Miracle-dz/UniFAR-FTUs huggingface dataset:
Extracellular Vesicles as Mediators of Cellular Crosstalk Between Immune System and Kidney Graft

Extracellular vesicles (EVs) are known immune-modulators exerting a critical role in kidney transplantation (KT). EV bioactive cargo includes graft antigens, costimulatory/inhibitory molecules, cytokines, growth factors, and functional microRNAs (miRNAs) that may modulate expression of recipient cell genes. As paracrine factors, neutrophil- and macrophage-derived EVs exert immunosuppressive and immune-stimulating effects on dendritic cells, respectively. Dendritic cell-derived EVs mediate alloantigen spreading and modulate antigen presentation to T lymphocytes. At systemic level, EVs exert pleiotropic effects on complement and coagulation. Depending on their biogenesis, they can amplify complement activation or shed complement inhibitors and prevent cell lysis. Likewise, endothelial- and platelet-derived EVs can exert procoagulant/prothrombotic effects and also promote endothelial survival and angiogenesis after ischemic injury. Kidney endothelial- and tubular-derived EVs play a key role in ischemia–reperfusion injury (IRI) and during the healing process; additionally, they can trigger rejection by inducing both alloimmune and autoimmune responses. Endothelial EVs have procoagulant/pro-inflammatory effects and can release sequestered self-antigens, generating a tissue-specific autoimmunity. Renal tubule-derived EVs shuttle pro-fibrotic mediators (TGF-β and miR-21) to interstitial fibroblasts and modulate neutrophil and T-lymphocyte influx. These processes can lead to peritubular capillary rarefaction and interstitial fibrosis–tubular atrophy. Different EVs, including those from mesenchymal stromal cells (MSCs), have been employed as a therapeutic tool in experimental models of rejection and IRI. These particles protect tubular and endothelial cells (by inhibition of apoptosis and inflammation–fibrogenesis or by inducing autophagy) and stimulate tissue regeneration (by triggering angiogenesis, cell proliferation, and migration). Finally, urinary and serum EVs represent potential biomarkers for delayed graft function (DGF) and acute rejection. In conclusion, EVs sustain an intricate crosstalk between graft tissue and innate/adaptive immune systems. EVs play a major role in allorecognition, IRI, autoimmunity, and alloimmunity and are promising as biomarkers and therapeutic tools in KT.
Ocular melanoma: A rare entity

Malignant melanoma of eyeball is a rare entity. The overall incidence of malignant melanoma is 3%–7% per year worldwide. Early detection and meticulous treatment planning and its execution are challenge to surgeons. A 38-year-old male presented with loss of visionin one eye, and after investigating, it turned out to be malignant melanoma of the eyeball. He was treated surgically by enucleation of the eyeball followed by prosthetic rehabilitation.
Excessive Daytime Sleepiness Measurements in Children With Attention Deficit Hyperactivity Disorder

Attention deficit hyperactivity disorder (ADHD) is the most commonly diagnosed neurodevelopmental disorder in childhood. It is a heterogeneous disorder in terms of clinical presentation that is probably due to the frequent occurrence of comorbidity. Children with ADHD more frequently report sleep disorders (notably delayed sleep phase syndrome) and excessive daytime sleepiness (EDS) than typically developing children. The aim of this article is to propose a narrative review of the assessment of EDS in the context of ADHD with first a summary of the subjective and objective tools used to measure it. Secondly, perspectives in terms of electroencephalogram (EEG) markers and neurofeedback are proposed. Then, possibilities for new kinds of evaluation are discussed (virtual reality, ecological momentary assessment, etc.). Lastly, we discuss specific clinical situations with EDS in the context of ADHD as links with narcolepsy, the comorbidity with other psychiatric disorders, and the context of sluggish cognitive tempo.
Effect of Positive Psychology Interventions on the Quality of Prenatal Care Offered by Midwives: A Field Trial

