Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison on Three Algorithms of Reconstructing Time-series MODIS EVI

With the rapid development of remote sensing techniques, higher precisions of the vegetation remote sensing are required. Therefore, before using the time-series data, how to select the optimal algorithms to reconstruct it has been a hot research topic. Based on the five main land cover types in Northeast China, the reconstruction quality of three commonly used algorithms that included in TIMESAT tools has been qualitatively analyzed.Then, the fidelity performance and the capability to keep main characteristics of the three algorithms on EVI with respect to different land cover types were compared. The result shows that the S- G algorithm has a better performance in reconstructing the peak and the width of the EVI curves in the growing seasons, but it is prone to keep the noise data due to excessive fittings, especially common in land cover types of steppe and shrub. AG and DL algorithms generally present similar performances and the results are much closer to the true values for land cover types of steppe, shrub and arable land. But AG algorithm is easily influenced by noises for fitting the peak of the cures, which reduces the maximum EVI and causes the decline of vegetation growth. Spatial patterns of the fidelity performance and the capability to keep main characteristics of the three algorithms are all related to the distribution of vegetation types. Finally, we found that AG is a better algorithm to be used for the land cover types of steppe and shrub, DL is better for arable land, while S-G is better for the broadleaved deciduous forest and coniferous deciduous forest.
Unveiling Mesenchymal Stem Cells’ Regenerative Potential in Clinical Applications: Insights in miRNA and lncRNA Implications

It is now widely recognized that mesenchymal stem cells (MSCs) possess the capacity to differentiate into a wide array of cell types. Numerous studies have identified the role of lncRNA in the regulation of MSC differentiation. It is important to elucidate the role and interplay of microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) in the regulation of signalling pathways that govern MSC function. Furthermore, miRNAs and lncRNAs are important clinical for innovative strategies aimed at addressing a wide spectrum of existing and emerging disease. Hence it is important to consider their impact on MSC function and differentiation. Examining the data available in public databases, we have collected the literature containing the latest discoveries pertaining to human stem cells and their potential in both fundamental research and clinical applications. Furthermore, we have compiled completed clinical studies that revolve around the application of MSCs, shedding light on the opportunities presented by harnessing the regulatory potential of miRNAs and lncRNAs. This exploration of the therapeutic possibilities offered by miRNAs and lncRNAs within MSCs unveils exciting prospects for the development of precision therapies and personalized treatment approaches. Ultimately, these advancements promise to augment the efficacy of regenerative strategies and produce positive outcomes for patients. As research in this field continues to evolve, it is imperative to explore and exploit the vast potential of miRNAs and lncRNAs as therapeutic agents. The findings provide a solid basis for ongoing investigations, fuelling the quest to fully unlock the regenerative potential of MSCs.
Retinoic acid metabolism-related enzyme signature identi�ed prognostic and immunotherapeutic e�ciency in sarcoma

Background: Dynamic balance of retinoic acid metabolism plays a major role in a variety of biological functions including cell proliferation and differentiation, while its dysregulation often leads to cancer progression and disordered immunity. Targeting retinoic acid signaling has shown effectivity in re-educates tumor microenvironment so that they could enhance the e�cacy of immunotherapies and received better outcome. However, a comprehensive analysis of retinoic acid metabolism abnormality in sarcoma is still lacking, which limits the development and application of related targeted drugs. Methods: The RA metabolism related enzymes data set was collected from several database. Then we systematically analyzed the molecular features of 19 retinoic acid metabolism-related enzymes based on TCGA/TARGET/GSE datasets and revealed two subtypes with distinct metabolic status and prognostic value. And we further generated a 7 genes signature to predict retinoic acid metabolism index based on LASSO-penalized Cox regression model. Results: Gene set enrichment analysis indicated a set of immune and oncogenic pathways were enriched in poor-prognosis group. Connectivity Map screened 56 potential small molecules speci�c to different sub-groups. Survival analysis demonstrated signi�cant prognostic difference between high-and low-risk groups among all datasets. Several immune cells including CD8+ T cells, Treg cells, Monocytes, and Macrophages showed different abundance between these groups, and immune checkpoint blockade therapy response prediction indicated potential immunotherapeutic e�ciency of poor-prognosis group. Conclusions: Taken together, our study elaborated two different retinoic acid metabolism status of sarcoma, which revealed the metabolic heterogeneity of sarcoma. Robust and powerful metabolic index risk model could provide insightful suggestions to explore the molecular functions and mechanisms of retinoic acid metabolism.
Transcriptome Analysis of Diploid and Autotetraploid Hemerocallis Response to Drought Stress

