Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Large-scale Heterogeneous Graph Representation Learning for Code Review Recommendations

Code review is an integral part of any mature software development process, and identifying the best reviewer for a code change is a well-accepted problem within the software engineering community. Selecting a reviewer who lacks expertise and understanding can slow development or result in more defects. To date, most reviewer recommendation systems rely primarily on historical file change and review information; those who changed or reviewed a file in the past are the best positioned to review in the future. We posit that while these approaches are able to identify and suggest qualified reviewers, they may be blind to reviewers who have the needed expertise and have simply never interacted with the changed files before. To address this, we present Coral, a novel approach to reviewer recommendation that leverages a socio-technical graph built from the rich set of entities (developers, repositories, files, pull requests, work-items, etc.) and their relationships in modern source code management systems. We employ a graph convolutional neural network on this graph and train it on two and a half years of history on 332 repositories. We show that Coral is able to model the manual history of reviewer selection remarkably well. Further, based on an extensive user study, we demonstrate that this approach identifies relevant and qualified reviewers who traditional reviewer recommenders miss, and that these developers desire to be included in the review process. Finally, we find that “classical” reviewer recommendation systems perform better on smaller (in terms of developers)
Molecular dynamics analyses of CLDN15 pore size and charge selectivity

The Claudin-15 (CLDN15) channel is important for nutrient, electrolyte, and water transport in the gastrointestinal tract. We used cell culture studies and molecular dynamics simulations to elucidate its structure and permeability mechanisms. We provide a model that underscores the crucial role of the D55 residue in the CLDN15 selectivity filter, which interacts with permeating cations. Our studies demonstrated the mechanisms whereby the size and charge of the D55 residue influence paracellular permeability. By altering D55 to larger, negatively charged glutamic acid (E) or similarly sized neutral asparagine (N), we observed changes in pore size and selectivity, respectively. D55E mutation decreased pore size, favoring small ion permeability without affecting charge selectivity, while D55N mutation led to reduced charge selectivity without markedly altering size selectivity. These findings shed light on the complex interplay of size and charge selectivity of CLDN15 channels. This knowledge can inform the development of strategies to modulate the function of CLDN15 and similar channels, which has implications for tight junction modulation in health and disease. SUMMARY Using cell culture and molecular dynamics simulations, we have clarified the role of amino acid D55 in the claudin-15 channel function. D55 is important for channel permeability and selectivity in both charge- and size-selective manners. This insight into the structural determinant of claudin cation channel function provides potential insight for designing approaches to alter the claudin channel function to benefit human health.
BMP7 promotes cardiomyocyte regeneration

Zebrafish has a remarkable and lifelong ability for cardiac regeneration after severe damage, whereas mammals lose their innate capacity for heart regeneration during early postnatal development. This study aimed to investigate whether the decreased production of growth factors during postnatal mammalian development contributes to the exit of cardiomyocytes from the cell cycle and the reduction in cardiac regenerative ability. We identified growth factors with declining expression levels during early postnatal life in the mouse model and assessed the pro-proliferative ability of these factors on neonatal murine primary cardiomyocytes in vitro. Our findings confirmed the previously reported pro-proliferative effects of NRG1, IL1b, RANKL, IGF2 and IL6, while also identifying novel potential pro-regenerative growth factors. Among them, BMP7 exhibited the most pronounced efficacy. Bmp7 knockdown interfered with the proliferation of neonatal mouse cardiomyocytes in culture and adult bmp7 mutant zebrafish displayed reduced cardiomyocyte proliferation during heart regeneration, indicating that Bmp7 is crucial for cardiomyocyte proliferation in the regenerative stages of mouse and zebrafish hearts. Conversely, bmp7 overexpression was sufficient to boost cardiomyocyte cycling in regenerating zebrafish hearts, while BMP7 administration stimulated mouse cardiomyocyte cycling at postnatal-day-7, when cardiomyocytes ceased to proliferate, and enhanced cardiomyocyte regeneration in vivo in adult mice following myocardial infarction. Mechanistically, BMP7-induced proliferation was mediated by type I BMP receptors BMPR1A and ACVR1, and type II receptors ACVR2A and BMPR2. Downstream signalling involved SMAD5, ERK and AKT. In conclusion, the administration of BMP7 holds promise as a strategy to stimulate heart regeneration following cardiac injury.
Integrative assessment of total and intact HIV-1 reservoir by a five-region multiplexed Rainbow digital PCR assay

