Miracle-dz/UniFAR-FTUs huggingface dataset:
Large-scale integrated analysis of genetics and metabolomic data reveals potential links between lipids and colorectal cancer risk.

BACKGROUND The etiology of Colorectal cancer(CRC) is not fully understood. METHODS Using genetic variants and metabolomics data including 217 metabolites from the Framingham Heart Study(n=1,357), we built genetic prediction models for circulating metabolites. Models with prediction R2>0.01(Nmetabolite=58) were applied to predict levels of metabolites in two large consortia with a combined sample size of ~46,300 cases and 59,200 controls of European and ~21,700 cases and 47,400 controls of East Asian(EA) descent. Genetically predicted levels of metabolites were evaluated for their associations with CRC risk in logistic regressions within each racial group, after which the results were combined by meta-analysis. RESULTS Of the 58 metabolites tested, 24 metabolites were significantly associated with CRC risk(BH-FDR<0.05) in the European population(odds ratios [ORs] ranged from 0.91 to 1.06;P-values ranged from 0.02 to 6.4x10-8). Twenty one of the twenty-four associations were replicated in the EA population(ORs ranged from 0.26 to 1.69,BH-FDR<0.05). Additionally, the genetically predicted levels of C16:0 cholesteryl ester was significantly associated with CRC risk in the EA population only(OREA:1.94,1.60-2.36,P=2.6x10-11;OREUR:1.01,0.99-1.04,P=0.3). Nineteen of the 25 metabolites were glycerophospholipids and triacylglycerols(TAG). Eighteen associations exhibited significant heterogeneity between the two racial groups(PEUR-EA-Het<0.005), which were more strongly associated in the EA population. This integrative study suggested a potential role of lipids, especially certain glycerophospholipids and TAGs, in the etiology of CRC. CONCLUSIONS This study identified potential novel risk biomarkers for colorectal cancer by integrating genetics and circulating metabolomics data. IMPACT The identified metabolites could be developed into new tools for risk assessment of colorectal cancer in both European and East Asian populations.
Differentiation of bronchial epithelial spheroids in the presence of IL‐13 recapitulates characteristic features of asthmatic airway epithelia

EDTA used as a positive control for epithelial barrier impairment S3). Only luminated spheroids were included in the measurements. IL- 13 treatment resulted in an impaired epithelial barrier development evi-denced with a high ‘lumen- to- background intensity ratio’ (Figure 2A) and decreased occludin mRNA and ZO- 1 staining intensity on Day 16 (Figures 2B,C). These results demonstrated that bronchial epithelial spheroid development under the influence of a type 2 cytokine, IL- 13, is leading to an epithelial barrier disorder similar to asthma. 3 In conclusion, we successfully developed an in vitro asthma model with a 3D airway organoid model termed here bronchial epithelial spheroids that recapitulates characteristic features of asthmatic airway epithelia. It demonstrates decreased ciliated and club cells and skew to a high mucus- producing and goblet cell- dominant phenotype with disrupted epithelial barrier by IL- 13. These results demonstrate that IL- 13 impairs the epithelial barrier by affecting both epithelial differentiation and tight junction protein expression. To the best of our knowledge, this study is the first in vitro asthma model that investigates epithelial barrier development and differentiation process in a 3D culture of primary bronchial epithelial cells with a detailed analysis including functional permeability, gene expression and immunofluorescence studies. The model is adaptable for high-throughput experiments, airway epithelial morphogenesis and development studies.
Postweaning positive modulation of α5GABAA receptors improves autism‐like features in prenatal valproate rat model in a sex‐specific manner

