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Ketamine is used as an analgesic adjuvant in patients with chronic cancer-related pain. However, ketamine's short half-life requires frequent dose administration. Our aim was to develop a sustained release formulation of ketamine with high loading and to evaluate the in vivo pharmacokinetics and biodistribution in mice. Here, ketamine hydrochloride sustained-release lipid particles (KSL) were developed using the thin-film hydration method. The mean (± SD) encapsulation efficiency (EE) and drug loading (DL) of KSL were 65.6 (± 1.7)% and 72.4 (± 0.5)% respectively, and the mean (± SD) size of the lipid particles and the polydispersity index were 738 (± 137) nm and 0.44 (± 0.02) respectively. The release period of KSL in pH 7.4 medium was 100% complete within 8 h in vitro but a sustained-release profile was observed for more than 5 days after intravenous injection in mice. Importantly, the KSL formulation resulted in a 27-fold increase in terminal half-life, a threefold increase in systemic exposure (AUC

Acetate has been shown to have neuroprotective and anti-inflammatory effects. It is oxidized by astrocytes and can thus provide auxiliary energy to the brain in addition to glucose. Therefore, we hypothesized that it may protect against seizures, which is investigated here by feeding glyceryl triacetate (GTA), to provide high amounts of acetate without raising sodium or acid levels.CD1 male mice were fed controlled diets with or without GTA for up to three weeks. Body weights, blood glucose levels, plasma short-chain fatty acid levels, and other hematological parameters were monitored. Seizure thresholds were determined in 6 Hz and maximal electroshock seizure threshold (MEST) tests. Antioxidant capacities were evaluated in the cerebral cortex and plasma using a ferric reducing antioxidant power (FRAP) assay and Trolox equivalent antioxidant capacity assay.Body weight gain was similar with both diets with and without GTA in two experiments. Glyceryl triacetate-fed groups showed 2-3- and 1.6-fold increased acetate and propionate levels in plasma, respectively. Glucose levels were unaltered in blood collected from the tail tip but increased in trunk blood. No differences were found in the activity of cerebral cortex acetyl-CoA synthetase. In the 6 Hz threshold test, seizure thresholds were lower by 3 mA and 2.4 mA after 8 and 14 days, respectively, in the GTA compared to the control diet-fed group, but showed no difference on day 16, showing that GTA has small, but inconsistent proconvulsant effects in this model. In MEST tests, a slightly increased seizure threshold (1 mA) was found on day 19 in the GTA-fed group, but not in another experiment on day 21. There were no differences in antioxidant capacity in plasma or cortex between the two groups.Glyceryl triacetate feeding showed no antioxidant effects nor beneficial changes in acute electrical seizure threshold mouse models, despite its ability to increase plasma acetate levels.

Most of the syntax-based metrics obtain the similarity by comparing the sub-structures extracted from the trees of hypothesis and reference. These sub-structures cannot represent all the information in the trees because their lengths are limited. To sufficiently use the reference syntax information, a new automatic evaluation metric is proposed based on the dependency parsing model. First, a dependency parsing model is trained using the reference dependency tree for each sentence. Then, the hypothesis is parsed by this dependency parsing model and the corresponding hypothesis dependency tree is generated. The quality of hypothesis can be judged by the quality of the hypothesis dependency tree. Unigram F-score is included in the new metric so that lexicon similarity is obtained. According to experimental results, the proposed metric can perform better than METEOR and BLEU on system level and get comparable results with METEOR on sentence level. To further improve the performance, we also propose a combined metric which gets the best performance on the sentence level and on the system level.

How to reduce the content placement cost of cloud content delivery networks (CCDNs) is a hot topic in recent years. Traditional content placement methods mainly reduce the cost of content placement by constructing delivery trees, but they cannot adapt to the dynamic deployment of cloud proxy servers in the CCDNs. In addition, the traditional content placement method only provides delivery paths according to local decision-making without considering global dynamics of the congestion in the CCDNs, which is also one of the main factors causing high cost of content placement. To solve these problems, we propose a content placement model based on Q-learning for the dynamic CCDNs, called Q-content placement model (Q-CPM). This Q-learning approach can lead to better routing decisions due to up-to-date and more reliable congestion values. Then, based on the Q-CPM model, an algorithm is proposed to construct the Q-adaptive delivery tree (Q-ADT). In this algorithm, local and nonlocal congestion information is propagated over network learning packets. Through this algorithm, the paths with low congestion cost will be selected and can adapt to the dynamic cloud delivery environment. The experimental results show that the method can adapt to the dynamic changes of the CCDNs flexibly and reduce the overall congestion cost of content placement effectively.

