Miracle-dz/UniFAR-FTUs huggingface dataset:
Histology and transcriptome insights into the early processes of intestinal anastomotic healing: a rat model

Abstract Background Understanding the early processes underlying intestinal anastomotic healing is crucial to comprehend the pathophysiology of anastomotic leakage. The aim of this study was to assess normal intestinal anastomotic healing and disturbed healing in rats to investigate morphological, cellular and intrinsic molecular changes in the anastomotic tissue. Method Anastomoses were created in two groups of Wistar rats, using four sutures or 12 sutures to mimic anastomotic leakage and anastomotic healing respectively. At 6, 12, 24 hours and 2, 3, 5 and 7 days, anastomotic tissue was assessed macroscopically using the anastomotic complication score and histologically using the modified Ehrlich–Hunt score. Transcriptome analysis was performed to assess differences between anastomotic leakage and anastomotic healing at the first three time points to find affected genes and biological processes. Results Ninety-eight rats were operated on (49 animals in the anastomotic leakage and 49 in the anastomotic healing group) and seven rats analysed at each time point. None of the animals with 12 sutures developed anastomotic leakage macroscopically, whereas 35 of the 49 animals with four sutures developed anastomotic leakage. Histological analysis showed increasing influx of inflammatory cells up to 3 days in anastomotic healing and up to 7 days in anastomotic leakage, and this increase was significantly higher in anastomotic leakage at 5 (P = 0.041) and 7 days (P = 0.003). Transcriptome analyses revealed large differences between anastomotic leakage and anastomotic healing at 6 and 24 hours, mainly driven by an overall downregulation of genes in anastomotic leakage. Conclusion Transcriptomic analyses revealed large differences between normal and disturbed healing at 6 hours after surgery, which might eventually serve as early-onset biomarkers for anastomotic leakage.
If you feel you can’t, you won’t: the role of subjective and objective cognitive competence on psychosocial functioning in depression

Abstract Background The purpose of this exploratory study is to examine the role of sociodemographic, clinical, and cognitive – both objective and subjective – factors in overall and in specific domains of psychosocial functioning, in patients with depression at different clinical states of the disease (remitted and non-remitted). Methods A sample of 325 patients with major depressive disorder, 117 in remission and 208 in non-remission, were assessed with a semi-structured interview collecting sociodemographic, clinical, cognitive (with neuropsychological tests and the Perceived Deficit Questionnaire), and functional (Functioning Assessment Short Test) characteristics. Backward regression models were conducted to determine associations of global and specific areas of functioning with independent factors, for both clinical states. Results Residual depressive symptomatology and self-appraisal of executive competence were significantly associated with psychosocial functioning in remitted patients, in overall and some subdomains of functioning, particularly cognitive and interpersonal areas. While depressive symptoms, executive deficits and self-appraisal of executive function were significantly related to functional outcomes in non-remitted patients, both in overall functioning and in most of subdomains. Discussion This study evidences the strong association of one’s appraisal of executive competence with psychosocial functioning, together with depressive symptoms, both in remitted and non-remitted patients with depression. Therefore, to achieve full recovery, clinical management of patients should tackle not only the relief of core depressive symptoms, but also the cognitive ones, both those that are objectified with neuropsychological tests and those that are reported by the patients themselves.
Appropriate reduction of importin‐α gene expression enhances yellow dwarf disease resistance in common wheat

