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Objectives To define the core competencies essential for specialist training in neurocritical care in China.Design Modified Delphi method and nominal group (NG) technique.Setting National.Participants A total of 1094 respondents from 33 provinces in China participated in the online survey. A NG of 11 members was organised by the Neuro-Critical Care Committee affiliated with the Chinese Association of Critical Care Physicians and the National Center for Healthcare Quality Management in Neurological Diseases.Results 1094 respondents from 33 provinces in China participated in the online survey. A formal list containing 329 statements was generated for the rating by a NG. After five rounds of NG meetings and one round of comments and iterative review, 198 core competencies (54 on neurological diseases, 64 on general medical diseases, 42 on monitoring of practical procedures, 20 on professionalism and system management, five on ethical and legal aspects, three on the principles of research and certification and 10 on scoring systems) formed the final list.Conclusion By using consensus techniques, we have developed a list of core competencies for neurocritical care training, which may serve as a reference for future specialist training programmes in China.

Hydraulic actuator becomes an increasingly concerned driver for human-like robots. However, its dynamic performance under the control should be still further improved because hydraulic system is a typical nonlinearity system. Interval type-2 fuzzy logic controller is an advanced control method featured with high performance to deal with uncertain and nonlinear dynamics, so designing an interval type-2 fuzzy logic controller for the control of hydraulic is a feasible method. In this article, an improved drone squadron optimization-based approach is proposed to optimize interval type-2 fuzzy logic controller parameters. To verify the feasibility and priority of improved drone squadron optimization, a comparison on three different typical plants including proportional-derivative (PD) system, proportional-integral (PI) system, and PI nonlinear system between improved drone squadron optimization and other meta-heuristic algorithms is carried out. Simulation results demonstrate that improved drone squadron optimization not only gets an appropriate interval type-2 fuzzy logic controller for system control but also outperforms other popular algorithms in accuracy of performance.

Summary: The death receptor CD95 is expressed in every cancer cell, thus providing a promising tool to target cancer. Activation of CD95 can, however, lead to apoptosis or proliferation. Yet the molecular determinants of CD95’s mode of action remain unclear. Here, we identify an optimal distance between CD95Ligand molecules that enables specific clustering of receptor-ligand pairs, leading to efficient CD95 activation. Surprisingly, efficient CD95 activation leads to apoptosis in cancer cells in vitro and increased tumor growth in vivo. We show that allowing a 3D aggregation of cancer cells in vitro switches the apoptotic response to proliferation. Indeed, we demonstrate that the absence or presence of cell-cell contacts dictates the cell response to CD95. Cell contacts increase global levels of phosphorylated tyrosines, including CD95’s tyrosine. A tyrosine-to-alanine CD95 mutant blocks proliferation in cells in contact. Our study sheds light into the regulatory mechanism of CD95 activation that can be further explored for anti-cancer therapies. : Gülcüler Balta et al. show that CD95 receptor activation is determined through the presentation of its ligand at a certain intermolecular distance. The type of signaling triggered by CD95 is, however, decided by the cellular environment. CD95 triggers survival in cancer cells in contact with other cells and death in isolated ones. Keywords: CD95, CD95 ligand, death receptors, apoptosis, survival, cell-cell contact, cancer, tyrosine kinase, supported membrane

Background Sepsis represents a significant healthcare problem worldwide and causes a high number of deaths every year but remains to be fully understood. During and after sepsis, the host immune response is complex and involves an initial excessive host inflammatory response to infection that is closely related to tissue damage and leads to organ failure. Over the past three decades, immunotherapy for sepsis has vastly improved, but in this area, the most influential articles, journals, authors, and countries have not yet been completely summarized and analyzed. Objective Performed a bibliometric analysis on all the articles concerning immunotherapy for sepsis from 1962 to 2019 was our objective, and we also explored the potential correlations between publications of different countries and their gross domestic product (GDP). Methods All articles about immunotherapy for sepsis were extracted from the Scopus database and analyzed. We also retrieved GDP data from all the countries that have published information from the World Bank. Results In summary, we have retrieved 1,483 related articles from the Scopus database starting from the first publication on immunotherapy for sepsis in 1962 through March 16, 2019. Over the past decade, the number of the articles published has increased year by year to reach 866 in total, which accounts for about 58% of all publications, with 2017 being the most prolific year when 179 articles were published. The US published 604 articles (41%), followed by China (n = 163, 11%), and Germany (n = 158, 11%). In terms of publishing media, the journal that published the highest number of the articles was Journal of Critical Care Medicine with 65 articles (4%), followed by Shock with 55 articles (4%), and Critical Care with 35 articles (2%). There was a strong correlation between the GDP of the different countries and their publication numbers (r = 0.811, P < 0.001). Conclusions Our present study analyzed all types of articles concerning immunotherapy for sepsis over the past 57 years and countries with high GDP tends to make more contributions to the medical field of this field. In the meantime, these studies highlight the importance of immunotherapy in the treatment of sepsis patients. The recognition of the historical status and development trend of this field can promote inter-agency cooperation, guide future research, and ultimately provide the basis for clinical practice guidelines.

Abstract Super‐small nanoclusters may intrinsically trigger specific molecular pathway for disease treatment in vitro/vivo. To prove the hypothesis the super‐small nanoclusters, e.g., Au clusters, are directly used to treat rheumatoid arthritis (RA) in vitro/vivo. RA is a chronic autoimmune disease that is characterized by the inflammation of joints and the unreversible destruction of the cartilage/bone. Au clusters significantly suppress lipopolysaccharide (LPS)‐induced proinflammatory mediator production in the murine macrophage cell line by inhibiting the signaling pathways that regulate the major proinflammatory mediator genes. In preclinical rat RA studies, Au clusters strongly prevent type II collagen‐induced rat RA without systemic side effects. Compared with the clinical first‐line anchored anti‐RA drug, methotrexate, Au clusters equally inhibit inflammation in vivo. Type II collagen‐induced rat RA is characterized with the destruction of cartilage/bone; treatment with Au clusters reverses the destruction of cartilage/bone to its normal state. This is because Au clusters directly inhibit receptor activator of nuclear factor‐κB ligand (RANKL)‐induced osteoclast differentiation and function through the downregulation of osteoclast‐specific genetic marker expression. However the methotrexate almost has no positive effect for this key issue in rat RA therapy. These data prove that the super‐small nanoclusters, e.g., Au clusters, could be a novel candidate nanodrug for RA treatment.