Chromosome doubling in ornamental plants, as shown by our study in daylilies (Hemerocallis spp.), has great potential to increase tolerance to abiotic stress. Drought is the most critical growth-limiting factor in a changing climate. Drought tolerance is one of the decisive factors for the survival, productivity, and appearance of perennial ornamental plants. Understanding and elucidating the molecular mechanisms that determine plant response to abiotic stress is essential. De novo transcriptome assembly of diploid and autotetraploid Hemerocallis spp. cv. Trahlyta was performed under artificially induced stress to elucidate the molecular mechanisms related to plant response to drought. In daylily mRNA, 237,886 transcripts were detected, and 42.4% of them were identified as annotated unigenes. In the experiment, diploid plants were more stressed, with 2871 upregulated or downregulated DEGs (differentially expressed genes) responding to drought, while tetraploid plants had 1599 DEGs. The proportion of upregulated DEGs differed by 1.3 times between diploid and autotetraploid genotypes, whereas the proportion of downregulated DEGs was 1.8 times greater in diploid plants. Signaling pathways related to the drought response were activated in daylilies, and key candidate genes were identified in both ploidy genotypes. In autotetraploid plants, more drought-related pathways were activated than in diploids—43 and 19, respectively. The most abundant DEGs in both cases were KEGG (Kyoto Encyclopedia of Genes and Genomes), metabolic (ko01100), and biosynthesis of secondary metabolites (ko01110) pathways. Summarizing the data, it was found that autotetraploid plants of the daylily have a wider potential for adaptation to drought stress. Therefore, they adapt faster and better to adverse drought conditions by activating alternative signaling pathways. The comparative transcriptome analysis of diploid and autotetraploid plants allows us to understand the molecular mechanisms of drought resistance and it is also essential for daylily breeding programs to develop drought-resistant genotypes in the future.
A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges

Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of deep learning, Deep RL (DRL) has witnessed great success over a wide spectrum of complex control tasks. Despite the encouraging results achieved, the deep neural network-based backbone is widely deemed as a black box that impedes practitioners to trust and employ trained agents in realistic scenarios where high security and reliability are essential. To alleviate this issue, a large volume of literature devoted to shedding light on the inner workings of the intelligent agents has been proposed, by constructing intrinsic interpretability or post-hoc explainability. In this survey, we provide a comprehensive review of existing works on eXplainable RL (XRL) and introduce a new taxonomy where prior works are clearly categorized into model-explaining, reward-explaining, state-explaining, and task-explaining methods. We also review and highlight RL methods that conversely leverage human knowledge to promote learning efficiency and performance of agents while this kind of method is often ignored in XRL field. Some challenges and opportunities in XRL are discussed. This survey intends to provide a high-level summarization of XRL and to motivate future research on more effective XRL solutions. Corresponding open source codes are collected and categorized at https://github.com/Plankson/awesome-explainable-reinforcement-learning.
Variability assessment of seed traits in Jatropha curcas L. for improvement of oil yield

Jatropha curcas has received considerable attention from researchers as a potential source of nonedible vegetable oil which is eminently suitable for production of liquid bio fuel, meeting international standards. For any tree improvement and breeding programme, study of variation among the populations is prerequisite as it helps in the detection of relative performance of various traits of economic value. 28 candidate plus trees were selected from the states of Jharkhand, West Bengal and Bihar in Eastern India. Nine seed parameters were measured and their genetic values and correlation was calculated to serve as base information for further improvement and breeding of J. curcas in Eastern India. Significant genetic differences exist in all the seed characteristics among the different candidate plus trees of J. curcas. Positive direct effect of seed width and protein content was observed on seed oil content. Indirect effect of seed traits via seed width was positive. Thus, seed width can be taken as a criterion for selecting trees with good oil yield. The variations in most studied parameters were under genotypic control among seed sources. Highest coefficient of variation was found for seed oil content emphasizing the need of wide scale screening and selection of superior genotypes to capture the existing variability.
Enhancing prediction of tooth caries using significant features and multi-model classifier

