The growing demand for head magnetic resonance imaging (MRI) examinations, along with a global shortage of radiologists, has led to an increase in the time taken to report head MRI scans around the world. For many neurological conditions, this delay can result in increased morbidity and mortality. An automated triaging tool could reduce reporting times for abnormal examinations by identifying abnormalities at the time of imaging and prioritizing the reporting of these scans. In this work, we present a convolutional neural network for detecting clinically-relevant abnormalities in T 2-weighted head MRI scans. Using a validated neuroradiology report classifier, we generated a labelled dataset of 43,754 scans from two large UK hospitals for model training, and demonstrate accurate classification (area under the receiver operating curve (AUC) = 0.943) on a test set of 800 scans labelled by a team of neuroradiologists. Importantly, when trained on scans from only a single hospital the model generalized to scans from the other hospital (∆AUC ≤ 0.02). A simulation study demonstrated that our model would reduce the mean reporting time for abnormal examinations from 28 days to 14 days and from 9 days to 5 days at the two hospitals, demonstrating feasibility for use in a clinical triage environment.

Intestinal atresia and Hirschsprung disease are two common causes of bowel obstruction in neonates,simultaneous occurrence is rare. This report delineates a 36-week newborn with ileal atresia and total colonic Hirschsprung who was referred to our unit due to failure of meconium passage during the first 48 hours after birth

Visual perceptual learning (VPL) has great potential implications for clinical populations, but adequate improvement often takes weeks to months to obtain; therefore, practical applications of VPL are limited. Strategies that enhance visual performance acquisition make great practical sense. Transcranial direct current stimulation (tDCS) could be beneficial to VPL, but thus far, the results are inconsistent. The current study had two objectives: (1) investigate the effect of anodal tDCS on VPL and (2) determine whether the timing sequence of anodal tDCS and training influences VPL. Anodal tDCS was applied on the left human middle temporal (hMT+) during training on a coherent motion discrimination task (online), anodal tDCS was also applied before training (offline), and sham tDCS was applied during training (sham). The coherent thresholds were measured without stimulation before, 2 days after and one month after training. All participants trained for 5 consecutive days. Anodal tDCS resulted in more performance improvement when applied during daily training but not when applied before training. Additionally, neither within-session improvement nor between-session improvement differed among the online, offline and sham tDCS conditions. These findings contribute to the development of efficient stimulation protocols and a deep understanding of the mechanisms underlying the effect of tDCS on VPL.

In order to improve the motion accuracy and stability of six degrees of freedom (6-DOF) motion platform, a control strategy of 6-DOF motion platform based on hybrid heuristic algorithm is proposed in this article. Based on the kinematics and dynamics equations of the 6-DOF motion platform, the PID control system of the 6-DOF motion platform is constructed, the optimization model of the 6-DOF motion platform is established by combining MATLAB/Simulink, and the optimization results are verified. The results show that, compared with the Z-N method PID control strategy, the system step overshoot is 3.80% and the dynamic performance is improved by 75.86% when the hybrid heuristic algorithm is used for PID control, the motion error of each electric cylinder is ±0.1 mm when controlling the 6-DOF motion platform, and the motion accuracy is improved by 67%. The optimized control strategy proposed in this article provides a technical reference for the performance control research of 6-DOF motion platforms and applications in vehicle vibration testing.

Neuronal sensitivity to light stimulation can be a significant confounding factor for assays that use light to study neuronal processes, such as optogenetics and fluorescent imaging. While continuous light stimulation has been shown to be responsible for a decrease in firing activity in several neuronal subtypes, discontinuous light stimulation commonly used in optogenetic experiments is supposed to have a negligible action. In the present report, we experimentally test this theoretical prediction by evaluating the effect produced by ten of the most commonly used patterns of discontinuous light stimulation under several electrophysiological parameters.

A new algorithm optimization-based hybrid neural network model is proposed in the present study for the multiaxial fatigue life prediction of various metallic materials. Firstly, a convolutional neural network (CNN) is applied to extract the in-depth features from the loading sequence comprised of the critical fatigue loading conditions. Meanwhile, the multiaxial historical loading information with time-series features is retained. Then, a long short-term memory (LSTM) network is adopted to capture the time-series features and in-depth features of the CNN output. Finally, a full connection layer is used to achieve dimensional transformation, which makes the fatigue life predictable. Herein, the hyperparameters of the LSTM network are automatically determined using the slime mould algorithm (SMA). The test results demonstrate that the proposed model has pleasant prediction performance and extrapolation capability, and it is suitable for the life prediction of various metallic materials under uniaxial, proportional multiaxial, non-proportional multiaxial loading conditions.

