without dyskinesia at baseline for a maximum of 36 months. Univariable and multivariable Cox regression with stepwise variable selection was used to investigate risk factors for dyskinesia. The performance of the model was assessed by the time-dependent area under the receiver-operating characteristic curve (AUC). At the end of follow-up, 32.8% of patients developed dyskinesia. Patients with bad sleep quality had a significantly higher proportion of dyskinesia compared with those with good sleep quality (48.1% vs. 20.6%, p = 0.023). Multivariable Cox regression selected duration of PD, sleep quality, cognition, mood, and levodopa dose. Notably, high Pittsburgh sleep quality index (PSQI) score was independently associated with an increased risk of dyskinesia (HR = 2.96, 95% CI 1.05–8.35, p = 0.041). The model achieved a good discriminative ability, with the highest AUC being 0.83 at 35 months. Our results indicated that high PSQI score may increase the risk of developing dyskinesia in PD, implying that therapeutic intervention targeting improving sleep quality may be a promising approach to prevent or delay the development of dyskinesia in PD.

Plasmid DNA offers extended transgene expression duration compared to mRNA technologies. Here, using a multi-step screening platform, the authors report the best performing nanoparticle formulations for liver-targeted plasmid DNA expression in vivo.

Photothermal therapy has been intensively investigated for treating cancer in recent years. However, the long-term therapeutic outcome remains unsatisfying due to the frequently occurred metastasis and recurrence. To address this challenge, immunotherapy has been combined with photothermal therapy to activate anti-tumor immunity and relieve the immunosuppressive microenvironment within tumor sites. Here, we engineered silica-based core‒shell nanoparticles (JQ-1@PSNs-R), in which silica cores were coated with the photothermal agent polydopamine, and a bromodomain-containing protein 4 (BRD4) inhibitor JQ-1 was loaded in the polydopamine layer to combine photothermal and immune therapy for tumor elimination. Importantly, to improve the therapeutic effect, we increased the surface roughness of the nanoparticles by hydrofluoric acid (HF) etching during the fabrication process, and found that the internalization of JQ-1@PSNs-R was significantly improved, leading to a strengthened photothermal killing effect as well as the increased intracellular delivery of JQ-1. In the animal studies, the multifunctional nanoparticles with rough surfaces effectively eradicated melanoma via photothermal therapy, successfully activated tumor-specific immune responses against residual tumor cells, and further prevented tumor metastasis and recurrence. Our results indicated that JQ-1@PSNs-R could serve as an innovative and effective strategy for combined cancer therapy.

Introduction: The purpose of this study was to evaluate the effect of running-induced fatigue on the characteristic asymmetry of running gait and to identify non-linear differences in bilateral lower limbs and fatigued gait by building a machine learning model.Methods: Data on bilateral lower limb three-dimensional ground reaction forces were collected from 14 male amateur runners before and after a running-induced fatigue experiment. The symmetry function (SF) was used to assess the degree of symmetry of running gait. Statistical parameter mapping (Paired sample T-test) algorithm was used to examine bilateral lower limb differences and asymmetry changes pre- and post-fatigue of time series data. The support vector ma-chine (SVM) algorithm was used to recognize the gait characteristics of both lower limbs before and after fatigue and to build the optimal algorithm model by setting different kernel functions.Results: The results showed that the ground reaction forces were asymmetrical (SF > 0.5) both pre-and post-fatigue and mainly concentrated in the medial-lateral direction. The asymmetry of the medial-lateral direction increased significantly after fatigue (p < 0.05). In addition, we concluded that the polynomial kernel function could make the SVM model the most accurate in classifying left and right gait features (accuracy of 85.3%, 82.4%, and 82.4% in medial-lateral, anterior-posterior and vertical directions, respectively). Gaussian radial basis kernel function was the optimal kernel function of the SVM algorithm model for fatigue gait recognition in the medial-lateral and vertical directions (accuracy of 54.2% and 62.5%, respectively). Moreover, polynomial was the optimal kernel function of the anterior-posterior di-rection (accuracy = 54.2%).Discussion: We proved in this study that the SVM algorithm model depicted good performance in identifying asymmetric and fatigue gaits. These findings can provide implications for running injury prevention, movement monitoring, and gait assessment.

