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Molecular dynamics simulations are used to study binary blends of an AB-type diblock and an AB

Establishing a concise and accurate analytical model is the key to developing a feasible progressive collapse design for engineering practice. However, existing models either focused on an individual force mechanism or required complicated computer programming. Among existing machine learning (ML) techniques, multi-gene genetic programming (MGGP) can be trained to obtain explicit formulas for engineering problems. In this study, a comprehensive database was established by data collection, Latin hypercube sampling and structural design, and was used to train the mathematical model for quantifying progressive collapse resistance of reinforced concrete (RC) beam-column substructures under middle column removal scenarios. Further, an energy-based error index was proposed to validate the accuracy of the MGGP model among others. The research outcomes can provide references for the development of simplified analytical models for calculating the progressive collapse progress of RC frame structures, and promote the development of the practical design method.

Traditional folk games inherit the history, culture and identity of a country and nation, and they have even more important meanings for China, which has a long history and many ethnic groups. However, with the rapid globalization of modern technology and economy, many Chinese traditional folk games are facing the crisis of disappearing. This article discusses how to develop and utilize the new functions of traditional folk games. That is to use them to train the resilience of preschool children, thus to initiate a new understanding of traditional folk games by teachers and parents. The article firstly discusses the importance of cultivating preschool children’s resilience, then analyzes the role of games in cultivating preschool children’s resilience, studies the traditional folk game classification method for preschool children’s resilience, and gives the development orientation traditional folk game teaching strategies and steps for the cultivation of resilience. This research not only inherits Chinese traditional culture in preschool education, but also trains preschool children’s resilience and strengthens their minds. It has important practical significance and reference value for the current training and education of preschool children in China and East Asia.

Background Data on clinical, laboratory, and radiographic characteristics and risk factors for in‐hospital mortality of lung cancer patients with COVID‐19 are scarce. Here, we aimed to characterize the early clinical features of lung cancer patients with COVID‐19 and identify risk factors associated with in‐hospital mortality. Methods All consecutive lung cancer patients with laboratory‐confirmed COVID‐19 admitted to 12 hospitals in Hubei province, China, from 3 January to 6 May 2020 were included in the study. Patients without definite clinical outcomes during the period were excluded. Data on initial clinical, laboratory and radiographic findings were compared between survivors and nonsurvivors. Univariable and multivariable logistic regression analyses were used to explore the risk factors associated with in‐hospital mortality. Results Of the 45 lung cancer patients (median [interquartile range] age, 66 [58–74] years; 68.9% males) included, 34 (75.6%) discharged and 11 (24.4%) died. Fever (73.3%) and cough (53.3%) were the dominant initial symptoms, and respiratory symptoms were common. Lung cancer patients also presented atypical appearances of COVID‐19. In the multivariable analysis, prolonged prolongation prothrombin time (PT) (OR = 2.1, 95% CI: 1.00–4.41, P = 0.0497) and elevated high sensitivity cardiac troponin I (hs‐TNI) (OR = 7.65, 95% CI: 1.24–47.39, P = 0.0287) were associated with an increased risk of in‐hospital mortality. Conclusions Lung cancer patients with COVID‐19 have high in‐hospital mortality. Prolonged PT and elevated hs‐TNI are independent risk factors for in‐hospital mortality of lung cancer patients with COVID‐19. Key points Significant findings of the study Lung cancer patients with COVID‐19 have atypical early symptoms and imaging features.The prolonged prothrombin time and elevated high sensitivity cardiac troponin I are independent risk factors for in‐hospital mortality of lung cancer patients with COVID‐19. What this study adds This study characterizes the early clinical features of lung cancer patients with COVID‐19 in China, and identifies the risk factors associated with in‐hospital mortality of lung cancer patients with COVID‐19., Lung cancer patients with COVID‐19 have high in‐hospital mortality with unique early symptoms and imaging features. Prolonged PT and elevated hs‐TNI are independent risk factors for in‐hospital mortality of lung cancer patients with COVID‐19. They may be used to help clinicians predict prognosis at an early stage for lung cancer patients with COVID‐19.

