Miracle-dz/UniFAR-FTUs huggingface dataset:
A Large Scale Study of Multiple Programming Languages and Code Quality

Nowadays, most software use multiple programming languages to implement certain functionalities based on the strengths and weaknesses of different languages. Researchers in the past have studied the impact of independent programming languages on software quality, however, there has been little or no research on the impact of multiple languages on the quality of software. Does the use of multiple languages cause more bugs? Are certain languages when used with other languages make software more bug prone? What are the relationships between multi-language usage and various bug categories? In this study, we perform a large scale empirical investigation to provide some answers to these questions. We gather a large dataset consisting of popular projects from GitHub (628 projects, 85 million SLOC, 134 thousand authors, 3 million commits, in 17 languages) to understand the impact of using multiple languages on software quality. We build multiple regression models to study the effects of using different languages on the number of bug fixing commits while controlling for factors such as project age, project size, team size, and the number of commits. Our results show that in general implementing a project with more languages has a significant effect on project quality, as it increases defect proneness. Moreover, we find specific languages that are statistically significantly more defect prone when they are used in a multi-language setting. These include popular languages like C++, Objective-C, and Java. Furthermore, we note that the use of more languages significantly increases bug proneness across all bug categories. The effect is strongest for memory, concurrency, and algorithm bugs.
The Governance Effect of the Media’s News Dissemination Role: Evidence from Insider Trading

We investigate whether the media plays a role in corporate governance by disseminating news. Using a comprehensive dataset of corporate and insider news coverage for the 2001-2012 period, we show that the media reduces insiders’ future trading profits by disseminating news on prior insiders’ trades available from regulatory filings. We find support for three economic mechanisms underlying the disciplining effect of news dissemination: the reduction of information asymmetry, concerns regarding litigation risk, and the impact on insiders’ personal wealth and reputation. Our findings provide new insights into the real effect of news dissemination.
Human NK Cell Subsets in Pregnancy and Disease: Toward a New Biological Complexity

In humans, NK cells are mainly identified by the surface expression levels of CD56 and CD16, which differentiate between five functionally different NK cell subsets. However, nowadays NK cells are considered as a more heterogeneous population formed by various subsets differing in function, surface phenotype, and anatomic localization. In human CMV- and hantaviruses-infected subjects, an increased frequency of a NKG2A−CD57+NKG2C+ NK cell subset has been observed, while the phenotype of the NK cell subpopulation associated with cancer may vary according to the specific kind of tumor and its anatomical location. The healthy human lymph nodes contain mainly the CD56bright NK cell subset while in melanoma metastatic lymph nodes the CD56dimCD57+KIR+CCR7+ NK cell subpopulation prevails. The five NK cell subpopulations are found in breast cancer patients, where they differ for expression pattern of chemokine receptors, maturation stage, functional capabilities. In pregnancy, uterine NK cells show a prevalence of the CD56brightCD16− NK cell compartment, whose activity is influenced by KIRs repertoire. This NK cell subset’s super specialization could be explained by (i) the expansion of single mature CD56dim clones, (ii) the recruitment and maturation of CD56bright NK cells through specific stimuli, and (iii) the in situ development of tumor-resident NK cells from tissue-resident CD56bright NK cells independently of the circulating NK cell compartment. This new and unexpected biological feature of the NK cell compartment could be an important source of new biomarkers to improve patients’ diagnosis.
GM-CSF Grown Bone Marrow Derived Cells Are Composed of Phenotypically Different Dendritic Cells and Macrophages

Granulocyte-macrophage colony stimulating factor (GM-CSF) has a role in inducing emergency hematopoiesis upon exposure to inflammatory stimuli. Although GM-CSF generated murine bone marrow derived cells have been widely used as macrophages or dendritic cells in research, the exact characteristics of each cell population have not yet been defined. Here we discriminated GM-CSF grown bone marrow derived macrophages (GM-BMMs) from dendritic cells (GM-BMDCs) in several criteria. After C57BL/6J mice bone marrow cell culture for 7 days with GM-CSF supplementation, two main populations were observed in the attached cells based on MHCII and F4/80 marker expressions. GM-BMMs had MHCIIlowF4/80high as well as CD11c+CD11bhighCD80−CD64+MerTK+ phenotypes. In contrast, GM-BMDCs had MHCIIhighF4/80low and CD11chighCD8α− CD11b+CD80+CD64−MerTKlow phenotypes. Interestingly, the GM-BMM population increased but GM-BMDCs decreased in a GM-CSF dose-dependent manner. Functionally, GM-BMMs showed extremely high phagocytic abilities and produced higher IL-10 upon LPS stimulation. GM-BMDCs, however, could not phagocytose as well, but were efficient at producing TNFα, IL-1β, IL-12p70 and IL-6 as well as inducing T cell proliferation. Finally, whole transcriptome analysis revealed that GM-BMMs and GM-BMDCs are overlap with in vivo resident macrophages and dendritic cells, respectively. Taken together, our study shows the heterogeneicity of GM-CSF derived cell populations, and specifically characterizes GM-CSF derived macrophages compared to dendritic cells.
Partial expansions for file organizations with an index

