Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of Biostimulant Mode of Action Using Novel Multi-Trait High-Throughput Screening of Arabidopsis Germination and Rosette Growth

Environmental stresses have a significant effect on agricultural crop productivity worldwide. Exposure of seeds to abiotic stresses, such as salinity among others, results in lower seed viability, reduced germination, and poor seedling establishment. Alternative agronomic practices, e.g., the use of plant biostimulants, have attracted considerable interest from the scientific community and commercial enterprises. Biostimulants, i.e., products of biological origin (including bacteria, fungi, seaweeds, higher plants, or animals) have significant potential for (i) improving physiological processes in plants and (ii) stimulating germination, growth and stress tolerance. However, biostimulants are diverse, and can range from single compounds to complex matrices with different groups of bioactive components that have only been partly characterized. Due to the complex mixtures of biologically active compounds present in biostimulants, efficient methods for characterizing their potential mode of action are needed. In this study, we report the development of a novel complex approach to biological activity testing, based on multi-trait high-throughput screening (MTHTS) of Arabidopsis characteristics. These include the in vitro germination rate, early seedling establishment capacity, growth capacity under stress and stress response. The method is suitable for identifying new biostimulants and characterizing their mode of action. Representatives of compatible solutes such as amino acids and polyamines known to be present in many of the biostimulant irrespective of their origin, i.e., well-established biostimulants that enhance stress tolerance and crop productivity, were used for the assay optimization and validation. The selected compounds were applied through seed priming over a broad concentration range and the effect was investigated simultaneously under control, moderate stress and severe salt stress conditions. The new MTHTS approach represents a powerful tool in the field of biostimulant research and development and offers direct classification of the biostimulants mode of action into three categories: (1) plant growth promotors/inhibitors, (2) stress alleviators, and (3) combined action.
The dynamic role of the ilio-sacral joint in jumping frogs

A striking feature among jumping frogs is a sharp pelvic bend about the ilio-sacral (IS) joint, unique to anurans. Although this sagittal plane hinge has been interpreted as crucial for the evolution of jumping, its mechanical contribution has not been quantified. Using a model based on Kassina maculata and animated with kinematics from prior experiments, we solved the ground contact dynamics in MuJoCo enabling inverse dynamics without force plate measurements. We altered the magnitude, speed and direction of IS extension (leaving remaining kinematics unaltered) to determine its role in jumping. Ground reaction forces (GRFs) matched recorded data. Prior work postulated that IS rotation facilitates jumping by aligning the torso with the GRF. However, our simulations revealed that static torso orientation has little effect on GRF due to the close proximity of the IS joint with the COM, failing to support the ‘torso alignment’ hypothesis. Rather than a postural role, IS rotation has a dynamic function whereby angular acceleration (i) influences GRF direction to modulate jump direction and (ii) increases joint loading, particularly at the ankle and knee, perhaps increasing tendon elastic energy storage early in jumps. Findings suggest that the pelvic hinge mechanism is not obligatory for jumping, but rather crucial for the fine tuning of jump trajectory, particularly in complex habitats.
Bidirectional evaluation with direct manipulation

We present an evaluation update (or simply, update) algorithm for a full-featured functional programming language, which synthesizes program changes based on output changes. Intuitively, the update algorithm retraces the steps of the original evaluation, rewriting the program as needed to reconcile differences between the original and updated output values. Our approach, furthermore, allows expert users to define custom lenses that augment the update algorithm with more advanced or domain-specific program updates. To demonstrate the utility of evaluation update, we implement the algorithm in Sketch-n-Sketch, a novel direct manipulation programming system for generating HTML documents. In Sketch-n-Sketch, the user writes an ML-style functional program to generate HTML output. When the user directly manipulates the output using a graphical user interface, the update algorithm reconciles the changes. We evaluate bidirectional evaluation in Sketch-n-Sketch by authoring ten examples comprising approximately 1400 lines of code in total. These examples demonstrate how a variety of HTML documents and applications can be developed and edited interactively in Sketch-n-Sketch, mitigating the tedious edit-run-view cycle in traditional programming environments.
FINN-R