Background: The quality of prenatal care has been recognized as critical to the effectiveness of care in optimizing maternal and child health outcomes. This study examined the effect of positive psychology interventions on the quality of prenatal care offered by midwives. Materials and Methods: This field trial was conducted on 60 midwives working in community health centers in Mashhad, Iran, from September 23, 2015 to March 20, 2016. Initially, centers No. 1 and No. 3 were selected via cluster sampling from among the five healthcare centers of Mashhad. Then, all subsidiaries of these centers were listed and assigned to intervention and control groups through simple random sampling. Thus, 60 midwives were randomly assigned to two equal intervention and control groups. The intervention, based on Seligman's Well-Being Theory, was presented weekly with homework in eight 2-h sessions. Before the interventions and immediately after the intervention, the Oxford Happiness Questionnaire (OHQ) and Ryff's Scales of Psychological Well-Being (SPWB) were completed by the midwives, and the Quality of Prenatal Care Questionnaire (QPCQ) was completed by two pregnant women for each midwife. Results: After the intervention, the mean [Standard Deviation (SD)] score of the overall quality of prenatal care in the intervention group was significantly higher than that of the control group [mean (SD) = 1.51 (0.49) vs. 0.05 (0.21); t43,12= 18.7, p < 0.001]. Conclusions: It seems that improving the well-being of midwives through positive psychology interventions is effective on the quality of prenatal care provided by them.
Management Strategies for POSEIDON Group 2

Although individualization of ovarian stimulation aims at maximal efficacy and safety in assisted reproductive treatments, in its current form it is far from ideal in achieving the desired success in women with a low prognosis. This could be due a failure to identify such women who are likely to have a low prognosis with currently used prognostic characteristics. Introduction of the patient-oriented strategies encompassing individualized oocyte number (POSEIDON) concept reinforces recognizing such low prognosis groups and stratifying in accordance with important prognostic factors. The POSEIDON concept provides a practical approach to the management of these women and is a useful tool for both counseling and clinical management. In this commentary, we focus on likely management strategies for POSEIDON group 2 criteria.
Deep Transcriptome Sequencing of Pediatric Acute Myeloid Leukemia Patients at Diagnosis, Remission and Relapse: Experience in 3 Malaysian Children in a Single Center Study

Among the many types of leukemia, acute myeloid leukemia (AML) affects 20% of diagnosed hematological malignancies in pediatric patients (Meshinchi and Arceci, 2007; de Rooij et al., 2015). Standard chemotherapy regimen remains as the first line treatment for pediatric AML, however nearly 40% of AML patients may suffer from relapse and eventually die from the disease (de Rooij et al., 2015). Similarly, it has been reported that 50% of the pediatric AML relapsed within 12–18 months of diagnosis and 45% of those relapsed were not expected to survive (Creutzig et al., 2014). Despite advances in cytogenetic analysis through fluorescence in situ hybridization and multiplex PCR, there is still a need for a better and comprehensive molecular profiling. For instance, microarray has long been used to study the gene expression profiles of AML patients. The different profile of gene expression has enabled clinicians to tailor better treatment for patients and predict whether patients have the tendency to relapse (Goswami et al., 2009). In a recent study, Handschuch et al. reported that three genes, ANXA3, S100A9, and WT1 can differentiate between different prognostic types of AML (Handschuh et al., 2018). The study outcome was in agreement with another study conducted by Shimada et al. (2012), where a high expression of WT1 gene showed prognostic impact in pediatric AML (Shimada et al., 2012). Another study by Jo et al. (2015) reported that high expression of EVI1 and MEL1 could predict the prognosis of pediatric AML (Jo et al., 2015). However, none of the biomarkers identified from these studies have been translated into clinical use. Therefore, the search continues for additional promising biomarkers, notably novel transcripts, novel fusion genes and non-coding RNAs which are not represented in the microarray platform. Transcriptome sequencing through next generation sequencing represents an effective approach to discover
Stress-Based Production, and Characterization of Glutathione Peroxidase and Glutathione S-Transferase Enzymes From Lactobacillus plantarum