Long-term hyperglycemia in patients with diabetes leads to human serum albumin (HSA) glycation, which may impair HSA function as a transport protein and affect the therapeutic efficacy of anticoagulants in patients with diabetes. In this study, a novel mass spectrometry approach was developed to reveal the differences in the profiles of HSA glycation sites between patients with diabetes and healthy subjects. K199 was the glycation site most significantly changed in patients with diabetes, contributing to different interactions of glycated HSA and normal HSA with two types of anticoagulant drugs, heparin and warfarin. An in vitro experiment showed that the binding affinity to warfarin became stronger when HSA was glycated, while HSA binding to heparin was not significantly influenced by glycation. A pharmacokinetic study showed a decreased level of free warfarin in the plasma of diabetic rats. A preliminary retrospective clinical study also revealed that there was a statistically significant difference in the anticoagulant efficacy between patients with diabetes and patients without diabetes who had been treated with warfarin. Our work suggests that larger studies are needed to provide additional specific guidance for patients with diabetes when they are administered anticoagulant drugs or drugs for treating other chronic diseases.

Background Climate change may lead to emerging and re-emerging infectious diseases and pose public health challenges to human health and the already overloaded healthcare system. It is therefore important to review current knowledge and identify further directions in China, the largest developing country in the world. Methods A comprehensive literature review was conducted to examine the relationship between climate variability and infectious disease transmission in China in the new millennium. Literature was identified using the following MeSH terms and keywords: climatic variables [temperature, precipitation, rainfall, humidity, etc.] and infectious disease [viral, bacterial and parasitic diseases]. Results Fifty-eight articles published from January 1, 2000 to May 30, 2018 were included in the final analysis, including bacterial diarrhea, dengue, malaria, Japanese encephalitis, HFRS, HFMD, Schistosomiasis. Each 1 °C rise may lead to 3.6%–14.8% increase in the incidence of bacillary dysentery disease in south China. A 1 °C rise was corresponded to an increase of 1.8%–5.9% in the weekly notified HFMD cases in west China. Each 1 °C rise of temperature, 1％ rise in relative humidity and one hour rise in sunshine led to an increase of 0.90%, 3.99% and 0.68% in the monthly malaria cases, respectively. Climate change with the increased temperature and irregular patterns of rainfall may affect the pathogen reproduction rate, their spread and geographical distribution, change human behavior and influence the ecology of vectors, and increase the rate of disease transmission in different regions of China. Conclusion Exploring relevant adaptation strategies and the health burden of climate change will assist public health authorities to develop an early warning system and protect China's population health, especially in the new 1.5 °C scenario of the newly released IPCC special report.

Low-temperature direct bonding is an effective method for joining two dissimilar materials into one composite. In this paper, we developed a universal method for fabricating single-crystalline SiC on Si, SiO2, and glass substrates via vacuum ultraviolet/ozone (VUV/O3) activated direct bonding at 200 °C. Our studies showed that VUV irradiation could lead to hydrophilic and smooth surfaces to be bonded. TEM observations confirmed that the transition layers of SiC/Si, SiC/SiO2, and SiC/glass direct bonded pairs were only nanoscale, which was beneficial for the miniaturization of power devices. On the basis of X-ray photoelectron spectroscopy (XPS) elemental depth profiles analysis, we also demonstrated that the enriched carbon layers at the bonding interfaces were originated from the SiC substrates during the VUV treatment. Additionally, the bonding mechanism was discussed combining all the experimental investigations.

SZNF (Sus scrofa zinc finger CCHC domain containing 3) is a post-transcription regulation factor, belonging to CCHC zinc finger proteins. Cyadox is a novel quinoxaline drug with antibacterial and growth promotion effects. In this study, we investigated the pharmacological mechanism of cyadox mediated by SZNF. Firstly, signaling pathways related to cyadox-induced SZNF expression were studied. The results showed that the mRNA level of SZNF reached the peak as early as 4 h after 2 μM cyadox treatment in swine hepatocytes. Several signaling pathways, including JAK2/STAT1, PI3K/Akt, TGF-β/Smad3 and p38, might play critical roles in regulation of SZNF. The JAK2/STAT1, JAK2/PI3K/Akt, PI3K/Akt and myD88 & TAK1 & ASK1 /P38 signaling pathways were firstly activated after cyadox treatment in swine hepatocyte, the TGF-β/Smad3 signaling pathway was activated later. Then given the characteristic of RNA binding of CCHC zinc finger proteins, the target mRNAs binding with SZNF were detected by RNA immunoprecipitation coupled to sequencing (RIP-seq) in PK-15 cells treated with cyadox. The RIP-Seq results showed that the bound mRNAs of 45 genes and 93 genes by SZNF protein were increased and decreased, respectively in cyadox-treated PK-15 cells compared with blank sample. With bioinformatics analysis, we showed that cyadox might exert its antibacterial and growth promotion effect by regulating SZNF-associated target genes in post-transcriptional level, such as genes related to growth (MLXIP, CKS2) and inflammation (LGALS3, PLAU). Thus, our results indicated that SZNF can post-transcriptionally regulate its target genes related to growth and inflammatory in cyadox-treated cells, which may explain the pharmacological mechanism of this drug.