Abstract Active temperature control devices are widely used for the thermal management of enclosures, including vehicles and buildings. Passive radiative cooling has been extensively studied; however, its integration with existing actively temperature regulated and decorative enclosures has slipped out of the research at status quo. Here, we present a photonic-engineered dual-side thermal management strategy for reducing the active power consumption of the existing temperature-regulated enclosure without sacrificing its aesthetics. By coating the exterior and interior of the enclosure roof with two visible-transparent films with distinctive wavelength-selectivity, simultaneous control over the energy exchange among the enclosure with the hot sun, the cold outer space, the atmosphere, and the active cooler can be implemented. A power-saving of up to 63% for active coolers of the enclosure is experimentally demonstrated by measuring the heat flux compared to the ordinary enclosure when the set temperature is around 26°C. This photonic-engineered dual-side thermal management strategy offers facile integration with the existing enclosures and represents a new paradigm toward carbon neutrality.

Background: Misdiagnosis and underdiagnosis of pulmonary hypertension caused by fibrosing mediastinitis (PH-FM) are considerably prevalent due to unspecific symptoms and as well as the lack of awareness of this fatal disease. Objectives: The aim of this study was to evaluate the diagnostic accuracy of the chest X-ray (CXR) for screening the patients with PH-FM from those with pulmonary hypertension (PH). Design: This was a retrospective observational cohort study. Methods: The patients with suspected PH were recruited between October 2014 and October 2020. All the clinical data and CXR findings were collected. The sensitivity, specificity, and likelihood ratio of the CXR features were calculated. Logistic regression was used to identify the factors associated with the CXR characteristics and FM and to generate a prediction model. Finally, the diagnostic efficiency of the prediction model was evaluated using nomogram and internal validation. Results: The patients with PH-FM ( n = 36) and PH caused by the diseases other than FM (PH-non-FM, n = 62) were enrolled. The CXR features, including atelectasis, pleural effusion, consolidation, nodules, calcification, interlobular septal thickening, and interstitial reticulation, were more prevalent in patients with PH-FM than in those with PH-non-FM (all p

In this study, we have obtained the corresponding relationship between SLM-NiTi alloy transition temperatures and energy density, and realized the accurate prediction and inverse prediction of SLM process parameters and NiTi alloy transition temperatures. And we verify the accuracy of the prediction model through DSC, XRD and SEM. Secondly, B19′NiTi and B2NiTi are successfully obtained through the prediction model. Finally, a composite gradient NiTi is established based on the idea of rigid-flexible coupling, which successfully achieved the simultaneous improvement of the strength and toughness of the SLM-NiTi alloy. The compressive fracture strength reached 2668 MPa, and the fracture strain reached 55.51%.

With the continuous advancement of global industrialization, a large amount of organic and inorganic pollutants have been discharged into the environment, which is essential for human survival. Consequently, the issue of water environment pollution has become increasingly severe. Photocatalytic technology is widely used to degrade water pollutants due to its strong oxidizing performance and non-polluting characteristics, and BiVO4-based photocatalysts are one of the ideal raw materials for photocatalytic reactions. However, a comprehensive global analysis of the factors influencing the photocatalytic performance of BiVO4-based photocatalysts is currently lacking. Here, we performed a meta-analysis to investigate the differences in specific surface area, kinetic constants, and the pollutant degradation performance of BiVO4-based photocatalysts under different preparation and degradation conditions. It was found that under the loading condition, all the performances of the photocatalysts can be attributed to the single BiVO4 photocatalyst. Moreover, loading could lead to an increase in the specific surface area of the material, thereby providing more adsorption sites for photocatalysis and ultimately enhancing the photocatalytic performance. Overall, the construct heterojunction and loaded nanomaterials exhibit a superior performance for BiVO4-based photocatalysts with 136.4% and 90.1% improvement, respectively. Additionally, within a certain range, the photocatalytic performance increases with the reaction time and temperature.

