Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of the NMDAR co-agonist D-serine on the structure and function of the optic tectum

The N-methyl-D-aspartate type glutamate receptor (NMDAR) is a molecular coincidence detector which converts correlated patterns of neuronal activity into cues for the structural and functional refinement of developing circuits in the brain. D-serine is an endogenous co-agonist of the NMDAR. In this study, we investigated the effects of potent enhancement of NMDAR-mediated currents by chronic administration of saturating levels of D-serine on the developing Xenopus retinotectal circuit. Chronic exposure to the NMDAR co-agonist D-serine resulted in structural and functional changes to the optic tectum. D-serine administration affected synaptogenesis and dendritic morphology in recently differentiated tectal neurons, resulting in increased arbor compaction, reduced branch dynamics, and higher synapse density. These effects were not observed in more mature neurons. Calcium imaging to examine retinotopic map organization revealed that tectal neurons of animals raised in D-serine had sharper visual receptive fields. These findings suggest that the availability of endogenous NMDAR co-agonists like D-serine at glutamatergic synapses may regulate the refinement of circuits in the developing brain. Significance statement N-methyl-D-aspartate receptors (NMDAR) are implicated in activity-dependent circuit plasticity. We used administration of the NMDAR co-agonist D-serine to further examine the role of the NMDAR in circuit development in vivo. D-serine stabilised dendritic arbors specifically of recently differentiated neurons, promoted synaptogenesis, and led to sharper retinotopic receptive fields in the optic tectum. Together, these results support the idea that signaling in response to synaptic current through NMDARs promotes the maturation of developing brain circuits.
Cerebrovascular super-resolution 4D Flow MRI – using deep learning to non-invasively quantify velocity, flow, and relative pressure

The development of cerebrovascular disease is tightly coupled to changes in cerebrovascular hemodynamics, with altered flow and relative pressure indicative of the onset, development, and acute manifestation of pathology. Image-based monitoring of cerebrovascular hemodynamics is, however, complicated by the narrow and tortuous vasculature, where accurate output directly depends on sufficient spatial resolution. To address this, we present a method combining dedicated deep learning and state-of-the-art 4D Flow MRI to generate super-resolution full-field images with coupled quantification of relative pressure using a physics-driven image processing approach. The method is trained and validated in a patient-specific in-silico cohort, showing good accuracy in estimating velocity (relative error: 12.0 ± 0.1%, mean absolute error (MAE): 0.07 ± 0.06 m/s at peak velocity), flow (relative error: 6.6 ± 4.7%, root mean square error (RMSE): 0.5 ± 0.1 mL/s at peak flow), and with maintained recovery of relative pressure through the circle of Willis (relative error: 11.0 ± 7.3%, RMSE: 0.3 ± 0.2 mmHg). Furthermore, the method is applied to an in-vivo volunteer cohort, effectively generating data at <0.5mm resolution and showing potential in reducing low-resolution bias in relative pressure estimation. Our approach presents a promising method to non-invasively quantify cerebrovascular hemodynamics, applicable to dedicated clinical cohorts in the future.
Kif9 is an active kinesin motor required for ciliary beating and proximodistal patterning of motile axonemes

Most motile cilia have a stereotyped structure of nine microtubule outer doublets and a single central pair of microtubules. The central pair microtubules are surrounded by a set of proteins, termed the central pair apparatus. A specific kinesin, Klp1 projects from the central pair and contributes to ciliary motility in Chlamydomonas. The vertebrate orthologue, Kif9 is required for beating in mouse sperm flagella, but the mechanism of Kif9/Klp1 function remains poorly defined. Here, using Xenopus epidermal multiciliated cells, we show that Kif9 is necessary for ciliary motility as well as leads to defects in the distal localization of not only central pair proteins, but also radial spokes and dynein arms. In addition, single-molecule assays in vitro revealed that Xenopus Kif9 is a processive motor, though like axonemal dyneins it displays no processivity in ciliary axonemes in vivo. Thus, our data suggest that Kif9 plays both indirect and direct role in ciliary motility.
Comparison of Machine-Learning and CASA Models for Predicting Apple Fruit Yields from Time-Series Planet Imageries

