Miracle-dz/UniFAR-FTUs huggingface dataset:
Native and Bioengineered Exosomes for Ischemic Stroke Therapy

Exosomes are natural cells-derived vesicles, which are at the forefront toward clinical success for various diseases, including cerebral ischemia. Exosomes mediate cell-to-cell communication in different brain cells during both physiological and pathological conditions. Exosomes are an extensively studied type of extracellular vesicle, which are considered to be the best alternative for stem cell–based therapy. They can be secreted by various cell types and have unique biological properties. Even though native exosomes have potential for ischemic stroke therapy, some undesirable features prevent their success in clinical applications, including a short half-life, poor targeting property, low concentration at the target site, rapid clearance from the lesion region, and inefficient payload. In this review, we highlight exosome trafficking and cellular uptake and survey the latest discoveries in the context of exosome research as the best fit for brain targeting owing to its natural brain-homing abilities. Furthermore, we overview the methods by which researchers have bioengineered exosomes (BioEng-Exo) for stroke therapy. Finally, we summarize studies in which exosomes were bioengineered by a third party for stroke recovery. This review provides up-to-date knowledge about the versatile nature of exosomes with a special focus on BioEng-Exo for ischemic stroke. Standard exosome bioengineering techniques are mandatory for the future and will lead exosomes toward clinical success for stroke therapy.
Increase in Beta Power Reflects Attentional Top-Down Modulation After Psychosocial Stress Induction

Selective attention depends on goal-directed and stimulus-driven modulatory factors, each relayed by different brain rhythms. Under certain circumstances, stress-related states can change the balance between goal-directed and stimulus-driven factors. However, the neuronal mechanisms underlying these changes remain unclear. In this study, we explored how psychosocial stress can modulate brain rhythms during an attentional task and a task-free period. We recorded the EEG and ECG activity of 42 healthy participants subjected to either the Trier Social Stress Test (TSST), a controlled procedure to induce stress, or a comparable control protocol (same physical and cognitive effort but without the stress component), flanked by an attentional task, a 90 s of task-free period and a state of anxiety questionnaire. We observed that psychosocial stress induced an increase in heart rate (HR), self-reported anxiety, and alpha power synchronization. Also, psychosocial stress evoked a relative beta power increase during correct trials of the attentional task, which correlates positively with anxiety and heart rate increase, and inversely with attentional accuracy. These results suggest that psychosocial stress affects performance by redirecting attentional resources toward internal threat-related thoughts. An increment of endogenous top-down modulation reflected an increased beta-band activity that may serve as a compensatory mechanism to redirect attentional resources toward the ongoing task. The data obtained here may contribute to designing new ways of clinical management of the human stress response in the future and could help to minimize the damaging effects of persistent stressful experiences.
Dancing With Non-duality for Healing Through the Shadows of the COVID-19 Pandemic

The COVID-19 pandemic has unleashed torrents of global suffering at a devastating scale, necessitating a strong response to alleviating suffering. This paper begins with noting that the conventional approach to suffering in North America is to be positive and not to be negative. The paper summarily explores the philosophy of positive psychology underlying the first- and the second-wave of positive psychology, commenting on the evolution from dualism and a binary conceptualization in the first wave (PP 1.0) to a non-dualism of integrating binaries in the second wave (PP 2.0). PP 2.0’s enhanced therapeutic efficacy is noted for its non-dual framework. The paper then explores and suggests a different conceptualization possibility of non-duality, fundamental non-duality, that is related to but distinct from the one in PP 2.0. A case is made that fundamental non-duality has a radical possibility of therapeutic efficacy. Being consistent with the philosophy of non-duality, further suggestions are made that non-duality of PP 2.0 and fundamental non-duality can be therapeutically deployed together for greatest efficacy. The exploration contained in the paper is largely philosophical, arts-based, and autobiographical, creating an enacted and lived experience of applying theory to practice.
Linking Self-Control to Voluntary Behaviors at Workplace: The Mediating Role of Job Satisfaction