Autism spectrum disorder (ASD), as a common neurodevelopmental disorder that encompasses impairments in social communication and interaction, as well as repetitive and restrictive behavior, still awaits an effective treatment strategy. The involvement of GABAergic neurotransmission, and especially a deficit of GABAA receptors that contain the α5 subunits, were implicated in pathogenesis of ASD. Therefore, we tested MP‐III‐022, a positive allosteric modulator (PAM) selective for α5GABAA receptors, in Wistar rats prenatally exposed to valproic acid, as an animal model useful for studying ASD. Postweaning rats of both sexes were treated for 7 days with vehicle or MP‐III‐022 at two doses pharmacokinetically determined as selective, and thereafter tested in a behavioral battery (social interaction test, elevated plus maze, spontaneous locomotor activity, and standard and reverse Morris water maze). Additional rats were used for establishing a primary neuronal culture and performing calcium imaging, and determination of hippocampal mRNA levels of GABRA5, NKCC1, and KCC2. MP‐III‐022 prevented impairments in many parameters connected with social, repetitive and restrictive behavioral domains. The lower and higher dose was more effective in males and females, respectively. Intriguingly, MP‐III‐022 elicited certain changes in control animals similar to those manifested in valproate animals themselves. Behavioral results were mirrored in GABA switch and spontaneous neuronal activity, assessed with calcium imaging, and also in expression changes of three genes analyzed. Our data support a role of α5GABAA receptors in pathophysiology of ASD, and suggest a potential application of selective PAMs in its treatment, that needs to be researched in a sex‐specific manner.
Spiropyrimidinetriones: a Class of DNA Gyrase Inhibitors with Activity against Mycobacterium tuberculosis and without Cross-Resistance to Fluoroquinolones

Described here is a series of spiropyrimidinetrione (SPT) compounds with activity against Mycobacterium tuberculosis through inhibition of DNA gyrase. The SPT class operates via a novel mode of inhibition, which involves Mg2+-independent stabilization of the DNA cleavage complex with DNA gyrase and is thereby not cross-resistant with other DNA gyrase-inhibiting antibacterials, including fluoroquinolones. ABSTRACT Described here is a series of spiropyrimidinetrione (SPT) compounds with activity against Mycobacterium tuberculosis through inhibition of DNA gyrase. The SPT class operates via a novel mode of inhibition, which involves Mg2+-independent stabilization of the DNA cleavage complex with DNA gyrase and is thereby not cross-resistant with other DNA gyrase-inhibiting antibacterials, including fluoroquinolones. Compound 22 from the series was profiled broadly and showed in vitro cidality as well as intracellular activity against M. tuberculosis in macrophages. Evidence for the DNA gyrase mode of action was supported by inhibition of the target in a DNA supercoiling assay and elicitation of an SOS response seen in a recA reporter strain of M. tuberculosis. Pharmacokinetic properties of 22 supported evaluation of efficacy in an acute model of M. tuberculosis infection, where modest reduction in CFU numbers was seen. This work offers promise for deriving a novel drug class of tuberculosis agent without preexisting clinical resistance.
Response to “Prognostic factors in patients with pancreatic ductal adenocarcinoma with neoadjuvant treatment and pancreatectomy”

We our study “ Predictors of early recurrence following neoadjuvant chemotherapy and surgical resection for localized pancreatic adenocarcinoma. We first want to clarify that we are strong proponents of neoadjuvant treatment (NAT) use in pancreatic ductal adenocarcinoma (PDAC), even in patients with resectable disease. NAT not only increases recurrence ‐ free survival, but also reduces tumor size, margin and lymph node positivity, and rates of postoperative complications, which are significant barriers to adjuvant therapy. Dr Kawada makes a critical and much ‐ debated observation that response to NAT is associated with improved overall survival. Yet, it does not equate to cure — even when a complete pathologic response is achieved. 3 Pathologic response to NAT did not reach significance in our patient cohort; however, our analysis was focused on identifying risk factors for early recurrence, not overall survival. Our study instead highlights that the beneficial effects of NAT are lost in the 23% of patients who develop early recurrence. We and others have shown that early recurrent PDAC tends to localize to the liver, while late recurrent tumors occur locoregionally or metastasize to the lung. 4 A recent study by Dreyer et al. demon-strates that liver recurrent PDAC activates distinct signaling pathways when
VidSfM: Robust and Accurate Structure-From-Motion for Monocular Videos

