Use of single-chain antibody derivatives for targeted drug delivery

Single-chain antibodies (scFvs), which contain only the variable domains of full-length antibodies, are relatively small molecules that can be used for selective drug delivery. In this review, we discuss how scFvs help improve the specificity and efficiency of drugs. Small interfering RNA (siRNA) delivery using scFv-drug fusion peptides, siRNA delivery using scFv-conjugated nanoparticles, targeted delivery using scFv-viral peptide-fusion proteins, use of scFv in fusion with cell-penetrating peptides for effective targeted drug delivery, scFv-mediated targeted delivery of inorganic nanoparticles, scFv-mediated increase of tumor killing activity of granulocytes, use of scFv for tumor imaging, site-directed conjugation of scFv molecules to drug carrier systems, use of scFv to relieve pain and use of scFv for increasing drug loading efficiency are among the topics that are discussed here. © 2016, University of Michigan. All rights reserved

Know abnormal, find evil : frequent pattern mining for ransomware threat hunting and intelligence

Emergence of crypto-ransomware has significantly changed the cyber threat landscape. A crypto ransomware removes data custodian access by encrypting valuable data on victims’ computers and requests a ransom payment to reinstantiate custodian access by decrypting data. Timely detection of ransomware very much depends on how quickly and accurately system logs can be mined to hunt abnormalities and stop the evil. In this paper we first setup an environment to collect activity logs of 517 Locky ransomware samples, 535 Cerber ransomware samples and 572 samples of TeslaCrypt ransomware. We utilize Sequential Pattern Mining to find Maximal Frequent Patterns (MFP) of activities within different ransomware families as candidate features for classification using J48, Random Forest, Bagging and MLP algorithms. We could achieve 99% accuracy in detecting ransomware instances from goodware samples and 96.5% accuracy in detecting family of a given ransomware sample. Our results indicate usefulness and practicality of applying pattern mining techniques in detection of good features for ransomware hunting. Moreover, we showed existence of distinctive frequent patterns within different ransomware families which can be used for identification of a ransomware sample family for building intelligence about threat actors and threat profile of a given target

THE EVALUATION OF GRAIN AND OIL PRODUCTION, SOME PHYSIOLOGICAL AND MORPHOLOGICAL TRAITS OF AMARANTH ‘CV. KONIZ’ AS INFLUENCED BY THE SALT STRESS IN HYDROPONIC CONDITIONS

The purpose of this study was investigation of salinity effect on some traits of Amaranth. A split plot designed with three replications with two factors: 5 salinity levels (control, 75, 150, 225, 300 mM NaCl) and applied time at 4 levels (plant establishment, branching, flowering, grain filling) in a greenhouse under hydroponic system. Application of 300 mM salinity after plant establishment led to death of amaranth. Salinity application after establishment decreased significantly plant height and number of branches as 44.9 and 31.8, respectively. Production of grain weight was not affected by 75 mM salinity, but at higher salinity showed significantly decrease. The highest decrease in grain weight obtained by applying 225 mM salt after the plant establishment and salinity at 300 mM after branching as 86.6 and 71.3 percent respectively, resulting in a decrease in both 1000 kernel weight and grain number, respectively. Salinity application increased H2O2, MDA and total phenolics contents, severely. Most of characteristics hadnot affect by 75 mM NaCl, but other concentrations had a negative effect on the growth and production of Amaranth and increasing salinity had more negative impact. In this study, the most sensitive to salinity was after plant establishment and grain filling stage was the most tolerant

Deep dive into ransomware threat hunting and intelligence at fog layer

Ransomware, a malware designed to encrypt data for ransom payments, is a potential threat to fog layer nodes as such nodes typically contain considerably amount of sensitive data. The capability to efficiently hunt abnormalities relating to ransomware activities is crucial in the timely detection of ransomware. In this paper, we present our Deep Ransomware Threat Hunting and Intelligence System (DRTHIS) to distinguish ransomware from goodware and identify their families. Specifically, DRTHIS utilizes Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), two deep learning techniques, for classification using the softmax algorithm. We then use 220 Locky, 220 Cerber and 220 TeslaCrypt ransomware samples, and 219 goodware samples, to train DRTHIS. In our evaluations, DRTHIS achieves an F-measure of 99.6% with a true positive rate of 97.2% in the classification of ransomware instances. Additionally, we demonstrate that DRTHIS is capable of detecting previously unseen ransomware samples from new ransomware families in a timely and accurate manner using ransomware from the CryptoWall, TorrentLocker and Sage families. The findings show that 99% of CryptoWall samples, 75% of TorrentLocker samples and 92% of Sage samples are correctly classified

