Miracle-dz/UniFAR-FTUs huggingface dataset:
Family Therapy for Autism Spectrum Disorders

Autism spectrum disorders (ASDs) refer to range of neurodevelopmental lifelong conditions with symptoms presenting in early childhood. ASDs are characterised by some degree of impaired communication, reciprocal social interactions and restricted and repetitive interests and behaviours (WHO, 1992). ASDs affect at least 1% of the population and often diagnosis is not made until late adolescence and early adulthood due to the diversity in the ASD symptom profile and clinical presentation (Brugha et al., 2011; NICE, 2012). The degree of impairment of core ASD characteristics varies widely amongst individuals diagnosed and individuals with ASD often rely on family support well into adulthood as daily living and self-sufficiency skills can be encumbered (Gray et al., 2014; Magiati, Tay, & Howlin, 2014). Difficulties with learning and psychiatric comorbidities such as anxiety disorders and depression are frequently associated with ASDs. These comorbidities further complicate and compound difficulties across multiple domains of functioning and intensify reliance on family members. It is usual for core characteristics of ASD to impact significantly on others in the family (Hoefman et al., 2014). Recent evidence suggests that individuals with ASD and their family members benefit from psychological interventions that focus on improving coping and resilience and reducing stress and mental health morbidity. The outcomes though only focus on the individual with ASD or the family members, not in combination. Family therapy is an inclusive systemic psychotherapeutic intervention that aims to enhance communication, relationships and functioning of all family members (Dallos & Draper, 2010). This intervention is designed to facilitate discussion between family members regarding difficult situations such as the impact of core ASD characteristics on the family unit. Family therapy can thus provide the mechanism to family members to develop collective strategies to manage these challenges.
Nicotine exposure of male mice produces behavioral impairment in multiple generations of descendants

Use of tobacco products is injurious to health in men and women. However, tobacco use by pregnant women receives greater scrutiny because it can also compromise the health of future generations. More men smoke cigarettes than women. Yet the impact of nicotine use by men upon their descendants has not been as widely scrutinized. We exposed male C57BL/6 mice to nicotine (200 μg/mL in drinking water) for 12 wk and bred the mice with drug-naïve females to produce the F1 generation. Male and female F1 mice were bred with drug-naïve partners to produce the F2 generation. We analyzed spontaneous locomotor activity, working memory, attention, and reversal learning in male and female F1 and F2 mice. Both male and female F1 mice derived from the nicotine-exposed males showed significant increases in spontaneous locomotor activity and significant deficits in reversal learning. The male F1 mice also showed significant deficits in attention, brain monoamine content, and dopamine receptor mRNA expression. Examination of the F2 generation showed that male F2 mice derived from paternally nicotine-exposed female F1 mice had significant deficits in reversal learning. Analysis of epigenetic changes in the spermatozoa of the nicotine-exposed male founders (F0) showed significant changes in global DNA methylation and DNA methylation at promoter regions of the dopamine D2 receptor gene. Our findings show that nicotine exposure of male mice produces behavioral changes in multiple generations of descendants. Nicotine-induced changes in spermatozoal DNA methylation are a plausible mechanism for the transgenerational transmission of the phenotypes. These findings underscore the need to enlarge the current focus of research and public policy targeting nicotine exposure of pregnant mothers by a more equitable focus on nicotine exposure of the mother and the father.
The Cost-effectiveness of a Point-of-Care Paper Transaminase Test for Monitoring Treatment of HIV/TB Co-Infected Persons

