Description of the data base

We present a scalable and precise context-sensitive points-to analysis with three key properties: (1) filtering out of unrealizable paths, (2) a context-sensitive heap abstraction, and (3) a context-sensitive call graph. Previous work [21] has shown that all three properties are important for precisely analyzing large programs, e.g., to show safety of downcasts. Existing analyses typically give up one or more of the properties for scalability. We have developed a refinement-based analysis that succeeds by simultaneously refining handling of method calls and heap accesses, allowing the analysis to precisely analyze important code while entirely skipping irrelevant code. The analysis is demanddriven and client-driven, facilitating refinement specific to each queried variable and increasing scalability. In our experimental evaluation, our analysis proved the safety of 61% more casts than one of the most precise existing analyses across a suite of large benchmarks. The analysis checked the casts in under 13 minutes per benchmark (taking less than 1 second per query) and required only 35MB of memory, far less than previous approaches.

Analysis, calculation and utilization of the k-balance attribute in interdependent networks

Event-driven systems maintain an ongoing dialog with their environment. Examples include: distributed programs, where each process reacts to received messages by performing computation and sending messages to peers; graphical user interfaces (GUI), where the interface software and the underlying application react to user inputs and web-based applications, where presentation, business logic and storage tier functionality react to user inputs and response from other web-based services. Such applications are difficult to test because the set of possible interaction sequences between the system and its environment can be very large and governed by complex constraints. The exhaustive nature of software model checking techniques offers hope for effectively validating such systems, however, they only work for closed systems. Previously, the authors developed the Bandera Environment Generator, which given an open system, called a unit under analysis, closes it with a model of its environment. The authors' previous work on environment generation has focused on developing broadly applicable mechanisms for modelling environment behaviour. The generality of this approach often makes it difficult to produce environment models that enable precise and efficient system analysis. The authors' experience shows that by exploiting information about the application domain, generated environments can be made both more precise and more efficient for model checking. This study presents the concept of domain-specific environment generation, details techniques for customising environment generation for the domain of event-driven software, and assesses those techniques on the domains of GUI and web-based applications.

Gradient retention prediction of acid–base analytes in reversed phase liquid chromatography: A simplified approach for acetonitrile–water mobile phases

Social recommendation systems based on the graph neural network (GNN) have received a lot of research-related attention recently because they can use social information to improve recommendation accuracy and because of the benefits derived from the excellent performance of the graph neural network in graphic data modeling. A large number of excellent studies in this area have been proposed one after another, but they all share a common requirement that the data should be centrally stored. In recent years, there have been growing concerns about data privacy. At the same time, the introduction of numerous stringent data protection regulations, represented by general data protection regulations (GDPR), has challenged the recommendation models with conventional centralized data storage. For the above reasons, we have designed a flexible model of recommendation algorithms for social scenarios based on federated learning. We call it the federated graph neural network for recommendation systems (FedGR). Previous related work in this area has only considered GNN, social networks, and federated learning separately. Our work is the first to consider all three together, and we have carried out a detailed design for each part. In FedGR, we used the graph attention network to assist in modeling the implicit vector representation learned by users from social relationship graphs and historical item graphs. In order to protect data privacy, we used FedGR flexible data privacy protection by incorporating traditional cryptography encryption techniques with the proposed “noise injection” strategy, which enables FedGR to ensure data privacy while minimizing the loss of recommended performance. We also demonstrate a different learning paradigm for the recommendation model under federation. Our proposed work has been validated on two publicly available popular datasets. According to the experimental results, FedGR has decreased MAE and RMSE compared with previous work, which proves its rationality and effectiveness.

The concept of similarity measurement is is systematically proposed. Although there are some previous related works on this issue, similarity learning has not attracted the attention it deserves. We formulate the problem of similarity measurement learning and propose a framework to solve it. In our framework, the similarity measure is the distance of the samples in some feature space, therefore to learn the similarity measure is to learn a feature mapping function. Previous works are surveyed and they are integrated in this framework. Some applications of similarity measure learning are discussed, including fingerprint, face recognition and content-based image retrieval. A kernel-based method to learning the similarity measure is proposed and experimental result is given and discussed.

Recently, wearable devices have become increasingly popular in our lives because of their neat features and stylish appearance. However, their tiny sizes bring about new challenges to human-device interaction such as texts input. Although some novel methods have been put forward, they possess different defects and are not applicable to deal with the problem. As a result, we propose an acoustic-based texts-entry system, i.e., EchoWrite, by which texts can be entered with a finger writing in the air without wearing any additional device. More importantly, different from many previous works, EchoWrite runs in a training-free style which reduces the training overhead and improves system scalability. We implement EchoWrite with commercial devices and conduct comprehensive experiments to evaluate its texts-entry performance. Experimental results show that EchoWrite enables users to enter texts at a speed of 7.5 WPM without practice, and 16.6 WPM after about 30- minute practice. This speed is better than touch screen-based method on smartwatches, and comparable with previous related works.

