Miracle-dz/UniFAR-FTUs huggingface dataset:
An SPX-RLI1 Module Regulates Leaf Inclination in Response to Phosphate Availability in Rice[OPEN]

SPX and RLI1 regulate lamina joint cell expansion, thus increasing leaf inclination in response to Pi deficiency in rice. Leaf erectness is one of the key traits of plant architecture; in grains, plants with upright leaves can be planted close together, thus benefiting yield/unit area. Many factors, such as hormones, affect leaf inclination; however, how nutrition status, in particular phosphate (Pi) availability, affects leaf inclination remains largely unexplained. Here, we show that in rice (Oryza sativa), Pi deficiency stress inhibits lamina joint cell elongation, thus restricting lamina joint size and inducing leaf erectness in rice. The Pi starvation-induced proteins SPX1 (for Syg1/Pho81/XPR1) and SPX2 play a negative role in the regulation of leaf inclination. We further identified an SPX1-interacting protein, REGULATOR OF LEAF INCLINATION1 (RLI1), which positively regulates leaf inclination by affecting lamina joint cell elongation in rice. The rli1 mutants showed reduced leaf inclination and the RLI1 overexpressors showed increased leaf inclination. RLI1 directly activates the downstream genes BRASSINOSTEROID UPREGULATED1 (BU1) and BU1-LIKE 1 COMPLEX1 to control elongation of the lamina joint cells, therefore enhancing leaf inclination. We also found that Pi deficiency repressed the expression of RLI1. SPX1 protein interacts directly with RLI1, which could prevent RLI1 binding to the promoters of downstream genes. Therefore, SPX and RLI1 form a module to regulate leaf inclination in response to external Pi availability in rice.
Efficient and Fair Multi-programming in GPUs via Effective Bandwidth Management

Managing the thread-level parallelism (TLP) of GPGPU applications by limiting it to a certain degree is known to be effective in improving the overall performance. However, we find that such prior techniques can lead to sub-optimal system throughput and fairness when two or more applications are co-scheduled on the same GPU. It is because they attempt to maximize the performance of individual applications in isolation, ultimately allowing each application to take a disproportionate amount of shared resources. This leads to high contention in shared cache and memory. To address this problem, we propose new application-aware TLP management techniques for a multi-application execution environment such that all co-scheduled applications can make good and judicious use of all the shared resources. For measuring such use, we propose an application-level utility metric, called effective bandwidth, which accounts for two runtime metrics: attained DRAM bandwidth and cache miss rates. We find that maximizing the total effective bandwidth and doing so in a balanced fashion across all co-located applications can significantly improve the system throughput and fairness. Instead of exhaustively searching across all the different combinations of TLP configurations that achieve these goals, we find that a significant amount of overhead can be reduced by taking advantage of the trends, which we call patterns, in the way application's effective bandwidth changes with different TLP combinations. Our proposed pattern-based TLP management mechanisms improve the system throughput and fairness by 20% and 2x, respectively, over a baseline where each application executes with a TLP configuration that provides the best performance when it executes alone.
Effect of Multiwalled Carbon Nanotubes On Mechanical Properties of Concrete

This paper discusses the effect of multiwalled carbon nanotubes (CNT) on strength characteristics and du- rability of concrete. Sonication process is carried out by adding MWCNT with surfactants (super plasticizers - polycarboxylate 8H), 0.25% by weight of cement and also with water. 36 Specimens with MWCNTs of 0.015%, 0.03% and 0.045% of cement (by weight) were tested after 28 days of curing. Results show an increase in compressive and splitting-tensile strengths of the samples with increasing MWCNT. 0.045% of MWCNT has improved the 28 days compressive strength by 27 % while the split tensile strength increased by 45%. Crack propagation was reduced and water absorption decreased by 17% at 28 days curing.
Gated Fusion Network for Single Image Dehazing

In this paper, we propose an efficient algorithm to directly restore a clear image from a hazy input. The proposed algorithm hinges on an end-to-end trainable neural network that consists of an encoder and a decoder. The encoder is exploited to capture the context of the derived input images, while the decoder is employed to estimate the contribution of each input to the final dehazed result using the learned representations attributed to the encoder. The constructed network adopts a novel fusion-based strategy which derives three inputs from an original hazy image by applying White Balance (WB), Contrast Enhancing (CE), and Gamma Correction (GC). We compute pixel-wise confidence maps based on the appearance differences between these different inputs to blend the information of the derived inputs and preserve the regions with pleasant visibility. The final dehazed image is yielded by gating the important features of the derived inputs. To train the network, we introduce a multi-scale approach such that the halo artifacts can be avoided. Extensive experimental results on both synthetic and real-world images demonstrate that the proposed algorithm performs favorably against the state-of-the-art algorithms.
The Potential Role of Platelet-Related microRNAs in the Development of Cardiovascular Events in High-Risk Populations, Including Diabetic Patients: A Review