More attention has been recently directed toward glutathione peroxidase and s-transferase enzymes because of the great importance they hold with respect to their applications in the pharmaceutical field. This work was conducted to optimize the production and characterize glutathione peroxidase and glutathione s-transferase produced by Lactobacillus plantarum KU720558 using Plackett-Burman and Box-Behnken statistical designs. To assess the impact of the culture conditions on the microbial production of the enzymes, colorimetric methods were used. Following data analysis, the optimum conditions that enhanced the s-transferase yield were the De Man-Rogosa-Sharp (MRS) broth as a basal medium supplemented with 0.1% urea, 0.075% H2O2, 0.5% 1-butanol, 0.0125% amino acids, and 0.05% SDS at pH 6.0 and anaerobically incubated for 24 h at 40°C. The optimum s-transferase specific activity was 1789.5 U/mg of protein, which was ~12 times the activity of the basal medium. For peroxidase, the best medium composition was 0.17% urea, 0.025% bile salt, 7.5% Na Cl, 0.05% H2O2, 0.05% SDS, and 2% ethanol added to the MRS broth at pH 6.0 and anaerobically incubated for 24 h at 40°C. Furthermore, the optimum peroxidase specific activity was 612.5 U/mg of protein, indicating that its activity was 22 times higher than the activity recorded in the basal medium. After SDS-PAGE analysis, GST and GPx showed a single protein band of 25 and 18 kDa, respectively. They were able to retain their activities at an optimal temperature of 40°C for an hour and pH range 4–7. The 3D model of both enzymes was constructed showing helical structures, sheet and loops. Protein cavities were also detected to define druggable sites. GST model had two large pockets; 185Å3 and 71 Å3 with druggability score 0.5–0.8. For GPx, the pockets were relatively smaller, 71 Å3 and 32 Å3 with druggability score (0.65–0.66). Therefore, the present study showed that the consortium components as well as the stress-based conditions used could express both enzymes with enhanced productivity, recommending their application based on the obtained results.
The Potential of Stereotactic-EEG for Brain-Computer Interfaces: Current Progress and Future Directions

Stereotactic electroencephalogaphy (sEEG) utilizes localized, penetrating depth electrodes to measure electrophysiological brain activity. It is most commonly used in the identification of epileptogenic zones in cases of refractory epilepsy. The implanted electrodes generally provide a sparse sampling of a unique set of brain regions including deeper brain structures such as hippocampus, amygdala and insula that cannot be captured by superficial measurement modalities such as electrocorticography (ECoG). Despite the overlapping clinical application and recent progress in decoding of ECoG for Brain-Computer Interfaces (BCIs), sEEG has thus far received comparatively little attention for BCI decoding. Additionally, the success of the related deep-brain stimulation (DBS) implants bodes well for the potential for chronic sEEG applications. This article provides an overview of sEEG technology, BCI-related research, and prospective future directions of sEEG for long-term BCI applications.
Runtime multi-versioning and specialization inside a memoized speculative loop optimizer

In this paper, we propose a runtime framework that implements code multi-versioning and specialization to optimize and parallelize loop kernels that are invoked many times with varying parameters. These parameters may influence the code structure, the touched memory locations, the workload, and the runtime performance. They may also impact the validity of the parallelizing and optimizing polyhedral transformations that are applied on-the-fly. For a target loop kernel and its associated parameters, a different optimizing and parallelizing transformation is evaluated at each invocation, among a finite set of transformations (multi-versioning and specialization). The best performing transformed code version is stored and indexed using its associated parameters. When every optimizing transformation has been evaluated, the best performing code version regarding the current parameters, which has been stored, is relaunched at next invocations (memoization).
Increasing Diversity in Computing: A Focus on Retention

