Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated multi‐omic data and analyses reveal the pathways underlying key ornamental traits in carnation flowers

Summary Carnation (Dianthus caryophyllus) is one of the most popular ornamental flowers in the world. Although numerous studies on carnations exist, the underlying mechanisms of flower color, fragrance, and the formation of double flowers remain unknown. Here, we employed an integrated multi‐omics approach to elucidate the genetic and biochemical pathways underlying the most important ornamental features of carnation flowers. First, we assembled a high‐quality chromosome‐scale genome (636 Mb with contig N50 as 14.67 Mb) of D. caryophyllus, the ‘Scarlet Queen’. Next, a series of metabolomic datasets was generated with a variety of instrumentation types from different parts of the flower at multiple stages of development to assess spatial and temporal differences in the accumulation of pigment and volatile compounds. Finally, transcriptomic data were generated to link genomic, biochemical, and morphological patterns to propose a set of pathways by which ornamental traits such as petal coloration, double flowers, and fragrance production are formed. Among them, the transcription factors bHLHs, MYBs, and a WRKY44 homolog are proposed to be important in controlling petal color patterning and genes such as coniferyl alcohol acetyltransferase and eugenol synthase are involved in the synthesis of eugenol. The integrated dataset of genomics, transcriptomics, and metabolomics presented herein provides an important foundation for understanding the underlying pathways of flower development and coloration, which in turn can be used for selective breeding and gene editing for the development of novel carnation cultivars.
Neoantigen Identification and Response to Adoptive Cell Transfer in anti PD-1 Naïve and Experienced Patients with Metastatic Melanoma.

PURPOSE Immune checkpoint blockade (ICB) agents and adoptive cell transfer (ACT) of tumor infiltrating lymphocytes (TIL) are prominent immunotherapies used for the treatment of advanced melanoma. Both therapies rely on activation of lymphocytes that target shared tumor antigens or neoantigens. Recent analysis of patients with metastatic melanoma who underwent treatment with TIL ACT at the National Cancer Institute (NCI) demonstrated decreased responses in patients previously treated with anti PD-1 agents. We aimed to find a basis for the difference in response rates between anti PD-1 naïve and experienced patients. EXPERIMENTAL DESIGN We examined the tumor mutational burden (TMB) of resected tumors and the repertoire of neoantigens targeted by autologous TIL in a cohort of 112 anti PD-1 naïve and 69 anti PD-1 experienced patients. RESULTS Anti PD-1 naïve patients were found to have higher TMBs (352.0 vs. 213.5, p = 0.005) and more neoantigens (2 vs. 1, p = 0.003) compared to anti PD-1 experienced patients. Among patients treated with TIL ACT, TMB and number of neoantigens identified were higher in ACT responders than ACT non-responders in both anti PD-1naïve and experienced patients. Among patients with comparable TMBs and predicted neoantigen loads, patients naïve to anti PD-1 therapy were more likely to have identifiable neoantigens and tumor specific lymphocytes in their treatment products (2.5 vs. 1, p = 0.02). CONCLUSIONS These results indicate that decreases in TMB and targeted neoantigens only partially account for the difference in response to ACT and that additional factors likely influence responses in these patients.
CD74-NRG1 Fusions Are Oncogenic In Vivo and Induce Therapeutically Tractable ERBB2:ERBB3 Heterodimerization

Abstract NRG1 fusions are recurrent somatic genome alterations occurring across several tumor types, including invasive mucinous lung adenocarcinomas and pancreatic ductal adenocarcinomas and are potentially actionable genetic alterations in these cancers. We initially discovered CD74-NRG1 as the first NRG1 fusion in lung adenocarcinomas, and many additional fusion partners have since been identified. Here, we present the first CD74-NRG1 transgenic mouse model and provide evidence that ubiquitous expression of the CD74-NRG1 fusion protein in vivo leads to tumor development at high frequency. Furthermore, we show that ERBB2:ERBB3 heterodimerization is a mechanistic event in transformation by CD74-NRG1 binding physically to ERBB3 and that CD74-NRG1–expressing cells proliferate independent of supplemented NRG1 ligand. Thus, NRG1 gene fusions are recurrent driver oncogenes that cause oncogene dependency. Consistent with these findings, patients with NRG1 fusion-positive cancers respond to therapy targeting the ERBB2:ERBB3 receptors.
Famitinib with Camrelizumab and Nab-Paclitaxel for Advanced Immunomodulatory Triple-Negative Breast Cancer (FUTURE-C-Plus): An Open-Label, Single-Arm, Phase II Trial