Summary Barley yellow dwarf viruses (BYDVs) cause widespread damage to global cereal crops. Here we report a novel strategy for elevating resistance to BYDV infection. The 17K protein, a potent virulence factor conserved in BYDVs, interacted with barley IMP‐α1 and ‐α2 proteins that are nuclear transport receptors. Consistently, a nuclear localization signal was predicted in 17K, which was found essential for 17K to be transported into the nucleus and to interact with IMP‐α1 and ‐α2. Reducing HvIMP‐α1 and ‐α2 expression by gene silencing attenuated BYDV‐elicited dwarfism, accompanied by a lowered nuclear accumulation of 17K. Among the eight common wheat CRISPR mutants with two to four TaIMP‐α1 and ‐α2 genes mutated, the triple mutant α1aaBBDD/α2AAbbdd and the tetra‐mutant α1aabbdd/α2AAbbDD displayed strong BYDV resistance without negative effects on plant growth under field conditions. The BYDV resistance exhibited by α1aaBBDD/α2AAbbdd and α1aabbdd/α2AAbbDD was correlated with decreased nuclear accumulation of 17K and lowered viral proliferation in infected plants. Our work uncovers the function of host IMP‐α proteins in BYDV pathogenesis and generates the germplasm valuable for breeding BYDV‐resistant wheat. Appropriate reduction of IMP‐α gene expression may be broadly useful for enhancing antiviral resistance in agricultural crops and other economically important organisms.
Lateral Lumbar Interbody Fusion within Three-level for Patients with Neurological Symptoms due to Vertebral Fragility Fractures in the Lumbar Spine

There is a lack of agreement on whether minimally invasive lateral lumbar intervertebral fusion (LLIF) is a suitable treatment option for vertebral fragility fractures (VFFs). Hence, we sought to evaluate the efficacy and safety of LLIF in the management of VFF with neurological deficits in the lumbar spine. Between April 2015 and March 2020, we conducted a retrospective observational study of patients with VFF treated with three-level or less LLIF. The participants had previously received conservative treatment but had not been able to control their neurological symptoms. To assess the outcomes of the LLIF procedures, the patients were followed up for a minimum of 1 year. Clinical and radiological results, which include the timing and location of the bony fusion, were analyzed. The study involved 19 patients with 23 vertebral fracture levels. The residual height of the fractured vertebra was found to be 57.0 ± 12.3% of the height of the adjacent level. The mean Japanese Orthopedic Association score significantly improved postoperatively. Postoperative radiological parameters were significantly maintained at 1 year, and lumbar lordosis was maintained at the last follow-up (45.0 ± 26.7). In total 31 LLIF levels, bone fusion was observed in four levels at 6 months postoperatively, in 16 levels at 1 year, and in 23 levels at the last follow-up. The facet joint had the highest bony fusion location. LLIF within three levels can be safely performed in certain VFF cases with sufficient residual vertebral height.
G-quadruplexes in cancer-related gene promoters: from identification to therapeutic targeting

ABSTRACT Introduction Guanine-rich DNA sequences can fold into four-stranded noncanonical secondary structures called G-quadruplexes (G4s) which are widely distributed in functional regions of the human genome, such as telomeres and gene promoter regions. Compelling evidence suggests their involvement in key genome functions such as gene expression and genome stability. Notably, the abundance of G4-forming sequences near transcription start sites suggests their potential involvement in regulating oncogenes. Areas covered This review provides an overview of current knowledge on G4s in human oncogene promoters. The most representative G4-binding ligands have also been documented. The objective of this work is to present a comprehensive overview of the most promising targets for the development of novel and highly specific anticancer drugs capable of selectively impacting the expression of individual or a limited number of genes. Expert opinion Modulation of G4 formation by specific ligands has been proposed as a powerful new tool to treat cancer through the control of oncogene expression. Actually, most of G4-binding small molecules seem to simultaneously target a range of gene promoter G4s, potentially influencing several critical driver genes in cancer, thus producing significant therapeutic benefits.
Examining the bidirectional association between emotion recognition and autistic traits using observational and genetic analyses