Voluntary work behavior (VWB) refers to spontaneous workplace behaviors that extend beyond role norms, including extra-role behaviors that benefit the organization (i. e., organizational citizenship behavior, OCB) and negative behaviors that may harm the organization (i.e., counterproductive work behavior, CWB). This study examined the relationship between self-control and VWB and the mediating role of job satisfaction. A total of 1,101 full-time employees from China completed a battery of self-report measures online. The results show that self-control positively predicts employees' OCB and negatively predicts employees' CWB. Moreover, job satisfaction significantly mediates the relationship between self-control and OCB/CWB. The results confirm that employees with high self-control are more public-spirited, which previous studies have described as being “highly committed” (high OCB) or “less harmful” (low CWB). This finding closely relates to the observation that employees with high self-control tend to have more satisfying work outcomes or higher workplace status than those with low self-control.
Intrinsic Charge Trapping in Organic and Polymeric Semiconductors: A Physical Chemistry Perspective

We aim to understand the origins of intrinsic charge carrier traps in organic and polymeric semiconductor materials from a physical chemistry perspective. In crystalline organic semiconductors, we point out some of the inadequacies in the description of intrinsic charge traps using language and concepts developed for inorganic semiconductors. In π-conjugated polymeric semiconductors, we suggest the presence of a two-tier electronic energy landscape, a bimodal majority landscape due to two dominant structural motifs and a minority electronic energy landscape from intrinsic charged defects. The bimodal majority electronic energy landscape results from a combination of amorphous domains and microcrystalline or liquid-crystalline domains. The minority tier of the electronic density of states is comprised of deep Coulomb traps embedded in the majority electronic energy landscape. This minority electronic energy landscape may dominate transport properties at low charge carrier densities, such as those expected .
The Performance of the Diurnal Cycle of Precipitation from Blended Satellite Techniques over Brazil

The knowledge of the diurnal cycle of precipitation is of extreme relevance to understanding the physical/dynamic processes associated with the spatial and temporal distribution of precipitation. The main difficulty of this task is the lack of surface precipitation information over certain regions on an hourly time scale and the low spatial representativeness of these data (normally surface gauges). In order to overcome these difficulties, the main objective of this study is to create a 3-h precipitation accumulation database from the gauge-adjusted daily regional precipitation products to resolve the diurnal cycle properly. This study also proposes to evaluate different methodologies for partitioning gauge-adjusted daily precipitation products, i.e., a product made by the combination of satellite estimates and surface gauge observations, into 3-h precipitation accumulation. Two methodologies based on the calculation of a conversion factor F between a daily gauge-adjusted product, combined scheme (CoSch, hereafter), and a non-gauge-adjusted one, the integrated multi-satellite retrievals for GPM (IMERG)-Early (IMERG, hereafter) were tested for this research. Hourly rain gauge stations for the period of 2015–2018 over Brazil were used to assess the performance of the proposed methodologies over the whole region and five sub-regions with homogeneous precipitation regimes. Standard statistical metrics and categorical indices related with the capability to detect rainfall events were used to compare the ability of each product to represent the diurnal cycle. The results show that the new 3-h CoSch products show better agreement with rainfall gauge stations when compared with IMERG, better capturing the diurnal cycle of precipitation. The biggest improvement was over northeastern region close to the coast, where IMERG was not able to capture the diurnal cycle properly. One of the proposed methodologies (CoSchB) performed better on the critical success index and equitable threat score metrics, suggesting that this is the best product over the two. The downside, when compared with the other methodology (CoSchA), was a slight increase in the values of bias and mean absolute error, but still at acceptable levels.
Landsat 8 OLI Broadband Albedo Validation in Antarctica and Greenland