Platelet activation plays a pivotal role in the development and progression of atherosclerosis, which often leads to potentially fatal ischemic events at later stages of the disease. Platelets and platelet microvesicles (PMVs) contain large amounts of microRNA (miRNA), which contributes largely to the pool of circulating miRNAs. Hence, they represent a promising option for the development of innovative diagnostic biomarkers, that can be specific for the underlying etiology. Circulating miRNAs can be responsible for intracellular communication and may have a biological effect on target cells. As miRNAs associated to both cardiovascular diseases (CVD) and diabetes mellitus can be measured by means of a wide array of techniques, they can be exploited as an innovative class of smart disease biomarkers. In this manuscript, we provide an outline of miRNAs associated with platelet function and reactivity (miR-223, miR-126, miR-197, miR-191, miR-21, miR-150, miR-155, miR-140, miR-96, miR-98) that should be evaluated as novel biomarkers to improve diagnostics and treatment of CVD.
Verifying Parametric Thread Creation Igor

Automatic verification of concurrent systems is an active area of research since at least a quater of a century. We focus here on analyses of systems designed to operate with an arbitrary number of processes. German and Sistla, already in 1992, initiated in depth investigation of this problem for finite state systems. For infinite state systems, like pushdown systems, extra care is needed to avoid undecidability, as reachability is undecidable even for two identical pushdown processes communicating via single variable. Kahlon and Gupta in 2006 have proposed to use parametrization as means of bypassing this undecidability barrier. Indeed when instead of two pushdown processes we consider some unspecified number of them, the reachability problem becomes decidable. This idea of parametrization as an abstraction has been pursued further by Hague, who in 2011 has shown that the problem is still decidable when one of the pushdown processes is made different from the others: there is one leader process and many contributor processes. We discuss how the idea of parametrization as an abstraction leads to decidability, and in some cases even efficient algorithms, for verification of systems which combine recursion with dynamic thread creation.
Analysis of peripheral doses for base of tongue treatment by linear accelerator and helical TomoTherapy IMRT

The purpose of this study was to compare the peripheral doses to various organs from a typical head and neck intensity‐modulated radiation therapy (IMRT) treatment delivered by linear accelerator (linac) and helical TomoTherapy. Multiple human CT data sets were used to segment critical structures and organs at risk, fused and adjusted to an anthropomorphic phantom. Eighteen contours were designated for thermoluminescent dosimeter (TLD) placement. Following the RTOG IMRT Protocol 0522, treatment of the primary tumor and involved nodes (PTV70) and subclinical disease sites (PTV56) was planned utilizing IMRT to 70 Gy and 56 Gy. Clinically acceptable treatment plans were produced for linac and TomoTherapy treatments. TLDs were placed and each treatment plan was delivered to the anthropomorphic phantom four times. Within 2.5 cm (one helical TomoTherapy field width) superior and inferior to the field edges, normal tissue doses were on average 45% lower using linear accelerator. Beyond 2.5 cm, the helical TomoTherapy normal tissue dose was an average of 52% lower. The majority of points proved to be statistically different using the Student's t‐test with p<0.05. Using one method of calculation, probability of a secondary malignancy was 5.88% for the linear accelerator and 4.08% for helical TomoTherapy. Helical TomoTherapy delivers more dose than a linac immediately above and below the treatment field, contributing to the higher peripheral doses adjacent to the field. At distances beyond one field width (where leakage is dominant), helical TomoTherapy doses are lower than linear accelerator doses. PACS number: 87.50. cm Dosimetry/exposure assessment
Investigating the climate impacts of urbanization and the potential for cool roofs to counter future climate change in Southern California

The climate warming effects of accelerated urbanization along with projected global climate change raise an urgent need for sustainable mitigation and adaptation strategies to cool urban climates. Our modeling results show that historical urbanization in the Los Angeles and San Diego metropolitan areas has increased daytime urban air temperature by 1.3 ° C, in part due to a weakening of the onshore sea breeze circulation. We ﬁ nd that metropolis-wide adoption of cool roofs can meaningfully offset this daytime warming, reducing temperatures by 0.9 ° C relative to a case without cool roofs. Residential cool roofs were responsible for 67% of the cooling. Nocturnal temperature increases of 3.1 ° C from urbanization were larger than daytime warming, while nocturnal temperature reductions from cool roofs of 0.5 ° C were weaker than corresponding daytime reductions. We further show that cool roof deployment could partially counter the local impacts of global climate change in the Los Angeles metropolitan area. Assuming a scenario in which there are dramatic decreases in greenhouse gas emissions in the 21st century ( RCP2.6 ) , mid- and end-of-century temperature increases from global change relative to current climate are similarly reduced by cool roofs from 1.4 ° C to 0.6 ° C. Assuming a scenario with continued emissions increases throughout the century ( RCP8.5 ) , mid-century warming is signi ﬁ cantly reduced by cool roofs from 2.0 ° C to 1.0 ° C. The end-century warming, however, is signi ﬁ cantly offset only in small localized areas containing mostly industrial / commercial buildings where cool roofs with the highest albedo are adopted. We conclude that metropolis-wide adoption of cool roofs can play an important role in mitigating the urban heat island effect, and offsetting near-term local warming from global climate change. Global-scale reductions in greenhouse gas emissions are the only way of avoiding long-term warming, however. We further suggest that both climate mitigation and adaptation can be pursued simultaneously using ‘ cool photovoltaics ’
Information-Driven Business Cycles: A Primal Approach