The accuracy of the rain distribution could be enhanced by assimilating the remotely sensed and gauge-based precipitation data. In this study, a new nonparametric general regression (NGR) framework was proposed to assimilate satellite- and gauge-based rainfall data over southeast China (SEC). The assimilated rainfall data in Meiyu and Typhoon seasons, in different months, as well as during rainfall events with various rainfall intensities were evaluated to assess the performance of this proposed framework. In rainy season (Meiyu and Typhoon seasons), the proposed method obtained the estimates with smaller total absolute deviations than those of the other satellite products (i.e., 3B42RT and 3B42V7). In general, the NGR framework outperformed the original satellites generally on root-mean-square error (RMSE) and mean absolute error (MAE), especially on Nash-Sutcliffe coefficient of efficiency (NSE). At monthly scale, the performance of assimilated data by NGR was better than those of satellite-based products in most months, by exhibiting larger correlation coefficients (CC) in 6 months, smaller RMSE and MAE in at least 9 months and larger NSE in 9 months, respectively. Moreover, the estimates from NGR have been proven to perform better than the two satellite-based products with respect to the simulation of the gauge observations under different rainfall scenarios (i.e., light rain, moderate rain and heavy rain).

Personality is an important psychological construct accounting for individual differences in people. To reliably, validly, and efficiently recognize an individual's personality is a worthwhile goal; however, the traditional ways of personality assessment through self-report inventories or interviews conducted by psychologists are costly and less practical in social media domains, since they need the subjects to take active actions to cooperate. This paper proposes a method of big five personality recognition (PR) from microblog in Chinese language environments with a new machine learning paradigm named label distribution learning (LDL), which has never been previously reported to be used in PR. One hundred and thirteen features are extracted from 994 active Sina Weibo users' profiles and micro-blogs. Eight LDL algorithms and nine non-trivial conventional machine learning algorithms are adopted to train the big five personality traits prediction models. Experimental results show that two of the proposed LDL approaches outperform the others in predictive ability, and the most predictive one also achieves relatively higher running efficiency among all the algorithms.

The aim of this study is to investigate the effect of statins type or even when grouping statins by hydrophilic or hydrophobic nature on prostate cancer risk. A literature search was performed without language restrictions using the databases of PubMed (1984.1-2015.3), MEDLINE (1984.1-2015.3), and EMBASE (1990.1-2015.3). Two independent reviewers appraised eligible studies and extracted data. Weighted averages were reported as relative risk (RR) with 95% confidence intervals (CI). Statistic heterogeneity scores were assessed with the standard Cochran′s Q-test and I2 statistic. Publication bias was detected using the Begg′s and Egger′s tests. All statistical analyses were conducted by STATA version 10. Finally, fourteen studies were included in the meta-analysis. Both hydrophilic and hydrophobic statins showed no association with incidence of prostate cancer (RR = 1.00, 95% CI: 0.82-1.17; RR = 0.90, 95% CI: 0.73-1.08, respectively). Meanwhile, the risk of prostate cancer was not reduced in simvastatin (RR = 0.89, 95% CI: 0.72-1.05), pravastatin (RR = 1.02, 95% CI: 0.94-1.11), atorvastatin (RR = 0.89, 95% CI: 0.76-1.02), fluvastatin (RR = 0.99, 95% CI: 0.97-1.01), or lovastatin users (RR = 0.94, 95% CI: 0.79-1.08). The funnel plot showed that there was no publication bias. The results showed that statins had a neutral effect on prostate cancer risk; hydrophilic and hydrophobic statins as well as any subtype of statins did not affect the risk of prostate cancer.

In organic photovoltaics, non-fullerene acceptors relax matching rules and allow for the development of new donor polymers. Here, Bin et al. design a donor polymer and obtain high photoconversion efficiencies despite the low energy offset for hole transfer between the acceptor and the donor.

Embryoid body (EB) formation and adherent culture (AD) paradigms are equivalently thought to be applicable for neural specification of human pluripotent stem cells. Here, we report that sonic hedgehog-induced ventral neuroprogenitors under EB conditions are fated to medial ganglionic eminence (MGE), while the AD cells mostly adopt a floor-plate (FP) fate. The EB-MGE later on differentiates into GABA and cholinergic neurons, while the AD-FP favors dopaminergic neuron specification. Distinct developmental, metabolic, and adhesion traits in AD and EB cells may potentially account for their differential patterning potency. Gene targeting combined with small-molecule screening experiments identified that concomitant inhibition of Wnts, STAT3, and p38 pathways (3i) could largely convert FP to MGE under AD conditions. Thus, differentiation paradigms and signaling regulators can be integrated together to specify distinct neuronal subtypes for studying and treating related neurological diseases, such as epilepsy, Alzheimer's disease, and Parkinson's disease.

