Miracle-dz/UniFAR-FTUs huggingface dataset:
Observation-based unit test generation at Meta

TestGen automatically generates unit tests, carved from serialized observations of complex objects, observed during app execution. We describe the development and deployment of TestGen at Meta. In particular, we focus on the scalability challenges overcome during development in order to deploy observation-based test carving at scale in industry. So far, TestGen has landed 518 tests into production, which have been executed 9,617,349 times in continuous integration, finding 5,702 faults. Meta is currently in the process of more widespread deployment. Our evaluation reveals that, when carving its observations from 4,361 reliable end-to-end tests, TestGen was able to generate tests for at least 86\% of the classes covered by end-to-end tests. Testing on 16 Kotlin Instagram app-launch-blocking tasks demonstrated that the TestGen tests would have trapped 13 of these before they became launch blocking.
Controllable Expensive Multi-objective Learning with Warm-starting Bayesian Optimization

Pareto Set Learning (PSL) is a promising approach for approximating the entire Pareto front in multi-objective optimization (MOO) problems. However, existing derivative-free PSL methods are often unstable and inefficient, especially for expensive black-box MOO problems where objective function evaluations are costly. In this work, we propose to address the instability and inefficiency of existing PSL methods with a novel controllable PSL method, called Co-PSL. Particularly, Co-PSL consists of two stages: (1) warm-starting Bayesian optimization to obtain quality Gaussian Processes priors and (2) controllable Pareto set learning to accurately acquire a parametric mapping from preferences to the corresponding Pareto solutions. The former is to help stabilize the PSL process and reduce the number of expensive function evaluations. The latter is to support real-time trade-off control between conflicting objectives. Performances across synthesis and real-world MOO problems showcase the effectiveness of our Co-PSL for expensive multi-objective optimization tasks.
Bayesian Modelling Approaches for Breath-Hold Induced Cerebrovascular Reactivity

Cerebrovascular reactivity (CVR) reflects the ability of blood vessels to dilate and constrict in response to a vasoactive stimulus and is an important indicator of cerebrovascular health. CVR can be mapped non-invasively with functional magnetic resonance imaging (fMRI) based on blood oxygen level-dependent (BOLD) contrast in combination with a breath-hold (BH) task. There are several ways to analyse this type of data and retrieve individual CVR amplitude and timing information. The most common approach involves employing a time-shifted general linear model with the measured end-tidal carbon dioxide signal as a regressor of interest. In this work, we introduce a novel method for CVR mapping based on a variational Bayesian approach. We analysed BOLD fMRI data from six participants that performed a BH task in ten different sessions each, and computed the corresponding CVR amplitude and delay maps for each session/subject. No statistically significant differences were observed between the modelling approaches in the CVR delay and amplitude maps in grey matter. Notably, the largest difference between methods was apparent in the case of low CVR amplitude, attributed to how each method addressed noisy voxels, particularly in white matter and cerebral spinal fluid. Both approaches showed highly reproducible CVR amplitude maps where between-subject variability was significantly larger than between-session variability. Furthermore, our results illustrated that the Bayesian approach is more computationally efficient, and future implementations could incorporate more complex noise models, non-linear fitting, and physiologically meaningful information into the model in the form of priors. This work demonstrates the utility of variational Bayesian modelling for CVR mapping and highlights its potential for characterising BOLD fMRI dynamics in the study of cerebrovascular health and its application to clinical settings.
PluMu - a Mu-like bacteriophage infecting Actinobacillus pleuropneumoniae

Actinobacillus pleuropneumoniae is the causative agent of pleuropneumonia, an economically important lung disease of pigs. In draft genomes of two Cypriot clinical A. pleuropneumoniae isolates (MIDG3457 and MIDG3459), we previously identified single genomic regions with homology to Mu-like bacteriophage and presented preliminary evidence of active phage. Here, updated Phastest genomic analysis identified two loci in both MIDG3457 and MIDG3459 that were predicted to encode proteins with high homology to, and whose organization was characteristic of, Mu-like phages. Phylogenetically, the closest matches were with Mannheimia Vb and Glaesserella SuMu phages. Phastest scored the loci as “complete”, indicating they produced active phage. PCR amplification of the Mu-like phage c and tail genes from DNase-treated polyethylene glycol 8000 (PEG)-precipitated supernatants of MIDG3457 and MIDG3459 (grown in either Brain Heart Infusion-NAD or Grace’s Insect Medium-NAD broth) indicated the presence of intact virions. The phages from MIDG3457 and MIDG3459 were named PluMu 3457-1, 3457-2, and PluMu 3459-1 and PluMu 3459-2, respectively. Transmission electron microscopy (TEM) of PEG-precipitated supernatants of broth-grown MIDG3459 identified virions with icosahedral heads and tails, consistent with other Mu-like phages. We conclude that MIDG3459 produces an active Mu- like phage.
Intrinsic structural disorder on proteins is involved in the interactome evolution