Wavelength‐selective light trapping has been widely applied in fields such as energy utilization, optical sensing, optical imaging, and so forth. Though metasurfaces have exhibited to efficiently trap the light for diverse optical responses with specific configurations, the manufacturing costs and processing precision both limit the extensive applications. Here, a compact optical coating to trap light either at a single wavelength or across a broadband within a nanometer‐thick single‐layer metallic coating based on mirror|phase tuning layer|absorptive (MPA) and MPA|anti‐reflection coating stacks is proposed. The intermediate phase‐tuning dielectric provides a specific phase shift for the cavity to achieve destructive or constructive interference for ≈100% absorption or reflection correspondingly by switching from ultrahigh‐index dielectric behavior to epsilon‐near‐zero material behavior. And the additional dielectric is introduced to reduce the increasing reflection for the final broadband absorption with a thicker absorptive metal. Furthermore, the efficient color‐preserving solar–thermal conversion based on this compact coating (4‐layer) with the entire thickness of ≈300 nm is demosntrated experimentally. Various perceived colors are produced by simply changing the thickness of the top dielectric layer with the efficient solar–thermal conversion remained, verified by the temperature difference between the fabricated device and the dye‐based plastic exceeding 13 °C with the solar irradiance ≈850 W m−2.

BackgroundAmyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease whose primary hallmark is the progressive degeneration of motor neurons in the brainstem, spinal cord, and cerebral cortex that leads to weakness, spasticity, fatigue, skeletal muscle atrophy, paralysis, and even death. Exercise, as a non-pharmacological tool, may generally improve muscle strength, cardiovascular function, and quality of life. However, there are conflicting reports about the effect of exercise training in adults with ALS.AimsThis systematic review and network meta-analysis aim to conduct a mixed comparison of different exercise interventions for function, respiratory, fatigue, and quality of life in adults with ALS.MethodsRandomized controlled trials with ALS participants were screened and included from the databases of PubMed, Medline, and Web of Science. Physical exercise interventions were reclassified into aerobic exercise, resistance training, passive exercise, expiratory muscle exercise, and standard rehabilitation. Patient-reported outcome measures would be reclassified from perspectives of function, respiratory, fatigue, and quality of life. The effect size would be transferred into the percentage change of the total score.ResultThere were 10 studies included, with the agreement between authors reaching a kappa-value of 0.73. The network meta-analysis, which was conducted under the consistency model, identified that a combined program of aerobic exercise, resistance exercise, and standard rehabilitation showed the highest potential to improve quality of life (0.64 to be the best) and reduce the fatigue (0.39 to be the best) for ALS patients, while exercise program of aerobic and resistance training showed the highest potential (0.51 to be the best) to improve ALS patients' physical function. The effect of exercise on the respiratory was still unclear.ConclusionA multi-modal exercise and rehabilitation program would be more beneficial to ALS patients. However, the safety and guide for practice remain unclear, and further high-quality randomized controlled trials (RCTs) with a larger sample are still needed.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021253442, CRD42021253442.

Objective: To explore potential causal genetic variants and genes underlying the pathogenesis of uterine leiomyomas (ULs).Methods: We conducted the summary data-based Mendelian randomization (SMR) analyses and performed functional mapping and annotation using FUMA to examine genetic variants and genes that are potentially involved in the pathogenies of ULs. Both analyses used summarized data of a recent genome-wide association study (GWAS) on ULs, which has a total sample size of 244,324 (20,406 cases and 223,918 controls). We performed separate SMR analysis using CAGE and GTEx eQTL data.Results: Using the CAGE eQTL data, our SMR analysis identified 13 probes tagging 10 unique genes that were pleiotropically/potentially causally associated with ULs, with the top three probes being ILMN_1675156 (tagging CDC42, PSMR = 8.03 × 10−9), ILMN_1705330 (tagging CDC42, PSMR = 1.02 × 10−7) and ILMN_2343048 (tagging ABCB9, PSMR = 9.37 × 10−7). Using GTEx eQTL data, our SMR analysis did not identify any significant genes after correction for multiple testing. FUMA analysis identified 106 independent SNPs, 24 genomic loci and 137 genes that are potentially involved in the pathogenesis of ULs, seven of which were also identified by the SMR analysis.Conclusions: We identified many genetic variants, genes, and genomic loci that are potentially involved in the pathogenesis of ULs. More studies are needed to explore the exact underlying mechanisms in the etiology of ULs.

