Miracle-dz/UniFAR-FTUs huggingface dataset:
Passive Compliance Control of Aerial Manipulators

This paper presents a passive compliance control for aerial manipulators to achieve stable environmental interactions. The main challenge is the absence of actuation along body-planar directions of the aerial vehicle which might be required during the interaction to preserve passivity. The controller proposed in this paper guarantees passivity of the manipulator through a proper choice of end-effector coordinates, and that of vehicle fuselage is guaranteed by exploiting time domain passivity technique. Simulation studies validate the proposed approach.
K-means++ vs. Behavioral Biometrics: One Loop to Rule Them All

Behavioral biometrics, a field that studies patterns in an individual’s unique behavior, has been researched actively as a means of authentication for decades. Recently, it has even been adopted in many real world scenarios. In this paper, we study keystroke dynamics, the most researched of such behavioral biometrics, from the perspective of an adversary. We designed two adversarial agents with a standard accuracy convenience tradeoff: Targeted K-means++, which is an expensive, but extremely effective adversarial agent, and Indiscriminate K-means++, which is slightly less powerful, but adds no overhead cost to the attacker. With Targeted K-means++ we could compromise the security of 40-70% of users within ten tries. In contrast, with Indiscriminate K-means++, the security of 30-50% of users was compromised. Therefore, we conclude that while keystroke dynamics has potential, it is not ready for security critical applications yet. Future keystroke dynamics research should use such adversaries to benchmark the performance of the detection algorithms, and design better algorithms to foil these. Finally, we show that the K-means++ adversarial agent generalizes well to even other types of behavioral biometrics data by applying it on a dataset of touchscreen swipes.
Effects of cognitive-behavioural therapy on anxiety, depression and condom use in people with HIV in Mexico City: a pilot study

ABSTRACT Anxiety and depression in people living with HIV (PLWH) are negatively associated with healthy sexual behaviours. We pilot-tested a Cognitive-Behavioural Therapy (CBT)-based intervention to reduce anxiety and depression, aimed to increase serostatus disclosure to sexual partners, quality of sexual life (QoSL) and condom use. The study had a single-case experimental design (AB) with follow-up measures. Eleven PLWH with moderate/severe anxiety/depression received six-module CBT intervention delivered in ten one-hour individual weekly sessions. Anxiety, depression, consistent/correct condom use and QoSL were measured. Depression and anxiety decreased after the intervention (depression baseline [BL] Mdn = 21, final [F] Mdn = 3, z = -2.934, p = .003; anxiety BL Mdn = 30, F Mdn = 4, z = -2.941, p = .003). QoSL improved (BL Mdn = 28, F Mdn = 13, z = -2.625, p = .009), along with participants’ ability to use condoms (57.14 vs.100, z = -2.937, p = .003). Effect size was large, changes were maintained at follow-up measurements. The CBT intervention had positive effects in reducing anxiety and depression, which could facilitate the acquisition of healthy sexual behaviours. Further studies are important to clarify the benefits of targeting emotional variables to improve wellbeing and prevention behaviours in PLWH.
Use of lung-protective strategies during one-lung ventilation surgery: a multi-institutional survey.

