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Purpose: To investigate the clinical efficacy of anti-programmed cell death-1 (PD-1) immunotherapy in combination with chemotherapy in patients suffering from locally advanced non-small cell lung cancer (NSCLC). Methods: A total of 110 NSCLC patients admitted in The Second Affiliated Hospital of Hainan Medical University, Haikou, China were randomly divided into two groups using envelope method. The control group was administered pemetrexed and cisplatin, and the study group was further treated with nivolumab. Each treatment cycle lasted for 21 days, and a total of 6 cycles were administered for the duration of therapy. The short-term objective response rate, adverse reactions, programmed cell death5 (PDCD-5), tumor necrosis factor-α (TNF-α), tumor necrosis factor-β1 (TGF-β1), and immune status indices were determined. Results: The objective remission rate in study group was 79.25 % (42/53), which was higher than that in control group (61.40 %, 35/57; p < 0.05). After treatment, both groups showed higher levels of PDCD5 and TNF-α compared to their pre-treatment values, while demonstrating lower levels of TGF-β1. Furthermore, PDCD-5 and TNF-α in study group were higher than those in control group, while TGF-β1 was lower than that in control group (p < 0.05). In control group, the values of clusters of differentiation 3+(CD3+), 4+(CD4+), and 8+(CD8+) decreased after treatment, while the values of CD8+ increased after treatment. The CD3+, CD4+, and CD4+/CD8+ in study group were higher than those in control group, while CD8+ was lower than those in control group (p < 0.05). Conclusion: Anti-PD-1 immunotherapy combination with chemotherapy in the treatment of local NSCLC stabilizes the immune system and improves the short-term efficacy of patients with advanced NSCLC. Further clinical trials are, however, required prior to application of this combination clinical practice.

Computer-aided X-ray pneumonia lesion recognition is important for accurate diagnosis of pneumonia. With the emergence of deep learning, the identification accuracy of pneumonia has been greatly improved, but there are still some challenges due to the fuzzy appearance of chest X-rays. In this paper, we propose a deep learning framework named Attention-Based Contrastive Learning for Class-Imbalanced X-Ray Pneumonia Lesion Recognition (denoted as Deep Pneumonia). We adopt self-supervised contrastive learning strategy to pre-train the model without using extra pneumonia data for fully mining the limited available dataset. In order to leverage the location information of the lesion area that the doctor has painstakingly marked, we propose mask-guided hard attention strategy and feature learning with contrastive regulation strategy which are applied on the attention map and the extracted features respectively to guide the model to focus more attention on the lesion area where contains more discriminative features for improving the recognition performance. In addition, we adopt Class-Balanced Loss instead of traditional Cross-Entropy as the loss function of classification to tackle the problem of serious class imbalance between different classes of pneumonia in the dataset. The experimental results show that our proposed framework can be used as a reliable computer-aided pneumonia diagnosis system to assist doctors to better diagnose pneumonia cases accurately., Comment: 9 pages, 9 figures

Wire and arc additive manufacturing (WAAM) has been used to manufacture ER2319 aluminum alloy lattice structure, whereas a relationship of the struts diameter of lattice structure with the size and number of droplets, struts angle with the inter-layer lift and offset of the arc torch was developed. The manufacturing strategy of lattice structure by WAAM was formulated, and the aluminum alloy lattice structure demonstration was prepared where compressive properties were measured. The results show that the size of the droplet can be controlled by adjusting the current, voltage, and period within a pulse period; and then the struts diameter can be controlled by adjusting the size and number of droplets. The struts with a diameter of no less than 2.50mm were manufactured by WAAM, and its relative error was less than 2.0%. The struts angle can be controlled by adjusting the inter-layer lift and offset of the arc torch. The angle of the strut was from 15° to 90°, and its relative error was below 4.0%. A 3-layer pyramid lattice structure demonstration of the aluminum alloy composed of struts with a diameter of 3 mm and an angle of 45° was fabricated by WAAM. The average compressive strength of ER2319 aluminum Alloy lattice structure was 58.53 MPa.

With the rapid development of smart grid, traditional cloud computing architectures struggle to meet the needs of new power applications with low latency and large connectivity in the context of big data. Hence, edge computing has emerged. Edge computing is closer to the edge of the network where data is generated, enabling fast data processing and supporting swift user requests. This paper describes the system architecture of edge computing and the principles of task offloading in smart grid. It finally concludes with a summary of existing issues and future trends.

Corona loss is the key parameter for evaluating operating economy of ultra-high voltage (UHV) and extra-high voltage (EHV) alternating current (AC) transmission lines and is one of important factors that need to be considered in line design and operation. At present, corona losses of 8 × LGJ-630 conductors, which are commonly used in ultra-high voltage alternating current (UHV AC) double-circuit transmission lines on the same tower, and 8 × LGJ-720, and 10 × LGJ-720 bundle conductors with a larger cross-section and more bundle number in high-altitude areas have not yet been studied. In the paper, the corona losses of the 8 × LGJ-630, 8 × LGJ-720, and 10 × LGJ-720 conductors were measured under conditions of dryness, artificial moderate rain, heavy rain and a wet conductor, based on the UHV corona cage built in Ping’an County, Xi’ning, China at an altitude of 2,200 m. By taking typical 1,000 kV UHV-AC double-circuit transmission lines on the same tower as an example, the electric field strengths on the surface of conductors in the corona cage and the actual lines were calculated. On this basis, the corona loss of the UHV-AC double-circuit transmission line in a high-altitude area was analyzed. The research results can provide data reference for constructing UHV transmission lines in high-altitude areas.