Background and objective For early-stage lung cancer, segmentectomy can get the same oncological benefits as lobectomy. Accurate identification of the intersegmental border is the key to segmentectomy. This study used extended segmentectomy and extended subsegmentectomy to treat lung intersegmental and intersubsegmental ground-glass nodules (GGN) by utilizing modified inflation-deflation methods to distinguish the intersegmental and intersubsegmental borders. The accuracy of modified inflation-deflation methods and the effectiveness of extended resection to guarantee a safe surgical margin were evaluated. Methods A retrospective analysis of 83 cases of extended segmentectomy and extended subsegmentectomy was conducted. Preoperative three-dimensional computed tomography bronchography and angiography (3D-CTBA) revealed that nodules were involved in intersegmental or intersubsegmental veins. Based on preoperative three-dimensional reconstruction, the surgery was designed to extendedly remove the dominant lung segment or subsegment with nodules involved. When the dominant lung segment or subsegment could not be identified, the simpler lung segment or subsegment was selected for the resection. After the target vessel and bronchus were cut off during the operation, modified inflation-deflation method was used to determine the border, and a stapler was used to resect the adjacent lung segment or subsegment tissue by 2 cm-3 cm around the inflation-deflation boundary line. Then, the relationship between the inflation-deflation boundary line and the nodule and the width of the surgical margin were measured. Clinical data were collected during the perioperative period. Results 56 extended segmentectomies and 27 extended subsegmentectomies were performed. The average diameter of pulmonary nodules was (0.9±0.3) cm. There were 79 cases with clearly inflation-deflation boundary lines. The average time needed for the appearance of the lines was (13.6±6.5) min. In 55 cases, the nodules were involved with the inflation-deflation boundary lines. Meanwhile, the remaining 24 cases revealed an average minimum distance of (0.6±0.3) cm between nodules and the boundary lines. The average width of surgical margin was (2.1±0.3) cm in these 79 cases. No deaths or major complications appeared during 30 d after operation. Conclusion The modified inflation-deflation method can effectively define the intersegmental and intersubsegmental borders, and guarantee the safe surgical margins of extended segmentectomy and extended subsegmentectomy to treat intersegmental and intersubsegmental small lung tumors.

Limited-angle computed tomography (CT) has arisen in some medical and industrial applications. It is also a challenging problem since some scan views are missing and the directly reconstructed images often suffer from severe distortions. For such kind of problems, we analyze the features of limited-angle CT images and propose a multi-scale dilated convolution neural network (MSD-CNN) to correct the artifacts and to restore the image. In this network, the dilated convolution layer and multi-scale pooling layer are combined to form a group and exited in the whole encoder-decoder process. Since the dilated convolutions support an exponential expansion of the receptive field without losing resolution and coverage, the obtained artifact features possess the multi-scale characteristic. Furthermore, to improve the effectiveness and accuracy of the training step, we employ a preprocessing method, which extracts image patches. Numerical experiments verify the out-performance of the proposed method compared with some conventional methods, such as Unet based deep learning,TV- and L0 -based optimization methods.

The x-ray computed tomography (CT) is essential for medical diagnosis and industrial nondestructive testing. The aim of CT is to recover or reconstruct image from projection data. However, in particular, the reconstructed image usually suffers from complex artifacts and noise, such as the sampling is insufficient or low-dose CT. In order to deal with such issues and achieve reconstruction, a full automatic reconstruction (FAR) net is proposed for CT reconstruction via deep learning technique. Different with the usual network in deep learning reconstruction, the proposed neural network is an end-to-end network by which the image is predicted directly from projection data. The main challenge for such a FAR-net is the space complexity of the CT reconstruction in full-connected (FC) network. For a CT image with the size N × N, a typical requirement of memory space for the image reconstruction is O(N4), for which is unacceptable by conventional calculation device, e.g. GPU workstation. In this paper, we utilize a series of smaller fully connected layers (FCL) to replace the huge Radon transform matrix based on the sparse nonnegative matrix factorization (SNMF) theory. By applying such an approach, the FAR-net is able to reconstruct images with the size 512×512 on only single workstation. The results of numerical experiments show that the projection matrix and the FAR-net is able to reconstruct the CT image from projection data with a superior quality to conventional methods such as optimization based approach. Meanwhile, the factorization for the inverse projection matrix is validated in simulation and real experiments.

