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The aim: Prove that the use of GLS by speckle tracking ECHO study is more sensitive to detect early systolic dysfunction. Materials and methods: In this case control study 40 diabetic patients 22 males (55%) & 18 females (45%) who have no symptom or clinical finding of a cardiac problem with normal LVEF and a 30 healthy control subjects. The ECHO study includes measuring their LVEF &comparing it with the result global longitudinal strain by speckle tracking for assessment of systolic function. Results: A 62.5% of diabetic patients who are have no CVS complaint neither they have systolic dysfunction with normal LVEF they have early systolic dysfunction revealed by speckle tacking technique in compares to a healthy control group where only 10% have systolic dysfunction with mean GLS of the patients was -17.43±3.016, while that for the control group -20.58 ± 1.729 A P value of 0. 012. Also there is a significant correlation between the duration of DM & the systolic dysfunction as detected by GLS. Conclusions: Diabetic patients got early systolic dysfunction before they show any symptoms, even their LVEF is normal, which can be detected by speckle tracking ECHO study.

This paper presents a client bootstrapping protocol for proxy-based moving target defense system for the cloud. The protocol establishes the identity of prospective clients who intend to connect to web services behind obscure proxy servers in a cloud-based network. In client bootstrapping, a set of initial line of defense services receive new client requests, execute an algorithm to assign them to a proxy server, and reply back with the address of the chosen proxy server. The bootstrapping protocol only reveals one proxy address to each client, maintaining the obscurity of the addresses for other proxy servers. Hiding the addresses of proxy servers aims to lower the likelihood that a proxy server becomes the victim of a denial-of-service (DoS) attack. Existing works address this problem by requiring the solution of computationally intensive puzzles from prospective clients. This solution slows the progression of attacks as well as new clients. This paper presents an alternative idea by observing that limited capacity of handling initial network requests is the primary cause of denial-of-service attacks. Thus, the suggested alternative is to utilize cost-effective high-capacity networks to handle client bootstrapping, thus thwarting attacks on the initial line of defense. The prototype implementation of the protocol using Google’s firebase demonstrates the proof of concept for web services that receive network requests from clients on mobile devices.

The current study aimed to characterize a novel bee venom –loaded polyacrylic acid carrier intended for cancer therapy. Honey bee venom was collected by the electric shock method. Poly acrylic acid and venom-loaded polyacrylic acid were prepared using potassium peroxodisulfate as an initiator. Nanoformulation was achieved by emulsion-solvent diffusion. Characterization was achieved by FT-IR, 1H-NMR, TEM, DFED, and UV spectrophotometry. TEM showed that the resultant spherical nanoparticles were either dispersed or in a nodular form and their diameter was 17-210 nm. DFED showed that the resultant nanoparticles did not show crystalline edges. FTIR and 1H-NMR concluded that there was no marked interaction between drug and selected polymer except in the physical encapsulation process. UV spectrophotometry indicated a 95% uniformity of drug content in the resultant nanoparticles. The study concluded that the PAA carrier may be a promising suitable carrier for bee venom.

The aim: Prove that the use of GLS by speckle tracking ECHO study is more sensitive to detect early systolic dysfunction.Materials and methods: In this case control study 40 diabetic patients 22 males (55%)18 females (45%) who have no symptom or clinical finding of a cardiac problem with normal LVEF and a 30 healthy control subjects. The ECHO study includes measuring their LVEFcomparing it with the result global longitudinal strain by speckle tracking for assessment of systolic function.Results: A 62.5% of diabetic patients who are have no CVS complaint neither they have systolic dysfunction with normal LVEF they have early systolic dysfunction revealed by speckle tacking technique in compares to a healthy control group where only 10% have systolic dysfunction with mean GLS of the patients was -17.43±3.016, while that for the control group -20.58 ± 1.729 A P value of 0. 012. Also there is a significant correlation between the duration of DMthe systolic dysfunction as detected by GLS.Conclusions: Diabetic patients got early systolic dysfunction before they show any symptoms, even their LVEF is normal, which can be detected by speckle tracking ECHO study.