A new way to increase file space in dynamically growing files is introduced in which substantial improvement in file utilization can be achieved. It makes use of partial expansions in which, instead of doubling the space associated with some part of the file, the space grows at a slower rate. Unlike previous versions of partial expansion in which the number of buckets involved in file growth is increased by less than a factor of two, the new method expands file space by increasing bucket size via “elastic buckets. ” This permits partial expansions to be used with a wide range of indexed files, including B-trees. The results of using partial expansions are analyzed, and the analysis confirmed by a simulation study. The analysis and simulation demonstrate that the file utilization gains are substantial and that fears of excessive insertion cost resulting from more frequent file growth are unfounded.
Caching function calls using precise dependencies

This paper describes the implementation of a purely functionalprogramming language for building software systems. In this language,external tools like compilers and linkers are invoked by function calls. Because some function calls are extremely expensive, it is obviously important to reuse the results of previous function calls whenever possible. Caching a function call requires the language interpreter to record all values on which the function call depends. For optimal caching, it is important to record precise dependencies that are both dynamic and fine-grained. The paper sketches how we compute such dependencies, describes the implementation of an efficient function cache, and evaluates our implementation's performance.
Effect of Heat Stress on Lipopolysaccharide-Induced Vascular Permeability Change in Mice

The effect of heat shock protein (hsp) induction on lipopolysaccharide (LPS)-induced increase in vascular permeability was studied in mice as a model of inflammatory mediator-induced inflammatory response. Mice were exposed to an ambient temperature of 43°C for 1 h and then returned to 23°C to recover up to 24 h. Dermal contents of hsp70 and hsp90 but not heat shock cognate protein (hsc)70 increased at 6 h after heat exposure and returned to the basal level at 24 h. LPS was injected subcutaneously at 0, 2, 4, 6, or 24 h after heat exposure. Two hours after LPS injection, vascular permeability was assessed by dermal accumulation of intravenously injected dye. LPS-induced dye leakage was reduced by 42 and 49% in heat-exposed mice after recovery for 4 and 6 h, respectively. Increases in dermal tumor necrosis factor-α (TNF-α) and prostaglandin E2 (PGE2) contents induced by LPS were significantly reduced in the heat-stressed mice recovered for 6 h. LPS-induced increase in cyclooxygenase-2 but not TNF-α mRNA was attenuated in heat-stressed mice. Deoxyspergualin, an inhibitor of hsc70 and hsp90, and geldanamycin, a specific hsp90 inhibitor, dose dependently reversed the inhibitory effect of heat stress on LPS-induced dye leakage and dermal TNF-α content but not PGE2 content. These results suggest that heat stress attenuated LPS-induced vascular permeability change by inducing hsp90, leading to inhibition of TNF-α production.
Lumen segmentation and stenosis quantification of atherosclerotic carotid arteries in CTA utilizing a centerline intensity prior.

PURPOSE The degree of stenosis is an important biomarker in assessing the severity of cardiovascular disease. The purpose of our work is to develop and evaluate a semiautomatic method for carotid lumen segmentation and subsequent carotid artery stenosis quantification in CTA images. METHODS The authors present a semiautomatic stenosis detection and quantification method following lumen segmentation. The lumen of the carotid arteries is segmented in three steps. First, centerlines of the internal and external carotid arteries are extracted with an iterative minimum cost path approach in which the costs are based on a measure of medialness and intensity similarity to lumen. Second, the lumen boundary is delineated using a level set procedure which is steered by gradient information, regional intensity information, and spatial information. Special effort is made in adding terms based on local centerline intensity prior so as to exclude all possible plaque tissues from the segmentation. Third, side branches in the segmented lumen are removed by applying a shape constraint to the envelope of the maximum inscribed spheres of the segmentation. From the segmented lumen, the authors detect and quantify the cross-sectional area-based and cross-sectional diameter-based stenosis degrees according to the North American Symptomatic Carotid En-darterectomy Trial criterion. RESULTS The method is trained and tested on a publicly available database from the cls2009 challenge. For the segmentation, the authors obtain a Dice similarity coefficient of 90.2% and a mean absolute surface distance of 0.34 mm. For the stenosis quantification, the authors obtain an average error of 15.7% for cross-sectional diameter-based stenosis and 19.2% for cross-sectional area-based stenosis quantification. CONCLUSIONS With these results, the method ranks second in terms of carotid lumen segmentation accuracy, and first in terms of carotid artery stenosis quantification.
Concomitant Tumor Expression of EGFR and TATI/SPINK1 Associates with Better Prognosis in Colorectal Cancer