Background Bladder cancer (BCa) is a common malignancy characterized by high heterogeneity, yet the current treatment modalities are limited. The aim of the present investigation was to unravel the functional role of Karyopherin alpha 2 (KPNA2), a tumor facilitator identified in multiple malignancies, in the progression of BCa. Methods BCa tissues and adjacent normal tissues were surgically resected and analyzed from patients with BCa to determine the expression profile of KPNA2 and Chromobox 8 (CBX8) by RT-qPCR, Western blot analysis and immunohistochemistry. The relationship among KPNA2, CBX8 and PR domain zinc finger protein 1 (PRDM1) was explored by co-immunoprecipitation and chromatin-immunoprecipitation. The functions of KPNA2, CBX8 and PRDM1 on BCa cell proliferation, migration and invasion were evaluated. Next, a nude mouse model of BCa was established for validating the roles of KPNA2, CBX8 and PRDM1 in vivo. Results KPNA2 and CBX8 were highly expressed in BCa and are in association with dismal oncologic outcomes of patients with BCa. KPNA2 promoted nuclear import of CBX8. CBX8 downregulated PRDM1 by recruiting BCOR in the promoter region of PRDM1. Overexpression of KPNA2 promoted the malignant behaviors of BCa cells, which was counteracted by silencing of CBX8. Overexpressing PRDM1 attenuated the progression of BCa by inhibiting c-FOS expression. The tumor-promoting effects of KPNA2 via the PRDM1/c-FOS pathway were also validated in vivo. Conclusion Collectively, our findings attached great importance to the interplay between KPNA2 and CBX8 in BCa in mediating the development and progression of BCa, thus offering a promising candidate target for better BCa patient management.

Long noncoding RNAs (lncRNAs) exert an increasingly important effect on genome instability and the prognosis of cancer patients. The present research established a computational framework originating from the mutation assumption combining lncRNA expression profile and somatic mutation profile in the genome of renal cancer to assess the effect of lncRNAs on the gene instability of renal cancer. A total of 45 differentially expressed lncRNAs were evaluated to be genome-instability-associated from the high and low cumulative somatic mutations groups. Then we established a prognosis model based on three genome-instability-associated lncRNAs (AC156455.1, AC016405.3, and LINC01234)-GlncScore. The GlncScore was then verified in testing cohort and the total TCGA renal cancer cohort. The GlncScore was evaluated to have an accurate prediction for the survival of patients. Furthermore, GlncScore was associated with somatic mutation patterns, indicating its capacity of reflecting genome instability in renal cancer. In conclusion, this study evaluated the effect of lncRNAs on genome instability of renal cancer and provided new hidden cancer biomarkers related to genome instability in renal cancer.

The paper firstly analyses the advantages of modern informational teaching over traditional informational teaching, constructs a framework of blending informational teaching for the course of Kindergarten Music Education. Then the paper discusses online and offline blending teaching method, studies blending educational resources based on knowledge types, organizes blending informational teaching platform and related teaching processes, and discusses the mixing of teaching roles based on learning tasks. After that, a case study of blending informational teaching is given. Finally, the teaching data of the past 3 years are used to demonstrate the effectiveness of mixed information teaching.

