Miracle-dz/UniFAR-FTUs huggingface dataset:
Aspects of the ecology of the hitch lavinia exilicauda cyprinidae a persistent native cyprinid in clear lake california usa

Growth, diet, and fecundity of Clear Lake hitch (Lavinia exilicauda chi) were investigated to see if any changes had taken place following the establishment of a large population of Mississippi silversides (Menidia audens) in the lake. No changes were found. Hitch seem to avoid competing with silversides for zooplankton by being limnetic in all except the early life history stages. Fecundity was found to be higher than that of Beardsley Reservoir hitch, but considerably lower than a previous estimate had indicated. The Clear Lake hitch (L. e. chi Hopkirk 1973) is one of only four (out of 12) native species that has managed to persist in large numbers in Clear Lake, Lake Co., California, following various environmental perturbations and the establishment of 16 exotic species (Moyle 1976). The hitch is a large (to 35 cm standard length), plankton-feeding cyprinid that spawns in the intermittent streams that enter the lake and is taken in small numbers by a commercial fishery. Although brief studies of its feeding habits were undertaken by Lindquist, Deonier, and Hanley (1943), and of its growth and reproduction by Murphy (1948), no studies have been undertaken since the establishment in the lake of the Mississippi silversides (Menidia audens) in 1967 (Cook and Moore 1967). The silversides is a small planktivore that now dominates the inshore areas. It has been introduced into reservoirs in Oklahoma and California and appears to be extending its range rapidly (Moyle 1976). An additional aspect of hitch biology needing to be examined was the extremely high fecundity (112,000 eggs/female) reported by Murphy (1948), which was in contrast to the much lower fecundities (about 3,000 to 26,000 eggs/female) reported by Nicola (1974) for L. e. exilicauda from Beardsley Reservoir, California. The purpose of this study, therefore, was to examine the feeding habits and growth rates of Clear Lake hitch, for comparison with pre-silversides studies, and to reexamine their fecundity. 1 Present address: Dept. Engineering Research, Pacific Gas and Electric Co., 3400 Crow Canyon Road, San Ramon, CA 94584.
Alcohol Use Disorder and Associated Factors Among Jimma University Undergraduate Students

Purpose Harmful alcohol use among University students is a problem throughout the world. However, little is known about alcohol use disorders among JFD university students in Ethiopia. Therefore, this study aimed to assess the prevalence and associated factors of alcohol use disorder (AUD) among Jimma University undergraduate students. Methods Institution-based cross-sectional study was conducted among Jimma university students. Data were collected from 741 sampled students who were selected through a multi-stage stratified sampling technique. Alcohol Use Disorder Identification Test (AUDIT), Oslo 3 items social support scale (OSS-3), and Kessler-6 (K6) tools were used to assess alcohol use disorders, social support, and psychological distress, respectively. Data were analyzed through SPSS version 20.0. Bivariate and multivariate logistic regression analyses were conducted and adjusted odds ratio (AOR) at a 95% confidence interval (CI) was used to determine the independently associated factors of AUD. Results The overall prevalence of AUD among Jimma University undergraduate students was 26.5%. There was a positive and independent association between AUDs and being single AOR= 1.98, 95% CI [1.21, 3.22], having a history of mental illness AOR= 1.98, 95% CI [1.04, 3.75], having a history of suicidal attempt AOR= 3.63, 95% CI [1.18, 11.11], smoking cigarette AOR= 5.04, 95% CI [2.02, 12.57], having peer pressure to drink alcohol AOR= 2.72, 95% CI [1.76, 4.19] and presence of mental distress AOR= 2.81, 95% CI [1.83, 4.32]. Conclusion The findings of this study showed that the prevalence of AUD was high in the sampled undergraduate students of Jimma University. AUD was positively associated with mental, substance, and behavioral risk factors. Therefore, concerted actions needed to emplace to increase the student’s awareness of the effect of harmful alcohol use. Moreover, it is recommended that further studies need to be conducted to develop strategies for evidence-based interventions.
Lysophosphatidic Acid Receptor 1 Specifically Labels Seizure-Induced Hippocampal Reactive Neural Stem Cells and Regulates Their Division

