Miracle-dz/UniFAR-FTUs huggingface dataset:
REM sleep in naps differentially relates to memory consolidation in typical preschoolers and children with Down syndrome

Significance This paper demonstrates that typical children have enhanced learning of new words across sleep periods (naps) which is linked to the amount of time in rapid eye movement (REM) sleep and shows sleep-dependent learning losses in an atypically developing group of children with REM deficits (e.g., Down syndrome). The work yields both medical and theoretical impacts by (i) highlighting a modifiable mechanism of intellectual disability in Down syndrome that has not been described before and (ii) emphasizing the important role of REM sleep in children’s learning. Sleep is recognized as a physiological state associated with learning, with studies showing that knowledge acquisition improves with naps. Little work has examined sleep-dependent learning in people with developmental disorders, for whom sleep quality is often impaired. We examined the effect of natural, in-home naps on word learning in typical young children and children with Down syndrome (DS). Despite similar immediate memory retention, naps benefitted memory performance in typical children but hindered performance in children with DS, who retained less when tested after a nap, but were more accurate after a wake interval. These effects of napping persisted 24 h later in both groups, even after an intervening overnight period of sleep. During naps in typical children, memory retention for object-label associations correlated positively with percent of time in rapid eye movement (REM) sleep. However, in children with DS, a population with reduced REM, learning was impaired, but only after the nap. This finding shows that a nap can increase memory loss in a subpopulation, highlighting that naps are not universally beneficial. Further, in healthy preschooler’s naps, processes in REM sleep may benefit learning.
Causal Shannon–Fisher Characterization of Motor/Imagery Movements in EEG

The electroencephalogram (EEG) is an electrophysiological monitoring method that allows us to glimpse the electrical activity of the brain. Neural oscillations patterns are perhaps the best salient feature of EEG as they are rhythmic activities of the brain that can be generated by interactions across neurons. Large-scale oscillations can be measured by EEG as the different oscillation patterns reflected within the different frequency bands, and can provide us with new insights into brain functions. In order to understand how information about the rhythmic activity of the brain during visuomotor/imagined cognitive tasks is encoded in the brain we precisely quantify the different features of the oscillatory patterns considering the Shannon–Fisher plane H×F. This allows us to distinguish the dynamics of rhythmic activities of the brain showing that the Beta band facilitate information transmission during visuomotor/imagined tasks.
Comparison of Animation and Static-picture based Instruction: Effects on Performance and Cognitive Load for Learning Genetics

This study aimed to examine the relative effectiveness of using an animation versus static pictures in terms of supporting the learning of genetics. To provide a methodologically sound comparison, the two sessions were constructed to be equivalent and were designed based on principles provided by the cognitive theory of multimedia learning and the cognitive load theory. Genetics was chosen as the instructional content, which included learning about the processes of cell division, mitosis, and meiosis. The results indicate that students in the animation group perceived less extraneous cognitive load and achieved a better learning outcome than those in the static pictures group. Therefore, this study supports the superiority of the animation over static picture instruction when learning micro-scientific phenomena.
Multiscale Entropy Analysis for Recognition of Visually Elicited Negative Stress From EEG Recordings

Automatic identification of negative stress is an unresolved challenge that has received great attention in the last few years. Many studies have analyzed electroencephalographic (EEG) recordings to gain new insights about how the brain reacts to both short- and long-term stressful stimuli. Although most of them have only considered linear methods, the heterogeneity and complexity of the brain has recently motivated an increasing use of nonlinear metrics. Nonetheless, brain dynamics reflected in EEG recordings often exhibit a multiscale nature and no study dealing with this aspect has been developed yet. Hence, in this work two nonlinear indices for quantifying regularity and predictability of time series from several time scales are studied for the first time to discern between visually elicited emotional states of calmness and negative stress. The obtained results have revealed the maximum discriminant ability of 86.35% for the second time scale, thus suggesting that brain dynamics triggered by negative stress can be more clearly assessed after removal of some fast temporal oscillations. Moreover, both metrics have also been able to report complementary information for some brain areas.
The Shiraz Breast Cancer Registry (SBCR): study design and primary reports.