Convolutional Neural Networks (CNNs) have become one of the most successful machine learning techniques for image and video processing. The most computationally intensive part of the CNN is the convolutional layers, which have the multi-channel image and multiple kernels. However, due to the network pruning operation and the application of RELU activation function operation in the training process, numerous zero values are generated in the network. This paper proposes the convolution split compression calculation (CSCC) algorithm, which improves the performance of the convolution layer by utilizing the sparse characteristic of the feature map. In the CSCC algorithm, first, the feature map is directly converted into a sparse matrix of compressed sparse row (CSR) format, which avoids expanding feature map to an intermediate matrix and reduces the memory space consumption. Second, the convolution kernel is converted into a vector. Finally, the convolution result is obtained by the sparse matrix vector multiplication (SpMV). The experimental results show that the CSCC algorithm has a good advantage in computation speed and memory consumption compared with the other convolution algorithms.

Peptides hold promise as therapeutics, as they have high bioactivity and specificity, good aqueous solubility, and low toxicity. However, they typically suffer from short circulation half-lives in the body. To address this issue, here, we have developed a method for encapsulation of an innate-immune targeted hexapeptide into nanoparticles using safe non-toxic FDA-approved materials. Peptide-loaded nanoparticles were formulated using a two-stage microfluidic chip. Microfluidic-related factors (i.e., flow rate, organic solvent, theoretical drug loading, PLGA type, and concentration) that may potentially influence the nanoparticle properties were systematically investigated using dynamic light scattering and transmission electron microscopy. The pharmacokinetic (PK) profile and biodistribution of the optimised nanoparticles were assessed in mice. Peptide-loaded lipid shell-PLGA core nanoparticles with designated size (~400 nm) and a sustained in vitro release profile were further characterized in vivo. In the form of nanoparticles, the elimination half-life of the encapsulated peptide was extended significantly compared with the peptide alone and resulted in a much higher distribution into the lung. These novel nanoparticles with lipid shells have considerable potential for increasing the circulation half-life and improving the biodistribution of therapeutic peptides to improve their clinical utility, including peptides aimed at treating lung-related diseases.

How cancer cells cope with high levels of replication stress during rapid proliferation is currently unclear. Here, we show that macrophage migration inhibitory factor (MIF) is a 3’ flap nuclease that translocates to the nucleus in S phase. Poly(ADP-ribose) polymerase 1 co-localizes with MIF to the DNA replication fork, where MIF nuclease activity is required to resolve replication stress and facilitates tumor growth. MIF loss in cancer cells leads to mutation frequency increases, cell cycle delays and DNA synthesis and cell growth inhibition, which can be rescued by restoring MIF, but not nuclease-deficient MIF mutant. MIF is significantly upregulated in breast tumors and correlates with poor overall survival in patients. We propose that MIF is a unique 3’ nuclease, excises flaps at the immediate 3’ end during DNA synthesis and favors cancer cells evading replication stress-induced threat for their growth., Replication stress is associated with cancer formation and progression. Here the authors reveal that the macrophage migration inhibitory factor (MIF) functions as 3’ flap nuclease involved in resolving replication stress affecting overall tumor progression.

Ketamine in sub-anaesthetic doses has analgesic properties and an opioid-sparing effect. Intrathecal (i.t.) delivery of analgesics bypasses systemic metabolism and delivers the analgesic agent adjacent to the target receptors in the spinal cord and so small doses are required to achieve effective pain relief. In order to relieve intractable cancer-related pain, sustained-release ketamine formulations are required in combination with a strong opioid because frequent i.t. injection is not practical. In this study, ketamine or ketamine-loaded porous silicon (pSi) were encapsulated into poly(lactic-co-glycolic acid) (PLGA) microparticles by a novel supercritical carbon dioxide (scCO