Background Tooth decay, also known as dental caries, is a common oral health problem that requires early diagnosis and treatment to prevent further complications. It is a chronic disease that causes the gradual breakdown of the tooth’s hard tissues, primarily due to the interaction of bacteria and dietary sugars. Results While numerous investigations have focused on addressing this issue using image-based datasets, the outcomes have revealed limitations in their effectiveness. In a novel approach, this study focuses on feature-based datasets, coupled with the strategic integration of Principle Component Analysis (PCA) and Chi-square (chi2) for robust feature engineering. In the proposed model, features are generated using PCA, utilizing a voting classifier ensemble consisting of Extreme Gradient Boosting (XGB), Random Forest (RF), and Extra Trees Classifier (ETC) algorithms. Discussion Extensive experiments were conducted to compare the proposed approach with the chi2 features and machine learning models to evaluate its efficacy for tooth caries detection. The results showed that the proposed voting classifier using PCA features outperformed the other approaches, achieving an accuracy, precision, recall, and F1 score of 97.36%, 96.14%, 96.84%, and 96.65%, respectively. Conclusion The study demonstrates that the utilization of feature-based datasets and PCA-based feature engineering, along with a voting classifier ensemble, significantly improves tooth caries detection accuracy compared to image-based approaches. The achieved high accuracy, precision, recall, and F1 score emphasize the potential of the proposed model for effective dental caries detection. This study provides new insights into the potential of innovative methodologies to improve dental healthcare by evaluating their effectiveness in addressing prevalent oral health issues.
Plexus: Optimizing Join Approximation for Geo-Distributed Data Analytics

Modern applications are increasingly generating and persisting data across geo-distributed data centers or edge clusters rather than a single cloud. This paradigm introduces challenges for traditional query execution due to increased latency when transferring data over wide-area network links. Join queries in particular are heavily affected, due to their large output size and amount of data that must be shuffled over the network. Join sampling---computing a uniform sample from the join results---is a useful technique for reducing resource requirements. However, applying it to a geo-distributed setting is challenging, since acquiring independent samples from each location and joining on the samples does not produce uniform and independent tuples from the join result. To address these challenges, we first generalize an existing join sampling algorithm to the geo-distributed setting. We then present our system, Plexus, which introduces three additional optimizations to further reduce the network overhead and handle network and data heterogeneity: (i) weight approximation, (ii) heterogeneity awareness and (iii) sample prefetching. We evaluate Plexus on a geo-distributed system deployed across multiple AWS regions, with an implementation based on Apache Spark. Using three real-world datasets, we show that Plexus can reduce query latency by up to 80% over the default Spark join implementation on a wide class of join queries without substantially impacting sample uniformity.
Learning I/O Variables from Scientiﬁc Software’s User Manuals

Scientiﬁc software often involves many input and output variables. Identifying these variables is important for such software engineering tasks as metamorphic testing. To reduce the manual work, we report in this paper our investigation of machine learning algorithms in classifying variables from software’s user manuals. We identify thirteen natural-language features, and use them to develop a multi-layer solution where the ﬁrst layer distinguishes variables from non-variables and the second layer classiﬁes the variables into input and output types. Our experimental results on three scientiﬁc software systems show that random forest and feedforward neural network can be used to best implement the ﬁrst layer and second layer respectively.
The Hydro-ABC model (Version 2.0): a simplified convective-scale model with moist dynamics

Abstract. The prediction of convection (in terms of position, timing, and strength) is important to achieve for high-resolution weather forecasting. This problem requires not only good convective-scale models, but also data assimilation systems that give initial conditions which neither improperly hinder nor improperly hasten convection in the ensuing forecasts. Solving this problem is difficult and expensive using operational-scale numerical weather prediction systems, and so a simplified model of convective-scale flow is under development (called the “ABC model”). This paper extends the existing ABC model of dry convective-scale flow to include mixing ratios of vapour and condensate phases of water. The revised model is called “Hydro-ABC”. Hydro-ABC includes transport of the vapour and condensate mixing ratios within a dynamical core, and it transitions between these two phases via a micro-physics scheme. A saturated mixing ratio is derived from model quantities, which helps determine whether evaporation or condensation happens. Latent heat is exchanged with the buoyancy variable (ABC's potential-temperature-like variable) in such a way to conserve total energy, where total energy is the sum of dry energy and latent heat. The model equations are designed to conserve the domain-total mass, water, and energy. An example numerical model integration is performed and analysed, which shows the development of a realistic looking anvil cloud and excitation of inertio-gravity and acoustic modes over a wide range of frequencies. This behaviour means that Hydro-ABC is a sufficiently challenging model which will allow experimentation with innovative data assimilation strategies in future work. An ensemble of Hydro-ABC integrations is performed in order to study the possible forecast error covariance statistics (knowledge of which is necessary for data assimilation). These show patterns that are dependent on the presence of convective activity (at any model's vertical column), thus giving a taste of flow-dependent error statistics. Candidate indicators/harbingers of convection are also evaluated (namely relative humidity, hydrostatic imbalance, horizontal divergence, convective available potential energy, convective inhibition, vertical wind, and the condensate mixing ratio), some of which appear to be reliable diagnostics concerning the presence of convection. These diagnostics will be useful in the selection of the relevant forecast error covariance statistics when data assimilation for Hydro-ABC is developed.
Influence of Ethnic Identities on Voting Behaviour in 2019 National Assembly Elections in Gombe State, Nigeria

