Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel stress response pathway regulates rRNA biogenesis

Production of ribosomes is an energy-intensive process owing to the intricacy of these massive macromolecular machines. Each human ribosome contains 80 ribosomal proteins and four non-coding RNAs. Accurate assembly requires precise regulation of protein and RNA subunits. In response to stress, the integrated stress response (ISR) rapidly inhibits global translation. How rRNA is coordinately regulated with the rapid inhibition of ribosomal protein synthesis is not known. Here we show that stress specifically inhibits the first step of rRNA processing. Unprocessed rRNA is stored within the nucleolus, and, when stress resolves, it re-enters the ribosome biogenesis pathway. Retention of unprocessed rRNA within the nucleolus aids in the maintenance of this organelle. This response is independent of the ISR or inhibition of cellular translation but represents an independent stress-response pathway that we term Ribosome Biogenesis Stress Response (RiBiSR). Failure to coordinately regulate ribosomal protein translation and rRNA production results in nucleolar fragmentation. Our study unveils a novel stress response pathway that aims at conserving energy, preserving the nucleolus, and prevents further stress by regulation of rRNA processing.
Divergent features of the coenzyme Q:cytochrome c oxidoreductase complex in Toxoplasma gondii parasites

The mitochondrion is critical for the survival of apicomplexan parasites. Several major anti-parasitic drugs, such as atovaquone and endochin-like quinolones, act through inhibition of the mitochondrial electron transport chain at the coenzyme Q:cytochrome c oxidoreductase complex (Complex III). Despite being an important drug target, the protein composition of Complex III of apicomplexan parasites has not been elucidated. Here, we undertake a mass spectrometry-based proteomic analysis of Complex III in the apicomplexan Toxoplasma gondii. Along with canonical subunits that are conserved across eukaryotic evolution, we identify several novel or highly divergent Complex III components that are conserved within the apicomplexan lineage. We demonstrate that one such subunit, which we term TgQCR11, is critical for parasite proliferation, mitochondrial oxygen consumption and Complex III activity, and establish that loss of this protein leads to defects in Complex III integrity. We conclude that the protein composition of Complex III in apicomplexans differs from that of the mammalian hosts that these parasites infect. Author summary Apicomplexan parasites cause numerous diseases in humans and animals, including malaria (Plasmodium species) and toxoplasmosis (Toxoplasma gondii). The coenzyme Q:cytochrome c oxidoreductase protein complex (Complex III) performs a central role in the mitochondrial electron transport chain of many eukaryotes. Despite being the target of several major anti-apicomplexan drugs, the protein composition of Complex III in apicomplexans was previously unknown. Our work identifies novel proteins in Complex III of apicomplexans, one of which is critical for complex function and integrity. Our study highlights divergent features of Complex III in apicomplexans, and provides a broader understanding of Complex III evolution in eukaryotes. Our study also provides important insights into what sets this major drug target apart from the equivalent complex in host species.
An Automatic Encoding and Decoding Method for Differentiating Alzheimer's Disease with Functional MRI

In recent years, promising performance of classifying the Alzermerzer's Disease has been achieved by using functional resting-state MRI to extract features by functional connectivity and brain activation in different brain regions such as ReHO, ALFF and so on. However current studies focus on the feature extraction by analyzing the whole time series extracted from the functional images, without considering the variation of the signature changes in the brain regions, which might cause fluctuations of the brain signature activation or the analysis of functional connectivity. This study focus on the image feature automatic encoding and decoding in sequence by a network, where convolutional neural network is used to extract abstract image features in each time step and a long-short term recurrent neural network used to combine features at all time. And finally we use the network to carry out experiments to identify the Alzermerzer's Disease. Our CNN network is developed from the U-net, where we only use the first half of the network to encode the images. Finally we have gained a considerable accuracy in average.
Geography-Aware Sequential Location Recommendation

Sequential location recommendation plays an important role in many applications such as mobility prediction, route planning and location-based advertisements. In spite of evolving from tensor factorization to RNN-based neural networks, existing methods did not make effective use of geographical information and suffered from the sparsity issue. To this end, we propose a Geography-aware sequential recommender based on the Self-Attention Network (GeoSAN for short) for location recommendation. On the one hand, we propose a new loss function based on importance sampling for optimization, to address the sparsity issue by emphasizing the use of informative negative samples. On the other hand, to make better use of geographical information, GeoSAN represents the hierarchical gridding of each GPS point with a self-attention based geography encoder. Moreover, we put forward geography-aware negative samplers to promote the informativeness of negative samples. We evaluate the proposed algorithm with three real-world LBSN datasets, and show that GeoSAN outperforms the state-of-the-art sequential location recommenders by 34.9%. The experimental results further verify significant effectiveness of the new loss function, geography encoder, and geography-aware negative samplers.
UFold: fast and accurate RNA secondary structure prediction with deep learning