Persistent latent reservoirs of intact HIV-1 proviruses, capable of rebounding despite suppressive ART, hinder efforts towards an HIV-1 cure. Hence, assays specifically quantifying intact proviruses are crucial to assess the impact of curative interventions. Clinical trials have utilized two recent assays: intact proviral DNA assay (IPDA) and Q4PCR. While IPDA is more sensitive due to amplifying short fragments, it may overestimate intact fractions by relying only on two small regions. Q4PCR is sequencing-based and its performance might be subjected to bias against full-length proviruses. Leveraging digital PCR (dPCR) advancements, we developed the ‘Rainbow’ 5-plex proviral HIV-1 DNA assay, assessing it with standard materials and samples from 69 PLWH. The Rainbow assay proved equally sensitive but more specific than IPDA, is not subjected to bias against full-length proviruses, enabling high-throughput quantification of total and intact reservoir size. This innovation offers potential for targeted evaluation and monitoring of rebound-competent reservoirs, contributing to HIV-1 management and cure strategies. Teaser The 5 color ‘HIV-1 Rainbow’ digital PCR assay offers a multi-level view on the HIV reservoir in one snapshot reaction.
PROTAC-mediated Degradation of HIV-1 Nef Efficiently Restores Cell-surface CD4 and MHC-I Expression and Blocks HIV-1 Replication

The HIV-1 Nef accessory factor is critical to the viral life cycle in vivo where it promotes immune escape of HIV-infected cells and viral persistence. While these features identify Nef as an attractive antiretroviral drug target, Nef lacks enzymatic activity and an active site, complicating development of occupancy-based drugs. Here we describe the development of proteolysis targeting chimeras (PROTACs) for the targeted degradation of Nef. Nef-binding compounds, based on a previously reported hydroxypyrazole core, were coupled to ligands for ubiquitin E3 ligases via flexible linkers. The resulting bivalent PROTACs induced formation of a ternary complex between Nef and the Cereblon E3 ubiquitin ligase, resulting in ubiquitylation of Nef and proteolytic degradation. Nef-directed PROTACs efficiently rescued Nef-mediated MHC-I and CD4 downregulation in T cells and suppressed HIV-1 replication in donor PBMCs. Targeted degradation of Nef is anticipated to reverse all HIV-1 Nef functions and may help restore adaptive immune responses against HIV-1 reservoir cells in vivo.
Direct RNA-binding by MYCN mediates feedback from RNA processing to transcription control

The MYCN oncoprotein broadly binds active promoters in a heterodimer with its partner protein MAX. MYCN also interacts with the nuclear exosome, a 3’-5’ exoribonuclease complex, suggesting a function in RNA metabolism. Here we show that MYCN forms stable high molecular weight complexes with the exosome and multiple RNA-binding proteins. In cells, MYCN binds to thousands of intronic RNAs; recombinant MYCN directly binds RNA via a short, highly conserved sequence termed MYCBoxI. Perturbing exosome function results in global re-localization of MYCN from promoters to intronic RNAs. At promoters, MYCN is then replaced by the MNT(MXD6) repressor protein, which inhibits MYCN-dependent transcription. MYCN promotes the degradation of its bound introns via the nuclear exosome targeting (NEXT) complex. Our data demonstrate that MYCN is an RNA-binding protein that regulates nascent transcript turnover and show that competition between its RNA– and DNA-bound states links the dynamics of the MYCN/MAX/MXD network to mRNA processing.
Magnetic Resonance Imaging Segmentation Techniques of Brain Tumors: A Review