In this work, a high-fidelity nonlinear and time-periodic six degrees-of-freedom dynamic model of a flapping wing micro aerial vehicle has been developed. The model utilized Lagrange’s equations for quasi-coordinates. A quasi-steady aerodynamic model that includes the effect of leading-edge vortex and rotational circulation was implemented. Averaging technique was used to obtain a time-invariant dynamic model for the purpose of designing a trajectory tracking control law. The sensitivity of aerodynamic forces and moments to the control parameters, namely: flapping frequency, flapping bias and stroke plane angles, was determined exploiting control derivatives. The developed controller has two loops. The outer-loop, employing integral sliding mode controller, generates desired cycle-averaged aerodynamic forces and moments. The inner loop employs a novel control allocation method. The proposed inner-loop controller provides a closed-loop feedback solution that ensures that the chosen control parameters provide the desired aerodynamic forces and moments. Simulation studies indicate that the proposed controller applied on the high-fidelity six degrees-of-freedom dynamic model successfully achieves trajectory tracking.

Malware collusion is a technique utilized by attackers to evade standard detection. It is a new threat where two or more applications, appearing benign, communicate to perform a malicious task. Most proposed approaches aim at detecting stand-alone malicious applications. We point out the need for analyzing data flows across multiple Android apps, a problem referred to as end-to-end flow analysis . In this work, we present a flow analysis for app pairs that computes the risk level associated with their potential communications. Our approach statically analyzes the sensitivity and context of each inter-app flow based on inter-component communication (ICC) between communicating apps, and defines fine-grained security policies for inter-app ICC risk classification. We perform an empirical study on 7,251 apps from the Google Play store to identify the apps that communicate with each other via ICC channels. Our results report four times fewer warnings on our dataset of 197 real app pairs communicating via explicit external ICCs than the state-of-the-art permission-based collusion detection.

Seasonal and annual prevalence of intestinal protozoans were studied for a period of 18 years in an urban/suburban Los Angeles area. This is the first such study anywhere in the world. A total of 7766 fecal specimens from 3883 patients in the Los Angeles County, from 1996 to 2013 were tested at Parasitology Center, Inc. (PCI), Scottsdale, Arizona. During this period, 1629 (41%) of patients were found infected with one or more protozoan parasites. The most prevalent parasites were Blastocystis hominis (19%), Entamoeba histolytica/E. dispar (6%) E. hartmanni (6%), and Cryptosporidium parvum (5%). Blastocystis hominis made up 45% of all infections. Infections with Blastocystis hominis progressively declined through 2013 while those of C. parvum increased. Infections with B. hominis were more prevalent in colder weather and lowest in August and September. Infections with C. parvum were most prevalent from March to June and lowest in August. The overall monthly prevalence for all protozoan parasites varied between 34% in August and 51% in February. The composition of the parasitic fauna diagnosed, annual prevalence rates, and seasonality were discussed in comparison with other studies. Key words: Intestinal protozoans, Los Angeles, 1996-2013, Prevalence, Seasonality.

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-Cov2), is the causative pathogen of coronavirus disease 2019 (COVID-19), which is primarily described as a respiratory illness. However, a wide array of cardiovascular complications has also been described in the setting of COVID-19. Wellens' syndrome, also regarded as a left anterior descending coronary T-wave syndrome, is an electrocardiography (EKG) pattern that indicates critical proximal left anterior descending (LAD) artery stenosis. It is characterized by deeply inverted T-waves or biphasic T-waves in the anterior precordial chest leads in a patient with unstable angina. Patients typically present with symptoms consistent with acute coronary syndrome. To our knowledge, we present the first case of Wellens' syndrome in a patient with a COVID-19 infection. Furthermore, this case describes stenosis of the left circumflex artery, a variant of the unusual angiographic findings associated with Wellens', as it is usually associated with occlusion of the proximal LAD. The pathophysiology of cardiovascular complications associated with COVID-19 is not well-understood; nevertheless, it was reported that mortality from coronary artery disease (CAD) complications is significantly higher in these patient populations. Healthcare providers should also be aware of identifying Wellens' syndrome, as urgent coronary angiography is superior to stress testing.

The coronavirus disease 2019 (COVID-19) creates a significant burden on the cardiovascular system. Moreover, diagnosing coronary artery disease in patients with COVID-19 may be clinically challenging. Herein, we present a case of in-hospital stent thrombosis and thrombotic occlusion of the right coronary artery after initial revascularization and adherence to antiplatelet therapy.

