A multiscale hybrid mathematical model of epidermal-dermal interactions during skin wound healing.

Following injury, skin activates a complex wound healing programme. While cellular and signalling mechanisms of wound repair have been extensively studied, the principles of epidermal-dermal interactions and their effects on wound healing outcomes are only partially understood. To gain new insight into the effects of epidermal-dermal interactions, we developed a multiscale, hybrid mathematical model of skin wound healing. The model takes into consideration interactions between epidermis and dermis across the basement membrane via diffusible signals, defined as activator and inhibitor. Simulations revealed that epidermal-dermal interactions are critical for proper extracellular matrix deposition in the dermis, suggesting these signals may influence how wound scars form. Our model makes several theoretical predictions. First, basal levels of epidermal activator and inhibitor help to maintain dermis in a steady state, whereas their absence results in a raised, scar-like dermal phenotype. Second, wound-triggered increase in activator and inhibitor production by basal epidermal cells, coupled with fast re-epithelialization kinetics, reduces dermal scar size. Third, high-density fibrin clot leads to a raised, hypertrophic scar phenotype, whereas low-density fibrin clot leads to a hypotrophic phenotype. Fourth, shallow wounds, compared to deep wounds, result in overall reduced scarring. Taken together, our model predicts the important role of signalling across dermal-epidermal interface and the effect of fibrin clot density and wound geometry on scar formation. This hybrid modelling approach may be also applicable to other complex tissue systems, enabling the simulation of dynamic processes, otherwise computationally prohibitive with fully discrete models due to a large number of variables

Impact of Credit Default Swaps on Firms’ Operational Efficiency

As one of the most important financial innovations in the last two decades, credit default swap (CDS) contracts have been initiated and actively traded in the market to hedge against credit risks. However, little is known about how these financial innovations affect an underlying firm’s operations. In this empirical study, we find that an underlying firm’s operational efficiency is significantly improved with the inception of CDS trading. Our results are robust to multiple causal identification strategies. Further analysis suggests that the inception of CDS tends to enhance the operational efficiency of a firm through the supply chain financing capability and trade credit. We also postulate that CDS leads to enhanced efficiency through institutional monitoring and improvements in management effectiveness. We then obtain suggestive evidence. Our findings have direct implications concerning the ongoing policy debate surrounding CDS. We contribute to operations management research by exploring how innovations in the financial market would, in turn, affect the operational performance of firms

Fault diagnosis of a mixed-flow pump under cavitation condition based on deep learning techniques

Deep learning technique is an effective mean of processing complex data that has emerged in recent years, which has been applied to fault diagnosis of a wide range of equipment. In the present study, three types of deep learning techniques, namely, stacked autoencoder (SAE) network, long short term memory (LSTM) network, and convolutional neural network (CNN) are applied to fault diagnosis of a mixed-flow pump under cavitation conditions. Vibration signals of the mixed-flowed pump are collected from experiment measurements, and then employed as input datasets for deep learning networks. The operation status is clarified into normal, minor cavitation, and severe cavitation conditions according to visualized bubble density. The techniques of FFT and dropout algorithms are also applied to improve diagnosis accuracy. The results show that the diagnosis accuracy based on SAE and LSTM networks is lower than 50%, while is higher than 68% when using CNN. The maximum accuracy can reach 87.2% by mean of a combination of CNN, BN, MLP, and using frequency domain data by FFT as inputs, which validates the feasibility of applying CNN in mixed-flow pumps

Clinical efficacy and safety of robotic retroperitoneal lymph node dissection for testicular cancer: a systematic review and meta-analysis