Context: Medical imaging technologies are an indispensable tool in medicine today developed to satisfy the significant demand for information on medical imaging by visualizing internal organs for clinical analysis. This enables the radiologists and clinicians to accurately understand the patient’s condition and makes medical practices easier, more effective for patients, and cheaper for the healthcare system. Objective: The current study aimed at presenting a comprehensive review on the recent classification and segmentation techniques of brain tumors in magnetic resonance image (MRI). Data Source: Google Scholar, ScienceDirect, Web of Knowledge, Springer, and manual search of reference lists from 1990 to 2018. Inclusion Criteria: The current study considered brain tumors since they are relatively less common and more important compared with other tumors due to their high morbidity rate. Results: Many automated brain tumors segmentation algorithms of magnetic resonance imaging (MRI) were reviewed and discussed including their advantages and limitations to provide a clear insight into these algorithms. The review concentratedon the state-of-art methods of segmentation of MRI brain tumors since they attracted a significant attention in the recent two decades resulting in many algorithms being developed for automated, semi-automated, and interactive segmentation of brain tumors. While there is a significant development of segmentation algorithms, they are rarely used clinically due to lack of interaction between developers and clinicians. Conclusions: Most studies did not consider grading of brain tumors and did not distinguish to which grade the brain tumor belonged. This enables the developers to understand how the margins of brain tumors appear in medical images. Limitations: The most important limitations that make brain tumors segmentation remaina challenging task are the variety of the shape and intensity of tumors in addition to the probability of inhomogeneity of tumorous tissue.
Smart Subtitles for Language Learning

Language learners often use subtitled videos to help them learn the language. However, standard subtitles are suboptimal for vocabulary acquisition, as translations are nonliteral and made at the phrase level, making it hard to find connections between the subtitle text and the words in the video. This paper presents Smart Subtitles, which are interactive subtitles tailored towards vocabulary acquisition. They provide features such as vocabulary definitions on hover, and dialog-based video navigation. Our user study shows that Chinese learners learn over twice as much vocabulary with Smart Subtitles than with dual Chinese-English subtitles, with similar levels of comprehension and enjoyment.
Calculating Production Rate of each Branch of a Multilateral Well Using Multi-Segment Well Model: Field Example

Multilateral wells require a sophisticated type of well model to be applied in reservoir simulators to represent them. The model must be able to determine the flow rate of each fluid and the pressure throughout the well. The production rate calculations are very important because they give an indication about some main issues associated with multi-lateral wells such as one branch may produce water or gas before others, no production rate from one branch, and selecting the best location of a new branch for development process easily. This paper states the way to calculate production rate of each branch of a multilateral well-using multi-segment well model. The pressure behaviour of each branch is simulated dependent on knowing its production rate. This model has divided a multi-lateral well into an arbitrary number of segments depending on the required degree of accuracy and run time of the simulator. The model implemented on a field example (multi-lateral well HF-65ML) in Halfaya Oil Field/Mishrif formation. The production rate and pressure behaviour of each branch are simulated during the producing interval of the multilateral well. The conclusion is that production rate of the main branch is slightly larger than a lateral branch.
Exploring Creativity of Designers in Children's Library Navigation APP Design with Design Thinking Workshop

Design Thinking is characterized by integrating professional knowledge and experience from different fields to help teams work together to develop feasible solutions. It emphasizes user-centeredness and helps designers better understand user needs through five steps. Therefore, for design beginners, design thinking can deconstruct complex problems, actively discover and define them, and continuously organize creative thinking to find the most appropriate solutions. We took the design of a children's library navigation APP as the research topic. In the workshop, we introduced design thinking to the students to understand the problems faced by children and parents, clarified and dismantled the fragmented findings, and brainstormed new design insights to help two groups of students develop different ideas of APP design in a co-creative way. We will investigate whether a design thinking workshop can help students transform their traditionally passive role into an active one, and systematically, integrate and augment their design ideas, to develop a children's library navigation APP design with creative thinking in the study.
Investigating Responsible Nudge Design for Informed Decision-Making Enabling Transparent and Reflective Decision-Making