Regadenoson is a pharmacological stress agent that has been widely used since its approval by the Food and Drug Administration (FDA) in 2008. For many years, dipyridamole and adenosine, which are non-selective adenosine receptor agonists, were more popular. However, these agents are less preferred now due to their undesirable adverse effects as compared to regadenoson. In the ADVANCE (ADenoscan Versus regAdenosoN Comparative Evaluation) phase 3 clinical trial, regadenoson demonstrated non-inferiority to adenosine for detecting reversible myocardial ischemia. This review summarizes the clinical utilities of regadenoson as the most widely used pharmacological stress agent. Moreover, the use of regadenoson has been documented in specific patient populations. Although regadenoson has established safety and efficacy in most patients with chronic diseases, there are equivocal results in the literature for other chronic diseases. It is warranted to highlight that the use of regadenoson has not been studied in patients of low socioeconomic class; it is a condition that carries a significant burden on the cardiovascular system.

Amid the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the search for effective treatment and vaccines has been exponentially on the rise. Finding effective treatment has been the core of attention of many scientific reports and antivirals are in the center of those treatments. Numerous antivirals are being studied for the management of the coronavirus disease 2019 (COVID-19) pneumonia caused by the SARS-CoV-2. Remdesivir was the first drug to gain emergency FDA approval to be used in COVID-19. Similarly, favipiravir, an anti-influenza drug, is being studied as a potential agent against COVID-19. Contrastingly, hydroxychloroquine has been a controversial drug in the management of COVID-19. Nevertheless, the National Institute of Health (NIH), along with the World Health Organization (WHO), have discontinued clinical trials for hydroxychloroquine as the drug showed little or no survival benefit. Ivermectin, an antihelminthic drug, has shown antiviral properties previously. Additionally, it was described to be effective in vivo against the SARS-CoV-2. However, its survival benefit in patients with COVID-19 has not been documented. We herein propose the theory of inhaled ivermectin which can attain the desired lung concentration that will render it effective against SARS-CoV-2.

Hydralazine induced lupus syndrome (HILS), a form of Drug-Induced Lupus (DIL), was first reported in 1953. Since then, studies have shown an increasing incidence of HILS. It presents with lupus-like symptoms such as arthralgia, fever, chest pain, anorexia, fatigue, petechiae, and rash. Though rare, HILS may initially present with pericardial effusion. Lab findings of HILS usually show positive anti-nuclear antibody (ANA) in >95% of cases, antihistone abs in >95% of cases, rheumatoid factor ab in 20%, and anti-double-strand DNA in

IntroductionCoronavirus disease-2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is causing dramatic morbidity and mortality worldwide. The Red Blood Cell Distribution Width (RDW) has been strongly associated with increased morbidity and mortality in multiple diseases.ObjectiveTo assess if elevated RDW is associated with unfavorable outcomes in hospitalized COVID-19.MethodsWe retrospectively studied clinical outcomes of hospitalized COVID-19 patients for their RDW values. In-hospital mortality was defined as primary outcome, while septic shock, need for mechanical ventilation, and length of stay (LOS) were secondary outcomes.ResultsA total of 294 COVID-19 patients were finally studied. Overall prevalence of increased RDW was 49.7% (146/294). RDW was associated with increased risk of in-hospital mortality (aOR, 4.5; 95%CI, 1.4-14.3) and septic shock (aOR, 4.6; 95%CI, 1.4-15.1) after adjusting for anemia, ferritin, and lactate. The association remained unchanged even after adjusting for other clinical confounders such as age, sex, body mass index, coronary artery disease, hypertension, diabetes mellitus, and chronic obstructive pulmonary disease. No association was found instead with mechanical ventilation and median LOS.ConclusionElevated RDW in hospitalized COVID-19 patients is associated with a significantly increased risk of mortality and septic shock.

Levetiracetam is a widely used, effective and usually well-tolerated anti-epileptic medicine. It is mostly excreted by kidneys and requires dose adjustment according to the glomerular filtration rate. Very few case reports have been published in the literature about levetiracetam causing acute kidney injury (AKI). We present a case of a 26-year-old male with a seizure disorder on levetiracetam, presented with status epilepticus requiring intubation for airway protection. He received 4 g of intravenous levetiracetam as a loading dose and continued with a maintenance dose of 750 mg intravenous every 12 hours. He had signs of AKI on day two and creatinine eventually reached a maximum level of 12.2 mg/dL. His kidney function improved to his new baseline in a period of 30 days without requiring renal replacement therapy. He did not have significant rhabdomyolysis and his kidney function started improving right after his anti-epileptic therapy was switched to valproic acid pointing towards levetiracetam as the primary cause of kidney injury. Clinicians should be aware that levetiracetam can cause AKI on patients with a seizure disorder, especially when administered in high doses. Kidney function should be monitored closely and patients should be treated aggressively with intravenous fluids when they have any signs of rhabdomyolysis to prevent further kidney damage.