New mathematical tools help to understanding cell functions, adaptability and evolvability to discover hidden variables to predict phenotypes that could be tested in the future in wet labs. Different models have been successfully used to discover properties of the protein-protein interaction network or interactome. We found that in the hyperbolic Popularity-Similarity model cellular proteins with highest contents of structural intrinsic disorder cluster together in many different eukaryotic interactomes and not the prokaryotic E. coli, where proteins with high levels of intrinsic disorder are very low. We also found that the normalized theta variable from the Popularity-Similarity model for a protein family correlate to the seniority of the organisms in analysis.
A three-dimensional, discrete-continuum model of blood pressure in microvascular networks

We present a 3D discrete-continuum model to simulate blood pressure in large microvascular tissues in the absence of known capillary network architecture. Our hybrid approach combines a 1D Poiseuille flow description for large, discrete arteriolar and venular networks coupled to a continuum-based Darcy model, point sources of flux, for transport in the capillary bed. We evaluate our hybrid approach using a vascular network imaged from the mouse brain medulla/pons using multi-fluorescence high-resolution episcopic microscopy (MF-HREM). We use the fully-resolved vascular network to predict the hydraulic conductivity of the capillary network and generate a fully-discrete pressure solution to benchmark against. Our results demonstrate that the discrete-continuum methodology is a computationally feasible and effective tool for predicting blood pressure in real-world microvascular tissues when capillary microvessels are poorly defined.
Deciphering SH-SY5Y neuroblastoma cell proliferation: A detailed investigation of seeding concentrations, cultivation surfaces, and long-term maintenance in a sealed chamber

This study investigated the proliferation dynamics of SH-SY5Y neuroblastoma cells in a confined environment. The effects of seeding concentrations, culture surfaces and long-term maintenance in a sealed chamber were systematically investigated. Seeding concentration experiments revealed a clear correlation between initial cell number and proliferation rate, highlighting the importance of precise adjustments for optimal cell culture results. The study of available surface effects showed a nuanced relationship between chamber dimensions and cell growth, with confluence stability observed in larger chambers with diameters greater than 10 mm. The study also compared SH-SY5Y cells with HeLaGFP and 16HBE14σ cells, revealing growth patterns influenced by chamber size. A properly designed sealed chamber with continuous real-time microscopic monitoring showed comparable cell growth to standard culture chambers. The research provides valuable insights into optimising SH-SY5Y cell culture practices, particularly in microfluidic systems.
Inferring the location and orientation of cell divisions on time-lapse image sequences

We propose a two-stage method to characterize cell divisions. In a first stage, the division detection problem is recast into a semantic segmentation task on image sequences. In a second stage, a local regression on individual divisions yields the orientation and distance between daughter cells. We apply our formalism to confocal image sequences of neural tube formation in chicken embryos, where divisions occur within a well-defined plane. We show that our two-stage method can be implemented using simple networks, e.g. a U-Net for the segmentation and a 4-layer CNN for the regression. Optimization of the networks was achieved through a systematic exploration of hyperparameters. In particular, we show that considering several frames as inputs significantly improves the segmentation performance. We reach a performance of 96% in the F1 measure for the detection and errors for the angle, which are within the bounds of the uncertainty of the ground-truth annotation dataset.
Resistance genes are distinct in protein-protein interaction networks according to drug class and gene mobility

With growing calls for increased surveillance of antibiotic resistance as an escalating global health threat, improved bioinformatic tools are needed for tracking antibiotic resistance genes (ARGs) across One Health domains. Most studies to date profile ARGs using sequence homology, but such approaches provide limited information about the broader context or function of the ARG in bacterial genomes. Here we introduce a new pipeline for identifying ARGs in genomic data that employs machine learning analysis of Protein-Protein Interaction Networks (PPINs) as a means to improve predictions of ARGs while also providing vital information about the context, such as gene mobility. A random forest model was trained to effectively differentiate between ARGs and nonARGs and was validated using the PPINs of ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter cloacae), which represent urgent threats to human health because they tend to be multi-antibiotic resistant. The pipeline exhibited robustness in discriminating ARGs from nonARGs, achieving an average area under the precision-recall curve of 88%. We further identified that the neighbors of ARGs, i.e., genes connected to ARGs by only one edge, were disproportionately associated with mobile genetic elements, which is consistent with the understanding that ARGs tend to be mobile compared to randomly sampled genes in the PPINs. This pipeline showcases the utility of PPINs in discerning distinctive characteristics of ARGs within a broader genomic context and in differentiating ARGs from nonARGs through network-based attributes and interaction patterns. The code for running the pipeline is publicly available at https://github.com/NazifaMoumi/PPI-ARG-ESKAPE
Serine mistranslation induces the integrated stress response without accumulation of uncharged tRNAs