Consent interfaces are habitually designed to coerce people into sharing the maximum amount of data, rather than making decisions that align with their intentions and privacy attitudes, by leveraging cognitive biases to nudge users toward certain decision outcomes through interface design. Reflection and transparency have been proposed as two design dimensions of a choice architecture constituting a responsible nudge approach capable of counteracting these mechanisms by prompting reflected choice. In a crowdsourced experiment, we evaluate these capabilities of a proposed data-disclosure consent interface design deploying the responsible nudge approach within a realistic setting by exploiting a status quo bias during the sign-up of an online survey platform as a secondary task within a crowdsourcing context. Our results provide insights into a responsible design of consent interfaces, suggesting that prompting reflection significantly decreases the discrepancy between users’ privacy attitudes and decision outcomes. Meanwhile, making the presence of a nudge transparent had no significant effect on its influence. Furthermore, identifying individuals’ attitudes as a significant predictor of privacy behavior provides a promising direction for future research.
Image hashing algorithm to defend FGSM attacks on Neural Network

In this research, we present a performance evaluation of existing image hashing algorithms on defending deep learning models against adversarial attacks as an initial work to developing a new, time-efficient image hashing algorithm. More specifically, we aim to set a maximum time complexity of 𝑂 ( 𝑁 2 ) as a constraint to the algo-rithm, such that it can be used in time-critical systems and/or low computing resources systems. Upon experimenting with existing image hashing algorithms, we conclude that the wavelet hashing algorithm achieves the highest accuracy (75%) when detecting images generated from Neural Networks attacked by the FGSM, with a time complexity of 𝑂 ( 𝑁 ) .
Deep Learning-Based Pneumonia Classification Based on Respiratory Sounds

A crucial aspect of pneumonic pathology is attributed and analyzed by respiratory sound, which also provides information on the patient's lung symptoms. Almost all health institutions worldwide acquire pneumonia as a common and therapeutically challenging diagnosis that can lead to a severe body response to an infection, critical illness, and respiratory failure. It is well acknowledged that clinical diagnosis and prognosis are insufficient for the proper assessment of the severity of the disease due to the complexity of its pathogenesis. Auscultation and chest X-ray (CXR) images are currently the primary methods for identifying and diagnosing pneumonia, but it can be difficult for a trained radiologist to identify pneumonia from a CXR image or for a pathologist to do so using a stethoscope because accurate interpretation of respiratory sounds requires significant clinical expertise. We attempted to create an automated classification of respiratory sounds to detect pneumonia as well as a more complicated classification to distinguish between different forms of pathology by using a deep learning convolutional neural network to classify the six classes (healthy, COPD, URTI, bronchiectasis, pneumonia, and bronchiolitis) of sounds recorded in a clinical setting. In the proposed method for pneumonia detection and pathological respiratory sound classification, a pertained picture feature extractor of series, respiratory sound, and CNN classifier are all used. The CNN model had an average precision of 98.8%, with a split among the classes of 97.80%, 87.60%, 95.0%, 85%, 100%, and 100% for COPD, healthy, URTI, bronchiectasis, pneumonia, and bronchiolitis, respectively. It detected abnormal sounds with an accuracy of 94.2% and pneumonia with an accuracy of 100%. The performance results obtained suggest that CNN is a viable tool for detecting specific characteristics in respiratory data and is capable of accurately classifying pneumonia.
Text Document Clustering Using Dimension Reduction Technique