Abstract Background Persons with HIV and tuberculosis (TB) co-infection require transaminase monitoring while on hepatotoxic medications. A novel paper-based, point-of-care transaminase test is in development at an anticipated cost of $1 per test. Methods To project long-term clinical outcomes and estimate the cost-effectiveness of using a paper-based fingerstick test to monitor for drug-induced liver injury (DILI), as compared with automated testing and with no laboratory monitoring. The design was a decision analytic model, including deterministic and probabilistic sensitivity analyses. Data sources were observational cohorts and a validation study of the paper-based test. The target population was HIV/TB co-infected persons in South Africa on antiretroviral therapy who were initiating TB therapy. Interventions: (1) clinical (no laboratory) monitoring; (2) monitoring using the paper-based test with a ≥120 IU/mL threshold for positivity; (3) monitoring using the paper-based test with a ≥200 IU/mL threshold for positivity; (4) monitoring using the paper-based test using 1 of 3 categories: <120 IU/mL, 120 to 200 IU/mL, and >200 IU/mL (“bin placement”); (5) monitoring using automated ALT testing using the same 3 categories (“automated testing”). The outcome measures were discounted quality-adjusted life-years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs). Results The ICER of automated testing was $5180/QALY. Use of the paper-based test with the bin placement strategy was cost-effective compared with clinical monitoring alone. Conclusion At its current performance, monthly DILI monitoring by bin placement using the paper-based test was cost-effective, compared with clinical monitoring, in HIV/TB co-infected persons in South Africa.
Can you make morphometrics work when you know the right answer? Pick and mix approaches for apple identification

Morphological classification of living things has challenged science for several centuries and has led to a wide range of objective morphometric approaches in data gathering and analysis. In this paper we explore those methods using apple cultivars, a model biological system in which discrete groups are pre-defined but in which there is a high level of overall morphological similarity. The effectiveness of morphometric techniques in discovering the groups is evaluated using statistical learning tools. No one technique proved optimal in classification on every occasion, linear morphometric techniques slightly out-performing geometric (72.6% accuracy on test set versus 66.7%). The combined use of these techniques with post-hoc knowledge of their individual successes with particular cultivars achieves a notably higher classification accuracy (77.8%). From this we conclude that even with pre-determined discrete categories, a range of approaches is needed where those categories are intrinsically similar to each other, and we raise the question of whether in studies where potentially continuous natural variation is being categorised the level of match between categories is routinely set too high.
Association of Optical Coherence Tomography Angiography of Collaterals in Retinal Vein Occlusion With Major Venous Outflow Through the Deep Vascular Complex

Importance Analysis of collateral vessel formation following retinal vein occlusion may advance our understanding of the venous outflow anatomy in the macula. Objective To determine the location of collateral vessels with optical coherence tomography (OCT) angiography imaging. Design, Setting, and Participants Observational retrospective cohort study. Collateral vessel formation was studied with OCT angiography (OCTA) in patients with retinal vein occlusion (RVO). The study took place at 2 retinal practices (Vitreous Retina Macula Consultants of New York and Stein Eye Institute, University of California, Los Angeles), with patient records retrieved from March 2015 to August 2017. Data analysis was completed in November 2017. Exposures Collaterals identified with fundus photography and/or fluorescein angiography were analyzed with OCTA to determine their course through the superficial vascular plexus (SVP) and the deep vascular complex (DVC). Main Outcomes and Measures Collateral vessel pathways through the SVP and DVC were analyzed with cross-sectional and en face OCT and OCTA segmentation and color-coded volume renderings prepared from raw OCTA voxel data. Results From 23 eyes (22 branch and 1 hemispheric retinal vein occlusion ) of 23 patients (mean [SD] age, 73 [11] years), 101 collateral vessels were identified and analyzed (mean [SD], 4.4 [2.0]; range, 2-9 collateral per eye). On OCTA, the collaterals appeared as curvilinear dilated flow signals that connected veins across the horizontal raphe or veins on opposite sides of an occluded venous segment within the same retinal hemisphere. Of the 101 collaterals analyzed, all showed greater flow signal in the DVC, and all had some portion of their course identified within the DVC. No collaterals were found exclusively in the SVP. Volume renderings for 3 cases confirmed qualitatively that retinal collateral vessels course through the retina predominantly at the level of the DVC. Conclusions and Relevance Based on a limited number of cases, all collateral vessels associated with retinal vein occlusion were found to course through the DVC. The absence of collaterals isolated to the SVP supports a serial arrangement of the SVP and DVC, with venous drainage predominantly coursing through the DVC.
Ontology-Supported Design Parameter Management for Change Impact Analysis