Aminoacyl-tRNA synthetases (aaRSs) are essential enzymes that support robust and accurate protein synthesis. A rapidly expanding number of studies show that mutations in aaRSs lead to multiple human diseases, including neurological disorders and cancer. Much remains unknown about how aaRS mutations impact human health. In particular, how aminoacylation errors affect stress responses and fitness in eukaryotic cells remains poorly understood. The integrated stress response (ISR) is an adaptive mechanism in response to multiple stresses. However, chronic activation of the ISR contributes to the development of multiple diseases (e.g., neuropathies). Here we show that Ser misincorporation into Ala and Thr codons, resulting from aaRS editing defects or mutations in tRNAs, constitutively active the ISR. Such activation does not appear to depend on the accumulation of uncharged tRNAs, implicating that Ser mistranslation may lead to ribosome stalling and collision.
Competitive advantages of secondary adsorption-triggered lysis inhibition for T-even phages

T-even bacteriophages are known to employ lysis inhibition (LIN), where the lysis of an infected host is delayed in response to secondary adsorptions. Upon the eventual burst of the host, significantly more phage progenies are released. Here, we analysed the competitive advantage of LIN using a mathematical model. In batch culture, LIN provides a bigger phage yield at the end of the growth and, in addition, gives a competitive advantage against LIN mutants with rapid lysis by letting them adsorb to already infected hosts in the LIN state. By simulating plaque formation in a spatially structured environment, we show that, while LIN phages will produce a smaller zone of clearance, the area over which the phages spread is actually comparable to those without LIN. The analysis suggests that LIN induced by secondary adsorption is favourable in terms of competition, both in spatially homogeneous and inhomogeneous environments. Author Summary T-even bacteriophages can delay the lysis of their hosts when they detect more phages are adsorbing to the hosts, increasing the progeny production per host. Using a mathematical model, we provide a quantitative analysis of this strategy’s competitive advantages and disadvantages in different environments. The model predicts that phage adsorption to lysis-inhibited cells provides a significant competitive advantage against fast-lysing cells. We also find that secondary infection-triggered delay does not hinder the spreading of the phage in a lawn of uninfected cells, even though the apparent plaque size is small. The analysis suggests that lysis inhibition provides a robust competitive advantage for a virulent phage.
Engineering an anti-CD206-synNotch receptor: insights into the development of novel synthetic receptors

Immune cells play a pivotal role in the establishment, growth and progression of tumors at primary and metastatic sites. Macrophages, in particular, play a critical role in suppressing immune responses and promoting an anti-inflammatory environment through both direct and indirect cell-cell interactions. However, our understanding of the mechanisms underlying such interactions is limited due to a lack of reliable tools for studying transient interactions between cancer cells and macrophages within the tumor microenvironment. Recent advances in mammalian synthetic biology have introduced a wide range of synthetic receptors that have been used in diverse biosensing applications. One such synthetic receptor is the synNotch receptor, which can be tailored to sense specific ligands displayed on the surface of target cells. With this study, we aimed at developing a novel αCD206-synNotch receptor, targeting CD206+ macrophages, a population of macrophages that play a crucial role in promoting metastatic seeding and persistent growth. Engineered in cancer cells and used in mouse metastasis models, such tool could help monitor and understand the effects cell-cell interactions between macrophages and cancer cells have on metastasis establishment. Here, we report the development of cancer landing pad cells for versatile applications, the engineering of αCD206-synNotch cells, report the measurements of their activity and specificity, and discuss the unexpected caveats when considering their in vivo applications.
Cross-species prediction of transcription factor binding by adversarial training of a novel nucleotide-level deep neural network

Cross-species prediction of TF binding remains a major challenge due to the rapid evolutionary turnover of individual TF binding sites, resulting in cross-species predictive performance being consistently worse than within-species performance. In this study, we first propose a novel Nucleotide-Level Deep Neural Network (NLDNN) to predict TF binding within or across species. NLDNN regards the task of TF binding prediction as a nucleotide-level regression task. Beyond predictive performance, we also assess model performance by locating potential TF binding regions, discriminating TF-specific single-nucleotide polymorphisms (SNPs), and identifying causal disease-associated SNPs. Then, we design a dual-path framework for adversarial training of NLDNN to further improve the cross-species prediction performance by pulling the domain space of human and mouse species closer.
Hydrodynamic Radii of Intrinsically Disordered Proteins: Fast Prediction by Minimum Dissipation Approximation and Experimental Validation

