Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of rainstorm modes along the upper mainstream of Yangtze River during 2003–2016

Accurate knowledge of extrveme rainfall mode (time distribution) is crucial for many fields, such as hydraulic structure design, flood control and reservoir operation. The rainstorm modes along the upper mainstream of Yangtze River (UMYR) during 2003–2016 were investigated by fuzzy mode identification method, linear regression and Mann–Kendall test. Results show that unimodal rainstorms are more frequently seen than bimodal ones, and those with late peaks are dominant along UMYR. Moreover, rainstorms tend to centre on downstream UMYR. Trend analysis reveals moderate decreases in regional mean rainstorm frequency and amount, but an increase in regional mean peak intensity. Unimodal rainstorms with early peaks and bimodal rainstorms with early and late peaks have increased significantly. However, unimodal rainstorms with late peaks and bimodal rainstorms with middle and late peaks show decreases. Spatially, decreases in rainstorm frequency, amount and peak intensity are found in downstream, while peak intensity shows an increasing trend in the upper UMYR. Along the river reach, mean rainstorm levels are generally below 100 mm. In comparison to bimodal rainstorms, unimodal rainstorms are more intense, and those with middle peaks are considered most intense with mean levels of peak intensity generally ranging from 25 to 50 mm h−1. For other rainstorm modes, the mean levels of peak intensity are generally below 25 mm h−1. Finally, we found there is a potential change in the dominant rainstorm mode in downstream UMYR, where unimodal rainstorms with early peaks have become the prevalent local rainstorm mode in recent years, replacing unimodal rainstorms with late peaks.
The Framingham study.

Background and Purpose We sought to modify existing sex-specific health risk appraisal functions (profile functions) for the prediction of first stroke that better assess the effects of the use of antihypertensive medication. Methods Health risk appraisal functions were previously developed from the Framingham Study cohort. These functions were Cox proportional hazards regression models relating age, systolic blood pressure, diabetes mellitus, cigarette smoking, prior cardiovascular disease, atrial fibrillation, left ventricular hypertrophy by electrocardiogram, and the use of antihypertensive medication to the occurrence of stroke. Closer examination of the data indicated that antihypertensive therapy effect is present only for systolic blood pressures between 110 and 200 mm Hg. Adjustments to the regressions to better fit the observed data were developed and tested for statistical significance and goodness-of-fit of the model residuals. Results Modified functions more consistent with the data were developed, and, from these, tables to evaluate 10-year risk of first stroke were computed. Conclusions The stroke profile can be used for evaluation of the risk of stroke and suggestion of risk factor modification to reduce risk. The effect of antihypertensive therapy in the evaluation of stroke risk can now be better evaluated.
Direct Products in Communication Complexity

We give exponentially small upper bounds on the success probability for computing the direct product of any function over any distribution using a communication protocol. Let suc(μ, f, C) denote the maximum success probability of a 2-party communication protocol for computing the boolean function f(x, y) with C bits of communication, when the inputs (x, y) are drawn from the distribution μ. Let μ<sup>n</sup> be the product distribution on n inputs and f<sup>n</sup> denote the function that computes n copies of f on these inputs. We prove that if T log<sup>3/2</sup> T ≪ (C - 1)√n and suc(μ, f, C) <; 2/3, then suc(μ<sup>n</sup>, f<sup>n</sup>, T) ≤ exp(-Ω(n)). When μ is a product distribution, we prove a nearly optimal result: as long as T log<sup>2</sup> T ≪ Cn, we must have suc(μ<sup>n</sup>, f<sup>n</sup>, T) ≤ exp(-Ω(n)).
The glypican Dally is required in the niche for the maintenance of germline stem cells and short-range BMP signaling in the Drosophila ovary