The ad-hoc retrieval task is to rank related documents given a query and a document collection. A series of deep learning based approaches have been proposed to solve such problem and gained lots of attention. However, we argue that they are inherently based on local word sequences, ignoring the subtle long-distance document-level word relationships. To solve the problem, we explicitly model the document-level word relationship through the graph structure, capturing the subtle information via graph neural networks. In addition, due to the complexity and scale of the document collections, it is considerable to explore the different grain-sized hierarchical matching signals at a more general level. Therefore, we propose a Graph-based Hierarchical Relevance Matching model (GHRM) for ad-hoc retrieval, by which we can capture the subtle and general hierarchical matching signals simultaneously. We validate the effects of GHRM over two representative ad-hoc retrieval benchmarks, the comprehensive experiments and results demonstrate its superiority over state-of-the-art methods., To appear at WWW 2021

There is increasing evidence showing that oxidative glucose metabolism in mitochondria is impaired in epilepsy and the involved metabolic mechanisms have been further characterized by recent work. Glucose is the main energy source in brain in people who eat a conventional mixed diet with carbohydrates, fats, and proteins. In the cytoplasm, glucose is metabolized to pyruvate by glycolysis, which produces small amounts of energy. Entry of pyruvate into mitochondria and subsequent metabolism via the tricarboxylic acid cycle generates large amounts of adenosine triphosphate as well as precursors for cellular biosynthesis of lipids and amino acids. Sufficient energy is important for the brain to be able to signal normally and to keep the sodium and potassium gradients stable across cellular membranes. In contrast, insufficient energy can contribute to surges in extracellular potassium levels, which destabilizes membrane potentials and signaling and can result in seizure generation in the chronic epileptic brain. The known biochemical mechanisms leading to mitochondrial impairments of glucose carbon metabolism in epilepsy are summarized in this chapter, including reduced glucose utilization, decreases in enzyme activities, such as pyruvate dehydrogenase, as well as increased anaplerotic demand. Based on this current knowledge, auxiliary brain fuels would be useful to provide extra energy to the epileptic brain. This includes ketones, tricarboxylic acid cycle intermediates, and precursors as well as even medium chain fatty acids and triheptanoin.

For patients with intractable cancer-related pain, administration of strong opioid analgesics and adjuvant agents by the intrathecal (i.t.) route in close proximity to the target receptors/ion channels, may restore pain relief. Hence, the aim of this study was to use bioerodable polymers to encapsulate an opioid analgesic (hydromorphone) and an adjuvant drug (ketamine) to produce prolonged-release formulations for i.t. injection. A two-stage microfluidic method was used to fabricate nanoparticles (NPs). The physical properties were characterised using dynamic light scattering and transmission electron microscopy. A pilot in vivo study was conducted in a rat model of peripheral neuropathic pain. The in vitro release of encapsulated payload from NPs produced with a polymer mixture (CPP-SA/PLGA 50:50) was sustained for 28 days. In a pilot in vivo study, analgesia was maintained over a three day period following i.t. injection of hydromorphone-loaded NPs at 50 μg. Co-administration of ketamine-loaded NPs at 340 μg did not increase the duration of analgesia significantly. The two-stage microfluidic method allowed efficient production of analgesic/adjuvant drug-loaded NPs. Our proof-of-principle in vivo study shows prolonged hydromorphone analgesic for 78 h after single i.t. injection. At the i.t. dose administered, ketamine released from NPs was insufficient to augment hydromorphone analgesia.

Ketamine in sub-anaesthetic doses is an analgesic adjuvant with a morphine-sparing effect. Co-administration of a strong opioid with an analgesic adjuvant such as ketamine is a potential treatment option, especially for patients with cancer-related pain. A limitation of ketamine is its short in vivo elimination half-life. Hence, our aim was to develop biocompatible and biodegradable ketamine-loaded poly(ethylene glycol) (PEG)-block-poly(lactic-co-glycolic acid) (PLGA) nanoparticles for sustained release. Ketamine-encapsulated single polymer PEG-PLGA nanoparticles and double polymer PEG-PLGA/shellac (SH) nanoparticles with a high drug loading of 41.8% (drug weight/the total weight of drug-loaded nanoparticles) were prepared using a new sequential nanoprecipitation method. These drug-loaded nanoparticles exhibited a sustained-release profile for up to 21 days in vitro and for more than 5 days after intravenous injection in mice. Our study demonstrates that high drug loading and a sustained release profile can be achieved with ketamine-loaded PEG-PLGA nanoparticles prepared using this new nanoprecipitation method.

