Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating the impact of a community-based parent training programme for children with cerebral palsy in Ghana

Background In low and middle-income settings, where access to support and rehabilitation services for children with disabilities are often lacking, the evidence base for community initiatives is limited. This study aimed to explore the impact of a community-based training programme for caregivers of children with cerebral palsy in Ghana. Methods A pre and post evaluation of an 11-month participatory training programme (“Getting to Know Cerebral Palsy”) offered through a parent group model, was conducted. Eight community groups, consisting of a total of 75 caregivers and their children with cerebral palsy (aged 18 months-12 years), were enrolled from 8 districts across Ghana. Caregivers were interviewed at baseline, and again at 2 months after the completion of the programme, to assess: quality of life (PedsQL™ Family Impact Module); knowledge about their child’s condition; child health indicators; feeding practices. Severity of cerebral palsy, reported illness, and anthropometric measurements were also assessed. Results Of the child-caregiver pairs, 64 (84%) were included in final analysis. There were significant improvements in caregiver quality of life score (QoL) (median total QoL 12.5 at baseline to 51.4 at endline, P<0.001). Caregivers reported significant improvements in knowledge and confidence in caring for their child (p<0.001), in some aspects of child feeding practices (p<0.001) and in their child’s physical and emotional heath (p< 0.001). Actual frequency of reported serious illness over 12-months remained high (67%) among children, however, a small reduction in recent illness episodes (past 2 weeks) was seen (64% to 50% p < 0.05). Malnutrition was common at both time points; 63% and 65% of children were classified as underweight at baseline and endline respectively (p = 0.5). Conclusion Children with cerebral palsy have complex care and support needs which in low and middle-income settings need to be met by their family. This study demonstrates that a participatory training, delivered through the establishment of a local support group, with an emphasis on caregiver empowerment, resulted in improved caregiver QoL. Despite less effect on effect on child health and no clear effect on nutritional status, this alone is an important outcome. Whilst further development of these programmes would be helpful, and is underway, there is clear need for wider scale-up of an intervention which provides support to families.
Interpersonal Sensitivity as Mediator of the Relations Between War Experiences and Mental Illness in War-Affected Youth in Northern Uganda: Findings From the WAYS Study

The pathways from war experiences to mental health problems are poorly understood. The current study aims to assess the role of interpersonal sensitivity in the relations between war experiences and mental health problems based on data from the War-Affected Youth Survey cohort study. The War-Affected Youth Survey is an ongoing research project of formerly abducted children in Northern Uganda assessing their war experiences and the risk and protective factors in the development of mental health problems. Mediation of the relations between war experiences and mental health problems by interpersonal sensitivity was analyzed using structural equation modeling. War experiences were related to posttraumatic stress disorder through interpersonal sensitivity accounting for 55% of the variance in their relations, to depression/anxiety through interpersonal sensitivity accounting for 89% of the variance in their relations (i.e., near complete mediation), and to psychotic symptoms through interpersonal sensitivity accounting for 53% of the variance in their relations. The direct relation between war experiences, on the one hand, and posttraumatic stress disorder and psychotic symptoms, on the other hand, attenuated but remained statistically significant. For depression/anxiety, the direct relationship ceased to be significant after including interpersonal sensitivity in the model. Interpersonal sensitivity is an important determinant of long-term mental health problems in war-affected youth. Interventions to improve mental health should target youth with high scores on interpersonal sensitivity. Cognitive–behavioral therapy to recognize and change cognitive schemas in youth prone to interpersonal sensitivity is recommended.
Clinical performance of LOCATOR® attachments: A retrospective study with 1–8 years of follow‐up