Nitrated aromatic compounds (NACs) are a group of key chromophores for brown carbon (light-absorbing organic carbon, i.e., BrC) aerosol, which affects radiative forcing. The chemical composition and sources of NACs and their contributions to BrC absorption, however, are still not well understood. In this study, PM2:5-bound NACs in Xi'an, Northwest China, were investigated for 112 daily PM2:5 filter samples from 2015 to 2016. Both the total concentrations and contributions from individual species of NACs show distinct seasonal variations. The seasonally averaged concentrations of NACs are 2.1 (spring), 1.1 (summer), 12.9 (fall), and 56 ngm3 (winter). Thereinto, 4-nitrophenol is the major NAC component in spring (58 %). The concentrations of 5-nitrosalicylic acid and 4-nitrophenol dominate in summer (70 %), and the concentrations of 4-nitrocatechol and 4-nitrophenol dominate in fall (58 %) and winter (55 %). The NAC species show different seasonal patterns in concentrations, indicating differences in emissions and formation pathways. Source apportionment results using positive matrix factorization (PMF) further show large seasonal differences in the sources of NACs. Specifically, in summer, NACs were highly influenced by secondary formation and vehicle emissions (80 %), while in winter, biomass burning and coal combustion contributed the most (75 %). Furthermore, the light absorption contributions of NACs to BrC are wavelength-dependent and vary greatly by season, with maximum contributions at 330 nm in winter and fall and 320 nm in summer and spring. The differences in the contribution to light absorption are associated with the higher mass fractions of 4-nitrocatechol (max D345 nm) and 4-nitrophenol (max D310 nm) in fall and winter, 4-nitrophenol in spring, and 5-nitrosalicylic acid (max D315 nm) and 4-nitrophenol in summer. The mean contributions of NACs to BrC light absorption at a wavelength of 365 nm in different seasons are 0.14% (spring), 0.09% (summer), 0.36% (fall), and 0.91% (winter), which are about 6-9 times higher than their mass fractional contributions of carbon in total organic carbon. Our results indicate that the composition and sources of NACs have profound impacts on the BrC light absorption.

Images obtained in real-world low-light conditions are not only low in brightness, but they also suffer from many other types of degradation, such as color distortion, unknown noise, detail loss and halo artifacts. In this paper, we propose a Degradation-Aware Deep Retinex Network (denoted as DA-DRN) for low-light image enhancement and tackle the above degradation. Based on Retinex Theory, the decomposition net in our model can decompose low-light images into reflectance and illumination maps and deal with the degradation in the reflectance during the decomposition phase directly. We propose a Degradation-Aware Module (DA Module) which can guide the training process of the decomposer and enable the decomposer to be a restorer during the training phase without additional computational cost in the test phase. DA Module can achieve the purpose of noise removal while preserving detail information into the illumination map as well as tackle color distortion and halo artifacts. We introduce Perceptual Loss to train the enhancement network to generate the brightness-improved illumination maps which are more consistent with human visual perception. We train and evaluate the performance of our proposed model over the LOL real-world and LOL synthetic datasets, and we also test our model over several other frequently used datasets without Ground-Truth (LIME, DICM, MEF and NPE datasets). We conduct extensive experiments to demonstrate that our approach achieves a promising effect with good rubustness and generalization and outperforms many other state-of-the-art methods qualitatively and quantitatively. Our method only takes 7 ms to process an image with 600x400 resolution on a TITAN Xp GPU., Comment: 16 pages, 17 figures

Low-light image enhancement is a challenging low-level computer vision task because after we enhance the brightness of the image, we have to deal with amplified noise, color distortion, detail loss, blurred edges, shadow blocks and halo artifacts. In this paper, we propose a Two-Stage Network with Channel Attention (denoted as TSN-CA) to enhance the brightness of the low-light image and restore the enhanced images from various kinds of degradation. In the first stage, we enhance the brightness of the low-light image in HSV space and use the information of H and S channels to help the recovery of details in V channel. In the second stage, we integrate Channel Attention (CA) mechanism into the skip connection of U-Net in order to restore the brightness-enhanced image from severe kinds of degradation in RGB space. We train and evaluate the performance of our proposed model on the LOL real-world and synthetic datasets. In addition, we test our model on several other commonly used datasets without Ground-Truth. We conduct extensive experiments to demonstrate that our method achieves excellent effect on brightness enhancement as well as denoising, details preservation and halo artifacts elimination. Our method outperforms many other state-of-the-art methods qualitatively and quantitatively., Comment: 8 pages, 8 figures

Additional file 2. Trends in the prevalence of HIV infection among PWIDs by regions.

Aiming to minimize the surface distortion of large-aperture laser transport mirrors in high-power laser facilities, an assembly design and mounting method are proposed for the mirror. First, a theoretical model on the mirror surface deformation is established. With a new assembly design, the mirror is fastened on its neutral plane and its optical surface distortion can be precisely compensated through several adjustable forces on the sides, which will generate bending moments on the mirror body. Furthermore, a dynamic kinematic joint is designed, in which a corresponding relationship between spring compression and screw rotation ensures the accurate control of the magnitude of mirror preload. Finally, the performance of the presented method has been validated through field experiments and numerical simulations. This transport mirror assembly and mounting design have obvious technical advantages on simple mechanical structure, high operational efficiency, and precise preload control. The results show that the assembly design and optimized mounting strategy can keep the total surface distortion of the mirror within 350 nm (peak-valley).