Interband cascade laser arrays with continuous-wave (CW) watt-level output power at room temperature are demonstrated. A three-emitter laser array episide-down bonded on a diamond submount exhibited a CW output power in excess of 1 W at 10℃. The wall-plug efficiency of the laser array is almost the same as that of a single emitter, indicating that there is little heat accumulation caused by the thermal interference between the emitters in the array structure.

With the advancement of technology, new problems and challenges also follow, among which the more serious problem is media forgery. Forged media information brings a lot of inconvenience to life. It is difficult to distinguish the truth from the false. In this article, various types of forgery are listed and elaborated with a focus on the copy-move forgery category, the study and research involving copy-move forgery(CMF) detection techniques using the Local Binary Pattern (LBP) are presented. The technical review of recent state-of-the-art LBP-based is provided.

A novel parameter estimation method is proposed for the permanent magnet synchronous generator (PMSG), which is implemented by an enhanced self-learning particle swarm optimization algorithm with Levy flight (SLPSO), and the problem of lower parameter estimation precision of standard PSO is obviated. This method injects currents of different intensities into the d-axis in a time-sharing manner to solve the problem of equation under-ranking, and the mathematical model for full-rank parameter estimation is developed. The speed term of PSO is simplified to expedite the convergence of PSO, and a strategy with Chaotic decline for the inertia weight of PSO is adopted to strengthen its ability to jump out of the local optimum. Moreover, the self-learning dense fleeing strategy (SLDF) is proposed where particles perform diffusion learning based on population density information and Levy flight, the evolutionary unitary problem and human intervention in the evolutionary process is averted. Furthermore, the memory tempering annealing algorithm (MTA) and greedy algorithm (GA) is integrated into the algorithm, MTA can facilitate the exploration of potentially better regions, and GA for local optimization enhances the convergence speed and accuracy in late stage of the algorithm. Comparing the proposed method with several existing PSO algorithms through simulation and experiments, the experimental data show that the proposed method can effectively track variable parameters under different working conditions and has better robustness.

Objective: To evaluate the histological modifications of the vaginal mucosa after repeated microablative fractional CO2 lasers treatments. As secondary objectives we evaluated the clinical effects associated with repeated microablative fractional CO2 lasers treatments using validated questionnaires. Design: Prospective intervention study. Setting: Division of Gynecology and Obstetrics, Urogynecology Unit, IRCCS San Raffaele Scientific Institute Sample: 15 postmenopausal women complaining of genitourinary syndrome of menopause symptoms. Methods: one cohort of patients submitted to at least two previous laser treatment cycles in the past years. Main outcome measures: Vaginal Health Index (VHI), Visual Analog Scale (VAS), Female Sexual Function Index (FSFI), Urinary Distress Inventory-6 (UDI-6), International Consultation on Incontinence Questionnaire – Urinary Incontinence (ICIQ-UI) and 5-point Likert scale. Moreover an histological examinations were carried out on all samples. Results: At 4 weeks after the last treatment VHI score and all FSFI items resulted significantly increased compared baseline. We observed a statistically significant decrease both in frequency and severity for all urinary symptoms after the follow up. We observed a statistically significant increase in the number of epithelial cell layer with a consequent increase in epithelial thickness, in the number of glycogen filled cells, and in the number of papillae, after the laser treatment. No signs of fibrosis were observed since neovascularization was observed in each single woman. Conclusions: This is the first study demonstrating the histological persistency of efficacy in repeated annually laser treatment cycles, with tissue changes always leading to regenerative results without any sign of fibrosis.

