Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome-Wide Analysis Reveals Dynamic Epigenomic Differences in Soybean Response to Low-Phosphorus Stress

Low-phosphorus (low-P) stress has a significant limiting effect on crop yield and quality. Although the molecular mechanisms of the transcriptional level responsible for the low-P stress response have been studied in detail, the underlying epigenetic mechanisms in gene regulation remain largely unknown. In this study, we evaluated the changes in DNA methylation, gene expression and small interfering RNAs (siRNAs) abundance genome-wide in response to low-P stress in two representative soybean genotypes with different P-efficiencies. The DNA methylation levels were slightly higher under low-P stress in both genotypes. Integrative methylation and transcription analysis suggested a complex regulatory relationship between DNA methylation and gene expression that may be associated with the type, region, and extent of methylation. Association analysis of low-P-induced differential methylation and gene expression showed that transcriptional alterations of a small part of genes were associated with methylation changes. Dynamic methylation alterations in transposable element (TE) regions in the CHH methylation context correspond with changes in the amount of siRNA under low-P conditions, indicating an important role of siRNAs in modulating TE activity by guiding CHH methylation in TE regions. Together, these results could help to elucidate the epigenetic regulation mechanisms governing the responses of plants to abiotic stresses.
Study on Fatigue Characteristics of Bionic Functional Surface of Hardened Steel

In this study, we aimed to process the biomimetic function surface by designing a prototype for modeling the pits on a dung beetle body and the abdomen of a desert viper, and by using high speed milling and controlling the ratio of row spacing to feed rate. Firstly, we conducted three-dimensional parametric modeling and static analysis of the bionic functional surface using 3D modeling software UGNX (12.0, SIEMENS AG, Munich, Germany) and finite element analysis software ABAQUS (2018, Dassault, Providence, RI, USA). Then, the analysis results were imported into the fatigue life analysis software nCode (2018, HBM United Kingdom Ltd., South Yorkshire, UK) to simulate the fatigue characteristics of different bionic pit morphology models. Per the simulated tensile fatigue testing machine, the result shows that the minimum fatigue life value of the quadrilateral pit surface of the simulated dung beetle is one and four times higher than the hexagonal pit morphology and the irregular pit morphology, respectively, whereas the maximum fatigue damage is lower by one and five orders of magnitude, respectively. The quadrilateral pit surface on the biomimetic dung beetle body has better fatigue resistance, which can considerably improve the fatigue damage distribution state and the fatigue life of hardened steel die surfaces. The influential regulation of milling parameters on fatigue performance was studied and the results show that the fatigue resistance of the model is optimal when milling parameters are: row spacing of 0.4 mm, loading space of 0.2 mm, and milling depth of 0.3 mm. The quadrilateral dimensions formed by milling are highly similar to those of a dung beetle body proving that a certain reduction in milling process depth can increase the structural fatigue resistance. From the perspective of fatigue crack growth analysis, the quadrilateral dimples on the surface of the dung beetle improve fatigue crack growth inhibition and fatigue resistance.
Palynological characteristics of selected Lamioideae taxa and its taxonomic significance

Lamioideae comprised the second‐largest subfamily in Lamiaceae. Although considerable progress has recently been made in the taxonomic study of Lamioideae, the subfamily remains one of the most poorly investigated subfamily in Lamiaceae. Therefore, the present study was designed with the aim to document the pollen micromorphology of some selected Lamioideae taxa and its taxonomic significance from Pakistan. Pollen micromorphological features were observed using scanning electron microscopy. The pollen grains are monad, tricolpate, radially/bilateral symmetrical. The pollen grains were small to medium‐sized having oblate, oblate/subspheroidal, and subspheroidal shape. Exine sculpturing was observed as reticulate, microreticulate, and bireticulate. The colpus surface ornamentation was found as verrucate, gemmate, scabrate, and psilate. There was a considerable variation between the species in the micromorphology, that is, the coarseness of the reticulum, thickness of the muri comprising the reticulum and the number of secondary lumina per primary lumen. Hence, this study documented the pollen morphology of some selected taxa of the subfamily Lamioideae from Pakistan and strengthens the taxonomic identification of subfamily based on pollen characters, which helps in the correct identification, discrimination of the species of Lamioideae at generic and species level.
Diagnosis, Treatment and Prevention of Sarcopenia in Hip Fractured Patients: Where We Are and Where We Are Going: A Systematic Review

