A fast-flexible strategy based approach to solving employee scheduling problem considering soft work time.

Employee scheduling aims to assign employees to shifts to satisfy daily workload and constraints. Some employee scheduling problems and their variants have been proven NP-hard, and a series of works have been done. However, the existing algorithms consider the fixed work time, which may cause plenty of overstaffing and understaffing phenomenons. Hence, this paper proposes a fast-flexible strategy based approach (FFS) to solve it. FFS introduces the idea of soft work time, which allows the work time of employees can be adjusted in a range. Based on this, we set the flextime strategy to decide the specific work time of each employee every day. Besides, FFS adopts a pairwise-allocated strategy and proficiency average matrix to boost its efficiency and effectiveness. Finally, the extensive experimental evaluation shows that FFS is more effective and efficient than the baselines for solving the employee scheduling problem considering soft work time.

Development of a risk score for myopia: A cohort study conducted among school-aged children in China.

PURPOSE: To evaluate the myopia risk in school-aged children one year after lifting a pandemic-related lockdown and develop a tool to identify high-risk groups. METHODS: In total, 38,079 children without myopia from 38 schools were included. The outcomes were myopia incidence and progression in 1 year after the COVID-19 lockdown was lifted, both obtained by the spherical equivalent refraction (SER). We separated the population into an exploratory (75%) and a validation sample (25%) to construct the risk score model. RESULTS: In total, 9811 (29.57%) students became myopic, and the overall myopia progression was 0.22 ± 0.62 D. Even less myopia progression was noted in the pre-myopia group at baseline (All: P = 0.045, Boy: P = 0.005). The risk score model included seven predictors: gender, grade, SER at baseline, residence, parental myopia, eye discomfort symptoms, and online courses. The model had a score range of 0-46 and an optimal cutoff of 34. The area under the receiver operating curve of the model was 0.726 (0.719-0.732) for the exploratory sample and 0.731 (0.720-0.742) for the validation sample. CONCLUSIONS: The risk score can serve as a practical tool for classifying the risk of myopia in school-aged children.

Comparison of the effects of stepwise intracranial decompression and decompressive craniectomy in the treatment of severe traumatic brain injury: A randomized controlled trial.

BACKGROUND: To compare the effects of stepwise intracranial decompression (SID) and decompressive craniectomy (DC) on severe traumatic brain injury. METHODS: This prospective randomized study was conducted at The Third Affiliated Hospital of Soochow University. Ninety two patients were divided into 2 groups according to the random number table method. The study group received SID, whereas the control group received DC. The surgical time and intraoperative bleeding of the 2 groups of patients were recorded, neurological function and glasgow coma score before and after treatment in both groups, incidence of complications, prognostic situation, and levels of brain oxygen metabolism indicators before and after treatment. RESULTS: Among the 92 patients who agreed, 46 were assigned to the study and control groups, and 6 patients were excluded. Finally, 86 patients were analyzed, including 43 in the study group and 43 in the control group. After treatment, the glasgow coma score scores of the 2 groups increased compared to before treatment; the study group had a higher score, The National Institutes of Health Stroke Scale score decreased compared to before treatment, and the study group had a lower score (P < .05). The incidence of complications in the study group (4.65%) was significantly lower than that in the control group (18.60%) (P < .05). The good prognosis rate of the research group (41.86%) was significantly higher than that of the control group (16.28%) (P < .05). CONCLUSION: Compared with DC, using SID to treat severe traumatic brain injury can shorten surgical time and reduce intraoperative bleeding, more effectively improve patients neurological function and consciousness state, reduce the incidence of complications, and regulate brain oxygen metabolism status, which is beneficial for improving prognosis and ensuring a good outcome of the disease.

Photoacoustic digital brain and deep-learning-assisted image reconstruction.