Women refuse to open bank accounts due to long queues in banks and not being able to access a bank loan due to a lack of information and time spent in loan processing. Banks face challenges in managing queues in providing financial services. There is no evidence of research using Anylogic to address long periods spent in loan processing. This study attempts to fill the research gap by reviewing the literature, data collection, simulation run, and system mapping. The study collected secondary district-wise data from two hundred (200) bank branches in Punjab State of India and computed the average number of loans processed per day by seven banks in Punjab State India, simulation run was carried out in three experiments using the Analogic model to determine the average number of loans processing per day in India based on regular eight working hours per day by bank in India, using one credit consultant, one security specialist, one bank clerk, and one credit analyst. Both online and offline credit submission is supported by the Anylogic simulation application, and the mapping of women’s loans granted by banks in India was designed using Kumu application software. The result of the study revealed four (4) loan approval per day against the current practice of one loan approval in India for 2 to 7days. This study can guide bank stakeholders and researchers in selecting interventions to ensure customer satisfaction.

Biological conversion of waste methane to biodegradable plastics is a way of reducing their production cost due to expensive raw materials. This study addresses the computational modeling of the growth phase reactor of this innovative process. The model was used for investigating the effect of gas recycling and inlet gas retention time on the performance of the reactor. The bioreactor model was implemented with the use of a genome-scale metabolic network of Methylocystis hirsuta using dynamic flux balance analysis with and without consideration of axial distributions within the reactor. The reactor has been modeled for two separate scenarios. The first scenario is a pure methane feed in a reactor with 0.5 micro-meter diffuser pore size, and the second is a biogas feed in a reactor with two micro-meter diffusers pore size. As the reactions of this process occur in the liquid phase, the mass transfer coefficient is an important parameter. For both reactors, this parameter was predicted in dependence on superficial gas velocities with the combination of data from experiments and our model. The results show an increase of removal efficiency by 35% and biomass concentration by 1.7 g/L with the increase of gas recycle ratio from 0 to 30 at the empty bed residence time of 60 min.

In the intelligent traffic management system, it is necessary not only to grasp the real-time traffic flow status, but also to understand the future traffic change and development trend, and the future traffic data can be obtained through short-term traffic flow forecasting. In this paper, a short-term traffic flow forecasting model based on DBN-BILSTM combination model based on variational mode decomposition (VMD) is proposed. The prediction method proposed in this paper is reflected in two aspects: data decomposition and model optimization: 1. In terms of data decomposition, this paper uses variational mode decomposition (VMD) to decompose the traffic flow time series data into multiple modal components (IMF), so that the relatively stationary input data can be obtained; 2. In terms of model, this paper combines the advantages of deep belief network (DBN) and two-way short-term memory network (BILSTM) to propose DBN-BILSTM short-term traffic flow forecasting model. Variational Modal Decomposition (VMD) decomposes the original traffic flow sequence, and then pre-trains it through a deep belief network to extract and reduce the dimension of spatial features, which greatly reduces the time required for model learning; At the same time, the bidirectional LSTM neural network is added to optimize the problem that the deep belief network is difficult to capture the long-term dependencies in the time series data: the bidirectional LSTM neural network is an extension of the traditional LSTM. It can further improve the accuracy of prediction results and make the network learn faster and more fully. Finally, experiments are carried out on the model with actual data, and through comparative analysis, it is proved that the short-term traffic flow forecasting model based on variational mode decomposition (VMD) DBN-BILSTM combination model is reliable in terms of prediction accuracy and stability.

In this study, we aim at investigating the geometry of surfaces corresponding to the geometry of solutions of the geometric curve flows in Euclidean 3-space R 3 considering the Frenet frame. In particular, we express some geometric properties and some characterizations of u-parameter curves and t-parameter curves of some trajectory surfaces including the Hasimoto surface, shortening trajectory surface, minimal trajectory surface, τ -normal trajectory surface in R 3 .

In order to evaluate the effectiveness of space target damage caused by projectile explosion in weapon range, it is difficult to give a scientific calculation model for target damage due to the uncertainty of projectile explosion position and target intersection state. In this paper, we study the probability calculation function of multiple projectiles hitting the target area by combining the principle of air defense and anti-missile mesh intersection and explosion. According to the fragment dispersion characteristics formed by the explosion of the projectile and the intersection penetration area of the fragment and the target, studying the calculation method of the target damage condition probability under the condition of shooting multiple projectiles in one test, and establishing the calculation model of the target damage probability of multiple projectiles' explosion fragments under the condition that the projectile effectively hits the target. Through the experimental test of the five element acoustic array projectile explosion point position testing system, we calculate the damage probability of multiple projectile explosion points to the target according to the probability function of the projectile hitting the target in the explosion and the damage function of the target scattered by fragments. The experimental results show that the theoretical simulation results are basically consistent with the target damage results calculated from the actual projectile explosion test data.

