Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey of Code Search Based on Deep Learning

Code writing is repetitive and predictable, inspiring us to develop various code intelligence techniques. This survey focuses on code search, that is, to retrieve code that matches a given natural language query by effectively capturing the semantic similarity between the query and code. Deep learning, being able to extract complex semantics information, has achieved great success in this field. Recently, various deep learning methods, such as graph neural networks and pretraining models, have been applied to code search with significant progress. Deep learning is now the leading paradigm for code search. In this survey, we provide a comprehensive overview of deep learning-based code search. We review the existing deep learning-based code search framework which maps query/code to vectors and measures their similarity. Furthermore, we propose a new taxonomy to illustrate the state-of-the-art deep learning-based code search in a three-steps process: query semantics modeling, code semantics modeling, and matching modeling which involves the deep learning model training. Finally, we suggest potential avenues for future research in this promising field.
Temporal-aware Multimodal Feature Fusion for Sentiment Analysis

In this paper, we present a solution to the MuSe-Personalisation sub-challenge in the Multimodal Sentiment Analysis Challenge 2023. The task of MuSe-Personalisation aims to predict a time-continuous emotional value (i.e., arousal and valence) by using multimodal data. The MuSe-Personalisation sub-challenge faces the individual variations problem, resulting in poor generalization on unknown test sets. To solve the above problem, we first extract several informative visual features, and then propose a framework containing feature selection, feature learning and fusion strategy to discover the best combination of features for sentiment analysis. Finally, our method achieved the Top 1 performance in the MuSe-Personalisation sub-challenge, and the result in the combined CCC of physiological arousal and valence was 0.8681, outperforming the baseline system by a large margin (i.e., 10.42%) on the test set.
ECG-Coupled Multimodal Approach for Stress Detection

Psychological stress undoubtedly impacts the lives of many people on a regular basis, and it is, therefore, necessary to have systems capable of adequately identifying the level of stress an individual is experiencing. With the emergence of compact devices capable of capturing biosignals such as ECG, it is now feasible to use such signals for different applications, including stress monitoring, in addition to the more popular audio and video modalities. As part of the MuSe-Personalisation sub-challenge for 2023, we develop a GRU-based regressor to continuously predict stress levels from features extracted from raw ECG signals, with our unimodal model achieving a reasonable combined CCC, which is comparable to almost all the baseline audio and video models. Moreover, we utilised this model in combination with the two best performing MuSe-Personalisation baseline models to construct a number of multimodal models via late fusion, with our best model attaining a combined CCC of . 7808, thereby improving upon the best baseline system.
Gigue: A JIT Code Binary Generator for Hardware Testing

Just-in-time compilers are the main virtual machine components responsible for performance. They recompile frequently used source code to machine code directly, avoiding the slower interpretation path. Hardware acceleration and performant security primitives would benefit the generated JIT code directly and increase the adoption of hardware-enforced primitives in a high-level execution component. The RISC-V instruction set architecture presents extension capabilities to design and integrate custom instructions. It is available as open-source and several capable open-source cores coexist, usable for prototyping. Testing JIT-compiler-specific instruction extensions would require extending the JIT compiler itself, other VM components, the underlying operating system, and the hardware implementation. As the cost of hardware prototyping is already high, a lightweight representation of the JIT compiler code region in memory would ease prototyping and implementation of new solutions. In this work, we present Gigue, a binary generator that outputs bare-metal executable code, representing a JIT code region snapshot composed of randomly filled methods. Its main goal is to speed up hardware extension prototyping by defining JIT-centered workloads over the newly defined instructions. It is modular and heavily configurable to qualify different JIT code regions' implementations from VMs and different running applications. We show how the generated binaries can be extended with three custom extensions, whose execution is guaranteed by Gigue's testing framework. We also present different application case generation and execution on top of a fully-featured RISC-V core.
Adapting Segment Anything Model for Shield Tunnel Water Leakage Segmentation

