Miracle-dz/UniFAR-FTUs huggingface dataset:
Desert Hedgehog Promotes Ischemia-Induced Angiogenesis by Ensuring Peripheral Nerve Survival

Rationale: Blood vessel growth and patterning have been shown to be regulated by nerve-derived signals. Desert hedgehog (Dhh), one of the Hedgehog family members, is expressed by Schwann cells of peripheral nerves. Objective: The purpose of this study was to investigate the contribution of Dhh to angiogenesis in the setting of ischemia. Methods and Results: We induced hindlimb ischemia in wild-type and Dhh–/– mice. First, we found that limb perfusion is significantly impaired in the absence of Dhh. This effect is associated with a significant decrease in capillary and artery density in Dhh–/–. By using mice in which the Hedgehog signaling pathway effector Smoothened was specifically invalidated in endothelial cells, we demonstrated that Dhh does not promote angiogenesis by a direct activation of endothelial cells. On the contrary, we found that Dhh promotes peripheral nerve survival in the ischemic muscle and, by doing so, maintains the pool of nerve-derived proangiogenic factors. Consistently, we found that denervation of the leg, immediately after the onset of ischemia, severely impairs ischemia-induced angiogenesis and decreases expression of vascular endothelial growth factor A, angiopoietin 1, and neurotrophin 3 in the ischemic muscle. Conclusions: This study demonstrates the crucial roles of nerves and factors regulating nerve physiology in the setting of ischemia-induced angiogenesis.
Requirement for Rac1-Dependent NADPH Oxidase in the Cardiovascular and Dipsogenic Actions of Angiotensin II in the Brain

We have shown that intracellular superoxide (O2·−) production in CNS neurons plays a key role in the pressor, bradycardic, and dipsogenic actions of Ang II in the brain. In this study, we tested the hypothesis that a Rac1-dependent NADPH oxidase is a key source of O2·− in Ang II–sensitive neurons and is involved in these central Ang II–dependent effects. We performed both in vitro and in vivo studies using adenoviral (Ad)-mediated expression of dominant-negative Rac1 (AdN17Rac1) to inhibit Ang II–stimulated Rac1 activation, an obligatory step in NADPH oxidase activation. Ang II induced a time-dependent increase in Rac1 activation and O2·− production in Neuro-2A cells, and this was abolished by pretreatment with AdN17Rac1 or the NADPH oxidase inhibitors apocynin or diphenylene iodonium. AdN17Rac1 also inhibited Ang II–induced increases in NADPH oxidase activity in primary neurons cultured from central cardiovascular control regions. In contrast, overexpression of wild-type Rac1 (AdwtRac1) caused more robust NADPH oxidase-dependent O2·− production to Ang II. To extend the in vitro studies, the pressor, bradycardic, and drinking responses to intracerebroventricularly (ICV) injected Ang II were measured in mice that had undergone gene transfer of AdN17Rac1 or AdwtRac1 to the brain. AdN17Rac1 abolished the increase in blood pressure, decrease in heart rate, and drinking response induced by ICV injection of Ang II, whereas AdwtRac1 enhanced these physiological effects. The exaggerated physiological responses in AdwtRac1-treated mice were abolished by O2·− scavenging. These results, for the first time, identify a Rac1-dependent NADPH oxidase as the source of central Ang II–induced O2·− production, and implicate this oxidase in cardiovascular diseases associated with dysregulation of brain Ang II signaling, including hypertension.
Achieving Anatomic Acetabular Fracture Reduction—When is the Best Time to Operate?

