Miracle-dz/UniFAR-FTUs huggingface dataset:
Confirmation Bias and Other Flaws in Citing Pass Rate Studies

There have been four principal studies on pass rates in introductory programming courses, in 2007, 2014, and two in 2019. These studies, the first two in particular, are extremely widely cited, but it appears that some of the citing papers misrepresent what was written in the original papers. We present an analysis of nearly 600 papers that cite one or more of these four papers, indicating what sorts of message they impute to the papers. We find that well over half of the citing papers fail to accurately represent the papers' findings, instead citing the papers as evidence either of a commonly accepted belief or of their own -- sometimes quite bizarre -- assertions. This raises significant concerns for the computing education community: for authors, reviewers, and readers. To help ensure the legitimacy of our discipline we suggest that when authors intend to cite a paper as evidence of some assertion, they carefully read the paper and confirm that it does actually include that assertion; and also confirm that the paper actually establishes the assertion rather than, for example, presenting it as hearsay or as a common belief.
Cohesive Subgraph Search over Big Heterogeneous Information Networks: Applications, Challenges, and Solutions

With the advent of a wide spectrum of recent applications, querying heterogeneous information networks (HINs) has received a great deal of attention from both academic and industrial societies. HINs involve objects (vertices) and links (edges) that are classified into multiple types; examples include bibliography networks, knowledge networks, and user-item networks in E-business. An important component of these HINs is the cohesive subgraph, or a subgraph containing vertices that are densely connected internally. Searching cohesive subgraphs over HINs has found many real applications, such as community search, product recommendation, fraud detection, and so on. Consequently, how to design effective cohesive subgraph models and how to efficiently search cohesive subgraphs on large HINs become important research topics in the era of big data. In this tutorial, we first highlight the importance of cohesive subgraph search over HINs in various applications and the unique challenges that need to be addressed. Subsequently, we conduct a thorough review of existing works of cohesive subgraph search over HINs. Then, we analyze and compare the models and solutions in these works. Finally, we point out new research directions. We believe that this tutorial not only helps researchers to have a better understanding of existing cohesive subgraph search models and solutions, but also provides them insights for future study.
LDAX: A Learning-based Fast Design Space Exploration Framework for Approximate Circuit Synthesis

The majority of existing frameworks for automated synthesis of Approximate Circuits (AxCs) employ a search-based Design Space Exploration (DSE) approach. This includes an iterative process where approximate circuit instances are created and evaluated in terms of quality and performance metrics. The quality evaluation of each instance via either verification or testing results in extremely long computation times, imposing a practical limit on the number of nodes that can be explored in the design space. To overcome this problem, we exploit Random Forests (RFs) to develop extremely fast estimators to evaluate the quality and performance of AxC instances while avoiding time-consuming computations. Utilizing these estimators we build LDAX, an efficient design space exploration framework that attempts to improve the runtime of the AxCs synthesis process. LDAX is based on the fact that, in an iterative search space exploration, a large number of validations can be skipped by leveraging a high-accuracy predictor. To efficiently explore the design space, which is usually represented by a tree and each node denotes an AxC, we propose a learning-based search technique that can quickly analyze a large set of nodes even very deep nodes in the tree. Our experimental results reveal that LDAX can achieve average speed-up of 41 × on a set of practical benchmarks from different application domains in comparison with the most competitive state-of-the-art framework.
Secure Yannakakis: Join-Aggregate Queries over Private Data

In this paper, we describe a secure version of the classical Yannakakis algorithm for computing free-connex join-aggregate queries. This protocol can be used in the secure two-party computation model, where the parties would like to evaluate a query without revealing their own data. Our protocol presents a dramatic improvement over the state-of-the-art protocol based on Yao's garbled circuit. In theory, its cost (both running time and communication) is linear in data size and polynomial in query size, whereas that of the garbled circuit is polynomial in data size and exponential in query size. This translates to a reduction in running time in practice from years to minutes, as tested on a number of TPC-H queries of varying complexity.
Endothelial ontogeny and the establishment of vascular heterogeneity