A population of neural stem cells (NSCs) dwelling in the dentate gyrus (DG) is able to generate neurons throughout adult life in the hippocampus of most mammals. These NSCs generate also astrocytes naturally and are capable of generating oligodendrocytes after gene manipulation. It has been more recently shown that adult hippocampal NSCs after epileptic seizures as well as subventricular zone NSCs after stroke can give rise to reactive astrocytes (RAs). In the hippocampus, the induction of seizures triggers the conversion of NSCs into reactive NSCs (React-NSCs) characterized by a drastic morphological transformation, abnormal migration, and massive activation or entry into the cell cycle to generate more React-NSCs that ultimately differentiate into RAs. In the search for tools to investigate the properties of React-NSCs, we have explored the LPA1–green fluorescent protein (GFP) transgenic line of mice in which hippocampal NSCs are specifically labeled due to the expression of lysophosphatidic acid receptor 1 (LPA1). We first addressed the validity of the transgene expression as true marker of LPA1 expression and then demonstrated how, after seizures, LPA1-GFP labeled exclusively React-NSCs for several weeks. Then React-NSCs lost LPA1-GFP expression as neurons of the granule cell layer started to express it. Finally, we used knockout for LPA1 transgenic mice to show that LPA1 plays a functional role in the activation of React-NSCs. Thus, we confirmed that LPA1-GFP expression is a valid tool to study both NSCs and React-NSCs and that the LPA1 pathway could be a target in the intent to preserve NSCs after seizures.
High-Intensity Interval Training Restores Glycolipid Metabolism and Mitochondrial Function in Skeletal Muscle of Mice With Type 2 Diabetes

High-intensity interval training has been reported to lower fasting blood glucose and improve insulin resistance of type 2 diabetes without clear underlying mechanisms. The purpose of this study was to investigate the effect of high-intensity interval training on the glycolipid metabolism and mitochondrial dynamics in skeletal muscle of high-fat diet (HFD) and one-time 100 mg/kg streptozocin intraperitoneal injection-induced type 2 diabetes mellitus (T2DM) mice. Our results confirmed that high-intensity interval training reduced the body weight, fat mass, fasting blood glucose, and serum insulin of the T2DM mice. High-intensity interval training also improved glucose tolerance and insulin tolerance of the T2DM mice. Moreover, we found that high-intensity interval training also decreased lipid accumulation and increased glycogen synthesis in skeletal muscle of the T2DM mice. Ultrastructural analysis of the mitochondria showed that mitochondrial morphology and quantity were improved after 8 weeks of high-intensity interval training. Western blot analysis showed that the expression of mitochondrial biosynthesis related proteins and mitochondrial dynamics related proteins in high-intensity interval trained mice in skeletal muscle were enhanced. Taken together, these data suggest high-intensity interval training improved fasting blood glucose and glucose homeostasis possibly by ameliorating glycolipid metabolism and mitochondrial dynamics in skeletal muscle of the T2DM mice.
Interactions Between Motor Thalamic Field Potentials and Single-Unit Spiking Are Correlated With Behavior in Rats

Field potential (FP) oscillations are believed to coordinate brain activity over large spatiotemporal scales, with specific features (e.g., phase and power) in discrete frequency bands correlated with motor output. Furthermore, complex correlations between oscillations in distinct frequency bands (phase-amplitude, amplitude-amplitude, and phase-phase coupling) are commonly observed. However, the mechanisms underlying FP-behavior correlations and cross-frequency coupling remain unknown. The thalamus plays a central role in generating many circuit-level neural oscillations, and single-unit activity in motor thalamus (Mthal) is correlated with behavioral output. We, therefore, hypothesized that motor thalamic spiking coordinates motor system FPs and underlies FP-behavior correlations. To investigate this possibility, we recorded wideband motor thalamic (Mthal) electrophysiology as healthy rats performed a two-alternative forced-choice task. Delta (1–4 Hz), beta (13–30 Hz), low gamma (30–70 Hz), and high gamma (70–200 Hz) power were strongly modulated by task performance. As in the cortex, the delta phase was correlated with beta/low gamma power and reaction time. Most interestingly, subpopulations of Mthal neurons defined by their relationship to the behavior exhibited distinct relationships with FP features. Specifically, neurons whose activity was correlated with action selection and movement speed were entrained to delta oscillations. Furthermore, changes in their activity anticipated power fluctuations in beta/low gamma bands. These complex relationships suggest mechanisms for commonly observed FP-FP and spike-FP correlations, as well as subcortical influences on motor output.
Mapping Essential Urban Land Use Categories in Nanjing by Integrating Multi-Source Big Data