Background Epidermal growth factor receptor (EGFR) activation plays a role in colorectal cancer (CRC) carcinogenesis, and anti-EGFR drugs are used in treatment of advanced CRC. One of the EGFR ligands is tumor-associated trypsinogen inhibitor TATI, also called serine protease inhibitor Kazal type1 (SPINK 1), which we recently showed to be an independent prognostic marker in CRC. Methods We studied the prognostic value of immunohistochemical expression of EGFR and concomitant expression of EGFR and TATI/SPINK1 in a series of 619 colorectal cancer patients. Results Of the samples, 92% were positive for EGFR. EGFR+/TATI+ was seen in 62.8%, EGFR+/TATI− in 29.5%, EGFR−/TATI+ in 4.9%, and EGFR−/TATI− in 2.7% of patients. EGFR expression correlated with WHO grade (p = 0.040). In univariate analysis, EGFR expression correlated with favourable survival (p = 0.006). EGFR+/TATI+ patients showed better survival than did those with other combinations (p<0.001). In multivariate analysis, EGFR+/TATI+ was an independent prognostic factor of favourable prognosis (p<0.001). Conclusion Concomitant positivity of EGFR and TATI/SPINK1 predicts favourable prognosis in CRC.
Coordinationof secondarymetabolismanddevelopment in fungi : thevelvet familyof regulatoryproteins

Filamentous fungi produce a number of small bioactive molecules as part of their secondary metabolism ranging from benign antibiotics such as penicillin to threatening mycotoxins such as aflatoxin. Secondary metabolism can be linked to fungal developmental programs in response to various abiotic or biotic external triggers. The velvet family of regulatory proteins plays a key role in coordinating secondary metabolism and differentiation processes such as asexual or sexual sporulation and sclerotia or fruiting body formation. The velvet family shares a protein domain that is present in most parts of the fungal kingdom from chytrids to basidiomycetes. Most of the current knowledge derives from the model Aspergillus nidulans where VeA, the founding member of the protein family, was discovered almost half a century ago. Different members of the velvet protein family interact with each other and the nonvelvet protein LaeA, primarily in the nucleus. LaeA is a methyltransferase-domain protein that functions as a regulator of secondary metabolism and development. A comprehensive picture of the molecular interplay between the velvet domain protein family, LaeA and other nuclear regulatory proteins in response to various signal transduction pathway starts to emerge from a jigsaw puzzle of several recent studies.
Kinematics analysis for the leg mechanism of a wheel-leg hybrid rescue robot

A novel wheel-leg hybrid rescue robot is proposed and the design is given from the 3D modularization concept. The proposed hybrid robot provides great mobility in rough and unstructured terrains, with a simplified mechanism. It can be used in a variety of dangerous and dirty tasks, such as earthquake, nuclear release and other catastrophe disaster. The legs of the hybrid rescue robot equipped with a (2-UPS+U)R series-parallel mechanism is designed. This makes the robot has a high payload capacity and great stiffness on both leg mode and wheel mode. The mathematical model of each leg is established and its degree of freedom is solved. In addition, the inverse position of the leg is analyzed and deprived using the vector method. Finally, the workspace of the leg is developed using MATLAB software. The study of the hybrid robot lays a theoretical foundation for future development of the dynamics, control system design, and the development of the prototype.
Increasing the Clinical Efficacy of NK and Antibody-Mediated Cancer Immunotherapy: Potential Predictors of Successful Clinical Outcome Based on Observations in High-Risk Neuroblastoma

Disease recurrence is frequent in high-risk neuroblastoma (NBL) patients even after multi-modality aggressive treatment [a combination of chemotherapy, surgical resection, local radiation therapy, autologous stem cell transplantation, and cis-retinoic acid (CRA)]. Recent clinical studies have explored the use of monoclonal antibodies (mAbs) that bind to disialoganglioside (GD2), highly expressed in NBL, as a means to enable immune effector cells to destroy NBL cells via antibody-dependent cell-mediated cytotoxicity (ADCC). Preclinical data indicate that ADCC can be more effective when appropriate effector cells are activated by cytokines. Clinical studies have pursued this by administering anti-GD2 mAb in combination with ADCC-enhancing cytokines (IL2 and GM-CSF), a regimen that has demonstrated improved cancer-free survival. More recently, early clinical studies have used a fusion protein that consists of the anti-GD2 mAb directly linked to IL2, and anti-tumor responses were seen in the Phase II setting. Analyses of genes that code for receptors that influence ADCC activity and natural killer (NK) cell function [Fc receptor (FcR), killer immunoglublin-like receptor (KIR), and KIR-ligand (KIR-L)] suggest patients with anti-tumor activity are more likely to have certain genotype profiles. Further analyses will need to be conducted to determine whether these genotypes can be used as predictive markers for favorable therapeutic outcome. In this review, we discuss factors that affect response to mAb-based tumor therapies such as hu14.18-IL2. Many of our observations have been made in the context of NBL; however, we will also include some observations made with mAbs targeting other tumor types that are consistent with results in NBL. Therefore, we hypothesize that the NBL observations discussed here may also be relevant to mAb therapy for other cancers, in which ADCC is known to play a role.
Claims and Supporting Evidence for Self-adaptive Systems: an Initial Study