Vitamin D works by binding to vitamin D receptor (VDR). The muscle involvement in hypovitaminosis D was broadly named osteomalacic myopathy.A case control study involved 20 female patients diagnosed with osteomalacic myopathy compared with 15 age-matched healthy female controls. We assessed both for VDR genotype single-nucleotide polymorphisms (SNP) at 3 sites (ApaI, BsmI, and FokI).ApaI and BsmI genotypes distribution in both groups showed non-significant difference unlike FokI genotypes in which we found significantly higher percentages of single allele mutation in patients vs. controls.The relation of VDR gene SNPs to muscle function was studied before but in healthy subjects. We tried to correlate if presence/absence of a certain mutation is responsible for the appearance of osteomalacic myopathy.

Android malware (malicious apps) families share common attributes and behavior through sharing core malicious code. However, as the number of new malware increases, the task of identifying the correct family becomes more challenging. Two prominent approaches tackle this problem, either using dynamic analysis that captures the runtime behavior of the malware or using static analysis methods that can reveal malicious behavior by analyzing the underlying logic and code patterns. A third emerging way is to use the various sources of identification features to analyze the architectural and external attributes of a malicious app. For example, two malicious apps can have different behavioral patterns but share common attributes. We hypothesize that this malware can belong to the same family but attempt to mislead dynamic and code-level static analysis tools by randomizing their behavior. In this work, we utilize a promising set of Android-oriented code metrics that guide a supervised classification learning process for identifying malware families in Android. Our empirical results on 2,869 malware apps, across 35 different malware families, show that these metrics are very effective to identify malware families. In particular, we achieve low false positive rate (1.2%) and AUC score of 0.984 for family identification by using Random Forest (RF) classifier.

This brief introduces a successive approximation time-to-digital converter based on a novel algorithm denoted as successive approximation register with continuous disassembly (SAR-CD). The main advantage of the proposed SAR-CD algorithm is that it moves the conditioning between the evaluated bits to the digital domain, after all the bits are evaluated. Simulation results show promising enhancements in power consumption compared with the state-of-the-art designs. A full 10-bit architecture is introduced using 65-nm CMOS technology as a case study with simulation power consumption of 2.8 mW at a sampling rate of 29.4 Msample/s from 1-V power supply with an effective number of bits value of 8.63 bits and a maximum differential nonlinearity of 1 least significant bit.

Medicine recommender systems can assist the medical care providers with the selection of an appropriate medication for the patients. The advanced technologies available nowadays can help developing such recommendation systems which can lead to more concise decisions. Many existing medicine recommendation systems are developed based on different algorithms. Thus, it is crucial to understand the state-of-the-art developments of these systems, their advantages and disadvantages as well as areas which require more research. In this paper, we conduct a literature review on the existing solutions for medicine recommender systems, describe and compare them based on various features, and present future research directions.

The ability to predict prolonged length of hospital stay for newborn children has clinical value as an indicator of newborn health status but also can assist in such health system resource considerations as improved utilization of hospital wards and beds. In this paper, we describe the application of machine learning-based prediction to a Healthcare Cost and Utilization Project dataset and report on the performance of various developed predictive models. Via only utilizing administrative data and minimal clinical data available near to the time of admission/birth, we are able to demonstrate high performing models. The use of HCUP data for building newborn prolonged length of stay models potentially applicable across health care providers is an important contribution, and additionally the models represent high-performing models in the field of published predictive models of newborn length of stay in general.

This paper provides a framework where autonomous vehicles (AV) enable better Smart Cities. Large amounts of data are constantly collected from AVs. To manage the constant influx of data, a real-time data model is proposed. To demonstrate how the database works, four use cases have been introduced as follows: (1) Detecting traffic congestion (2) Identifying empty parking spots (3) Determining weather conditions for a specific location, and (4) Detecting objects or dead animals on the road. The proposed data model will enable real-time data-driven decision-making. The framework can be easily expanded to include other use cases in the future.