High-spatial-resolution (HSR) urban land use maps are very important for urban planning, traffic management, and environmental monitoring. The rapid urbanization in China has led to dramatic urban land use changes, however, so far, there are no such HSR urban land use maps based on unified classification frameworks. To fill this gap, the mapping of 2018 essential urban land use categories in China (EULUC-China) was jointly accomplished by a group of universities and research institutes. However, the relatively lower classification accuracy may not sufficiently meet the application demands for specific cities. Addressing these challenges, this study took Nanjing city as the case study to further improve the mapping practice of essential urban land use categories, by refining the generation of urban parcels, resolving the problem of unbalanced distribution of point of interest (POI) data, integrating the spatial dependency of POI data, and evaluating the size of training samples on the classification accuracy. The results revealed that (1) the POI features played the most important roles in classification performance, especially in identifying administrative, medical, sport, and cultural land use categories, (2) compared with the EULUC-China, the overall accuracy for Level I and Level II in EULUC-Nanjing has increased by 11.1% and 5%, to 86.1% and 80% respectively, and (3) the classification accuracy of Level I and Level II would be stable when the number of training samples was up to 350. The methods and findings in this study are expected to better inform the regional to continental mappings of urban land uses.
Heat Shock Responsive Gene Expression Modulated by mRNA Poly(A) Tail Length

Poly(A) tail length (PAL) has been implicated in the regulation of mRNA translation activities. However, the extent of such regulation at the transcriptome level is less understood in plants. Herein, we report the development and optimization of a large-scale sequencing technique called the Assay for PAL-sequencing (APAL-seq). To explore the role of PAL on post-transcriptional modification and translation, we performed PAL profiling of Arabidopsis transcriptome in response to heat shock. Transcripts of 2,477 genes were found to have variable PAL upon heat treatments. Further study of the transcripts of 14 potential heat-responsive genes identified two distinct groups of genes. In one group, PAL was heat stress-independent, and in the other, PAL was heat stress-sensitive. Meanwhile, the protein expression of HSP70 and HSP17.6C was determined to test the impact of PAL on translational activity. In the absence of heat stress, neither gene demonstrated protein expression; however, under gradual or abrupt heat stress, both transcripts showed enhanced protein expression with elongated PAL. Interestingly, HSP17.6C protein levels were positively correlated with the severity of heat treatment and peaked when treated with abrupt heat. Our results suggest that plant genes have a high variability of PALs and that PAL contributes to swift posttranslational stress responses.
Surfactant-Mediated Airway and Acinar Interactions in a Multi-Scale Model of a Healthy Lung

We present a computational multi-scale model of an adult human lung that combines dynamic surfactant physicochemical interactions and parenchymal tethering between ~16 generations of airways and subtended acini. This model simulates the healthy lung by modeling nonlinear stress distributions from airway/alveolar interdependency. In concert with multi-component surfactant transport processes, this serves to stabilize highly compliant interacting structures. This computational model, with ~10 k degrees of freedom, demonstrates physiological processes in the normal lung such as multi-layer surfactant transport and pressure–volume hysteresis behavior. Furthermore, this model predicts non-equilibrium stress distributions due to compliance mismatches between airway and alveolar structures. This computational model provides a baseline for the exploration of multi-scale interactions of pathological conditions that can further our understanding of disease processes and guide the development of protective ventilation strategies for the treatment of acute respiratory distress syndrome (ARDS).
Fire and grass-bedding construction 200 thousand years ago at Border Cave, South Africa