The albedo is a fundamental component of the processes that govern the energy budget, and particularly important in the context of climate change. However, a satellite-based high-resolution (30 m) albedo product which can be used in the polar regions up to 82.5° latitude during the summer seasons is lacking. To cover this gap, in this study we calculate satellite-based broadband albedo from Landsat 8 OLI and validate it against broadband albedo measurements from in situ stations located on the Antarctic and Greenland icesheets. The model to derive the albedo from raw satellite data includes an atmospheric and topographic correction and conversion from narrow-band to broadband albedo, and at each step different options were taken into account, in order to provide the best combination of corrections. Results, after being cleaned from anomalous data, show a good agreement with in situ albedo measurements, with a mean absolute error between in situ and satellite albedo of 0.021, a root mean square error of 0.026, a standard deviation of 0.015, a correlation coefficient of 0.995 (p < 0.01) and a bias estimate of −0.005. Considering the structure of the model, it could be applied to data from previous sensors of the Landsat family and help construct a record to analyze albedo variations in the polar regions.
Scene Complexity: A New Perspective on Understanding the Scene Semantics of Remote Sensing and Designing Image-Adaptive Convolutional Neural Networks

Scene understanding of remote sensing images is of great significance in various applications. Its fundamental problem is how to construct representative features. Various convolutional neural network architectures have been proposed for automatically learning features from images. However, is the current way of configuring the same architecture to learn all the data while ignoring the differences between images the right one? It seems to be contrary to our intuition: it is clear that some images are easier to recognize, and some are harder to recognize. This problem is the gap between the characteristics of the images and the learning features corresponding to specific network structures. Unfortunately, the literature so far lacks an analysis of the two. In this paper, we explore this problem from three aspects: we first build a visual-based evaluation pipeline of scene complexity to characterize the intrinsic differences between images; then, we analyze the relationship between semantic concepts and feature representations, i.e., the scalability and hierarchy of features which the essential elements in CNNs of different architectures, for remote sensing scenes of different complexity; thirdly, we introduce CAM, a visualization method that explains feature learning within neural networks, to analyze the relationship between scenes with different complexity and semantic feature representations. The experimental results show that a complex scene would need deeper and multi-scale features, whereas a simpler scene would need lower and single-scale features. Besides, the complex scene concept is more dependent on the joint semantic representation of multiple objects. Furthermore, we propose the framework of scene complexity prediction for an image and utilize it to design a depth and scale-adaptive model. It achieves higher performance but with fewer parameters than the original model, demonstrating the potential significance of scene complexity.
SSNCSE_NLP@Dravidian-CodeMix-FIRE2020: Sentiment Analysis for Dravidian Languages in Code-Mixed Text

Social media has become a place for expressing people’s emotions, love, and hatred. With the ubiquitous availability of the internet entertains individuals to spend more time and express their feelings good or bad openly on social media platforms. In this study, we compare and analyze the methods for comment-level text polarity classification task using the Dravidian-CodeMix-FIRE2020 data-set. We contrast machine learning models with features extracted from techniques such as TF, TFIDF, BERT, fastText, and LSTM. The TF and the BERT embedding gave the best results compared. Our models scored F1 scores of 0.61 and 0.71 for the Tamil-English and the Malayalam-English tasks respectively.
Capability of IMERG V6 Early, Late, and Final Precipitation Products for Monitoring Extreme Precipitation Events

The monitoring of extreme precipitation events is an important task in environmental research, but the ability of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) precipitation products to monitor extreme precipitation events remains poorly understood. In this study, three precipitation products for IMERG version 6, early-, late-, and final-run products (IMERG-E, IMERG-L, and IMERG-F, respectively), were used to capture extreme precipitation, and their applicability to monitor extreme precipitation events over Hubei province in China was evaluated. We found that the accuracy of the three IMERG precipitation products is inconsistent in areas of complex and less complex topography. Compared with gauge-based precipitation data, the results reveal the following: (1) All products can accurately capture the spatiotemporal variation patterns in precipitation during extreme precipitation events. (2) The ability of IMERG-F was good in areas of complex topography, followed by IMERG-E and IMERG-L. In areas of less complex topography, IMERG-E and IMERG-L produced outcomes that were consistent with those of IMERG-F. (3) The three IMERG precipitation products can capture the actual hourly precipitation tendencies of extreme precipitation events. (4) In areas of complex topography, the rainfall intensity estimation ability of IMERG-F is better than those of IMERG-E and IMERG-L.
Feasibility Analysis of GNSS-Reflectometry for Monitoring Coastal Hazards