Photoacoustic tomography (PAT) is a newly developed medical imaging modality, which combines the advantages of pure optical imaging and ultrasound imaging, owning both high optical contrast and deep penetration depth. Very recently, PAT is studied in human brain imaging. Nevertheless, while ultrasound waves are passing through the human skull tissues, the strong acoustic attenuation and aberration will happen, which causes photoacoustic signals' distortion. In this work, we use 180 T1 weighted magnetic resonance imaging (MRI) human brain volumes along with the corresponding magnetic resonance angiography (MRA) brain volumes, and segment them to generate the 2D human brain numerical phantoms for PAT. The numerical phantoms contain six kinds of tissues, which are scalp, skull, white matter, gray matter, blood vessel and cerebrospinal fluid. For every numerical phantom, Monte-Carlo based optical simulation is deployed to obtain the photoacoustic initial pressure based on optical properties of human brain. Then, two different k-wave models are used for the skull-involved acoustic simulation, which are fluid media model and viscoelastic media model. The former one only considers longitudinal wave propagation, and the latter model takes shear wave into consideration. Then, the PA sinograms with skull-induced aberration is taken as the input of U-net, and the skull-stripped ones are regarded as the supervision of U-net to train the network. Experimental result shows that the skull's acoustic aberration can be effectively alleviated after U-net correction, achieving conspicuous improvement in quality of PAT human brain images reconstructed from the corrected PA signals, which can clearly show the cerebral artery distribution inside the human skull.

E2F1-Deficient Adipose-Derived Stem Cells Improve Wound Closure in Mice by Up-Regulating Expression of VEGF and TGF-ß1.

BACKGROUND: Wound healing is a widespread health problem that imposes a financial burden on health systems. Cell therapy with genetically modified adipose-derived stem cells (ADSCs) is a promising strategy for dysregulated wound repair. E2F transcription factor 1 (E2F1) is a bidirectional regulator of cytokines. Here, the authors aimed to investigate the impact and potential mechanism of E2F1 -/- ADSCs in promoting the wound healing process. METHODS: Forty-five C57BL/6 mice (specific pathogen-free, male) with 10-mm full-thickness wounds were randomly treated with subcutaneous injection of 2 × 10 6 wild-type ADSCs, 2 × 10 6 E2F1 -/- ADSCs, or phosphate-buffered saline. The wound closure rate was monitored at days 0, 3, 7, 10, and 14 after treatment. The collagen synthesis, angiogenesis, and wound contraction were calculated by Masson, immunohistochemistry, and immunofluorescent staining (CD31 and KI67), Western blotting (α-smooth muscle actin, collagen I, vascular endothelial growth factor, and transforming growth factor-ß1) separately at day 14. In vitro, the conditioned media (CM) of wild-type ADSCs and E2F1 -/- ADSCs were collected to evaluate the impact on proliferation, migration, and angiogenesis. RESULTS: In vivo, the E2F1 -/- ADSC group exhibited increased healing rate, proliferating vessels, and collagen synthesis compared with control at day 14 ( P < 0.05). Moreover, E2F1 -/- ADSCs showed enhanced vascular endothelial growth factor and transforming growth factor-ß1 expression in the wound site and CM, and the CM from E2F1 -/- ADSCs promoted the proliferation, migration, and tube formation of co-cultured cells in vitro ( P < 0.05). CONCLUSION: The E2F1 -/- ADSCs exhibited a strong paracrine ability to improve the vascularization process and collagen deposition, thereby accelerating wound healing in the rodent model. CLINICAL RELEVANCE STATEMENT: These findings show that targeting transcription factor E2F1 could regulate the paracrine function of ADSCs, developing E2F1-modified ADSCs as an effective therapeutic option for wound healing and regeneration.

A jointed feature fusion framework for photoacoustic image reconstruction.

The standard reconstruction of Photoacoustic (PA) computed tomography (PACT) image could cause the artifacts due to interferences or ill-posed setup. Recently, deep learning has been used to reconstruct the PA image with ill-posed conditions. In this paper, we propose a jointed feature fusion framework (JEFF-Net) based on deep learning to reconstruct the PA image using limited-view data. The cross-domain features from limited-view position-wise data and the reconstructed image are fused by a backtracked supervision. A quarter position-wise data (32 channels) is fed into model, which outputs another 3-quarters-view data (96 channels). Moreover, two novel losses are designed to restrain the artifacts by sufficiently manipulating superposed data. The experimental results have demonstrated the superior performance and quantitative evaluations show that our proposed method outperformed the ground-truth in some metrics by 135% (SSIM for simulation) and 40% (gCNR for in-vivo) improvement.

A Novel Data-Driven Evaluation Framework for Fork after Withholding Attack in Blockchain Systems.