Research progresses on Cherenkov and transit-time high-power microwave (HPM) sources in National University of Defense Technology (NUDT) of China are presented. The research issues are focused on the following aspects. The pulse-shortening phenomenon in O-type Cerenkov HPM devices is suppressed. The compact coaxial relativistic backward-wave oscillators (RBWOs) at low bands are developed. The power efficiency in M-Type HPM tubes without guiding magnetic field increased. The power capacities and power efficiencies in the triaxial klystron amplifier (TKA) and relativistic transit-time oscillator (TTO) at higher frequencies increased. In experiments, some exciting results were obtained. The X-band source generated 2 GW microwave power with a pulse duration of 110 ns in 30 Hz repetition mode. Both L- and P-band compact RBWOs generated over 2 GW microwave power with a power efficiency of over 30%. There is approximately a 75% decline of the volume compared with that of conventional RBWO under the same power capacity conditions. A 1.755 GHz MILO produced 3.1 GW microwave power with power efficiency of 10.4%. A 9.37 GHz TKA produced the 240 MW microwave power with the gain of 34 dB. A 14.3 GHz TTO produced 1 GW microwave power with power efficiency of 20%.

Selective plane illumination microscopy (SPIM) is capable of high-resolution, high-speed 3D imaging of single cells, but application to multicellular samples is challenging. Here the authors develop tiling light sheet SPIM to image large multicellular specimens in 3D with subcellular resolution.

Lipids in breastmilk play a critical role in infant growth and development. However, few studies have investigated sources of variability of both high- and low-abundant milk lipids. The objective of our study was to investigate individual and morning–evening differences in the human milk lipidome. In this study, a modified two-phase method (MTBE: Methanol 7:2) was validated for the extraction of lipids from human breastmilk. This method was then applied to samples from a group of 20 healthy women to measure inter- and intra-individual (morning versus evening) variability of the breastmilk lipidome. We report here the levels of 237 lipid species from 13 sub-classes using reversed-phase liquid chromatography mass spectrometry (RP-LCMS) and direct-infusion mass spectrometry (DI-MS). About 85% of lipid species showed stable inter-individual differences across time points. Half of lipid species showed higher concentrations in the evening compared with the morning, with phosphatidylethanolamines (PEs) and triacylglycerols (TAGs) exhibiting the largest changes. In morning and evening samples, the biological variation was greater for diacylglycerols (DAGs) and TAGs compared with phospholipids and sphingolipids, and the variation in DAGs and TAGs was greater in evening samples compared with morning samples. These results demonstrate that variation in the milk lipidome is strongly influenced by individual differences and time of day.

The interface crack of a slab track is a fracture of mixed-mode that experiences a complex loading–unloading–reloading process. A reasonable simulation of the interaction between the layers of slab tracks is the key to studying the interface crack. However, the existing models of interface disease of slab track have problems, such as the stress oscillation of the crack tip and self-repairing, which do not simulate the mixed mode of interface cracks accurately. Aiming at these shortcomings, we propose an improved cohesive zone model combined with an unloading/reloading relationship based on the original Park–Paulino–Roesler (PPR) model in this paper. It is shown that the improved model guaranteed the consistency of the cohesive constitutive model and described the mixed-mode fracture better. This conclusion is based on the assessment of work-of-separation and the simulation of the mixed-mode bending test. Through the test of loading, unloading, and reloading, we observed that the improved unloading/reloading relationship effectively eliminated the issue of self-repairing and preserved all essential features. The proposed model provides a tool for the study of interface cracking mechanism of ballastless tracks and theoretical guidance for the monitoring, maintenance, and repair of layer defects, such as interfacial cracks and slab arches.

Uneven subgrade frost heave has been a severe problem for the operation of high-speed railways in cold regions. In order to reveal the influencing mechanism of frost heave on the vehicle-track system, a novel FEM (finite element method) model based on an explicit algorithm was proposed. In the novel model, the existence of the leverage effect in slab track, which was caused by frost heave, was realistically reproduced at first, and then the vehicle model started running for evaluating the influence of the frost heave on the whole dynamic system. Results show that the leverage effect plays a key role in analyzing the influence of frost heave on the vehicle-track system, besides for track irregularity and contact loss. Specifically, the leverage effect decreases the stability of the slab track and causes an increase in dynamic irregularity. The roles of the track irregularity and the contact loss in the influencing mechanism were also revealed. With the ratio of wavelength to amplitude increasing, the track irregularity is gradually dominant in the influence mechanism of frost heave on the vehicle-track system. The research could provide a reference for the management and maintenance of the slab track in cold regions.

Cervical cells classification is a crucial component of computer-aided cervical cancer detection. Fine-grained classification is of great clinical importance when guiding clinical decisions on the diagnoses and treatment, which remains very challenging. Recently, convolutional neural networks (CNN) provide a novel way to classify cervical cells by using automatically learned features. Although the ensemble of CNN models can increase model diversity and potentially boost the classification accuracy, it is a multi-step process, as several CNN models need to be trained respectively and then be selected for ensemble. On the other hand, due to the small training samples, the advantages of powerful CNN models may not be effectively leveraged. In order to address such a challenging issue, this paper proposes a transfer learning based snapshot ensemble (TLSE) method by integrating snapshot ensemble learning with transfer learning in a unified and coordinated way. Snapshot ensemble provides ensemble benefits within a single model training procedure, while transfer learning focuses on the small sample problem in cervical cells classification. Furthermore, a new training strategy is proposed for guaranteeing the combination. The TLSE method is evaluated on a pap-smear dataset called Herlev dataset and is proved to have some superiorities over the exiting methods. It demonstrates that TLSE can improve the accuracy in an ensemble manner with only one single training process for the small sample in fine-grained cervical cells classification.