As mobile computing becomes an integral part of the modern user experience, malicious applications have infiltrated open marketplaces for mobile platforms. Malware apps stealthily launch operations to retrieve sensitive user or device data or abuse system resources. We describe a highly accurate classification approach for detecting malicious Android apps. Our method statically extracts a data-flow feature on how user inputs trigger sensitive API invocations, a property referred to as the user-trigger dependence. Our evaluation with 1433 malware apps and 2684 free popular apps gives a classification accuracy (2.1% false negative rate and 2.0% false positive rate) that is better than, or at least competitive against, the state-of-the-art. Our method also discovers new malicious apps in the Google Play market that cannot be detected by virus scanning tools. Our thesis in this mobile app classification work is to advocate the approach of benign property enforcement, i.e., extracting unique behavioral properties from benign programs and designing corresponding classification policies.

Big Data has become increasingly popular due to its ability to deliver information and optimize current business, health, economic, and research processes. Even though the use of Big Data spans over multiple industries, there is still a lack of security standards and regulations surrounding what data can be captured and how it can be used. This absence of laws and regulations surrounding Big Data security has potentially led to many data breaches. Since there are many participants within the Big Data information flow, each one needs to be further inspected to determine what security measures should be implemented at each stage to prevent these data breaches from occurring. The objective of this position paper is to point out the need for practical security standards in Big Data information flow. In particular, we identify which security standards should be applied at each stage of the Big Data information flow to ensure the privacy and security of valuable and sensitive data.

Migraine is a common disease throughout the world. Not only does it affect the life of people tremendously, but it also leads to high costs, e.g. due to inability to work or various required drug-taking cycles for finding the best drug for a patient. Solving the latter aspect could help to improve the life of patients and decrease the impact of the other consequences. Therefore, in this paper, we present an approach for a drug recommendation system based on the highly scalable native graph database Neo4J. The presented system uses simulated patient data to help physicians gain more transparency about which drug fits a migraine patient best considering her individual features. Our evaluation shows that the proposed system works as intended. This means that only drugs with highest relevance scores and no interactions with the patient's diseases, drugs or pregnancy are recommended.

Inter-Component Communication (ICC) enables useful interactions between mobile apps. However, misuse of ICC exposes users to serious threats, allowing malicious apps to access privileged user data via another app. Unfortunately, existing ICC analyses are largely insufficient in both accuracy and scalability. Most approaches rely on single-app ICC analysis which results in inaccurate and excessive alerts. A few recent works use pairwise app analysis, but are limited by small data sizes and scalability. In this paper, we present MR-Droid, a MapReduce-based computing framework for accurate and scalable inter-app ICC analysis in Android. MR-Droid extracts data-flow features between multiple communicating apps to build a large-scale ICC graph. We leverage the ICC graph to provide contexts for inter-app communications to produce precise alerts and prioritize risk assessments. This scheme requires quickly processing a large number of app-pairs, which is enabled by our MapReduce-based program analysis. Extensive experiments on 11,996 apps from 24 app categories (13 million pairs) demonstrate the effectiveness of our risk prioritization scheme. Our analyses also reveal new real-world hijacking attacks and collusive app pairs. Based on our findings, we provide practical recommendations for reducing inter-app communication risks.

Inter-Component Communication (ICC) enables useful interactions between mobile apps. However, misuse of ICC exposes users to serious threats such as intent hijacking/spoofing and app collusions, allowing malicious apps to access privileged user data via another app. Unfortunately, existing ICC analyses are largely incompetent in both accuracy and scale. This poster points out the need and technical challenges of prioritized analysis of inter-app ICC risks. In this poster, we propose MR-Droid, a MapReduce-based computing framework for accurate and scalable inter-app ICC analysis in Android. MR-Droid extracts data-flow features between multiple communicating apps and the target apps to build a large-scale ICC graph. Our approach is to leverage the ICC graph to provide contexts for inter-app communications to produce precise alerts and prioritize risk assessments. This process requires large app-pair data, which is enabled by our MapReduce-based program analysis. Our initial extensive experiments on 11,996 apps from 24 app categories (13 million pairs) demonstrate the scalability of our approach.

Refactoring is the process of improving the design of existing code by changing its internal structure without affecting its external behavior. Refactoring tends to improve software quality by improving design, improving readability and reducing ‘bugs’. There are many different refactoring methods, each having a particular purpose and effect. Consequently, the effect of refactoring methods on software quality attributes may vary. Moreover, it is often unclear to software designers how to use refactoring methods to improve specific quality attributes. In this paper, we propose a classification of refactoring methods based on their measurable effect on software quality attributes. This, in turn, helps software designers choose appropriate refactoring methods that will improve the quality of their designs, based on the design objectives. It also enables them to predict the quality drift caused by using particular refactoring methods.