Infected areas and damage levels due to crop pest and disease have been growing seriously according to the climate change. We aim to develop an automatic system to provide national pest and disease dynamic monitoring and early forecasting products, by integrating multisource information (Earth Observation, meteorological, ecological, entomological, and plant pathological, etc.) and cutting edge research on pest and disease modeling to support decision making in the sustainable management of pest and disease. First, we selected the sensitive indexes for pest and disease habitat monitoring and early forecasting, and then optimized the forecasting model's parameters to enhance its applicability in national level. Second, we developed an automatic system based on web GIS platform to efficiently realize the national pest and disease dynamic habitat monitoring and early forecasting. Finally, we released the pest and disease forecasting thematic maps. China's national disease wheat yellow rust (Puccinia striiformis) and national pest oriental migratory locust (Locusta migratoria manilensis (Meyen)) are taking as the experimental objects. Based on the developed system, we forecasted the infected areas of rust and locust in China, in 2019, with these R-square values are higher than 0.87. This system would not only promote the efficacy of pest and disease management and prevention by improving accuracy of monitoring and forecasting, but also help to reduce the amount of chemical pesticides, which could thus guarantee food security and agriculture sustainable development in China.

Background: Esophageal squamous cell carcinoma (ESCC) is one of the most lethal malignancies with poor prognosis. Cancer-testis genes (CTGs) have been vigorously pursued as targets for cancer immunotherapy, but the expressive patterns and functional roles of CTGs remain unclear in ESCC. Methods: A systematic screening strategy was adopted to screen CTGs in ESCC by integrating multiple public databases and RNA expression microarray data from 119 ESCC subjects. For the newly identified ESCC prognosis-associated CTGs, an independent cohort of 118 patients with ESCC was recruited to validate the relationship via immunohistochemistry. Furthermore, functional assays were performed to determine the underlying mechanisms. Findings: 21 genes were recognized as CTGs, in particular, CDCA5 was aberrantly upregulated in ESCC tissues and significantly associated with poor prognosis (HR = 1.85, 95%CI: 1.14–3.01, P = .013). Immunohistochemical staining confirmed that positive CDCA5 expression was associated with advanced TNM staging and a shorter overall survival rate (45.59% vs 28.00% for CDCA5−/+ subjects, P = 1.86 × 10−3). H3K27 acetylation in CDCA5 promoter might lead to the activation of CDCA5 during ESCC tumorigenesis. Functionally, in vitro assay of gain- and loss-of-function of CDCA5 suggested that CDCA5 could promote ESCC cells proliferation, invasion, migration, apoptosis resistance and reduce chemosensitivity to cisplatin. Moreover, in vivo assay showed that silenced CDCA5 could inhibit tumor growth. Mechanistically, CDCA5 knockdown led to an arrest in G2/M phase and changes in the expression of factors that played fundamental roles in the cell cycle pathway. Interpretation: CDCA5 contributed to ESCC progression and might serve as an attractive target for ESCC immunotherapy. Fund: This work was supported by the Natural Science Foundation of Jiangsu Province (No. BK20181083 and BK20181496), Jiangsu Top Expert Program in Six Professions (No. WSW-003 and WSW-007), Major Program of Science and Technology Foundation of Jiangsu Province (No. BE2016790 and BE2018746), Jiangsu Medical Young Talent Project (No. QNRC2016566), the Program of Jiangsu Medical Innovation Team (No. CXTDA2017006), Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX18_1487) and Jiangsu Province 333 Talents Project (No. BRA2017545). Keywords: Esophageal squamous cell cancer, Cancer-testis gene, CDCA5, Immunotherapy, Biomarker