Intelligent algorithms have a significant driving force in smart engineering. As one of the common engineering projects, water leakage detection in shield tunnel is critical. Manual inspection methods for detecting leakage areas are time-consuming and may yield inconsistent results. To improve efficiency and reliability, automated methods should be developed. Recently, pre-trained foundational segmentation model on huge datasets are gaining attention. By modifying Segment Anything Model (SAM) without retraining or fine-tuning the entire model, we provide an accurate automatic water leakage segmentation approach using adaptation technique. Our method simplifies tunnel maintenance, ensuring the integrity and safety of these critical infrastructures. Our study highlights the limitations of SAM and the need for its adaptation in specific task. The contributions of this paper include the efficient adaptation of SAM for shield tunnel water leakage segmentation and the demonstration of the application effect by data experiments. With AI models we improve the efficiency of engineering challenges.
CD1d-dependent neuroinflammation impairs tissue repair and functional recovery following a spinal cord injury

Tissue damage resulting from a spinal cord injury (SCI) is primarily driven by a robust neuroimmune/neuroinflammatory response. This intricate process is mainly governed by a multitude of cytokines and cell surface proteins in the central nervous system (CNS). However, the critical components of the neuroimmune/neuroinflammatory response during SCI are still not well-defined. In this study, we investigated the impact of CD1d, an MHC class I-like molecule mostly known for presenting lipid antigens to natural killer T (NKT) cells and regulating immune/inflammatory responses, on neuroimmune/neuroinflammatory responses induced by SCI. We observed an increased expression of CD1d on various cell types within the spinal cord, including microglia/macrophages, oligodendrocytes (ODCs), and endothelial cells (DCs), but not on neurons or astrocytes post-SCI. In comparison to wildtype (WT) mice, a T10 contusive SCI in CD1d knockout (CD1dKO or Cd1d-/-) mice resulted in markedly reduced proinflammatory cytokine release, microglia/macrophage activation and proliferation. Following SCI, the levels of inflammatory cytokines and activation/proliferation of microglia/macrophages were dramatically reduced, while anti-inflammatory cytokines such as IL-4 and growth factors like VEGF were substantially increased in the spinal cord tissues of CD1dKO mice when compared to WT mice. In the post-acute phase of SCI (day 7 post-SCI), CD1dKO mice had a significantly higher frequency of tissue-repairing macrophages, but not other types of immune cells, in the injured spinal cord tissues compared to WT mice. Moreover, CD1d-deficiency protected spinal cord neuronal cells and tissue, promoting functional recovery after a SCI. However, the neuroinflammation in WT mouse spinal cords was independent of the canonical CD1d/NKT cell axis. Finally, treatment of injured mice with a CD1d-specific monoclonal antibody significantly enhanced neuroprotection and improved functional recovery. Therefore, CD1d promotes the proinflammatory response following a SCI and represents a potential therapeutic target for spinal cord repair. Significance Statement The cell surface molecule, CD1d, is known to be recognized by cells of the immune system. To our knowledge, this is the first observation that the CD1d molecule significantly contributes to neuroinflammation following a spinal cord injury (SCI) in a manner independent of the CD1d/NKT cell axis. This is important, because this work reveals CD1d as a potential therapeutic target following an acute SCI for which there are currently no effective treatments.
Climatic Impacts of Parameterized Local and Remote Tidal Mixing

AbstractTurbulent mixing driven by breaking internal tides plays a primary role in the meridional overturning and oceanic heat budget. Most current climate models explicitly parameterize only the local dissipation of internal tides at the generation sites, representing the remote dissipation of low-mode internal tides that propagate away through a uniform background diffusivity. In this study, a simple energetically consistent parameterization of the low-mode internal-tide dissipation is derived and implemented in the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The impact of remote and local internal-tide dissipation on the ocean state is examined using a series of simulations with the same total amount of energy input for mixing, but with different scalings of the vertical profile of dissipation with the stratification and with different idealized scenarios for the distribution of the low-mode internal-tide energy dissipation: uniformly over ocean basins, co.
Low- and high-level information analyses of transcriptome connecting endometrial-decidua-placental origin of preeclampsia subtypes: A preliminary study

