Miracle-dz/UniFAR-FTUs huggingface dataset:
Discovery of Triggering Relations and Its Applications in Network Security and Android Malware Detection

An increasing variety of malware, including spyware, worms, and bots, threatens data confidentiality and system integrity on computing devices ranging from backend servers to mobile devices. To address these threats, exacerbated by dynamic network traffic patterns and growing volumes, network security has been undergoing major changes to improve accuracy and scalability in the security analysis techniques. This dissertation addresses the problem of detecting the network anomalies on a single device by inferring the traffic dependence to ensure the root-triggers. In particular, we propose a dependence model for illustrating the network traffic causality. This model depicts the triggering relation of network requests, and thus can be used to reason about the occurrences of network events and pinpoint stealthy malware activities. The triggering relationships can be inferred by means of both rule-based and learning-based approaches. The rule-based approach originates from several heuristic algorithms based on the domain knowledge. The learning-based approach discovers the triggering relationship using a pairwise comparison operation that converts the requests into event pairs with comparable attributes. Machine learning classifiers predict the triggering relationship and further reason about the legitimacy of requests by enforcing their root-triggers. We apply our dependence model on the network traffic from a single host and a mobile device. Evaluated with real-world malware samples and synthetic attacks, our findings confirm that the traffic dependence model provides a significant source of semantic and contextual information that detects zero-day malicious applications. This dissertation also studies the usability of visualizing the traffic causality for domain experts. We design and develop a tool with a visual locality property. It supports different levels of visual based querying and reasoning required for the sensemaking process on complex network data. The significance of this dissertation research is in that it provides deep insights on the dependency of network requests, and leverages structural and semantic information, allowing us to reason about network behaviors and detect stealthy anomalies.
Depletion layer dynamics of polyelectrolyte solutions under Poiseuille flow

Significance The interfacial physics of complex liquids under flow remains a long-standing problem in fluid mechanics that is important for fields ranging from lubrication to nanofluidics. Liquids containing small amounts of high-molecular weight polymers are found to flow through channels faster than expected, a phenomenon attributed to the formation of boundary depletion layers that relaxes the no-slip boundary condition and allows the bulk of the fluid to slip past the walls. This work provides the most direct measurement to date of the dimension and composition of depletion layers of a polymer solution under flow. We anticipate extending this approach to help understand fluid dynamics in different regimes, such as flow in nanoconfinement and turbulence. Complex liquids flow through channels faster than expected, an effect attributed to the formation of low-viscosity depletion layers at the boundaries. Characterization of depletion layer length scale, concentration, and dynamics has remained elusive due in large part to the lack of suitable real-space experimental techniques. The short length scales associated with depletion layers have traditionally prohibited direct imaging. By overcoming this limitation via adaptations of stimulated emission depletion (STED) microscopy, we directly measure the concentration profile of polymer solutions at a nonadsorbing wall under Poiseuille flow. Using this approach, we 1) confirm the theoretically predicted concentration profile governed by entropically driven depletion, 2) observe depletion layer narrowing at low to intermediate shear rates, and 3) report depletion layer composition that approaches pure solvent at unexpectedly low shear rates.
Spatiotemporal variability in activity patterns of urban street cattle as function of environmental factors

ABSTRACT In the current study, we studied behavioral patterns, such as foraging, activity, standing, and laying in a population of cattle wandering on streets of Raipur city. We also determined the effects of four environmental factors (temperature, humidity, light, and noise intensities) on these behaviors. We recorded all four behavioral variables at four time points each day over a period of three consecutive days at 10 randomly selected locations using on-site human observation method, digital video, and still camera. We used Cosinor rhythmometry to analyze the time series data on all four behavioral variables. One-way ANOVA was performed to determine the impact of the factor “time of the day” on all observed behavioral variables. Pearson correlation was also employed to evaluate the relationship between the environmental and behavioral variables. Cosinor analyses revealed statistically significant 24-h rhythms in all observed behavioral variables, at the group level, with peaks of foraging, activity, standing, and laying located at 11.54 h, 14.54 h, 18.66 h, and 23.88 h, respectively. Factor “time of the day” had statistically significant effect on all observed behavioral variables. Foraging and activity were found to be positively correlated with temperature, light, and noise intensities; and negatively correlated with humidity. The laying behavior was found to be negatively associated with temperature, light, and noise intensities; and positively correlated with humidity. The findings of this study might be helpful in the management of cattle menace that has become an annoying phenomenon on the streets and highways of almost all Indian cities.
Metamorphosis into mother after 35 years of age: a study of Grounded Theory.