Convolutional Neural Networks have rapidly become the most successful machine-learning algorithm, enabling ubiquitous machine vision and intelligent decisions on even embedded computing systems. While the underlying arithmetic is structurally simple, compute and memory requirements are challenging. One of the promising opportunities is leveraging reduced-precision representations for inputs, activations, and model parameters. The resulting scalability in performance, power efficiency, and storage footprint provides interesting design compromises in exchange for a small reduction in accuracy. FPGAs are ideal for exploiting low-precision inference engines leveraging custom precisions to achieve the required numerical accuracy for a given application. In this article, we describe the second generation of the FINN framework, an end-to-end tool that enables design-space exploration and automates the creation of fully customized inference engines on FPGAs. Given a neural network description, the tool optimizes for given platforms, design targets, and a specific precision. We introduce formalizations of resource cost functions and performance predictions and elaborate on the optimization algorithms. Finally, we evaluate a selection of reduced precision neural networks ranging from CIFAR-10 classifiers to YOLO-based object detection on a range of platforms including PYNQ and AWS F1, demonstrating new unprecedented measured throughput at 50 TOp/s on AWS F1 and 5 TOp/s on embedded devices.
Supervised Search Result Diversification via Subtopic Attention

Search result diversification aims to retrieve diverse results to satisfy as many different information needs as possible. Supervised methods have been proposed recently to learn ranking functions and they have been shown to produce superior results to unsupervised methods. However, these methods use implicit approaches based on the principle of Maximal Marginal Relevance (MMR). In this paper, we propose a learning framework for explicit result diversification where subtopics are explicitly modeled. Based on the information contained in the sequence of selected documents, we use the attention mechanism to capture the subtopics to be focused on while selecting the next document, which naturally fits our task of document selection for diversification. As a preliminary attempt, we employ recurrent neural networks and max pooling to instantiate the framework. We use both distributed representations and traditional relevance features to model documents in the implementation. The framework is flexible to model query intent in either a flat list or a hierarchy. Experimental results show that the proposed method significantly outperforms all the existing search result diversification approaches.
Bayesian adaptive assessment of the reading function for vision: The qReading method

Reading is a fundamental skill that can be significantly affected by visual disabilities. Reading performance, which typically is measured as reading speed with a reading chart, is a key endpoint for quantifying normal or abnormal vision. Despite its importance for clinical vision, existing reading tests for vision are time consuming and difficult to administer. Here, we propose a Bayesian adaptive method, the qReading method, for automated assessment of the reading speed versus print size function. We implemented the qReading method with a word/nonword lexical decision task and validated the method with computer simulations and a psychophysical experiment. Computer simulations showed that both the interrun standard deviation and intrarun half width of the 68.2% credible interval of the estimated reading speeds from the qReading method were less than 0.1 log10 units after 150 trials, with a bias of 0.05 log10 units. In the psychophysical experiment, reading functions measured by the qReading and Psi methods (Kontsevich & Tyler, 1999) in a word/nonword lexical decision task were compared. The estimated reading functions obtained with the qReading and Psi methods were highly correlated (r = 0.966 ± 0.004, p < 0.01). The precision of the qReading method with 225 trials was comparable to that of the Psi method with 450 trials. We conclude that the qReading method can precisely and accurately assess the reading function in much reduced time, with great promise in both basic research and clinical applications.
Divergence in sex peptide-mediated female post-mating responses in Drosophila melanogaster