Background Limited evidence suggests that intraoperative lung-protective ventilation (LPV) during one-lung ventilation (OLV) may reduce respiratory complications after thoracic surgery. Little is known about LPV practices during OLV. Our purpose was to assess the state of practice/perspectives of anesthesiologists regarding LPV during elective OLV. Methods We conducted a multi-institutional cross-sectional survey of anesthesiologists performing OLV at high-volume Canadian tertiary/university centers. The survey was designed, refined and distributed by a multi-disciplinary team using the Dillman method. Univariable and multivariable analyses were used. Results Seventy-five (63%) of 120 eligible respondents completed the survey. Although the critical care literature focuses on minimizing tidal volume (TV) as the central strategy of LPV, most respondents (89%, n=50/56) focused on minimizing peak airway pressure (PAP) as their primary strategy of intraoperative LPV. Only 64% (n=37/58) reported actively trying to minimize TV. While 32% (n=17/54) were unsure about the current evidence regarding LPV, 67% (n=36/54) believed that the evidence favoured their use during OLV. Perceived clinical and institutional barriers were the only predictors of reduced attempts to minimize TV on univariate analyses. In multivariable/adjusted analyses, perceived institutional barriers were the only predictors of reduced attempts to minimize TV with adjusted odds ratio of 0.1 (95% CI: 0.03-0.6). Conclusions Most anesthesiologists defined low PAP as the primary strategy of LPV during OLV and attempted to minimize it. This study is the first to assess the practice/perspectives of anesthesiologists regarding LPV during OLV and also the first to explore predictors of LPV use. Randomized trials are currently ongoing. However, this study suggests that institutional barriers may subvert future knowledge translation and need to be addressed.
‘It’s been a long haul, a big haul, but we’ve made it’: hepatitis C virus treatment in post-transplant patients with virus recurrence: An interpretative phenomenological analysis

The lived experience of both interferon-based and new interferon-free treatments in patients with hepatitis C virus remains understudied. To explore their journey through hepatitis C virus treatment, we interviewed seven post-transplant patients with recurrent hepatitis C virus. Three themes were identified using interpretative phenomenological analysis. Participants reported an ongoing sense of ontological uncertainty characterized by lack of control over their condition and treatment. Furthermore, an apposition of scepticism and hope accompanying each stage of hepatitis C virus treatment was described. A staged approach to psychological intervention tailored to the needs of the patient and their associated ‘stage’ of hepatitis C virus treatment was recommended.
When Sexual Trauma Survivors with Severe Psychiatric Disabilities Experience Institutional Care

ABSTRACT Individuals with severe psychiatric disabilities face many challenges from their experience of mental health problems, but also from disenfranchisement, marginalization, and stigmatization from a sociocultural experience. Those who are sexual trauma survivors also have unique challenges and intersecting experiences, which are not historically well understood, acknowledged, or treated in U.S. systems of care. Both in historic and modern mental health systems there have been methods thought of as interventions that have been or are currently reported as traumatizing to those who experience them. This article highlights the specific challenges, needs, and organizational shifts that U.S. systems of care need to be aware of and embark upon in order to provide treatment that is more efficacious with and cognizant of the experiences of those who are sexual trauma survivors. Furthermore, new paradigms for providing mental health treatment are offered in the context of providing trauma-informed as well as trauma treatment to those who are sexual trauma survivors and experience severe psychiatric disabilities.
A Deep Learning Algorithm for Personalized Blood Glucose Prediction

A convolutional neural network (CNN) model is presented to forecast the future glucose levels of the patients with type 1 diabetes. The model is a modified version of a recently proposed model called WaveNet, which becomes very useful in acoustic signal processing. By transferring the task into a classification problem, the model is mainly built by casual dilated CNN layers and employs fast WaveNet algorithms. The OhioT1DM dataset is the source of the four input fields: glucose levels, insulin events, carbohydrate intake and time index. The data is fed into the network along with the targets of the glucose change in 30 minutes. Several pre-processing approaches such as interpolation, combination and filtering are used to fill up the missing data in the training sets, and they improve the performance. Finally, we obtain the predictions of the testing dataset and evaluate the results by the root mean squared error (RMSE). The mean value of the best RMSE of six patients is 21.72.
Are self‐assessment of search ability and performance reliable?

This study examined users' self‐rated search ability with their objective search performance, their subjective assessed search performance, and the accuracy of subjective assessments. Thirty students were recruited for the lab mode online search competition. Participants were asked to rate their search abilities before searching, and predict search performance after searching. The results showed that users' self‐rated search ability was not related to their objective search performance in this search competition. As for subjective evaluation of their search performance, no significant difference was found on predicted‐as‐right, predicted‐as‐unsureness rate, or the correct and incorrect prediction rates. Besides 70% of correct predictions, all searchers tended to overestimate their search performance, but self‐rated low searchers had more chance to predict their answers to the search tasks as wrong and underestimated their search performance sometimes. This study is an exploratory examination on users' self‐rated search ability, and has implications to understand how searchers evaluate their search abilities and search performance.
Exploring Data-driven Worked Examples for Block-based Programming