The Drosophila ovary is an excellent system with which to study germline stem cell (GSC) biology. Two or three female GSCs are maintained in a structure called a niche at the anterior tip of the ovary. The somatic niche cells surrounding the GSCs include terminal filament cells, cap cells and escort stem cells. Mounting evidence has demonstrated that BMP-like morphogens are the immediate upstream signals to promote GSC fate by preventing the expression of Bam, a key differentiation factor. In contrast to their morphogenic long-range action in imaginal epithelia, BMP molecules in the ovarian niche specify GSC fate at single-cell resolution. How this steep gradient of BMP response is achieved remains elusive. In this study, we found that the glypican Dally is essential for maintaining GSC identity. Dally is highly expressed in cap cells. Cell-specific Dally-RNAi, mutant clonal analysis and cell-specific rescue of the GSC-loss phenotype suggest that Dally acts in the cap cells adjacent to the GSCs. We confirmed that Dally facilitated BMP signaling in GSCs by examining its downstream targets in various dally mutants. Conversely, when we overexpressed Dally in somatic cells outside the niche, we increased the number of GSC-like cells apparently by expanding the pro-GSC microenvironment. Furthermore, in a genetic setting we revealed a BMP-sensitivity distinction between germline and somatic cells, namely that Dally is required for short-range BMP signaling in germline but not in somatic cells. We propose that Dally ensures high-level BMP signaling in the ovarian niche and thus female GSC determination.
A Novel Feature Evaluation Method in Mapping Forest AGB by Fusing Multiple Evaluation Metrics Using PolSAR Data

For mapping forest above-ground biomass (AGB), a novel feature evaluation method, named integrating multiple metrics with collinearity considered (IMC), is proposed by fusing different types of metrics with considering information redundancy among features. The optimal feature set was obtained using IMC metric from various types of features extracted from L-band ALOS-2 PALSAR data in planted Chinese fir forest. To evaluate the effectiveness of the IMC metric, three common feature evaluation metrics, Pearson correlation coefficient, importance derived from random forest (RF), and the sensitivity index (SI), were also used to obtain the optimal feature set, respectively. Moreover, the combination effect among features was also analyzed to filter the global best feature set in mapping for forest AGB. The results showed that the accuracy of forest AGB estimation was obviously improved using the proposed IMC metric [0.45–0.67 for $R^{2}$ and from 24.51% to 32.16% for relative root mean square error (rRMSE)]. Furthermore, it is also implied that the combination effect among features has the great potential to improve the capability of forest AGB estimation.
Task-driven Support Polygon Reshaping for Humanoids

In this paper we address a task-driven motion generation method that allows a humanoid robot to make whole-body motions including support polygon reshaping to achieve the given tasks. In the proposed method, generalized inverse kinematics (IK) is employed with floating-base to generate humanoid whole-body motions that enable the robot to accomplish the tasks according to given priorities. During the motion, several criteria such as manipulability or end-effector position error are tracked. If the desired task cannot be done because of those criteria, a geometric planner for support polygon is activated. Then the whole-body motion is computed again always using the generalized IK solver including stepping motion that realizes the deformed support polygon by using dynamic walking pattern generator. This method provides a way to perform tasks that could not be done without changing the humanoid's support state. We have verified the proposed framework through simulations and experiments using humanoid robot HRP-2
Dynamic Algorithm for Graph Clustering Using Minimum Cut Tree

In this paper we introduce a dynamic algorithm for clustering undirected graphs, whose edge property is continuously changing. The algorithm can maintain high-quality clusters efficiently in presence of insertion and deletion (update) of edges. The algorithm is motivated by the minimum-cut tree based partitioning algorithm presented in G. W. Flake et al. (2004) and G. W. Flake (2000). It takes O(k3) time for each update processing, where k is the maximum size of any cluster. This is the worst case time complexity, and in general time taken is much less. To the best of our knowledge, this is the first clustering algorithm, for evolving graphs, providing strong theoretical quality guarantee on clusters
Finding optimal Bayesian networks by dynamic programming