The use of LOCATOR® attachments in implant‐supported removable dental prostheses (ISRDPs) has been evidenced with conflicting clinical behavior in literature. This retrospective study aimed to investigate the long‐term clinical performance of LOCATOR® attachments by evaluating the frequency of the encountered mechanical complication events (MCEs) and the factors that play a role in attachment wear (AW). The study recruited participants with ISRDPs on LOCATOR® attachments. Clinical parameters, number of MCEs (attachment replacements, attachment loosenings, denture cap‐related events, loss of retention and/or insert, and implant fractures), and AW were recorded. Nonparametric tests were applied for statistical analyses (𝛼=0.05). Baseline demographics for the recruited 47 participants (mean age: 72.0 ± 9.0 years) revealed an implant survival rate of 94.9% (mean observation period: 54.8 months), average peri‐implant probing depths, bleeding on probing scores, and plaque scores of 1.80 ± 1.50 mm, 0.70 ± 0.90, and 0.81 ± 0.90, respectively.
Innovation for hepatotoxicity in vitro research models: A review

Many categories of drugs can induce hepatotoxicity, so improving the prediction of toxic drugs is important. In vitro models using human hepatocytes are more accurate than in vivo animal models. Good in vitro models require an abundance of metabolic enzyme activities and normal cellular polarity. However, none of the in vitro models can completely simulate hepatocytes in the human body. There are two ways to overcome this limitation: enhancing the metabolic function of hepatocytes and changing the cultural environment. In this review, we summarize the current state of research, including the main characteristics of in vitro models and their limitations, as well as improved technology and developmental prospects. We hope that this review provides some new ideas for hepatotoxicity research.
Semantic mapping for orchard environments by merging two‐sides reconstructions of tree rows

Measuring semantic traits for phenotyping is an essential but labor‐intensive activity in horticulture. Researchers often rely on manual measurements which may not be accurate for tasks, such as measuring tree volume. To improve the accuracy of such measurements and to automate the process, we consider the problem of building coherent three‐dimensional (3D) reconstructions of orchard rows. Even though 3D reconstructions of side views can be obtained using standard mapping techniques, merging the two side‐views is difficult due to the lack of overlap between the two partial reconstructions. Our first main contribution in this paper is a novel method that utilizes global features and semantic information to obtain an initial solution aligning the two sides. Our mapping approach then refines the 3D model of the entire tree row by integrating semantic information common to both sides, and extracted using our novel robust detection and fitting algorithms. Next, we present a vision system to measure semantic traits from the optimized 3D model that is built from the RGB or RGB‐D data captured by only a camera. Specifically, we show how canopy volume, trunk diameter, tree height, and fruit count (FC) can be automatically obtained in real orchard environments. The experiment results from multiple data sets quantitatively demonstrate the high accuracy and robustness of our method.
Fast Phenomics in Vineyards: Development of GRover, the Grapevine Rover, and LiDAR for Assessing Grapevine Traits in the Field

This paper introduces GRover (the grapevine rover), an adaptable mobile platform for the deployment and testing of proximal imaging sensors in vineyards for the non-destructive assessment of trunk and cordon volume and pruning weight. A SICK LMS-400 light detection and ranging (LiDAR) radar mounted on GRover was capable of producing precise (±3 mm) 3D point clouds of vine rows. Vineyard scans of the grapevine variety Shiraz grown under different management systems at two separate locations have demonstrated that GRover is able to successfully reproduce a variety of vine structures. Correlations of pruning weight and vine wood (trunk and cordon) volume with LiDAR scans have resulted in high coefficients of determination (R2 = 0.91 for pruning weight; 0.76 for wood volume). This is the first time that a LiDAR of this type has been extensively tested in vineyards. Its high scanning rate, eye safe laser and ability to distinguish tissue types make it an appealing option for further development to offer breeders, and potentially growers, quantified measurements of traits that otherwise would be difficult to determine.
Eye-Movement Intervention Enhances Extinction via Amygdala Deactivation