Significant advances in face recognition tasks have been made with the development of convolutional neural networks. With large scale convolutional neural networks we are now able to achieve 99% accuracy on many public face test sets. However, the face recognition networks commonly used for mobile are limited by their narrow network width which makes it difficult to learn better distributed features in face recognition feature embedding learning. In this work, based on MobileFaceNets we propose an attention-guided hierarchical feature fusion model. Compared with the traditional cascading convolutional neural network classification model, our approach can explicitly consider both global and local features. And our approach requires only a small increase in computation to obtain better results than directly increasing the feature dimension.

Log interpretation is usually a significant job for asset development with relative high uncertainties and low efficiency. Incorrect results are associated with several negative effects. Remarkable efforts have been made to develop machine learning method to solve this problem. However, with only a single algorithm, the performance may be limited. To maximize prediction accuracy, we present another option using stacking machine learning technique, within which different types of base machine learning algorithms function together through a mega - classifier. Compared with the best performed single algorithm, stacking algorithm performs better, achieving 85.3% in prediction accuracy and 81.2% in F1 score. These results suggest that this strategy could lead to better prediction and improve the prediction accuracy.

Rolling bearings play an important part in rotating machinery. As they work in complex conditions, faults will occur sometimes. Therefore, it is necessary to detect the faults early. Traditional bearing fault diagnosis methods are often based on mechanism analysis and feature selection, and the process is relatively complicated. Deep learning methods, however, have the ability to extract and select features automatically, which greatly reduces the workload. In recent years, deep learning-based methods have been successfully used in many fields, such as computer vision, voice recognition, medical diagnosis. In this paper, the end-to-end fault methods based on deep learning are proposed. The Long Short-Term Memory (LSTM) network, Gated Recurrent Unit (GRU) network and One-Dimensional Convolutional Neural Network (1D CNN) are used to build the deep learning network architecture respectively. A methodology is proposed for rolling bearing fault diagnosis, including data preprocessing, network modeling, training, validation and testing. Test bench data is used for fault diagnosis and the results show that deep learning based end-to-end methods are effective for the fault diagnosis of rolling bearings and that the model based on 1D CNN has the best performance.

The sources, formation mechanism and amount of organosulfates (OS) in atmospheric aerosol are not yet well understood, partly due to the lack of authentic standards for quantification. In this study, we report an improved robust procedure for the synthesis of organosulfates with different functional groups. Nine authentic organosulfate standards were synthesized and four standards (benzyl sulfate, phenyl sulfate, glycolic acid sulfate, and hydroxyacetone sulfate) were used to quantify their ambient concentrations. The authentic standards and ambient aerosol samples were analyzed using an optimized ultra performance liquid chromatography-electrospray ionization-tandem mass spectrometric method (UPLC-ESI-MS/MS). The recovery ranged from 80.4 to 93.2%, the limits of detection and limits of quantification obtained were 1.1-16.7 and 3.4-55.6pgmĝ'3, respectively. Measurements of ambient aerosol particle samples collected in winter 2013/2014 in urban Xi'an, northwestern China, show that glycolic acid sulfate (77.3±49.2ngmg-3) is the most abundant species of the identified organosulfates followed by hydroxyacetone sulfate (1.3±0.5ngmg-3), phenyl sulfate (0.14±0.09ngmg-3), and benzyl sulfate (0.04±0.01ngmg-3). Except for hydroxyacetone sulfate, which seems to form mainly from biogenic emissions in this region, the organosulfates quantified during winter in Xi'an show an increasing trend with an increase in the mass concentrations of organic carbon indicating their anthropogenic origin.

With the increasing demand of shipping capacity, modern ships are moving towards the trend to greater sizes and more complex structures. At the same time, the operational safety, economy and environmental protection of ships are facing challenges. As a result, the concept of transformation from the “buying good ships” to “using ships well” is proposed for the shipbuilding and shipping industry. With the goal of upgrading in equipment intelligence, industrial chain capability and management intelligence upgrading, the overall architecture of Cyber-enabled ship control and management system based on “Two Terminals & Two Clouds” (2T2C), which consists of a perception and control terminal, an intelligent management terminal and a hybrid cloud platform, is proposed. The SOMS system based on 2T2C architecture is applied to iDolphin 38,800-ton intelligent bulk carrier and has achieved good results with real ship data: the intelligent system can effectively reduce the difficulty of using the ship, save fuel consumption and reduce accidents and control the full-life operating costs and energy consumption.

Background It is difficult to differentiate patients with cerebellopontine angle (CPA) cholesteatoma and patients with primary trigeminal neuralgia just according to early symptoms. We aimed to explore the clinical characteristics, early diagnosis, and microneurosurgical techniques for CPA cholesteatoma that presented as trigeminal neuralgia. Methods The data of 26 patients who complained trigeminal neuralgia with CPA cholesteatoma between January 2009 and December 2015 were collected and studied retrospectively, they were diagnosed by magnetic resonance imaging preoperatively and confirmed by pathology postoperatively. All the tumors were resected through a retrosigmoidal approach. In 26 cases, 14 patients who underwent cholesteatoma resection and microvascular decompression were assigned to group A and 12 patients who underwent only cholesteatoma resection were assigned to group B. The clinical features and surgical results between groups A and B were compared. The complications and surgical results were followed up, and surgical techniques were summarized. Results All patients presented as trigeminal neuralgia at the same side of the cholesteatoma. There was no statistical difference between the 2 groups in clinical features and surgical results between groups A and B. All patients with cholesteatoma showed clear and significant imaging characteristics. The tumors were totally removed in 18 patients and subtotally removed in 8 patients. Pain relief was satisfactory in all patients. Surgical complications included transient aseptic meningitis in 2 patients, facial numbness in 2 patients, mild tinnitus in 2 patients, mild and facial weakness in 1 patient. No death, hematoma, or acute hydrocephalus were reported in this series. During the follow-up of 12–80 months, no patient experienced recurrence of pain or tumor. Conclusions Cholesteatoma of the cerebellopontine angle often presented as trigeminal neuralgia. Magnetic resonance imaging is helpful for early diagnosis according to its distinct signal. Surgical treatment is often the first choice, the neuralgia relief was satisfactory after operation. Microvascular decompression is recommended simultaneously if some offending vessels were founded during the surgical resection of the tumor.