Objectives: We hypothesize that earlier operative intervention for acetabular fractures improves the probability of achieving an anatomic reduction. Design: Retrospective review. Setting: Academic level I trauma center. Patients/Participants: Six hundred fifty acetabular fractures treated through open reduction and internal fixation (ORIF) between September 2001 and February 2014. Intervention: Acetabular fracture ORIF. Main Outcome Measurements: Reduction quality was assessed through postoperative radiographs. Displacement of ⩽1 mm was considered an anatomic reduction, 2–3 mm imperfect, and >3 mm poor. Results: Anatomic reductions were observed in 85% (n = 553) of cases, imperfect reductions in 11% (n = 74) of cases, and poor reductions in 4% (n = 23) of cases. Patients with anatomic reductions had significantly shorter times from injury to ORIF [odds ratio (OR) interval] (median, 3 d) when compared with either imperfect (median, 4.5 days, P = 0.02) or poor reductions (median, 7 days, P < 0.001) reductions. The OR interval of imperfect reductions was also significantly shorter than that of poor reductions (P = 0.02). Logistic regression analysis demonstrated that OR interval had an effect of −0.12, meaning that the log odds of anatomic reduction decreases by 0.12 with each day from injury to ORIF. Conclusion: The interval from injury to operative fixation of acetabular fractures affects reduction quality. Earlier intervention improves the probability of achieving an anatomic reduction; therefore, ORIF should be performed as early as possible, provided the patient is optimized for surgery. Level of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.
Financing Through Asset Sales

Most research on …rm …nancing studies the choice between debt and equity. We model an alternative source –non-core asset sales –and allow asset sales to occur for operational as well as …nancing motives. We identify three new factors that drive a …rm’s choice between selling assets and equity. First, equity investors own a claim to the cash raised. Since cash is certain, this mitigates the
Virtual Wind-up Toys for Animation

The strips are designed to be manufactured from relatively thin or light gauge metal or plastic; each having an elongate base section designed to be secured by nails and/or adhesive to a floor, and having an offset carpet-gripping section which in one embodiment is designed in use to be hammered or bent downwardly relative to the base section and over the edge of a section of carpeting to secure the latter in place, and which in a second embodiment has formed in its longitudinal edge a plurality of spaced teeth which imbed in the bottom of a carpet to hold its edge in place. In the second embodiment a plurality of longitudinally spaced scallops or dimples are formed in the strip to increase its structural rigidity; and in each embodiment adhesive strips are used to help secure the carpeting to the strips.
Reversible Jump Attack to Textual Classifiers with Modification Reduction

Recent studies on adversarial examples expose vulnerabilities of natural language processing (NLP) models. Existing techniques for generating adversarial examples are typically driven by deterministic hierarchical rules that are agnostic to the optimal adversarial examples, a strategy that often results in adversarial samples with a suboptimal balance between magnitudes of changes and attack successes. To this end, in this research we propose two algorithms, Reversible Jump Attack (RJA) and Metropolis-Hasting Modification Reduction (MMR), to generate highly effective adversarial examples and to improve the imperceptibility of the examples, respectively. RJA utilizes a novel randomization mechanism to enlarge the search space and efficiently adapts to a number of perturbed words for adversarial examples. With these generated adversarial examples, MMR applies the Metropolis-Hasting sampler to enhance the imperceptibility of adversarial examples. Extensive experiments demonstrate that RJA-MMR outperforms current state-of-the-art methods in attack performance, imperceptibility, fluency and grammar correctness.
Magnonic Superconductivity

We uncover a new superconducting state with partial spin polarization induced by a magnetic field. This state, which we call"magnonic superconductor", lacks a conventional pairing order parameter, but is characterized instead by a composite order parameter that represents the binding of electron pairs and magnons. We rigorously demonstrate the existence of magnonic superconductivity with high transition temperature in a triangular lattice Hubbard model with repulsive interaction. We further show that magnonic Cooper pairs can attract to form higher-charge bound states, which can give rise to charge-$6e$ superconductivity.
Non-Convex Robust Hypothesis Testing using Sinkhorn Uncertainty Sets