Coastal hazards, such as a tsunamis and storm surges, are a critical threat to coastal communities that lead to significant loss of lives and properties. To mitigate their impact, event-driven water level changes should be properly monitored. A tide gauge is one of the conventional water level measurement sensors. Still, alternative measurement systems can be needed to compensate for the role of tide gauge for contingency (e.g., broken and absence, etc.). Global Navigation Satellite System (GNSS) is an emerging water level measurement sensor that processes multipath signals reflected by the water surface that is referred to as GNSS-Reflectometry (GNSS-R). In this study, we adopted the GNSS-R technique to monitor tsunamis and storm surges by analyzing event-driven water level changes. To detect the extreme change of water level, enhanced GNSS-R data processing methods were applied which included the utilization of multi-band GNSS signals, determination of optimal processing window, and Kalman filtering for height rate determination. The impact of coastal hazards on water level retrievals was assessed by computing the confidence level of retrieval (CLR) that was computed based on probability of dominant peak representing the roughness of the water surface. The proposed approach was validated by two tsunami events, induced by 2012 Haida Gwaii earthquake and 2015 Chile earthquake, and two storm surge events, induced by 2017 Hurricane Harvey and occurred in Alaska in 2019. The proposed method successfully retrieved the water levels during the storm surge in both cases with the high correlation coefficients with the nearby tide gauge, 0.944, 0.933, 0.987, and 0.957, respectively. In addition, CLRs of four events are distinctive to the type of coastal events. It is confirmed that the tsunami causes the CLR deduction, while for the storm surges, GNSS-R keep high CLR during the event. These results are possibly used as an indicator of each event in terms of storm surge level and tsunami arrival time. This study shows that the proposed approach of GNSS-R based water level retrieval is feasible to monitor coastal hazards that are tsunamis and storm surges, and it can be a promising tool for investigating the coastal hazards to mitigate their impact and for a better decision making.
TNNG: Total Nuclear Norms of Gradients for Hyperspectral Image Prior

We introduce a novel regularization function for hyperspectral image (HSI), which is based on the nuclear norms of gradient images. Unlike conventional low-rank priors, we achieve a gradient-based low-rank approximation by minimizing the sum of nuclear norms associated with rotated planes in the gradient of a HSI. Our method explicitly and simultaneously exploits the correlation in the spectral domain as well as the spatial domain. Our method exploits the low-rankness of a global region to enhance the dimensionality reduction by the prior. Since our method considers the low-rankness in the gradient domain, it more sensitively detects anomalous variations. Our method achieves high-fidelity image recovery using a single regularization function without the explicit use of any sparsity-inducing priors such as ℓ0, ℓ1 and total variation (TV) norms. We also apply this regularization to a gradient-based robust principal component analysis and show its superiority in HSI decomposition. To demonstrate, the proposed regularization is validated on a variety of HSI reconstruction/decomposition problems with performance comparisons to state-of-the-art methods its superior performance.
Spatiotemporal Fusion of Formosat-2 and Landsat-8 Satellite Images: A Comparison of "Super Resolution-Then-Blend" and "Blend-Then-Super Resolution" Approaches