Background: There is mixed evidence for an association between autism spectrum disorder (ASD) and emotion recognition deficits. We sought to assess the bidirectionality of this association using phenotypic and genetic data in a large community sample. Methods: Analyses were conducted in three stages. First, we examined the bidirectional association between autistic traits at age 8 years and emotion recognition task (ERT) responses at age 24 years (Study 1; N=3,562); and between Diagnostic Analysis of Non-Verbal Accuracy (DANVA) emotion recognition responses at age 8 years and autistic traits at age 10 years (Study 2; N=9,071). Next, we used genetic analyses (Study 3) to examine the association between polygenic risk scores for ASD and these phenotypes. The genetic correlation between ASD and ERT responses at age 24 was also estimated. Analyses were conducted in the Avon Longitudinal Study of Parents and Children. Results: Autistic traits at age 8 years were negatively associated with later total correct responses on ERT in Study 1 (b=- 0.18; 95% CI: -0.27 to -0.09). We also found evidence of an association in Study 2 ( b =-0.04; 95% CI: -0.05 to -0.03). We found the opposite association i.e., positive, between the ASD polygenic risk score and ERT ( b =0.40; 95% CI: 0.10 to 0.70); however, this association varied across different p-value thresholds so should be interpreted with caution. We did not find evidence of a genetic correlation between ASD and ERT. Conclusion: We found an observational association between poorer emotion recognition and increased autistic traits. Our genetic analyses revealed an association between ASD polygenic risk and the ERT outcome, which may suggest a shared genetic aetiology between these or a potential causal pathway. Our results may inform interventions targeting emotion recognition.
RK-core: An Established Methodology for Exploring the Hierarchical Structure within Datasets

Recently, the field of machine learning has undergone a transition from model-centric to data-centric. The advancements in diverse learning tasks have been propelled by the accumulation of more extensive datasets, subsequently facilitating the training of larger models on these datasets. However, these datasets remain relatively under-explored. To this end, we introduce a pioneering approach known as RK-core, to empower gaining a deeper understanding of the intricate hierarchical structure within datasets. Across several benchmark datasets, we find that samples with low coreness values appear less representative of their respective categories, and conversely, those with high coreness values exhibit greater representativeness. Correspondingly, samples with high coreness values make a more substantial contribution to the performance in comparison to those with low coreness values. Building upon this, we further employ RK-core to analyze the hierarchical structure of samples with different coreset selection methods. Remarkably, we find that a high-quality coreset should exhibit hierarchical diversity instead of solely opting for representative samples. The code is available at https://github.com/yaolu-zjut/Kcore.
High Dynamic Range mmWave Massive MU-MIMO with Householder Reflections

All-digital massive multiuser (MU) multiple-input multiple-output (MIMO) at millimeter-wave (mmWave) frequencies is a promising technology for next-generation wireless systems. Low-resolution analog-to-digital converters (ADCs) can be utilized to reduce the power consumption of all-digital basestation (BS) designs. However, simultaneously transmitting user equipments (UEs) with vastly different BS-side receive powers either drown weak UEs in quantization noise or saturate the ADCs. To address this issue, we propose high dynamic range (HDR) MIMO, a new paradigm that enables simultaneous reception of strong and weak UEs with low-resolution ADCs. HDR MIMO combines an adaptive analog spatial transform with digital equalization: The spatial transform focuses strong UEs on a subset of ADCs in order to mitigate quantization and saturation artifacts; digital equalization is then used for data detection. We demonstrate the efficacy of HDR MIMO in a massive MU-MIMO mmWave scenario that uses Householder reflections as spatial transform.
Patch-CLIP: A Patch-Text Pre-Trained Model