AIM This is a description of the largest breast cancer (BC) registry in Iran, termed the Shiraz Breast Cancer Registry (SBCR). METHODS Data on baseline and clinical characteristics, socioeconomic status, imaging, physical examination, histopathology, treatment and prognosis have been recorded for each individual. RESULTS Overall, 5937 were included in the report. Mean age of first presentation was 49.05 ± 11.69 years. Mean tumor size was 2.78 ± 1.76 cm. Most patients had stage 2 (46.9%) and 3 (25.5%) BCs, respectively. Most common type of BC was invasive ductal carcinoma (83.3%), followed by medullary carcinoma (3.8%). Overall, 12.9% were triple negative (HER2-, ER- and PR-). CONCLUSION The study provides an overview on the status of BC's in Iran and a wide opportunity for future studies.
Generating Self-Guided Dense Annotations for Weakly Supervised Semantic Segmentation

Learning semantic segmentation models under image-level supervision is far more challenging than under fully supervised setting. Without knowing the exact pixel-label correspondence, most weakly-supervised methods rely on external models to infer pseudo pixel-level labels for training semantic segmentation models. In this paper, we aim to develop a single neural network without resorting to any external models. We propose a novel self-guided strategy to fully utilize features learned across multiple levels to progressively generate the dense pseudo labels. First, we use high-level features as class-specific localization maps to roughly locate the classes. Next, we propose an affinity-guided method to encourage each localization map to be consistent with their intermediate level features. Third, we adopt the training image itself as guidance and propose a self-guided refinement to further transfer the image's inherent structure into the maps. Finally, we derive pseudo pixel-level labels from these localization maps and use the pseudo labels as ground truth to train the semantic segmentation model. Our proposed self-guided strategy is a unified framework, which is built on a single network and alternatively updates the feature representation and refines localization maps during the training procedure. Experimental results on PASCAL VOC 2012 segmentation benchmark demonstrate that our method outperforms other weakly-supervised methods under the same setting.
Sharp Analysis of Learning with Discrete Losses

The problem of devising learning strategies for discrete losses (e.g., multilabeling, ranking) is currently addressed with methods and theoretical analyses ad-hoc for each loss. In this paper we study a least-squares framework to systematically design learning algorithms for discrete losses, with quantitative characterizations in terms of statistical and computational complexity. In particular, we improve existing results by providing explicit dependence on the number of labels for a wide class of losses and faster learning rates in conditions of low-noise. Theoretical results are complemented with experiments on real datasets, showing the effectiveness of the proposed general approach.
Application of Digital Technologies in Maxillofacial Prosthetics Literature: A 10-Year Observation of Five Selected Prosthodontics Journals.

PURPOSE To identify trends in the application of various types of digital technologies in maxillofacial prosthetics by identifying these digital technologies and their characteristics and reviewing the prevalence of applied digital technologies and their recent trends in the maxillofacial prosthetics literature. MATERIALS AND METHODS Five leading peer-reviewed prosthodontics journals were manually searched for maxillofacial prosthetics articles on the use of digital technologies published from January 2008 to December 2017. Descriptive statistics were used to analyze data collected on productivity, type of digital technology used, type of article, type of defect, number of authors, and geographic distribution. RESULTS Of the 336 maxillofacial prosthetics articles screened, 87 (26%) were selected for analysis. A remarkable increase was found in article productivity in the last 5 years (63%) compared to the first 5 years (37%). There was also a notable increase over the last 10 years in all digital technologies used except for evaluation technologies, which remained almost constant. Case reports most frequently mentioned use of digitization technologies (61%), design technologies (66%), and rapid prototyping technologies (66%). Original research articles most frequently mentioned use of evaluation technologies (89%); 39% of these technologies were used for intraoral defects and 27% for extraoral defects. Most often, articles had four to five authors (54%). Most published articles were from the Asia-Pacific region (44%), followed by North America (22%) and Europe (20%). CONCLUSION Although the application of digital technologies reported in the maxillofacial prosthetics literature has increased notably in leading prosthodontics journals over the last 10 years, total article productivity has remained relatively small.
Towards sustainable technology-enhanced innovation in higher education: Advancing learning design by understanding and supporting teacher design practice