Abstract Purpose: Camrelizumab, an mAb against programmed cell death protein 1 (PD-1), plus nab-paclitaxel exhibited promising antitumor activity in refractory metastatic immunomodulatory triple-negative breast cancer (TNBC). Famitinib is a tyrosine kinase inhibitor targeting VEGFR2, PDGFR, and c-kit. We aimed to assess the efficacy and safety of a novel combination of famitinib, camrelizumab, and nab-paclitaxel in advanced immunomodulatory TNBC. Patients and Methods: This open-label, single-arm, phase II study enrolled patients with previously untreated, advanced, immunomodulatory TNBC (CD8 IHC staining ≥10%). Eligible patients received 20 mg of oral famitinib on days 1 to 28, 200 mg of i.v. camrelizumab on days 1 and 15, and i.v. nab-paclitaxel 100 mg/m2 on days 1, 8, and 15 in 4-week cycles. The primary endpoint was objective response rate (ORR), as assessed by investigators per RECIST v1.1. Key secondary endpoints were progression-free survival (PFS), overall survival (OS), duration of response (DOR), safety, and exploratory biomarkers. Results: Forty-eight patients were enrolled and treated. Median follow-up was 17.0 months (range, 8.7–24.3). Confirmed ORR was 81.3% [95% confidence interval (CI), 70.2–92.3], with five complete and 34 partial responses. Median PFS was 13.6 months (95% CI, 8.4–18.8), and median DOR was 14.9 months [95% CI, not estimable (NE)–NE]. Median OS was not reached. No treatment-related deaths were reported. Among 30 patients with IHC, 13 (43.3%) were programmed death-ligand 1 (PD-L1)–negative, and PD-L1 was associated with favorable response. PKD1 and KAT6A somatic mutations were associated with therapy response. Conclusions: The triplet regimen was efficacious and well tolerated in previously untreated, advanced, immunomodulatory TNBC. The randomized controlled FUTURE-SUPER trial is under way to validate our findings. See related commentary by Salgado and Loi, p. 2728
Prognostic and Predictive Factors in Patients with Advanced HCC and Elevated Alpha-Fetoprotein Treated with Ramucirumab in Two Randomized Phase III Trials

Abstract Purpose: Ramucirumab is an effective treatment for patients with advanced hepatocellular carcinoma (aHCC) and baseline alpha-fetoprotein (AFP) ≥400 ng/mL. We aimed to identify prognostic and predictive factors of response to ramucirumab in patients with aHCC with AFP ≥400 ng/mL from the phase III REACH and REACH-2 randomized trials. Patients and Methods: Patients with aHCC, Child-Pugh class A with prior sorafenib treatment were randomized in REACH and REACH-2 (ramucirumab 8 mg/kg or placebo, biweekly). Meta-analysis of individual patient-level data (pooled population) from REACH (AFP ≥400 ng/mL) and REACH-2 was performed. A drug exposure analysis was conducted for those with evaluable pharmacokinetic data. To identify potential prognostic factors for overall survival (OS), multivariate analyses were performed using a Cox proportional hazards regression model. To define predictors of ramucirumab benefit, subgroup-by-treatment interaction terms were evaluated. Results: Of 542 patients (316 ramucirumab, 226 placebo) analyzed, eight variables had independent prognostic value associated with poor outcome (geographical region, Eastern Cooperative Oncology Group performance score ≥1, AFP >1,000 ng/mL, Child-Pugh >A5, extrahepatic spread, high neutrophil-to-lymphocyte ratio, high alkaline phosphatase and aspartate aminotransferase). Ramucirumab survival benefit was present across all subgroups, including patients with very aggressive HCC [above median AFP; HR: 0.64; 95% confidence interval (CI): 0.49–0.84] and nonviral aHCC (HR: 0.56; 95% CI: 0.40–0.79). While no baseline factor was predictive of a differential OS benefit with ramucirumab, analyses demonstrated an association between high drug exposure, treatment-emergent hypertension (grade ≥3), and increased ramucirumab benefit. Conclusions: Ramucirumab provided a survival benefit irrespective of baseline prognostic covariates, and this benefit was greatest in patients with high ramucirumab drug exposure and/or those with treatment-related hypertension.
Counterdiabatic Optimized Local Driving