Transfer and receipt of seminal fluid proteins crucially affect reproductive processes in animals. Evolution in these male ejaculatory proteins is explained with post-mating sexual selection, but we lack a good understanding of the evolution of female post-mating responses (PMRs) to these proteins. Some of these proteins are expected to mediate sexually antagonistic coevolution generating the expectation that females evolve resistance. One candidate in Drosophila melanogaster is the sex peptide (SP) which confers cost of mating in females. In this paper, we compared female SP-induced PMRs across three D. melanogaster wild-type populations after mating with SP-lacking versus control males including fitness measures. Surprisingly, we did not find any evidence for SP-mediated fitness costs in any of the populations. However, female lifetime reproductive success and lifespan were differently affected by SP receipt indicating that female PMRs diverged among populations. Injection of synthetic SP into virgin females further supported these findings and suggests that females from different populations require different amounts of SP to effectively initiate PMRs. Molecular analyses of the SP receptor suggest that genetic differences might explain the observed phenotypical divergence. We discuss the evolutionary processes that might have caused this divergence in female PMRs.
Feature extraction from a depth map for human detection

Human detection is challenging and important task for computer vision-based researchers. Histogram of Oriented Gradients (HOG) descriptors show significantly outperforms the existing feature sets for a human detection. Machine learning algorithms based on HOG have been widely used for classification, human detection and recognition. However this method has the amount of type I and II errors. The human detection using a visible light image was often affected by the change of illumination conditions, the pose and coordinates change, the interference of complex backgrounds, the camera sensor noise, the shadow of detecting objects and moving objects of self-occlusions or mutual-occlusions. The amount of these errors can be decreased by using the object distance information. In this paper we propose a human detection method based on HOG features. The method uses a depth map instead of visible light image. We have additional information about the scene through the comprehensive distance information analysis. During the experiment, we used maps of depth received from the Kinect v2 visual sensor. During the first step in our detection experiment we processed the whole depth map frame with the preprocessing noise redundant filters. Then on the second step we sliced the depth by the layers which contain definite distance points. After that step we made segmentation on the every layer and send segment to the pre-trained HOG-classifier. The experimental results show that the new proposed method of HOG on the depth map provides high precision and recall. It gives opportunities to train more sensitive classifiers, which can provide the higher recall values.
What Does a Modern Anatomist Look like? Current Trends in the Training of Anatomy Educators

Anatomical sciences are foundational to the health professions, yet little is known about the qualifications of anatomy educators at the graduate and professional level in the United States. Moreover, there is concern that the number of qualified anatomy educators being trained may be insufficient to meet the growing demand posed by new and expanded programs in medicine and allied health specialties. The authors surveyed anatomists from across the country to (i) characterize the educational credentials of current anatomy educators and (ii) assess the perceived need for education‐focused postdoctoral positions or formal mentorships to prepare anatomists for teaching‐intensive faculty positions. To probe the survey responses more deeply, one‐on‐one interviews were conducted with eight individuals selected to represent a diverse sample of respondents in terms of institution, gender, and academic rank. Results indicate that 30–40% of educators at the graduate level and approximately 60% of those at the undergraduate level lack graduate coursework in histology, embryology, and neuroanatomy. Forty‐five percent of respondents had completed a postdoctoral fellowship. Eighty‐six percent replied “yes/maybe” to the question of whether an anatomy education postdoctoral fellowship would benefit doctoral graduates. The top 3 reasons for this recommendation were to (i) establish independent educational research, (ii) improve a publication record, and (iii) gain additional teaching experience. Notable weaknesses of education‐focused postdoctoral training were related to finances, fear of exploitation, and undervaluing of teaching. Moving forward, postdoctoral fellowships and other forms of postgraduate training may represent a key strategy for training anatomists in the current educational climate. Anat Sci Educ 00: 000–000. © 2018 American Association of Anatomists.
Acoustic cue variability affects eye movement behaviour during non-native speech perception: a GAMM model