The visual simulation system which the docking process of replenishment at sea is constructed based on MultiGen Creator and Vega Prime. In a bid to modeling and driving of the visual simulation, a large number of the real-time data should be transformed for simulation in which the entire process of replenishment at sea. According to the complicated power and movement state of the docking mechanism under different levels of waves, the finite element analysis of the key components of the docking mechanism based on ABAQUS software is carried out, due to the uncertainty of the change of the real marine environment. Which can provide the theoretical basis for the objective and realistic on establishment of the visual simulation system which the docking process of replenishment at sea.

Compressive sensing (CS) implements sampling and compression to sparse or compressible signals simultaneously. Compressive signal processing is a new signal processing scheme base on compressive sensing theory. In this paper, the problem of signal compressive reconstruction base on narrow-band interference is researched. The reconstruction performance of BP, MP, and OMP algorithms with narrow-band interference is analyzed by computer simulations.

Space deployable mechanism plays an important role in the orbital deployment and stable support of the antenna reflection surface, which is an important component for the spacecraft radar antenna. In order to improve the structural stiffness of the ring truss deployable antenna mechanism when it has a large diameter, a scissors double-ring truss deployable mechanism is proposed in this paper. First, structure analysis of general ring truss deployable antenna and construction of the scissors double-ring truss deployable mechanism are conducted, and the whole mechanism is decomposed to a plurality of mechanism units. Then, degree of freedom (DOF) of the scissors double-ring truss deployable mechanism is analyzed based on screw theory, the result showed that it has only one DOF. Furthermore, based on screw theory, kinematic characteristics of the scissors double-ring truss deployable mechanism are examined, velocities and accelerations of the components in the mechanism are obtained, as well as the Jacobian matrixes. Finally, based on Newton-Euler equation and the principle of virtual work, a dynamic model of the whole mechanism is established, numerical calculation and simulation verification are carried out, and the results verified the correctness of the theoretical analysis. The scissors double-ring truss deployable mechanism proposed in this paper can be well applied in the field of space antennas, and the theoretical analysis method based on screw theory used in this paper can provide insights into other spatial deployable mechanisms.

In response to the global energy crisis, the State Grid Corporation of China has put forward the idea of global energy Internet, which focuses on using ultra-high voltage (UHV) transmission technology for power delivery across continents. Reasonable prediction of UHV transmission line can effectively assist global energy Internet scheme comparison and project decision in the early stage. Based on mathematical model, this paper selects 11 influencing factors to obtain mathematical equation of UHV transmission line. It indicates that the mathematical model obtains good results and the deviation rate between the predicted value and the actual value is less than 10%. The prediction technology of UHV transmission line in global energy Internet can meet the requirements of scheme comparison and project decision in the early stage.

As a complex network, microgrid must have some weakness. Risk assessment of microgrid will help to find out these weakness so as to put forward the targeted improvement measures and provide strong support for the optimal operation of microgrid. According to the risk assessment of microgrid, this paper establishes a microgrid risk index system, which includes the first-level indexes and corresponding sub-indexes of structural safety, operation safety and technical safety, and innovatively establishes a credibility entropy weight coefficient evaluation model, and calculates the weight of each index layer and the comprehensive risk assessment score of the microgid. Furthermore, the risk assessment of a microgrid system is analyzed by quantitative calculation in order to make the assessment results more reliable and provide reference for further improving the risk assessment of the microgrid.

Lithium-ion batteries (LIBs) technology have been widely utilized as these batteries show great promise as power sources in our life. However, the commercial anode is mainly carbon materials which facing safety issues owing to a low Li-intercalation potential. The spinel Li4Ti5O12 (LTO) has attracted special attention for its structural changes lightly, better safety performance and stable cycle reversibility. Herein, we increased the electronic conductivity of LTO via improving the transport properties of the material with sucrose as carbon sources. The modified LTO with sucrose were successfully fabricated and found to exhibit better electronic conductivity. XRD results showed that all the major diffractions from the spinel structure of LTO can be found in the modified LTO such as (1 1 1), (3 1 1), (4 0 0) but there are no observations of the carbon diffraction peaks. Electrochemical impedance spectroscopy (EIS) data shows modified LTO resistance was nearly one third of the standard LTO value, which indicates modifying LTO with sucrose is a promising approach to prepare the high performance LTO anode. Keywords: Lithium-ion batteries, Li4Ti5O12, Electronic conductivity, Sucrose

The effective energy density at the target of inertial confinement fusion facility is subject to the problem of the low efficiency of second harmonic generation of large-aperture KDP frequency converters over a long time. To overcome this problem, we propose the optomechanical analysis method to correlate the mechanical factors with its critical physical performance in theory. Further, combined with experiments and simulations, the mechanical sensitivity of the frequency converter is completely explained from the microscopic and macroscopic perspectives. Finally, we introduce a moment-dominated clamping scheme that is expected to greatly improve the online service performance of the converter.