Apple (Malus domestica Borkh. cv. “Fuji”), an important cash crop, is widely consumed around the world. Accurately predicting preharvest apple fruit yields is critical for planting policy making and agricultural management. This study attempted to explore an effective approach for predicting apple fruit yields based on time-series remote sensing data. In this study, time-series vegetation indices (VIs) were derived from Planet images and analyzed to further construct an accumulated VI (∑VIs)-based random forest (RF∑VI) model and a Carnegie–Ames–Stanford approach (CASA) model for predicting apple fruit yields. The results showed that (1) ∑NDVI was the optimal predictor to construct an RF model for apple fruit yield, and the R2, RMSE, and RPD values of the RF∑NDVI model reached 0.71, 16.40 kg/tree, and 1.83, respectively. (2) The maximum light use efficiency was determined to be 0.499 g C/MJ, and the CASASR model (R2 = 0.57, RMSE = 19.61 kg/tree, and RPD = 1.53) performed better than the CASANDVI model and the CASAAverage model (R2, RMSE, and RPD = 0.56, 24.47 kg/tree, 1.22 and 0.57, 20.82 kg/tree, 1.44, respectively). (3) This study compared the yield prediction accuracies obtained by the models using the same dataset, and the RF∑NDVI model (RPD = 1.83) showed a better performance in predicting apple fruit yields than the CASASR model (RPD = 1.53). The results obtained from this study indicated the potential of the RF∑NDVI model based on time-series Planet images to accurately predict apple fruit yields. The models could provide spatial and quantitative information of apple fruit yield, which would be valuable for agronomists to predict regional apple production to inform and develop national planting policies, agricultural management, and export strategies.
A novel thiocarbohydrazide derivative for preconcentration of copper(II), nickel(II), lead(II), and cadmium(II) in water samples for flame atomic absorption spectrophotometry

A preconcentration procedure has been developed by using a thiocarbohydrazide derivative (1,5-bis(phenylthiocarbamoyl)thiocarbohydrazide) as a novel sorbent. The ligand was synthesized and used for preconcentration of Cu(II), Ni(II), Pb(II), and Cd(II) ions from water samples by batch and column procedures to enrich metal concentrations appropriate for flame atomic absorption spectrophotometry (FAAS). The optimum pHs for effective solid-phase extractions were 5 and 7 for Cu(II), and Ni(II), Pb(II) and Cd(II) adsorptions, respectively. The method detection limits 0.73, 0.05, 1.14 and 0.04 ng mL–1; the method determination limits 2.4, 0.15, 3.76 and 0.13 ng mL–1 and the enrichment factors 30, 65, 26 and 50 were found for Cu(II), Ni(II), Pb(II) and Cd(II), respectively. The sorption and desorption yields and overall recoveries of metals were almost quantitative except Pb(II) of which sorption yield was around 72% with relative standard deviation value around 1.8%. Langmuir, Freundlich and Temkin models were examined for the metal adsorptions. The Langmuir isotherm model provided a better fit with high correlation coefficients. Regarding the Langmuir parameters, the maximal adsorption capacities on 1,5-bis(phenylthiocarbamoyl)thiocarbohydrazide were 18.25, 7.99, 13.26 and 12.96 mg g–1 for Cu(II), Ni(II), Pb(II) and Cd(II), respectively. The adsorption kinetics for the adsorptions of metals were studied by using linear forms of pseudo-first-order and pseudo-second-order kinetic models. Pseudo-second-order kinetic model fitted very well. The developed method has been successfully applied to the preconcentration of Cu(II), Ni(II), Pb(II) and Cd(II) in water samples (tap water, seawater and mineral water samples), and satisfactory and reproducible results were obtained in FAAS analysis.
Towards Monitoring Waterlogging with Remote Sensing for Sustainable Irrigated Agriculture