Background: Sarcopenia is defined as a progressive loss of muscle mass and muscle strength associated to increased adverse events, such as falls and hip fractures. The aim of this systematic review is to analyse diagnosis methods of sarcopenia in patients with hip fracture and evaluate prevention and treatment strategies described in literature. Methods: Three independent authors performed a systematic review of two electronic medical databases using the following inclusion criteria: Sarcopenia, hip fractures, diagnosis, treatment, and prevention with a minimum average of 6-months follow-up. Any evidence-level studies reporting clinical data and dealing with sarcopenia diagnosis, or the treatment and prevention in hip fracture-affected patients, were considered. Results: A total of 32 articles were found. After the first screening, we selected 19 articles eligible for full-text reading. Ultimately, following full-text reading, and checking of the reference list, seven articles were included. Conclusions: Sarcopenia diagnosis is challenging, as no standardized diagnostic and therapeutic protocols are present. The development of medical management programs is mandatory for good prevention. To ensure adequate resource provision, care models should be reviewed, and new welfare policies should be adopted in the future.
Targeting Receptors on Cancer Cells with Protein Toxins

Cancer cells frequently upregulate surface receptors that promote growth and survival. These receptors constitute valid targets for intervention. One strategy involves the delivery of toxic payloads with the goal of killing those cancer cells with high receptor levels. Delivery can be accomplished by attaching a toxic payload to either a receptor-binding antibody or a receptor-binding ligand. Generally, the cell-binding domain of the toxin is replaced with a ligand or antibody that dictates a new binding specificity. The advantage of this “immunotoxin” approach lies in the potency of these chimeric molecules for killing cancer cells. However, receptor expression on normal tissue represents a significant obstacle to therapeutic intervention.
An epidemiological cross-sectional study of prevalence of mental disorders in Dulong nationality of Southwest China

Abstract The Dulong nationality is one of the 5 smallest ethnic minorities in China. The suicide rate among people of the Dulong nationality is very serious. To address this issue, we conducted cross-sectional epidemiological studies on the prevalence of mental disorders in ethnic groups. Studying the unique situation of ethnic minorities can help us better understand their mental state and improve their quality of life. We conducted a cross-sectional epidemiological survey on a minority group in Southwest China. We used the cluster sampling method, and 2129 people were included in the study. The highest 1-month disorder prevalence was for alcohol dependence/abuse (4.16%), and the prevalence of lifelong mood disorders was 9.82%. The results of multivariate analysis showed that women faced a higher risk of mood disorders and anxiety disorders. This epidemiological survey of the prevalence of mental disorders in ethnic minorities in Southwest China provides a significant reference for mental health interventions for other ethnic minorities around the world.
Availability Versus Utilization of Supplemental Breast Cancer Screening Post Passage of Breast Density Legislation.