With the popularization of smartphones, larger collection of videos with high quality is available, which makes the scale of scene reconstruction increase dramatically. However, high-resolution video produces more match outliers, and high frame rate video brings more redundant images. To solve these problems, a tailor-made framework is proposed to realize an accurate and robust structure-from-motion based on monocular videos. The key ideas include two points: one is to use the spatial and temporal continuity of video sequences to improve the accuracy and robustness of reconstruction; the other is to use the redundancy of video sequences to improve the efficiency and scalability of system. Our technical contributions include an adaptive way to identify accurate loop matching pairs, a cluster-based camera registration algorithm, a local rotation averaging scheme to verify the pose estimate and a local images extension strategy to reboot the incremental reconstruction. In addition, our system can integrate data from different video sequences, allowing multiple videos to be simultaneously reconstructed. Extensive experiments on both indoor and outdoor monocular videos demonstrate that our method outperforms the state-of-the-art approaches in robustness, accuracy and scalability.
Circulating Tregs accumulate in omental tumors and acquire adipose-resident features.

Tumors that metastasize in the peritoneal cavity typically end up in the omental adipose tissue, a particularly immune-suppressive environment that includes specialized adipose-resident regulatory T cells (Tregs). Tregs rapidly accumulate in the omentum after tumor implantation and potently suppress anti-tumor immunity. However, it is unclear whether these Tregs are recruited from the circulation or derived from pre-existing adipose-resident Tregs by clonal expansion. Here we show that Tregs in tumor-bearing omenta predominantly have thymus-derived characteristics. Moreover, naïve tumor antigen-specific CD4+ T cells fail to differentiate into Tregs in tumor-bearing omenta. In fact, Tregs derived from the pre-tumor repertoire are sufficient to suppress anti-tumor immunity and promote tumor growth. However, tumor implantation in the omentum does not promote Treg clonal expansion, but instead leads to increased clonal diversity. Parabiosis experiments show that despite tissue-resident (non-circulating) characteristics of omental Tregs in naïve mice, tumor implantation promotes a rapid influx of circulating Tregs, many of which come from the spleen. Finally, we show that newly recruited Tregs rapidly acquire characteristics of adipose-resident Tregs in tumor-bearing omenta. These data demonstrate that most Tregs in omental tumors are recruited from the circulation and adapt to their environment by altering their homing, transcriptional and metabolic properties.
CRISPR/Cas9‐mediated P‐CR domain‐specific engineering of CESA4 heterodimerization capacity alters cell wall architecture and improves saccharification efficiency in poplar

Summary Cellulose is the most abundant unique biopolymer in nature with widespread applications in bioenergy and high‐value bioproducts. The large transmembrane‐localized cellulose synthase (CESA) complexes (CSCs) play a pivotal role in the biosynthesis and orientation of the para‐crystalline cellulose microfibrils during secondary cell wall (SCW) deposition. However, the hub CESA subunit with high potential homo/heterodimerization capacity and its functional effects on cell wall architecture, cellulose crystallinity, and saccharification efficiency remains unclear. Here, we reported the highly potent binding site containing four residues of Pro435, Trp436, Pro437, and Gly438 in the plant‐conserved region (P‐CR) of PalCESA4 subunit, which are involved in the CESA4‐CESA8 heterodimerization. The CRISPR/Cas9‐knockout mutagenesis in the predicted binding site results in physiological abnormalities, stunt growth, and deficient roots. The homozygous double substitution of W436Q and P437S and heterozygous double deletions of W436 and P437 residues potentially reduced CESA4‐binding affinity resulting in normal roots, 1.5–2‐fold higher plant growth and cell wall regeneration rates, 1.7‐fold thinner cell wall, high hemicellulose content, 37%–67% decrease in cellulose content, high cellulose DP, 25%–37% decrease in cellulose crystallinity, and 50% increase in saccharification efficiency. The heterozygous deletion of W436 increases about 2‐fold CESA4 homo/heterodimerization capacity led to the 50% decrease in plant growth and increase in cell walls thickness, cellulose content (33%), cellulose DP (20%), and CrI (8%). Our findings provide a strategy for introducing commercial CRISPR/Cas9‐mediated bioengineered poplars with promising cellulose applications. We anticipate our results could create an engineering revolution in bioenergy and cellulose‐based nanomaterial technologies.
A Decision-making Framework Based on Artificial Neural Networks and Intelligent Agents for Transmission Grid Operation