In the blockchain system, mining pools are popular for miners to work collectively and obtain more revenue. Nowadays, there are consensus attacks that threaten the efficiency and security of mining pools. As a new type of consensus attack, the Fork After Withholding (FAW) attack can cause huge economic losses to mining pools. Currently, there are a few evaluation tools for FAW attacks, but it is still difficult to evaluate the FAW attack protection capability of target mining pools. To address the above problem, this paper proposes a novel evaluation framework for FAW attack protection of the target mining pools in blockchain systems. In this framework, we establish the revenue model for mining pools, including honest consensus revenue, block withholding revenue, successful fork revenue, and consensus cost. We also establish the revenue functions of target mining pools and other mining pools, respectively. In particular, we propose an efficient computing power allocation optimization algorithm (CPAOA) for FAW attacks against multiple target mining pools. We propose a model-solving algorithm based on improved Aquila optimization by improving the selection mechanism in different optimization stages, which can increase the convergence speed of the model solution and help find the optimal solution in computing power allocation. Furthermore, to greatly reduce the possibility of falling into local optimal solutions, we propose a solution update mechanism that combines the idea of scout bees in an artificial bee colony optimization algorithm and the constraint of allocating computing power. The experimental results show that the framework can effectively evaluate the revenue of various mining pools. CPAOA can quickly and accurately allocate the computing power of FAW attacks according to the computing power of the target mining pool. Thus, the proposed evaluation framework can effectively help evaluate the FAW attack protection capability of multiple target mining pools and ensure the security of the blockchain system.

Spatiotemporal dynamic network for regional maritime vessel flow prediction amid COVID-19.

The COVID-19 pandemic has stifled international trade and the global maritime industry. Its impact on the routing of the regional vessel traffic flow provides supportive data to port authorities, ship owners, shippers, and consignees. This study proposes a spatiotemporal dynamic graph neural network (STDGNN) model that includes the usual primary part of the vessel flow and an auxiliary part of newly confirmed COVID-19 cases near the port. The primary part consists of a time-embedding (TE) block, two dynamic graph neural network (DGNN) blocks, and a gated recurrent unit block, to capture the spatiotemporal dependence in the regional vessel traffic flow. The auxiliary part is made of multiple blocks to exploit the dynamic temporal relationships in hours, days, and weeks. Moreover, the performance of the STDGNN model is verified by utilising real vessel traffic flow data (i.e. inflow, outflow, and volume) and the new cases of COVID-19 near the port of New York, USA, provided by the automatic identification system and the Johns Hopkins University Centre for Systems Science and Engineering. The 2-h prediction result shows a 37.7%, 17.23%, and 11.4% improvement in the mean absolute error (MAE) over the gated recurrent unit (GRU), STGCN, and TGCN models, respectively. The delicate and adaptable prediction of vessel traffic flow could help the port relieve congestion, enhance efficiency, and further assist the recovery of regional maritime industries in the post-COVID era.

FAWPA: A FAW Attack Protection Algorithm Based on the Behavior of Blockchain Miners.

Blockchain has become one of the key techniques for the security of the industrial internet. However, the blockchain is vulnerable to FAW (Fork after Withholding) attacks. To protect the industrial internet from FAW attacks, this paper proposes a novel FAW attack protection algorithm (FAWPA) based on the behavior of blockchain miners. Firstly, FAWPA performs miner data preprocessing based on the behavior of the miners. Then, FAWPA proposes a behavioral reward and punishment mechanism and a credit scoring model to obtain cumulative credit value with the processed data. Moreover, we propose a miner's credit classification mechanism based on fuzzy C-means (FCM), which combines the improved Aquila optimizer (AO) with strong solving ability. That is, FAWPA combines the miner's accumulated credit value and multiple attack features as the basis for classification, and optimizes cluster center selection by simulating Aquila's predation behavior. It can improve the solution update mechanism in different optimization stages. FAWPA can realize the rapid classification of miners' credit levels by improving the speed of identifying malicious miners. To evaluate the protective effect of the target mining pool, FAWPA finally establishes a mining pool and miner revenue model under FAW attack. The simulation results show that FAWPA can thoroughly and efficiently detect malicious miners in the target mining pool. FAWPA also improves the recall rate and precision rate of malicious miner detection, and it improves the cumulative revenue of the target mining pool. The proposed algorithm performs better than ND, RSCM, AWRS, and ICRDS.