In this paper, the authors manage to design and control a kind of hexapod wall-climbing robot, which is a radial symmetric, with 18 actuators - 3 steering motors allocated at each leg - supposed to be effectively mobile. Suckers, solenoid valves and vacuum pump are equipped to enable the function of wall climbing. In order to reach the target, mathematic model is optimized in advance. And it is designed to be light but meanwhile has a certain capacity of load-carrying. By alternating the gait between the tripod and the non-tripod one, or adjusting the open-loop control strategy, the load-carrying capacity can be extended a lot at the price of reducing the mobility.

Converging evidences show that face categorization and recognition are represented in specific brain areas. It is also known from behavioral studies that spatial frequency of pictures takes different roles in different face categorizations, e.g. identity information is carried more by low frequency features, and gender is represented by both high and low frequencies while high frequency is more important for face expression. However, there is no solid experimental data about the neural mechanism of the different contributions of spatial frequencies on face categorization up to now. ECoG was collected from epilepsy patients while they underwent a monitoring session to locate their epilepsy foci with implanted subdural surface electrodes. Pictures with different gender, facial expression and identities were presented to the patients, while they were required to perform a simple detection task to maintain their focus on the pictures. The changes of traditional face related components, N170, were analyzed by event related potentials and the significant changes was also verified on each electrode by permutation test. The N170 latency is found to be delayed for high spatial frequency (HSF) pictures. For faces of unknown person, low spatial frequency (LSF) presentation results in a longer N170 latency, but not for faces of well-known movie stars. Female faces show longer N170 latency in HSF conditions compared to LSF, but male faces have no such effect. No significant effect was found on N170 component for expression. However, analysis based on individual electrodes showed that more frontal electrodes are involved in expression representation; the identity-specific sites are more likely to respond to LSF stimuli, and the gender-specific sites have equal responses to both LSF and HSF stimuli, while the expression-specific sites are also more likely to respond to the LSF stimuli, which is inconsistent with existing behavioral studies, and show significant differences as early as 114 milliseconds. It fits with the cognitive model that expression related information has been analyzed briefly in occipital-temporal areas in the early stage before other brain areas are involved for further processing. In summary, spatial frequency's contributions to identity and gender could be represented by N170 in traditional face related brain areas while expression information, instead of carrying by N170, is distributed in a wider area in occipital temporal region for fast analysis and relayed to other areas, e.g. frontal cortex. This is the first study to study the neural mechanism of spatial frequency's contributions in face categorization by ECoG, and provides a new perspective to understand the brain dynamics of feature processes for face perception.

Summary Purpose: To describe the clinical, electrophysiologic, neuroradiologic, and histologic findings in our patients with drug-resistant hypermotor seizures (HMSs) and to evaluate the outcome of their surgical treatment. Methods: Twenty-three patients were identified by criteria for drug-resistant HMS. Surgical treatment and presurgical evaluation modalities including semiology, magnetic resonance imaging (MRI), interictal/ictal scalp video-EEG (electroencephalography), and intracranial recording were analyzed retrospectively. Results: The common seizure frequency of 60–300 per month was observed among 15 patients. Sixteen patients (69.6%) experienced auras such as fear and palpitation. Marked agitation was observed in 12 patients and mild agitation in 11 patients. Groaning/shouting and asymmetric posturing were common accompanying symptoms. Asymmetric posturing was observed more often in patients with mild agitation than in those with marked agitation (p = 0.027). MRI detected focal abnormalities in six patients. Intracranial recording was conducted in 16 patients. The origins of seizures were localized in mesial frontal cortex in four patients, dorsolateral frontal cortex in four patients, and mesial temporal cortex in two patients. The epileptogenic zones (EZs) were resected from the frontal lobe in 21 patients and from the temporal lobe in 2 patients. The follow-up ranged from 12–60 months. Seventeen patients (73.9%) had been seizure-free, 11 of whom had presented with marked agitation (11 of 12) and 6 with mild agitation (6 of 11) (p = 0.069). Histologic examinations demonstrated focal cortical dysplasia (FCD) in 18 patients. Discussion: The HMSs can originate from both the mesial and dorsolateral frontal cortex, and occasionally from the temporal lobe. Patients with drug-resistant HMSs should be recommended for resective surgical treatment.

Previously, we proposed a physically-inspired method to construct data points into an effective in-tree (IT) structure, in which the underlying cluster structure in the dataset is well revealed. Although there are some edges in the IT structure requiring to be removed, such undesired edges are generally distinguishable from other edges and thus are easy to be determined. For instance, when the IT structures for the 2-dimensional (2D) datasets are graphically presented, those undesired edges can be easily spotted and interactively determined. However, in practice, there are many datasets that do not lie in the 2D Euclidean space, thus their IT structures cannot be graphically presented. But if we can effectively map those IT structures into a visualized space in which the salient features of those undesired edges are preserved, then the undesired edges in the IT structures can still be visually determined in a visualization environment. Previously, this purpose was reached by our method called IT-map. The outstanding advantage of IT-map is that clusters can still be found even with the so-called crowding problem in the embedding. In this paper, we propose another method, called IT-Dendrogram, to achieve the same goal through an effective combination of the IT structure and the single link hierarchical clustering (SLHC) method. Like IT-map, IT-Dendrogram can also effectively represent the IT structures in a visualization environment, whereas using another form, called the Dendrogram. IT-Dendrogram can serve as another visualization method to determine the undesired edges in the IT structures and thus benefit the IT-based clustering analysis. This was demonstrated on several datasets with different shapes, dimensions, and attributes. Unlike IT-map, IT-Dendrogram can always avoid the crowding problem, which could help users make more reliable cluster analysis in certain problems., 13 pages, 6 figures. IT-Dendrogram: An Effective Method to Visualize the In-Tree structure by Dendrogram