Background Existing proposed pathogenesis for preeclampsia (PE) was only applied for early onset subtype and did not consider pre-pregnancy and competing risks. We aimed to decipher PE subtypes by identifying related transcriptome that represents endometrial maturation and histologic chorioamnionitis. Methods We utilized eight arrays of mRNA expression for discovery (n=289), and other eight arrays for validation (n=352). Differentially expressed genes (DEGs) were overlapped between those of: (1) healthy samples from endometrium, decidua, and placenta, and placenta samples under histologic chorioamnionitis; and (2) placenta samples for each of the subtypes. They were all possible combinations based on four axes: (1) pregnancy-induced hypertension; (2) placental dysfunction-related diseases (e.g., fetal growth restriction [FGR]); (3) onset; and (4) severity. Results The DEGs of endometrium at late-secretory phase, but none of decidua, significantly overlapped with those of any subtypes with: (1) early onset (p-values ≤0.008); (2) severe hypertension and proteinuria (p-values ≤0.042); or (3) chronic hypertension and/or severe PE with FGR (p-values ≤0.042). Although sharing the same subtypes whose DEGs with which significantly overlap, the gene regulation was mostly counter-expressed in placenta under chorioamnionitis (n=13/18, 72.22%; odds ratio [OR] upper bounds ≤0.21) but co-expressed in late-secretory endometrium (n=3/9, 66.67%; OR lower bounds ≥1.17). Neither the placental DEGs at first-nor second-trimester under normotensive pregnancy significantly overlapped with those under late-onset, severe PE without FGR. Conclusions We identified the transcriptome of endometrial maturation in placental dysfunction that distinguished early- and late-onset PE, and indicated chorioamnionitis as a PE competing risk. This study implied a feasibility to develop and validate the pathogenesis models that include pre-pregnancy and competing risks to decide if it is needed to collect prospective data for PE starting from pre-pregnancy including chorioamnionitis information.
Phospholipid isotope tracing reveals β-catenin-driven suppression of phosphatidylcholine metabolism in hepatocellular carcinoma

Background and Aims Activating mutations in the CTNNB1 gene encoding β-catenin are among the most frequently observed oncogenic alterations in hepatocellular carcinoma (HCC). HCC with CTNNB1 mutations show profound alterations in lipid metabolism including increases in fatty acid oxidation and transformation of the phospholipidome, but it is unclear how these changes arise and whether they contribute to the oncogenic program in HCC. Methods We employed untargeted lipidomics and targeted isotope tracing to quantify phospholipid production fluxes in an inducible human liver cell line expressing mutant β-catenin, as well as in transgenic zebrafish with activated β-catenin-driven HCC. Results In both models, activated β-catenin expression was associated with large changes in the lipidome including conserved increases in acylcarnitines and ceramides and decreases in triglycerides. Lipid flux analysis in human cells revealed a large reduction in phosphatidylcholine (PC) production rates as assayed by choline tracer incorporation. We developed isotope tracing lipid flux analysis for zebrafish and observed similar reductions in phosphatidylcholine synthesis flux accomplished by sex-specific mechanisms. Conclusions The integration of isotope tracing with lipid abundances highlights specific lipid class transformations downstream of β-catenin signaling in HCC and suggests future HCC-specific lipid metabolic targets. Synopsis In this work, we show by lipid specific isotope tracing that mutations in the oncogene CTNNB1 leads to conserved changes in lipid metabolism in hepatocellular carcinoma. These include the stimulation of fatty acid oxidation and a suppression of phosphorylcholine synthesis.
A classification-based generative approach to selective targeting of global slow oscillations during sleep