Improving extinction learning is essential to optimize psychotherapy for persistent fear-related disorders. In two independent studies (both n = 24), we found that goal-directed eye movements activate a dorsal frontoparietal network and transiently deactivate the amygdala (ηp2 = 0.17). Connectivity analyses revealed that this downregulation potentially engages a ventromedial prefrontal pathway known to be involved in cognitive regulation of emotion. Critically, when eye movements followed memory reactivation during extinction learning, it reduced spontaneous fear recovery 24 h later (ηp2 = 0.21). Stronger amygdala deactivation furthermore predicted a stronger reduction in subsequent fear recovery after reinstatement (r = 0.39). In conclusion, we show that extinction learning can be improved with a noninvasive eye-movement intervention that triggers a transient suppression of the amygdala. Our finding that another task which taxes working memory leads to a similar amygdala suppression furthermore indicates that this effect is likely not specific to eye movements, which is in line with a large body of behavioral studies. This study contributes to the understanding of a widely used treatment for traumatic symptoms by providing a parsimonious account for how working-memory tasks and goal-directed eye movements can enhance extinction-based psychotherapy, namely through neural circuits (e.g., amygdala deactivation) similar to those that support cognitive control of emotion. SIGNIFICANCE STATEMENT Fear-related disorders represent a significant burden on individual sufferers and society. There is a high need to optimize treatment, in particular via noninvasive means. One potentially effective intervention is execution of eye movements following trauma recall. However, a neurobiological understanding of how eye movements reduce traumatic symptoms is lacking. We demonstrate that goal-directed eye-movements, like working-memory tasks, deactivate the amygdala, the core neural substrate of fear learning. Effective connectivity analyses revealed amygdala deactivation potentially engaged dorsolateral and ventromedial prefrontal pathways. When applied during safety learning, this deactivation predicts a reduction in later fear recovery. These findings provide a parsimonious and mechanistic account of how behavioral manipulations taxing working memory and suppressing amygdala activity can alter retention of emotional memories.
Multi-Modal Coreference Resolution with the Correlation between Space Structures

Multi-modal data is becoming more common in big data background. Finding the semantically similar objects from different modality is one of the heart problems of multi-modal learning. Most of the current methods try to learn the inter-modal correlation with extrinsic supervised information, while intrinsic structural information of each modality is neglected. The performance of these methods heavily depends on the richness of training samples. However, obtaining the multi-modal training samples is still a labor and cost intensive work. In this paper, we bring a extrinsic correlation between the space structures of each modalities in coreference resolution. With this correlation, a semi-supervised learning model for multi-modal coreference resolution is proposed. We firstly extract high-level features of images and text, then compute the distances of each object from some reference points to build the space structure of each modality. With a shared reference point set, the space structures of each modality are correlated. We employ the correlation to build a commonly shared space that the semantic distance between multi-modal objects can be computed directly. The experiments on two multi-modal datasets show that our model performs better than the existing methods with insufficient training data.
T Cell–Inflamed versus Non-T Cell–Inflamed Tumors: A Conceptual Framework for Cancer Immunotherapy Drug Development and Combination Therapy Selection

Immunotherapies such as checkpoint-blocking antibodies and adoptive cell transfer are emerging as treatments for a growing number of cancers. Despite clinical activity of immunotherapies across a range of cancer types, the majority of patients fail to respond to these treatments and resistance mechanisms remain incompletely defined. Responses to immunotherapy preferentially occur in tumors with a preexisting antitumor T-cell response that can most robustly be measured via expression of dendritic cell and CD8+ T cell–associated genes. The tumor subset with high expression of this signature has been described as the T cell–“inflamed” phenotype. Segregating tumors by expression of the inflamed signature may help predict immunotherapy responsiveness. Understanding mechanisms of resistance in both the T cell–inflamed and noninflamed subsets of tumors will be critical in overcoming treatment failure and expanding the proportion of patients responding to current immunotherapies. To maximize the impact of immunotherapy drug development, pretreatment stratification of targets associated with either the T cell–inflamed or noninflamed tumor microenvironment should be employed. Similarly, biomarkers predictive of responsiveness to specific immunomodulatory therapies should guide therapy selection in a growing landscape of treatment options. Combination strategies may ultimately require converting non-T cell–inflamed tumors into T cell–inflamed tumors as a means to sensitize tumors to therapies dependent on T-cell killing. Cancer Immunol Res; 6(9); 990–1000. ©2018 AACR.
Discriminative Learning of Open-Vocabulary Object Retrieval and Localization by Negative Phrase Augmentation