It is recognized that diversity of perspectives results in solutions that serve a broad base. This is critical to the area of computing, which has applications to science, humanities, as well as foundational work to advance the field of computing. Cultivating an environment that values and retains diversity, however, is not an easy task. This is especially the case in the "classroom", whereby the students are in the space for only a few hours per week. Hence, in terms of retention, it is necessary to complement "classroom" activities to foster and retain diversity, with extra-curricular activities. The importance of retention of diverse students, which results in an increase in the graduation of diverse students, resulted in the Center for Minorities and People with Disabilities in IT (CMD-IT) establishing the CMD-IT University Award for the Retention of Minorities and People with Disabilities in Computing. In this talk, I will discuss lessons learned from the award as well as other organizations and alliances focused on retention of diverse students and present some challenges to the CS Education community.
Routing Authentication Chain Mechanism to Resist AODV Black Hole Attacks

The black hole attack is a main kind of attack against the ad hoc on demand distance vector (AODV) protocol in ad hoc networks. A much greater threat is a joint attack of multiple black holes. To prevent black hole attacks, this paper proposes an authentication chain mechanism based on the secure recursive function, by which the successive nodes of a route are unified only on the deterministic state transition relation of the recursive function to implement a unique association for security, such that the whole route forms an authentication chain. With the mechanism of this paper, even if a pseudorandom linear sequence is adopted as a kind of simple recursive function, as long as its linear complexity is greater than the number of nodes in the authenticating chain, the attacker cannot obtain continuous state values of more than two times the linear complexity of the sequence, Thus the secure authentication of the route can be guaranteed. Therefore, the mechanism and method presented in this paper represent a novel and effective approach to defend against black hole attacks.
Graph-based network analysis of transcriptional regulation pattern divergence in duplicated yeast gene pairs

The genome and interactome of Saccharomyces cerevisiae have been characterized extensively over the course of the past few decades. However, despite many insights gained over the years, both functional studies and evolutionary analyses continue to reveal many complexities and confounding factors in the construction of reliable transcriptional regulatory network models. We present here a graph-based technique for comparing transcriptional regulatory networks based on network motif similarity for gene pairs. We construct interaction graphs for duplicated transcription factor pairs traceable to the ancestral whole-genome duplication as well as other paralogues in Saccharomyces cerevisiae. We create a set of network divergence metrics predicated on the presence and size of bi-fan arrays that are associated in the literature with gene duplication, within other network motifs. We compare the developed metrics to paralogue protein, gene and promoter alignment-free sequence dissimilarity to validate our results. We observe that our network divergence metrics generally agree with paralogue protein and gene sequence dissimilarity, and notice a weaker agreement with promoter dissimilarity. Our findings indicate that genetic divergence between paralogues is accompanied by a corresponding divergence in their interaction networks, and that our approach may be useful for investigating structural similarity in the interaction networks of paralogous genes.
Practical Urban Localization for Mobile AR

Emerging mobile apps render AR effects based on the places of interest (POI) that a user is currently in. To obtain the needed POI labels and a smartphone's camera position and orientation, such apps demand inexpensive localization. Yet, existing localization solutions either work poorly in urban areas or require expensive data collection. To this end, we advocate for an inexpensive, practical localization pipeline by integrating commodity vision operators. To instantiate the pipeline, we propose a system with three key designs: the cloud indexes image features as a forest rather than a monolithic tree; smartphones incrementally prefetch image features for on-device matching rather than uploading features to the cloud; smartphones tune the camera positioning algorithm dynamically based on its physical environment. Our preliminary results show that these designs can reduce the cost of image data collection by up to three orders of magnitude, reduce user-perceived delays, and scale to diverse AR resource demands and environments.
SRC: Discovering Human Activity Community in A City