Abstract Security margins have been reduced in restructured and deregulated power systems, and as a result, these systems have been operated close to their security limits. Therefore, it is of utmost importance that power system operation be tracked in a real-time fashion, making decisions as fast as possible to ensure operating points within security limits. In this context, this article proposes a practical decision-making framework for transmission grid operation featuring artificial neural networks and intelligent agents. In this framework, the system operating point is tracked by means of voltage stability margins estimated by artificial neural networks,while the decision-making process is supported by means of intelligent agents. The output of this framework is a qualitative answer that supports system operators in making decisions to enhance security margins. A 6-bus test-system and the CIGRE 32-bus system were used for validating the neural network approach for voltage stability margin estimations; the proposed framework was validated with the IEEE 300-bus system. Results show that such a framework can be readily applied to support decisions aimed at ensuring secure system operating points.
Hydrodynamic signatures in thermal transport in devices based on 2D materials: an ab initio study

We investigate the features arising from hydrodynamic effects in graphene and phosphorene devices with finite heat sources, using ab initio calculations and solving the Phonon Boltzmann Transport Equation through energy-based deviational Monte Carlo methods. We explain the mechanisms that create those hydrodynamic features, showing that boundary scattering and geometry are determinant factors, and that the length scales at which they can appear depend solely on the ability of intrinsic scattering to randomize the heat flux. We relate this latter point to the non-local lengths and mean free paths, additionally providing an insight into how the scattering operator must be treated to obtain a proper description of the hydrodynamic behavior.
Heterogeneous Sparse Matrix-Vector Multiplication via Compressed Sparse Row Format

Sparse matrix-vector multiplication (SpMV) is one of the most important kernels in high-performance computing (HPC), yet SpMV normally suffers from ill performance on many devices. Due to ill performance, SpMV normally requires special care to store and tune for a given device. Moreover, HPC is facing heterogeneous hardware containing multiple different compute units, e.g., many-core CPUs and GPUs. Therefore, an emerging goal has been to produce heterogeneous formats and methods that allow critical kernels, e.g., SpMV, to be executed on different devices with portable performance and minimal changes to format and method. This paper presents a heterogeneous format based on CSR, named CSR-k, that can be tuned quickly and outperforms the average performance of Intel MKL on Intel Xeon Platinum 8380 and AMD Epyc 7742 CPUs while still outperforming NVIDIA's cuSPARSE and Sandia National Laboratories' KokkosKernels on NVIDIA A100 and V100 for regular sparse matrices, i.e., sparse matrices where the number of nonzeros per row has a variance $\leq$ 10, such as those commonly generated from two and three-dimensional finite difference and element problems. In particular, CSR-k achieves this with reordering and by grouping rows into a hierarchical structure of super-rows and super-super-rows that are represented by just a few extra arrays of pointers. Due to its simplicity, a model can be tuned for a device and used to select super-row and super-super-rows sizes in constant time.
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence)

Prior work on differential privacy analysis of randomized SGD algorithms relies on composition theorems, where the implicit (unrealistic) assumption is that the internal state of the iterative algorithm is revealed to the adversary. As a result, the R\'enyi DP bounds derived by such composition-based analyses linearly grow with the number of training epochs. When the internal state of the algorithm is hidden, we prove a converging privacy bound for noisy stochastic gradient descent (on strongly convex smooth loss functions). We show how to take advantage of privacy amplification by sub-sampling and randomized post-processing, and prove the dynamics of privacy bound for"shuffle and partition"and"sample without replacement"stochastic mini-batch gradient descent schemes. We prove that, in these settings, our privacy bound converges exponentially fast and is substantially smaller than the composition bounds, notably after a few number of training epochs. Thus, unless the DP algorithm converges fast, our privacy analysis shows that hidden state analysis can significantly amplify differential privacy.
Prediction of single-atom-thick transition metal nitride 
CrN4
 with a square-planar network and high-temperature ferromagnetism