This paper presents an ontology-supported approach to the management of design parameters in engineering. This approach aims specifically at enabling Change Impact Analysis through Requirements Traceability and acquainted expert knowledge of design parameters. The approach is suitable for both software and hardware designs. The activities and features are mainly obtained by (1) the application of an ontology-based universal system modeling procedure proposal for model integration, (2) the utilization of a knowledge base for capturing expert knowledge and (3) a semantic Mission Profile Aware Design platform. OWL is used to represent information and the underlying data model can improve knowledge transfer among heterogeneous systems which are common in complex engineering projects. At the same time, effort to perform reasoning on such models can be reduced. A demonstration and hands-on description of two illustrative use cases complements the paper.
The anti-biofilm effect of silver-nanoparticle-decorated quercetin nanoparticles on a multi-drug resistant Escherichia coli strain isolated from a dairy cow with mastitis

Background Escherichia coli is an important opportunistic pathogen that could cause inflammation of the udder in dairy cows resulting in reduced milk production and changes in milk composition and quality, and even death of dairy cows. Therefore, mastitis is the main health issue which leads to major economic losses on dairy farms. Antibiotics are routinely used for the treatment of bovine mastitis. The ability to form biofilm increases the antibiotic resistance of E. coli. Nanoparticles (NPs), a nanosized, safe, and highly cost-effective antibacterial agent, are potential biomedical tools. Given their antibacterial activities, silver nanoparticles (Ag NPs) have a broad range of applications. Methods In this study, we performed antibacterial activity assays, biofilm formation assays, scanning electron microscopy (SEM) experiments, and real-time reverse transcription PCR (RT-PCR) experiments to investigate the antibacterial and anti-biofilm effect of quercetin, Ag NPs, and Silver-nanoparticle-decorated quercetin nanoparticles (QA NPs) in E. coli strain ECDCM1. Results In this study, QA NPs, a composite material combining Ag NPs and the plant-derived drug component quercetin, exhibited stronger antibacterial and anti-biofilm properties in a multi-drug resistant E. coli strain isolated from a dairy cow with mastitis, compared to Ag NPs and Qe. Discussion This study provides evidence that QA NPs possess high antibacterial and anti-biofilm activities. They proved to be more effective than Ag NPs and Qe against the biofilm formation of a multi-drug resistant E. coli isolated from cows with mastitis. This suggests that QA NPs might be used as a potential antimicrobial agent in the treatment of bovine mastitis caused by E. coli.
Early and late complications following hematopoietic stem cell transplantation in pediatric patients – A retrospective analysis over 11 years

Hematopoietic stem cell transplantation (HSCT) has been an effective method for treating a wide range of malignant or non-malignant disorders. In case of an autologous HSCT, patients receive their own stem cells after myeloablation before extraction. Allogeneic HSCT uses stem cells derived from a donor. Despite being associated with a high risk of early and long-term complications, it is often the last curative option. 229 pediatric patients, who between 1 January 2005 and 31 December 2015 received an HSCT at the University Children’s Hospital Wuerzburg, were studied. Correlations between two groups were calculated with the Chi square test or with a 2x2-contingency table. To calculate metric variables, the Mann-Whitney-U-test was used. Survival curves were calculated according to Kaplan and Meier. Significance was assumed for results with a p-value <0.05 (CI (Confident Interval) 95%). We retrospectively analyzed 229 pediatric patients (105 females, 124 males) for early and late complications of allogeneic and autologous hematopoietic stem cell transplantation. Median age at HSCT was seven years. Underlying diseases were leukemia (n = 73), lymphoma (n = 22), solid tumor (n = 65), CNS (central nervous system)- tumor (n = 41), and “other diseases” (n = 28). Survival times, overall survival, and event-free survival were calculated. Of all patients, 80.8% experienced complications of some degree, including mild and transient complications. Allo-HSCT (allogeneic HSCT) carried a significantly higher risk of complications than auto-HSCT (autologous HSCT) (n = 118 vs. n = 67; p = < .001) and the remission rate after allo-HSCT was also higher (58.7% vs. 44,7%; p = .032). Especially infection rates and pulmonary complications are different between auto- and allo-HSCT. Leukemia patients had the highest risk of early and late complications (95,0%; p < .001). Complications within HSCT are major risk factors following morbidity and mortality. In order to detect complications and risk factors early, strict recordings are needed to reduce the rate of complication by recognition and prevention of triggering factors. In the future, these factors should receive greater attention in the planning of HSCT post-transplantation care in order to improve the results of the transplantation and establish protocols to prevent their occurrence.
Resting-State Functional Connectivity Magnetic Resonance Imaging and Outcome After Acute Stroke