Biredox-Ionic Anthraquinone-Coupled Ethylviologen Composite Enables Reversible Multielectron Redox Chemistry for Li-Organic Batteries.

Organic compounds bearing redox-active ionic pairs as electrode materials for high-performance rechargeable batteries have gained growing attention owing to the properties of synthetic tunability, high theoretical capacity, and low solubility. Herein, an innovative biredox-ionic composite, i.e., ethylviologen dianthraquinone-2-sulfonate (EV-AQ2 ), affording multiple and reversible active sites as a cathode material in lithium-organic batteries is reported. EV-AQ2 exhibits a high initial capacity of 199.2 mAh g-1 at 0.1 C rate, which corresponds to the transfer of two electrons from one redox couple EV2+ /EV0 and four electrons from two redox-active AQ- anions. It is notable that EV-AQ2 shows remarkably improved cyclability compared to the precursors. The capacity retention is 89% and the Coulombic efficiency approaches 100% over 120 cycles at 0.5 C rate. The results offer evidence that AQ- into the EV2+ scaffold with multiple redox sites are promising in developing high-energy-density organic rechargeable batteries.

Effects of atorvastatin doses on serum level of procalcitonin and predictors for major adverse cardiovascular events in patients with acute myocardial infarction: a pilot study and post hoc analysis.

BACKGROUND: Inflammation plays an important role in acute myocardial infarction (AMI). Procalcitonin levels rise in response to proinflammatory stimuli. This study aimed to investigate the effects of different doses of atorvastatin on the serum inflammatory profiles, especially procalcitonin and major adverse cardiovascular events (MACEs) in patients with AMI during hospitalization. METHODS: The patients who were admitted to the Coronary Care Unit of The Third Medical Center of PLA General Hospital (Beijing, China) between January 2015 and December 2015 with a diagnosis of AMI were enrolled, and randomized to atorvastatin 20 mg/day postoperatively (20-mg group), 40 mg/day postoperatively (40-mg group) and 80 mg preoperatively+40 mg/day postoperatively (80/40-mg group). Serum procalcitonin and high-sensitivity C-reactive protein (hs-CRP) were evaluated before and at 1 and 3 days after percutaneous coronary intervention (PCI). RESULTS: A total of 112 patients with AMI (23 women and 89 men) were prospectively eligible for the study. There were no significant differences in most clinical data among the three groups. The 80/40-mg group showed significantly reduced serum procalcitonin levels at 1 and 3 days after PCI (P < 0.001) and reduced hs-CRP levels at 3 days P = 0.001) compared with 20-mg and 40-mg groups. Serum procalcitonin (OR, 4.593; 95% CI, 1.476-8.387; P = 0.005), hs-CRP (OR, 1.149; 95% CI, 1.012-1.338; P = 0.018), highly sensitive cardiac troponin T (OR, 1.255; 95% CI, 1.004-1.569, P = 0.009) and Gensini score (OR, 1.022; 95% CI, 1.045-1.062; P = 0.013) were independently associated with MACEs during hospitalization. CONCLUSION: The use of atorvastatin 80 mg before and 40 mg/day after PCI in patients with AMI can effectively reduce serum inflammatory factors. procalcitonin and hs-CRP were independently associated with in-hospital MACEs.

A multi-purpose high-pressure and high temperature gas-flow cell for operando optical Raman spectroscopy.

A multi-purpose in operando optical cell with temperature and gas pressure control is described. This device allows for in operando Raman spectroscopy measurements for different applications. Its original design includes a temperature control from room temperature up to 1000°C and a heating stage that provides uniform and stable thermal conditions. The pressure control of the cell ranges from 10-1 Pa to 6 MPa, and it is compatible with different gases. Both oxidizing and reducing gases are allowed to pass through the sample surface under study. The device has been calibrated and applied to collect Raman spectra for different reaction systems under various operating temperatures and pressures.

Long-range ordering and local structural disordering of BiAgSe2 and BiAgSeTe thermoelectrics.