Previously, we proposed a physically inspired rule to organize the data points in a sparse yet effective structure, called the in-tree (IT) graph, which is able to capture a wide class of underlying cluster structures in the datasets, especially for the density-based datasets. Although there are some redundant edges or lines between clusters requiring to be removed by computer, this IT graph has a big advantage compared with the k-nearest-neighborhood (k-NN) or the minimal spanning tree (MST) graph, in that the redundant edges in the IT graph are much more distinguishable and thus can be easily determined by several methods previously proposed by us. In this paper, we propose a general framework to re-construct the IT graph, based on an initial neighborhood graph, such as the k-NN or MST, etc, and the corresponding graph distances. For this general framework, our previous way of constructing the IT graph turns out to be a special case of it. This general framework 1) can make the IT graph capture a wider class of underlying cluster structures in the datasets, especially for the manifolds, and 2) should be more effective to cluster the sparse or graph-based datasets., 17 pages

Scientists in many fields have the common and basic need of dimensionality reduction: visualizing the underlying structure of the massive multivariate data in a low-dimensional space. However, many dimensionality reduction methods confront the so-called "crowding problem" that clusters tend to overlap with each other in the embedding. Previously, researchers expect to avoid that problem and seek to make clusters maximally separated in the embedding. However, the proposed in-tree (IT) based method, called IT-map, allows clusters in the embedding to be locally overlapped, while seeking to make them distinguishable by some small yet key parts. IT-map provides a simple, effective and novel solution to cluster-preserving mapping, which makes it possible to cluster the original data points interactively and thus should be of general meaning in science and engineering., Comment: 13 pages, 6 figures

Previously in 2014, we proposed the Nearest Descent (ND) method, capable of generating an efficient Graph, called the in-tree (IT). Due to some beautiful and effective features, this IT structure proves well suited for data clustering. Although there exist some redundant edges in IT, they usually have salient features and thus it is not hard to remove them. Subsequently, in order to prevent the seemingly redundant edges from occurring, we proposed the Nearest Neighbor Descent (NND) by adding the "Neighborhood" constraint on ND. Consequently, clusters automatically emerged, without the additional requirement of removing the redundant edges. However, NND proved still not perfect, since it brought in a new yet worse problem, the "over-partitioning" problem. Now, in this paper, we propose a method, called the Hierarchical Nearest Neighbor Descent (H-NND), which overcomes the over-partitioning problem of NND via using the hierarchical strategy. Specifically, H-NND uses ND to effectively merge the over-segmented sub-graphs or clusters that NND produces. Like ND, H-NND also generates the IT structure, in which the redundant edges once again appear. This seemingly comes back to the situation that ND faces. However, compared with ND, the redundant edges in the IT structure generated by H-NND generally become more salient, thus being much easier and more reliable to be identified even by the simplest edge-removing method which takes the edge length as the only measure. In other words, the IT structure constructed by H-NND becomes more fitted for data clustering. We prove this on several clustering datasets of varying shapes, dimensions and attributes. Besides, compared with ND, H-NND generally takes less computation time to construct the IT data structure for the input data., Comment: 19 pages, 9 figures

A recently proposed clustering method, called the Nearest Descent (ND), can organize the whole dataset into a sparsely connected graph, called the In-tree. This ND-based Intree structure proves able to reveal the clustering structure underlying the dataset, except one imperfect place, that is, there are some undesired edges in this In-tree which require to be removed. Here, we propose an effective way to automatically remove the undesired edges in In-tree via an effective combination of the In-tree structure with affinity propagation (AP). The key for the combination is to add edges between the reachable nodes in In-tree before using AP to remove the undesired edges. The experiments on both synthetic and real datasets demonstrate the effectiveness of the proposed method.

In our physically inspired in-tree (IT) based clustering algorithm and the series after it, there is only one free parameter involved in computing the potential value of each point. In this work, based on the Delaunay Triangulation or its dual Voronoi tessellation, we propose a nonparametric process to compute potential values by the local information. This computation, though nonparametric, is relatively very rough, and consequently, many local extreme points will be generated. However, unlike those gradient-based methods, our IT-based methods are generally insensitive to those local extremes. This positively demonstrates the superiority of these parametric (previous) and nonparametric (in this work) IT-based methods., Comment: 7 pages; 6 figures

Most density-based clustering methods largely rely on how well the underlying density is estimated. However, density estimation itself is also a challenging problem, especially the determination of the kernel bandwidth. A large bandwidth could lead to the over-smoothed density estimation in which the number of density peaks could be less than the true clusters, while a small bandwidth could lead to the under-smoothed density estimation in which spurious density peaks, or called the "ripple noise", would be generated in the estimated density. In this paper, we propose a density-based hierarchical clustering method, called the Deep Nearest Neighbor Descent (D-NND), which could learn the underlying density structure layer by layer and capture the cluster structure at the same time. The over-smoothed density estimation could be largely avoided and the negative effect of the under-estimated cases could be also largely reduced. Overall, D-NND presents not only the strong capability of discovering the underlying cluster structure but also the remarkable reliability due to its insensitivity to parameters., Comment: 28 pages, 14 figures