Improving teacher design promises to be a scalable, sustainable approach to building capacity amongst a workforce faced with complex and evolving drivers of change in higher education worldwide. While design has long been recognised as a routine part of teaching, there has been renewed interest in supporting and understanding the design work that teachers do to foster innovation, particularly in technology‐enhanced learning, at institutional scale by influencing teachers' practices. Re‐framing teaching as design usefully emphasises the creative problem‐solving needed to balance pedagogical, logistical and technical considerations within specific educational contexts, tailored to learners' needs. There is potential for this re‐framing to build on and advance work in "learning design" and "design for learning" that has generated a wide range of practical supports and tools. In this article, we explore, problematise and conceptualise the notion of "teacher as designer" within the complexity of contemporary higher education through a critical review of existing empirical and conceptual work internationally. We offer insights into the current state of knowledge about teacher design in higher education, highlight gaps and possibilities, offer a new conceptualisation drawing on practice theory and set out propositions to provoke further debate about teacher design as a vehicle for sustainable innovation in higher education. [ABSTRACT FROM AUTHOR]
Feasibility study by a single-blind randomized controlled trial of self-management of mobility with a gait-speed feedback device by older persons at risk for falling

ABSTRACT This single-blind randomized pilot study explored feasibility and safety of a self-management fall prevention program, hypothesizing that older persons can comply with this program, while it does not result in more (injurious) falls, or a decrease in mental wellbeing as an adverse effect of being focused on falls prevention. Eighty-six persons, community-dwelling or home for the aged (mean age 80.3 years [SD: 6.3], 56 women (65.1%)) participated. The intervention group measured their gait speed by using the Mobility Feedback Device (MFD) weekly for 6 months. The control group was monitored for the outcomes without an intervention. Change scores involving health perception and mental wellbeing (Medical Outcomes Study 20-item short form (MOS-20)) were compared between groups. Feasibility was assessed by drop-out rate and compliance to measure gait speed. Safety was assessed by fall incidence during follow-up. MOS-20 decreased significantly in the control group (p = 0.024) but remained stable in the intervention group. Drop-out rate was low (9.3%), and compliance was good. Fall incidence was the same for both groups (p = 0.155). The self-management fall prevention program is feasible and safe in a community-dwelling and home for the aged population, making it worthwhile to further explore self-management fall-prevention studies.
Compositional Attention Networks for Interpretability in Natural Language Question Answering

MAC Net is a compositional attention network designed for Visual Question Answering. We propose a modified MAC net architecture for Natural Language Question Answering. Question Answering typically requires Language Understanding and multi-step Reasoning. MAC net's unique architecture - the separation between memory and control, facilitates data-driven iterative reasoning. This makes it an ideal candidate for solving tasks that involve logical reasoning. Our experiments with 20 bAbI tasks demonstrate the value of MAC net as a data-efficient and interpretable architecture for Natural Language Question Answering. The transparent nature of MAC net provides a highly granular view of the reasoning steps taken by the network in answering a query.
Neural Net Classification Combined With Movement Analysis to Evaluate Setaria viridis as a Model System for Time of Day of Anther Appearance