We present a new framework to address the non-convex robust hypothesis testing problem, wherein the goal is to seek the optimal detector that minimizes the maximum of worst-case type-I and type-II risk functions. The distributional uncertainty sets are constructed to center around the empirical distribution derived from samples based on Sinkhorn discrepancy. Given that the objective involves non-convex, non-smooth probabilistic functions that are often intractable to optimize, existing methods resort to approximations rather than exact solutions. To tackle the challenge, we introduce an exact mixed-integer exponential conic reformulation of the problem, which can be solved into a global optimum with a moderate amount of input data. Subsequently, we propose a convex approximation, demonstrating its superiority over current state-of-the-art methodologies in literature. Furthermore, we establish connections between robust hypothesis testing and regularized formulations of non-robust risk functions, offering insightful interpretations. Our numerical study highlights the satisfactory testing performance and computational efficiency of the proposed framework.
Knowing Where Things Are in the Second Year of Life: Implications for Hippocampal Development

Prior data have revealed striking contrasts between 18 and 24-month-old children in place learning, an ability knownto dependon the hippo campus(Newcombe, Huttenlocher, Drummey, & Wiley, 1998). The current research examined the development of three other basic abilities of mature spatial competence: the representation of multiple locations, the learning of relations among objects, and the recall of a single location after a substantial filled delay. Results indicated a transition from 18 to 24 months in all three abilities. This evidence supports a general transition in spatial representation that occurs towards the end of infancy. Existing neurobehavioral data suggest that a corresponding change in hippocampal functioning underlies this development.
Flight Capacity of the Walnut Twig Beetle (Coleoptera: Scolytidae) on a Laboratory Flight Mill

Abstract The walnut twig beetle, Pityophthorus juglandis Blackman, and associated fungus Geosmithia morbida Kolařík, Freeland, Utley, & Tisserat constitute the insect–fungal complex that causes thousand cankers disease in walnut, Juglans spp. , and wingnut, Pterocarya spp. Thousand cankers disease is responsible for the decline of Juglans species throughout the western United States and more recently, the eastern United States and northern Italy. We examined the flight capacity of P. juglandis over 24-h trials on a flight mill in the laboratory. The maximum total flight distance observed was ~3.6 km in 24 h; however, the mean and median distances flown by beetles that initiated flight were ~372 m and ~158 m, respectively. Beetles flew for 34 min on average within a 24-h flight trial. Male and female flight capacities were similar, even though males were larger than females (0.64 vs. 0.57 mm pronotal width). Age postemergence had no effect on flight distance, flight time, or mean flight velocity. The propensity to fly, however, decreased with age. We integrated results of flight distance with propensity to fly as beetles aged in a Monte Carlo simulation to estimate the maximum dispersal capacity over 5 d, assuming no mortality. Only 1% of the insects would be expected to fly >2 km, whereas one-third of the insects were estimated to fly <100 m. These results suggest that nascent establishments remain relatively localized without anthropogenic transport or wind-aided dispersal, which has implications for management and sampling of this hardwood pest.
Involvement of de novo ceramide synthesis in gamma-tocopherol and gamma-tocotrienol-induced apoptosis in human breast cancer cells.

SCOPE This study further examines mechanisms involved in the pro-apoptotic action of gamma-tocopherol (γT) and gamma-tocotrienol (γT3) in human breast cancer cell lines. METHODS AND RESULTS γT upregulates phospho-JNK (pJNK), CCAAT/enhancer-binding protein homologous protein (CHOP), and death receptor-5 (DR5) protein expression as detected by Western blot assays. siRNA knockdown of JNK, CHOP, or DR5 shows that γT-induced apoptosis is JNK/CHOP/DR5 signaling dependent, which is similar to γT3-mediated apoptotic signaling. Furthermore, both γT and γT3 induce increased levels of cellular ceramides and dihydroceramides as determined by LC-MS/MS analyses. Inhibition of de novo ceramide synthesis using chemical inhibitors blocked the ability of γT and γT3 to induce apoptosis as detected by Annexin V-FITC/PI assay and to activate JNK/CHOP/DR5 pro-apoptotic signaling thereby demonstrating the involvement of de novo ceramide synthesis in γT- and γT3-induced apoptosis. CONCLUSION Taken together, data show that both γT and γT3 induce apoptosis via de novo ceramide synthesis dependent activation of JNK/CHOP/DR5 pro-apoptotic signaling.
Use of recommendation models to provide support to dyslexic students