Background Given sleep’s crucial role in health and cognition, numerous sleep-based brain interventions are being developed, aiming to enhance cognitive function, particularly memory consolidation, by improving sleep. Research has shown that Transcranial Alternating Current Stimulation (tACS) during sleep can enhance memory performance, especially when used in a closed-loop (cl-tACS) mode that coordinates with sleep slow oscillations (SOs, 0.5-1.5Hz). However, sleep tACS research is characterized by mixed results across individuals, which are often attributed to individual variability. Objective/Hypothesis This study targets a specific type of SOs, widespread on the electrode manifold in a short delay (“global SOs”), due to their close relationship with long-term memory consolidation. We propose a model-based approach to optimize cl-tACS paradigms, targeting global SOs not only by considering their temporal properties but also their spatial profile. Methods We introduce selective targeting of global SOs using a classification-based approach. We first estimate the current elicited by various stimulation paradigms, and optimize parameters to match currents found in natural sleep during a global SO. Then, we employ an ensemble classifier trained on sleep data to identify effective paradigms. Finally, the best stimulation protocol is determined based on classification performance. Results Our study introduces a model-driven cl-tACS approach that specifically targets global SOs, with the potential to extend to other brain dynamics. This method establishes a connection between brain dynamics and stimulation optimization. Conclusion Our research presents a novel approach to optimize cl-tACS during sleep, with a focus on targeting global SOs. This approach holds promise for improving cl-tACS not only for global SOs but also for other physiological events, benefiting both research and clinical applications in sleep and cognition.
Five amino acid mismatches in the zinc-finger domains of CELLULOSE SYNTHASE 5 and CELLULOSE SYNTHASE 6 modulate their incorporation into cellulose synthase complexes in Arabidopsis

Main conclusion Different capacities for the homodimerization of CESA5 and CESA6 are modulated by five amino acid mismatches in their zinc-finger domains and motivate discriminative incorporation of these CESAs into CSCs Cellulose synthase 5 (CESA5) and CESA6 are known to share substantial functional overlap. In the zinc-finger domain of CESA5, there are five amino acid (AA) mismatches when compared to CESA6. These mismatches in CESA5 were replaced with their CESA6 counterparts one by one until all were replaced, generating nine engineered CESA5s. Each N-terminal enhanced yellow fluorescent protein-tagged engineered CESA5 was introduced to prc1-1, a cesa6 null mutant, and resulting mutants were subjected to phenotypic analyses. We found that five single AA-replaced CESA5 proteins partially rescue the prc1-1 mutant phenotypes to different extents. Multi-AA replaced CESA5s further rescued the mutant phenotypes in an additive manner, culminating in full recovery by CESA5G43R+S49T+S54P+S80A+Y88F. Investigations in cellulose content, cellulose synthase complex (CSC) motility, and cellulose microfibril organization in the same mutants support the results of the phenotypic analyses. Bimolecular fluorescence complementation assays demonstrated that the level of homodimerization in every engineered CESA5 is substantially higher than CESA5. The mean fluorescence intensity of CSCs carrying each engineered CESA5 fluctuates with the degree to which the prc1-1 mutant phenotypes are rescued by introducing a corresponding engineered CESA5, indicating that these mismatches modulate the incorporation of both CESAs into CSCs by controlling their ability to homodimerize.
Bayesian inference of relative fitness on high-throughput pooled competition assays

The tracking of lineage frequencies via DNA barcode sequencing enables the quantification of microbial fitness. However, experimental noise coming from biotic and abiotic sources complicates the computation of a reliable inference. We present a Bayesian pipeline to infer relative microbial fitness from high-throughput lineage tracking assays. Our model accounts for multiple sources of noise and propagates uncertainties throughout all parameters in a systematic way. Furthermore, using modern variational inference methods based on automatic differentiation, we are able to scale the inference to a large number of unique barcodes. We extend this core model to analyze multi-environment assays, replicate experiments, and barcodes linked to genotypes. On simulations, our method recovers known parameters within posterior credible intervals. This work provides a generalizable Bayesian framework to analyze lineage tracking experiments. The accompanying open-source software library enables the adoption of principled statistical methods in experimental evolution.
The guide RNA sequence dictates the slicing kinetics and conformational dynamics of the Argonaute silencing complex