The establishment of distinct cellular identities was pivotal during the evolution of Metazoa, enabling the emergence of an array of specialized tissues with different functions. In most animals including vertebrates, cell specialization occurs in response to a combination of intrinsic (e.g., cellular ontogeny) and extrinsic (e.g., local environment) factors that drive the acquisition of unique characteristics at the single‐cell level. The first functional organ system to form in vertebrates is the cardiovascular system, which is lined by a network of endothelial cells whose organ‐specific characteristics have long been recognized. Recent genetic analyses at the single‐cell level have revealed that heterogeneity exists not only at the organ level but also between neighboring endothelial cells. Thus, how endothelial heterogeneity is established has become a key question in vascular biology. Drawing upon evidence from multiple organ systems, here we will discuss the role that lineage history may play in establishing endothelial heterogeneity.
Systematic dissection of a complex gut bacterial community

Efforts to model the gut microbiome have yielded important insights into the mechanisms of interspecies interactions, the impact of priority effects on ecosystem dynamics, and the role of diet and nutrient availability in determining community composition. However, the model communities studied to date have been defined or complex but not both, limiting their utility. Here, we construct a defined community of 104 bacterial strains composed of the most common taxa from the human gut microbiota. By propagating this community in growth media missing one amino acid at a time, we show that branched-chain amino acids have an outsize impact on community structure and identify a pathway in Clostridium sporogenes for generating ATP from arginine. We constructed and propagated the complete set of single-strain dropout communities, revealing a sparse network of strain-strain interactions including a novel interaction between C. sporogenes and Lactococcus lactis driven by metabolism. This work forms a foundation for studying strain-strain and strain-nutrient interactions in highly complex defined communities, and it provides a starting point for interrogating the rules of synthetic ecology at the 100+ strain scale.
Cellular heterogeneity and MTH1 play key roles in galactose mediated signaling of the GAL switch to utilize the disaccharide melibiose

Yeast metabolizes the disaccharide melibiose by hydrolyzing it into equimolar concentrations of glucose and galactose by MEL1-encoded α-galactosidase. Galactose metabolizing genes (including MEL1) are induced by galactose and repressed by glucose, which are the products of melibiose hydrolysis. Therefore, how melibiose catabolization and utilization take place by circumventing the glucose repression is an enigma. Other than the galactose metabolizing genes MTH1, a negative regulator of glucose signal pathway has Gal4p binding sites and is induced by galactose and repressed by high glucose concentration. But, at low or no glucose MTH1 along with its paralogue STD1 represses hexose transporters, that are involved in glucose transport. This sort of tuning of glucose and galactose regulation motivated us to delineate the role of MTH1 as a regulator of MEL1 expression and melibiose utilization. The deletion mutant of MTH1 shows growth defect on melibiose and this growth defect is enhanced upon the deletion of both MTH1 and its paralogue STD1. Microscopy and flowcytometry analysis, suggest, that even though MEL1 and GAL1 promoter are under Gal4p and Gal80p regulation, upon deletion of MTH1 it hampers only MEL1 expression, but not the GAL1 gene expression. By using 2-Deoxy galactose toxicity assay, we observed phenotypic heterogeneity in cells grown on melibiose i.e. after cleaving of melibiose a fraction of cell population utilizes glucose and another fraction utilizes galactose and coexist together. Understanding GAL/MEL gene expression patterns in melibiose will have great implication to understand various other complex sugar utilizations, tunable gene expressions and complex feedback gene regulations. Significance Sugar metabolism is an important phenomenon to understand the regulation of gene expression. Glucose is the most preferred carbon source. Yeast follows glycolytic pathways like cancer cells for metabolism of sugars and understanding this will throw more light to the metabolism of cancer cells. In this communication we observed cell-to-cell heterogeneity in yeast cells playing a key role in metabolism of a complex disaccharide melibiose, which gets cleaved into glucose and galactose by α-galactosidase. Glucose represses α-galactosidase and galactose induces it. Because of the heterogeneous population of cells one fraction consumes glucose liberated by melibiose hydrolysis, therefore it is not sufficient to repress α-galactosidase and other GAL genes. Therefore, GAL genes are expressed and help in metabolizing melibiose and galactose.
Response of tomato (Lycopersicon esculentum Mill.) to application of effective microorganisms