Thermoelectric materials are promising for energy harvesting using waste heat. The thermal management of the thermoelectric materials attract scientific and technological interests. The narrow bandgap semiconductor BiAgSe2 is a good candidate for thermoelectric materials due to its ultralow thermal conductivity. The mother compound BiAgSe2 crystallizes in hexagonal symmetry at room temperature, but experiences structural transitions to cubic phase at high temperature. By contrast, the daughter compound BiAgSeTe exhibits long range ordering and crystallizes into cubic phase at room temperature. Nevertheless, the local structural disorderings due to the Bi3+ and Ag+ anti-site defects, as well as local structural distortions, are ubiquitous in both parent BiAgSe2 and BiAgSeTe. BiAgSeTe exhibits distinct transport properties owing to the disordering-induced drastic changes in the electronic band structure, as well as the scattering dictated by the point defects. It is suggested that BiAgSe2 and BiAgSeTe could be good candidates for phonon glass and crystal glass (PGEC)-type thermoelectrics.

[Finite element optimization analysis of minimally invasive screw treatment for Sanders typeâ ¡calcaneal fracture].

OBJECTIVE: To explore biomechanical characteristics of minimally invasive different screw fixations in treating Sanders typeⅡcalcaneal fractures. METHODS: Dicom data of calcaneus by CT scan were input into Mimics 21.0 software and Ansys15.0 software to construct three-dimensional finite element digital model of calcaneus;this model was input into UG NX 10.0 software, and calcaneus was cut according to Sanders classification to establish Sanders typeⅡ calcaneus model with posterior articular surface collapse;then simulated minimally invasive screw internal fixation after calcaneal fracture:a screw from posterior articular surface was used to outside-in fix sustentaculum tali, other 4 screws were used to fix calcaneus by different methods through calcaneal tuberosity, and 4 different calcaneal models were obtained. Under the same conditions, 4 types of internal fixation models were loaded respectively, and nonlinear finite element analysis was performed to calculate the stress distribution of different internal fixation models. RESULTS: Under the same condition of loading, the model 3 had smaller displacement value, maximum calcaneus displacement value and maximum equivalent stress value of the screw than other three internal fixation models, and the stress was more dispersed. CONCLUSION: In minimally invasive screw internal fixation of calcaneus fracture, after 1 sustentaculum tali screw fixation, 2 screws crossed fix posterior articular surface from calcaneal tuberosity, 2 screws fix parallelly calcaneocuboid joint from calcaneal tuberosity are more suitable for biomechanical requirements, and could provide basic theory for clinical treatment.

[Effect of early goal directed sedation on cerebral oxygen metabolism in patients with acute brain injury].