Background and Purpose— Physiological effects of stroke are best assessed over entire brain networks rather than just focally at the site of structural damage. Resting-state functional magnetic resonance imaging can map functional-anatomic networks by analyzing spontaneously correlated low-frequency activity fluctuations across the brain, but its potential usefulness in predicting functional outcome after acute stroke remains unknown. We assessed the ability of resting-state functional magnetic resonance imaging to predict functional outcome after acute stroke. Methods— We scanned 37 consecutive reperfused stroke patients (age, 69±14 years; 14 females; 3-day National Institutes of Health Stroke Scale score, 6±5) on day 3 after symptom onset. After imaging preprocessing, we used a whole-brain mask to calculate the correlation coefficient matrices for every paired region using the Harvard-Oxford probabilistic atlas. To evaluate functional outcome, we applied the modified Rankin Scale at 90 days. We used region of interest analyses to explore the functional connectivity between regions and graph-computation analysis to detect differences in functional connectivity between patients with good functional outcome (modified Rankin Scale score ⩽2) and those with poor outcome (modified Rankin Scale score >2). Results— Patients with good outcome had greater functional connectivity than patients with poor outcome. Although 3-day National Institutes of Health Stroke Scale score was the most accurate independent predictor of 90-day modified Rankin Scale (84.2%), adding functional connectivity increased accuracy to 94.7%. Preserved bilateral interhemispheric connectivity between the anterior inferior temporal gyrus and superior frontal gyrus and decreased connectivity between the caudate and anterior inferior temporal gyrus in the left hemisphere had the greatest impact in favoring good prognosis. Conclusions— These data suggest that information about functional connectivity from resting-state functional magnetic resonance imaging may help predict 90-day stroke outcome.
Influence of activation pattern estimates and statistical significance tests in fMRI decoding analysis

The use of Multi-Voxel Pattern Analysis (MVPA) has increased considerably in recent functional magnetic resonance imaging studies. A crucial step consists in the choice of methods for the estimation of responses and their statistical significance. However, a systematic comparison of these and their adequacy to predominant experimental design is missing. In the current study, we compared three pattern estimation methods: Least-Squares Unitary (LSU), based on run-wise estimation, Least-Squares All (LSA) and Least-Squares Separate (LSS), which rely on trial-wise estimation. We compared the efficiency of these methods in an experiment where sustained activity had to be isolated from zero-duration events as well as in a block-design approach and in an event-related design. We evaluated the sensitivity of the t-test in comparison with two non-parametric methods based on permutation testing: one proposed in Stelzer et al. (2013), equivalent to performing a permutation in each voxel separately and the Threshold-Free Cluster Enhancement (Smith and Nichols, 2009). LSS resulted the most accurate approach to address the large overlap of signal among close events in the event-related designs. We found a larger sensitivity of Stelzer’s method in all settings, especially in the event-related designs, where voxels close to surpass the statistical threshold with the other approaches were now marked as informative regions. Our results provide evidence that LSS is the most accurate approach for unmixing events with different duration and large overlap of signal, consistent with previous studies showing better handling of collinearity in LSS. Moreover, Stelzer’s potentiates this better estimation with its larger sensitivity.
Propensity Score–Based Estimators With Multiple Error-Prone Covariates