Field experiments were conducted at Silate Agricultural Project in Khartoum State during the period 2004-05 and University of Gezira Research Farm in Wad Medani during the period 2005-06 to determine the response of tomato ( Lycopersicon esculentum Mill) to the application of effective microorganisms (EM). Effective microorganisms solution was applied at three rates of 0.01%, 0.02% and 0.05% either alone or in combinations with chicken manure or urea. Chicken manure was added to the soil at the rate of 6 tons/ha, whereas urea was applied at the rate of 0.10 ton/ha. Spraying intervals were 7 and 14 days and application methods were soil and foliar application. Results showed significant differences between the different treatments. EM sprayed at a dilution rate of 0.05% every seven days in combination with chicken manure gave significant increases in plant height, number of primary branches, number of fruits per vine and total yield. Soil application of EM gave better results than foliar application, especially when applied with chicken manure. Results also revealed that when EM was applied to the soil with chicken manure, it gave the earliest flowering at 54.0 days and resulted in the tallest plants and the highest leaf area index.
Age-related decline of the unfolded protein response in the heart promotes protein misfolding and cardiac pathology

Cardiac myocyte death during heart failure is particularly detrimental, given that cardiac muscle exhibits limited regenerative potential. Protein aggregation was previously observed in end-stage heart failure, suggesting protein-misfolding in cardiac myocytes as a contributor to the disease process. However, the relationship between protein-misfolding, cardiac myocyte death, and myocardial dysfunction is yet to be clearly established. Here, we showed that protein synthesis and the unfolded protein response (UPR) declined as a function of mammalian postnatal development, especially in tissues with low mitotic activity, such as the heart. A deeper examination in animals models showed that compared to neonatal cardiac myocytes, adult cardiac myocytes expressed lower levels of the adaptive UPR transcription factor, ATF6, as well as lower levels of numerous ATF6-regulated genes, which was associated with susceptibility to ER stress-induced cell death. Further reduction of the ATF6-dependent gene program in ATF6 knock-out mice led to the accumulation of misfolded proteins in the myocardium and impaired myocardial function in response to cardiac stress, indicating that ATF6 plays a critical adaptive role in the setting of cardiac disease. Thus, strategies to increase ATF6 aimed at balancing proteostasis in cardiac myocytes might be a fruitful avenue for the development of novel therapies for heart disease and other age-associated diseases. Highlights The unfolded protein response (UPR) declines as a function of age in adult mammalian tissues with low mitotic activity, such as the heart Decreases in the UPR in adult cardiac myocytes is associated with impaired survival during ER stress ATF6 loss of function in adult hearts increases protein misfolding and cardiac disfunction during stress
Attrition Rate in Infant fNIRS Research: A Meta-Analysis

Understanding the trends and predictors of attrition rate, or the proportion of collected data that is excluded from the final analyses, is important for accurate research planning, assessing data integrity, and ensuring generalizability. In this pre-registered meta-analysis, we reviewed 182 publications in infant (0-24 months) functional near-infrared spectroscopy (fNIRS) research published from 1998 to April 9, 2020 and investigated the trends and predictors of attrition. The average attrition rate was 34.23% among 272 experiments across all 182 publications. Among a subset of 136 experiments which reported the specific reasons of subject exclusion, 21.50% of the attrition were infant-driven while 14.21% were signal-driven. Subject characteristics (e.g., age) and study design (e.g., fNIRS cap configuration, block/trial design, and stimulus type) predicted the total and subject-driven attrition rates, suggesting that modifying the recruitment pool or the study design can meaningfully reduce the attrition rate in infant fNIRS research. Based on the findings, we established guidelines on reporting the attrition rate for scientific transparency and made recommendations to minimize the attrition rates. We also launched an attrition rate calculator (LINK) to aid with research planning. This research can facilitate developmental cognitive neuroscientists in their quest toward increasingly rigorous and representative research. Highlights Average attrition rate in infant fNIRS research is 34.23% 21.50% of the attrition are infant-driven (e.g., inattentiveness) while 14.21% are signal-driven (e.g., poor optical contact) Subject characteristics (e.g., age) and study design (e.g., fNIRS cap configuration, block/trial design, and stimulus type) predict the total and infant-driven attrition rates Modifying the recruitment pool or the study design can meaningfully reduce the attrition rate in infant fNIRS research
ultraID: a compact and efficient enzyme for proximity-dependent biotinylation in living cells