The diffusion coefficients of globular and fully unfolded proteins can be predicted with high accuracy solely from their mass or chain length. However, this approach fails for intrinsically disordered proteins (IDPs) containing structural domains. We propose a rapid predictive methodology for estimating the diffusion coefficients of IDPs. The methodology uses accelerated conformational sampling based on self-avoiding random walks and includes hydrodynamic interactions between coarse-grained protein subunits, modeled using the generalized Rotne-Prager-Yamakawa approximation. To estimate the hydrodynamic radius, we rely on the minimum dissipation approximation recently introduced by Cichocki et al. Using a large set of experimentally measured hydrodynamic radii of IDPs over a wide range of chain lengths and domain contributions, we demonstrate that our predictions are more accurate than the Kirkwood approximation and phenomenological approaches. Our technique may prove valuable in predicting the hydrodynamic properties of both fully unstructured and multidomain disordered proteins. TOC Graphic
Brief Interventions for Cannabis Use in Healthcare Settings: Systematic Review and Meta-analyses of Randomized Trials.

OBJECTIVES The efficacy of brief interventions for cannabis use was assessed in a systematic review and meta-analyses. METHODS Systematic searches in academic databases were conducted, and reference lists of included studies were reviewed. Randomized trials were included that compared brief interventions with minimal control interventions for improving cannabis-specific outcomes among participants recruited from healthcare settings. Mean differences (MDs) based on change-from-baseline measurements were pooled using random-effects meta-analyses, with stratification by short term (≤3 months) and long term (>3 months). RESULTS Ten reports from 9 studies were included. Most studies were conducted in the United States, including participants who were adults and were recruited from primary care or emergency departments. There were no significant effects of brief interventions on cannabis-specific Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) scores in the short term (MD -1.27 points; 95% confidence interval [CI] -3.75, 1.21; I 84.40%). The null pattern of findings was also observed for number of days of cannabis use in the past 30 days in the short term (MD -0.22 days; 95% CI -2.27, 1.82; I 60.30%) and long term (MD -0.28 days; 95% CI -2.42, 1.86; I 60.50%). The evidence base for other outcomes not subjected to meta-analyses was limited and mixed. CONCLUSIONS Brief interventions did not result in reductions in cannabis-specific ASSIST scores or number of days of cannabis use, whereas the evidence base for other outcomes was limited and mixed. As such, brief interventions in healthcare settings may not be efficacious for cannabis use.
TALISMAN: Tamper Analysis for Reference Monitors

—Correct access control enforcement is a critical foundation for data security. The reference monitor is the key component for enforcing access control, which is supposed to provide tamperproof mediation of all security-sensitive operations. Since reference monitors are often deployed in complex software handling a wide variety of operation requests, such as operating systems and server programs, a question is whether reference monitor implementations may have flaws that prevent them from achieving these requirements. In the past, automated analyses detected flaws in complete mediation. However, researchers have not yet developed methods to detect flaws that may tamper with the reference monitor, despite the many vulnerabilities found in such programs. In this paper, we develop T ALISMAN , an automated analysis for detecting flaws that may tamper the execution of reference monitor implementations. At its core, T ALISMAN implements a precise information flow integrity analysis to detect violations that may tamper the construction of authorization queries. T ALISMAN applies a new, relaxed variant of noninterference that eliminates several spurious implicit flow violations. T ALISMAN also provides a means to vet expected uses of untrusted data in authorization using endorsement. We apply T ALISMAN on three reference monitor implementations used in the Linux Security Modules framework, SELinux, AppArmor, and Tomoyo, verifying 80% of the arguments in authorization queries generated by these LSMs. Using T ALISMAN , we also found vulnerabilities in how pathnames are used in authorization by Tomoyo and AppArmor allowing adversaries to circumvent authorization. T ALISMAN shows that tamper analysis of reference monitor implementations can automatically verify many cases and also enable the detection of critical flaws.
Procedures and strategies in wheat breeding for resistance to diseases.

The primary objective of the winter wheat breeding program at the Agricultural Institute Osijek is to enhance the profitability of wheat production by developing and releasing improved wheat varieties. This means selection for early maturity, lodging resistance, disease resistance, freeze tolerance, along with high yield and good quality. Diseases can be the most limiting factor in expression of genetic potential. The most important wheat diseases are powdery mildew, leaf rust, septoria diseases and fusarium head blight. Traditionally, breeders have introduced resistance traits into wheat by crossing commercial plants with other wheat varieties that are able to fend off a pathogen, which is time-consuming process. Germplasm from Agricultural Institute Osijek was screened for Type I (incidence), Type II (spread in the spike), Type III (DON resistance) resistance to fusarium head blight, and leaf and stem rust seedling resistance using appropriated published methodologies. Combining various types of resistance to different diseases is expected to generate lines with higher levels of resistance and more effective resistance under high inoculum loads.