Empirical studies show that worked examples (WEs) are effective in improving students' learning efficiency in a variety of domains. I aim to create and evaluate data-driven intelligent WEs for novices using the Snap! block-based programming environment. First, I will design and evaluate WEs with self-explanation prompts in Snap! Then I will develop a data-driven method to generate WEs based on student solutions and compare it with manually-curated WEs.
Efficacy of adjunctive photodynamic therapy in the treatment of generalized aggressive periodontitis: A randomized controlled clinical trial

Generalized aggressive periodontitis (GAgP) is a distinct type of periodontal disease characterized by rapid loss of attachment and alveolar bone occurring in young individuals. Photodynamic therapy (PDT) was introduced in periodontology as an adjunctive approach to non‐surgical periodontal treatment (NPT) in periodontitis patients. In this trial, the aim was to evaluate the clinical and microbiological effects of adjunctive PDT to NPT in patients with GAgP.
Investigating singapore's altmetric landscape

Altmetrics is an emerging measure for academic impact and it is gaining in global importance. In this paper, we analyse the altmetric landscape of Singapore, a young nation with a fast growing international research sector. We aim to find out if the coverage of altmetrics across the different disciplines is increasing along with the fast increase in the amount of research publications in recent years. We also look into how altmetrics relate to traditional citation counts as a measure of research impact. From our results, we see that there is overall an 18% coverage of altmetrics of Singapore publications from 2009 to 2013. The number of publications with available altmetric data has also been increasing over the years for most disciplines. Correlation results between citation counts and altmetrics show medium to low correlations with distinct differences amongst the various disciplines. A high coverage of altmetrics however does not seem to lead to larger correlations with citation counts. Singapore thus remains an intriguing case study to watch in the coming years.
An Agent Based WCET Analysis for Top-View Person Re-Identification

Person re-identification is a challenging task for improving and personalising the shopping experience in an intelligent retail environment. A new Top View Person Re-Identification (TVPR) dataset of 100 persons has been collected and described in a previous work. This work estimates the Worst Case Execution Time (WCET) for the features extraction and classification steps. Such tasks should not exceed the WCET, in order to ensure the effectiveness of the proposed application. In fact, after the features extraction, the classification process is performed by selecting the first passage under the camera for training and using the others as the testing set. Furthermore, a gender classification is exploited for improving retail applications. We tested all feature sets using k-Nearest Neighbors, Support Vector Machine, Decision Tree and Random Forest classifiers. Experimental results prove the effectiveness of the proposed approach, achieving good performance in terms of Precision, Recall and F1-score.
Pharmacological induction of heat shock proteins ameliorates toxicity of mutant PKCγ in spinocerebellar ataxia type 14

Amyloid and amyloid-like protein aggregations are hallmarks of multiple, varied neurodegenerative disorders, including Alzheimer's and Parkinson's diseases. We previously reported that spinocerebellar ataxia type 14 (SCA14), a dominant-inherited neurodegenerative disease that affects cerebellar Purkinje cells, is characterized by the intracellular formation of neurotoxic amyloid-like aggregates of genetic variants of protein kinase Cγ (PKCγ). A number of protein chaperones, including heat shock protein 70 (Hsp70), promote the degradation and/or refolding of misfolded proteins and thereby prevent their aggregation. Here, we report that, in various SCA14-associated, aggregating PKCγ variants, endogenous Hsp70 is incorporated into aggregates and that expression of these PKCγ mutants up-regulates Hsp70 expression. We observed that PKCγ binds Hsp70 and that this interaction is enhanced in the SCA14-associated variants, mediated by the kinase domain that is involved in amyloid-like fibril formation as well as the C2 domain of PKCγ. Pharmacological up-regulation of Hsp70 by the Hsp90 inhibitors celastrol and herbimycin A attenuated the aggregation of mutant PKCγ in primary cultured Purkinje cells. Up-regulation of Hsp70 diminished net PKCγ aggregation by preventing aggregate formation, resulting in decreased levels of apoptotic cell death among primary cultured Purkinje cells expressing the PKCγ variant. Of note, herbimycin A also ameliorated abnormal dendritic development. Extending our in vitro observations, administration of celastrol to mice up-regulated cerebellar Hsp70. Our findings identify heat shock proteins as important endogenous regulators of pathophysiological PKCγ aggregation and point to Hsp90 inhibition as a potential therapeutic strategy in the treatment of SCA14.
Monitoring Health In Smart Homes Using Simple Sensors