Propensity score methods are an important tool to help reduce confounding in nonexperimental studies. Most propensity score methods assume that covariates are measured without error. However, covariates are often measured with error, which leads to biased causal effect estimates if the true underlying covariates are the actual confounders. Although some groups have investigated the impact of a single mismeasured covariate on estimating a causal effect and proposed methods for handling the measurement error, fewer have investigated the case where multiple covariates are mismeasured, and we found none that discussed correlated measurement errors. In this study, we examined the consequences of multiple error-prone covariates when estimating causal effects using propensity score-based estimators via extensive simulation studies and real data analyses. We found that causal effect estimates are less biased when the propensity score model includes mismeasured covariates whose true underlying values are strongly correlated with each other. However, when the measurement errors are correlated with each other, additional bias is introduced. In addition, it is beneficial to include correctly measured auxiliary variables that are correlated with confounders whose true underlying values are mismeasured in the propensity score model.
Sexual dimorphism and heightened conditional expression in a sexually selected weapon in the Asian rhinoceros beetle

Among the most dramatic examples of sexual selection are the weapons used in battles between rival males over access to females. As with ornaments of female choice, the most “exaggerated” sexually selected weapons vary from male to male more widely than other body parts (hypervariability), and their growth tends to be more sensitive to nutritional state or physiological condition compared with growth of other body parts (“heightened” conditional expression). Here, we use RNAseq analysis to build on recent work exploring these mechanisms in the exaggerated weapons of beetles, by examining patterns of differential gene expression in exaggerated (head and thorax horns) and non‐exaggerated (wings, genitalia) traits in the Asian rhinoceros beetle, Trypoxylus dichotomus. Our results suggest that sexually dimorphic expression of weaponry involves large‐scale changes in gene expression, relative to other traits, while nutrition‐driven changes in gene expression in these same weapons are less pronounced. However, although fewer genes overall were differentially expressed in high‐ vs. low‐nutrition individuals, the number of differentially expressed genes varied predictably according to a trait's degree of condition dependence (head horn > thorax horn > wings > genitalia). Finally, we observed a high degree of similarity in direction of effects (vectors) for subsets of differentially expressed genes across both sexually dimorphic and nutritionally responsive growth. Our results are consistent with a common set of mechanisms governing sexual size dimorphism and condition dependence.
Considering Non-sequential Control Flows for Process Prediction with Recurrent Neural Networks

Predictive business process monitoring aims to predict how an ongoing process instance will unfold up to its completion, thereby facilitating proactively responding to anticipated problems. Recurrent Neural Networks (RNNs), a special form of deep learning techniques, gain interest as a prediction technique in BPM. However, non-sequential control flows may make the prediction task more difficult, because RNNs were conceived for learning and predicting sequences of data. Based on an industrial dataset, we provide experimental results comparing different alternatives for considering non-sequential control flows. In particular, we consider cycles and parallelism for business process prediction with RNNs.
Infrastructure Based Calibration of a Multi-Camera and Multi-LiDAR System Using Apriltags

This paper proposes a framework for the calibration of a multi-camera and multi-LiDAR system. It utilizes the Apriltags to build a calibration environment to solve the poses and extrinsics of cameras and then deploy ICP-like algorithm to solve the extrinsics of LiDARs. Compared to previous extrinsic calibration methods, this proposed framework naturally applies to systems with different numbers and configurations of cameras and LiDARs. The calibration procedure produces extrinsics not only accurately and robustly, but efficiently without inconvenient human manipulation. The framework provides general, efficient and standardizable calibration solution for autonomous driving platforms with cameras and LiDARs.