Finding the Bayesian network that maximizes a score function is known as structure learning or structure discovery. Most approaches use local search in the space of acyclic digraphs, which is prone to local maxima. Exhaustive enumeration requires super-exponential time. In this paper we describe a “merely” exponential space/time algorithm for finding a Bayesian network that corresponds to a global maxima of a decomposable scoring function, such as BDeu or BIC. NSF IIS-0325581, NSERC PGS-B
Speech Stream Detection in Short-wave Channel Based on Cepstral of Auditory Wavelet Packets

This paper is aimed at non-stationary characteristic of noise and speech signal in short-wave channel. A novel speech stream detection method based on the cepstral of auditory wavelet packet was proposed. The results of experiments show that this method is a good one for speech stream detection, with nice combat noise performance and adaptation
High expression of TROP2 characterizes different cell subpopulations in androgen-sensitive and androgen-independent prostate cancer cells

Progression of castration-resistant tumors is frequent in prostate cancer. Current systemic treatments for castration-resistant prostate cancer only produce modest increases in survival time and self-renewing Tumor-Initiating Cells (TICs) are suspected to play an important role in resistance to these treatments. However it remains unclear whether the same TICs display both chemo-resistance and self-renewing abilities throughout progression from early stage lesions to late, castration resistant tumors. Here, we found that treatment of mice bearing LNCaP-derived xenograft tumors with cytotoxic (docetaxel) and anti-androgen (flutamide) compounds enriched for cells that express TROP2, a putative TIC marker. Consistent with a tumor-initiating role, TROP2high cells from androgen-sensitive prostate cancer cell lines displayed an enhanced ability to re-grow in culture following treatment with taxane-based chemotherapy with or without androgen blockade. TROP2 down-regulation in these cells reduced their ability to recur after treatment with docetaxel, in the presence or absence of flutamide. Accordingly, in silico analysis of published clinical data revealed that prostate cancer patients with poor prognosis exhibit significantly elevated TROP2 expression level compared to low-risk patients, particularly in the case of patients diagnosed with early stage tumors. In contrast, in androgen-independent prostate cancer cell lines, TROP2high cells did not exhibit a differential treatment response but were characterized by their high self-renewal ability. Based on these findings we propose that high TROP2 expression identifies distinct cell sub-populations in androgen-sensitive and androgen-independent prostate tumors and that it may be a predictive biomarker for prostate cancer treatment response in androgen-sensitive tumors.
S2E: Towards an End-to-End Entity Resolution Solution from Acoustic Signal

Traditional cascading Entity Resolution (ER) pipeline suffers from propagated errors from upstream tasks. We address this issue by formulating a new end-to-end (E2E) ER problem, Signal-to-Entity (S2E), resolving query entity mentions to actionable entities in textual catalogs directly from audio queries instead of audio transcriptions in raw or parsed format. Additionally, we extend the E2E Spoken Language Understanding framework by introducing a novel dimension to ER research. We adapt three public datasets for the S2E task, and propose a novel solution, which aligns the multimodal signals via an effective retrieval co-attention mechanism and refined multimodal objectives. Despite 42% smaller in terms of the total model size, the proposed design outperforms the cascading baseline by 2.6%, 47.0%, and 73.3% across the three datasets respectively with different acoustic conditions.
Object- and pixel-based classifications of macroalgae farming area with high spatial resolution imagery

Abstract Macroalgae plays an important role in coastal ecosystems. The accurate delineation of macroalgae areas is important for environmental management. This study compared the pixel- and object-based methods using Gaofen satellite no. 2 image to explore an efficient classification approach. Expert system rules and nearest neighbour classifier were adopted for object-based classification, whereas maximum likelihood classifier was implemented in the pixel-based approach. Normalized difference vegetation index, normalized difference water index, mean value of the blue band and geometric characteristics were selected as features to distinguish macroalgae farms by considering the spectral and spatial characteristics. Results show that the object-based method achieved a higher overall accuracy and kappa coefficient than the pixel-based method. Moreover, the object-based approach displayed superiority in identifying Porphyra class. These findings suggest that the object-based method can delineate macroalgae farming areas efficiently and be applied in the future to monitor the macroalgae farms with high spatial resolution imagery.
Elevated cytokinin content in ipt transgenic creeping bentgrass promotes drought tolerance through regulating metabolite accumulation