OBJECTIVE To understand the transition process of women through maternity status after 35 years of age. METHOD The methodology of constructivist Grounded Theory was used in the design of research, data collection and analysis. RESULTS The results allowed the construction of a comprehensive model of the transition to motherhood experience after the age of 35, called "Metamorphosis in Mother". This process integrates several categories, being addressed in this article only two of them: "Confronting the dark side" and "Caring for herself". CONCLUSION From the results, the difficulties experienced by the mothers around the 2nd month are highlighted, which revealed an obscure side of motherhood unknown to mothers and the need to take care of themselves to overcome these difficulties.
Transactivation Function-1-Mediated Partial Agonist Activity of Selective Estrogen Receptor Modulator Requires Homo-Dimerization of the Estrogen Receptor α Ligand Binding Domain

The isolation of estrogen receptor alpha (ERα) cDNA was successful around 30 years ago. The characteristics of ERα protein have been examined from various aspects, primarily through in vitro cell culture studies, but more recently using in vivo experimental models. There remains, however, some uncharacterized ERα functionalities. In particular, the mechanism of partial agonist activity of selective estrogen receptor modulators (SERMs) that involves control of the N-terminal transcription function of ERα, termed AF-1, is still an unsolved ERα functionality. We review the possible mechanism of SERM-dependent regulation of ERα AF-1-mediated transcriptional activity, which includes the role of helix 12 of ERα ligand binding domain (LBD) for SERM-dependent AF-1 regulation. In addition, we describe a specific portion of the LBD that associates with blocking AF-1 activity with an additional role of the F-domain in mediating SERM activity.
Integration of Pediatric Palliative Care Into Cardiac Intensive Care: A Champion-Based Model

We propose a novel conceptual model for PPC integration into the traditionally cure-oriented CICU using interdisciplinary PPCCs. Integration of pediatric palliative care (PPC) into management of children with serious illness and their families is endorsed as the standard of care. Despite this, timely referral to and integration of PPC into the traditionally cure-oriented cardiac ICU (CICU) remains variable. Despite dramatic declines in mortality in pediatric cardiac disease, key challenges confront the CICU community. Given increasing comorbidities, technological dependence, lengthy recurrent hospitalizations, and interventions risking significant morbidity, many patients in the CICU would benefit from PPC involvement across the illness trajectory. Current PPC delivery models have inherent disadvantages, insufficiently address the unique aspects of the CICU setting, place significant burden on subspecialty PPC teams, and fail to use CICU clinician skill sets. We therefore propose a novel conceptual framework for PPC-CICU integration based on literature review and expert interdisciplinary, multi-institutional consensus-building. This model uses interdisciplinary CICU-based champions who receive additional PPC training through courses and subspecialty rotations. PPC champions strengthen CICU PPC provision by (1) leading PPC-specific educational training of CICU staff; (2) liaising between CICU and PPC, improving use of support staff and encouraging earlier subspecialty PPC involvement in complex patients’ management; and (3) developing and implementing quality improvement initiatives and CICU-specific PPC protocols. Our PPC-CICU integration model is designed for adaptability within institutional, cultural, financial, and logistic constraints, with potential applications in other pediatric settings, including ICUs. Although the PPC champion framework offers several unique advantages, barriers to implementation are anticipated and additional research is needed to investigate the model’s feasibility, acceptability, and efficacy.
A Novel Human Respiration Pattern Recognition Using Signals of Ultra-Wideband Radar Sensor