In recent years, the functionally graded materials (FGM) with cellular structure have become a hot spot in the field of materials research. For the continuously varying cellular structure in the layer-wise FGM, the connection of gradient cellular structures has become the main problem. Unfortunately, the effect of gradient connection method on the overall structural performance lacks attention, and the boundary mismatch has enormous implications. Using the homogenization theory and the level set method, this article presents an efficient topology optimization method to solve the connection issue. Firstly, a simple but efficient hybrid level set scheme is developed to generate a new level set surface that has the partial features of two candidate level sets. Then, when the new level set surface is formed by considering the level set functions of two gradient base cells, a special transitional cell can be constructed by finding the zero level set of this generated level set surface. Since the transitional cell has the geometric features of two gradient base cells, the shape of the transitional cell fits perfectly with its connected gradient cells on both sides. Thus, the design of FGM can have a smooth connectivity with C1 continuity without any complex numerical treatments during the optimization. A number of examples on both 2D and 3D are provided to demonstrate the characteristics of the proposed method. Finite element simulation has also been employed to calculate the mechanical properties of the designs. The simulation results show that the FGM devised by the proposed method exhibits better mechanical performances than conventional FGM with only C0 continuity.

During the long-term service of slab track, various external factors (such as complicated temperature) can result in a series of slab damages. Among them, slab arching changes the structural mechanical properties, deteriorates the track geometry conditions, and even threatens the operation of trains. Therefore, it is necessary to detect slab arching accurately to achieve effective maintenance. However, the current damage detection methods cannot satisfy high accuracy and low cost simultaneously, making it difficult to achieve large-scale and efficient arching detection. To this end, this paper proposed a vision-based arching detection method using track geometry data. The main works include: (1) data nonlinear deviation correction and arching characteristics analysis; (2) data conversion and augmentation; (3) design and experiments of convolutional neural network- based detection model. The results show that the proposed method can detect arching damages effectively, and the F1-score reaches 98.4%. By balancing the sample size of each pattern, the performance can be further improved. Moreover, the method outperforms the plain deep learning network. In practice, the proposed method can be employed to detect slab arching and help to make maintenance plans. The method can also be applied to the data-based detection of other structural damages and has broad prospects.

Introduction: CNS ventriculitis is a serious complication following an intracranial insult that demands immediate treatment with broad-spectrum antibiotics in a critical care setting. Infections due to multi/extensive drug resistance (MDR/XDR) microorganisms are very challenging, which may demand an additional approach to the ongoing practice; intravenous and intraventricular administration of antibiotics.Aim: To study the efficacy and safety of thorough ventricular irrigation followed by daily intraventricular antibiotic administration in patients with MDR/XDR ventriculitis.Materials and Methods: A retrospective analysis was done on 19 inpatients with ventriculitis caused by Acinetobacter baumannii (AB) or Klebsiella pneumonia (KP), at Shanghai Tenth People's Hospital from January 2016 to October 2017. We reviewed our experience; the role of thorough ventricular irrigation with Colistin mixed normal saline, followed by intraventricular Colistin therapy. Treatment outcomes were evaluated based on the clinical symptoms, Cerebro-Spinal Fluid (CSF) culture, laboratory findings and complications.Results: A total of 19 patients were included (15 males and 4 females), with a mean age in years of 51, which ranged from 18–67. Fourteen patients had Acinetobacter baumannii (AB) and 5 had Klebsiella pneumoniae (KP). The average CSF sterilization period following ventricular irrigation and intraventricular Colistin was 6 days. Sixteen patients (84%) were cured, and 3 patients (15%) died during the course of the treatment.Conclusion: In addition to Intraventricular Colistin, thorough ventricular irrigation could increase the cure rate up to 84% in patients suffering from MDR/XDR CNS ventriculitis.

Uncertainties widely exist in complex engineering systems. Robust design is one of the most used method for designing under uncertainty and has been gaining more attention. For the wide range of uncertainties, this article proposes a multidisciplinary robust design optimization method based on the set strategy. In this method, a robust design model that utilizes the maximum variation analysis is developed for uncertainty analysis. Then, a set strategy–based approach is employed to build a system optimization model, which is used to coordinate the coupling variables between full autonomy subsystems and obtains a new design space. Finally, the system robust optimal solution and the optimal robust design space are obtained through the sequential optimization, which provide a direction for the subsequent analysis. Two mathematics examples and the speed reducer design problem are taken to verify the validity and accuracy of the proposed method. A practical engineering problem, namely, air cooling battery thermal management system design problem, is successfully solved by the proposed method.

Deep learning is robust to the perturbation of a point cloud, which is an important data form in the Internet of Things. However, it cannot effectively capture the local information of the point cloud and recognize the fine-grained features of an object. Different levels of features in the deep learning network are integrated to obtain local information, but this strategy increases network complexity. This paper proposes an effective point cloud encoding method that facilitates the deep learning network to utilize the local information. An axis-aligned cube is used to search for a local region that represents the local information. All of the points in the local region are available to construct the feature representation of each point. These feature representations are then input to a deep learning network. Two well-known datasets, ModelNet40 shape classification benchmark and Stanford 3D Indoor Semantics Dataset, are used to test the performance of the proposed method. Compared with other methods with complicated structures, the proposed method with only a simple deep learning network, can achieve a higher accuracy in 3D object classification and semantic segmentation.

Although the high-speed railway (HSR) system has been widely agreed to be a sustainable and convenient means of transportation, the vibration induced has already been deemed an urgent environmental problem. For the sake of investigating the vibration characteristics of the ballastless track on bridges in the HSR system from the point of view of energy, a numerical model of the vehicle–track–bridge coupled system is developed herein and the energy method based on power flow theory is employed. In addition, a corresponding evaluation method of the power flow theory is developed to evaluate the vibration characteristics of the track–bridge system. The conclusions indicate that (1) the vibration energy gradually attenuates from top to bottom of the track–bridge system in its transfer process. Moreover, the attenuation effects are mainly the result of the elasticity and damping effects of the fasteners and the slab mat layer. (2) With increasing slab mat layer stiffness, the vibration energy of the rail slightly decreases; on the contrary, that of the slab track and the bridge obviously increases. (3) With increasing fastener stiffness, the vibration energy of the entire track–bridge system increases. (4) With increasing running speed, the vibration energy of the entire track–bridge system rises obviously. The results reveal that the reasonable stiffness levels of the fasteners and the slab mat layer are 40 to 60 kN/mm and 40 to 60 MPa/m, respectively, under the investigated condition in this work. This work also presents a novel way to study the vibration characteristics of the ballastless track on bridges of HSRs in terms of energy.