Proximity-dependent biotinylation (PDB) combined with mass spectrometry analysis has established itself as a key technology to study protein-protein interactions in living cells. A widespread approach, BioID, uses an abortive variant of the E. coli BirA biotin protein ligase, a quite bulky enzyme with slow labeling kinetics. To improve PDB versatility and speed, various enzymes have been developed by different approaches. Here we present a novel small-size engineered enzyme: ultraID. We show its practical use to probe the interactome of Argonaute-2 after a 10 min labeling pulse and expression at physiological levels. Moreover, using ultraID, we provide a membrane-associated interactome of coatomer, the coat protein complex of COPI vesicles. To date, ultraID is the smallest and most efficient biotin ligase available for PDB and offers the possibility of investigating interactomes at a high temporal resolution.
Human motor cortical gamma activity relates to GABAergic signalling and to behaviour

Gamma activity (γ, >30 Hz) is universally demonstrated across brain regions and species. However, the physiological basis and functional role of γ sub-bands (slow-γ, mid-γ, fast-γ) have been predominantly studied in rodent hippocampus; γ activity in the human neocortex is much less well understood. Here we combined neuroimaging and non-invasive brain stimulation to examine the properties of γ activity sub-bands in the primary motor cortex (M1), and their relationship to both local GABAergic activity and to motor learning. In 33 healthy individuals, we quantified movement-related γ activity in M1 using magnetoencephalography, assessed GABAergic signaling using transcranial magnetic stimulation (TMS), and estimated motor learning via a serial reaction time task. We characterised two distinct γ sub-bands (slow-γ, mid-γ) which show movement-related increase in activity during unilateral index finger movements and are characterised by distinct temporal-spectral-spatial profiles. Bayesian correlation analysis revealed strong evidence for a positive relationship between slow-γ (∼30-60Hz) peak frequency and endogenous GABA signalling during movement preparation (as assessed using the TMS-metric short interval intracortical inhibition). There was also moderate evidence for a relationship between power of the movement-related mid-γ activity (60-90Hz) and motor learning. These relationships were neurochemically- and frequency-specific. These data provide new insights into the neurophysiological basis and functional roles of γ activity in human M1 and allow the development of a new theoretical framework for γ activity in the human neocortex. Significance Statement Gamma (γ) activity is ubiquitous in the brain, yet our understanding of the mechanisms and function of γ activity in the human neocortex, and particularly in the human motor cortex, is limited. Using a multimodal approach, we characterised two patterns of movement-related γ activity in the human motor cortex (slow-γ and mid-γ), with different spatial, temporal and spectral properties. Slow-γ peak frequency was correlated to local GABA-A activity, whereas mid-gamma power predicted performance in a subsequent motor learning task. Based on these findings and previous research, we propose a theoretical framework to explain how human motor cortical γ activities may arise and their potential role in plasticity and motor learning, providing new hypotheses to be tested in future studies. Key Points We combined neuroimaging (i.e. MEG) and non-invasive brain stimulation (i.e. TMS) to examine the properties of γ activity sub-bands in the primary motor cortex. Two distinct γ sub-bands (slow-γ, mid-γ) show a movement-related increase in activity during finger movements and are characterised by distinct temporal-spectral-spatial profiles. We found strong evidence for a positive relationship between slow-γ (∼30-60Hz) peak frequency and endogenous GABA signalling during movement preparation (as assessed using the TMS-metric short interval intracortical inhibition).
15 years of introgression studies: quantifying gene flow across Eukaryotes