Waterlogging is an increasingly important issue in irrigated agriculture that has a detrimental impact on crop productivity. The above-ground effect of waterlogging on crops is hard to distinguish from water deficit stress with remote sensing, as responses such as stomatal closure and leaf wilting occur in both situations. Currently, waterlogging as a source of crop stress is not considered in remote sensing-based evaporation algorithms and this may therefore lead to erroneous interpretation for irrigation scheduling. Monitoring waterlogging can improve evaporation models to assist irrigation management. In addition, frequent spatial information on waterlogging will provide agriculturalists information on land trafficability, assist drainage design, and crop choice. This article provides a scientific perspective on the topic of waterlogging by consulting literature in the disciplines of agronomy, hydrology, and remote sensing. We find the solution to monitor waterlogging lies in a multi-sensor approach. Future scientific routes should focus on monitoring waterlogging by combining remote sensing and ancillary data. Here, drainage parameters deduced from high spatial resolution Digital Elevation Models (DEMs) can play a crucial role. The proposed approaches may provide a solution to monitor and prevent waterlogging in irrigated agriculture.
Understanding Spatio-Temporal Patterns of Land Use/Land Cover Change under Urbanization in Wuhan, China, 2000-2019

Exploring land use structure and dynamics is critical for urban planning and management. This study attempts to understand the Wuhan development mode since the beginning of the 21st century by profoundly investigating the spatio-temporal patterns of land use/land cover (LULC) change under urbanization in Wuhan, China, from 2000 to 2019, based on continuous time series mapping using Landsat observations with a support vector machine. The results indicated rapid urbanization, with large LULC changes triggered. The built-up area increased by 982.66 km2 (228%) at the expense of a reduction of 717.14 km2 (12%) for cropland, which threatens food security to some degree. In addition, the natural habitat shrank to some extent, with reductions of 182.52 km2, 23.92 km2 and 64.95 km2 for water, forest and grassland, respectively. Generally, Wuhan experienced a typical urbanization course that first sped up, then slowed down and then accelerated again, with an obvious internal imbalance between the 13 administrative districts. Hanyang, Hongshan and Dongxihu specifically presented more significant land dynamicity, with Hanyang being the active center. Over the past 19 years, Wuhan mainly developed toward the east and south, with the urban gravity center transferred from the northwest to the southeast of Jiang’an district. Lastly, based on the predicted land allocation of Wuhan in 2029 by the patch-generating land use simulation (PLUS) model, the future landscape dynamic pattern was further explored, and the result shows a rise in the northern suburbs, which provides meaningful guidance for urban planners and managers to promote urban sustainability.
Matrix SegNet: A Practical Deep Learning Framework for Landslide Mapping from Images of Different Areas with Different Spatial Resolutions

Practical landslide inventory maps covering large-scale areas are essential in emergency response and geohazard analysis. Recently proposed techniques in landslide detection generally focused on landslides in pure vegetation backgrounds and image radiometric correction. There are still challenges in regard to robust methods that automatically detect landslides from images with multiple platforms and without radiometric correction. It is a significant issue in practical application. In order to detect landslides from images over different large-scale areas with different spatial resolutions, this paper proposes a two-branch Matrix SegNet to semantically segment input images by change detection. The Matrix SegNet learns landslide features in multiple scales and aspect ratios. The pre- and post- event images are captured directly from Google Earth, without radiometric correction. To evaluate the proposed framework, we conducted landslide detection in four study areas with two different spatial resolutions. Moreover, two other widely used frameworks: U-Net and SegNet, were adapted to detect landslides via the same data by change detection. The experiments show that our model improves the performance largely in terms of recall, precision, F1-score, and IOU. It is a good starting point to develop a practical, deep learning landslide detection framework for large scale application, using images from different areas, with different spatial resolutions.
Big Data Analytics for Energy Consumption Prediction in Smart Grid Using Genetic Algorithm and Long Short Term Memory