This study investigates human activity community in a city by conceptualizing it as a network embedding problem. In order to learn the latent representations of activity-travel patterns from individual daily trajectories, network embedding learns a vector space representation for each type of activity place as a node connected by movement links to preserve the structure of individual activities. The proposed approach is applied to mobile positioning data at the individual level obtained for a weekday from volunteers at Guangzhou City. Assessments are conducted to validate individual decision making for several types of activities by a field survey. This study contributes to a general framework for discovering individual activity-travel patterns from human movement trajectories.
HALO: post-link heap-layout optimisation

Today, general-purpose memory allocators dominate the landscape of dynamic memory management. While these solutions can provide reasonably good behaviour across a wide range of workloads, it is an unfortunate reality that their behaviour for any particular workload can be highly suboptimal. By catering primarily to average and worst-case usage patterns, these allocators deny programs the advantages of domain-specific optimisations, and thus may inadvertently place data in a manner that hinders performance, generating unnecessary cache misses and load stalls. To help alleviate these issues, we propose HALO: a post-link profile-guided optimisation tool that can improve the layout of heap data to reduce cache misses automatically. Profiling the target binary to understand how allocations made in different contexts are related, we specialise memory-management routines to allocate groups of related objects from separate pools to increase their spatial locality. Unlike other solutions of its kind, HALO employs novel grouping and identification algorithms which allow it to create tight-knit allocation groups using the entire call stack and to identify these efficiently at runtime. Evaluation of HALO on contemporary out-of-order hardware demonstrates speedups of up to 28% over jemalloc, out-performing a state-of-the-art data placement technique from the literature.
The Practical Details of Building a CS Concept Inventory

Concept inventories (CIs) allow researchers and practitioners to measure student conceptual learning within a course or topic area. While they have enabled meaningful pedagogical change in other disciplines, there are relatively few CIs in computer science. In this paper, we report on our experiences as recent developers of a CI for basic data structures. We discuss each step along the route to a CI and offer tips based on what we have learned. We encourage others to create CIs, and we hope that this paper will serve as a practical guide through the process.
Didactic Methods of Integrating Programming in Mathematics in Primary School: Findings from a Swedish National Project

The association between mathematics and programming in an educational context is not new. Today, programming has been introduced into curricula worldwide for younger children. In the Swedish case, primary school teachers are expected to integrate programming in mathematics education from autumn 2018. However, Swedish teachers' knowledge of programming and programming didactics is limited. Meanwhile, there is little research on K-9 programming education. This has led to the dilemma that the mathematics teachers have limited support in didactic knowledge and good examples. This study reports on a teacher professional development project in programming. More specifically, teachers used Lesson Study to plan, execute, and evaluate lessons that integrated programming into various school subjects in elementary school. This study analyzed the didactic strategies developed in 10 lesson studies, as well as mapped the opportunities and challenges of pupils' learning in the mathematics subject. The result was the identification of three didactic strategies, which were analog programming, robot programming and block programming, as well as 11 didactic methods applied within these strategies. The paper contributes with examples of the didactic methods that teachers have developed and evaluated using lesson study. The paper further provides insights on how teachers can take progression into account by applying the three didactic strategies. At last but not least, the study shows a great need for teachers to develop computational thinking abilities.
Oak: a scalable off-heap allocated key-value map

Efficient ordered in-memory key-value (KV-)maps are paramount for the scalability of modern data platforms. In managed languages like Java, KV-maps face unique challenges due to the high overhead of garbage collection (GC). We present Oak, a scalable concurrent KV-map for environments with managed memory. Oak offloads data from the managed heap, thereby reducing GC overheads and improving memory utilization. An important consideration in this context is the programming model since a standard object-based API entails moving data between the on- and off-heap spaces. In order to avoid the cost associated with such movement, we introduce a novel zero-copy (ZC) API. It provides atomic get, put, remove, and various conditional put operations such as compute (in-situ update). We have released an open-source Java version of Oak. We further present a prototype Oak-based implementation of the internal multidimensional index in Apache Druid. Our experiments show that Oak is often 2x faster than Java's state-of-the-art concurrent skiplist.