Text document clustering is used to group a set of documents based on the information it contains and to provide retrieval results when a user browses the internet. Experimental evidences have shown that Information Retrieval applications can benefit from document clustering and it has been used as a tool to improve the performance of retrieval of information. Information retrieval is an interdisciplinary field of knowledge management and text mining. Dimensionality Reduction (DR) is a typical step in many text mining problems which involves transforming sparse data into a shorter and more compact one. DR can be done in 2 ways: feature reduction and feature selection. This study implements dimensionality reduction through feature selection with k-means algorithm. Feature Selection is implemented through the InfoGain DR technique. This paper presents an experimental analysis of the performance of the document clustering with the InfoGain technique and proves that this method significantly improves the performance in terms of Accuracy, Precision and Recall for the BBC Sports Dataset.
SOCIO-ECONOMIC AND ECOLOGICAL EFFECTS IN PROBABILISTIC APPRAISAL OF RAILWAY PROJECT

The article justifies the economic appraisal of investments in the construction of railway lines with the allowance for socio-economic and ecological effects, as well as the probabilistic nature of the project environment. Examples of external benefits associated with railway project realization would be tax payments to the federal budgets, an increase in regional employment level, the reduction of environmental costs due to decrease in levels of air pollution, noise and climate change as a whole. The study aims to develop a probabilistic model of cash flows, effects and costs to compute the effectiveness of budget investments in complex infrastructure facilities
Autonomous Navigation in Complex Indoor and Outdoor Environments with Micro Aerial Vehicles

AUTONOMOUS NAVIGATION IN COMPLEX INDOOR AND OUTDOOR ENVIRONMENTS WITH MICRO AERIAL VEHICLES Shaojie Shen Vijay Kumar Nathan Michael Micro aerial vehicles (MAVs) are ideal platforms for surveillance and search and rescue in confined indoor and outdoor environments due to their small size, superior mobility, and hover capability. In such missions, it is essential that the MAV is capable of autonomous flight to minimize operator workload. Despite recent successes in commercialization of GPS-based autonomous MAVs, autonomous navigation in complex and possibly GPS-denied environments gives rise to challenging engineering problems that require an integrated approach to perception, estimation, planning, control, and high level situational awareness. Among these, state estimation is the first and most critical component for autonomous flight, especially because of the inherently fast dynamics of MAVs and the possibly unknown environmental conditions. In this thesis, we present methodologies and system designs, with a focus on state estimation, that enable a light-weight off-the-shelf quadrotor MAV to autonomously navigate complex unknown indoor and outdoor environments using only onboard sensing and computation. We start by developing laser and vision-based state estimation methodologies for indoor autonomous flight. We then investigate fusion from heterogeneous sensors to improve robustness and enable operations in complex indoor and outdoor environments. We further propose estimation algorithms for on-the-fly initialization and online failure recovery. Finally, we present planning, control, and environment coverage strategies for integrated high-level autonomy behaviors. Extensive online experimental results are presented throughout the thesis. We conclude by proposing future research opportunities. iii
CONSIDERATION OF DESIGN SYSTEMS FOR SOLAR POWERED REVERSE OSMOSIS SEAWATER DESALINATION

Solar-powered reverse osmosis (RO) seawater desalination systems are one of the best solutions for providing fresh, clean water. This paper presents research concerning solar-powered reverse osmosis (RO) seawater desalination. The research consists of a review of solar-powered RO desalination, previous designs of solar-powered RO seawater desalination systems, and a design of a solar-powered RO seawater desalination system in Durban. The review includes solved issues regarding the cost and efficiency of the RO seawater desalination system. The review also focuses on how to make the RO desalination portable and how to improve the RO system processes. The main aim and objective of this research was to conduct research on previous findings regarding solar-powered RO seawater desalination, and to propose a design system that will provide a reasonable amount of water for the community of Durban. The proposed design system produces 16 830 000 L of water per year using solar energy with super-capacitors and battery as a power source. The solar powered RO seawater desalination systems can be improved by having a two-stage RO process, and by having three energy sources (solar energy, battery/capacitor, and wind energy) in order to increase freshwater produced per annum. Although having a battery in a system creates energy losses and batteries are a reliable source of energy