Miracle-dz/UniFAR-FTUs huggingface dataset:
Nintendo Wii versus resistance training to improve upper-limb function in children ages 7 to 12 with spastic hemiplegic cerebral palsy: a home-based pilot study

This pilot, home-based study compared a Nintendo Wii intervention to a single-joint upper-limb resistance training of a similar intensity, in n=6 children ages of 7 to 12 with spastic hemiplegic CP with respect to upper limb function, compliance and motivation levels. The main results of this study found that all participants in the Wii intervention group (n=3) experienced positive changes in more than one assessment from pre-test to follow-up, and these changes were on average greater than those experienced by the resistance training group (n=3). Also, the Nintendo Wii group was found to have a higher compliance rate with the study’s protocols, and higher parent-reported motivation levels throughout the study, as compared to the resistance-training group. This suggests that Nintendo Wii interventions for the upper limbs may be a more effective home-based rehabilitation strategy than the single-joint upper limb resistance training program used in this study for this population, primarily due to greater participant motivation to comply with Nintendo Wii training. These findings warrant further research with larger sample sizes.
GRAPHENE NANORIBBONS TRANSPORT PROPERTIES CALCULATION

Abstract Graphene, a two-dimensional sheet of sp-bonded carbon arranged in a honeycomb lattice, is a potential candidate for use in future nanodevices. The lateral confinement of the two-dimensional electron gas in graphene nanoribbons (GNR) can tune their electrical properties. We performed density-functional theory (DFT) based calculations to obtain electronic structure of both zig-zag and armchair GNRs with lateral constrictions. The spin-dependent exchange-correlation potential is approximated within the generalized gradient approximation using the QuantumWise toolkit ATK, which employs local numerical double-zeta polarized basis orbitals. The spin-dependent transport properties of the electrode-device-electrode geometry were calculated by means of non-equilibrium Green’s function formalism as implemented in ATK. Band structures calculated by the DFT were compared with results of the semi-empirical method (Extended Huckel model). Our results show that GNRs with the constrictions exhibit novel transport properties. The horizontal layout of the GNR constrictions determines main features important for electrical and spintronic applications of these structures. The constrictions could be realized by means of local anodic oxidation using scanning probe microscope.
Exploring Use Cases for an Artificial Intelligence Poet

I report on the iterative process of designing a mobile AI poetry system, along with a series of broad scale use cases in which different variants of the system has been tested in the wild. The project has so far resulted in the generation of about 20 million individual poems, co-created by the system together with millions of users. Apart from the design of the technical side of the system, my focus has been on how the system could be adapted to and deployed in different commercial settings. I discuss my insights related to systems support for creative processes, and how findings from these use cases could be applicable also to other AI content generation systems.
Affect-Focused Psychodynamic Internet-Based Therapy for Adolescent Depression: Randomized Controlled Trial

Background Adolescent depression is one of the largest health issues in the world and there is a pressing need for effective and accessible treatments. Objective This trial examines whether affect-focused internet-based psychodynamic therapy (IPDT) with therapist support is more effective than an internet-based supportive control condition on reducing depression in adolescents. Methods The trial included 76 adolescents (61/76, 80% female; mean age 16.6 years), self-referred via an open access website and fulfilling criteria for major depressive disorder. Adolescents were randomized to 8 weeks of IPDT (38/76, 50%) or supportive control (38/76, 50%). The primary outcome was self-reported depressive symptoms, measured with the Quick Inventory of Depressive Symptomatology for Adolescents (QIDS-A17-SR). Secondary outcomes were anxiety severity, emotion regulation, self-compassion, and an additional depression measure. Assessments were made at baseline, postassessment, and at 6 months follow-up, in addition to weekly assessments of the primary outcome measure as well as emotion regulation during treatment. Results IPDT was significantly more effective than the control condition in reducing depression (d=0.82, P=.01), the result of which was corroborated by the second depression measure (d=0.80, P<.001). IPDT was also significantly more effective in reducing anxiety (d=0.78, P<.001) and increasing emotion regulation (d=0.97, P<.001) and self-compassion (d=0.65, P=.003). Significantly more patients in the IPDT group compared to the control group met criteria for response (56% vs 21%, respectively) and remission (35% vs 8%, respectively). Results on depression and anxiety symptoms were stable at 6 months follow-up. On average, participants completed 5.8 (SD 2.4) of the 8 modules. Conclusions IPDT may be an effective intervention to reduce adolescent depression. Further research is needed, including comparisons with other treatments. Trial Registration International Standard Randomised Controlled Trial Number (ISRCTN) 16206254; http://www.isrctn. com/ISRCTN16206254
Automatic tag suggestions using a deep learning recommender system