External stimuli such as injury, learning, or stress influence the production of neurons by neural stem cells (NSCs) in the adult mammalian brain. These external stimuli directly impact stem cell activity by influencing areas directly connected or in close proximity to the neurogenic niches of the adult brain. However, very little is known on how distant injuries affect NSC activation state. In this study, we demonstrate that a thoracic spinal transection injury activates the distally located hippocampal-NSCs. This activation leads to a transient increase production of neurons that functionally integrate to improve animal’s performance in hippocampal-related memory tasks. We further show that interferon-CD95 signaling is required to promote injury-mediated activation of remote NSCs. Thus, we identify an immune-CNS axis responsible for injury-mediated activation of remotely located NSCs.

Under the background of the rapid development of today’s aerospace industry, the bearing characteristics of planetary roller screw drive under high overload conditions are studied. Designing a high-load, high-performance precision planetary roller screw drive is of practical and significant. An axially spaced RV type planetary roller screw with a single nut is designed under aerospace conditions. Under the conditions of the aerospace working load and efficiency, the parameters and structures of the planetary roller screw are completed. Based on this, the bearing characteristics of the planetary roller screw are studied and the equivalent finite element model of the planetary roller screw is analyzed. Those analyses include stress and strain, elastic–plastic properties, transmission efficiency, and dynamic load capacity. The results show that the design of planetary roller screw meets the design requirements for aerospace conditions. And the results can provide a basis for the design and manufacture of planetary roller screws under high loads.

Articular cartilage defects may initiate osteoarthritis. Subchondral drilling, a widely applied clinical technique to treat small cartilage defects, does not yield cartilage regeneration. Various translational studies aiming to improve the outcome of drilling have been performed; however, a robust systematic analysis of its translational evidence was still lacking. Here, we performed a systematic review of the outcome of subchondral drilling for knee cartilage repair in translational animal models. A total of 12 relevant publications studying 198 animals was identified, detailed study characteristics were extracted, and methodological quality and risk of bias were analyzed. Subchondral drilling led to improved repair outcome compared with defects that were untreated or treated with abrasion arthroplasty for cartilage repair in multiple translational models. Within the 12 studies, considerable subchondral bone changes were observed, including subchondral bone cysts and intralesional osteophytes. Furthermore, extensive alterations of the subchondral bone microarchitecture appeared in a temporal pattern in small and large animal models, together with specific topographic aspects of repair. Moreover, variable technical aspects directly affected the outcomes of osteochondral repair. The data from this systematic review indicate that subchondral drilling yields improved short-term structural articular cartilage repair compared with spontaneous repair in multiple small and large animal models. These results have important implications for future investigations aimed at an enhanced translation into clinical settings for the treatment of cartilage defects, highlighting the importance of considering specific aspects of modifiable variables such as improvements in the design and reporting of preclinical studies, together with the need to better understand the underlying mechanisms of cartilage repair following subchondral drilling.

With the development of advanced manufacturing technology, more and more enterprises utilize automated guided vehicles to transfer materials for computer numerical control machines in the workshop. Because automated guided vehicle scheduling problem is non-deterministic polynomial–hard, highly efficient automated guided vehicle scheduling algorithms are necessary to improve productivity of the workshop. In order to solve this problem, a mathematical model with a new objective function is established firstly, which incorporates two indicators, that is, the standard deviation of the waiting time of computer numerical control material buffers and the total travel distance of automated guided vehicle, reflecting the overall capacity of computer numerical control machines and the energy efficiency of automated guided vehicles during the actual production, respectively. Then, an improved harmony search algorithm is proposed, which includes an effective discrete encoding scheme of harmony, a new initialization method for harmony memory based on opposition-based learning strategy, a dynamic harmony memory considering rate parameter and a local search strategy. The property of the proposed algorithm is evaluated through three cases from the real-world manufacturing system for producing back cover of smart phone. Compared with the competing algorithms, the results demonstrate the superiority of proposed algorithm for solving automated guided vehicle scheduling problem obviously.

Generally, hypoxia is a normal physiological condition in the flaccid penis, which is interrupted by regular nocturnal erections in men with normal erectile function. [1] Lack of spontaneous and nocturnal erections after radical prostatectomy due to neuropraxia results in persistent hypoxia of cavernosal tissue, which leads to apoptosis and degeneration of cavernosal smooth muscle fibers. Therefore, overcoming hypoxia is believed to play a crucial role during neuropraxia. The use of a vacuum erectile device (VED) in penile rehabilitation is reportedly effective and may prevent loss of penile length. The corporal blood after VED use is increased and consists of both arterial and venous blood, as revealed by color Doppler sonography and blood gas analysis. A similar phenomenon was observed in negative pressure wound therapy (NPWT). However, NPWT employs a lower negative pressure than VED, and a hypoperfused zone, which increases in response to negative pressure adjacent to the wound edge, was observed. Nonetheless, questions regarding ideal subatmospheric pressure levels, modes of action, and therapeutic duration of VED remain unanswered. Moreover, it remains unclear whether a hypoperfused zone or PO 2 gradient appears in the penis during VED therapy. To optimize a clinical VED protocol in penile rehabilitation, further research on the mechanism of VED, especially real-time PO 2 measurements in different parts of the penis, should be performed.