In many plant species, the time of day at which flowers open to permit pollination is tightly regulated. Proper time of flower opening, or Time of Day of Anther Appearance (TAA), may coordinate flowering opening with pollinator activity or may shift temperature sensitive developmental processes to cooler times of the day. The genetic mechanisms that regulate the timing of this process in cereal crops are unknown. To address this knowledge gap, it is necessary to establish a monocot model system that exhibits variation in TAA. Here, we examine the suitability of Setaria viridis, the model for C4 photosynthesis, for such a role. We developed an imaging system to monitor the temporal regulation of growth, flower opening time, and other physiological characteristics in Setaria. This system enabled us to compare Setaria varieties Ames 32254, Ames 32276, and PI 669942 variation in growth and daily flower opening time. We observed that TAA occurs primarily at night in these three Setaria accessions. However, significant variation between the accessions was observed for both the ratio of flowers that open in the day vs. night and the specific time of day where the rate is maximal. Characterizing this physiological variation is a requisite step toward uncovering the molecular mechanisms regulating TAA. Leveraging the regulation of TAA could provide researchers with a genetic tool to improve crop productivity in new environments.
No Difference on Adherence Between Immediate‐Release Versus Extended‐Release Dopamine Agonists in Uninsured Subjects with Parkinson's Disease

Pharmacological management of subjects with Parkinson's disease (PD) is complex. Regardless of drug selection, adherence is one of the main concerns. Nonadherence is associated with poor symptomatic control and low quality of life. In general, adherence to once‐a‐day formulations is thought to be better in comparison to 3‐times‐daily dosing.
Generation and characterization of pathogenic Mab21l2(R51C) mouse model

MAB21L2(R51C) is one of the five documented MAB21L2 mutations in human patients with bilateral eye malformations identified via whole exome sequencing. In addition to the eye abnormality, patients with MAB21L2 R51C/+ mutation also have skeletal dysplasia and intellectual disability. To evaluate the pathology of this mutant allele systematically in understanding the functional role of MAB21L2 in human development, we introduce the R51C mutation into the mouse genome by CRISPR/Cas9 system to generate a mouse model for detailed characterization. The Mab21l2 R51C/+ mice have eyeless phenotype and skeletal abnormalities. Micro‐computed tomography (micro‐CT) analysis showed the Mab21l2 R51C/+ mice have no eye balls but with two abnormal tissues underneath the brain. Histological analysis revealed that the early eye development in the mutant embryos is interrupted. In addition, Mab21l2 R51C/+ mice also have joint fusion phenotype; the humerus is fused with radius, whereas femur is fused with tibia. Limbs in the mutant animals are distinctly shorter than the wild type; and deltoid tuberosities in humeri are absent in these Mab21l2 R51C/+ mice. In summary, we showed that our Mab21l2 R51C/+ mutant mice have recapitulated the pathological features in eye and bone of human patients. Further analyses of the mutant phenotype with molecular markers will provide insight on how MAB21L2 guides the optic differentiation and skeletogenesis, revealing specific underlying pathogenic mechanism of the MAB21L2(R51C) mutation.
Volatile Compound-Mediated Recognition and Inhibition Between Trichoderma Biocontrol Agents and Fusarium oxysporum

Certain Trichoderma strains protect plants from diverse pathogens using multiple mechanisms. We report a novel mechanism that may potentially play an important role in Trichoderma-based biocontrol. Trichoderma virens and T. viride significantly increased the amount/activity of secreted antifungal metabolites in response to volatile compounds (VCs) produced by 13 strains of Fusarium oxysporum, a soilborne fungus that infects diverse plants. This response suggests that both Trichoderma spp. recognize the presence of F. oxysporum by sensing pathogen VCs and prepare for attacking pathogens. However, T. asperellum did not respond to any, while T. harzianum responded to VCs from only a few strains. Gene expression analysis via qPCR showed up-regulation of several biocontrol-associated genes in T. virens in response to F. oxysporum VCs. Analysis of VCs from seven F. oxysporum strains tentatively identified a total of 28 compounds, including six that were produced by all of them. All four Trichoderma species produced VCs that inhibited F. oxysporum growth. Analysis of VCs produced by T. virens and T. harzianum revealed the production of compounds that had been reported to display antifungal activity. F. oxysporum also recognizes Trichoderma spp. by sensing their VCs and releases VCs that inhibit Trichoderma, suggesting that both types of VC-mediated interaction are common among fungi.
Sound regular expression semantics for dynamic symbolic execution of JavaScript

