Miracle-dz/UniFAR-FTUs huggingface dataset:
������������ : Automated Refactoring of the N+1 Problem in Database-Backed Applications

An Object-Relational Mapping (ORM) provides an object-oriented interface to a database and facilitates the development of database-backed applications. In an ORM, programmers do not need to write queries in a separate query language such as SQL, they instead write ordinary method calls that are mapped by the ORM to data-base queries. This added layer of abstraction hides the signi � cant performance cost of database operations, and misuse of ORMs can lead to far more queries being generated than necessary. Of particular concern is the infamous “N+1 problem”, where an initial query yields N results that are used to issue N subsequent queries. This anti-pattern is prevalent in applications that use ORMs, as it is natural to iterate over collections in object-oriented languages. However, iterating over data that originates from a database and calling an ORM method in each iteration may result in suboptimal performance. In such cases, it is often possible to reduce the number of round-trips to the database by issuing a single, larger query that fetches all desired results at once. We propose an approach for automatically refactoring applications that use ORMs to eliminate instances of the “N+1 problem”, which relies on static analysis to detect data � ow between ORM API calls. We implement this approach in a tool called ������������ , targeting the Sequelize ORM in JavaScript, and evaluate it on 8 JavaScript projects. We found 44 N+1 query pairs in these projects, and ������������ refactored all of them successfully, resulting in improved performance (up to 7.67x) while preserving program behavior. Further experiments demonstrate that the relative performance improvements grew as the database size was increased (up to 38.58x), and that front-end page load times were improved.
SAFA: A Tool for Supporting Safety Analysis in Evolving Software Systems

Many organizations seek to increase their agility in order to deliver more timely and competitive products. However, in safety-critical systems such as medical devices, autonomous vehicles, or factory floor robots, the release of new features has the potential to introduce hazards that potentially lead to run-time failures that impact software safety. As a result, many projects suffer from a phenomenon referred to as the big freeze. SAFA is designed to address this challenge. Through the use of cutting-edge deep-learning solutions, it generates trees of requirements, designs, code, tests, and other artifacts that visually depict how hazards are mitigated in the system, and it automatically warns the user when key artifacts are missing. It also uses a combination of colors, annotations, and recommendations to dynamically visualize change across software versions and augments safety cases with visual annotations to aid users in detecting and analyzing potentially adverse impacts of change upon system safety. A link to our tool demo can be found at https://www.youtube. com/watch? v=r-CwxerbSVA.
Precise (Un)Affected Version Analysis for Web Vulnerabilities

Web applications are attractive attack targets given their popularity and large number of vulnerabilities. To mitigate the threat of web vulnerabilities, an important piece of information is their affected versions. However, it is non-trivial to build accurate affected version information because confirming a version as affected or unaffected requires security expertise and huge efforts, while there are usually hundreds of versions to examine. As a result, such information is maintained in a low-quality manner in almost every public vulnerability database. Therefore, it is extremely useful to have a tool that can automatically and precisely examine a large part (even if not all) of the software versions as affected or unaffected. To this end, this paper proposes a vulnerability-centric approach for precise (un)affected version analysis for web vulnerabilities. The key idea is to extract the vulnerability logic from a patch and directly use the vulnerability logic to check whether a version is (un)affected or not. Compared with existing works, our vulnerability-centric approach helps to tolerate the code changes across different software versions. We construct a high-quality dataset with 34 CVEs and 299 software versions to evaluate our approach. The results show that our approach achieves a precision of 98.15% and a recall of 85.01% in identifying (un)affected versions and significantly outperforms existing tools (e.g., V-SZZ, ReDebug, V0Finder).
Exoskeletons: A challenge for development

Abstract The development of exoskeletons is currently a lengthy process full of challenges. We are proposing a framework to accelerate the process and make the resulting exoskeletons more user-centered. The needed accomplishments in science are described in an effort to lay the foundation for future research projects. Since the early 2000s, exoskeletons have been discussed as an emerging technology in industrial, medical, or military applications. Those systems are designed to support people during manual tasks. At first, those systems lacked broad acceptance. Many models found their niches in ongoing developments and more diverse systems entering the market. There are still applications that are in dire need of such assistance. Due to the lack of experience with body-worn robotics, the development of such systems has been shaped by trial and error. The lack of legacy products results in longer development times. In this paper, a process to generate a framework is presented to display the required research to enable future exoskeleton designers. Owing to their proximity to the user’s body, exoskeletons are highly complex systems that need sophisticated subsystems, such as kinematic, control, interaction design, or actuators, to be accepted by users. Due to the wide variety of fields and high user demands, a synchronized multidisciplinary effort is necessary. To achieve this, a process to develop a modular framework for exoskeleton design is proposed. It focuses on user- and use-case-centered solutions for matching kinematics, actuation, and control. To ensure the usefulness of the framework, an evaluation of the incorporated solutions is required.
Study on nutrient supplying capacity of purple soil under long-term rice-wheat rotation