Gliomas are primary brain tumors presumably derived from glial cells. The WHO grade IV glioblastoma (GBM), characterized by rapid cell proliferation, easily recrudescent, high morbidity, and mortality, is the most common, devastating, and lethal gliomas. Molecular mechanisms underlying the pathogenesis and progression of GBMs with potential diagnostic and therapeutic value have been explored industriously. With the advent of high-throughput technologies, numerous long noncoding RNAs (lncRNAs) aberrantly expressed in GBMs were discovered recently, some of them probably involved in GBM initiation, malignant progression, relapse and resistant to therapy, or showing diagnostic and prognostic value. In this review, we summarized the profile of lncRNAs that has been extensively investigated in glioma research, with a focus on their regulatory mechanisms. Then, their diagnostic, prognostic, and therapeutic implications were also discussed.

Clustering and routing are two key techniques to improve the energy efficiency of wireless sensor networks. As clustering and routing for improving the energy efficiency of wireless sensor networks are NP-hard problems, increasing meta-heuristic algorithms are introduced for solving them. However, due to their discreteness and strong constraints, most meta-heuristics are unsuitable or inefficient to optimize them. Harmony search algorithm is one of the most suitable meta-heuristics for solving these problems. This article proposes a new energy-efficient clustering and routing algorithm based on harmony search algorithm to improve the energy efficiency of wireless sensor networks. The proposed approach contains two parts: clustering phase and routing phase. First, a new objective function model, which has considered balancing the energy consumption of both gateways and regular nodes as well as considered routing, is established for the clustering phase. Then, a new energy-efficient clustering algorithm is designed based on several improvements made to harmony search algorithm: (1) a discrete encoding scheme of a harmony for clustering is proposed; (2) a roulette wheel selection method is designed to choose a gateway for a regular sensor node to join, which is employed by two steps (i.e. initialization of harmony and improvisation of a new harmony); (3) the dynamically changed harmony memory considering rate is designed for improvisation of a new harmony; (4) a local search scheme is proposed to improve the best harmony within the harmony memory in iterations. In addition, the improved harmony search based energy-efficient routing algorithm that we proposed previously is employed to balance the energy consumption of gateways in the routing phase. The proposed approach is compared with several popular meta-heuristic-based clustering algorithms over extensive wireless sensor networks cases. The experimental results clearly demonstrate the superiority of the proposed approach.

Background: Few studies have examined the acute exercise-induced changes in cognitive performance in different thermal environments and the time course effects.Objective: Investigate the time-dependent effects of acute exercise on university students’ processing speed, working memory and cognitive flexibility in temperate and cold environments.Method: Twenty male university students (age 23.5 ± 2.0 years) with moderate physical activity level participated in a repeated-measures within-subjects design. Processing speed, working memory and cognitive flexibility were assessed using CogState test battery at baseline (BASE), followed by a 45-min rest (REST), immediately after (EX) and 30 min after (POST-EX) 30-min moderate-intensity treadmill running in both temperate (TEMP; 25°C) and cold (COLD; 10°C) environments. Mean skin temperature (MST) and thermal sensation (TS) were also recorded. Two-way repeated measures ANOVA was performed to analyze each variable. Spearman’s rho was used to identify the correlations between MST, TS and cognitive performance.Results: Reaction time (RT) of processing speed and working memory decreased immediately after exercise in both conditions (processing speed: p = 0.003; working memory: p = 0.007). The facilitating effects on processing speed disappeared within 30 min after exercise in TEMP (p = 0.163) and COLD (p = 0.667), while improvements on working memory remained 30 min after exercise in TEMP (p = 0.047), but not in COLD (p = 0.663). Though RT of cognitive flexibility reduced in both conditions (p = 0.003), no significance was found between EX and REST (p = 0.135). Increased MST and TS were significantly associated with reductions in processing speed RT (MST: r = -0.341, p < 0.001; TS: r = -0.262, p = 0.001) and working memory RT (MST: r = -0.282, p < 0.001; TS: r = -0.2229, p = 0.005), and improvements in working memory accuracy (MST: r = 0.249, p = 0.002; TS: r = 0.255, p = 0.001).Conclusion: The results demonstrate different time-dependent effects of acute exercise on cognition in TEMP and COLD. Our study reveals facilitating effects of exercise on university students’ processing speed and working memory in both environments. However, in contrast to TEMP, effects on working memory in COLD are transient.

Most traditional scheduling problems prioritize optimizing production efficiency, cost, and quality. However, with gradually increasing energy consumption and environmental pollution, the novel “energy-efficient scheduling” model has received increasing attention from scholars and engineers. This scheduling model focuses on reducing workshop energy consumption and environmental pollution and has become a hot topic in the scheduling area. This article proposes a new energy-efficient scheduling mathematical model considering productivity, energy efficiency, and noise reduction with flexible spindle speed for the job shop environment. This model considers the machining spindle speed that impacts the production time, power, and noise to be flexible and an independent decision-making variable. In addition, the evaluation methods of productivity, energy consumption, and noise are presented in this model. To cleanly solve this mixed integer programming model, an effective multi-objective genetic algorithm based on simplex lattice design is proposed. The corresponding encoding/decoding method, fitness function, and crossover/mutation operators are designed based on the features of this problem. To evaluate the performance of this method, three instances with different scales have been designed. The results demonstrate the effectiveness of the proposed model for the energy-efficient job shop scheduling problem.

Wheel flats have become a major problem affecting the long-term service of railway systems. Wheels with flats create intermittent impact loads to trains and rails. This not only accelerates the deterioration of vehicle and track components but also leads to abnormal wheel-rail contact conditions. An effective method for detecting wheel conditions is urgently needed to ensure the operation of the railway and provide guidance for the repair of wheels. However, most previous researches have used qualitative detection methods, and hence have been unable to achieve accurate positioning of the wheel flats. In addition, the theoretical basis for the layout scheme for wheel flat detection sensors is lacking, making it impossible to meet the needs of field applications. In this study, we simulated the spatial distribution characteristics of rail strain, under different wheel flat conditions, and based on this, a layout scheme of multisensor arrays was proposed which more effectively captured the responses of the wheel flats. A wheel flat recognition and precise positioning method based on multisensor fusion was designed. The algorithm was validated through the combination of experimental and simulation methods. The result shows that the algorithm can ideally detect and locate the wheel flats under complex conditions.