The spatiotemporal fusion technique has the advantages of generating time-series images with high-spatial and high-temporal resolution from coarse-resolution to fine-resolution images. A hybrid fusion method that integrates image blending (i.e., spatial and temporal adaptive reflectance fusion model, STARFM) and super-resolution (i.e., very deep super resolution, VDSR) techniques for the spatiotemporal fusion of 8 m Formosat-2 and 30 m Landsat-8 satellite images is proposed. Two different fusion approaches, namely Blend-then-Super-Resolution and Super-Resolution (SR)-then-Blend, were developed to improve the results of spatiotemporal fusion. The SR-then-Blend approach performs SR before image blending. The SR refines the image resampling stage on generating the same pixel-size of coarse- and fine-resolution images. The Blend-then-SR approach is aimed at refining the spatial details after image blending. Several quality indices were used to analyze the quality of the different fusion approaches. Experimental results showed that the performance of the hybrid method is slightly better than the traditional approach. Images obtained using SR-then-Blend are more similar to the real observed images compared with images acquired using Blend-then-SR. The overall mean bias of SR-then-Blend was 4% lower than Blend-then-SR, and nearly 3% improvement for overall standard deviation in SR-B. The VDSR technique reduces the systematic deviation in spectral band between Formosat-2 and Landsat-8 satellite images. The integration of STARFM and the VDSR model is useful for improving the quality of spatiotemporal fusion.
Evaluation of IMERG Level-3 Products in Depicting the July to October Rainfall over Taiwan: Typhoon Versus Non-Typhoon

This study assesses the performance of satellite precipitation products (SPPs) from the latest version, V06B, Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) Level-3 (including early, late, and final runs), in depicting the characteristics of typhoon season (July to October) rainfall over Taiwan within the period of 2000–2018. The early and late runs are near-real-time SPPs, while final run is post-real-time SPP adjusted by monthly rain gauge data. The latency of early, late, and final runs is approximately 4 h, 14 h, and 3.5 months, respectively, after the observation. Analyses focus on the seasonal mean, daily variation, and interannual variation of typhoon-related (TC) and non-typhoon-related (non-TC) rainfall. Using local rain-gauge observations as a reference for evaluation, our results show that all IMERG products capture the spatio-temporal variations of TC rainfall better than those of non-TC rainfall. Among SPPs, the final run performs better than the late run, which is slightly better than the early run for most of the features assessed for both TC and non-TC rainfall. Despite these differences, all IMERG products outperform the frequently used Tropical Rainfall Measuring Mission 3B42 v7 (TRMM7) for the illustration of the spatio-temporal characteristics of TC rainfall in Taiwan. In contrast, for the non-TC rainfall, the final run performs notably better relative to TRMM7, while the early and late runs showed only slight improvement. These findings highlight the advantages and disadvantages of using IMERG products for studying or monitoring typhoon season rainfall in Taiwan.
ICENETv2: A Fine-Grained River Ice Semantic Segmentation Network Based on UAV Images

Accurate ice segmentation is one of the most crucial techniques for intelligent ice monitoring. Compared with ice segmentation, it can provide more information for ice situation analysis, change trend prediction, and so on. Therefore, the study of ice segmentation has important practical significance. In this study, we focused on fine-grained river ice segmentation using unmanned aerial vehicle (UAV) images. This has the following difficulties: (1) The scale of river ice varies greatly in different images and even in the same image; (2) the same kind of river ice differs greatly in color, shape, texture, size, and so on; and (3) the appearances of different kinds of river ice sometimes appear similar due to the complex formation and change procedure. Therefore, to perform this study, the NWPU_YRCC2 dataset was built, in which all UAV images were collected in the Ningxia–Inner Mongolia reach of the Yellow River. Then, a novel semantic segmentation method based on deep convolution neural network, named ICENETv2, is proposed. To achieve multiscale accurate prediction, we design a multilevel features fusion framework, in which multi-scale high-level semantic features and lower-level finer features are effectively fused. Additionally, a dual attention module is adopted to highlight distinguishable characteristics, and a learnable up-sampling strategy is further used to improve the segmentation accuracy of the details. Experiments show that ICENETv2 achieves the state-of-the-art on the NWPU_YRCC2 dataset. Finally, our ICENETv2 is also applied to solve a realistic problem, calculating drift ice cover density, which is one of the most important factors to predict the freeze-up data of the river. The results demonstrate that the performance of ICENETv2 meets the actual application demand.