Increased endogenous plant cytokinin (CK) content through transformation with an adenine isopentyl transferase (ipt) gene has been associated with improved plant drought tolerance. The objective of this study is to determine metabolic changes associated with elevated CK production in ipt transgenic creeping bentgrass (Agrostis stolonifera L.) with improved drought tolerance. Null transformants (NTs) and plants transformed with ipt controlled by a stress- or senescence-activated promoter (SAG12-ipt) were exposed to well-watered conditions or drought stress by withholding irrigation in an environmental growth chamber. Physiological analysis confirmed that the SAG12-ipt line (S41) had improved drought tolerance compared with the NT plants. Specific metabolite changes over the course of drought stress and differential accumulation of metabolites in SAG12-ipt plants compared with NT plants at the same level of leaf relative water content (47% RWC) were identified using gas chromatography–mass spectroscopy. The metabolite profiling analysis detected 45 metabolites differentially accumulated in response to ipt expression or drought stress, which included amino acids, carbohydrates, organic acids, and organic alcohols. The enhanced drought tolerance of SAG12-ipt plants was associated with the maintenance of accumulation of several metabolites, particularly amino acids (proline, γ-aminobutyric acid, alanine, and glycine) carbohydrates (sucrose, fructose, maltose, and ribose), and organic acids that are mainly involved in the citric acid cycle. The accumulation of these metabolites could contribute to improved drought tolerance due to their roles in the stress response pathways such as stress signalling, osmotic adjustment, and respiration for energy production.
Pharmacological Activities, Therapeutic Effects, and Mechanistic Actions of Trigonelline

Trigonelline (TRG) is a natural polar hydrophilic alkaloid that is found in many plants such as green coffee beans and fenugreek seeds. TRG potentially acts on multiple molecular targets, including nuclear factor erythroid 2-related factor 2 (Nrf2), peroxisome proliferator-activated receptor γ, glycogen synthase kinase, tyrosinase, nerve growth factor, estrogen receptor, amyloid-β peptide, and several neurotransmitter receptors. In this review, we systematically summarize the pharmacological activities, medicinal properties, and mechanistic actions of TRG as a potential therapeutic agent. Mechanistically, TRG can facilitate the maintenance and restoration of the metabolic homeostasis of glucose and lipids. It can counteract inflammatory constituents at multiple levels by hampering pro-inflammatory factor release, alleviating inflammatory propagation, and attenuating tissue injury. It concurrently modulates oxidative stress by the blockage of the detrimental Nrf2 pathway when autophagy is impaired. Therefore, it exerts diverse therapeutic effects on a variety of pathological conditions associated with chronic metabolic diseases and age-related disorders. It shows multidimensional effects, including neuroprotection from neurodegenerative disorders and diabetic peripheral neuropathy, neuromodulation, mitigation of cardiovascular disorders, skin diseases, diabetic mellitus, liver and kidney injuries, and anti-pathogen and anti-tumor activities. Further validations are required to define its specific targeting molecules, dissect the underlying mechanistic networks, and corroborate its efficacy in clinical trials.
Electroacupuncture induces acute changes in cerebral cortical miRNA profile, improves cerebral blood flow and alleviates neurological deficits in a rat model of stroke