Scattering due to interface-roughness (IR) and longitudinal-optical (LO) phonons are primary transport mechanisms in terahertz quantum-cascade lasers (QCLs). By choosing GaAs/Al0.10Ga0.90As heterostructures with short-barriers, the effect of IR scattering is mitigated, leading to low operating current-densities. A series of resonant-phonon terahertz QCLs developed over time, achieving some of the lowest threshold and peak current-densities among published terahertz QCLs with maximum operating temperatures above 100 K. The best result is obtained for a three-well 3.1 THz QCL with threshold and peak current-densities of 134 A/cm2 and 208 A/cm2 respectively at 53 K, and a maximum lasing temperature of 135 K. Another three-well QCL designed for broadband bidirectional operation achieved lasing in a combined frequency range of 3.1−3.7 THz operating under both positive and negative polarities, with an operating current-density range of 167−322 A/cm2 at 53 K and maximum lasing temperature of 141 K or 121 K depending on the polarity of the applied bias. By showing results from QCLs developed over a period of time, here we show conclusively that short-barrier terahertz QCLs are effective in achieving low current-density operation at the cost of a reduction in peak temperature performance.

In order to meet the requirement of multimedia information acquisition in wireless sensor network,a new wire-less multimedia sensor node is designed and implemented with the high-performance DSP processor as the core.ZigBee module is used to implement the wireless communication.In virtue of the powerful computing ability and peripheral in-terfaces of DSP processor,the multimedia node owns binocular vision and various sensors such as accelerometer.There-fore,it can attain the real-time perception of environmental information and acquire image/video data.Based on the Zig-Bee protocol,the sensor is able to fulfill short-distance wireless communication and monitoring duty in indoor environ-ment.Experimental results show that the multimedia sensor node designed can accurately obtain and handle the syntheti-cal sensing data in real time.It is suitable for the application of wireless multimedia sensor network.

Objective To analyze surgical methods and evaluate treatment efficacy and safety for managing adrenal cystic lesions. Materials and methods All patients presenting with adrenal lesions of the West China Hospital were reviewed retrospectively from January 2003 to April 2013 and 47 were diagnosed as adrenal cysts. Basic information, clinical history, physical examination, laboratory investigations, abdominal ultrasound and enhanced computed tomography were detailed noted. Cysts with different surgical management were analyzed and surgery option operative time, postoperative complications and after-surgery hospital stay were all noted. The final diagnosis was judged by histopathology. Patients were followed from 3 month to 10 years. Results All the 47 patients with a mean age of 43.8 years were managed by surgical intervention. Compared laparoscopic technology with open technology, the laparoscopic has the advantage of a shorter operation time, shorter hospital stay after surgery and enhanced cosmesis. The histopathologic result was: 23 (50%) were endothelial cysts and 16 (35%) were pseudocysts. One patient had evidence to recurrence at the followed-up stage. Conclusion Adrenal cysts are rare and with the development of imaging techniques many of these are diagnosed incidentally. CT has advantages in detecting the cysts with haemorrhage, intracystic debris, calcification and mixed adrenal mass. Minimally invasive surgery offers equivalent efficacy to traditional open procedures, while providing a shorter operation time, shorter convalescence and improved cosmesis. Patients after surgical resection should be followed up closely especially if functional cysts and histopathology of cystic tumor are present.

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Ballast flight significantly affects the safe operation of high-speed railway. A wind tunnel experiment and computational fluid dynamics simulation were performed to investigate the occurrence and development mechanism of ballast flight from the perspective of individual particles, which was based on the capture, reconstruction, and analysis approach of ballast features. The dynamic characteristics of ballast particles under wind load, the flow field characteristics such as the distribution of air pressure and streamline, and the influence of the ballast mass and shape, windward direction, and wind speed on the ballast flight were investigated. The results show that the critical velocity of the ballast weighing from 5 g to 200 g is between 14.22 m/s and 29.89 m/s. The massive ballast with the shape of an oblate is unlikely to fly. The air pressure difference is proportional to the square of the wind speed, and it increases 14.9 times when the wind speed increases from 10 m/s to 40 m/s. In order to avoid ballast flight, recommended suggestions include limiting the proportions of the small and the ellipsoid ballast praticles, laying the ballast with high density and a large size on the top of the ballast bed, and reducing the degradation and fouling of the ballast bed.

The present methods of diagnosing depression are entirely dependent on self-report ratings or clinical interviews. Those traditional methods are subjective, where the individual may or may not be answering genuinely to questions. In this paper, the data has been collected using self-report ratings and also using electronic smartwatches. This study aims to develop a weighted average ensemble machine learning model to predict major depressive disorder (MDD) with superior accuracy. The data has been pre-processed and the essential features have been selected using a correlation-based feature selection method. With the selected features, machine learning approaches such as Logistic Regression, Random Forest, and the proposed Weighted Average Ensemble Model are applied. Further, for assessing the performance of the proposed model, the Area under the Receiver Optimization Characteristic Curves has been used. The results demonstrate that the proposed Weighted Average Ensemble model performs with better accuracy than the Logistic Regression and the Random Forest approaches.