Dyslexia is the most widespread specific learning disorder and significantly impair different cognitive domains. This, in turn, negatively affects dyslexic students during their learning path. Therefore, specific support must be given to these students. In addition, such a support must be highly personalized, since the problems generated by the disorder can be very different from one to another. In this work, we explored the possibility of using AI to suggest the most suitable supporting tools for dyslexic students, so as to provide a targeted help that can be of real utility. To do this, we relied on recommendation algorithms, which are a branch of machine learning, that aim to detect personal preferences and provide the most suitable suggestions. We hence implemented and trained three collaborative-filtering recommendation models, namely an item-based, a user-based and a weighted-hybrid model, and studied their performance on a large database of 1237 students' information, collected with a self-evaluating questionnaire regarding all the most used supporting strategies and digital tools. Each recommendation model was tested with three different similarity metrics, namely Pearson correlation, Euclidean distance and Cosine similarity. The obtained results showed that a recommendation system is highly effective in suggesting the optimal help tools/strategies for everyone. This demonstrates that the proposed approach is successful and can be used as a new and effective methodology to support students with dyslexia.
Program Seminal Behaviors: Automating Input Characterization for Large-Scope Proactive Behavior Prediction

Accurately forecasting how a program behaves is crucial for optimizations in compilers, as well as in other layers in the software execution stack, such as operating systems, virtual machines. This fundamental problem has drawn decades of research endeavors, with various techniques produced, ranging from the estimation in static compilers to profilingbased projection to dynamic sampling in runtime systems. Despite the continuous progress achieved so far, the insufficient treatments to program inputs in existing techniques have caused either limited prediction accuracy, or a series of constraints imposed by reactive runtime sampling. Recent studies have shown that, addressing those limitations by modeling the relations between program inputs and its behaviors, may improve the efficiency in various computing layers significantly. But unfortunately, the complexities in program inputs form a hard challenge for the modeling of input-behavior relations, severely impairing the feasibility of input-based behavior prediction. In this work, we tackle that obstacle by initiating the concept of seminal behaviors and the recognition of such behaviors through explorations in program behavior correlations. Seminal behaviors, referring to the set of behaviors that strongly correlate with other program behaviors, offer an opportunity for automating input characterizations. By employing a set of statistical learning techniques, we empirically demonstrate the existence of seminal behaviors for a variety of benchmarks. We evaluate the effectiveness of seminal behaviors in predicting a set of other kinds of program behaviors, in both code and data levels, such as calling frequencies, block access frequencies, data locality. The results suggest that the recognition of seminal behaviors is promising in automating input-based behavior prediction, and thus opens many opportunities for advancing current dynamic optimizations.
Circumnutations: From Darwin to Space Flights

WHAT ARE CIRCUMNUTATIONS? More than a century ago, plant physiologists were aware that plant organs-roots, hypocotyls, shoots, branches, flower stalks-rarely grow in just one direction. The mean growth direction may be maintained for long intervals, but the organ's instantaneous growth direction usually oscillates slowly about that mean. The plant organ tip, as seen from a dista1 viewpoint, describes an ellipse. The axes of the ellipse can vary; at one extreme the ellipse approximates a line and at the other a circle. As the organ grows, its tip advances and (in three dimensions) traces a somewhat irregular helix. That oscillating growth pattern was well known to nineteenth century plant scientists as "revolving nutation" until Darwin (Darwin and Darwin, 1880) introduced the terms "circumnutate" and "circumnutation," which are used today. Thus, circumnutational oscillations are manifestations of the radially asymmetric growth rate typical of elongating plant organs.
Pixel classification using variable string genetic algorithms with chromosome differentiation