The N, P and K natural supplying capacities of purple soil and the basic productivity of purple soil and their varieties were studied with 10 years long-term fertilization experiment in the wheat-rice rotation system. The results of the experiment indicated that the contribution of the natural productivity of purple soil for the yields of wheat and rice were in 51% and 61%, respectively. The contributions of chemical fertilizers for the yield increases of rice and wheat were in 39% and 49%, respectively. The natural nutrient supplying capacities of purple soil to wheat were in 60% for N, 70% for P and 91% for K, and to rice were in 70% for N, 90% for P and 92% for K, respectively. The amount of nutrient demand in wheat was higher than that in rice. The average natural supplying amounts of N, P and K from purple soil with 10-year long-term experiment were 37.1, 5.4 and 46.0kg/(hm~2·a) in wheat seasons, and were 81.8, 13.6 and 103.0 kg/(hm~2·a) in rice seasons, respectively. In rice-wheat rotation system, the amounts of N, P, and K natural supplying by purple soil gradually decreased in wheat season year by year, while they were stable in rice seasons. That wheat crops gradually depleted the soil fertility was more distinct than rice. The nutrients input of the natural environment played an important role on keeping the stable paddy soil fertility. The natural productivity of purple soil could support some yields of crop, which were about 1.4 t/(hm~2·a) for wheat and 3.5 t/(hm~2·a) for rice.
Six Human-Centered Artificial Intelligence Grand Challenges

Abstract Widespread adoption of artificial intelligence (AI) technologies is substantially affecting the human condition in ways that are not yet well understood. Negative unintended consequences abound including the perpetuation and exacerbation of societal inequalities and divisions via algorithmic decision making. We present six grand challenges for the scientific community to create AI technologies that are human-centered, that is, ethical, fair, and enhance the human condition. These grand challenges are the result of an international collaboration across academia, industry and government and represent the consensus views of a group of 26 experts in the field of human-centered artificial intelligence (HCAI). In essence, these challenges advocate for a human-centered approach to AI that (1) is centered in human well-being, (2) is designed responsibly, (3) respects privacy, (4) follows human-centered design principles, (5) is subject to appropriate governance and oversight, and (6) interacts with individuals while respecting human’s cognitive capacities. We hope that these challenges and their associated research directions serve as a call for action to conduct research and development in AI that serves as a force multiplier towards more fair, equitable and sustainable societies.
Enhanced toughness and stable crack propagation in a novel tungsten fibre-reinforced tungsten composite produced by chemical vapour infiltration

Tungsten is a promising candidate for the plasma-facing components of a future fusion reactor, but its use is strongly restricted by its inherent brittleness. An innovative concept to overcome this problem is tungsten fibre-reinforced tungsten composite. In this paper we present the first mechanical test of such a composite material using a sample containing multiple fibres. The in situ fracture experiment was performed in a scanning electron microscope for close observation of the propagating crack. Stable crack propagation accompanied with rising load bearing capacity is observed. The fracture toughness is estimated using the test results and the surface observation.
Aluminum-doped n-type ZnTe layers grown by molecular-beam epitaxy

N-type ZnTe layers with high electron concentration are grown by molecular-beam epitaxy using aluminum as the donor species. The ZnTe:Al layers show a high structural quality with a narrow x-ray diffraction linewidth (24 arcsec) and a high carrier concentration up to n=4×1018 cm−3 with low resistivity (ρ=0.017 Ω cm). The dependence of the electron mobility on the carrier concentration suggests that the dominant scattering mechanisms in the ZnTe:Al layers are ionized impurity scattering and polar optical phonon scattering. The photoluminescence spectrum of moderately doped ZnTe layers shows strong Al–donor-related bound exciton lines: I2 (2.378 eV) and donor–acceptor pair emission (zero phonon energy=2.324 eV) with a weak deep-level emission (2.19 eV). Highly Al-doped layers show an increase in the deep-level emission intensity and a decrease in carrier mobility, which are interpreted in terms of the increase in the carrier compensation.
A Quantitative Theory for Genomic Offset Statistics