Tamarix chinensis Lour. is a shrub or 3-6-meter-tall small tree with high salt- and alkali- tolerance and aggressive invasiveness, mainly distributed in the eastern part of China in warm-temperate and subtropical climate zones. Molecular evidence indicated it expanded eastward from the western part of China quite recently in late Pleistocene about 0.02 Ma. With highly polymorphic microsatellites markers, we evaluated genetic structure and adaptation of nine T. chinensis populations mainly distributed in two estuarine flats of two climate zones: Yellow River Delta of warm-temperate and Hangzhou Bay of the subtropical zone. Despite a low population differentiation of FST= 0.0518, Bayesian clustering analysis, Discriminant Analysis of Principal Components (DAPC) and the unweighted pair group method with arithmetic mean (UPGMA) clearly identified three genetic clusters correlated to the populations’ geographic origin. Isolation by distance (IBD) and isolation by environment (IBE) patterns were detected at significant levels. A series of redundancy analyses (RDA) revealed significant contributions of geographical and environmental factors to genetic variation measured with environmental factors explaining a larger variance. The FST-based test (BayeScan) determined 5 out of 8 SSRs as outlier loci possibly at balancing selection. Further landscape genetic analysis identified some alleles significantly correlated to environmental variables including maximum temperature of the warmest month, minimum temperature of coldest month, and isothermality, signaling the adaptation of T. chinensis to temperature in two climate zones. Our result showed rapid evolution happened at a small temporal scale in a woody species with short generation time.

Platelet-Derived Growth Factor Receptor Beta (PDGFRB) is critically implicated in development. Germline pathogenic variants (GPVs) in PDGFRB result in several distinct inherited syndromes including primary familial brain calcifications (PFBC) and infantile myofibromatosis. To date, GPVs in PDGFRB have not been identified in children with brain tumors. Here, we describe the clinicopathological features of a 9-year-old male with a medulloblastoma and an 11-year-old female with a glioma. Sequencing of the blood and tumor samples revealed the same PDGFRB c.2959C>T (p. Arg987Trp) GPV in both children. Additional fusion genes DCTN1-ALK and TRIM24-MET were also identified in the patients’ tumors through RNAseq.

In midst of recent dengue outbreak in Nepal, 2022, the risk of co-infection increases and may lead to fatal outcomes if the diagnosis of multiple infections is delayed. Thus, all available diagnostic approaches must be taken to decrease the burden of illness and lessen mortality.

Caligula japonica is a forestry pest due to its damage to multiple trees. Recently, it served as a potential natural medical mesh biomaterial in the medical industry. However, studies on karyotype evolution and functional genomics of C. japonica are limited by the absent of genomic resource and its several months of diapause. Here, we conducted high-throughput sequencing of its genome and firstly obtained the chromosome-level genome. we successfully assembled a high-quality genome of 584,506,556bp with Contig N50 of 12 Mb and 31 chromosomes. About 342 Mb repeat sequences were identified, accounting for 58.53% of C. japonica genome. Genome annotation by de novo gene prediction and homologous gene search yields 24791 protein-coding genes. We also applied manual annotation of diapause genes in C. japonica genome. The genome abstained in this research will not only support resource for future study on diapause mechanism of C. japonica but also help to progress comparative genomic analyses in Lepidoptera.

Although rare, the association between simple bone cysts and cemento-osseous dysplasia is very important to know. It is essential to be able to establish a correct diagnosis through regular follow-up to detect clinical and radiological changes of this entity over time and to be able to intervene when necessary.