In recent years, patch representation learning has emerged as a necessary research direction for exploiting the capabilities of machine learning in software generation. These representations have driven significant performance enhancements across a variety of tasks involving code changes. While the progress is undeniable, a common limitation among existing models is their specialization: they predominantly excel in either predictive tasks, such as security patch classification, or in generative tasks such as patch description generation. This dichotomy is further exacerbated by a prevalent dependency on potentially noisy data sources. Specifically, many models utilize patches integrated with Abstract Syntax Trees (AST) that, unfortunately, may contain parsing inaccuracies, thus acting as a suboptimal source of supervision. In response to these challenges, we introduce PATCH-CLIP, a novel pre-training framework for patches and natural language text. PATCH-CLIP deploys a triple-loss training strategy for 1) patch-description contrastive learning, which enables to separate patches and descriptions in the embedding space, 2) patch-description matching, which ensures that each patch is associated to its description in the embedding space, and 3) patch-description generation, which ensures that the patch embedding is effective for generation. These losses are implemented for joint learning to achieve good performance in both predictive and generative tasks involving patches. Empirical evaluations focusing on patch description generation, demonstrate that PATCH-CLIP sets new state of the art performance, consistently outperforming the state-of-the-art in metrics like BLEU, ROUGE-L, METEOR, and Recall.
Non-Autoregressive Sentence Ordering

Existing sentence ordering approaches generally employ encoder-decoder frameworks with the pointer net to recover the coherence by recurrently predicting each sentence step-by-step. Such an autoregressive manner only leverages unilateral dependencies during decoding and cannot fully explore the semantic dependency between sentences for ordering. To overcome these limitations, in this paper, we propose a novel Non-Autoregressive Ordering Network, dubbed \textit{NAON}, which explores bilateral dependencies between sentences and predicts the sentence for each position in parallel. We claim that the non-autoregressive manner is not just applicable but also particularly suitable to the sentence ordering task because of two peculiar characteristics of the task: 1) each generation target is in deterministic length, and 2) the sentences and positions should match exclusively. Furthermore, to address the repetition issue of the naive non-autoregressive Transformer, we introduce an exclusive loss to constrain the exclusiveness between positions and sentences. To verify the effectiveness of the proposed model, we conduct extensive experiments on several common-used datasets and the experimental results show that our method outperforms all the autoregressive approaches and yields competitive performance compared with the state-of-the-arts. The codes are available at: \url{https://github.com/steven640pixel/nonautoregressive-sentence-ordering}.
Trapped acoustic waves and raindrops: high-order accurate integral equation method for localized excitation of a periodic staircase

We present a high-order boundary integral equation (BIE) method for the frequency-domain acoustic scattering of a point source by a singly-periodic, infinite, corrugated boundary. We apply it to the accurate numerical study of acoustic radiation in the neighborhood of a sound-hard two-dimensional staircase modeled after the El Castillo pyramid. Such staircases support trapped waves which travel along the surface and decay exponentially away from it. We use the array scanning method (Floquet--Bloch transform) to recover the scattered field as an integral over the family of quasiperiodic solutions parameterized by their on-surface wavenumber. Each such BIE solution requires the quasiperiodic Green's function, which we evaluate using an efficient integral representation of lattice sum coefficients. We avoid the singularities and branch cuts present in the array scanning integral by complex contour deformation. For each frequency, this enables a solution accurate to around 10 digits in a couple of seconds. We propose a residue method to extract the limiting powers carried by trapped modes far from the source. Finally, by computing the trapped mode dispersion relation, we use a simple ray model to explain an observed acoustic"raindrop"effect (chirp-like time-domain response).
STANLEY: Stochastic Gradient Anisotropic Langevin Dynamics for Learning Energy-Based Models

We propose in this paper, STANLEY, a STochastic gradient ANisotropic LangEvin dYnamics, for sampling high dimensional data. With the growing efficacy and potential of Energy-Based modeling, also known as non-normalized probabilistic modeling, for modeling a generative process of different natures of high dimensional data observations, we present an end-to-end learning algorithm for Energy-Based models (EBM) with the purpose of improving the quality of the resulting sampled data points. While the unknown normalizing constant of EBMs makes the training procedure intractable, resorting to Markov Chain Monte Carlo (MCMC) is in general a viable option. Realizing what MCMC entails for the EBM training, we propose in this paper, a novel high dimensional sampling method, based on an anisotropic stepsize and a gradient-informed covariance matrix, embedded into a discretized Langevin diffusion. We motivate the necessity for an anisotropic update of the negative samples in the Markov Chain by the nonlinearity of the backbone of the EBM, here a Convolutional Neural Network. Our resulting method, namely STANLEY, is an optimization algorithm for training Energy-Based models via our newly introduced MCMC method. We provide a theoretical understanding of our sampling scheme by proving that the sampler leads to a geometrically uniformly ergodic Markov Chain. Several image generation experiments are provided in our paper to show the effectiveness of our method.
Far-infrared Luminosity Bursts Trace Mass Accretion onto Protostars