Genomic offset statistics predict the maladaptation of populations to rapid habitat alteration based on association of genotypes with environmental variation. Despite substantial evidence for empirical validity, genomic offset statistics have well-identified limitations, and lack a theory that would facilitate interpretations of predicted values. Here, we clarified the theoretical relationships between genomic offset statistics and unobserved fitness traits controlled by environmentally selected loci, and proposed a geometric measure to predict fitness after rapid change in local environment. he predictions of our theory were verified in computer simulations and in empirical data on African pearl millet (Cenchrus americanus) obtained from a common garden experiment. Our results proposed a unified perspective on genomic offset statistics, and provided a theoretical foundation necessary when considering their potential application in conservation management in the face of environmental change.
Learning cortical hierarchies with temporal Hebbian updates

A key driver of mammalian intelligence is the ability to represent incoming sensory information across multiple abstraction levels. For example, in the visual ventral stream, incoming signals are first represented as low-level edge filters and then transformed into high-level object representations. These same hierarchical structures routinely emerge in artificial neural networks (ANNs) trained for image/object recognition tasks, suggesting that a similar process might underlie biological neural networks. However, the classical ANN training algorithm, backpropagation, is considered biologically implausible, and thus several alternative biologically plausible methods have been developed. For instance, several cortical-inspired ANNs in which the apical dendrite of a pyramidal neuron encodes top-down prediction signals have been proposed. In this case, akin to theories of predictive coding, a prediction error can be calculated locally inside each neuron for updating its incoming weights. Notwithstanding, from a neuroscience perspective, it is unclear whether neurons could compare their apical vs. somatic spiking activities to compute prediction errors. Here, we propose a solution to this problem by adapting the framework of the apical-somatic prediction error to the temporal domain. In particular, we show that if the apical feedback signal changes the postsynaptic firing rate, we can use differential Hebbian updates, a rate-based version of the classical spiking time-dependent plasticity (STDP) updates. To the best of our knowledge, this is the first time a cortical-like deep ANN has been trained using such time-based learning rules. Overall, our work removes a key requirement of biologically plausible models for deep learning that does not align with plasticity rules observed in biology and proposes a learning mechanism that would explain how the timing of neuronal activity can allow supervised hierarchical learning.
Investigating the Unbinding of Muscarinic Antagonists from the Muscarinic 3 Receptor

Patient symptom relief is often heavily influenced by the residence time of the inhibitor-target complex. For the human muscarinic receptor 3 (hMR3), tiotropium is a long-acting bronchodylator used in conditions such as asthma or chronic obstructive pulmonary disease (COPD). The mechanistic insights of this inhibitor remain unclear, specifically, elucidation of the main factors determining the unbinding rates could help develop the next generation of antimuscarinic agents. Using our novel unbinding algorithm, we were able to investigate ligand dissociation from hMR3. The unbinding paths of tiotropium and two of its analogues, N-methylscopolamin and homatropine methylbromide show a consistent qualitative mechanism and allowed us to identify the structural bottleneck of the process. Furthermore, our machine learning-based analysis identified key roles of the ECL2/TM5 junction involved at the transition state. Additionally, our results point at relevant changes at the intracellular end of the TM6 helix leading to the ICL3 kinase domain, highlighting the closest residue L482. This residue is located right between two main protein binding sites involved in signal transduction for hMR3’s activation and regulation. We also highlight key pharmacophores of tiotropium that play determining roles in the unbinding kinetics and could aid towards drug design and lead optimization. Description Graphical abstract of the work, showing the unbinding for ligands 1 (tiotropium, TTP), 2 (N-methylscopolamin, NMS) and 3 (homatropine methylbromide, CPD2). Using TTP’s downhill simulations from its unbinding transition state, different protein-ligand and proteinprotein interactions were analyzed with MLTSA to find relevant CVs driving the different outcomes.
Different Coexisting Mpox Lineages Were Continuously Circulating in Humans Prior to 2022

The origin and hazardous potential of human mpox is obscured by a lack of genomic data between the 2018, when exportations from Nigeria were recorded, and 2022 when the global outbreak started. Here, 18 genomes from patients across southern Nigeria in 2019/20 reveal multiple lineages of Monkeypox virus have achieved sustained human-to-human transmission, co-existing in humans for several years and accumulating mutations consistent with APOBEC3 activity suggesting the virus in humans is now segregated from its natural reservoir. Remarkably, three genomes have disruptions in the A46R gene, which contributes to innate immune modulation. The data demonstrates that the A.2 lineage, multiply exported to North America since 2021 independently of the global outbreak, has persisted in Nigeria for more than two years prior to its latest exportation. One-Sentence Summary Mpox is now a human diseae evolving in humans with multiple variants taking separate paths towards adaptation, some analogous to those of Variola
Functional Organization of Auditory and Reward Systems in Aging