Crack propagation produced by frost heave affects the durability of slab-track structures in high-humidity and cold regions in China. This work is intended to reveal the evolution laws of frost-heave crack propagation, establish evaluation criteria for crack propagation, and investigate factors involved in frost-heave crack propagation. Firstly, by preparing slab-track specimens with initial cracks, an experiment of frost-heave crack propagation was designed. The process of frost-heave crack propagation was carried out by means of digital image correlation (DIC) technology and acoustic emission (AE) technology, respectively. These experiments revealed the evolution laws of generalized strain and AE events’ location during crack initiation and propagation, respectively, and the key parameters of micro-crack initiation strain and unstable propagation strain were obtained. By using theoretical and experimental analysis, a double-strain criterion for frost-heave crack propagation was proposed. Finally, factors involved in frost-heave crack propagation were investigated. The results show that crack initiation can be reflected by the crack-tip strain. The average micro-crack initiation strain and unstable propagation strain were found to be 224 με and 243 με, respectively. Moreover, it was found that the frost-heave crack propagation was caused by an ice plug which formed at the crack opening. When the crack width is larger than 2.7 mm and the external temperature is lower than −6.6 °C, cracks propagate easily under the frost-heave force.

Gas sensors are becoming increasingly important for the safety and quality of human life.

In this paper, we propose a variable block insertion heuristic (VBIH) algorithm to solve the permutation flow shop scheduling problem (PFSP). The VBIH algorithm removes a block of jobs from the current solution. It applies an insertion local search to the partial solution. Then, it inserts the block into all possible positions in the partial solution sequentially. It chooses the best one amongst those solutions from block insertion moves. Finally, again an insertion local search is applied to the complete solution. If the new solution obtained is better than the current solution, it replaces the current solution with the new one. As long as it improves, it retains the same block size. Otherwise, the block size is incremented by one and a simulated annealing-based acceptance criterion is employed to accept the new solution in order to escape from local minima. This process is repeated until the block size reaches its maximum size. To verify the computational results, mixed integer programming (MIP) and constraint programming (CP) models are developed and solved using very recent small VRF benchmark suite. Optimal solutions are found for 108 out of 240 instances. Extensive computational results on the VRF large benchmark suite show that the proposed algorithm outperforms two variants of the iterated greedy algorithm. 236 out of 240 instances of large VRF benchmark suite are further improved for the first time in this paper. Ultimately, we run Taillard’s benchmark suite and compare the algorithms. In addition to the above, three instances of Taillard’s benchmark suite are also further improved for the first time in this paper since 1993.

Recently, deep learning has achieved state-of-the-art performance in more aspects than traditional shallow architecture-based machine-learning methods. However, in order to achieve higher accuracy, it is usually necessary to extend the network depth or ensemble the results of different neural networks. Increasing network depth or ensembling different networks increases the demand for memory resources and computing resources. This leads to difficulties in deploying depth-learning models in resource-constrained scenarios such as drones, mobile phones, and autonomous driving. Improving network performance without expanding the network scale has become a hot topic for research. In this paper, we propose a cross-architecture online-distillation approach to solve this problem by transmitting supplementary information on different networks. We use the ensemble method to aggregate networks of different structures, thus forming better teachers than traditional distillation methods. In addition, discontinuous distillation with progressively enhanced constraints is used to replace fixed distillation in order to reduce loss of information diversity in the distillation process. Our training method improves the distillation effect and achieves strong network-performance improvement. We used some popular models to validate the results. On the CIFAR100 dataset, AlexNet’s accuracy was improved by 5.94%, VGG by 2.88%, ResNet by 5.07%, and DenseNet by 1.28%. Extensive experiments were conducted to demonstrate the effectiveness of the proposed method. On the CIFAR10, CIFAR100, and ImageNet datasets, we observed significant improvements over traditional knowledge distillation.

Machine learning (ML) is extensively applied in chemistry, particularly in vibrational spectroscopy. However, few teaching examples effectively demonstrate the capabilities of ML in classifying polymeric materials, exhibiting subtle spectral differences that elude visual discrimination. This study presents a teaching example specifically tailored for undergraduate students to acquire the skills necessary to employ ML models in the classification of infrared spectral data from different types of polyvinyl butyrals (PVBs). The course encompasses fundamental knowledge of PVB structure and synthesis, a comprehensive spectral analysis workflow for constructing classification models, specific data processing techniques, and practical implementation of a student-synthesized PVB product in a laboratory demonstration. Assessment of students' knowledge acquisition is conducted through assignments, and student attitudes toward this course via submitted self-reflection surveys are discussed. This study underscores the efficacy of classroom examples in developing students' abilities and fostering their interest in amalgamating chemistry and artificial intelligence. The knowledge and techniques acquired in this course hold practical implications for quality control, process monitoring, and material identification in industry.

Solitary pulmonary nodule (SPN or coin lesion) is a mass in the lung and can be commonly found in chest X-rays or computerized tomography (CT) scans. However, despite the advancement of imaging technologies, it is still difficult to distinguish malignant cancer from benign SPNs. Here we investigated the metabolic profiling of patients with benign and malignant pulmonary nodules. A combination of gas chromatography/mass spectrometry (GC/MS) and liquid chromatography/mass spectrometry (LC/MS) was used to profile the plasma metabolites in 17 patients with malignant SPNs, 15 patients with benign SPNs and 20 healthy controls. The metabolic profiles were assayed using OPLS-DA, and further analyzed to identify marker metabolites related to diseases. Both GC/MS- and LC/MS-derived models showed clear discriminations in metabolic profiles among three groups. It was found that 63 metabolites (12 from GC/MS, 51 from LC/MS) contributed to the differences. Of these, 48 metabolites showed same change trend in both malignant and benign SPNs as compared with healthy controls, indicating some common pathways including inflammation and oxidative injury shared by two diseases. In contrast, 14 metabolites constituted distinct profiles that differentiated malignant from benign SPNs, which might be a unique biochemical feature associated with lung cancer. Overall, our data suggested that integration of two highly sensitive and complementary metabolomics platforms could enable a comprehensive metabolic profiling and assist in discrimination malignant from benign SPNs.