Bedding of grass and ashes The Border Cave site in the KwaZulu-Natal region of South Africa has been a rich source of archaeological knowledge about Stone Age humans because of its well-preserved stratigraphic record. Wadley et al. now report the discovery of grass bedding in Border Cave, dated to approximately 200,000 years ago. The bedding, identified with a range of microscopic and spectroscopic techniques, was mingled with layers of ash. It also incorporated debris from lithics, burned bone, and rounded ochre grains, all of which were of clear anthropogenic origin. The authors speculate that the ash may have been deliberately used in bedding to inhibit the movement of ticks and other arthropod irritants. These discoveries extend the record of deliberate construction of plant bedding by at least 100,000 years. Science, this issue p. 863 Stone Age humans in southern Africa used a mixture of grasses and ash for bedding. Early plant use is seldom described in the archaeological record because of poor preservation. We report the discovery of grass bedding used to create comfortable areas for sleeping and working by people who lived in Border Cave at least 200,000 years ago. Sheaves of grass belonging to the broad-leafed Panicoideae subfamily were placed near the back of the cave on ash layers that were often remnants of bedding burned for site maintenance. This strategy is one forerunner of more-complex behavior that is archaeologically discernible from ~100,000 years ago.
Adaptive Latency Reduction in LoRa for Mission Critical Communications in Mines

Reliable communication is essential to alleviate incidents and escalate rescue operations. However, wireless communication is very challenging in underground mine due to irregular confined shapes and rough environments. A recent wireless standard LoRa (Long Range) is promising in mine environments because of its ultralow power consumption, long range, and deep penetration capabilities. In underground mine, sensors are deployed for continuous monitoring the working environments as well as tracking objects and miners. Therefore, different types of traffic are generated with different QoS requirements. LoRaWAN, the standardized medium access control (MAC) protocol is based on pure ALOHA that can not meet the requirements of mission-critical communications. The mission-critical applications require very low latency and high reliability. In this paper, we evaluate the performance of LoRa and LoRaWAN technologies in an underground mine in the presence of different kinds of traffic; and subsequently we propose redundant retransmission aided adaptive latency reduction protocol for low latency communication. In this protocol the ACK-TIMEOUT is adjusted based on the air time of the previous uplink transmission and the contention stage. Simulation results demonstrate that the proposed protocol significantly improves the performance of the system and outperforms LoRaWAN in terms of data extraction ratio (DER) and average transmission delay.
Using UAV-Based SOPC Derived LAI and SAFY Model for Biomass and Yield Estimation of Winter Wheat

Knowledge of sub-field yield potential is critical for guiding precision farming. The recently developed simulated observation of point cloud (SOPC) method can generate high spatial resolution winter wheat effective leaf area index (SOPC-LAIe) maps from the unmanned aerial vehicle (UAV)-based point cloud data without ground-based measurements. In this study, the SOPC-LAIe maps, for the first time, were applied to the simple algorithm for yield estimation (SAFY) to generate the sub-field biomass and yield maps. First, the dry aboveground biomass (DAM) measurements were used to determine the crop cultivar-specific parameters and simulated green leaf area index (LAI) in the SAFY model. Then, the SOPC-LAIe maps were converted to green LAI using a normalization approach. Finally, the multiple SOPC-LAIe maps were applied to the SAFY model to generate the final DAM and yield maps. The root mean square error (RMSE) between the estimated and measured yield is 88 g/m2, and the relative root mean squire error (RRMSE) is 15.2%. The pixel-based DAM and yield map generated in this study revealed clearly the within-field yield variation. This framework using the UAV-based SOPC-LAIe maps and SAFY model could be a simple and low-cost alternative for final yield estimation at the sub-field scale.
The indirect shared steering control under double loop structure of driver and automation