Towards Autonomous Robotic Valve Turning

In this paper an autonomous intervention robotic task to learn the skill of grasping and turning a valve is described. To resolve this challenge a set of different techniques are proposed, each one realizing a specific task and sending information to the others in a Hardware-In-Loop (HIL) simulation. To improve the estimation of the valve position, an Extended Kalman Filter is designed. Also to learn the trajectory to follow with the robotic arm, Imitation Learning approach is used. In addition, to perform safely the task a fuzzy system is developed which generates appropriate decisions. Although the achievement of this task will be used in an Autonomous Underwater Vehicle, for the first step this idea has been tested in a laboratory environment with an available robot and a sensor

Association between self-efficacy and quality of life in women with breast cancer undergoing chemotherapy

Background: Self-efficacy is known as a factor which influences health behaviors, chronic diseases management and quality of life in patients with cancer. Objective: The aim of this study was to investigate the association of self-efficacy and quality of life in women with breast cancer undergoing chemotherapy. Methods: This cross sectional study was conducted in 100 women with breast cancer referred to Seyed Al-Shohada Hospital, Isfahan in 2015. The study subjects were selected by simple random sampling method. The measurement tools were the Sherer self-efficacy scale and the World Health Organization WHOQOL-BREF quality of life assessment. Data were analyzed using one-way ANOVA and Pearson’s correlation coefficient. Findings: Mean age was 48.25±11.93 years. The mean self-efficacy score and quality of life score were 55.78± 11 and 75.91±15.28, respectively and both of them were average. There was positive significant correlation between self-efficacy and quality of life. There was also significant association between self-efficacy and quality of life domains including physical health, mental health, social relationships and environment. Conclusion: With regards to the results, it seems that activities such as workshops for patients, presence of a psychologist in department of chemotherapy, and providing health facilities can be effective for increasing self-efficacy and quality of life in patients with cance

Extracting Predictor Variables to Construct Breast Cancer Survivability Model with Class Imbalance Problem

Application of data mining methods as a decision support system has a great benefit to predict survival of new patients. It also has a great potential for health researchers to investigate the relationship between risk factors and cancer survival. But due to the imbalanced nature of datasets associated with breast cancer survival, the accuracy of survival prognosis models is a challenging issue for researchers. This study aims to develop a predictive model for 5-year survivability of breast cancer patients and discover relationships between certain predictive variables and survival. The dataset was obtained from SEER database. First, the effectiveness of two synthetic oversampling methods Borderline SMOTE and Density based Synthetic Oversampling method (DSO) is investigated to solve the class imbalance problem. Then a combination of particle swarm optimization (PSO) and Correlation-based feature selection (CFS) is used to identify most important predictive variables. Finally, in order to build a predictive model three classifiers decision tree (C4.5), Bayesian Network, and Logistic Regression are applied to the cleaned dataset. Some assessment metrics such as accuracy, sensitivity, specificity, and G-mean are used to evaluate the performance of the proposed hybrid approach. Also, the area under ROC curve (AUC) is used to evaluate performance of feature selection method. Results show that among all combinations, DSO + PSO_CFS + C4.5 presents the best efficiency in criteria of accuracy, sensitivity, G-mean and AUC with values of 94.33%, 0.930, 0.939 and 0.939, respectively

Systematic Review and Meta-analysis of Genetically Informed Research: Associations between Parent Anxiety and Offspring Internalizing Problems

OBJECTIVE: Parent anxiety is associated with offspring internalizing problems (emotional problems related to anxiety and depression). This may reflect causal processes, whereby exposure to parent anxiety directly influences offspring internalizing (and/or vice versa). However, parent-offspring associations could also be attributable to their genetic relatedness. We present a systematic review and meta-analysis to investigate whether exposure to parent anxiety is associated with offspring internalizing after controlling for genetic relatedness. METHOD: A literature search in five databases identified 429 records. Publications were retained if they used a quasi-experimental design in a general population sample to control for participant relatedness in associations between parent anxiety and offspring internalizing outcomes. Publications were excluded if they involved an experimental exposure or intervention. Studies of pre- and post-natal anxiety exposure were meta-analysed separately. Pearson's correlation coefficient estimates (r) were pooled using multilevel random effects models. RESULTS: Eight publications were retained. Data were drawn from four population cohorts, each unique to a quasi-experimental design: adoption, sibling-comparison, children-of-twins or in-vitro-fertilisation. Cohorts were located in northern Europe or America. Families were predominantly of European ancestry. Three publications (Nfamilies>11,700; offspring aged 0.5-10 years) showed no association between prenatal anxiety exposure and offspring internalizing outcomes after accounting for participant relatedness (r=.04, CI -.07,.14). Six publications (Nfamilies>12,700; offspring aged 0.75-22 years) showed a small but significant association between concurrent symptoms in parents and offspring, after accounting for participant relatedness (r=.13, CI .04,.21). CONCLUSION: Initial literature, derived from homogenous populations, suggests that prenatal anxiety exposure does not cause offspring internalizing outcomes. However, postnatal anxiety exposure may be causally associated with concurrent offspring internalizing, via non-genetic pathways. Longitudinal stability, child-to-parent effects, and the role of moderators and methodological biases require attention