With the rise of affordable next generation sequencing technology, introgression — or the exchange of genetic materials between taxa — is widely perceived to be a ubiquitous phenomenon in nature. Although this claim is supported by several keystone studies, no thorough assessment on the frequency of introgression in nature has been performed to date. In this manuscript, we aim to address this knowledge gap by providing a meta-analysis of the most comprehensive survey of introgression studies in Eukaryotes to date (724 papers with claims of introgression). We first examined the evidence given to support introgression, and if/how the lines of evidence have changed across time. We then collated a single statistic, Patterson’s D, that quantifies the strength of introgression across 123 studies to further assess how taxonomic group, divergence time, and aspects of life history influence introgression. We find three main results. Studies on introgression are much more frequent in plants and mammals than any other taxonomic group. The study of introgression has shifted from a largely qualitative assessment of whether introgression happens, to a focus on when and how much introgression has occurred across taxa. The most often used introgression statistic, Patterson’s D, shows several intriguing patterns suggesting introgression reports may be biased by both differences in reporting criteria and sequencing technology, but may also differ across taxonomic systems and throughout the process of speciation. Together, these results suggest the need for a unified approach to quantifying introgression in natural communities, and highlight important areas of future research that can be better assessed once this unified approach is met.
Transcriptional reprogramming and constitutive PD-L1 expression in melanoma are associated with dedifferentiation and activation of interferon and tumor necrosis factor signaling pathways

Melanoma is the most aggressive type of skin cancer, with increasing incidence worldwide. Advances in targeted therapy and immunotherapy have improved the survival of melanoma patients experiencing recurrent disease, but unfortunately treatment resistance frequently reduces patient survival. Resistance to targeted therapy is associated with transcriptomic changes, and has also been shown to be accompanied by increased expression of programmed death ligand 1 (PD-L1), a potent inhibitor of immune response. Intrinsic upregulation of PD-L1 is associated with genome-wide DNA hypomethylation and widespread alterations in gene expression in melanoma cell lines. However, an in-depth analysis of the transcriptomic landscape of melanoma cells with intrinsically upregulated PD-L1 expression is lacking. To determine the transcriptomic landscape of intrinsically upregulated PD-L1 expression in melanoma, we investigated transcriptomes in melanomas with constitutive versus inducible PD-L1 expression (referred to as PD-L1CON and PD-L1IND). RNA-Seq analysis was performed on seven PD-L1CON melanoma cell lines and ten melanoma cell lines with low inducible PD-L1IND expression. We observed that PD-L1CON melanoma cells had a reprogrammed transcriptome with a characteristic pattern of dedifferentiated gene expression, together with active interferon (IFN) and tumor necrosis factor (TNF) signalling pathways. Furthermore, we identified key transcription factors that were also differentially expressed in PD-L1CON versus PD-L1IND melanoma cell lines. Overall, our studies describe transcriptomic reprogramming of melanomas with PD-L1CON expression. Simple Summary Melanoma, an aggressive form of skin cancer, is frequently associated with drug resistance in the advanced stages. For instance, frequently resistance is observed to sequential treatment of melanoma with targeted therapy and immunotherapy. In this research, the authors investigated whether potential transcriptional mechanisms and pathways associated with PD-L1 protein expression could underlie targeted therapy drug resistance in melanoma. The authors found a PD-L1 expression transcriptional pattern underlies resistance to targeted therapy in a subgroup of melanomas. These melanomas were markedly dedifferentiated, as compared to melanomas that were not drug resistant. Understanding changes in transcription and molecular pathways that lead to drug resistance could allow researchers to develop interventions to prevent drug resistance from occurring in melanoma, which could also be relevant to other cancer types.