MnO x nanomaterials are regarded as promising catalysts for degrading formaldehyde (HCHO). However, the catalytic nanoparticles (NPs) suffer from the catalysis performance and stability loss due to their weak combination with the substrates and easy-aggregation nature. Herein, we develop a facile method to synthesize hollow porous MnO x @NPs fibers via electrospinning and two-step oxidation. The NPs, e.g., TiO2 and SnO2, can be efficiently loaded into MnO x fibers’ inner frame through a simple dispersion process. The hollow porous structures obtained through two-step oxidation increase the HCHO removal efficiency by 8 times, while MnO x @SnO2 fibers exhibit the enhanced catalytic performance by 23.0% more.

O-GlcNAcylation of carbohydrate-responsive element-binding protein (ChREBP) is believed as an important modulator of ChREBP activities, however little direct evidence of O-GlcNAcylation on ChREBP and no exact O-GlcNAcylation sites have been reported so far. Here, we validate O-GlcNAcylation on ChREBP in cell-free coupled transcription/translation system and in cells by chemoenzymatic and metabolic labeling, respectively. Moreover, for the first time, we identify O-GlcNAcylation on Ser614 in the C-terminus of ChREBP by mass spectrometry and validate two important sites, Thr517 and Ser839 for O-GlcNAcylation and their function via molecular and chemical biological method. Under high glucose conditions, Ser514 phosphorylation enhances ChREBP O-GlcNAcylation, maintaining the transcriptional activity of ChREBP; Ser839 O-GlcNAcylation is essential for Mlx-heterodimerization and DNA-binding activity enhancement, consequently inducing transcriptional activity. Ser839 O-GlcNAcylation is also crucial for ChREBP nuclear export partially by strengthening interactions with CRM1 and 14-3-3. This work is a detailed study of ChREBP O-GlcNAcylation and highlights the biological consequences of the site-specific O-GlcNAcylation dynamics of ChREBP.

Xue Li,1 Feng Shi,2 Liping Gong,2 Baojian Hang,2 Daoyuan Li,1 Lianli Chi1 1National Glycoengineering Research Center, Shandong Provincial Key Laboratory of Carbohydrate Chemistry and Glycobiology, and State Key Laboratory of Microbial Technology, Shandong University, Jinan, Shandong, People’s Republic of China; 2Scientific Research Division, Shandong Institute for Food and Drug Control, Jinan, Shandong, People’s Republic of China Abstract: Collacoriiasini (CCA) is a protein-based traditional Chinese medicine made from donkey skins. Because it has the ability to nourish blood, its demand is increasing rapidly. The shortage of donkey skins increases the risk of the adulteration of CCA products with other animal skins. To ensure the drug efficacy and safety of CCA products, a proteomics technique was applied to reveal proteins in the skins of donkey, horse, cattle, and pig. Species-specific peptides for each animal species were predicted using bioinformatics, and their presence in the skins and gelatin samples was examined by nano-liquid chromatography-tandem mass spectrometry (nano-LC-MS/MS). One unique marker peptide for each animal species was selected to develop an LC-MS/MS multiple reaction monitoring method. The capability of this method to identify donkey, horse, cattle, and pig materials was demonstrated by analyzing in-house-made donkey gelatins containing different amounts of other animal skins and commercial CCA products. The adulteration of non-donkey species could be sensitively detected at a low level of 0.5%. Hybrid animals, such as mules and hinnies, were also differentiated from donkeys. We provide a practical tool for the quality control of CCA products. The strategy can also be used to study other important traditional Chinese medicines which contain animal proteins. Keywords: Colla corii asini, collagen, proteomics, adulteration, mass spectrometry

One single code change can significantly influence a wide range of software systems and their users. For example, 1) adding a new feature can spread defects in several modules, while 2) changing an API method can improve the performance of all client programs. Developers often may not clearly know whether their or others’ changes are influential at commit time. Rather, it turns out to be influential after affecting many aspects of a system later. This paper investigates influential software changes and proposes an approach to identify them early, i.e., immediately when they are applied. We first conduct a post- mortem analysis to discover existing influential changes by using intuitions such as isolated changes and changes referred by other changes in 10 open source projects. Then we re-categorize all identified changes through an open-card sorting process. Subsequently, we conduct a survey with 89 developers to confirm our influential change categories. Finally, from our ground truth we extract features, including metrics such as the complexity of changes, terms in commit logs and file centrality in co-change graphs, to build ma- chine learning classifiers. The experiment results show that our prediction model achieves overall with random samples 86.8% precision, 74% recall and 80.4% F-measure respectively.