Objective: Despite the lack of evidence that supplemental screening in women with dense breasts reduces breast cancer mortality, 38 states have passed breast density legislation, with some including recommendations for supplemental screening. The objective of this study is to compare the availability versus use of supplemental breast cancer screening modalities and determine factors driving use of supplemental screening in rural versus urban settings. Methods: A 50-item mailed survey using the Tailored Design Method was sent to American College of Radiology mammography-accredited facilities in North Carolina in 2017. Respondents included 94 facilities (48 rural and 46 urban locations). Survey questions focused on breast cancer and supplemental screening services, breast density, risk factors/assessment, and facility demographics. Results: The survey response rate was 60.3% (94/156). Among the 94 respondents, 64.0% (n = 60) reported availability of any type of supplemental screening (digital breast tomosynthesis [DBT], ultrasound, or magnetic resonance imaging [MRI]). In facilities where supplemental screening modalities were available, the most commonly utilized supplemental screening modality was DBT (96.4%), compared with ultrasound (35.7%) and MRI (46.7%). Facilities reported using supplemental screening based on patient breast density (48.3%), referring physician recommendation (63.3%), reading radiologist recommendation (63.3%), breast cancer risk factors (48.3%), and patient request (40.0%). Urban facilities were more likely than rural facilities to base supplemental screening on breast cancer risk factors (62.5% vs. 32.1%; p-value = 0.019), referring physician (75.0% vs. 50.0%; p-value = 0.045), and reading radiologist (78.1% vs. 46.4%; p-value = 0.011). Conclusion: In our study, supplemental screening modalities were widely available, with facilities more likely to use DBT for supplemental screening compared to other modalities.
Age, Sex Hormones, and Circadian Rhythm Regulate the Expression of Amyloid-Beta Scavengers at the Choroid Plexus

Accumulation of amyloid-beta (Aβ) in the brain is thought to derive from the impairment of Aβ clearance mechanisms rather than from its overproduction, which consequently contributes to the development of Alzheimer’s disease. The choroid plexus epithelial cells constitute an important clearance route for Aβ, either by facilitating its transport from the cerebrospinal fluid to the blood, or by synthesizing and secreting various proteins involved in Aβ degradation. Impaired choroid plexus synthesis, secretion, and transport of these Aβ-metabolizing enzymes have been therefore associated with the disruption of Aβ homeostasis and amyloid load. Factors such as aging, female gender, and circadian rhythm disturbances are related to the decline of choroid plexus functions that may be involved in the modulation of Aβ-clearance mechanisms. In this study, we investigated the impact of age, sex hormones, and circadian rhythm on the expression of Aβ scavengers such as apolipoprotein J, gelsolin, and transthyretin at the rat choroid plexus. Our results demonstrated that mRNA expression and both intracellular and secreted protein levels of the studied Aβ scavengers are age-, sex-, and circadian-dependent. These data suggest that the Aβ-degradation and clearance pathways at the choroid plexus, mediated by the presence of Aβ scavengers, might be compromised as a consequence of aging and circadian disturbances. These are important findings that enhance the understanding of Aβ-clearance-regulating mechanisms at the blood–cerebrospinal fluid barrier.
Interactive Text-to-Speech System via Joint Style Analysis

While modern TTS technologies have made significant advancements in audio quality, there is still a lack of behavior naturalness compared to conversing with people. We propose a style-embedded TTS system that generates styled responses based on the speech query style. To achieve this, the system includes a style extraction model that extracts a style embedding from the speech query, which is then used by the TTS to produce a matching response. We faced two main challenges: 1) only a small portion of the TTS training dataset has style labels, which is needed to train a multi-style TTS that respects different style embeddings during inference. 2) The TTS system and the style extraction model have disjoint training datasets. We need consistent style labels across these two datasets so that the TTS can learn to respect the labels produced by the style extraction model during inference. To solve these, we adopted a semi-supervised approach that uses the style extraction model to create style labels for the TTS dataset and applied transfer learning to learn the style embedding jointly. Our experiment results show user preference for the styled TTS responses and demonstrate the style-embedded TTS system's capability of mimicking the speech query style.
Transmitter-Oriented Dual-Mode SWIPT With Deep-Learning-Based Adaptive Mode Switching for IoT Sensor Networks