Support for regular expressions in symbolic execution-based tools for test generation and bug finding is insufficient. Common aspects of mainstream regular expression engines, such as backreferences or greedy matching, are ignored or imprecisely approximated, leading to poor test coverage or missed bugs. In this paper, we present a model for the complete regular expression language of ECMAScript 2015 (ES6), which is sound for dynamic symbolic execution of the test and exec functions. We model regular expression operations using string constraints and classical regular expressions and use a refinement scheme to address the problem of matching precedence and greediness. We implemented our model in ExpoSE, a dynamic symbolic execution engine for JavaScript, and evaluated it on over 1,000 Node.js packages containing regular expressions, demonstrating that the strategy is effective and can significantly increase the number of successful regular expression queries and therefore boost coverage.
Two sphingomyelin synthase homologues regulate body weight and sphingomyelin synthesis in female brown planthopper, N. lugens (Stål)

Although sphingomyelins known to be are lipid constituents of the plasma membrane in vertebrates, much remains obscure about the metabolism of sphingomyelins in insects. With ultra performance liquid chromatography‐time‐of‐flight‐tandem mass spectrometry analysis, we revealed for the first time that sphingomyelins are abundant in Nilaparvata lugens (Stål), the brown planthopper (BPH), and their biosynthesis is carried out by sphingomyelin synthase‐like protein 2 (SMSL2), which is homologous to sphingomyelin synthase‐related protein (SMSr). Unlike other insect species, high concentrations of sphingomyelins rather than ceramide phosphoethanolamines exist in the BPH. Two putative genes, which are homologous to SMSr, are named Nilaparvata lugens SMS‐like 1 (NlSMSL1) and 2 (NlSMSL2). Knockdowns of both NlSMSL2 and NlSMSL1 were conducted but only the first decreased concentrations of sphingomyelins in the BPH, indicating that NlSMSL2 plays a role in the biosynthesis of sphingomyelins. Real‐time quantitative PCR analysis revealed both NlSMSL1 and NlSMSL2 are highly expressed in BPH adults, with NlSMSL1 specifically highly expressed in reproductive organs (ovaries and testes) whereas NlSMSL2 was highly expressed in the malpighian tubules. The knockdown of NlSMSL1 or NlSMSL2 increased BPH female body weight but not that of males, suggesting sex‐specific roles for SMSLs in influencing BPH body weight. The results suggest that NlSMSL2 catalyses the synthesis of sphingomyelins and maintains female BPH body weight through alteration of sphingolipid content.
Tracing Transactions Across Cryptocurrency Ledgers

One of the defining features of a cryptocurrency is that its ledger, containing all transactions that have evertaken place, is globally visible. As one consequenceof this degree of transparency, a long line of recent re-search has demonstrated that even in cryptocurrenciesthat are specifically designed to improve anonymity it is often possible to track money as it changes hands,and in some cases to de-anonymize users entirely. With the recent proliferation of alternative cryptocurrencies, however, it becomes relevant to ask not only whether ornot money can be traced as it moves within the ledgerof a single cryptocurrency, but if it can in fact be tracedas it moves across ledgers. This is especially pertinent given the rise in popularity of automated trading platforms such as ShapeShift, which make it effortless to carry out such cross-currency trades. In this paper, weuse data scraped from ShapeShift over a thirteen-monthperiod and the data from eight different blockchains to explore this question. Beyond developing new heuristics and creating new types of links across cryptocurrency ledgers, we also identify various patterns of cross-currency trades and of the general usage of these platforms, with the ultimate goal of understanding whetherthey serve a criminal or a profit-driven agenda.