Thanks to the success of object detection technology, we can retrieve objects of the specified classes even from huge image collections. However, the current state-of-the-art object detectors (such as Faster R-CNN) can only handle pre-specified classes. In addition, large amounts of positive and negative visual samples are required for training. In this paper, we address the problem of open-vocabulary object retrieval and localization, where the target object is specified by a textual query (e.g., a word or phrase). We first propose Query-Adaptive R-CNN, a simple extension of Faster R-CNN adapted to open-vocabulary queries, by transforming the text embedding vector into an object classifier and localization regressor. Then, for discriminative training, we then propose negative phrase augmentation (NPA) to mine hard negative samples which are visually similar to the query and at the same time semantically mutually exclusive of the query. The proposed method can retrieve and localize objects specified by a textual query from one million images in only 0.5 seconds with high precision.
Aesthetic Features for Personalized Photo Recommendation

Many photography websites such as Flickr, 500px, Unsplash, and Adobe Behance are used by amateur and professional photography enthusiasts. Unlike content-based image search, such users of photography websites are not just looking for photos with certain content, but more generally for photos with a certain photographic "aesthetic". In this context, we explore personalized photo recommendation and propose two aesthetic feature extraction methods based on (i) color space and (ii) deep style transfer embeddings. Using a dataset from 500px, we evaluate how these features can be best leveraged by collaborative filtering methods and show that (ii) provides a significant boost in photo recommendation performance.
Adding Sparkle to Social Coding: An Empirical Study of Repository Badges in the npm Ecosystem

In fast-paced, reuse-heavy software development, the transparency provided by social coding platforms like GitHub is essential to decision making. Developers infer the quality of projects using visible cues, known as signals, collected from personal profile and repository pages. We report on a large-scale, mixed-methods empirical study of npm packages that explores the emerging phenomenon of repository badges, with which maintainers signal underlying qualities about the project to contributors and users. We investigate which qualities maintainers intend to signal and how well badges correlate with those qualities. After surveying developers, mining 294,941 repositories, and applying statistical modeling and time series analysis techniques, we find that non-trivial badges, which display the build status, test coverage, and up-to-dateness of dependencies, are mostly reliable signals, correlating with more tests, better pull requests, and fresher dependencies. Displaying such badges correlates with best practices, but the effects do not always persist.
Salivary and urinary metabolome analysis for pre-puberty-related biomarkers identification in porcine

Estrus synchronization is important for optimal management of gilt reproduction in pig farms. Hormonal treatments, such as synthetic progestogens, are used on a routine basis, but there is a growing demand for non-hormonal alternative breeding tools. Before puberty, gilts exhibit a ‘waiting period,’ related to the ovarian development and gonadotrophin secretions, during which external stimulations, such as boar exposure, could induce and synchronize first ovulation. Practical non-invasive tools for identification of this period in farms are lacking. During this period, urinary oestrone levels are high, but urine sampling is difficult in group-housed females. The aim of this work was to search for specific biomarkers of the ‘waiting period’ in saliva and urine. In total, nine 144- to 147-day-old Large White gilts were subjected to trans-abdominal ultrasonography three times a week for 5 weeks until puberty detection (week –5 to week –1 before puberty). Urine and saliva samples were collected for oestrone assay to detect the ‘waiting period’ and for metabolome analysis using 1H-nuclear magnetic resonance spectroscopy to detect potential biomarkers of the ‘waiting period. ’ Gilts were slaughtered 7 days after puberty detection for puberty confirmation. Results were consistent with ultrasonography data for six gilts. Urine and saliva samples from these six gilts were analyzed. Urinary estrone concentration significantly increased 2 weeks before puberty detection. Metabolome analysis of urine samples allowed the identification of 78 spectral bins, among them, 42 low-molecular-weight metabolites were identified. Metabolome analysis of salivary samples allowed the identification of 59 spectral bins, among them, 23 low-molecular-weight metabolites were detected and 17 were identified. No potential biomarker was identified in urinary samples. In saliva, butyrate and 2HOvalerate, 5.79 ppm (putatively uridine), formate, malonate and propionate could be biomarker candidates to ascertain the pre-puberty period in gilt reproduction. These results confirm that non-invasive salivary samples could allow the identification of the physiological status of the gilts and presumably the optimal time for application of the boar effect. This could contribute to synchronize puberty onset and hence to develop non-hormonal breeding tools.
Detail Preserving Depth Estimation from a Single Image Using Attention Guided Networks