In this article, we propose a dual-mode simultaneous wireless information and power transfer (SWIPT) system with a deep-learning-based adaptive mode switching (MS) algorithm to exploit both advantages of single-tone and multitone SWIPT. For self-powering of low-energy Internet-of-Things (IoT) devices, a duty-cycling operation is used with nonlinear energy harvesting. For this, we employ a new energy-assisted single-tone modulation which simplifies the receiver structure for information decoding. Considering the symbol-error rate performance, we formulate an adaptive MS problem to maximize the achievable rate under the energy-causality constraint by adjusting the MS threshold. To relieve the computational burden of the receiver, we introduce asymmetric processing for adaptive MS, for which the transmitter adapts the communication mode based on the feedback from the receiver. We invoke deep learning for adaptive MS at the transmitter that iteratively updates the MS threshold in a long-term scale via deep long short-term memory (LSTM) recurrent neural network (RNN) while deciding on the communication mode and modulation index in a short-term scale. We demonstrate the achievable rate improvement under an energy-neutral operation while providing interesting insights into designing the adaptive MS algorithm for the dual-mode SWIPT system.
Robust Method for Removing Dynamic Objects from Point Clouds

3D point cloud maps are an accumulation of laser scans obtained at different positions and times. Since laser scans represent a snapshot of the surrounding at the time of capture, they often contain moving objects which may not be observed at all times. Dynamic objects in point cloud maps decrease the quality of maps and affect localization accuracy, hence it is important to remove the dynamic objects from 3D point cloud maps. In this paper, we present a robust method to remove dynamic objects from 3D point cloud maps. Given a registered set of 3D point clouds, we build an occupancy map in which the voxels represent the occupancy state of the volume of space over an extended time period. After building the occupancy map, we use it as a filter to remove dynamic points in lidar scans before adding the points to the map. Furthermore, we accelerate the process of building occupancy maps using object detection and a novel voxel traversal method. Once the occupancy map is built, dynamic object removal can run in real-time. Our approach works well on wide urban roads with stopped or moving traffic and the occupancy maps get better with the inclusion of more lidar scans from the same scene.
Learning Robotic Assembly Tasks with Lower Dimensional Systems by Leveraging Physical Softness and Environmental Constraints

In this study, we present a novel control framework for assembly tasks with a soft robot. Typically, existing hard robots require high frequency controllers and precise force/torque sensors for assembly tasks. The resulting robot system is complex, entailing large amounts of engineering and maintenance. Physical softness allows the robot to interact with the environment easily. We expect soft robots to perform assembly tasks without the need for high frequency force/torque controllers and sensors. However, specific data-driven approaches are needed to deal with complex models involving nonlinearity and hysteresis. If we were to apply these approaches directly, we would be required to collect very large amounts of training data. To solve this problem, we argue that by leveraging softness and environmental constraints, a robot can complete tasks in lower dimensional state and action spaces, which could greatly facilitate the exploration of appropriate assembly skills. Then, we apply a highly efficient model-based reinforcement learning method to lower dimensional systems. To verify our method, we perform a simulation for peg-in-hole tasks. The results show that our method learns the appropriate skills faster than an approach that does not consider lower dimensional systems. Moreover, we demonstrate that our method works on a real robot equipped with a compliant module on the wrist.
Proximity Estimation Using Vision Features Computed On Sensor

This paper presents a monocular vision based proximity estimation system using abstract features, such as corner points, blobs and edges, as inputs to a neural network. An experimental vehicle was built using a vision system integrating the SCAMP-5 vision chip, a micro-controller, and an RC model car. The vision chip includes image sensor with embedded 256×256 processor SIMD array. The pixel processor array chip was programmed to capture images and run the feature algorithms directly on the focal plane, and then digest them so that only sparse feature description data were read-out in the form of 40 values. By logging the vision output and the output from three infrared proximity sensors, training data were obtained to train three fully connected layer-recurrent neural networks with fewer than 700 parameters each. The trained neural network was able to estimate the proximity to the level of accuracy sufficient for a reactive collision avoidance behaviour to be achieved. The latency of the control system, from image capture to neural network output, was under 4ms, enabling the vehicles to avoid obstacles while moving at 0.64m/s to 1.8m/s in the experiment.
An obstacle-interaction planning method for navigation of actuated vine robots