Most naturally occurring mammalian cancers and immortalized tissue culture cell lines share a common characteristic, the overexpression of full-length HMGA1 (high mobility group A1) proteins. The HMGA1 protooncogene codes for two closely related isoform proteins, HMGA1a and HMGA1b, and causes cancerous cellular transformation when overexpressed in either transgenic mice or "normal" cultured cell lines. Previous work has suggested that the in vivo types and patterns of the HMGA1 post-translational modifications (PTMs) differ between normal and malignant cells. The present study focuses on the important question of whether HMGA1a and HMGA1b proteins isolated from the same cell type have identical or different PTM patterns and also whether these isoform patterns differ between non-malignant and malignant cells. Two independent mass spectrometry methods were used to identify the types of PTMs found on specific amino acid residues on the endogenous HMGA1a and HMGA1b proteins isolated from a non-metastatic human mammary epithelial cell line, MCF-7, and a malignant metastatic cell line derived from MCF-7 cells that overexpressed the transgenic HMGA1a protein. Although some of the PTMs were the same on both the HMGA1a and HMGA1b proteins isolated from a given cell type, many other modifications were present on one but not the other isoform. Furthermore, we demonstrate that both HMGA1 isoforms are di-methylated on arginine and lysine residues. Most importantly, however, the PTM patterns on the endogenous HMGA1a and HMGA1b proteins isolated from non-metastatic and metastatic cells were consistently different, suggesting that the isoforms likely exhibit differences in their biological functions/activities in these cell types.

Insects are heterotherms that exhibit a close relationship between their ecology (especially temperature changes) and physiology. In the present study, selected genes associated with cell death and temperature were examined to determine gene expression in Bombyx mori in high and low temperature environments. We determined the amount of dsRNA, different concentrations of dsRNA, and different type of cells to set the conditions most efficient for RNAi. We then prepared dsRNA transcripts of the genes associated with cell death and temperature response. We analyzed cell damage via Trypan blue staining and found that cell viability was reduced after knockdown of these genes. The special transduced cell lines produced in the present study can be applied in various research fields. We also expect that these cell lines can be used as a research tool for the precise functional analysis of various genes.

The Liver X Receptors (LXRα and LXRβ) are members of the nuclear hormone receptor superfamily.1 These sterol-responsive transcription factors regulate the expression of a number of genes involved in intestinal cholesterol absorption, conversion of cholesterol to bile acids, and reverse cholesterol transport. Activation of LXRs blocks cholesterol absorption and induces cholesterol efflux from lipid-loaded cells such as macrophages. Because increased cholesterol efflux is predicted to limit the transformation of macrophages into atherosclerotic foam cells, LXR activity is predicted to be antiatherogenic. This hypothesis is supported by several studies using mice genetically deficient in LXR expression. Previous work from Tangirala and colleagues established that selective elimination of macrophage LXRα and LXRβ expression in the bone marrow compartment results in accelerated disease in both the spontaneous (ApoE−/−) and Western-diet inducible (LDLR−/−) models of atherosclerosis.2 Furthermore, LXRαβ double knockout mice fed a normal chow diet exhibit increased cholesterol accumulation in the macrophages of their arterial walls.2,3 In addition to their effects on cholesterol metabolism, activation of the LXRs by synthetic agonists has an inhibitory effect on inflammatory gene expression in macrophages, pointing to a second potential antiatherogenic mechanism of these receptors.4 Finally, recent studies suggest that the ability of microbial infections to modify the development of atherosclerosis may be accomplished in part through interference with the LXR signaling pathway and macrophage cholesterol metabolism.5 See page 135 Collectively, these studies point to LXRs as potential therapeutic targets for the treatment or prevention of atherosclerosis. However, it …

Immunofluorescent histochemical and ultrastructural studies on the innervation of kisspeptin/neurokinin B neurons to tuberoinfundibular dopaminergic neurons in the arcuate nucleus of rats

Cu films were deposited on Si(100) substrates by applying a negative substrate bias voltage using the non-mass-separated ion beam deposition method. Glow-discharge mass spectrometry was used to determine the impurity concentrations of the deposited Cu films and the 6N Cu target. It was found that the Cu film deposited at the substrate bias voltage of -50 V showed lower impurity contents than the Cu film deposited without the substrate bias voltage, although both the Cu films were contaminated during the deposition. The purification effect might result from the following reasons: (i) the Penning ionization and an ionization mechanism proposed in the present study, (ii) a difference in the kinetic energy of accelerated Cu+ ions toward the substrate with/without the negative substrate bias voltage.