The glycosylation of human chorionic gonadotropin (hCG) plays an important role in reproductive tumors. Detecting hCG N-glycosylation alteration may significantly improve the diagnostic accuracy and sensitivity of related cancers. However, developing an immunoassay directly against the N-linked oligosaccharides is unlikely because of the heterogeneity and low immunogenicity of carbohydrates. Here, we report a hydrogen/deuterium exchange and MS approach to investigate the effect of N-glycosylation on the binding of antibodies against different hCG glycoforms. Hyperglycosylated hCG was purified from the urine of invasive mole patients, and the structure of its N-linked oligosaccharides was confirmed to be more branched by MS. The binding kinetics of the anti-hCG antibodies MCA329 and MCA1024 against hCG and hyperglycosylated hCG were compared using biolayer interferometry. The binding affinity of MCA1024 changed significantly in response to the alteration of hCG N-linked oligosaccharides. Hydrogen/deuterium exchange-MS reveals that the peptide β65-83 of the hCG β subunit is the epitope for MCA1024. Site-specific N-glycosylation analysis suggests that N-linked oligosaccharides at Asn-13 and Asn-30 on the β subunit affect the binding affinity of MCA1024. These results prove that some antibodies are sensitive to the structural change of N-linked oligosaccharides, whereas others are not affected by N-glycosylation. It is promising to improve glycoprotein biomarker-based cancer diagnostics by developing combined immunoassays that can determine the level of protein and measure the degree of N-glycosylation simultaneously.

O-glycosylation-site characterization of individual glycoproteins is a major challenge because of the heterogeneity of O-glycan core structures. In proteomic studies, O-glycosylation-site analysis is even more difficult because of the complexity of the sample. In this work, we designed a rapid and convenient workflow for characterizing the O-glycosylation sites of individual proteins and the human-plasma proteome. A mixture of exoglycosidases was used to partially remove O-glycan chains and leave an N-acetylgalacosamine (GalNAc) residue attached to the Ser or Thr residues. The O-glycosylated peptides could then be identified by using liquid chromatography-tandem mass spectrometry (LC-MS-MS) to detect the 203 Da mass increase. Jacalin was used to selectively isolate O-GalNAc glycopeptides before LC-MS-MS analysis, which is optional for individual proteins and necessary for complex human-plasma proteins. Bovine fetuin and human chorionic gonadotropin (hCG) were used to test the analytical workflow. The workflow indicated superior sensitivity by not only covering most previously known O-glycosylation sites but also discovering several novel sites. Using only one drop of blood, a total of 49 O-GalNAc-linked glycopeptides from 36 distinctive glycoproteins in human plasma were identified unambiguously. The approach described herein is simple, sensitive, and global for site analysis of core 1 through core 4 O-glycosylated proteins.

The Android packaging model offers ample opportunities for malware writers to piggyback malicious code in popular apps, which can then be easily spread to a large user base. Although recent research has produced approaches and tools to identify piggybacked apps, the literature lacks a comprehensive investigation into such phenomenon. We fill this gap by 1) systematically building a large set of piggybacked and benign apps pairs, which we release to the community, 2) empirically studying the characteristics of malicious piggybacked apps in comparison with their benign counterparts, and 3) providing insights on piggybacking processes. Among several findings providing insights analysis techniques should build upon to improve the overall detection and classification accuracy of piggybacked apps, we show that piggy- backing operations not only concern app code, but also extensively manipulates app resource files, largely contradicting common beliefs. We also find that piggybacking is done with little sophistication, in many cases automatically, and often via library code.

To devise efficient approaches and tools for detecting malicious packages in the Android ecosystem, researchers are increasingly required to have a deep understanding of malware. There is thus a need to provide a framework for dissecting malware and locating malicious program fragments within app code in order to build a comprehensive dataset of malicious samples. Towards addressing this need, we propose in this work a tool-based approach called HookRanker, which provides ranked lists of potentially malicious packages based on the way malware behaviour code is triggered. With experiments on a ground truth set of piggybacked apps, we are able to automatically locate the malicious packages from piggybacked Android apps with an accuracy of 83.6% in verifying the top five reported items.