Electroacupuncture has been shown to improve cerebral blood flow in animal models of stroke. However, it is unclear whether electroacupuncture alters miRNA expression in the cortex. In this study, we examined changes in the cerebral cortical miRNA profile, cerebral blood flow and neurological function induced by electroacupuncture in a rat model of stroke. Electroacupuncture was performed at Renzhong (GV26) and Neiguan (PC6), with a frequency of 2 Hz, continuous wave, current intensity of 3.0 mA, and stimulation time of 1 minute. Electroacupuncture increased cerebral blood flow and alleviated neurological impairment in the rats. miRNA microarray profiling revealed that the vascular endothelial growth factor signaling pathway, which links cell proliferation with stroke, was most significantly affected by electroacupuncture. Electroacupuncture induced changes in expression of rno-miR-206-3p, rno-miR-3473, rno-miR-6216 and rno-miR-494-3p, and these changes were confirmed by quantitative real-time polymerase chain reaction. Our findings suggest that changes in cell proliferation-associated miRNA expression induced by electroacupuncture might be associated with the improved cerebral blood supply and functional recovery following stroke.
Mapping from Frame-Driven to Frame-Free Event-Driven Vision Systems by Low-Rate Rate Coding and Coincidence Processing--Application to Feedforward ConvNets

Event-driven visual sensors have attracted interest from a number of different research communities. They provide visual information in quite a different way from conventional video systems consisting of sequences of still images rendered at a given "frame rate. " Event-driven vision sensors take inspiration from biology. Each pixel sends out an event (spike) when it senses something meaningful is happening, without any notion of a frame. A special type of event-driven sensor is the so-called dynamic vision sensor (DVS) where each pixel computes relative changes of light or "temporal contrast. " The sensor output consists of a continuous flow of pixel events that represent the moving objects in the scene. Pixel events become available with microsecond delays with respect to "reality. " These events can be processed "as they flow" by a cascade of event (convolution) processors. As a result, input and output event flows are practically coincident in time, and objects can be recognized as soon as the sensor provides enough meaningful events. In this paper, we present a methodology for mapping from a properly trained neural network in a conventional frame-driven representation to an event-driven representation. The method is illustrated by studying event-driven convolutional neural networks (ConvNet) trained to recognize rotating human silhouettes or high speed poker card symbols. The event-driven ConvNet is fed with recordings obtained from a real DVS camera. The event-driven ConvNet is simulated with a dedicated event-driven simulator and consists of a number of event-driven processing modules, the characteristics of which are obtained from individually manufactured hardware modules.
The Anatomy of an Antibacterial Clay Deposit: A New Economic Geology

Argillic alteration zones associated with porphyry and epithermal deposits may contain clays that are antibacterial against antibiotic-resistant human pathogens, thus providing new economic potential for these deposits. This study examined the antibacterial activity of hydrothermal alteration zones in andesite porphyry and volcaniclastic rocks from a sulfurand pyrite-rich clay deposit in the Oregon Cascades. The deposit is located along a fault zone west of Crater Lake, in Eocene-age volcanic rocks. Mineralogical relationships were used to deduce emplacement temperatures of the antibacterial clays, and oxygen isotopes of secondary quartz were used to deduce fluid sources. High-sulfidation alteration from magmatic fluids (200°–300°C) produced highly illitic Black clay with elemental sulfur and up to 19 wt % pyrite. Low-sulfidation, mixed magmatic-meteoric fluids (150°–200°C) produced pervasive Blue clay containing reduced Fe-bearing rectorite and minor pyrite (4–5 wt %). White clay containing smectite and kaolinite formed along faults from cooler (≤100°C) meteoric water. Surficial Red clay contains illite-smectite and goethite from the oxidation of pyrite. Antibacterial activity is greatest in the pyrite-bearing clays but the nonsulfide-bearing White clays also inhibit bacterial growth. Red oxidized clays are not antibacterial. When antibacterial clays are rehydrated in deionized water (100 mg/ml), the pH (<4.5) drives mineral dissolution and metal hydrolysis to produce Fe2+, Al3+, and hydroxyl radicals that kill bacteria. The pH and Eh of the hydrated clay is important for stabilizing these aqueous reactants, therefore we find no antibacterial effect where carbonates or goethite are stable. Accordingly, the antibacterial effect is diminished with burial depth (meters) where the Eh is too low to drive pyrite oxidation.