This study was conducted to investigate how well deep learning can be applied to the field of tag recommender systems. In the context of an image item, tag recommendations can be given based on tags already existing on the item, or on item content information. In the current literature, there are no works which jointly models the tags and the item content information using deep learning. Two tag recommender systems were developed. The first one was a highly optimized hybrid baseline model based on matrix factorization and Bayesian classification. The second one was based on deep learning. The two models were trained and evaluated on a dataset of user-tagged images and videos from Flickr. A percentage of the tags were withheld, and the evaluation consisted of predicting them. The deep learning model attained the same prediction recall as the baseline model in the main evaluation scenario, when half of the tags were withheld. However, the baseline model generalized better to the sparser scenarios, when a larger number of tags were withheld. Furthermore, the computations of the deep learning model were much more time-consuming than the computations of the baseline model. These results led to the conclusion that the baseline model was more practical, but that there is much potential in using deep learning for the purpose of tag recommendation.
Expression of Cox-2 and Bcl-2 in Paget ’ s Disease of the Breast Arsenal

Paget’s disease (PD) is a rare form of intraepithelial adenocarcinoma that involves breast and extramammarian tissues. PD of the breast, a disorder of the nipple-areola complex first described by Sir James Paget in 1874 accounts for 1-3% of all breast cancers (Paget, 1874). It is often associated with ductal carcinoma in situ and/ or invasive ductal cancer. Clinical differential diagnosis of Paget’s disease of the breast include atopic or contact dermatitis of the nipple, chronic eczema, psoriasis and inflammatory changes such as chronic nipple discharge and sphylitic chancre (Karakas, 2011). There are two theories on the hypothesis of the nature and origin of PD: the epidermotropic theory and in-situ malignant transformation theory. The “epidermotrophic” theory postulates that Paget’s cells originate from the cancer cells of the ductus that have migrated along the basal membrane of the epidermis of the nipple (Muir, 1939; Ashikari et al., 1970). The similiarity of the
Bacillus lacus sp. nov., isolated from a water sample of Sambhar salt lake, India

Academy of Scientific and Innovative Research, (AcSIR), CSIR Campus, Chennai, India MTCC-Microbial Type Culture Collection & Gene Bank, CSIR-Institute of Microbial Technology, Chandigarh-160036, India Council of Scientific and Industrial Research (CSIR) Central Scientific Instruments Organisation, Chandigarh-160030, India CSIR-National Institute of Oceanography, Regional Centre, 176, Lawsons Bay Colony, Visakhapatnam-530017, India
Factors Associated with the Nutritional Status of Schoolchildren in the Main City of Benin Republic, Sub-Saharan Africa