We consider use of an ambient sensor network, installed in Smart Homes, to identify low level events taking place which can then be analysed to generate a resident's profile of activities of daily living (ADLs). These ADL profiles are compared to both the resident's typical profile and to known 'risky' profiles to support evidence-based interventions. Human activity recognition to identify ADLs from sensor data is a key challenge, a windowbased representation is compared on four existing datasets. We find that windowing works well, giving consistent performance. We also introduce FITsense, which is building a Smart Home environment to specifically identify increased risk of falls to allow interventions before falls occurs.
A Novel Alternative Splicing Mechanism That Enhances Human 5-HT1A Receptor RNA Stability Is Altered in Major Depression

The serotonin-1A (5-HT1A) receptor is a key regulator of serotonergic activity and is implicated in mood and emotion. However, its post-transcriptional regulation has never been studied in humans. In the present study, we show that the “intronless” human 5-HT1A gene (HTR1A) is alternatively spliced in its 3′-UTR, yielding two novel splice variants. These variants lack a ∼1.6 kb intron, which contains an microRNA-135 (miR135) target site. Unlike the human HTR1A, the mouse HTR1A lacks the splice donor/accepter sites. Thus, in the mouse HTR1A, splicing was not detected. The two spliced mRNAs are extremely stable, are resistant to miR135-induced downregulation, and have greater translational output than the unspliced variant. Moreover, alternative HTR1A RNA splicing is oppositely regulated by the splice factors PTBP1 and nSR100, which inhibit or enhance its splicing, respectively. In postmortem human brain tissue from both sexes, HTR1A mRNA splicing was prevalent and region-specific. Unspliced HTR1A was expressed more strongly in the hippocampus and midbrain versus the prefrontal cortex (PFC), and correlated with reduced levels of nSR100. Importantly, HTR1A RNA splicing and nSR100 levels were reduced in the PFC of individuals with major depression compared with controls. Our unexpected findings uncover a novel mechanism to regulate HTR1A gene expression through alternative splicing of microRNA sites. Altered levels of splice factors could contribute to changes in regional and depression-related gene expression through alternative splicing. SIGNIFICANCE STATEMENT Alternative splicing, which is prevalent in brain tissue, increases gene diversity. The serotonin-1A receptor gene (HTR1A) is a regulator of serotonin, which is implicated in mood and emotion. Here we show that human HTR1A RNA is alternately spliced. Splicing removes a microRNA site to generate ultrastable RNA and increase HTR1A expression. This splicing varies in different brain regions and is reduced in major depression. We also identify specific splice factors for HTR1A RNA, showing they are also reduced in depression. Thus, we describe a novel mechanism to regulate gene expression through splicing. Altered levels of splice factors could contribute to depression by changing gene expression.
Dynamic Cancellation: Selecting Time Warp Cancellation Strategies at Runtime

The performance of Time Warp parallel discrete event simulators can be affected by the cancellation strategy used to send anti-messages. Under aggressive cancellation, antimessage generation occurs immediately after a straggler message is detected. In contrast, lazy cancellation delays the sending of anti-messages until forward processing from a straggler message confirms that the premature computation did indeed generate an incorrect message. Previous studies have shown that neither approach is clearly superior to the other in all cases (even within the same application domain). Furthermore, no strategy exists to make a priori determination of the more favorable cancellation strategy. Most existing Time Warp systems merely provide a switch for the user to select the cancellation strategy employed. This paper explores the use of simulation time decision procedures to select cancellation strategies. The approach is termed Dynamic Cancellation and it assigns the capability for selecting cancellation strategies to the Logical Processes (LPs) in a Time Warp simulation. Thus, within a single parallel simulation both strategies may be employed by distinct LPs and even across the simulation lifetime of an LP. Empirical analysis using several control strategies show that dynamic cancellation always performs with the best static strategy and, in some cases, dynamic cancellation provides some nominal (5–10%) performance gain over the best static strategy.
Fifteen-degree clavicular hook plate achieves better clinical outcomes in the treatment of acromioclavicular joint dislocation