We previously reported that HOXB9 is overexpressed in colon cancer and predicts a favourable patient outcome, which is opposite to the tumour-promoting role of HOXB9 in other cancers. We hypothesized that HOXB9 acetylation may account for its inhibitory role in colon cancer. We aim to examine the role of acetylated HOXB9 in colon cancer cells and patients. The AcK27-HOXB9 levels in colon cancer cells and patients were analysed by Western blot analysis and immunohistochemistry separately. Correlation between AcK27-HOXB9 expression and patient survival was assessed by Kaplan-Meier analysis. HOXB9 target gene EZH2 was determined by luciferase assay in HOXB9-transfected colon cancer cells. Nucleocytoplasmic translocation of HOXB9 was detected by subcellular fractionation and immunofluorescence. The AcK27-HOXB9 level was decreased in colon cancer patients and predicted better outcome. HOXB9 upregulated oncogenic EZH2 expression, whereas AcK27-HOXB9 suppressed it by translocating HOXB9 from nuclei into cytoplasm. We demonstrated that AcK27-HOXB9 inhibits while non-acetylated HOXB9 promotes EZH2 expression and colon cancer progression. Thus, AcK27-HOXB9 underlies the tumour suppressive role of HOXB9. Detection of the ratio between AcK27-HOXB9 and HOXB9 is of differential diagnostic value for colon cancer patients.

Motion estimation (ME) is a time-consuming algorithm to find a matching block in the search area for video applications, such as video compression. Motion estimation algorithm includes full search algorithm and fast search algorithm. If the width of the motion estimation algorithm search area is twice the size of the block, half of the search area for adjacent blocks overlaps. In view of this, this paper proposes a search area data reuse method for fast search motion estimation. With the proposed data reuse implementation, only the first block needs to read the data of the entire search area from the off-chip memory. The other blocks only need to read half the original search area. In this way, time of access to external memory is reduced, and running time of the algorithm is also decreased. Experimental results of diamond search show that the search area data reuse methods can reduce the running time by 40% to 60% compared with the algorithm of no use data reuse, and it can also reduce the power consumption by 62% to 73%. Compared with other methods in the literature, the proposed method also performs better on running time and power consumption.

Motion estimation (ME) is a kernel algorithm in many video applications. Full search integer ME (FSIME) can find the best result but it usually takes plenty of time. Traditionally, only intra-frame data reuse is considered for FSIME. In this paper, a new inter-frame data reuse method is proposed to further utilize inter-frame data reuse for true ME. ME in frame rate up-conversion (FRUC-ME), a kind of true ME, is used as a case study. For FRUC-ME with the new inter-frame data reuse method, a frame is loaded to the on-chip buffer only once instead of twice and used for two interpolated frames. Two levels of the new method are proposed, Inter-D and new Inter-E, which give a good tradeoff between off-chip memory bandwidth and on-chip buffer size. New data access order is used to implement the new data reuse method. The proposed data reuse method (Inter-D) demands less off-chip memory bandwidth than its intra-frame counterpart (Intra-D), and the off-chip memory traffic and power consumption are both reduced by 37.5% for FRUC-ME.

2-D-to-3-D conversion is one way to make full use of 2-D contents to produce 3-D contents. Real-time 2-D-to-3-D conversion is required for 3-D consumer electronic devices, which demands fast processing speed especially for high-definition videos. In this paper, we propose a reconfigurable VLSI architecture for real-time 2-D-to-3-D conversion. Two different depth-retrieval methods are implemented in this architecture in order to support the choice of a best method or combination of different methods. The proposed architecture can also support different resolutions (4K, 1080p, and 720p) and the original view can be configured as either left view or right view. In order to overcome the problem of “Memory Wall”, we propose a data reuse method to reduce memory traffic for the proposed architecture so that the overall performance is improved to realize real-time conversion. The experiment result shows that the implemented 2-D-to-3-D architecture can achieve state-of-the-art throughput (4K@30f/s).

In modern e-commerce, the user’s sequential behavior contains a wealth of information. According to the user’s historical interaction behavior information, modeling the user’s preferences to predict the continuous items that the user may interact with is the main task of the sequential recommendation system. In this article, we conducted a systematic and comprehensive review of sequential recommendation systems based on user interaction behavior. Specifically, first of all, we briefly introduce the existing general recommendation methods, and point out the shortcomings of the general recommendation method, and then systematically expound the sequence recommendation, and finally summarize and forecast.