Adiabatic protocols are employed across a variety of quantum technologies, from implementing state preparation and individual operations that are building blocks of larger devices, to higher-level protocols in quantum annealing and adiabatic quantum computation. The problem of speeding up these processes has garnered a large amount of interest, resulting in a menagerie of approaches, most notably quantum optimal control and shortcuts to adiabaticity. The two approaches are complementary: optimal control manipulates control fields to steer the dynamics in the minimum allowed time while shortcuts to adiabaticity aim to retain the adiabatic condition upon speed-up. We outline a new method which combines the two methodologies and takes advantage of the strengths of each. The new technique improves upon approximate local counterdiabatic driving with the addition of time-dependent control fields. We refer to this new method as counterdiabatic optimised local driving (COLD) and we show that it can result in a substantial improvement when applied to annealing protocols, state preparation schemes, entanglement generation and population transfer on a lattice. We also demonstrate a new approach to the optimisation of control fields which does not require access to the wavefunction or the computation of system dynamics. COLD can be enhanced with existing advanced optimal control methods and we explore this using the chopped randomised basis method and gradient ascent pulse engineering.
Better Supervisory Signals by Observing Learning Paths

Better-supervised models might have better performance. In this paper, we first clarify what makes for good supervision for a classification problem, and then explain two existing label refining methods, label smoothing and knowledge distillation, in terms of our proposed criterion. To further answer why and how better supervision emerges, we observe the learning path, i.e., the trajectory of the model's predictions during training, for each training sample. We find that the model can spontaneously refine"bad"labels through a"zig-zag"learning path, which occurs on both toy and real datasets. Observing the learning path not only provides a new perspective for understanding knowledge distillation, overfitting, and learning dynamics, but also reveals that the supervisory signal of a teacher network can be very unstable near the best points in training on real tasks. Inspired by this, we propose a new knowledge distillation scheme, Filter-KD, which improves downstream classification performance in various settings.
FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context

We advance sketch research to scenes with the first dataset of freehand scene sketches, FS-COCO. With practical applications in mind, we collect sketches that convey scene content well but can be sketched within a few minutes by a person with any sketching skills. Our dataset comprises 10,000 freehand scene vector sketches with per point space-time information by 100 non-expert individuals, offering both object- and scene-level abstraction. Each sketch is augmented with its text description. Using our dataset, we study for the first time the problem of fine-grained image retrieval from freehand scene sketches and sketch captions. We draw insights on: (i) Scene salience encoded in sketches using the strokes temporal order; (ii) Performance comparison of image retrieval from a scene sketch and an image caption; (iii) Complementarity of information in sketches and image captions, as well as the potential benefit of combining the two modalities. In addition, we extend a popular vector sketch LSTM-based encoder to handle sketches with larger complexity than was supported by previous work. Namely, we propose a hierarchical sketch decoder, which we leverage at a sketch-specific"pre-text"task. Our dataset enables for the first time research on freehand scene sketch understanding and its practical applications.
Spitzer and Herschel studies of dust in supernova remnants in the Small Magellanic Cloud