Effective prediction of defect-prone software modules can enable software developers to focus quality assurance activities and allocate effort and resources more efficiently. Support vector machines (SVM) have been successfully applied for solving both classification and regression problems in many applications. This paper evaluates the capability of SVM in predicting defect-prone software modules and compares its prediction performance against eight statistical and machine learning models in the context of four NASA datasets. The results indicate that the prediction performance of SVM is generally better than, or at least, is competitive against the compared models.

This paper introduces a new algorithm and circuit design of Time-to-Digital Converter(TDC) with modified Successive Approximation Register(SAR) algorithm. This design enables continuous pulse disassemble. The input pulse is absolutely compared to pulses of widths proportional to Vfs/2, Vfs/4‥Vfs/N, and each bit is evaluated independent of the previous bit result. Then bits correction is applied after the sample evaluation. A 4bit case study circuit is realized using TSMC CMOS 65nm design technology. The design demonstrated 3.67 Effective Number Of Bits (ENOB) for a sampling frequency of 666 MS\s.

We present a new method of screening for malicious Android applications that uses two types of information about the application: the permissions that the application requests in its installation manifest and a metric called percentage of valid call sites (PVCS). PVCS measures the riskiness of the application based on a data flow graph. The information is used with machine learning algorithms to classify previously unseen applications as malicious or benign with a high degree of accuracy. Our classifier outperforms the previous state of the art by a significant margin, with particularly low false positive rates. Furthermore, the classifier evaluation is performed on malware families that were not used in the training phase, simulating the accuracy of the classifier on malware yet to be developed. We found that our PVCS metric and the SEND_SMS permission are the specific pieces of information that are most useful to the classifier.

Rationale and Objectives. When receiver operating characteristic (ROC) studies involve multiple detection tasks in the same patient, the correlations among the within-patient interpretations and the shortage of patients raise several analytic challenges. The authors propose methods for both parametric and nonparametric analysis of this type of data. Methods. First, they computed one accuracy statistic for each task. Then, one jackknife “pseudovalue” was calculated for each task and each patient. Then, they formed a summary index from the accuracy statistics and calculated the standard error of this summary index on the basis of the observed correlations among the jackknife pseudovalues. The method was extended to comparisons of two diagnostic systems and illustrated by using data from a clinical study. Results and Conclusion. This approach can be used with rating or quantitative data and with any index of accuracy, whether calculated parametrically or nonparametrically.

We describe an efficient approach to identify malicious Android applications through specialized static program analysis. Our solution – referred to as user intention program dependence analysis – performs offline analysis to find the dependence relations between user triggers and entry points to methods providing critical system functions. Analyzing these types of dependences in programs can identify the privileged operations (e.g., file, network operations and sensitive data access) that are not intended by users. We apply our technique on 708 free popular apps and 482 malware apps for Android OS, and the experimental results show that our technique can differentiate between legitimate and malware applications with high accuracy. We also explain the limitations of the user-intention-based approach and point out the need for practitioners to adopt multiple analysis tools for evaluating the assurance of Android applications.

Refactoring to patterns allows software designers to safely move their designs towards specific design patterns by applying multiple low-level refactorings. There are many different refactoring to pattern techniques, each with a particular purpose and a varying effect on software quality attributes. Thus far, software designers do not have a clear means to choose refactoring to pattern techniques to improve certain quality attributes. This paper takes the first step towards a classification of refactoring to pattern techniques based on their measurable effect on software quality attributes. This classification helps software designers in selecting the appropriate refactoring to pattern techniques that will improve the quality of their design based on their design objectives. It also enables them to predict the quality drift caused by using specific refactoring to pattern techniques.

In this work, we describe an eective device-based isolation approach for achieving data security. Device-based isolation leverages the proliferation of personal computing devices to provide strong run-time guarantees for the condentiality of secrets. To demonstrate our isolation approach, we show its use in protecting the secrecy of highly sensitive data that is crucial to security operations, such as cryptographic keys used for decrypting ciphertext or signing digital signatures. Private key is usually encrypted when not used, however, when being used, the plaintext key is loaded into the memory of the host for access. In our threat model, the host may be compromised by attackers, and thus the condentiality of the host memory cannot be preserved. We present a novel and practical solution and its prototype called DataGuard to protect the secrecy of the highly sensitive data through the storage isolation and secure tunneling enabled by a mobile handheld device. DataGuard can be deployed for the key protection of individuals or organizations.