BackgroundRetroperitoneal lymph node dissection (RPLND) is an effective treatment for testicular tumors. In recent years, with the development of robotics, many urological procedures performed via standard laparoscopy have been replaced by robots. Our objective was to compare the safety and efficacy of robotic retroperitoneal lymph node dissection (R-RPLND) versus Non-robotic retroperitoneal lymph node dissection (NR-RPLND) in testicular cancer.MethodsPubmed, Embase, Scopus, Cochrane Library, and Web of Science databases were searched for literature on robotic surgery for testicular germ cell tumors up to April 2023. The statistical and sensitivity analyses were performed using Review Manager 5.3. Meta-analysis was performed to calculate mean difference (MD), odds ratio(OR), and 95% confidence interval (CI) effect indicators.ResultsEight studies with 3875 patients were finally included in this study, 453 with R-RPLND and 3422 with open retroperitoneal lymph node dissection (O-RPLND)/laparoscopic retroperitoneal lymph node dissection (L-RPLND). The results showed that R-RPLND had lower rates of intraoperative blood loss (MD = -436.39; 95% CI -707.60 to -165.19; P = 0.002), transfusion (OR = 0.06; 95% CI 0.01 to 0.26; P = 0.0001), total postoperative complication rates (OR = 0.39; 95% CI 0.21 to 0.70; P = 0.002), and length of stay (MD=-3.74; 95% CI -4.69 to -2.78; P<0.00001). In addition, there were no statistical differences between the two groups regarding perioperative and oncological outcomes regarding total operative time, the incidence of postoperative complications grade≥III, abnormal ejaculation rate, lymph node yield, and postoperative recurrence rate.ConclusionsThe R-RPLND and O-RPLND/L-RPLND provide safe and effective retroperitoneal lymph node dissection for testicular cancer. Patients with R-RPLND have less intraoperative bleeding, shorter hospitalization period, fewer postoperative complications, and faster recovery. It should be considered a viable alternative to O-RPLND/L-RPLND.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO, identifier CRD42023411696

Interface engineering strategies towards Cs2AgBiBr6 single-crystalline photodetectors with good Ohmic contact behaviours

Lead-free double perovskite materials have attracted much interest for optoelectronic applications due to their nontoxicity and high stability. In this work, centimetre-sized Cs2AgBiBr6 single crystals were successfully grown using methylammonium bromide (MABr) as the flux by a top-seeded solution growth (TSSG) method. The low-temperature crystal structure of Cs2AgBiBr6 single crystals was determined and refined. To investigate the interface problems between Cs2AgBiBr6 single crystals and electrodes, the optical band gap, X-ray photoelectron spectroscopy (XPS), and ultraviolet photoemission spectroscopy (UPS) measurements were performed on Cs2AgBiBr6 single crystals. More importantly, we investigated the photodetectors based on Cs2AgBiBr6 single crystals with different contact electrodes (Au, Ag, and Al). It is found that a good Ohmic contact with Ag electrodes enables excellent photo-response behaviors. Furthermore, we studied the photodetectors based on Cs2AgBiBr6 single crystals using Ag electrodes under room and low temperature conditions, which underwent phase transition. Cs2AgBiBr6 single crystal photodetectors show clear differences at room and low temperatures, which is caused by the work function changes of Cs2AgBiBr6 single crystals induced by the reversible phase transition. These attractive properties may enable opportunities to apply emerging double perovskite single-crystalline materials for high-performance optoelectronic devices

Remote sensing and environmental assessment of wetland ecological degradation in the Small Sanjiang Plain, Northeast China