In this study, one lab-scale EGSB reactor (1.47 L volume) was designed to treat the antibiotic wastewater under different environmental factors, including the addition of cephalexin (CFX), Temperature (T) and Hydraulic Retention Time (HRT). The microbial community structure in EGSB reactor was analyzed with high-throughput sequencing technology to investigate their response to environmental factors changes, and then the random-matrix-theory (RMT)-based network analysis was used to investigate the microbial community’s molecular ecological network in EGSB systems treating antibiotics wastewater. Moreover, the explanatory value of each environmental factor on the change of microbial community structure was obtained through the result of redundancy analysis (RDA). The results showed that the addition of cephalexin (CFX), decline of T and decline of HRT (8 h) would decrease the removal efficiency of COD decreasing. And the removal efficiency of CFX would not be affected by decline of T and HRT, except the producing and degrading process of CFX by-products was changed obviously. The result of RDA analysis suggested the environmental factors mainly affected bacterial and fungal microbial community structure but not archaeal ones. The result of high-throughput sequencing showed the relative abundance (RA) of Firmicutes had been obviously affected by T and HRT, which might be main reason leading to the decrease of COD removal efficiency. In addition, molecular ecological network analysis showed the growth of Bacteroidetes occupied the niche of functional microorganism and led to the unstable operation of EGSB when T declined. What’s more, the molecular ecological network analysis revealed that Exophiala which belonged to fungi Ascomycota phylum was the hub genus to degrade complex refractory organic pollutants, and Aceticlastic methanogens Methanosaeta was the core functional archaea genus.

Material decomposition (MD) is an important application of computer tomography (CT). For phase contrast imaging, conventional MD methods are categorized into two types with respect to different operation sequences, i.e., "before" or "after" image reconstruction. Both categories come down to two-step methods, which have the problem of noise amplification. In this study, we incorporate both phase and absorption (PA) information into MD process, and correspondingly develop a simultaneous algebraic reconstruction technique (SART). The proposed method is referred to as phase & absorption material decomposition-SART (PAMD-SART). By iteratively solving an optimization problem, material composition and substance quantification are reconstructed directly from absorption and differential phase projections. Comparing with two-step MD, the proposed one-step method is superior in noise suppression and accurate decomposition. Numerical simulations and synchrotron radiation based experiments show that PAMD-SART outperforms the classical MD method (image-based and dual-energy CT iterative method), especially for the quantitative accuracy of material equivalent atomic number.

Computed tomography (CT) image reconstruction using classical total variation (TV)-based methods or its variations inevitably suffers from a blocky effect when the sampling number is low, because of the piecewise assumption. A low-rank based method is an effective way to circumvent this side effect. Normally, a nuclear norm is used to impose the low rank constraint, and its numerical computation depends on the sum of singular values, which are calculated by singular value decomposition. However, as larger singular values mainly deliver the structural information, treating all the singular values equally may lead to imperfect preservation of edges and textures. To deal with this problem, we here propose to reconstruct the CT image by explicitly exploring the nonlocal similarity in the target image with nonlocal weighted nuclear norm minimization. First, a matrix is constructed by grouping nonlocal patches similar to the current patch. Then, the original nuclear norm minimization is replaced by a weighted version, which treats the singular values differently according to their magnitudes. By doing this, we can eliminate noise and streak artifacts without introducing any side effects. The corresponding numerical algorithm is given by an alternating optimization strategy. Experimental results demonstrate that our method outperforms several existing reconstruction methods in both qualitative and quantitative aspects, including filtered back projection, TV, and total generalized variation methods.

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation.

Size-dependent phagocytosis is a well-characterized phenomenon in monocytes and macrophages. However, this size effect for preferential gene delivery to these important cell targets has not been fully exploited because commonly adopted stabilization methods for electrostatically complexed nucleic acid nanoparticles, such as PEGylation and charge repulsion, typically arrest the vehicle size below 200 nm. Here, we bridge the technical gap in scalable synthesis of larger submicron gene delivery vehicles by electrostatic self-assembly of charged nanoparticles, facilitated by a polymer structurally designed to modulate internanoparticle Coulombic and van der Waals forces. Specifically, our strategy permits controlled assembly of small poly(ß-amino ester)/messenger ribonucleic acid (mRNA) nanoparticles into particles with a size that is kinetically tunable between 200 and 1,000 nm with high colloidal stability in physiological media. We found that assembled particles with an average size of 400 nm safely and most efficiently transfect monocytes following intravenous administration and mediate their differentiation into macrophages in the periphery. When a CpG adjuvant is co-loaded into the particles with an antigen mRNA, the monocytes differentiate into inflammatory dendritic cells and prime adaptive anticancer immunity in the tumor-draining lymph node. This platform technology offers a unique ligand-independent, particle-size-mediated strategy for preferential mRNA delivery and enables therapeutic paradigms via monocyte programming. [ABSTRACT FROM AUTHOR]