This paper describes an efficient approach for identifying malicious Android mobile applications through specialized static program analysis. Our solution performs offline analysis and enforces the normal properties of legitimate dataflow patterns to identify programs that violate these properties. To demonstrate the feasibility of our user-centric dependence analysis, we implement a tool to generate a data dependence graph and perform preliminary evaluation to characterize both legitimate and malicious Android apps. Our preliminary results confirm our hypothesis on the differences in user-centric data dependence behaviors between legitimate and malicious apps. This work has been supported in part by Security and Software Engineering Research Center (S2ERC), a NSF sponsored multi-university Industry/ University Cooperative Research Center (I/UCRC).

Deficiency of vitamin D has been reported in patients with many types of musculoskeletal pain. The present study was designed to determine the association between serum 25-hydroxyvitamin D [25-(OH)D] deficiency and nonspecific skeletal pain.A total of 276 patients with nonspecific skeletal pain at different regions of the skeletal system diagnosed as leg pain, widespread pain, arthralgia, rib pain, back pain and fibromyalgia were compared with 202 matched controls with regard to mean serum 25-(OH)D level and 25-(OH)D deficiency. Serum 25-(OH)D was measured by enzyme-linked immunosorbent assay method and levels20 ng/mL were considered as deficient. Nonparametric one-way analysis of variance, Kruskal Wallis and Wilcoxon tests were used for group comparisons. Multiple logistic regression analysis with calculation of adjusted odds ratio (OR) and 95% confidence interval (95% CI) were performed to determine associations.In patients with nonspecific skeletal pain the mean 25-(OH)D was significantly lower (P = 0.0001) and the proportion of 25-(OH)D deficiency was significantly higher (63.4%vs. 36.1%, P = 0.0001) compared with controls. There was a significantly positive association between 25-(OH)D deficiency and skeletal pain (OR = 2.94, 95% CI = 1.01-4.3, P = 0.0001). The strength of association varied across the groups with strongest association observed with leg pain (OR = 7.4; 95% CI = 3.9-13.9, P = 0.0001) followed by arthralgia (OR = 3.9, 95% CI = 2.1-7.1, P = 0.0001) and widespread pain (OR = 2.8, 95% CI = 1.1-6.6, P = 0.020) but no association with back pain and fibromyalgia. There was a greater positive associations in women compared with men (OR = 2.1, 95% CI = 1.1-4.3, P = 0.001).The results of this study indicate a positive association of vitamin D deficiency with a variety of nonspecific bone pain, particularly in women. More studies with larger samples are required to confirm these findings. Increasing serum vitamin D to sufficient levels and longitudinal follow-up of patients may provide further evidence in relation to vitamin D deficiency and skeletal pain.

Refactoring, the process of improving the design of existing code by changing its internal structure without affecting its external behavior, tends to improve software quality by improving design, improving readability, and reducing bugs. There are many different refactoring methods, each having a particular purpose and effect. Consequently, the effect of refactorings on software quality attribute may vary. Software testing is an external software quality attributes that takes lots of time and effort to make sure that the software performs as intended. In this paper, we propose a classification of refactoring methods based on their measurable effect on software testing effort. This, in turn, helps the software developers decide which refactoring methods to apply in order to optimize a software system with regard to the testing effort.

Objective: To determine the causes and the intraocular pressure (IOP) outcome of neo-vascular glaucoma at the Guinness Eye Centre of the Lagos University Teaching Hospital, Lagos, Nigeria. Materials and Methods: The case records of all patients with the diagnosis of neo-vascular glaucoma who presented at Guinness Eye Centre from January 1st, 2008 to December 31st, 2012 were reviewed. Information extracted from their records included the bio-data, eye affected, best corrected visual acuity, intra-ocular pressure, etiology of neo-vascular glaucoma, ocular co-morbidity, systemic co-morbidity and forms of treatment received. Cases of retinoblastoma were excluded from the study. Data were analyzed using the SPSS V.17.1Results: A total of 31 cases of neovsacular glaucoma were reviewed. The male to female ratio was 1.8:1 and the age range was from 26 to 83 years. Patients aged 51 years or older were 22 (70.4%). The presenting best corrected visual acuity in the affected eye or in the worse affected eye was counting fi ngers in 30 (96%) patients. The presenting IOP was 30 mmHg or higher in 26 (83.2%). Both eyes were affected in three (9.6%) patients. Patients with diabetes mellitus, hypertension or both constituted 57.8% of the total number of patients. Primary open angle glaucoma and retina vein occlusion were the etiologies of neo-vascular glaucoma in 14 (45.2%) patients. A total of 15 (48.4) patients failed to turn up for repeat appointments. Only 5 (16.0%) patients (who received ocular medications, intravitreal bevacizumab and pan-retina photocoagulation) showed a significant IOP reduction.Conclusion: Late presentation, loss to follow up and lack of full and complete treatment regime were important findings in this study. A significant IOP reduction occurred with only in the patients who had a combination therapy of IOP lowering medications, intravitreal bevacizumab and pan-retina photocoagulation. A routine eye examination for patients at risk is essential for early detection and treatment in order to minimize visual loss in neo-vascular glaucoma.Keywords: Combination therapy, early detection, late presentation, neo-vascular glaucoma