Background: Malnutrition has become a real concern today. It is a pathological condition resulting from the relative or absolute deficiency or excess of one or more essential nutrients and can occur through either clinical or anthropometric signs or physiological or biochemical analyses. General objective: The purpose of this study was to determine the factors associated with and the extent of malnutrition in schoolchildren in Cotonou, the main city of Benin Republic. Method: This was a cross-sectional study conducted in July 2016. It involved data collection regarding food and nutritional knowledge, nutritional habits and practices, physical activity level and health status which were obtained using survey research. The study took place in four schools from the Friendly School of Nutrition Initiative (FNSI) in Cotonou and focused on schoolchildren aged from eight to eleven (n = 182). Anthropometric data including weight and height were collected, BMI indices weight-for age and height-forage were calculated, making it thereby possible to assess the nutritional status of the schoolchildren. Factors associated with malnutrition were determined by logistic regression (p < 0.05). Results: From the outcome of this study, 13% of the children were wasted; 5% were overweight or obese. The proportion of stunting was 5% of which 1% were severely stunted. Thirty-four percent (34%) of schoolchildren were anemic. Factors associated with wasting in schoolchildren were age (OR = 4.99; p = 0.007), gender (OR = 0.36; p = 0.049) and maternal employment status (OR = 5.58; p = 0.021). Maternal employment status had a high influence on stunting (OR = 7.77; p = 0.033). Conclusion: It appears that the double burden of malnutrition is a reality in Cotonou’s schools and is characterized by coexistence of wasting, stunting, overweight and obesity in these schoolchildren. Efficient strategies are needed to improve the nutritional status of Cotonou’s schoolchildren.
Identification of biological characteristics associated with RNA-binding proteins (RBPs) target sites : Identificação de características biológicas associadas a sítios-alvo de proteínas de ligação ao RNA (RBPs)

Stress granules are protein and RNA aggregates found in the cytoplasm of cells, in general are produced in response to a source of stress (ex. Hypoxia, viral infections, nutriente deprivation and heat shock). However, several neurodegenerative diseases, such as amyotrophic lateral sclerosis and Alzheimer’s disease, have already been associated with pathogenic inclusions of these aggregates with harmful consequences for the cells. Amongst the proteins presente in the stress granule, the complex G3BP1-CAPRIN1-USP10 is essential for the condensation fo the granule and it’s association with ribosomal subunits, with the ectopic expression of CAPRIN1 being suufficient to induce the formation of these aggregates. Although the molecular mechanisms associated with stress granules are not completely elucidated, the main functions postulated for these structures are: protection of RNAs from harmful situations, degradation of target mRNAs, selection of stress-response mRNAs for translation and reprogamming of overall gene expression. In order to characterize the mRNA-targets of Caprin-1 in stress granules, we used a combination of eCLIP-seq, RIP-seq and RNA-seq techiniques to identify the RNA binding sites for Caprin-1, as well as identifying which functional classes of transcripts are enriched in these samples. Our analysis revealed that Caprin-1 posesses a preference for binding to stemloops RNA secondary structures, additionally these regions were also enriched in GG repeats, which might suggest the formation of secondary structures known as G-quadruplexes, which are more stable than the stemloop model. From the functional standpoint, we found that targets identified by eCLIP-seq are enriched in transcripts coding for other RNA-binding proteins, associated mostly with catabolic processes and cell cycle control. Additionally, we compared the enriched categories with predicted alterations in metabolic pathways obtained from RNA-seq data. Overall, we found 19 pathways which are simultaneously enriched in eCLIP-seq targets and had significant alterations in their activation after CAPRIN1 ectopic expression. Lastly, we identified that Caprin1 binding are also enriched in target sites for microRNAs and are associated with PUM2, another RNA binding protein. Taken together, our data allowed us to gather important information on the role of Caprin-1 in the stress granules, both for the selection of binding targets as well as the functional alterations resulting from the ectopic expression of this protein. Our finds corroborate, with new approaches, data previously suggested in the literature as well as propose novel mechanisms previously unreported for this model.
Real-time and multimodality image-guided intelligent HIFU therapy for uterine fibroid