// Fei Wang 1, * , Dinglan Wu 2, * , Housheng Fu 1 , Fengrong He 1 , Congjie Xu 1 , Jiaquan Zhou 1 , Daoyuan Li 1 , Guoping Li 1 , Jianbing Xu 1 , Qinghui Wu 1 , Jianxiang Chen 1 , Liangju Su 1 , Weifu Wang 1 and Shufang Zhang 3 1 Department of Urology, People’s Hospital of Hainan Province, Haikou, China 2 The Clinical Innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical University, Shenzhen, China 3 Central Laboratory, Haikou People’s Hospital, Central South University Xiangya School of Medicine Affiliated Haikou Hospital, Haikou, China * These have authors contributed equally to this work Correspondence to: Shufang Zhang, email: haikoushufang@126.com Weifu Wang, email: 13698987211@163.com Keywords: bladder cancer, Cofilin 1, transcription factor 7-like 2 (TCF7L2), depolymerizing factor (ADF), luciferase assay Received: February 02, 2017 Accepted: June 27, 2017 Published: September 06, 2017 ABSTRACT Earlier reports demonstrated that Cofilin expression is increased in bladder cancer samples, though its function remains unknown. Here, we found that Cofilin 1 expression was higher in bladder cancer tissues than in paracancerous tissues. Overexpression of Cofilin 1 promoted, while Cofilin 1 knockdown inhibited, proliferation, migration, and invasion in the T24 and RT4 bladder cancer cell lines. In addition, Cofilin 1 overexpression increased, while Cofilin 1 knockdown decreased, bladder tumor volumes in mouse xenograft experiments. Transcription factor 7-like 2 (TCF7L2) targeted the promoter of the Cofilin 1 gene, and TCF7L2 knockdown or mutations in the Cofilin 1 promoter dramatically decreased Cofilin 1 transcription. TCF7L2 promoted cell proliferation and migration and increased Cofilin 1 protein levels in RT4 and T24 cells. Thus, TCF7L2 contributed to Cofilin 1-induced promotion of bladder cancer development by binding to the Cofilin 1 promoter and increasing its expression.

The mammalian Sirt1 deacetylase is generally thought to be a nuclear protein, but some pilot studies have suggested that Sirt1 may also be involved in orchestrating nucleolar functions. Here, we show that nucleolar stress response is a ubiquitous cellular reaction that can be induced by different types of stress conditions, and Sirt1 is an endogenous suppressor of nucleolar stress response. Using stable isotope labeling by amino acids in cell culture approach, we have identified a physical interaction of between Sirt1 and the nucleolar protein nucleophosmin, and this protein–protein interaction appears to be necessary for Sirt1 inhibition on nucleolar stress, whereas the deacetylase activity of Sirt1 is not strictly required. Based on the reported prerequisite role of nucleolar stress response in stress‐induced p53 protein accumulation, we have also provided evidence suggesting that Sirt1‐mediated inhibition on nucleolar stress response may represent a novel mechanism by which Sirt1 can modulate intracellular p53 accumulation independent of lysine deacetylation. This process may represent an alternative mechanism by which Sirt1 regulates functions of the p53 pathway.

Time series mining has become essential for extracting knowledge from the abundant data that flows out from many application domains. To overcome storage and processing challenges in time series mining, compression techniques are being used. In this paper, we investigate the loss/gain of performance of time series classification approaches when fed with lossy-compressed data. This extended empirical study is essential for reassuring practitioners, but also for providing more insights on how compression techniques can even be effective in smoothing and reducing noise in time series data. From a knowledge engineering perspective, we show that time series may be compressed by 90% using discrete wavelet transforms and still achieve remarkable classification accuracy, and that residual details left by popular wavelet compression techniques can sometimes even help to achieve higher classification accuracy than the raw time series data, as they better capture essential local features.

Time series mining has become essential for extracting knowledge from the abundant data that flows out from many application domains. To overcome storage and processing challenges in time series mining, compression techniques are being used. In this paper, we investigate the loss/gain of performance of time series classification approaches when fed with lossy-compressed data. This empirical study is essential for reassuring practitioners, but also for providing more insights on how compression techniques can even be effective in reducing noise in time series data. From a knowledge engineering perspective, we show that time series may be compressed by 90% using discrete wavelet transforms and still achieve remarkable classification ac- curacy, and that residual details left by popular wavelet compression techniques can sometimes even help achieve higher classification accuracy than the raw time series data, as they better capture essential local features.