OBJECTIVE: To observe the effect of early goal directed sedation (EGDS) on cerebral oxygen metabolism in patients with acute brain injury. METHODS: A prospective cohort study was conducted. A total of 108 patients with acute brain injury admitted to the intensive care unit (ICU) of the Third Medical Center of the PLA General Hospital from January 2015 to December 2019 were enrolled. According to the patient's condition, dexmedetomidine contraindication and tolerance, and combined with the wishes of patients' families, they were divided into EGDS group and on-demand sedation group. Routine treatments such as surgery, mechanical ventilation, dehydration and reduction of intracranial pressure with mannitol, hemostasis or antiplatelets therapy were given according to the patient's condition. All patients were continuously given sufentanil by intravenous infusion for analgesia. Patients in the EGDS group were sedated by continuously intravenous infusion of dexmedetomidine (0.2-0.7 µg×kg-1×min-1) for 72 consecutive hours. Patients in the on-demand sedation group received intravenous bolus of propofol (0.5-1.0 mg/kg) when treatments were interfered due to agitation. Hemodynamic indexes [heart rate (HR), mean arterial pressure (MAP), cerebral perfusion pressure (CPP), intracranial pressure (ICP)], sedation indexes [bispectral index (BIS)], severity indexes [acute physiology and chronic health evaluation II (APACHE II) score, Glasgow coma score (GCS)] and cerebral oxygen metabolism indexes [jugular venous blood lactate (Lac), jugular venous oxygen saturation (SjvO2), cerebral arterial oxygen content (CaO2), cerebral extraction rate of oxygen (CERO2), cerebral arteriovenous blood oxygen content difference (a-vDO2)] were compared between the two groups before sedation and at 24, 48 and 72 hours of sedation. RESULTS: (1) Among the 108 patients, 3 patients with cerebral hemorrhage received secondary surgery or had worsening of cerebral hernia were excluded. 105 patients were enrolled in the study, including 54 patients in the EGDS group and 51 patients in the on-demand sedation group. There were no statistically significant differences in gender, age, type of craniocerebral injury, GCS score, proportion of mechanical ventilation and operation ratio between the two groups. (2) Compared with before sedation, Lac, CERO2 and a-vDO2 of both groups gradually reduced over time of sedation while SjvO2 and CaO2 were gradually higher. Those changes were more quickly in the EGDS group, Lac, SjO2, CERO2 and a-vDO2 significantly improved at 24 hours of sedation compared with those before sedation. Above indexes at 72 hours of sedation in the EGDS group were obviously better than those in the on-demand sedation group [Lac (mmol/L): 1.81±0.31 vs. 2.19±0.12, SjvO2: 0.714±0.125 vs. 0.683±0.132, CaO2 (mL/L): 201.21±15.25 vs. 179.65±14.07, CERO2: (27.87±3.66)% vs. (33.00±2.58)%, a-vDO2 (mL/L): 44.32±5.68 vs. 48.57±8.22, all P < 0.05]. (3) Compared with before sedation, HR, MAP and ICP decreased in the two groups over time while CPP, BIS and GCS score showed increasing trend, especially more quickly in the EGDS group, HR at 24 hours of sedation, MAP, CPP, BIS and GCS score at 48 hours significantly improved as compared with those before sedation. Hemodynamics and sedation related parameters and GCS score at 72 hours of sedation in the EGDS group were significantly better than those in the on-demand sedation group [HR (bpm): 70.69±7.80 vs. 79.85±9.77, MAP (mmHg, 1 mmHg = 0.133 kPa): 84.23±8.76 vs. 89.97±9.48, ICP (mmHg): 14.23±8.76 vs. 15.97±9.48, BIS: 60.56±24.58 vs. 56.86±33.44, GCS score: 8.06±3.63 vs. 7.86±2.98, all P < 0.05]. The APACHE II scores were significantly reduced at 72 hours of sedation in both groups as compared with those before sedation, while there was no statistical difference between the two groups. CONCLUSIONS: Compared with the on-demand sedation, EGDS could reduce cerebral oxygen metabolism, improve the coma degree, and reduce the severity of the disease in patients with acute brain injury.

Review of deep learning for photoacoustic imaging.

Machine learning has been developed dramatically and witnessed a lot of applications in various fields over the past few years. This boom originated in 2009, when a new model emerged, that is, the deep artificial neural network, which began to surpass other established mature models on some important benchmarks. Later, it was widely used in academia and industry. Ranging from image analysis to natural language processing, it fully exerted its magic and now become the state-of-the-art machine learning models. Deep neural networks have great potential in medical imaging technology, medical data analysis, medical diagnosis and other healthcare issues, and is promoted in both pre-clinical and even clinical stages. In this review, we performed an overview of some new developments and challenges in the application of machine learning to medical image analysis, with a special focus on deep learning in photoacoustic imaging. The aim of this review is threefold: (i) introducing deep learning with some important basics, (ii) reviewing recent works that apply deep learning in the entire ecological chain of photoacoustic imaging, from image reconstruction to disease diagnosis, (iii) providing some open source materials and other resources for researchers interested in applying deep learning to photoacoustic imaging.

Compressed sensing for photoacoustic computed tomography based on an untrained neural network with a shape prior.

Photoacoustic (PA) computed tomography (PACT) shows great potential in various preclinical and clinical applications. A great number of measurements are the premise that obtains a high-quality image, which implies a low imaging rate or a high system cost. The artifacts or sidelobes could pollute the image if we decrease the number of measured channels or limit the detected view. In this paper, a novel compressed sensing method for PACT using an untrained neural network is proposed, which decreases a half number of the measured channels and recovers enough details. This method uses a neural network to reconstruct without the requirement for any additional learning based on the deep image prior. The model can reconstruct the image only using a few detections with gradient descent. As an unlearned strategy, our method can cooperate with other existing regularization, and further improve the quality. In addition, we introduce a shape prior to easily converge the model to the image. We verify the feasibility of untrained network-based compressed sensing in PA image reconstruction and compare this method with a conventional method using total variation minimization. The experimental results show that our proposed method outperforms 32.72% (SSIM) with the traditional compressed sensing method in the same regularization. It could dramatically reduce the requirement for the number of transducers, by sparsely sampling the raw PA data, and improve the quality of PA image significantly.