Self-adaptation is widely recognized as a key approach to tackle some of the hard challenges of engineering and managing complex distributed software systems [1,2,3]. Self-adaptation endows a software system with the capability to adapt itself to internal dynamics and dynamics in the environment in order to achieve certain quality goals. Over the last decade, researchers and engineers have developed a huge body of work on engineering self-adaptive systems. However, it is not clear how the research results have actually contributed to improvements of engineering complex software systems. Several survey articles have been written over the last years, e.g. [4,5,6,7]. These articles summarize achievements of the field and outline challenges for future work. However, to the best of our knowledge, no systematic study has been performed on the claims associated with self-adaptation and the evidence that exists for these claims. As a result, there is no clear view on how self-adaptation actually contributes to tacking the challenges of engineering and managing complex distributed software systems engineering. However, such insight is crucial for researchers and engineers. Our objective is to study and summarize existing research related to engineering self-adaptive software systems and shed light on what the expected benefits are of self-adaption and to what extent evidence exists for these benefits. In particular, we aim to a) assess the quality of current research in self-adaptive systems, b) collect affirmation for the claims made for self-adaptation and the available evidence for these claims, and c) identify research trends, open problems, gaps, and areas for future work in the field. Different loosely connected communities have studied self-adaptation. Prominent examples are coexistence of different communities has led to the exploration of the field from different perspectives. However, it has also led to fragmentation of the field and the development of different vocabularies. In our study, we take a broad perspective on what is considered as a self-adaptive system. In particular, we consider a self-adaptive system as a system consisting of two parts of software: a
ARA: the active reading application

The Active Reading Application (ARA) brings the familiar experience of writing on paper to the tablet. The application augments paper-based practices with audio, the ability to review annotations, and sharing. It is designed to make it easier to review, annotate, and comment on documents by individuals and groups. ARA incorporates several patented technologies and draws on several years of research and experimentation.
A multiscale approach for the reconstruction of the fiber architecture of the human brain based on 3D-PLI

Structural connectivity of the brain can be conceptionalized as a multiscale organization. The present study is built on 3D-Polarized Light Imaging (3D-PLI), a neuroimaging technique targeting the reconstruction of nerve fiber orientations and therefore contributing to the analysis of brain connectivity. Spatial orientations of the fibers are derived from birefringence measurements of unstained histological sections that are interpreted by means of a voxel-based analysis. This implies that a single fiber orientation vector is obtained for each voxel, which reflects the net effect of all comprised fibers. We have utilized two polarimetric setups providing an object space resolution of 1.3 μm/px (microscopic setup) and 64 μm/px (macroscopic setup) to carry out 3D-PLI and retrieve fiber orientations of the same tissue samples, but at complementary voxel sizes (i.e., scales). The present study identifies the main sources which cause a discrepancy of the measured fiber orientations observed when measuring the same sample with the two polarimetric systems. As such sources the differing optical resolutions and diverging retardances of the implemented waveplates were identified. A methodology was implemented that enables the compensation of measured different systems' responses to the same birefringent sample. This opens up new ways to conduct multiscale analysis in brains by means of 3D-PLI and to provide a reliable basis for the transition between different scales of the nerve fiber architecture.
Determination of Minimum Training Sample Size for Microarray-Based Cancer Outcome Prediction–An Empirical Assessment

The promise of microarray technology in providing prediction classifiers for cancer outcome estimation has been confirmed by a number of demonstrable successes. However, the reliability of prediction results relies heavily on the accuracy of statistical parameters involved in classifiers. It cannot be reliably estimated with only a small number of training samples. Therefore, it is of vital importance to determine the minimum number of training samples and to ensure the clinical value of microarrays in cancer outcome prediction. We evaluated the impact of training sample size on model performance extensively based on 3 large-scale cancer microarray datasets provided by the second phase of MicroArray Quality Control project (MAQC-II). An SSNR-based (scale of signal-to-noise ratio) protocol was proposed in this study for minimum training sample size determination. External validation results based on another 3 cancer datasets confirmed that the SSNR-based approach could not only determine the minimum number of training samples efficiently, but also provide a valuable strategy for estimating the underlying performance of classifiers in advance. Once translated into clinical routine applications, the SSNR-based protocol would provide great convenience in microarray-based cancer outcome prediction in improving classifier reliability.