Evidence abounds that young stellar objects undergo luminous bursts of intense accretion that are short compared to the time it takes to form a star. It remains unclear how much these events contribute to the main-sequence masses of the stars. We demonstrate the power of time-series far-infrared (far-IR) photometry to answer this question compared to similar observations at shorter and longer wavelengths. We start with model spectral energy distributions that have been fit to 86 Class 0 protostars in the Orion molecular clouds. The protostars sample a broad range of envelope densities, cavity geometries, and viewing angles. We then increase the luminosity of each model by factors of 10, 50, and 100 and assess how these luminosity increases manifest in the form of flux increases over wavelength ranges of interest. We find that the fractional change in the far-IR luminosity during a burst more closely traces the change in the accretion rate than photometric diagnostics at mid-infrared and submillimeter wavelengths. We also show that observations at far-IR and longer wavelengths reliably track accretion changes without confusion from large, variable circumstellar and interstellar extinction that plague studies at shorter wavelengths. We close by discussing the ability of a proposed far-IR surveyor for the 2030s to enable improvements in our understanding of the role of accretion bursts in mass assembly.
RZ Piscium Hosts a Compact and Highly Perturbed Debris Disk

RZ Piscium (RZ Psc) is well known in the variable star field because of its numerous irregular optical dips in the past 5 decades, but the nature of the system is heavily debated in the literature. We present multiyear infrared monitoring data from Spitzer and WISE to track the activities of the inner debris production, revealing stochastic infrared variability as short as weekly timescales that is consistent with destroying a 90 km sized asteroid every year. ALMA 1.3 mm data combined with spectral energy distribution modeling show that the disk is compact (∼0.1–13 au radially) and lacks cold gas. The disk is found to be highly inclined and has a significant vertical scale height. These observations confirm that RZ Psc hosts a close to edge-on, highly perturbed debris disk possibly due to migration of recently formed giant planets that might be triggered by the low-mass companion RZ Psc B if the planets formed well beyond the snowlines.
Privately Answering Queries on Skewed Data via Per Record Differential Privacy

We consider the problem of the private release of statistics (like aggregate payrolls) where it is critical to preserve the contribution made by a small number of outlying large entities. We propose a privacy formalism, per-record zero concentrated differential privacy (PzCDP), where the privacy loss associated with each record is a public function of that record's value. Unlike other formalisms which provide different privacy losses to different records, PzCDP's privacy loss depends explicitly on the confidential data. We define our formalism, derive its properties, and propose mechanisms which satisfy PzCDP that are uniquely suited to publishing skewed or heavy-tailed statistics, where a small number of records contribute substantially to query answers. This targeted relaxation helps overcome the difficulties of applying standard DP to these data products.
Denoising Heat-inspired Diffusion with Insulators for Collision Free Motion Planning

Diffusion models have risen as a powerful tool in robotics due to their flexibility and multi-modality. While some of these methods effectively address complex problems, they often depend heavily on inference-time obstacle detection and require additional equipment. Addressing these challenges, we present a method that, during inference time, simultaneously generates only reachable goals and plans motions that avoid obstacles, all from a single visual input. Central to our approach is the novel use of a collision-avoiding diffusion kernel for training. Through evaluations against behavior-cloning and classical diffusion models, our framework has proven its robustness. It is particularly effective in multi-modal environments, navigating toward goals and avoiding unreachable ones blocked by obstacles, while ensuring collision avoidance. Project Website: https://sites.google. com/view/denoising-heat-inspired