With the entire Small Magellanic Cloud (SMC) mapped by the Spitzer Space Telescope and Herschel Space Observatory, we were able to search 8-250 micron images in order to identify infrared (IR) emission associated with SMC supernova remnants (SNRs). A valid detection had to correspond with known X-ray, Halpha and radio emission from the SNRs. From the 24 known SNRs, we made 5 positive detections with another 5 possible detections. Two detections are associated with pulsars or pulsar wind nebula, and another three detections are part of the extended nebulous emission from the SNRs. We modelled dust emission where fast moving electrons are predicted to collide and heat dust grains which then radiate in IR. With known distance (62.44+-0.47kpc), measured SNR sizes, electron densities, temperatures from X-ray emission as well as hydrogen densities, the modelling of SMC SNRs is straightforward. If the higher range of hydrogen and electron densities were to be accepted, we would expect almost all SMC SNRs to be detected in the IR, at least at 24 micron, but the actual detection rate is only 25%. One possible and common explanation for this discrepancy is that small grains have been destroyed by the SNRs shockwave. However, within the uncertainties of hydrogen and electron densities, we find that infrared dust emission can be explained reasonably well, without invoking dust destruction. There is no conclusive evidence that SNRs destroy swept-up ISM dust.
Indirect mechanism of Au adatom diffusion on the Si(100) surface

Calculations of the diffusion of a Au adatom on the dimer reconstructed Si(100)-2x1 surface reveal an interesting mechanism that differs significantly from a direct path between optimal binding sites, which are located in between dimer rows. Instead, the active diffusion mechanism involves promotion of the adatom to higher energy sites on top of a dimer row and then fast migration along the row, visiting ca. a hundred sites at room temperature, before falling back down into an optimal binding site. This top-of-row mechanism becomes more important the lower the temperature is. The calculations are carried out by finding minimum energy paths on the energy surface obtained from density functional theory within the PBEsol functional approximation followed by kinetic Monte Carlo simulations of the diffusion over a range of temperature from 200 K to 900 K. While the activation energy for the direct diffusion mechanism is calculated to be 0.84 eV, the effective activation energy for the indirect mechanism is on average 0.56 eV.
A comprehensive study on the processing of Co:ZnO ceramics: defect chemistry engineering and grain growth kinetics

In this report we present a systematic study on the preparation of Co:ZnO ceramics via standard solid-state route from different Co precursors (Co3O4, CoO and metallic Co) and atmospheres (O2 and Ar). Particular emphasis was done on the defect chemistry engineering and on the sintering growth kinetics. First-principles calculations based on density functional theory were employed to determine the formation energy of the main point defects in ZnO and Co:ZnO systems. Based on the theoretical results a set of chemical reactions was proposed. A detailed microstructural characterization was performed in order to determine the degree of Co incorporation into the ZnO lattice. The samples prepared in Ar atmosphere and from metallic Co presents the highest Co solubility limit (lower apparent Co incorporation activation energy) due to the incongruent ZnO decomposition. The determination of the parameters of the sintering growth kinetics reveals that Co3O4 is the best sintering additive in order to achieve higher densities in both sintering atmospheres. The results give evidences that the sintering in O2 is effective in promoting zinc vacancies in the ZnO structure, while the sintering in Ar promotes zinc interstitial defects. Our findings give valuable contribution to the understanding of the preparation of Co-doped ZnO ceramics and the sintering growth kinetics, what would allow to improve the state of the art on the processing of the material at both bulk and nanometric scales.
Non-transversal multilinear duality and joints

We develop a framework for a duality theory for general multilin- ear operators which extends that for transversal multilinear operators which has been established in [4]. We apply it to the setting of joints and multijoints, and obtain a “factorisation” theorem which provides an analogue in the discrete setting of results of Bourgain and Guth ([7] and [2]) from the Euclidean setting.
AutoMO-Mixer: An automated multi-objective Mixer model for balanced, safe and robust prediction in medicine

Accurately identifying patient’s status through medical images plays an important role in diagnosis and treatment. Artificial intelligence (AI), especially the deep learning, has achieved great success in many fields. However, more reliable AI model is needed in image guided diagnosis and therapy. To achieve this goal, developing a balanced, safe and robust model with a unified framework is desirable. In this study, a new unified model termed as automated multi-objective Mixer (AutoMO-Mixer) model was developed, which utilized a recent developed multiple layer perceptron Mixer (MLP-Mixer) as base. To build a balanced model, sensitivity and specificity were considered as the objective functions simultaneously in training stage. Meanwhile, a new evidential reasoning based on entropy was developed to achieve a safe and robust model in testing stage. The experiment on an optical coherence tomography dataset demonstrated that AutoMO-Mixer can obtain safer, more balanced, and robust results compared with MLP-Mixer and other available models.
Structure of propagating high-stress fronts in a shear-thickening suspension