We develop a methodology to estimate DSGE models with incomplete information, free of parametric restrictions on information structures. First, we define a “primal” economy in which deviations from full information are captured by wedges in agents' equilibrium expectations. Second, we provide implementability conditions, which ensure the existence of an information structure that implements these wedges. We apply the approach to estimate a New Keynesian model in which firms, households and the monetary authority have dispersed information about business conditions and productivity is the only aggregate fundamental. The estimated model fits the data remarkably well, with informational shocks able to account for the majority of U.S. business cycles. Output is driven mainly by household sentiments, whereas firm errors largely determine inflation. Our estimation indicates that firms and the central bank learn the aggregate state of the economy quickly, while household confusion about aggregate conditions is sizable and persistent.
The Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5): Development and Initial Psychometric Evaluation.

The Posttraumatic Stress Disorder Checklist (PCL) is a widely used DSM-correspondent self-report measure of PTSD symptoms. The PCL was recently revised to reflect DSM-5 changes to the PTSD criteria. In this article, the authors describe the development and initial psychometric evaluation of the PCL for DSM-5 (PCL-5). Psychometric properties of the PCL-5 were examined in 2 studies involving trauma-exposed college students. In Study 1 (N = 278), PCL-5 scores exhibited strong internal consistency (α = .94), test-retest reliability (r = .82), and convergent (rs = .74 to .85) and discriminant (rs = .31 to .60) validity. In addition, confirmatory factor analyses indicated adequate fit with the DSM-5 4-factor model, χ2 (164) = 455.83, p < . 001, standardized root mean square residual (SRMR) = . 07, root mean squared error of approximation (RMSEA) = . 08, comparative fit index (CFI) = . 86, and Tucker-Lewis index (TLI) = . 84, and superior fit with recently proposed 6-factor, χ2 (164) = 318.37, p < . 001, SRMR = . 05, RMSEA = . 06, CFI = . 92, and TLI = . 90, and 7-factor, χ2 (164) = 291.32, p < . 001, SRMR = . 05, RMSEA = . 06, CFI = . 93, and TLI = . 91, models. In Study 2 (N = 558), PCL-5 scores demonstrated similarly strong reliability and validity. Overall, results indicate that the PCL-5 is a psychometrically sound measure of PTSD symptoms. Implications for use of the PCL-5 in a variety of assessment contexts are discussed.
Aerosol Gemcitabine after Amputation Inhibits Osteosarcoma Lung Metastases but Not Wound Healing

Background In newly diagnosed osteosarcoma (OS) patients, the time between surgery and resumption of chemotherapy is 2–7 weeks. Delays > 16 days are associated with increased risk of relapse and decreased overall survival. Identifying an effective therapy that can be used postoperatively may prevent relapse. We investigated whether aerosol gemcitabine (GCB) initiated after tumor resection inhibited the growth of OS lung metastases without affecting the wound-healing process. Methods Mice were injected intratibially with OS cells. Amputation was performed when the tumor reached 1.5 cm. Full-thickness excisional wounds were also made on the dorsal skin and tail. Aerosol GCB or PBS was initiated 48 hours after amputation (3 times/week for 3 weeks). Wound sections were evaluated by immunohistochemistry for Ki-67 (proliferation), CD31 (vessels), VEGF, IL-10, bFGF, mast cells, macrophages, and M1/M2 macrophage ratios. The lungs were analyzed for macro- and micrometastases. Results Aerosol GCB inhibited the growth of the lung metastases but had no effect on the 3 phases of wound healing in the dorsal skin, tail, or bone. Production of cytokines at the wound sites was the same. Conclusion These data indicate that initiating aerosol GCB postoperatively may kill residual lung metastases thereby preventing relapse and improve survival.
Data Fusion-Based Multi-Object Tracking for Unconstrained Visual Sensor Networks