Single-atom-thick two-dimensional materials such as graphene usually have a hexagonal lattice while the square-planar lattice is uncommon in the family of two-dimensional materials. Here, we demonstrate that single-atom-thick transition metal nitride CrN$_4$ monolayer is a stable free-standing layer with a square-planar network. The stability of square-planar geometry is ascribed to the combination of N=N double bond, Cr-N coordination bond, and $\pi$-d conjugation, in which the double $\pi$-d conjugation is rarely reported in previous studies. This mechanism is entirely different from that of the reported two-dimensional materials, leading to lower formation energy and more robust stability compared to the synthesized g-C$_3$N$_4$ monolayer. On the other hand, CrN$_4$ layer has a ferromagnetic ground state, in which the ferromagnetic coupling between two Cr atoms is mediated by electrons of the half-filled large $\pi$ orbitals from $\pi$-d conjugation. The high-temperature ferromagnetism in CrN$_4$ monolayer is confirmed by solving the Heisenberg model with Monte Carlo method.
StyleBabel: Artistic Style Tagging and Captioning

We present StyleBabel, a unique open access dataset of natural language captions and free-form tags describing the artistic style of over 135K digital artworks, collected via a novel participatory method from experts studying at specialist art and design schools. StyleBabel was collected via an iterative method, inspired by `Grounded Theory': a qualitative approach that enables annotation while co-evolving a shared language for fine-grained artistic style attribute description. We demonstrate several downstream tasks for StyleBabel, adapting the recent ALADIN architecture for fine-grained style similarity, to train cross-modal embeddings for: 1) free-form tag generation; 2) natural language description of artistic style; 3) fine-grained text search of style. To do so, we extend ALADIN with recent advances in Visual Transformer (ViT) and cross-modal representation learning, achieving a state of the art accuracy in fine-grained style retrieval.
Widely Distributed Radar Imaging: Unmediated ADMM Based Approach

This paper presents a novel approach to reconstruct a unique image of an observed scene via synthetic apertures (SA) generated by employing widely distributed radar sensors. The problem is posed as a constrained optimization problem in which the global image which represents the aggregate view of the sensors is a decision variable. While the problem is designed to promote a sparse solution for the global image, it is constrained such that a relationship with local images that can be reconstructed using the measurements at each sensor is respected. Two problem formulations are introduced by stipulating two different establishments of that relationship. The proposed formulations are designed according to consensus ADMM (CADMM) and sharing ADMM (SADMM), and their solutions are provided accordingly as iterative algorithms. We drive the explicit variable updates for each algorithm in addition to the recommended scheme for hybrid parallel implementation on the distributed sensors and a central processing unit. Our algorithms are validated and their performance is evaluated by exploiting the Civilian Vehicles Dome dataset to realize different scenarios of practical relevance. Experimental results show the effectiveness of the proposed algorithms, especially in cases with limited measurements.
Do Small Code Changes Merge Faster? A Multi-Language Empirical Investigation

Code velocity, or the speed with which code changes are integrated into a production environment, plays a crucial role in Continuous Integration and Continuous Deployment. Many studies report factors influencing code velocity. However, solutions to increase code velocity are unclear. Meanwhile, the industry continues to issue guidelines on “ideal” code change size, believing it increases code velocity despite lacking evidence validating the practice. Surprisingly, this fundamental question has not been studied to date. This study investigates the practicality of improving code velocity by optimizing pull request size and composition (ratio of insertions, deletions, and modifications). We start with a hypothesis that a moderate correlation exists between pull request size and time-to-merge. We selected 100 most popular, actively developed projects from 10 programming languages on GitHub. We analyzed our dataset of 845,316 pull requests by size, composition, and context to explore its relationship to time-to-merge–a proxy to measure code velocity. Our study shows that pull request size and composition do not relate to time-to-merge. Regardless of the contextual factors that can influence pull request size or composition (e.g., programming language), the observation holds. Pull request data from two other platforms: Gerrit and Phabricator (401,790 code reviews) confirms the lack of relationship. This negative result as in “…eliminate useless hypotheses …” [75] challenges a widespread belief by showing that small code changes do not merge faster to increase code velocity.