Gombe state is a multi-ethnic society with diverse backgrounds, where distrust and animosity among the ethnic identities run deep. Contrary to the liberal principles of universal adult suffrage, politicians have been manipulating ethnic identities to influence voting behaviour to feather their political nests since the creation of Gombe state in 1996. This has led to the deprivation of the voters of their full rights to freedom of choice and the undermining of democracy in the state. The literature review shows scanty study has been carried out in the area. Therefore, this paper examined the influence of ethnic identities on voting behaviour in the 2019 National Assembly elections in Gombe state. A qualitative design and a case study approach are adopted. Purposive sampling and snowballing were used for the selection of informants. Apart from the review of secondary materials, primary data collection was through online Zoom, WhatsApp, and phone calls interviews under various categories of informants. Rational Choice Theory is employed and thematic analysis was used for data interpretation. Findings revealed abject poverty, low level of political education and culture among the voters mainly made it easier for the politicians to manipulate ethnic identities to influence voting behaviour.
The Use of Push Notification in Censorship Circumvention

Push notifications provide a way for applications to deliver time-sensitive information directly to users. In recent years, they have gained widespread adoption across mobile and desktop platforms. In this paper, we explore the use of push notification services for censorship circumvention. Supported with measurements, we argue that push notifications offer high availability, as blocking them would incur significant collateral damage, making them ideal candidates to tunnel circumvention traffic. We present two censorship circumvention systems that leverage push notification as a transport. PushRSS is a blocking-resistant content aggregator that tunnels RSS updates through a push notification network. Once bootstrapped, the tool remains operational even if the server IP is outright blocked. PushProxy is a general-purpose proxy that routes user’s downstream traffic through a push notification service, while keeping upstream an independent channel. By decoupling the downstream from upstream, PushProxy mitigates the ability of network adversaries to perform per-flow traffic analysis, while providing performance comparable to popular symmetric proxies. Although these systems have their limitations, we believe push notification still holds potential as a cir-cumvention transport that complements existing approaches.
Novel sulpiride-loaded solid lipid nanoparticles with enhanced intestinal permeability

Background: Solid lipid nanoparticles (SLN), novel drug delivery carriers, can be utilized in enhancing both intestinal permeability and dissolution of poorly absorbed drugs. The aim of this work was to enhance the intestinal permeability of sulpiride by loading into SLN. Methods: A unique ultrasonic melt-emulsification method with minimum stress conditions was used for the preparation of SLN. The mixture of the drug and the melted lipids was simply dispersed in an aqueous solution of a surfactant at a temperature that was 10 ° C higher than the melting points of the lipids using probe sonication, and was then simultaneously dispersed in cold water. Several formulation parameters were optimized, including the drug-to-lipid ratio, and the types of lipids and surfactants used. The produced SLN were evaluated for their particle size and shape, surface charge, entrapment efficiency, crystallinity of the drug and lipids, and the drug release profile. The rat everted sac intestine model was utilized to evaluate the change in intestinal permeability of sulpiride by loading into SLN. Results: The method adopted allowed successful preparation of SLN with a monodispersed particle size of 147.8–298.8 nm. Both scanning electron microscopic and atomic force microscopic images showed uniform spherical particles and confirmed the sizes determined by the light scattering technique. Combination of triglycerides with stearic acid resulted in a marked increase in zeta potential, entrapment efficiency, and drug loading; however, the particle size was increased. The type of surfactant used was critical for particle size, charge, drug loading, and entrapment efficiency. Generally, the in vitro release profile demonstrated by all formulations showed the common biphasic mode with a varying degree of burst release. The everted sac model showed markedly enhanced sulpiride permeability in the case of the SLN-loaded formulation. The in situ results showed a very good correlation with the in vitro release data. Conclusion: Incorporation of sulpiride into SLN results in enhanced intestinal permeability of sulpiride, that may in turn increase overall oral absorption of the drug. The superior attributes of the prepared SLN, specifically the high particle size uniformity and drug loading capacity, is considered novel, especially given the simplicity and modest nature of the sonication method used.