Convolutional Neural Networks have demonstrated superior performance on single image depth estimation in recent years. These works usually use stacked spatial pooling or strided convolution to get high-level information which are common practices in classification task. However, depth estimation is a dense prediction problem and low-resolution feature maps usually generate blurred depth map which is undesirable in application. In order to produce high quality depth map, say clean and accurate, we propose a network consists of a Dense Feature Extractor (DFE) and a Depth Map Generator (DMG). The DFE combines ResNet and dilated convolutions. It extracts multi-scale information from input image while keeping the feature maps dense. As for DMG, we use attention mechanism to fuse multi-scale features produced in DFE. Our Network is trained end-to-end and does not need any post-processing. Hence, it runs fast and can predict depth map in about 15 fps. Experiment results show that our method is competitive with the state-of-the-art in quantitative evaluation, but can preserve better structural details of the scene depth.
Imaging Brain Metastasis Patients With 18F-(2S,4R)-4-Fluoroglutamine

Purpose There is a need for an alternative PET probe, which does not show normal brain tissue uptake in the evaluation of metastasis to the brain. Therefore, we investigate the feasibility of 18F-labeled glutamine analog, 18F-(2S,4R)-4-fluoroglutamine (18F-FGln), as a new metabolic probe to detect brain metastasis. Methods Patients (7 men and 7 women; age, 25–67 years) with suspected brain metastasis were enrolled for this study. All patients were imaged first with 18F-FGln PET (3 patients for 1-hour dynamic whole-body PET/CT scans, and 11 patients for static whole-body scans at 30 ± 10 minutes after injection), followed by a whole-body 18F-FDG PET performed in the same week. The characteristics of 18F-FGln PET imaging in brain metastasis patients were compared with that of 18F-FDG PET and/or contrast-enhanced MRI patient-by-patient. A composite of all functional and anatomic imaging studies served as the imaging comparator. Results Initial study in 3 patients using 1-hour dynamic scan showed that 30 ± 10 minutes after injection is optimal for identifying brain metastasis with a high-contrast ratio. All patients were positive for brain metastasis on this studies that demonstrated 38 lesions in 6 anatomic regions on the imaging comparator. The per-lesion detection rates for 18F-FGln PET and 18F-FDG PET were 81.6% and 36.8%, respectively. The average tumor-to-normal brain ratio of 18F-FGln PET was significantly better than that of 18F-FDG PET in all patients (4.97 ± 2.23 vs 1.22 ± 0.69, P < 0.05). Furthermore, our results suggest that 18F-FGln uptake in brain metastasis appeared to be independent of tumor size and peripheral edema. In addition, in 14 brain metastatic lesions visualized by both 18F-FDG PET and 18F-FGln PET imaging, a positive correlation of SUVmax was observed (r = 0.780, P < 0.01). As to the extracranial metastasis, both tracers showed a concordant increased radioactive uptake except in liver and bone. Conclusions The initial imaging of 18F-FGln presenting a promising new PET radiotracer for patients with brain metastasis and its utility in the liver and bone metastatic lesions may require more caution due to uptake in normal structures.