Rationale: High-intensity focused ultrasound (HIFU) therapy represents a noninvasive surgical approach to treat uterine fibroids. The operation of HIFU therapy relies on the information provided by medical images. In current HIFU therapy, all operations such as positioning of the lesion in magnetic resonance (MR) and ultrasound (US) images are manually performed by specifically trained doctors. Manual processing is an important limitation of the efficiency of HIFU therapy. In this paper, we aim to provide an automatic and accurate image guidance system, intelligent diagnosis, and treatment strategy for HIFU therapy by combining multimodality information. Methods: In intelligent HIFU therapy, medical information and treatment strategy are automatically processed and generated by a real-time image guidance system. The system comprises a novel multistage deep convolutional neural network for preoperative diagnosis and a nonrigid US lesion tracking procedure for HIFU intraoperative image-assisted treatment. In the process of intelligent therapy, the treatment area is determined from the autogenerated lesion area. Based on the autodetected treatment area, the HIFU foci are distributed automatically according to the treatment strategy. Moreover, an image-based unexpected movement warning and other physiological monitoring are used during the intelligent treatment procedure for safety assurance. Results: In the experiment, we integrated the intelligent treatment system on a commercial HIFU treatment device, and eight clinical experiments were performed. In the clinical validation, eight randomly selected clinical cases were used to verify the feasibility of the system. The results of the quantitative experiment indicated that our intelligent system met the HIFU clinical tracking accuracy and speed requirements. Moreover, the results of simulated repeated experiments confirmed that the autodistributed HIFU focus reached the level of intermediate clinical doctors. Operations performed by junior- or middle-level operators with the assistance of the proposed system can reach the level of operation performed by senior doctors. Various experiments prove that our proposed intelligent HIFU therapy process is feasible for treating common uterine fibroid cases. Conclusion: We propose an intelligent HIFU therapy for uterine fibroid which integrates multiple medical information processing procedures. The experiment results demonstrated that the proposed procedures and methods can achieve monitored and automatic HIFU diagnosis and treatment. This research provides a possibility for intelligent and automatic noninvasive therapy for uterine fibroid.
Kindred Spirits: Predicting Fruitful Interactions with Emotional and Social Features

We study the problem of predicting meaningful interactions between users on the Experience Project, an online social network based around life experiences. Although most research on predicting friendships focuses around network structure, we believe that sensitivity to language, especially expressive, emotional language, is required to surface pairs of users that will form deep, meaningful friendships. This intuition guides our development of models to predict whether two users have interacted and to predict the strength of this interaction. We ﬁnd that network features alone are predictive of interactions, but models based solely on language features approach network-baed performance and models that combine network and language sensitivity appreciably outperform all other models.
FOOD DERIVED FROM INSECT-PROTECTED CORN LINE MIR162 APPROVAL REPORT

Food Standards Australia New Zealand (FSANZ) received an Application from Syngenta Seeds Pty Ltd (the Applicant) on 10 October 2007. The Applicant has requested an amendment to the Australia New Zealand Food Standards Code (the Code), specifically to Standard 1.5. 2 – Food produced using Gene Technology, to permit the sale and use of food derived from a new genetically modified (GM) variety of corn, MIR162. Standard 1.5. 2 prohibits a food produced using gene technology from being sold or used as an ingredient or component of any food unless it is listed in the Table to clause 2 of that Standard.
Root growth of sugarcane irrigated with wastewater through subsurface drip system

The use of wastewater in agriculture is an alternative to provide water and nutrients for plants. However, root system development can be affected by water quality and depth of wastewater applied. The objective of this study was to evaluate the sugarcane root system growth using a minirhizotron in a field irrigated with treated sewage effluent and freshwater by subsurface drip irrigation. The treatments tested were two drip line installation depths (0.2 and 0.4 m); two water sources (treated sewage effluent and freshwater) and non-irrigated plots as control. The experiment was a randomized block design with a 2×2 + 1 factorial, with three replications. The root system evaluation (root length and spatial distribution) was performed using a minirhizotron with an access tube buried in the soil profile and installed on a 45 degree angle. The soil moisture was determined using the time-domain reflectometry technique. The lowest moisture on the soil surface resulted in the highest root length density (0.18 cm cm -2 ) at the non-irrigated plots ( p >0.05). The application of treated sewage effluent and the installation depth of drip line did not change the root length of sugarcane in first ratoon ( p >0.05). Eighty percent of the root system was accumulated from the soil surface to 0.45 m.
Predictive Knowledge in Robots: An Empirical Comparison of Learning Algorithms