Camera node perception capability is one of the crucial issues for visual sensor networks, which belongs to the field of Internet of Things. Multi-object tracking is an important feature in analyzing object trajectories across multiple cameras, thus allowing synthesis of data and security analysis of images in various scenarios. Despite intensive research in the last decades, it remains challenging for tracking systems to perform in real-world situations. We therefore focus on key issues of multi-object state estimation for unconstrained multi-camera systems, e.g., data fusion of multiple sensors and data association. Unlike previous work that rely on camera network topology inference, we construct a graph from 2-D observations of all camera pairs without any assumption of network configuration. We apply the shortest path algorithm to the graph to find fused 3-D observation groups. Our approach is thus able to reject false positive reconstructions automatically, and also minimize computational complexity to guarantee feasible data fusion. Particle filters are applied as the 3-D tracker to form tracklets that integrate local features. These tracklets are modeled by a graph and linked into full tracks incorporating global spatial-temporal features. Experiments on the real-world PETS2009 S2/L1 sequence show the accuracy of our approach. Analyses of the different components of our approach provide meaningful insights for object tracking using multiple cameras. Evidence is provided for selecting the best view for a visual sensor network.
H3K36me3-mediated mismatch repair preferentially protects actively transcribed genes from mutation

Histone H3 trimethylation at lysine 36 (H3K36me3) is an important histone mark involved in both transcription elongation and DNA mismatch repair (MMR). It is known that H3K36me3 recruits the mismatch-recognition protein MutSα to replicating chromatin via its physical interaction with MutSα's PWWP domain, but the exact role of H3K36me3 in transcription is undefined. Using ChIP combined with whole-genome DNA sequencing analysis, we demonstrate here that H3K36me3, together with MutSα, is involved in protecting against mutation, preferentially in actively transcribed genomic regions. We found that H3K36me3 and MutSα are much more co-enriched in exons and actively transcribed regions than in introns and nontranscribed regions. The H3K36me3–MutSα co-enrichment correlated with a much lower mutation frequency in exons and actively transcribed regions than in introns and nontranscribed regions. Correspondingly, depleting H3K36me3 or disrupting the H3K36me3–MutSα interaction elevated the spontaneous mutation frequency in actively transcribed genes, but it had little influence on the mutation frequency in nontranscribed or transcriptionally inactive regions. Similarly, H2O2-induced mutations, which mainly cause base oxidations, preferentially occurred in actively transcribed genes in MMR-deficient cells. The data presented here suggest that H3K36me3-mediated MMR preferentially safeguards actively transcribed genes not only during replication by efficiently correcting mispairs in early replicating chromatin but also during transcription by directly or indirectly removing DNA lesions associated with a persistently open chromatin structure.
PlaneNet: Piece-Wise Planar Reconstruction from a Single RGB Image

This paper proposes a deep neural network (DNN) for piece-wise planar depthmap reconstruction from a single RGB image. While DNNs have brought remarkable progress to single-image depth prediction, piece-wise planar depthmap reconstruction requires a structured geometry representation, and has been a difficult task to master even for DNNs. The proposed end-to-end DNN learns to directly infer a set of plane parameters and corresponding plane segmentation masks from a single RGB image. We have generated more than 50,000 piece-wise planar depthmaps for training and testing from ScanNet, a large-scale RGBD video database. Our qualitative and quantitative evaluations demonstrate that the proposed approach outperforms baseline methods in terms of both plane segmentation and depth estimation accuracy. To the best of our knowledge, this paper presents the first end-to-end neural architecture for piece-wise planar reconstruction from a single RGB image. Code and data are available at https://github.com/art-programmer/PlaneNet.
Application of Logistic Regression in Assessing Stock Performances

Stock market prediction pertains to predicting the future value of stock of a company or other financial instrument traded on an exchange. Though the successful prediction of a stock's future price is a complex phenomenon, even a reasonable prediction would yield significant profit and this study attempts to address this unpredictability with the help of a data mining technique. In this study, the companies listed on BSE SENSEX are chosen as representative set of companies that are most actively traded. Logistic Regression is used on various important financial ratios of these companies and certain macro financial variables to analyze which ratios are important and how they are affecting the stock prices. The proposed model results in better classification accuracy when compared to a similar study in the literature.
Ionospheric signatures of acoustic waves generated by transient tropospheric forcing

Acoustic waves generated by tropospheric sources may attain significant amplitudes in the thermosphere and overlying ionosphere. Although they are weak precursors to gravity waves in the mesosphere below, acoustic waves may achieve temperature and vertical wind perturbations on the order of approximately tens of Kelvin and m/s throughout the E and F regions. Their perturbations to total electron content are predicted to be detectable by ground‐based radar and GPS receivers; they also drive field‐aligned currents that may be detectable in situ via magnetometers. Although transient and short lived, ionospheric signatures of acoustic waves may provide new and quantitative insight into the forcing of the upper atmosphere from below.