Participants in the ‘visual world’ paradigm simultaneously process both auditory and visual cues in order to match speech to target images. Previous research has shown that when native speakers listen to speech that has high within-category variability in the contrastive dimension, auditory perceptual uncertainty is reduced, resulting in increased looks to competitor objects. This suggests a cross-modal effect, where reduced reliability in the auditory domain leads to increased search for evidence in the visual domain. The present study investigated the effects of withincategory acoustic variability on eye movements during the acquisition of a new acoustic dimension not present in the native language, namely English speakers’ acquisition of lexical tone. All participants heard a bimodal distribution of stimuli, with distribution peaks at the prototypical pitch values for high and mid tones; however, presentation frequency differed between conditions: high-variance vs. low-variance. Based on previous research, we expected lower uncertainty and better learning in the low-variance condition. GAMM models of eye movement data showed that withincategory acoustic variance increases perceptual uncertainty in the auditory domain and hinders acquisition of a cue dimension. The results also show a cross-modal effect: lower relibility in the auditory domain leads to increased search for cues in the visual domain, even when visual cues are held constant across conditions.
Spine Fractures in Ankylosing Diseases: Recommendations of the Spine Section of the German Society for Orthopaedics and Trauma (DGOU)

Study Design: Review of literature and case series. Objectives: Update and review of current treatment concepts for spine fractures in patients with ankylosing spinal disorders. Methods: Case presentation and description of a diagnostic and therapeutic algorithm for unstable spinal injuries with an underlying ankylosing spinal disorder (ASD) of the cervical and thoracolumbar spine. Results: Nondisplaced fractures can be missed easily using conventional X-rays. Thus, computed tomography (CT) scans are recommended for all trauma patients with ASD. In doubt or presence of any neurologic involvement additional magnetic resonance imaging (MRI) scans should be obtained. Spine precautions should be maintained all times and until definitive treatment (<24 h). Nonoperative fracture treatment is not recommended given the mechanical instability of the most commonly seen fracture patterns (AOSpine B- and C-type, M2) in patients with ASD and inherent high risk of secondary neurologic deterioration. For patients with ankylosing spondylitis (AS) or diffuse idiopathic hyperostosis (DISH) sustaining cervical spine fractures, a combined anterior-posterior instrumentation for fracture fixation is recommended. Closed reduction and patient positioning can be challenging in presence of preexisting kyphotic deformities. In the thoracolumbar (TL) spine, a posterior instrumentation extending 2 to 3 levels above and below the fracture level is recommended to maintain adequate reduction and stability until fracture healing. Minimally invasive percutaneous pedicle screws and cement augmentation can help to minimize the surgical trauma and strengthen the construct stability in patients with diminished minor bone quality (osteopenia, osteoporosis). Conclusions: Current concepts, treatment options, and recommendations of the German Orthopedic Trauma Society–Spine Section for spinal fractures in the ankylosed spine have been outlined.
Role of calcium channels and endothelial factors in nickel induced aortic hypercontraction in Wistar rats

Aim: To investigate the mechanism of nickel augmented phenylephrine (PE)-induced contraction in isolated segments of Wistar rat aorta. Materials and Methods: Effect of varying concentrations of nickel on PE-induced contraction were investigated in isolated segments of Wistar rat aorta using an organ bath system. Aortic rings were pre-incubated with verapamil (1 µM and 20 µM), gadolinium, apocynin, indomethacin or N-G-nitro-L-arginine methyl ester (L-NAME) separately before incubation with nickel. Results: Endothelium intact aortic rings incubated with 100 nM, 1 µM or 100 µM of nickel exhibited 80%, 43% and 28% increase in PE-induced contraction, respectively, while no such enhancing responses were observed in endothelium denuded aorta. Incubation of aortic rings with 1 µM and 20 µM verapamil suggested an involvement of influx of calcium through T-type calcium channels in smooth muscle cells, while aortic rings pre-incubated with gadolinium showed no role of store operated calcium channels in the nickel effect on PE-induced contractions. The enhancing effect of nickel on PE-induced contractions was inhibited by apocynin, indomethacin or L-NAME. Conclusion: Nickel has caused augmentation of PE-induced contractions as a result of the endothelial generation of reactive oxygen species (ROS) and cyclooxygenase 2 (COX2) dependent endothelium contracting factors (EDCFs), which increases the influx of extracellular calcium through T-type Ca2+ channels in smooth muscle cells.
Clinical study of combined mirror and extracorporeal shock wave therapy on upper limb spasticity in poststroke patients