The RNA-induced silencing complex (RISC), which powers RNA interference (RNAi), consists of a guide RNA and an Argonaute protein that slices target RNAs complementary to the guide. We find that for different guide-RNA sequences, slicing rates of perfectly paired, bound targets can be surprisingly different (>250-fold range), and that faster slicing confers greater knockdown efficacy in cells. Four sequence determinants underlie much of this variation in slicing rates. Analyses of these determinants implicate a structural distortion at guide nucleotides 6–7 in promoting slicing, and indicate that, in contrast to the prevailing model, slicing is primarily limited by the rate at which the enzyme−substrate complex reaches a slicing-competent conformation. Moreover, different guide sequences have an unanticipated, 600-fold range in 3′-mismatch tolerance, which is attributable to the GC-content of their central regions. Together, our analyses identify sequence determinants of RISC activity and provide biochemical, conformational, and functional rationale for their action.
Long chain monomethyl branched-chain fatty acid levels in human milk vary with gestational weight gain

Breastfeeding is an important determinant of infant health and there is immense interest in understanding its metabolite composition so that key beneficial components can be identified. The aim of this research was to measure the fatty acid composition of human milk in an Irish cohort where we examined changes depending on lactation stage and gestational weight gain trajectory. Utilising a chromatography approach optimal for isomer separation, we identified 44 individual fatty acid species via GCMS and showed that monomethyl branched-chain fatty acids(mmBCFA’s), C15:0 and C16:1 are lower in women with excess gestational weight gain versus low gestational weight gain. To further explore the potential contribution of the activity of endogenous metabolic pathways to levels of these fatty acids in milk, we administered D2O to C57BL/6J dams fed a purified lard based high fat diet (HFD) or low-fat diet during gestation and quantified the total and de novo synthesized levels of fatty acids in their milk. We found that de novo synthesis over three days can account for between 10 and 50% of mmBCFAs in milk from dams on the low-fat diet dependent on the branched-chain fatty acid species. However, HFD fed mice had significantly decreased de novo synthesized fatty acids in milk resulting in lower total mmBCFAs and medium chain fatty acid levels. Overall, our findings highlight the diverse fatty acid composition of human milk and that human milk mmBCFA levels differ between gestational weight gain phenotypes. In addition, our data indicates that de novo synthesis contributes to mmBCFA levels in mice milk and thus may also be a contributory factor to mmBCFA levels in human milk. Given emerging data indicating mmBCFAs may be beneficial components of milk, this study contributes to our knowledge around the phenotypic factors that may impact their levels.
Inferring state-dependent diversification rates using approximate Bayesian computation (ABC)

State-dependent speciation and extinction (SSE) models provide a framework for quantifying whether species traits have an impact on evolutionary rates and how this shapes the variation in species richness among clades in a phylogeny. However, SSE models are becoming increasingly complex, limiting the application of likelihood-based inference methods. Approximate Bayesian computation (ABC), a likelihood-free approach, is a potentially powerful alternative for estimating parameters. One of the key challenges in using ABC is the selection of efficient summary statistics, which can greatly affect the accuracy and precision of the parameter estimates. In state-dependent diversification models, summary statistics need to capture the complex relationships between rates of diversification and species traits. Here, we develop an ABC framework to estimate state-dependent speciation, extinction and transition rates in the BiSSE (binary state dependent speciation and extinction) model. Using different sets of candidate summary statistics, we then compare the inference ability of ABC with that of using likelihood-based maximum likelihood (ML) and Markov chain Monte Carlo (MCMC) methods. Our results show the ABC algorithm can accurately estimate state-dependent diversification rates for most of the model parameter sets we explored. The inference error of the parameters associated with the species-poor state is larger with ABC than in the likelihood estimations only when the speciation rate is highly asymmetric between the two states (λ1 / λ0 = 5). Furthermore, we find that the combination of normalized lineage-through-time (nLTT) statistics and phylogenetic signal in binary traits (Fitz and Purvis’s D) constitute efficient summary statistics for the ABC method. By providing insights into the selection of suitable summary statistics, our work aims to contribute to the use of the ABC approach in the development of complex state-dependent diversification models, for which a likelihood is not available.