Convolutional neural networks (CNN) and long short-term memory (LSTM) have gained wide recognition in the field of natural language processing. However, due to the pre- and post-dependence of natural language structure, relying solely on CNN to implement text categorization will ignore the contextual meaning of words and bidirectional long short-term memory (BiLSTM). The feature fusion model is divided into a multiple attention (MATT) CNN model and a bi-directional gated recurrent unit (BiGRU) model. The CNN model inputs the word vector (word vector attention, part of speech attention, position attention) that has been labeled by the attention mechanism into our multi-attention mechanism CNN model. Obtaining the influence intensity of the target keyword on the sentiment polarity of the sentence, and forming the first dimension of the sentiment classification, the BiGRU model replaces the original BiLSTM and extracts the global semantic features of the sentence level to form the second dimension of sentiment classification. Then, using PCA to reduce the dimension of the two-dimensional fusion vector, we finally obtain a classification result combining two dimensions of keywords and sentences. The experimental results show that the proposed MATT-CNN+BiGRU fusion model has 5.94% and 11.01% higher classification accuracy on the MRD and SemEval2016 datasets, respectively, than the mainstream CNN+BiLSTM method.

Language is the main carrier of communication between cultures, but the translation between languages has become the biggest problem of people's communication. Machine translation is a process that uses computer to transform a natural language into another natural language. The automatic evaluation of machine translation is an important research content in machine translation technology. It can discover defects in translation system and promote its development. It has achieved rich fruits, and various evaluation methods emerge endlessly after several decades of development of the automatic evaluation method. In this paper, three kinds of representative evaluation methods are introduced and their respective advantages and disadvantages are analyzed. In addition, we describe the evaluation technique based on reference. It plays an important role in improving the performance of automatic evaluation methods although the coverage expansion of reference is not the main method. Finally, we summarize the development trends of automatic evaluation metric based on extended reference and related issues that need to be further addressed.

This article has been removed from the ACM Digital Library because it was found to plagiarize an earlier work written by Zhedong Zheng, Liang Zheng and Yi Yang, published by IEEE Xplore and entitled "Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro" 2017 IEEE International Conference on Computer Vision https://doi.org/10.1109/ICCV.2017.405. For further information, contact the ACM Director of Publications.

Basal-like breast cancer with a luminal progenitor gene expression profile is an aggressive subtype of breast cancer with a poorer prognosis compared with other subtypes. However, genes that specifically promote basal-like breast cancer development remain largely unknown. Here, we report that a novel gene C1orf106 plays an important role in maintaining the feature of basal-like/luminal progenitors. C1orf106 is frequently amplified and overexpressed in basal-like breast cancer and is associated with a poor outcome in patients. In human TCGA database, C1orf106 expression was correlated with upregulation of ELF5 and downregulation of GATA3, two transcription factors that regulate mammary gland stem cell fate. Enhanced expression of C1orf106 promotes tumor progression and expression of basal-like/luminal progenitor marker ELF5; depletion of C1orf106 suppresses tumorigenesis and expression of basal-like/luminal progenitor marker GATA3. These findings suggest that C1orf106 maintains the basal-like/luminal progenitor character through balancing the expression of ELF5 and GATA3. Taken together, we demonstrated that C1orf106 is an important regulator for basal-like/luminal progenitors and targeting C1orf106 is of therapeutic value for breast cancer.

Constructing a model of online film and television commentary sentiment classification can effectively guide film and television producers to comprehensively understand the audience acceptance of film and television works, and improve it. Traditional methods based on sentiment lexicon and machine learning exist in a series of Insufficient: ignore context semantics, too single word, sparse features, etc. Based on the existing convolutional neural network model, this paper systematically optimizes its internal structure, and proposes a NCNM (New Convolutional Neural Network model) model based on multi-sliding window and new pooling method, and uses feature vectors to cluster feature words. . In this paper, the Stanford SST dataset and Cornell MRD dataset are used to verify the classification effect of the proposed model. The experimental results show that ncnnm has a certain improvement in the accuracy of the emotional classification of short text video reviews compared with the existing mainstream methods..