We report a patient who developed delayed cerebrospinal fluid (CSF) rhinorrhea 11 years after gamma knife radiosurgery for a growth hormone (GH)-secreting adenoma. The treatment dose was 18 Gy for the tumor margin (50% isodose). One year later, an MRI of the head revealed that the tumor size had decreased. Eleven years later, the patient developed CSF rhinorrhea from the left nostril. Subsequent MRI examination revealed complete remission of the tumor in the sella turcica and the empty sella. The patient was admitted for direct endoscopic surgical repair of the skull base. We suggest that the cause of the CSF rhinorrhea is secondary empty sella. The other potential causes may be the original invasiveness of the tumor or delayed radiation damage to the mucous membranes of the skull. Long-term follow-up is required to monitor recurrence of CSF rhinorrhea.

Methyl salicylate 2-O-β-d-lactoside (MSL), whose chemical structure is similar to that of salicylic acid, is a natural product derivative isolated from a traditional Chinese herb. The aim of this study was to investigate the therapeutic effect of MSL in mice with collagen-induced arthritis (CIA) and explore its underlying mechanism.The anti-arthritic effects of MSL were evaluated on human rheumatoid fibroblast-like synoviocytes (FLS) in vitro and CIA in mice in vivo by obtaining clinical scores, measuring hind paw thickness and inflammatory cytokine levels, radiographic evaluations and histopathological assessments.Treatment with MSL after the onset of arthritis significantly prevented the progression and development of rheumatoid arthritis (RA) in CIA mice without megascopic gastric mucosa damage. In addition, MSL inhibited the production of pro-inflammatory mediators, the phosphorylation and translocation of NF-κB, and cell proliferation induced by TNF-α in FLS. MSL non-selectively inhibited the activity of COX in vitro, but was a more potent inhibitor of COX-2 than COX-1. MSL also inhibited the phosphorylation of inhibitor of NF-κB kinase, IκBα and p65, thus blocking the nuclear translocation of NF-κB in TNF-α-stimulated FLS.MSL exerts therapeutic effects on CIA mice, suppressing the inflammatory response and joint destruction by non-selectively inhibiting the activity of COX and suppressing activation of the NF-κB signalling pathway, but without damaging the gastric mucosa. Therefore, MSL has great potential to be developed into a novel therapeutic agent for the treatment of RA.

In this paper, we propose a physically inspired graph-theoretical clustering method, which first makes the data points organized into an attractive graph, called In-Tree, via a physically inspired rule, called Nearest Descent (ND). In particular, the rule of ND works to select the nearest node in the descending direction of potential as the parent node of each node, which is in essence different from the classical Gradient Descent or Steepest Descent. The constructed In-Tree proves a very good candidate for clustering due to its particular features and properties. In the In-Tree, the original clustering problem is reduced to a problem of removing a very few of undesired edges from this graph. Pleasingly, the undesired edges in In-Tree are so distinguishable that they can be easily determined in either automatic or interactive way, which is in stark contrast to the cases in the widely used Minimal Spanning Tree and k-nearest-neighbor graph. The cluster number in the proposed method can be easily determined based on some intermediate plots, and the cluster assignment for each node is easily made by quickly searching its root node in each sub-graph (also an In-Tree). The proposed method is extensively evaluated on both synthetic and real-world datasets. Overall, the proposed clustering method is a density-based one, but shows significant differences and advantages in comparison to the traditional ones. The proposed method is simple yet efficient and reliable, and is applicable to various datasets with diverse shapes, attributes and any high dimensionality, Comment: 28 pages: text part(1-14), supplementary material(15-28)

The Henan electric power video conference system is very important for the corporation's work such as manufacturing management, emergency training and so on. This paper mainly introduces the network architecture and schematization of Henan electric power video conference system, and it also analyzes the network characteristic. The gradual perfection and stability operation of Henan electric power video conference system will provide strong support to the corporation's operation and the improvement of work efficiency. KeywordsVideo conference; Network Architecture; Multi Control Unit; Conference Terminal

We report a patient with medically intractable musicogenic epilepsy (ME) who was treated with surgery. Using the non-invasive methods of ictal and interictal electroencephalography (EEG), MRI, interictal single photon emission computed tomography and clinical manifestations, we first localized the musicogenic seizures (MS). The ictal onset zone was then further localized using intracranial EEG to the middle part of the left superior temporal gyrus. Surgical resection of the epileptogenic zone was then performed. The patient had two seizures within 2 weeks post-operatively, but has then had no seizures during the following year (Engel class II). The results suggest that patients who have medically intractable ME combined with unilateral ictal onset zones should be considered for the surgical treatment of epilepsy.

Pure hereditary spastic paraparesis usually presents with progressive weakness and spasticity of the legs, which is similar to spastic cerebral palsy. In this study selective dorsal rhizotomy (SDR) was performed to improve the spasticity of pure hereditary spastic paraparesis and the long-term results were followed. A series of four patients with pure hereditary spastic paraparesis diagnosed by a multidisciplinary team received SDR. The dorsal rootlets from the L2 to S1 levels were selectively resected under electrophysiological monitoring. The patients were followed up for more than 2 years to evaluate the outcome of surgery. There was a significant reduction in muscular spasm after SDR. Standing and walking stability were improved in all patients which led to improvement in walking posture and longer walking distance without assistance. No urinary retention, cerebrospinal fluid leak, surgical infection or kyphosis occurred. For severe pure hereditary spastic paraparesis, SDR can reduce muscle spasm and improve standing and walking stability. These results were stable throughout follow-up. SDR performed at the level of the conus medullaris through a laminectomy from T12 to L1 or L1 to L2 requires a shorter incision, laminectomy of fewer segments, and has a shorter operation time than the usual method (laminectomy from L2 to S1). Intraoperative electrophysiological monitoring is helpful to discriminate abnormal rootlets and protect sphincter function.