As the concept of Internet of Things (IoT) develops, buildings are equipped with increasingly heterogeneous sensors to track building status as well as occupant activities. As users become more and more concerned with their privacy in buildings, explicit sensing techniques can lead to uncomfortableness and resistance from occupants. In this paper, we adapt a sensing by proxy paradigm that monitors building status and coarse occupant activities through agglomerative clustering of indoor temperature movements. Through extensive experimentation on 86 classrooms, offices and labs in a five-story school building in western Europe, we prove that indoor temperature movements can be leveraged to infer latent information about indoor environments, especially about rooms' relative physical locations and rough type of occupant activities. Our results evidence a cost-effective approach to extending commercial building control systems and gaining extra relevant intelligence from such systems.

We discuss the capability of a new feature set for malware detection based on potential component leaks (PCLs). PCLs are defined as sensitive data-flows that involve Android inter-component communications. We show that PCLs are common in Android apps and that malicious applications indeed manipulate significantly more PCLs than benign apps. Then, we evaluate a machine learning-based approach relying on PCLs. Experimental validations show high performance for identifying malware, demonstrating that PCLs can be used for discriminating malicious apps from benign apps.

Despite much effort in the community, the momentum of Android research has not yet produced complete tools to perform thorough analysis on Android apps, leaving users vulnerable to malicious apps. Because it is hard for a single tool to efficiently address all of the various challenges of Android programming which make analysis difficult, we propose to instrument the app code for reducing the analysis complexity, e.g., transforming a hard problem to a easy-resolvable one. To this end, we introduce in this paper Apkpler, a plugin-based framework for supporting such instrumentation. We evaluate Apkpler with two plugins, demonstrating the feasibility of our approach and showing that Apkpler can indeed be leveraged to reduce the analysis complexity of Android apps.

The abundance of time series data in various domains and their high dimensionality characteristic are challenging for harvesting useful information from them. To tackle storage and processing challenges, compression-based techniques have been proposed. Our previous work, Domain Series Corpus (DSCo), compresses time series into symbolic strings and takes advantage of language modeling techniques to extract from the training set knowledge about different classes. However, this approach was flawed in practice due to its excessive memory usage and the need for a priori knowledge about the dataset. In this paper we propose DSCo-NG, which reduces DSCo’s complexity and offers an efficient (linear time complexity and low memory footprint), accurate (performance comparable to approaches working on uncompressed data) and generic (so that it can be applied to various domains) approach for time series classification. Our confidence is backed with extensive experimental evaluation against publicly accessible datasets, which also offers insights on when DSCo-NG can be a better choice than others.

To devise efficient approaches and tools for detecting malicious packages in the Android ecosystem, researchers are increasingly required to have a deep understanding of malware. There is thus a need to provide a framework for dissecting malware and locating malicious program fragments within app code in order to build a comprehensive dataset of malicious samples. Towards addressing this need, we propose in this work a tool-based approach called HookRanker, which provides ranked lists of potentially malicious packages based on the way malware behaviour code is triggered. With experiments on a ground truth of piggybacked apps, we are able to automatically locate the malicious packages from piggybacked Android apps with an accuracy@5 of 83.6% for such packages that are triggered through method invocations and an accuracy@5 of 82.2% for such packages that are triggered independently.

Time series data are abundant in various domains and are often characterized as large in size and high in dimensionality, leading to storage and processing challenges. Symbolic representation of time series – which transforms numeric time series data into texts – is a promising technique to address these challenges. However, these techniques are essentially lossy compression functions and information are partially lost during transformation. To that end, we bring up a novel approach named Domain Series Corpus (DSCo), which builds per-class language models from the symbolized texts. To classify unlabeled samples, we compute the fitness of each symbolized sample against all per-class models and choose the class represented by the model with the best fitness score. Our work innovatively takes advantage of mature techniques from both time series mining and NLP communities. Through extensive experiments on an open dataset archive, we demonstrate that it performs similarly to approaches working with original uncompressed numeric data.