In this paper, based on the characteristics of web design and facture course, from the course introduction, teaching existence question analysis, training mode reform aspects and so on three aspects to illustrate how to improve the teaching effect of this course, and to explore the concept of how to use the OBE complete curriculum reform in teaching practice, thus improve the computer professional and visual communication professional social practice ability of students, in order to conform to the demand of the current web design position.

HOXB9 is a homeobox domain-containing transcription factor, playing an important role in embryonic development and cancer progression. However, the precise post-translational modifications (PTMs) of HOXB9 and the corresponding roles are unclear. Here, we report that acetyltransferase p300/CBP-associated factor (PCAF) interacts with and acetylates HOXB9 both in vivo and in vitro. Conversely, the acetylation of HOXB9 can be reversed by deacetylase SIRT1. Furthermore, we found that HOXB9 is acetylated at lysine 27 (AcK27). Functionally, in contrast to the wild type HOXB9, AcK27-HOXB9 decreased its capacity in promoting lung cancer cell migration and tumor growth in mice. Mechanistically, AcK27-HOXB9 suppresses the transcription of its target gene Jumonji domain-containing protein 6 (JMJD6) by direct occupying the promoter of JMJD6 gene. For clinical relevance, elevated HOXB9 acetylation at K27 predicts a better prognosis in lung adenocarcinoma patients. Taken together, we identified the first PTM of HOXB9 by demonstrating that HOXB9 can be acetylated and AcK27-HOXB9 counteracts the role of the wild-type HOXB9 in regulating lung adenocarcinoma progression.

Fluid animation often appears in applications such as games, films and cartoons. How to animate photo-realistic fluid motion efficiently is an important issue. We present an efficient parallel method for photo-realistic fluid animation in this paper. Our method is designed to generate fluid animation results with high efficiency on a cluster system. To do this, we categorize the computers in our cluster system into two classes, the server and the client. The server controls the process of the fluid animation while the clients are responsible for numerical computation. Given 3D virtual environment and fluid initial condition, we make pre-processing on the server so as to decompose the fluid animation task into several subtasks. Thus, the computation domain is divided into blocks and each client executes numerical computation for one block. The blocks of two adjacent clients are overlapped to keep the continuity of the solution across subdomain interface. We demonstrate the efficiency of our method ...

Stacked DRAM used as the last-level caches (LLCs) in multicore systems delivers performance enhancement due to its capacity benefit. While the performance of LLC depends heavily upon its block replacement policy, the conventional replacement policy needs redesigning to exploit the best of DRAM cache and avoid its drawbacks. The existing DRAM cache insertion policy blindly forwards victim lines replaced from L3 to the off-chip memory, regardless of the potential for increased hits by placing a fraction of them in the DRAM cache. Nevertheless, a naive design that steers all dirty victims to the DRAM cache introduces excessive writeback traffic, which aggravates capacity misses and DRAM interference. To leverage insertions in terms of writeback or fill requests, we propose a collective writeback and fill policy that adapts to the distinct access patterns of heterogeneous workloads based on runtime misses and writeback efficiency. The synthesis result shows that the new policy has only a small hardware overhead. According to the experimental results on the gem5 simulator, the new policy increases harmonic instruction per cycle throughput by 18%, 11.6%, and 11.7%, respectively, compared with the Always Writeback (AW)–Always Fill policy, Never Writeback Adaptive DRAM Placement policy, and AW Adaptive DRAM Placement policy on 64-MB alloy cache, while the improvement is 19.3%, 13.7%, and 14.5% on 64-MB MissMap cache.

An innovative retrofit scheme is proposed for the strengthening of old brick masonry buildings: the floor area is increased using newly added and isolated upper stories, and the lower brick masonry part is strengthened using precast concrete wall panels. To investigate the seismic performance of the innovative proposed strengthening scheme, a theoretical analysis and shaking-table tests were conducted, and the proposed scheme is compared with two conventional retrofitting schemes. The results of linear and nonlinear analyses of the three strengthening schemes indicated that all three schemes can mitigate seismic damage to old brick masonry buildings. The shaking-table tests confirmed that the proposed scheme is more effective than the two conventional schemes. Additionally, the seismic performance of the proposed strengthening scheme with post-test damage is investigated via a theoretical analysis and shaking-table tests. The results revealed that the stiffness degradation of the substructure can negatively affect the adding-story isolation system.