Due to the critical defects of techniques in fully autonomous vehicles, man-machine cooperative driving is still of great significance in todayʼ s transportation system. Unlike the previous shared control structure, this paper introduces a double loop structure which is applied to indirect shared steering control between driver and automation. In contrast to the tandem indirect shared control, the parallel indirect shared control put the authority allocation system of steering angle into the framework to allocate the corresponding weighting coefficients reasonably and output the final desired steering angle according to the current deviation of vehicle and the accuracy of steering angles. Besides, the active disturbance rejection controller ( ADRC ) is also added in the frame in order to track the desired steering angle fleetly and accurately as well as restrain the internal and external disturbances effectively which including the steering friction torque, wind speed and ground interference etc. Eventually, we validated the advantages of double loop framework through three sets of double lane change and slalom experiments, respectively. Exactly as we expected, the simulation results show that the double loop structure can effectively reduce the lateral displacement error caused by the driver or the controller, significantly improve the tracking precision and keep great performance in trajectory tracking characteristics when driving errors occur in one of driver and controller.
Evaluation of Sentinel-2 and Landsat 8 Images for Estimating Chlorophyll-a Concentrations in Lake Chad, Africa

Much effort has been applied in estimating the concentrations of chlorophyll-a (Chl a) in lakes. The optical complexity and lack of in situ data complicate estimating Chl a in such water bodies. We compared four established satellite reflectance algorithms—the two-band and three-band algorithms (2BDA, 3BDA), fluorescence line height (FLH), and normalized difference chlorophyll index (NDCI)—to estimate Chl a concentration in Lake Chad. We evaluated the performance and applicability of Landsat-8 (L8) and Sentinel-2 (S2) images with the four Chl a estimation algorithms. For accuracy, we compared the concentration levels from the four algorithms to those from Worldview-3 (WV3) images. We identified two promising algorithms that could be used alongside L8 and S2 satellite images to monitor Chl a concentrations in Lake Chad. With an averaged R2 of 0.8, the 3BDA and NDCI Chl a algorithms performed accurately with S2 and L8 images. For the S2 and L8 images, 3BDA had the highest performance when compared to the WV3 estimates. We demonstrate the usefulness of sensor images in improving water quality information for areas that are difficult to access or when conventional data are limited.
Fast skyline community search in multi-valued networks

Community search has been extensively studied in large networks, such as Protein-Protein Interaction (PPI) networks, citation graphs, and collaboration networks. However, in terms of widely existing multi-valued networks, where each node has d (d ≥ 1) numerical attributes, almost all existing algorithms either completely ignore the attributes of node at all or only consider one attribute. To solve this problem, the concept of skyline community was presented, based on the concepts of k-core and skyline recently. The skyline community is defined as a maximal k-core that satisfies some influence constraints, which is very useful in depicting the communities that are not dominated by other communities in multi-valued networks. However, the algorithms proposed on skyline community search can only work in the special case that the nodes have different values on each attribute, and the computation complexity degrades exponentially as the number of attributes increases. In this work, we turn our attention to the general scenario where multiple nodes may have the same attribute value. Specifically, we first present an algorithm, called MICS, which can find all skyline communities in a multi-valued network. To improve computation efficiency, we then propose a dimension reduction based algorithm, called P-MICS, using the maximum entropy method. Our algorithm can significantly reduce the skyline community searching time, while is still able to find almost all cohesive skyline communities. Extensive experiments on real-world datasets demonstrate the efficiency and effectiveness of our algorithms.
YOLO-Fine: One-Stage Detector of Small Objects Under Various Backgrounds in Remote Sensing Images

Object detection from aerial and satellite remote sensing images has been an active research topic over the past decade. Thanks to the increase in computational resources and data availability, deep learning-based object detection methods have achieved numerous successes in computer vision, and more recently in remote sensing. However, the ability of current detectors to deal with (very) small objects still remains limited. In particular, the fast detection of small objects from a large observed scene is still an open question. In this work, we address this challenge and introduce an enhanced one-stage deep learning-based detection model, called You Only Look Once (YOLO)-fine, which is based on the structure of YOLOv3. Our detector is designed to be capable of detecting small objects with high accuracy and high speed, allowing further real-time applications within operational contexts. We also investigate its robustness to the appearance of new backgrounds in the validation set, thus tackling the issue of domain adaptation that is critical in remote sensing. Experimental studies that were conducted on both aerial and satellite benchmark datasets show some significant improvement of YOLO-fine as compared to other state-of-the art object detectors.