We recently discovered a new gene, Desiccate (Desi), that is expressed in the epidermis and protects larvae from desiccation stress in Drosophila melanogaster. In the present study, we found that taste organs express more Desi than the epidermis both in larvae and adults. Green fluorescent protein (GFP) expression in larvae under the direction of a Desi promoter‐Gal4 line containing the 1,010‐bp 5′ flanking region of Desi produced no signal in the epidermis but strong signals in cells of the larval gustatory sense organs, indicating that this driver works specifically in the gustatory organs. In adults, GFP expression was also observed in basal cells of sensilla on labella, tarsi and wings. More precise morphological analysis of GFP expression located its expression in the outer accessory cells rather than neurons of the labial sensilla. Although Desi knockdown or induction of cell death in Desi‐expressing cells did not change the morphological or physiological characters of the larvae, larvae lacking Desi‐expressing cells failed to metamorphose normally, and all of them died inside puparia. Dying pharate adults were found to lack all labial sensilla. The proneural genes Achaete and Scute, which are involved in the development of the adult central and peripheral nervous system, were normally expressed in the pupae lacking Desi‐expressing cells. These results suggested that the lack of Desi‐expressing cells makes it impossible to produce outer accessory cells for development of the sensilla, thereby signifying that cells expressing Desi are essential for normal morphogenesis of the labial sensilla in Drosophila adults.

A major goal of any antithrombotic regimen administered during percutaneous coronary intervention (PCI) is the preservation of coronary microvascular perfusion, which is critical for myocardial survival. In addition to macrovascular thrombosis, microembolization into the downstream coronary artery bed that occurs during spontaneous plaque rupture and PCI plays a prominent role in the development of microvascular dysfunction that leads to myocardial infarction. In addition to physical obstruction of the lumen by the embolus, vasoconstriction and edema due to inflammation also impair microvascular flow.1 These events likely occur more frequently during PCI performed in the setting of unstable coronary syndromes.2 With Doppler guidewire technology, it was estimated that an average of 25 embolic events occurred during primary PCI for ST-segment elevation myocardial infarction.3 Article see p 784 Platelet aggregates are important components of coronary microemboli, along with leukocytes, erythrocytes, platelet-leukocyte aggregates, cholesterol crystals, and hyalin. Platelets and leukocytes within microemboli play a particularly important role in the pathophysiological changes of blood flow by promoting in situ microvascular thrombosis, vasoconstriction, and inflammation. Interestingly, the composition of the embolized material is similar in the settings of spontaneous and catheter-induced plaque rupture.1 In animal models, distal microembolization of inert microspheres is associated with an immediate reduction in flow due to vessel occlusion, followed by enhanced blood flow due to the effects of adenosine released from the embolized myocardium into the adjacent noninvolved myocardium.4 Microembolization results in overall reduced flow reserve and the progressive loss of contractile function, which often reverses within weeks. The degree of myocardial contractile dysfunction is disproportionate to the degree of infarction and is the result of inflammation.5 However, in the clinical condition, a more complex pathophysiology exists due to the influence of multiple components of the emboli. Platelets aggregate when fibrinogen binds to the active …

Accurate assessment of injury severity is critical for decision making related to the prevention, triage, and treatment of injured patients. Presently, the standard method of controlling for variations of injury severity between groups has been based upon the Injury Severity Score (ISS) and the Trauma Score and the Trauma and Injury Severity Score (TRISS) methodology. The purpose of this study was to attempt to build upon previous work using International Classification of Diseases, ninth revision (ICD-9) coded diagnosis, and procedure information available from standard hospital discharge abstracts (UB-82 Billing format) to create a hierarchical network to provide a tool for predicting injury severity and probability of survival. Data were obtained for this analysis from the North Carolina Medical Database. Data were available on all trauma patients admitted to hospitals in North Carolina from January 1, 1988 until June 30, 1992. The dependent variable of interest was the patient's survival after injury, coded as live or die. The independent variables used in the study included the ISS derived using the technique described by MacKenzie Abbreviated Injury Score (AIS) and body system maximum AIS scores, mortality risk ratios derived from the ICD-9-DM primary, secondary, and tertiary diagnoses, primary and secondary procedures as described in previous work, age and gender. Network generation used a commercial software package, AIM (Abtech Corp., Charlottesville, Va.), which is a numeric modeling tool that automatically "learns" knowledge from a data base of examples. In the test data set an ISS and a prediction of survival based upon the derived network were calculated for each and every patient. The relative predictive power of these two scores were compared by calculating the overall accuracy, sensitivity, and specificity and the false positive and false negative rates. The receiver operator characteristic curves demonstrate that the network is a more effective tool in predicting the outcome of trauma patients. All the measures of predictive power show that the network was the better predictor of outcome than the ISS. Given the recognized limitations of the ISS, the widespread availability of the ICD-9 coded diagnoses and procedures, and the availability of many state and regional data bases that have no ISS or Trauma Score, the purpose of this study was to assess the ability of a network derived from limited but widely available hospital discharge data to predict the outcome of injured patients. The study confirms previous work showing that the ICD-9 codes were strongly associated with outcome. The study demonstrated that the network created from these data was a better predictor of outcome than the derived ISS. When the results of the network were compared with other published series, the network, created without access to physiologic information, was almost as accurate, sensitive, and specific as reported values for TRISS and A Severity Characterization of Trauma (ASCOT). Because the present study is the first of its type, further investigations are needed to validate these findings. If other studies corroborate this study, a network model based upon ICD-9 codes could become the principal method for grading injury severity. This would provide superior predictive power of injury severity with important cost savings and universal application.