Receiver operating characteristic (ROC) analysis, which yields indices of accuracy such as the area under the curve (AUC), is increasingly being used to evaluate the performances of diagnostic tests that produce results on continuous scales. Both par ametric and nonparametric ROC approaches are available to assess the discriminant capacity of such tests, but there are no clear guidelines as to the merits of each, particularly with non-binormal data. Investigators may worry that when data are non- Gaussian, estimates of diagnostic accuracy based on a binormal model may be dis torted. The authors conducted a Monte Carlo simulation study to compare the bias and sampling variability in the estimates of the AUCs derived from parametric and nonparametric procedures. Each approach was assessed in data sets generated from various configurations of pairs of overlapping distributions; these included the binormal model and non-binormal pairs of distributions where one or both pair members were mixtures of Gaussian (MG) distributions with different degrees of departures from bi- normality. The biases in the estimates of the AUCs were found to be very small for both parametric and nonparametric procedures. The two approaches yielded very close estimates of the AUCs and of the corresponding sampling variability even when data were generated from non-binormal models. Thus, for a wide range of distributions, concern about bias or imprecision of the estimates of the AUC should not be a major factor in choosing between the nonparametric and parametric approaches. Key words: ROC analysis; quantitative diagnostic test; comparison, parametric; binormal model; LABROC; nonparametric procedure; area under the curve (AUC). (Med Decis Making 1997;17:94-102)

Rationale and Objectives. Several methods have been proposed for calculating the variances and covariances of nonparametric estimates of the area under receiver operating characteristic curves (AUC). The authors provide an explanation of the relationships between them and illustrate the factors that determine sampling variability. Methods. The authors investigated the algebraic links between two methods, that of “placements” and that of “pseudovalues” based on jackknifing. They also performed a numerical investigation of the comparative performance of the two methods. Results. The “placement” method has a simple structure that illustrates the determinants of the sampling variability and does not require specialized software. The authors show that the pseudovalues used in the jackknife method are directly linked to the placement values. Conclusion. Because of the close link, borne out in a numeric investigation of the sampling variation, and because of the ease of computation, the choice between the two methods can be based on users' preferences. For indexes other than the AUC, however, the use of pseudovalues holds greater promise.

Anthropometric measurements were performed on 51 normal, supine, barium enema examinations to determine the position of the lower pole of the caecum and the base of the appendix relative to palpable bony landmarks (the anterior superior iliac spine and the symphysis pubis). Four quadrants were defined (iliac, umbilical, inguinal and pelvic) by the intersection of the right lateral line and the interspinous line (the line joining the left and right anterior superior iliac spines). The position of the lower pole of the caecum was iliac in 12%, inguinal in 37%, and pelvic in 51%. The appendix or appendix stump was visualised on 53% of the barium examinations. The position of the appendix was iliac in 15%, umbilical in 15%, inguinal in 11%, and pelvic in 59%. The positions of the lower pole of the caecum and base of the appendix are lower and more medial than previously described. 70% of appendices were found to lie inferior to the interspinous line, contrary to established surgical teaching, which assumes McBurney's point to be the surface landmark for the appendix.

We describe an effective device-based isolation approach for achieving data security. We show its use in protecting the secrecy of highly sensitive data that is crucial to security operations, such as cryptographic keys used for decrypting ciphertext or signing digital signatures. Private key is usually encrypted in its storage when not used; however, when being used, the plaintext key is loaded into the memory of the host for access. We present a novel and practical solution and its prototype called DataGuard to protect the secrecy of the highly sensitive data through the storage isolation and secure tunneling enabled by a mobile handheld device. DataGuard can be deployed for the key protection of individuals or organizations. We implement three prototypes and conduct extensive experiments to evaluate the feasibility and performance of DataGuard. The results show that our approach performs well without significant overhead.