Mirror therapy is a simple, inexpensive, and patient-oriented method that has been shown to reduce phantom sensations and pain caused by amputation and improve range of motion, speed, and accuracy of arm movement and function. Extracorporeal shock wave therapy (ESWT) is a new, reversible, and noninvasive method for the treatment of spasticity after stroke. To investigate the therapeutic effect of the combination of mirror and extracorporeal shock wave therapy on upper limb spasticity in poststroke patients. We randomly assigned 120 patients into four groups: A, B, C, and D. All groups received conventional rehabilitation training for 30 min per day, five times a week, for 4 weeks. Moreover, participants in groups A, B, and C also added mirror therapy, ESWT, and a combination of mirror and ESWT, respectively, for 20 min per day. Motor recovery and spasticity were measured using Fugl–Meyer assessment and modified Ashworth scale. The differences in the Fugl–Meyer assessment and modified Ashworth scale scores in group C were significantly greater than those of group D at all observed time points after treatment and were significantly greater than those of groups A and B (P<0.05), but no significant differences were observed between groups A and B until 12 months. Upper extremity spasticity was improved by combined mirror and ESWT.
Track Fusion and Behavioral Reasoning for Moving Vehicles Based on Curvilinear Coordinates of Roadway Geometries

This paper presents track fusion and behavioral reasoning for moving vehicles in close proximity based on the curvilinear coordinates of roadway geometries. The inferred track and behavior of other vehicles can be used to perform safe actions in intelligent vehicle applications and autonomous driving. Vehicle detections from multiple perception sensors are integrated using track-to-track (T2T) fusion based on a cross-covariance method, and this T2T fusion is performed with curvilinear coordinates created using prebuilt roadway geometry on a digital map. The coordinate conversion to curvilinear space has many benefits for behavioral reasoning and tracking, such as constraining problem spaces and dimensions. A machine learning classifier based on a support vector machine is then applied to deduce the behavior of nearby vehicles. The algorithms presented here for track fusion and behavioral reasoning based on curvilinear coordinates have been verified through experiments in various real traffic scenarios.
Thermal Magnetic Field Activated Propolis Release From Liquid Crystalline System Based on Magnetic Nanoparticles

Intra-periodontal pocket drug delivery systems, such as liquid crystalline systems, are widely utilized improving the drug release control and the therapy. Propolis is used in the treatment of periodontal diseases, reducing the inflammatory and infectious conditions. Iron oxide magnetic nanoparticles (MNPs) can improve the treatment when an alternating external magnetic field (AEMF) is applied, increasing the local temperature. The aim of this study was to develop a liquid crystalline system containing MNPs for intra-periodontal pocket propolis release. MNPs were prepared using iron salts and the morphological, size, thermal, x-ray diffraction, magnetometry, and Mössbauer spectroscopy analyses were performed. Cytotoxicity studies using Artemia salina and fibroblasts were also accomplished. The systems were prepared using polyoxyethylene (10) oleyl ether, isopropyl myristate, purified water, and characterized by polarized optical microscopy, rheometry, and in vitro drug release profile using a periodontal pocket simulator apparatus. The antifungal activity of the systems was investigated against Candida spp. using an AEMF. MNPs displayed nanometric size, were monodisperse, and they displayed very low cytotoxicity. Microscopically homogeneous formulations were obtained displaying important physicochemical and biological properties. The system displayed prolonged release of propolis and important in vitro fungicide activity, which was increased when the AEMF was applied, indicating a potentially alternative therapy for the treatment of the periodontal disease.