The concept of chromosome differentiation, commonly witnessed in nature as male and female sexes, is incorporated in genetic algorithms with variable length strings for designing a nonparametric classification methodology. Its significance in partitioning different landcover regions from satellite images, having complex/overlapping class boundaries, is demonstrated. The classifier is able to evolve automatically the appropriate number of hyperplanes efficiently for modeling any kind of class boundaries optimally. Merits of the system over the related ones are established through the use of several quantitative measure.
Subjective Quality Assessment of Compressed Tone-Mapped High Dynamic Range Videos

High Dynamic Range (HDR) videos are able to represent wider ranges of contrasts and colors than Standard Dynamic Range (SDR) videos, giving more vivid experiences. Due to this, HDR videos are expected to grow into the dominant video modality of the future. However, HDR videos are incompatible with existing SDR displays, which form the majority of affordable consumer displays on the market. Because of this, HDR videos must be processed by tone-mapping them to reduced bit-depths to service a broad swath of SDR-limited video consumers. Here, we analyze the impact of tone-mapping operators on the visual quality of streaming HDR videos. To this end, we built the first large-scale subjectively annotated open-source database of compressed tone-mapped HDR videos, containing 15,000 tone-mapped sequences derived from 40 unique HDR source contents. The videos in the database were labeled with more than 750,000 subjective quality annotations, collected from more than 1,600 unique human observers. We demonstrate the usefulness of the new subjective database by benchmarking objective models of visual quality on it. We envision that the new LIVE Tone-Mapped HDR (LIVE-TMHDR) database will enable significant progress on HDR video tone mapping and quality assessment in the future. To this end, we make the database freely available to the community at https://live.ece. utexas.edu/research/LIVE_TMHDR/index.html
IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection

Bird's eye view (BEV) representation has emerged as a dominant solution for describing 3D space in autonomous driving scenarios. However, objects in the BEV representation typically exhibit small sizes, and the associated point cloud context is inherently sparse, which leads to great challenges for reliable 3D perception. In this paper, we propose IS-Fusion, an innovative multimodal fusion framework that jointly captures the Instance- and Scene-level contextual information. IS-Fusion essentially differs from existing approaches that only focus on the BEV scene-level fusion by explicitly incorporating instance-level multimodal information, thus facilitating the instance-centric tasks like 3D object detection. It comprises a Hierarchical Scene Fusion (HSF) module and an Instance-Guided Fusion (IGF) module. HSF applies Point-to-Grid and Grid-to-Region transformers to capture the multimodal scene context at different granularities. IGF mines instance candidates, explores their relationships, and aggregates the local multimodal context for each instance. These instances then serve as guidance to enhance the scene feature and yield an instance-aware BEV representation. On the challenging nuScenes benchmark, IS-Fusion outperforms all the published multimodal works to date. Code is available at: https://github.com/yinjunbo/IS-Fusion.
WSCLoc: Weakly-Supervised Sparse-View Camera Relocalization

Despite the advancements in deep learning for camera relocalization tasks, obtaining ground truth pose labels required for the training process remains a costly endeavor. While current weakly supervised methods excel in lightweight label generation, their performance notably declines in scenarios with sparse views. In response to this challenge, we introduce WSCLoc, a system capable of being customized to various deep learning-based relocalization models to enhance their performance under weakly-supervised and sparse view conditions. This is realized with two stages. In the initial stage, WSCLoc employs a multilayer perceptron-based structure called WFT-NeRF to co-optimize image reconstruction quality and initial pose information. To ensure a stable learning process, we incorporate temporal information as input. Furthermore, instead of optimizing SE(3), we opt for $\mathfrak{sim}(3)$ optimization to explicitly enforce a scale constraint. In the second stage, we co-optimize the pre-trained WFT-NeRF and WFT-Pose. This optimization is enhanced by Time-Encoding based Random View Synthesis and supervised by inter-frame geometric constraints that consider pose, depth, and RGB information. We validate our approaches on two publicly available datasets, one outdoor and one indoor. Our experimental results demonstrate that our weakly-supervised relocalization solutions achieve superior pose estimation accuracy in sparse-view scenarios, comparable to state-of-the-art camera relocalization methods. We will make our code publicly available.