Recently, various studies have been conducted on the quality of sleep in medical and health care fields. Sleep analysis in these areas is typically performed through polysomnography. However, since polysomnography involves attaching sensor devices to the body, accurate sleep measurements may be difficult due to the inconvenience and sensitivity of physical contact. In recent years, research has been focused on using sensors such as Ultra-wideband Radar, which can acquire bio-signals even in a non-contact environment, to solve these problems. In this paper, we have acquired respiratory signal data using Ultra-wideband Radar and proposed 1D CNN (1-Dimension Convolutional Neural Network) model that can classify and recognize five respiration patterns (Eupnea, Bradypnea, Tachypnea, Apnea, and Motion) from the signal data. Also, in the proposed model, we find the optimum parameter range through the recognition rate experiment on the combination of parameters (layer depth, size of kernel, and number of kernels). The average recognition rate of five breathing patterns experimented by applying the proposed method was 93.9%, which is about 3%~13% higher than that of conventional methods (LDA, SVM, and MLP).
A BCI based visual-haptic neurofeedback training improves cortical activations and classification performance during motor imagery

Objective. We proposed a brain–computer interface (BCI) based visual-haptic neurofeedback training (NFT) by incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. The goal of this work was to improve sensorimotor cortical activations and classification performance during motor imagery (MI). In addition, their correlations and brain network patterns were also investigated respectively. Approach. 64-channel electroencephalographic (EEG) data were recorded in nineteen healthy subjects during MI before and after NFT. During NFT sessions, the synchronous visual-haptic feedbacks were driven by real-time lateralized relative event-related desynchronization (lrERD). Main results. By comparison between previous and posterior control sessions, the cortical activations measured by multi-band (i.e. alpha_1: 8–10 Hz, alpha_2: 11–13 Hz, beta_1: 15–20 Hz and beta_2: 22–28 Hz) absolute ERD powers and lrERD patterns were significantly enhanced after the NFT. The classification performance was also significantly improved, achieving a ~9% improvement and reaching ~85% in mean classification accuracy from a relatively poor performance. Additionally, there were significant correlations between lrERD patterns and classification accuracies. The partial directed coherence based functional connectivity (FC) networks covering the sensorimotor area also showed an increase after the NFT. Significance. These findings validate the feasibility of our proposed NFT to improve sensorimotor cortical activations and BCI performance during motor imagery. And it is promising to optimize conventional NFT manner and evaluate the effectiveness of motor training.
Anthropogenic noise affects male house wren response to but not detection of territorial intruders

Anthropogenic noise decreases signal active space, or the area over which male bird song can be detected in the environment. For territorial males, noise may make it more difficult to detect and assess territorial challenges, which in turn may increase defense costs and influence whether males maintain territory ownership. We tested the hypothesis that noise affects the ability of male house wrens (Troglodytes aedon) near active nests to detect intruders and alters responses to them. We broadcast pre-recorded male song and pink noise on territories to simulate intrusions with and without noise, as well as to noise alone. We measured detection by how long males took to sing or approach the speaker after the start of a playback. To measure whether playbacks changed male behavior, we compared their vocal responses before and during treatments, as well as compared mean vocal responses and the number of flyovers and attacks on the speaker during treatments. Noise did not affect a male’s ability to detect an intruder on his territory. Males altered their responses to simulated intruders with and without noise compared to the noise-only treatment by singing longer songs at faster rates. Males increased peak frequency of songs during intrusions without noise compared to noise-only treatments, but frequency during intruder plus noise treatments did not differ from either. When confronting simulated intruders in noise, males increased the number of attacks on the speaker compared to intruders without noise, possibly because they were less able to assess intruders via songs and relied on close encounters for information. Although noise did not affect intruder detection, noise affected some aspects of singing and aggressive responses, which may be related to the challenge of discriminating and assessing territorial threats under elevated noise.
Sleep in Parental Caregivers and Children With Type 1 Diabetes