To describe the clinical, electrophysiologic, neuroradiologic, and histologic findings in our patients with drug-resistant hypermotor seizures (HMSs) and to evaluate the outcome of their surgical treatment.Twenty-three patients were identified by criteria for drug-resistant HMS. Surgical treatment and presurgical evaluation modalities including semiology, magnetic resonance imaging (MRI), interictal/ictal scalp video-EEG (electroencephalography), and intracranial recording were analyzed retrospectively.The common seizure frequency of 60-300 per month was observed among 15 patients. Sixteen patients (69.6%) experienced auras such as fear and palpitation. Marked agitation was observed in 12 patients and mild agitation in 11 patients. Groaning/shouting and asymmetric posturing were common accompanying symptoms. Asymmetric posturing was observed more often in patients with mild agitation than in those with marked agitation (p = 0.027). MRI detected focal abnormalities in six patients. Intracranial recording was conducted in 16 patients. The origins of seizures were localized in mesial frontal cortex in four patients, dorsolateral frontal cortex in four patients, and mesial temporal cortex in two patients. The epileptogenic zones (EZs) were resected from the frontal lobe in 21 patients and from the temporal lobe in 2 patients. The follow-up ranged from 12-60 months. Seventeen patients (73.9%) had been seizure-free, 11 of whom had presented with marked agitation (11 of 12) and 6 with mild agitation (6 of 11) (p = 0.069). Histologic examinations demonstrated focal cortical dysplasia (FCD) in 18 patients.The HMSs can originate from both the mesial and dorsolateral frontal cortex, and occasionally from the temporal lobe. Patients with drug-resistant HMSs should be recommended for resective surgical treatment.

Subject The objective of this study was to assess outcome with regard to seizure status and neurological function in patients undergoing resective surgery involving the perirolandic area. Method All 15 patients who underwent perirolandic cortical resection between October 2006 and September 2007 at the Comprehensive Epilepsy Centre of Beijing Xuanwu Hospital were included in the study. The locations of functional cortical areas, ictal onset zones and epileptogenic lesions were mapped by chronic intracranial EEG recordings and electric cortical stimulation. Seizure outcome was determined using the modified classification of Engel and colleagues. Motor and sensory deficits were monitored. Results At last follow-up 5 patients (33%) were in Engel class I, 4 (27%) were in class II, 3 (20%) were in class III, and 3 (20%) were in class IV. Nine patients suffered immediate functional deficits; 8 of these recovered completely within 2 weeks to 3 months of surgery. One had mild persistent loss of finger motor control. Conclusion After accurate presurgical evaluation using invasive recordings and functional brain mapping, epileptogenic cortical resection can give excellent results and few deficits in patients with perirolandic epilepsy.

Component metadata is one of the most effective methods to improve the testability of component-based software. We give a formal definition of component, and summarize meanings of component metadata. Based on these, an idea of grouped-metadata object (GMO) is introduced, which is divided into two types, respectively named descriptive metadata and operative metadata. And a general framework of descriptive metadata and operative metadata is further given, which is consisted of several groups. Each group includes several attributes, and their meanings are described. Furthermore, we give a formal reference model of GMO using class diagram of UML. And change model in GMO is described. Here we introduce a concept of method dependency graph (MDG) to implement mapping component changes to component interface changes. Then the changes are reflected in relevant attributes in GMO provided to component users in order to facilitate regression testing. Finally the case study based on previous model is done, and the corresponding results are given. All these show effectively that the models we presented are valid and helpful for component-based software testing.

Based on analyzing management system and modular organizational feature of Chinese current employment service system, this paper selects typical employment service system of municipal level, applies the method and idea of Balance Scorecard, designs indicator system and circular flow process of system performance evaluation, establishes model framework of comprehensive evaluation based on the method of analytical hierarchy process (AHP), and puts forward relevant advices on performance check-up. This research outcome could not only perform performance evaluation for respective employment service agencies and respective modular organizations, but also perform comprehensive evaluation for the performance of regional employment service system. Therefore, our research has an important meaning for both the theory and the practice.

A software component must be tested every time it is reused, to guarantee the quality of both the component itself and the system in which it is to be integrated. So how to increase testability of component has become a key technology in the software engineering community. This paper introduces a method to increase component testability. Firstly we analyze the meanings of component testability and the effective ways to increase testability. Then we give some definitions on component coupling testing criterion. And we further give the definitions of DU-I(definition-use information) and OP- Vs (observation-point values). Base on these, we introduce a definition-use table, which includes DU-I and OP-Vs item, to help component testers understanding and observing the component better. Then a framework of testable component based on above DU-table is given. These facilities provide ways to detect errors, to observe state variables by observation-points based monitor mechanism. And we adopt coupling-based testing using information DU-table provided. Lastly, we applied the method to our application software developed before, and generate some test cases. And our method is compared with Orso method and Kan method using the same example, presenting the comparison results. The relevant results illustrate the validity of our method, effectively generating test cases and killing more mutants.