Emergency evacuation after the earthquake is a vital means to prevent and reduce casualties, and has important practical significance. “Grouping behavior” is relatively common in crowd evacuation. That is, the evacuees must gather together with close-knit individuals before conducting collective evacuation. Therefore, by researching the herd behavior in the evacuated population after combining the ideas of firefly algorithm, the improved social force model by fusing relationship grouping and herd behavior are proposed, which is used to simulate the flow of people. The results show that the model can simulate the group phenomenon in the crowd evacuation more accurately. Moreover, it is found that the total evacuation time of the population is longer significantly with the augment of small-scale herd in the crowd. Thus the study provides a theoretical basis for large-scale population evacuation.

“Group behavior” is a common behavior characteristic of human groups evacuation. An improved herd social force model is proposed by combining an improved firefly algorithm (FA) to optimize the individual selection of movement path, basing on the herd behavior of pedestrian in this paper. Two herd behaviors, the follow and chase of the individual, are designed in this modal, which can gather individuals into the small-scale herd more quickly. The experimental results show that the proposed model is valid.

For high-dimensional data, the failure in distance calculation and the inefficient index tree that are respectively derived from equidistance and redundant attribute, have affected the performance of clustering algorithm seriously. To solve these problems, this paper introduces a clustering algorithm of high-dimensional data based on sequential Psim matrix and differential truncation. Firstly, the similarity of high-dimensional data is calculated with Psim function, which avoids the equidistance. Secondly, the data is organized with sequential Psim matrix, which improves the indexing performance. Thirdly, the initial clusters are produced with differential truncation. Finally, the K-Medoids algorithm is used to refine cluster. This algorithm was compared with K-Medoids and spectral clustering algorithms in two types of datasets. The experiment result indicates that our proposed algorithm reaches high value of Macro-F1 and Micro-F1 at the small number of iterations.

Kindlin-2, as an integrin-interacting protein, was known to be required for the maintenance of cardiac structure and function in zebrafish. However, the mechanism remains unclear. We found that Kindlin-2 interacts and colocalizes with α-actinin-2 at the Z-disc of mouse cardiac muscles and there Kindlin-2 also interacts with β1 integrin. Knockdown of Kindlin-2 influences the association of β1 integrin with α-actinin-2 and disrupts the structure of the Z-disc and leads to cardiac dysfunction. Our data indicated that Kindlin-2 is a novel α-actinin-2-interacting protein and plays an important role in the regulation of cardiac structure and function.

Enhancer of zeste homolog 2 (EZH2) is a key epigenetic regulator that catalyzes the trimethylation of H3K27 and is modulated by post-translational modifications (PTMs). However, the precise regulation of EZH2 PTMs remains elusive. We, herein, report that EZH2 is acetylated by acetyltransferase P300/CBP-associated factor (PCAF) and is deacetylated by deacetylase SIRT1. We identified that PCAF interacts with and acetylates EZH2 mainly at lysine 348 (K348). Mechanistically, K348 acetylation decreases EZH2 phosphorylation at T345 and T487 and increases EZH2 stability without disrupting the formation of polycomb repressive complex 2 (PRC2). Functionally, EZH2 K348 acetylation enhances its capacity in suppression of the target genes and promotes lung cancer cell migration and invasion. Further, elevated EZH2 K348 acetylation in lung adenocarcinoma patients predicts a poor prognosis. Our findings define a new mechanism underlying EZH2 modulation by linking EZH2 acetylation to its phosphorylation that stabilizes EZH2 and promotes lung adenocarcinoma progression.

Video Delivery Networks (VDNs) have been widely applied to the video content delivery of mobile Internet recently. However, the traditional VDNs are all infrastructure pre-deployed which makes them inflexible to adapt to the diversity and mass of live video delivery in mobile Internet. Thus, how to meet mobile users’ live video demands while minimizing the delivery cost is still a challenge task. To solve this problem, we propose a Software-defined live Video Delivery Network (S-VDN) framework with Device-to-Device (D2D) communication for mobile Internet. With a global view of the network, S-VDN is capable of managing the mobile users’ requests and the network topology conveniently as well as scheduling the radio resources. In addition, S-VDN enables content delivery among closely located mobile users without going through base stations by employing the D2D technique. Furthermore, we formulate this optimization problem as a Multi-commodity Flow (MCF) problem and design two heuristic algorithms to obtain the optimal solution. Simulation experiments show that our proposed algorithms are efficient in terms of both performance and rental cost.