The purpose of this cross-sectional, descriptive, pilot study was to examine the correlations in sleep between caregivers (≥18 years) and young (6–12 years) children with type 1 diabetes. Sleep was measured in both parent and child over 7 days using actigraphy and a sleep diary. Parents completed questionnaires on sleep, stress, depressive symptoms, and demographics. Children completed pediatric anxiety and fatigue questionnaires, and A1C (Hemoglobin A1c) was documented at clinic. Descriptive statistics and Pearson correlations were used to analyze data. Parents (N = 18, mean age: 39.3 ± 5.4 years, 100% Caucasian, 83% mothers) and children (N = 18, mean age: 9.6 ± 2.4 years, diagnosed for mean 3.0 ± 2.4 years, 66% female, mean A1C: 7.5 ± 0.8%) were recruited. Strong to moderate correlations were found for several measures including sleep measures based on actigraphy: mean sleep duration (hours; 7.6 ± 0.7 for parents and 8.8 ± 0.8 for children; r = .638, p = .004), mean sleep efficiency (r = .823, p < .001), and mean daily wake after sleep onset (minutes; r = .530, p = .024).
Follicular tinea faciei incognito: the perfect simulator*

REFERENCES 1. Haddad F, Helm TM. Wells syndrome. Cutis. 2014;93:38-9. 2. Silva CMR, Ottoni FA, Andrade-Filho JS, Pena GPM, Gontijo JRV. Do you know this syndrome? An Bras Dermatol. 2007;82:575-8. 3. Weins AB, Biedermann T, Weiss T, Weiss JM. Wells syndrome. J Dtsch Dermatol Ges. 2016;14:989-93. 4. Rajpara A, Liolios A, Fraga G, Blackmon J. Recurrent paraneoplastic wells syndrome in a patient with metastatic renal cell cancer. Dermatol Online J. 2014;20. pii: 13030/qt35w8r1g3. 5. Avelleira JCR, Viana FR, Boechat AM, Fleury RN. Well’s syndrome or eosinophilic cellulitis. An Bras Dermatol. 1999:74: 53-7.
Should we consider subcellular compartmentalization of metabolites, and if so, how do we measure them?

PURPOSE OF REVIEW To examine the consequences of metabolism compartmentalized at the subcellular level, provide prototypical examples of compartmentalized metabolism, and describe methods to examine compartmentalized metabolism. RECENT FINDINGS Progress in metabolomics and isotope tracing has underscored the importance of subcellular compartments of metabolism. The discovery of biological effects of metabolites as bioenergetic intermediates, anabolic building blocks, signaling mediators, and effectors in posttranslation modifications of proteins and nucleic acids have highlighted the role of compartmentalization in determining metabolic fate. Recent advances in both direct and indirect methods to quantify compartmentalized metabolism have improved upon historical approaches. Genetically encoded metabolite sensors, chemical probes, immunoaffinity purification, and compartment-resolved metabolic modeling have all been recently applied to study compartmentalization. SUMMARY Accurate measurement of metabolites in distinct subcellular compartments is important for understanding and pharmacologically targeting metabolic pathways in diverse disease contexts, including cancer, diabetes, heart failure, obesity, and regulation of the immune system. Direct and indirect approaches to quantify compartmentalized metabolism are advancing rapidly. Yet, major challenges remain in the generalizability, rigor, and interpretation of data from the available methods to quantify compartmentalized metabolism.
A new cardenolide glycoside from the roots of Streptocaulon juventas (lour.) merr. (Asclepiadaceae)

Abstract From remaining aqueous fraction of the roots of Streptocaulon juventas, one new cardiac glycoside named periplogenin 3-O-β-gentiobioside (1) together with six known ones (2–7) were isolated. Their relative structures were elucidated based on NMR spectroscopic analysis. Compound 1 showed moderate cytotoxicity against non-small cell lung carcinoma NCI-H460 and ovarian cancer HeLa cells. Moreover, compounds 2 and 3 exhibited remarkable cytotoxicity against NCI-H460 cell with the IC50 values of 0.34 and 0.068 μM, respectively.
Addressing medical errors: an intervention protocol for nursing professionals.