Knockout of E2F1 Inhibits Adipose Stem Cell Proliferation and Differentiation in Fat Transplantation by Repressing Peroxisome Proliferator-Activated Receptor Gamma Expression.

Adipose-derived stem cells (ADSCs) possess pluripotent differentiation potential and self-replication ability, which is highly significant in the field of tissue engineering. Cell-assisted lipotransfer (CAL) with ADSCs benefits fat survival. In this study, we focus on the effect of transcription factor E2F1 during CAL. The wild-type (WT) ADSCs were mixed with WT adipocytes, and the E2F1-/- ADSCs were mixed with E2F1-/- adipocytes. Then 2 cell mixtures were inoculated on the back 2 sides of E2F1-/- mice, respectively denoted as the WT group (WT ADSCs + WT adipose cells) and E2F1-/- group (E2F1-/- ADSCs + E2F1-/- adipose cells). At week 4, the fat graft was heavier in the WT group, with less necrotic area, more survival of mature adipocytes, and more proliferating ADSCs, compared with the E2F1-/- group. More capillaries were transformed from ADSCs in the WT group than in the E2F1-/- group. The in vitro protein levels of peroxisome proliferator-activated receptor gamma (PPAR-γ) were higher in WT ADSCs than in E2F1-/- ADSCs. Therefore, these findings suggest that knockout of E2F1 could affect ADSCs to inhibit the survival of fat grafts by downregulating PPAR-γ expression.

Prevalence and trend of low vision among primary and secondary school students in Wuhan from 2019 to 2020 / 中国学校卫生

Objective@#To provide a largescale assessment the prevalence of poor vision in 2020 among children and adolescents in Wuhan City, Hubei province and to provide basis for healthy vision promotion.@*Methods@#This crosssectional epidemiological study was conducted among 156 783 students, who lived in Wuhan during the COVID-19 period participated the vision screening through the online applet designed by Wuhan Center for Adolescent Poor Vision Prevetion and Control under the guidance of their guardians between June 19 and July 6, 2020. The demographic information and daily hours spent on various activities in the past week were investigated. The corresponding visual acuity data of students in 2019 before the COVID-19 outbreak was extracted from school vision monitoring records for each semester, which was measured by the experienced eye care professionals.@*Results@#The detection rate of poor vision (51.04%) in 2020 was significantly higher than that in 2019(43.04%)( χ 2=68 944.95, P <0.01). After adjustment for covariates, the odds ratio and 95% confidence interval for poor vision were 1.17(1.13-1.20), 1.07(1.04-1.10), 0.67 (0.65-0.69) and 0.62(0.60-0.64) in students with online class time, recreational screen time, indoor and outdoor activity time in the highest tertile, compared with the lowest tertile groups.@*Conclusion@#Increased rate of poor vision among primary and secondary schoool students deserves further concern. It is necessary to strengthen intervention of eyesight protection. Policies and programs aimed at improving opportunities for physical activities and decreasing multiple screen behaviors should be given priority.

Human Breast Numerical Model Generation Based on Deep Learning for Photoacoustic Imaging.

Photoacoustic imaging which combines high contrast of optical imaging and high resolution of ultrasound imaging, can provide functional information, potentially playing a crucial role in the study of breast cancer diagnostics. However, open source dataset for PA imaging research is insufficient on account of lacking clinical data. To tackle this problem, we propose a method to automatically generate breast numerical model for photoacoustic imaging. The different type of tissues is automatically extracted first by employing deep learning and other methods from mammography. And then the tissues are combined by mathematical set operation to generate a new breast image after being assigned optical and acoustic parameters. Finally, breast numerical model with proper optical and acoustic properties are generated, which are specifically suitable for PA imaging studies, and the experiment results indicate that our method is feasible with high efficiency.