Knowledge is central to intelligence. Intelligence can be thought of as the ability to acquire knowledge and apply it effectively. Despite being a subject of intense interest in artificial intelligence, it is not yet clear what the best approach is for an intelligent system to acquire and maintain a large body of knowledge. One interesting approach that we pursue in this thesis is based on the view that much of world knowledge is predictive. For example, to know that a box is heavy, is to predict that we need lots of effort to lift it. We call this predictive approach to maintaining and acquiring knowledge, the predictive knowledge approach. In this thesis, we implement an instance of this approach in order to explore and assess it further. To do so, we build upon the techniques and ideas of reinforcement learning. In particular, we use the idea of value functions. In conventional RL, value functions capture predictions about reward. Recently, value functions have been extended to capture more general predictions which can constitute knowledge. A value function in the extended form is called a general value function (GVF). GVFs provide a language to talk and think about predictions. More generally, we can think of GVFs as a language for representing predictive knowledge. In this thesis, we develop the predictive knowledge view using the language of GVFs and apply it to several robot domains. Our work has three main contributions. First, we contribute to the idea of predictive knowledge by providing several new examples of it on robot domains, gaining a more substantive understanding of knowledge as predictions. Second, we perform ii empirical comparisons of many off-policy temporal-difference (TD) learning algorithms including gradient-TD and emphatic-TD families of methods on robot data. Third, we systematically study the learning process on robots. Such studies provide insights about how to effectively evaluate and compare algorithms on real-world systems.
Part-of-speech and Morphology Tagging Old Swedish

Natural language processing for historical material almost inevitably runs into the problematic combination of large variation (leading to domain adaptation-like problems) and low resources (problematic for the standard statistical methods of the field). In this paper we present our ongoing efforts in part-of-speech and morphology tagging Old Swedish, the medieval Swedish of the 13th to early 16th century. In comparison to Contemporary Swedish, Old Swedish shows syntactic differences, e.g., in its word order and argument realization, but in particular it has a richer morphology, comparable to that of contemporary German and especially Icelandic. The morphology however was in the process of becoming simpler during the Old Swedish period itself.
Efficient Enclave Communication through Shared Memory

Open vSwitch is a virtual network switch commonly used to forward network packages between virtual machines. The switch routes network packets based on a set of flow rules stored in its flow tables. Open vSwitch does not provide confidentiality or integrity protection of its flow tables; therefore, an attacker can exploit software vulnerabilities in Open vSwitch to gain access to the host machine and observe or modify installed flow rules. Medina [1] brought integrity and confidentially guarantees to the flow tables of Open vSwitch, even in the presence of untrusted privileged software, by confining them inside of an Intel SGX enclave. However, using an enclave to protect the flow tables has significantly reduced the performance of Open vSwitch. This thesis investigates how and to what extent the performance overhead introduced by Intel SGX in Open vSwitch can be reduced. The method consisted of the development of a general-purpose communication library for Intel SGX enclaves, and two optimized SGX enabled Open vSwitch prototypes. The library enables efficient communication between the enclave and the untrusted application through shared memory-based techniques. Integrating the communication library in Open vSwitch, combined with other optimization techniques, resulted in two optimized prototypes that were evaluated on a set of common Open vSwitch use cases. The results of this thesis show that it is possible to reduce the overhead introduced by Intel SGX in Open vSwitch with several orders of magnitude, depending on the use case and optimization technique, without compromising its security guarantees.