The intrinsic organization of functional brain networks is known to change with age, and is affected by perceptual input and task conditions. Here, we compare functional activity and connectivity during music listening and rest between younger (N=24) and older (N=24) adults, using whole brain regression, seed-based connectivity, and ROI-ROI connectivity analyses. As expected, activity and connectivity of auditory and reward networks scaled with liking during music listening in both groups. Younger adults show higher within-network connectivity of auditory and reward regions as compared to older adults, both at rest and during music listening, but this age-related difference at rest was reduced during music listening, especially in individuals who self-report high musical reward. Furthermore, younger adults showed higher functional connectivity between auditory network and medial prefrontal cortex (mPFC) that was specific to music listening, whereas older adults showed a more globally diffuse pattern of connectivity, including higher connectivity between auditory regions and bilateral lingual and inferior frontal gyri. Finally, connectivity between auditory and reward regions was higher when listening to music selected by the participant. These results highlight the roles of aging and reward sensitivity on auditory and reward networks. Results may inform the design of music- based interventions for older adults, and improve our understanding of functional network dynamics of the brain at rest and during a cognitively engaging task.
Temperature sensitive contact modes allosterically gate TRPV3

TRPV Ion channels are sophisticated molecular sensors designed to respond to distinct temperature thresholds. The recent surge in cryo-EM structures has provided numerous insights into the structural rearrangements accompanying their opening and closing; however, the molecular mechanisms by which TRPV channels establish precise and robust temperature sensing remain elusive. In this work we employ molecular simulations, multi-ensemble contact analysis, graph theory, and machine learning techniques to reveal the temperature-sensitive residue-residue interactions driving allostery in TRPV3. We find that groups of residues exhibiting similar temperature-dependent contact frequency profiles cluster at specific regions of the channel. The dominant mode clusters on the ankyrin repeat domain and displays a linear melting trend while others display non-linear trends. These modes describe the residue-level temperature response patterns that underlie the channel’s functional dynamics. With network analysis, we find that the community structure of the channel changes with temperature. And that a network of high centrality contacts connects distant regions of the protomer to the gate, serving as a means for the temperature-sensitive contact modes to allosterically regulate channel gating. Using a random forest model, we show that the contact states of specific temperature-sensitive modes are indeed predictive of the channel gate’s state. Supporting the physical validity of these modes and networks are several residues identified with our analyses that are reported in literature to be functionally critical. Our results offer high resolution insight into thermo-TRP channel function and demonstrate the utility of temperature-sensitive contact analysis.
TOBF1 modulates mouse embryonic stem cell fate through co-transcriptional regulation of alternative splicing

Embryonic stem (ES) cells retain the ability to undergo lineage-specific differentiation that can eventually give rise to different cell types that constitute an organism. Although stem cell specific biological networks of transcription factors and epigenetic modifiers are well established, how the ES cell specific transcriptional and alternative splicing (AS) machinery regulate their expression has not been sufficiently explored. In this study, we show that the lncRNA associated protein TOBF1 regulates the co-transcriptional alternative splicing of transcripts necessary for maintaining stem cell identity in mouse ES cells. Overlaying information derived from TOBF1 chromatin occupancy, the distribution of its pluripotency-associated OCT-SOX binding motifs, and transcripts undergoing differential expression and alternative splicing upon its disruption unmasked local nuclear territories where these distinct events converge, ultimately leading to the maintenance of mouse ES cell identity.
Nirmatrelvir Resistance in SARS-CoV-2 Omicron_BA.1 and WA1 Replicons and Escape Strategies

The antiviral component of Paxlovid, nirmatrelvir (NIR), forms a covalent bond with Cys145 of SARS-CoV-2 nsp5. To explore NIR resistance we designed mutations to impair binding of NIR over substrate. Using 12 Omicron (BA.1) and WA.1 SARS-CoV-2 replicons, cell-based complementation and enzymatic assays, we showed that in both strains, E166V imparted high NIR resistance (∼55-fold), with major decrease in WA1 replicon fitness (∼20-fold), but not BA.1 (∼2-fold). WA1 replicon fitness was restored by L50F. These differences may contribute to a potentially lower barrier to resistance in Omicron than WA1. E166V is rare in untreated patients, albeit more prevalent in paxlovid-treated EPIC-HR clinical trial patients. Importantly, NIR-resistant replicons with E166V or E166V/L50F remained susceptible to a) the flexible GC376, and b) PF-00835231, which forms additional interactions. Molecular dynamics simulations show steric clashes between the rigid and bulky NIR t-butyl and β-branched V166 distancing the NIR warhead from its Cys145 target. In contrast, GC376, through “wiggling and jiggling” accommodates V166 and still covalently binds Cys145. PF-00835231 uses its strategically positioned methoxy-indole to form a β-sheet and overcome E166V. Drug design based on strategic flexibility and main chain-targeting may help develop second-generation nsp5-targeting antivirals efficient against NIR-resistant viruses.