IntroductionThe plain marsh wetland ecosystems are sensitive to changes in the natural environment and the intensity of human activities. The Sanjiang Plain is China’s largest area of concentrated marsh wetland, the Small Sanjiang Plain is the most important component of the Sanjiang Plain. However, with the acceleration of the urbanization and development of large-scale agricultural reclamation activities in the Small Sanjiang Plain in Northeast China, the wetland has been seriously damaged. In light of this degradation this study examines the Small Sanjiang Plain.MethodsFrom the four aspects of area, structure, function, and human activities, we try to construct a wetland degradation comprehensive index (WDCI) in cold region with expert scoring methods and analytic hierarchy process (AHP), coupled with network and administrative unit. The objective was to reveal the degradation of wetlands in Northeast China over three decades at a regional scale.ResultsThe results showed that (1) the overall wetland area decreased between 1990 and 2020 by 39.26×103 hm2. Within this period a significant decrease of 336.56×103 hm2 occurred between 1990 and 200 and a significant increase of 214.62×103 hm2 occurred between 2010 and 2020. (2) In terms of structural changes, the fractal dimension (FRAC) has the same trend as the Landscape Fragmentation Index (LFI) with little change. (3) In terms of functional changes, the average above-ground biomass (AGB) increased from 1029.73 kg/hm2 to 1405.38 kg/hm2 between 1990 and 2020 in the study area. (4) In terms of human activities, the average human disturbance was 0.52, 0.46, 0.57 and 0.53 in 1990, 2000, 2010 and 2020, with the highest in 2010. (5) The composite wetland degradation index shows that the most severe wetland degradation was 49.61% in 2010 occurred between 1990 and 2020. (6) Among the severely deteriorated trajectory types in 2010–2020, mild degradation → serious degradation accounted for the largest area of 240.23×103 hm2, and the significant improvement trajectory type in 1990–2000 accounted for the largest area of 238.50×103 hm2.DiscussionIn brief, we conclude that the degradation of the Small Sanjiang Plain wetland was caused mainly by construction, overgrazing, deforestation, and farmland reclamation. This study can also provide new views for monitoring and managing wetland degradation by remote sensing in cold regions

A deep learning method for foot-type classification using plantar pressure images

Background: Flat foot deformity is a prevalent and challenging condition often leading to various clinical complications. Accurate identification of abnormal foot types is essential for appropriate interventions.Method: A dataset consisting of 1573 plantar pressure images from 125 individuals was collected. The performance of the You Only Look Once v5 (YOLO-v5) model, improved YOLO-v5 model, and multi-label classification model was evaluated for foot type identification using the collected images. A new dataset was also collected to verify and compare the models.Results: The multi-label classification algorithm based on ResNet-50 outperformed other algorithms. The improved YOLO-v5 model with Squeeze-and-Excitation (SE), the improved YOLO-v5 model with Convolutional Block Attention Module (CBAM), and the multilabel classification model based on ResNet-50 achieved an accuracy of 0.652, 0.717, and 0.826, respectively, which is significantly higher than those obtained using the ordinary plantar-pressure system and the standard YOLO-v5 model.Conclusion: These results indicate that the proposed DL-based multilabel classification model based on ResNet-50 is superior in flat foot type detection and can be used to evaluate the clinical rehabilitation status of patients with abnormal foot types and various foot pathologies when more data on patients with various diseases are available for training

Phase transition induced recrystallization and low surface potential barrier leading to 10.91%-efficient CsPbBr3 perovskite solar cells

High efficiency and long-term stability are vital for further development of perovskite solar cells (PSCs). PSCs based on cesium lead halide perovskites exhibit better stability but lower power conversion efficiencies (PCEs), compared with organic-inorganic hybrid perovskites. Lower PCE is likely associated with trap defects, overgrowth of partial crystals and irreversible phase transition in the films. Here we introduce a strategy to fabricate high-efficiency CsPbBr3-based PSCs by controlling the ratio of CsBr and PbBr2 to form the perovskite derivative phases (CsPb2Br5/Cs4PbBr6) via a vapor growth method. Following post-annealing, the perovskite derivative phases as nucleation sites transform to the pure CsPbBr3 phase accompanied by crystal rearrangements and retard rapid recrystallization of perovskite grains. This growth procedure induced by phase transition not only makes the grain size of perovskite films more uniform, but also lowers the surface potential barrier that existsbetween the crystals and grain boundaries. Owing to the improved film quality, a PCE of 10.91% was achieved for n-i-p structured PSCs with silver electrodes, and a PCE of 9.86% for hole-transport-layer-free devices with carbon electrodes. Moreover, the carbon electrode-based devices exhibited excellent long-term stability and retained 80% of the initial efficiency in ambient air for more than 2000 h without any encapsulation