Smart Grids (SG) have smart meters and advance metering infrastu-rutre (AMI) which generates huge data. This data can be used for predicting energy consumption using big data analytics. A very limited work has been carried out in the literature which shows the utilization of big data in energy consumption prediction. In this paper, the proposed method is based on Genetic Algorithm – Long Short Term Memory (GA-LSTM). LSTM memorises values over an arbitrary interval that manages time series data very effictively while GA is an evolutionary process that is used for optimization. GA combines with LSTM to process hyper-parameters such as hidden layers, epochs, data intervals, batchsize and activation functions. Hence, GA creates a new vector for optimum solution that provides minimum error. These methods provide the best performance when compared with existing benchmarks. Moreover, GA-LSTM is used in a multi-threaded environment which increases the speed of convergence. Here, the multi-core platform is operated for solving one-dimensional GA-based inverse scattering problems. The result shows that GA-LSTM provides better convergence as compared to random approach techniques. For validating the results, Pennsylvania-New Jersey-Maryland Interconnection (PJM) energy consumption data has been used while adopting different performance evaluation metrics.
A Workbench for Corpus Linguistic Discourse Analysis

In this paper, we introduce the Swiss-AL workbench , an online tool for corpus linguistic discourse analysis. The workbench enables the analysis of Swiss-AL, a multilingual Swiss web corpus with sources from media, politics, industry, science, and civil society. The workbench differs from other corpus analysis tools in three characteristics: (1) easy access and tidy interface, (2) focus on visualizations, and (3) wide range of analysis options, ranging from classic corpus linguistic analysis (e.g., collocation analysis) to more recent NLP approaches (topic modeling and word embeddings). It is designed for researchers of various disciplines, practitioners, and students.
The Role of Privacy within the Realm of Healthcare Wearables' Acceptance and Use

The flexibility and vitality of the Internet along with technological innovation have fueled an industry focused on the design of portable devices capable of supporting personal activities and wellbeing. These compute devices, known as wearables, are unique from other computers in that they are portable, specific in function, and worn or carried by the user. While there are definite benefits attributable to wearables, there are also notable risks, especially in the realm of security where personal information and/or activities are often accessible to third parties. In addition, protecting one’s private information is regularly an afterthought and thus lacking in maturity. These concerns are amplified in the realm of healthcare wearable devices. Users must weigh the benefits with the risks. This is known as the privacy calculus. Often, users will opt for the wearable device despite the heightened concern that their information may or will be disclosed. This is known as the privacy paradox. While past research focused on specific wearable technologies, such as activity trackers and smartphones, the paradox of disclosure despite concern for privacy has not been the primary focus, particularly in the realm of the manifestation of the paradox when it comes to the acceptance and use of healthcare wearable devices. Accordingly, the objective of the present research was to propose and evaluate a research model specifically oriented towards the role of privacy in the realm of healthcare-related wearables’ acceptance and use. The presented model is composed of sixteen constructs informed from multiple theories including multiple technology acceptance theories, the Protection Motivation Theory (PMT), the Health Belief Model (HBM), and multiple privacy calculus theories. Using a survey-oriented approach to collect data, relationships among privacy, health, and acceptance constructs were examined using SmartPLS with intentions to validate the posited hypotheses and determine the influence of the various independent variables on the intention to disclose and the intention to adopt healthcare-wearables. Of particular interest is the posited moderating effects of perceived health status on intention to disclose personal information. The research endeavor confirmed significant evidence of the cost/benefit decision process, aka the privacy calculus, that takes place when deciding whether or not to disclose personal information in the healthcare wearables space. Perceived privacy risk was negatively correlated to intention to disclose while hedonic motivation and performance expectancy were positively correlated to intention to disclose. Furthermore, significant evidence was discovered
A Method for Quantifying Understory Leaf Area Index in a Temperate Forest through Combining Small Footprint Full-Waveform and Point Cloud LiDAR Data