Objective Clavicular hook plate application is one of the most commonly used treatment methods for acromioclavicular (AC) joint dislocation, although it may cause multiple postoperative complications. We modified the regularly used 0° hook plate to 15° and compared the clinical outcomes of these two hook plates for treatment of AC joint dislocation. Methods Forty-three patients with acute AC joint dislocation were randomly enrolled (0° hook plate, 20 patients; 15° hook plate, 23 patients). The American Shoulder and Elbow Surgeons (ASES) and visual analog scale for pain (VASP) scores were evaluated preoperatively and at 3 days and 1, 2, 3, and 6 months postoperatively and compared between the two groups. Results Compared with the preoperative scores, the 6-month postoperative ASES score gradually increased but the VASP score decreased in both groups. Furthermore, the ASES and VASP scores were significantly different between the two groups at every postoperative time point. Conclusion The 15° hook plate is superior to the 0° hook plate in reducing shoulder pain and improving postoperative recovery in the treatment of AC joint dislocation. Level of evidence Level III; Treatment study (retrospective comparative study).
Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data

Background: A major problem in treating acute kidney injury (AKI) is that clinical criteria for recognition are markers of established kidney damage or impaired function; treatment before such damage manifests is desirable. Clinicians could intervene during what may be a crucial stage for preventing permanent kidney injury if patients with incipient AKI and those at high risk of developing AKI could be identified. Objective: In this study, we evaluate a machine learning algorithm for early detection and prediction of AKI. Design: We used a machine learning technique, boosted ensembles of decision trees, to train an AKI prediction tool on retrospective data taken from more than 300 000 inpatient encounters. Setting: Data were collected from inpatient wards at Stanford Medical Center and intensive care unit patients at Beth Israel Deaconess Medical Center. Patients: Patients older than the age of 18 whose hospital stays lasted between 5 and 1000 hours and who had at least one documented measurement of heart rate, respiratory rate, temperature, serum creatinine (SCr), and Glasgow Coma Scale (GCS). Measurements: We tested the algorithm’s ability to detect AKI at onset and to predict AKI 12, 24, 48, and 72 hours before onset. Methods: We tested AKI detection and prediction using the National Health Service (NHS) England AKI Algorithm as a gold standard. We additionally tested the algorithm’s ability to detect AKI as defined by the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. We compared the algorithm’s 3-fold cross-validation performance to the Sequential Organ Failure Assessment (SOFA) score for AKI identification in terms of area under the receiver operating characteristic (AUROC). Results: The algorithm demonstrated high AUROC for detecting and predicting NHS-defined AKI at all tested time points. The algorithm achieves AUROC of 0.872 (95% confidence interval [CI], 0.867-0.878) for AKI detection at time of onset. For prediction 12 hours before onset, the algorithm achieves an AUROC of 0.800 (95% CI, 0.792-0.809). For 24-hour predictions, the algorithm achieves AUROC of 0.795 (95% CI, 0.785-0.804). For 48-hour and 72-hour predictions, the algorithm achieves AUROC values of 0.761 (95% CI, 0.753-0.768) and 0.728 (95% CI, 0.719-0.737), respectively. Limitations: Because of the retrospective nature of this study, we cannot draw any conclusions about the impact the algorithm’s predictions will have on patient outcomes in a clinical setting. Conclusions: The results of these experiments suggest that a machine learning–based AKI prediction tool may offer important prognostic capabilities for determining which patients are likely to suffer AKI, potentially allowing clinicians to intervene before kidney damage manifests.