OBJECTIVE To identify the types of interventions that should be included in an organizational protocol for responding to serious adverse events involving nursing staff. METHOD A descriptive exploratory study was conducted in the Autonomous Community of Madrid, Spain using a questionnaire. RESULTS 248 nurses have participated. The respondents prioritized the following interventions for inclusion in the protocol: legal advice (86.5% of participants) and counseling (82.4% of participants). Over two-thirds of the nurses (69.3%) showed that they would like to receive guidance on how to record adverse events on the patient's medical records, while 64.8% showed that they would like to receive advice on assurances and legal safeguards in relation to the health organization's medical error notification system and 54.5% endorsed refresher training. Compulsory temporary or permanent transfer of nursing staff involved in adverse events was one of the least popular interventions (3.3% of participants). CONCLUSION The nurses prioritized counseling, legal advice, training in communication techniques, and refresher training to address the consequences of adverse events and discarded the possibility of compulsory temporary or permanent transfer.
A Spoken Dialogue System for Enabling Information Behavior of Various Intention Levels

This paper describes a spoken dialogue system for accommodating a user’s information behaviors with various levels of information need. Our system, given a set of same-topic news articles, compiles a utterance plan that consists of a primary plan for delivering main news content, and the associated subsidiary plans for supplementing the main content. A primary plan is generated by applying text summarization and style conversion techniques. The subsidiary plans are compiled by considering potential user/system interactions. To make this mechanism work, we first classified user’s possible passive/active behaviors, and then designed the corresponding system actions. We empirically confirmed that our system was able to deliver the news content smoothly while dynamically adapting to the change of user’s intention levels. The smoothness of a conversation can be attributed to the pre-compiled utterance plan.
Combining patient visual timelines with deep learning to predict mortality

Background Deep learning algorithms have achieved human-equivalent performance in image recognition. However, the majority of clinical data within electronic health records is inherently in a non-image format. Therefore, creating visual representations of clinical data could facilitate using cutting-edge deep learning models for predicting outcomes such as in-hospital mortality, while enabling clinician interpretability. The objective of this study was to develop a framework that first transforms longitudinal patient data into visual timelines and then utilizes deep learning to predict in-hospital mortality. Methods and findings All adult consecutive patient admissions from 2008–2016 at a tertiary care center were included in this retrospective study. Two-dimensional visual representations for each patient were created with clinical variables on one dimension and time on the other. Predictors included vital signs, laboratory results, medications, interventions, nurse examinations, and diagnostic tests collected over the first 48 hours of the hospital stay. These visual timelines were utilized by a convolutional neural network with a recurrent layer model to predict in-hospital mortality. Seventy percent of the cohort was used for model derivation and 30% for independent validation. Of 115,825 hospital admissions, 2,926 (2.5%) suffered in-hospital mortality. Our model predicted in-hospital mortality significantly better than the Modified Early Warning Score (area under the receiver operating characteristic curve [AUC]: 0.91 vs. 0.76, P < 0.001) and the Sequential Organ Failure Assessment score (AUC: 0.91 vs. 0.57, P < 0.001) in the independent validation set. Class-activation heatmaps were utilized to highlight areas of the picture that were most important for making the prediction, thereby providing clinicians with insight into each individual patient’s prediction. Conclusions We converted longitudinal patient data into visual timelines and applied a deep neural network for predicting in-hospital mortality more accurately than current standard clinical models, while allowing for interpretation. Our framework holds promise for predicting several important outcomes in clinical medicine.