Understory vegetation plays an important role in the structure and function of forest ecosystems. Light detection and ranging (LiDAR) can provide understory information in the form of either point cloud or full-waveform data. Point cloud data have a remarkable ability to represent the three-dimensional structures of vegetation, while full-waveform data contain more detailed information on the interactions between laser pulses and vegetation; both types have been widely used to estimate various forest canopy structural parameters, including leaf area index (LAI). Here, we present a new method for quantifying understory LAI in a temperate forest by combining the advantages of both types of LiDAR data. To achieve this, we first estimated the vertical distribution of the gap probability using point cloud data to automatically determine the height boundary between overstory and understory vegetation at the plot level. We then deconvolved the full-waveform data to remove the blurring effect caused by the system pulse to restore the vertical resolution of the LiDAR system. Subsequently, we decomposed the deconvolved data and integrated the plot-level boundary height to differentiate the waveform components returned from the overstory, understory, and soil layers. Finally, we modified the basic LiDAR equations introducing understory leaf spectral information to quantify the understory LAI. Our results, which were validated against ground-based measurements, show that the new method produced a good estimation of the understory LAI with an R2 of 0.54 and a root-mean-square error (RMSE) of 0.21. Our study demonstrates that the understory LAI can be successfully quantified through the combined use of point cloud and full-waveform LiDAR data.
SAR Imaging Distortions Induced by Topography: A Compact Analytical Formulation for Radiometric Calibration

Modeling of synthetic aperture radar (SAR) imaging distortions induced by topography is addressed and a novel radiometric calibration method is proposed in this paper. An analytical formulation of the problem is primarily provided in purely geometrical terms, by adopting the theoretical notions of the differential geometry of surfaces. The novel and conceptually simple formulation relies on a cylindrical coordinate system, whose longitudinal axis corresponds to the sensor flight direction. A 3D representation of the terrain shape is then incorporated into the SAR imaging model by resorting to a suitable parametrization of the observed ground surface. Within this analytical framework, the area-stretching function quantitatively expresses in geometrical terms the inherent local radiometric distortions. This paper establishes its analytical expression in terms of the magnitude of the gradient of the look-angle function uniquely defined in the image domain, thus resulting in being mathematically concise and amenable to a straightforward implementation. The practical relevance of the formulation is also illustrated from a computational perspective, by elucidating its effective discrete implementation. In particular, an inverse cylindrical mapping approach is adopted, thus avoiding the drawback of pixel area fragmentation and integration required in forward-mapping-based approaches. The effectiveness of the proposed SAR radiometric calibration method is experimentally demonstrated by using COSMO-SkyMed SAR data acquired over a mountainous area in Italy.
Effect Analysis of the Digital Spectrometer FFT Algorithm on THz Atmospheric Limb Sounder (TALIS) System Sensitivity

THz Atmospheric Limb Sounder (TALIS) is a microwave radiometer designed by the National Space Science Center of the Chinese Academy of Sciences (NSSC, CAS) for measuring the vertical distribution of temperature and chemical constituents in the middle and upper atmosphere. The digital spectrometer is an important part of TALIS’ back end, which mainly realizes the function of spectral analysis. The radix 16 real-time complex fast Fourier transform (FFT) algorithm used in the digital spectrometer was obtained by improving and combining the parallel processing and complex processing of the FFT algorithm. In this study, the digital spectrometer parameter selection is systematically presented, and the effect of the digital spectrometer FFT algorithm on the TALIS system sensitivity is analyzed based on the TALIS system simulation model we established in the previous stage of this research. The results show that the actual full-band sensitivity obtained after using the FFT algorithm is consistent with the ideal full-band sensitivity of TALIS for different integration time, spectral resolutions, and quantization bits. However, the results of the comparison of the actual sub-band sensitivity after using the FFT algorithm with the ideal sub-band sensitivity show that the deterioration of the sub-band sensitivity will be caused by the FFT algorithm. The mean value of the deterioration ratio was found to be approximately 18%, and the maximum value of the deterioration ratio was approximately 33%.