Significance The surprising behavior of shear-thickening suspensions, such as the familiar cornstarch–water mixture studied here, is of both fundamental and practical importance, but many aspects are not well understood, and in particular the role of the suspending fluid in the dramatic thickening transition remains elusive. Here we show that the transition can be driven by the proliferation of stable propagating fronts and we directly observe relative flow between the cornstarch particles and the suspending fluid, providing a quantitative measure of the concentration changes and internal pressure gradients associated with the thickening transition. These results elucidate the complex structures associated with shear thickening and show that nonaffine flow, concentration fluctuations, and fluid migration are integral to the generation of high stresses.
Distinct Excitatory and Inhibitory Bump Wandering in a Stochastic Neural Field

Localized persistent cortical neural activity is a validated neural substrate of parametric working memory. Such activity "bumps" represent the continuous location of a cue over several seconds. Pyramidal (excitatory (E)) and interneuronal (inhibitory (I)) subpopulations exhibit tuned bumps of activity, linking neural dynamics to behavioral inaccuracies observed in memory recall. However, many bump attractor models collapse these subpopulations into a single joint E/I(lateral inhibitory) population and do not consider the role of interpopulation neural architecture and noise correlations. Both factors have a high potential to impinge upon the stochastic dynamics of these bumps, ultimately shaping behavioral response variance. In our study, we consider a neural field model with separate E/I populations and leverage asymptotic analysis to derive a nonlinear Langevin system describing E/I bump interactions. While the E bump attracts the I bump, the I bump stabilizes but can also repel the E bump, which can result in prolonged relaxation dynamics when both bumps are perturbed. Furthermore, the structure of noise correlations within and between subpopulations strongly shapes the variance in bump position. Surprisingly, higher interpopulation correlations reduce variance.
Overlap-based Vocabulary Generation Improves Cross-lingual Transfer Among Related Languages

Pre-trained multilingual language models such as mBERT and XLM-R have demonstrated great potential for zero-shot cross-lingual transfer to low web-resource languages (LRL). However, due to limited model capacity, the large difference in the sizes of available monolingual corpora between high web-resource languages (HRL) and LRLs does not provide enough scope of co-embedding the LRL with the HRL, thereby affecting the downstream task performance of LRLs. In this paper, we argue that relatedness among languages in a language family along the dimension of lexical overlap may be leveraged to overcome some of the corpora limitations of LRLs. We propose Overlap BPE (OBPE), a simple yet effective modification to the BPE vocabulary generation algorithm which enhances overlap across related languages. Through extensive experiments on multiple NLP tasks and datasets, we observe that OBPE generates a vocabulary that increases the representation of LRLs via tokens shared with HRLs. This results in improved zero-shot transfer from related HRLs to LRLs without reducing HRL representation and accuracy. Unlike previous studies that dismissed the importance of token-overlap, we show that in the low-resource related language setting, token overlap matters. Synthetically reducing the overlap to zero can cause as much as a four-fold drop in zero-shot transfer accuracy.
A Unified Spatially Coupled Code Design: Threshold, Cycles, and Locality

Spatially-Coupled (SC)-LDPC codes are known to have outstanding error-correction performance and low decoding latency. Whereas previous works on LDPC and SC-LDPC codes mostly take either an asymptotic or a finite-length design approach, in this paper we present a unified framework for jointly optimizing the codes' thresholds and cycle counts to address both regimes. The framework is based on efficient traversal and pruning of the code search space, building on the fact that the performance of a protograph-based SC-LDPC code depends on some characteristics of the code's partitioning matrix, which by itself is much smaller than the code's full parity-check matrix. We then propose an algorithm that traverses all nonequivalent partitioning matrices, and outputs a list of codes, each offering an attractive point on the trade-off between asymptotic and finite-length performance. We further extend the framework to designing SC-LDPC codes with sub-block locality, which is a recently introduced feature offering fast access to sub-blocks within the code block. Our simulations show that our framework results in SC-LDPC codes that outperform the state-of-the-art constructions, and that it offers the flexibility to choose low-SNR, high-SNR, or in-between SNR region as the primary design target.