The field of soft robotics is grounded on the idea that, due to their inherent compliance, soft robots can safely interact with the environment. Thus, the development of effective planning and control pipelines for soft robots should incorporate reliable robot-environment interaction models. This strategy enables soft robots to effectively exploit contacts to autonomously navigate and accomplish tasks in the environment. However, for a class of soft robots, namely vine-inspired, tip-extending or "vine" robots, such interaction models and the resulting planning and control strategies do not exist. In this paper, we analyze the behavior of vine robots interacting with their environment and propose an obstacle-interaction model that characterizes the bending and wrinkling deformation induced by the environment. Starting from this, we devise a novel obstacle-interaction planning method for these robots. We show how obstacle interactions can be effectively leveraged to enlarge the set of reachable workspace for the robot tip, and verify our findings with both simulated and real experiments. Our work improves the capabilities of this new class of soft robot, helping to advance the field of soft robotics.
Coexistence of 5G With Satellite Services in the Millimeter-Wave Band

In this study, a new method is proposed to confirm the possibility of coexistence between the existing satellite services and potential fifth-generation (5G) cellular services in the millimeter-wave band according to the frequency-designation agenda of International Mobile Telecommunications (IMT)-2020 for 5G. To evaluate the accumulated interference power of numerous 5G systems distributed globally at a satellite receiver, we extend the satellite’s interference reception area to the entire coverage area, from which only the land area is extracted using the geospatial terrain data of Earth in three dimensions. This enables more accurate interference assessment than conventional methods that only consider the footprint of the satellite’s 3-dB beamwidth. We also place the IMT-2020 (5G) systems in the coverage area using the IMT-2020 parameters and modeling documents for the International Telecommunication Union’s coexistence study. The propagation loss is modeled considering the clutter loss, building entry loss, and attenuation from atmospheric gases. Subsequently, we analyze the interference power received by a fixed satellite service (FSS) satellite operating in the same band and an Earth exploration satellite service (EESS) passive sensor operating in an adjacent channel. Our simulation shows that the FSS satellite receives up to 7.9dB more interference than that obtained from the existing method. Although this is a substantial difference, we find that the protection criteria is still satisfied. However, all EESS passive sensors do not meet the protection criteria in most scenarios, and additional frequency separation or interference mitigation techniques are required to protect these sensors. The proposed method is also applicable to the analysis of non-terrestrial network interference from airships, balloons, unmanned aerial vehicles, etc.
Fuzzy Adaptive Control-based Real-time Obstacle Avoidance under Uncertain Perturbations

Dynamic Movement Primitives (DMPs) framework is a powerful approach to imitate motor skills, which has outstanding characteristics, such as convergence to the goal position and good imitation performance. Considering complex motion scenes of manipulators, such as changing the goal position or adding obstacles, the original DMPs framework is not sufficient for the requirements. In this paper, we propose a learning control-based hierarchical control strategy to adapt to new goal positions and avoid obstacles: the high-level learning scheme is targeted at imitating the motor skill and generating the optimization trajectory for obstacle avoidance; the lower-level control scheme focuses on the safety and stability of the robot's movement with unknown disturbances. Firstly, the enhanced DMPs framework is presented to imitate the trajectory from human demonstrations, where the novel DMPs can adapt to new goal position with the changing goal, and avoid single or multiple obstacles. Then, the fuzzy adaptive control method is employed to control redundant manipulators, where the fuzzy logic system (FLS) is incorporated to approximate an unknown nonlinear function term of the unknown disturbance. Finally, the effectiveness of the proposed learning-control strategy is demonstrated with simulation results. The results show that the developed hierarchical strategy has good performance for new goal adaptation and obstacle avoidance.