Zhiting Peng, Department of Ophthalmology, The University of Hong Kong, Pokfulam Road, Hong Kong, ChinaYage Zhang, Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, ChinaResearch Centre for Biomimetic Tactile and Intelligent Sensing, Institute of Biomedical and Health Engineering, ChinaPengcheng Zhang, Research Centre for Biomimetic Tactile and Intelligent Sensing, Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, ChinaTianzhun Wu, Research Centre for Biomimetic Tactile and Intelligent Sensing, Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China Email: tz.wu@siat.ac.cnYau Kei Chan, Department of Ophthalmology, The University of Hong Kong, Pokfulam Road, Hong Kong, China, Telephone: (852) 39177904, Email: jchanyk@hku.hkAbstract:Nanoimprint lithography (NIL) is a cost-effective and high-throughput technology for replicating nanoscale structures that do not require expensive light sources for advanced photolithography equipment. NIL overcomes the limitations of light diffraction or beam scattering in traditional photolithography, suited to replicating nanoscale structures with high resolution. Roller nanoimprint lithography (R-NIL) is the most popular NIL technique by benefiting large-scale, continuous, and efficient industrial production. In the past two decades, a range of R-NIL equipment has emerged to meet the industrial needs for biomedical devices, semiconductors, flexible electronics, optical film, and interface functional material. R-NIL equipment achieves a simpler, more compact design, allowing multiple units to be clustered together for increased productivity. These units include transmission control, resist coating, resist curing, and imprinting. This critical review summarizes the hitherto R-NIL processes, their typical technical problems, and corresponding solutions and gives guidelines for developing advanced R-NIL equipment.Keywords: Roller nanoimprint lithography; Micro-nano machining; Micro-nano structure; Semiconductor; Microfluidics

Abstract Background and Purpose: Refractory wounds are a severe complication of diabetes mellitus with limited treatment regimens. Rho-associated protein kinase-1 (ROCK1) phosphorylates a series of substrates that trigger downstream signaling pathways, affecting multiple cellular processes. The present study investigated the role of ROCK1 in diabetic wound healing and molecular mechanisms. Experimental Approach: Streptozotocin (STZ)-induced diabetic mice with full-thickness excisional wound model and human umbilical vein endothelial cells (HUVECs) were used. Key Results: ROCK1 expression significantly increased in wound granulation tissues in both diabetic patients and diabetic mice. Wound healing and blood perfusion were dose-dependently improved by the ROCK1 inhibitor fasudil in diabetic mice. In endothelial cells, fasudil and ROCK1 siRNA significantly elevated the phosphorylation of adenosine monophosphate-activated protein kinase at Thr172 (pThr172-AMPKα), the activity of endothelial nitric oxide synthase (eNOS), and suppressed the levels of mitochondrial reactive oxygen species and nitrotyrosine formation. Experiments using integrated bioinformatics analysis and coimmunoprecipitation established that ROCK1 inhibited pThr172-AMPKα by binding to receptor-interacting serine/threonine kinase 4 (RIPK4). Conclusion and Implications: Fasudil accelerated wound repair and improved angiogenesis at least partially through the ROCK1/RIPK4/AMPK pathway. Fasudil may be a potential treatment for refractory wounds in diabetic patients.

Developers spend significant time locating and fixing bugs, which is often performed manually. Although spectrum-based fault localization (SFL) techniques aim at helping developers to locate faults, they are not yet used in practice. Recent studies have investigated how developers use SFL, presenting different conclusions about their effectiveness and usefulness. We carried out a user study to further enhance the understanding of SFL. We assessed whether SFL improves the developers’ performance and to what extent SFL leads developers to inspect faulty code excerpts. We also investigated the intention of the developers to use SFL and how they interact with SFL. Twenty-six participants performed debugging tasks using real programs, with and without using the Jaguar SFL tool. Using SFL, more developers located and fixed the bugs. SFL also led more developers to inspect the faulty code and locate the faulty method. However, they did not spend less time locating the faults. SFL was well accepted by the participants, who showed intention to use it in their daily activities. Our results indicate that SFL is useful even when the fault is not ranked among the first positions, leading developers to reach faulty code regions and find the bugs.

Parasitoid wasps are major causes of mortality of many species, and therefore traits related to host immune defence are usually favoured by natural selection. One powerful approach to detect functionally important genes under natural selection is through the analysis of directional selection acting upon protein-coding gene sequences across different species. Here, we investigated patterns of positive selection across three closely related leaf beetle species with different immune defence capacity against a shared parasitoid wasp using a Bayesian approach for the McDonald–Kreitman test. Focusing on single-copy orthologs for Coleoptera, as well as on candidate immune related genes, we detected species-specific positive selection on coding regions in each of the closely related Galerucella beetle species. Results indicated that more immune genes had experienced positive selection in the species with the greatest immunocompetence (G. pusilla) against parasitoid wasps, while almost no immune genes were under positive selection in the species with the least immunocompetence (G. calmariensis).