For many RNA molecules, the secondary structure is essential for the correction function of the RNA. Predicting RNA secondary structure from nucleotide sequences is a long-standing problem in genomics, but the prediction performance has reached a plateau over time. Traditional RNA secondary structure prediction algorithms are primarily based on thermodynamic models through free energy minimization. Here we propose a deep learning-based method, called UFold, for RNA secondary structure prediction, trained directly on annotated data without any thermodynamic assumptions. UFold improves substantially upon previous models, with approximately 31% improvement over traditional thermodynamic models and 24.5% improvement over other learning-based methods. It achieves an F1 score of 0.96 on base pair prediction accuracy. An online web server running UFold is publicly available at http://ufold.ics. uci.edu.
Region2vec: An Approach for Urban Land Use Detection by Fusing multiple Features

With the advancement of urbanization, urban land use detection has become a research hotspot. Numerous methods have been proposed to identify urban land use, in which points of interest (POI) data is widely used, and sometimes other data source like GPS trajectories is incorporated. However, previous works have hardly fully utilized the global spatial information contained in the POI data, or ignored correlations between features when integrating multiple data source, so resulting in information loss. In this study, we propose an integrated framework titled Region2vec to detect urban land use type by combining POI and mobile phone data. First, POI-based region embeddings are generated by applying Glove model and LDA model to mine the global spatial information and land use topic distributions respectively. The mobile phone data is utilized to generate human activity pattern-based embeddings. Then a similarity matrix is constructed according to POI-based and activity pattern-based embeddings. Finally, the similarity measures are regarded as clustering features to extract the urban land use results. Experiments are implemented and compared with other urban land use algorithms based on data in Sanya, China. The results demonstrate the effectiveness of the proposed framework. This research can provide effective information support for urban planning.
SEAL: Learning Heuristics for Community Detection with Generative Adversarial Networks

Community detection is an important task with many applications. However, there is no universal definition of communities, and a variety of algorithms have been proposed based on different assumptions. In this paper, we instead study the semi-supervised community detection problem where we are given several communities in a network as training data and aim to discover more communities. This setting makes it possible to learn concepts of communities from data without any prior knowledge. We propose the Seed Expansion with generative Adversarial Learning (SEAL), a framework for learning heuristics for community detection. SEAL contains a generative adversarial network, where the discriminator predicts whether a community is real or fake, and the generator generates communities that cheat the discriminator by implicitly fitting characteristics of real ones. The generator is a graph neural network specialized in sequential decision processes and gets trained by policy gradient. Moreover, a locator is proposed to avoid well-known free-rider effects by forming a dual learning task with the generator. Last but not least, a seed selector is utilized to provide promising seeds to the generator. We evaluate SEAL on 5 real-world networks and prove its effectiveness.
Effects of SARS‐CoV‐2 mutations on protein structures and intraviral protein–protein interactions

Since 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) has infected ten millions of people across the globe, and massive mutations in virus genome have occurred during the rapid spread of this novel coronavirus. Variance in protein sequence might lead to change in protein structure and interaction, then further affect the viral physiological characteristics, which could bring tremendous influence on the pandemic. In this study, we investigated 18 non-synonymous mutations in SARS-CoV-2 genome which incidence rates were all ≥1% as of July 15th, 2020, then modeled the mutated protein structures and compared them with the reference ones. The results showed that four types of mutations could cause dramatic changes in protein structures (RMSD ≥5.0 Å), which were Q57H and G251V in open reading frames 3a (ORF3a), S194L and R203K/G204R in nucleocapsid (N). Next, we found that these mutations could affect the binding affinity of intraviral protein interactions. In addition, the hot spots within these docking complexes were altered, among which the mutation Q57H was involved in both Orf3a-Orf8 and Orf3a-S protein interactions. Besides, these mutations were widely distributed all over the world, and their occurrences fluctuated as time went on. Notably, the incidences of R203K/G204R in N and Q57H in Orf3a were both over 50% in some countries. Overall, our findings suggest that SARS-CoV-2 mutations can change viral protein structure, binding affinity and hot spots of the interface, thereby may have impacts on SARS-CoV-2 transmission, diagnosis and treatment of COVID-19.
Screening and Identification of Sorghum Cultivars for Salinity Tolerance During Germination

【Objective】The objective of the current study was to explore the response of 35 different sorghum cultivars to alkali stress,and screen them for suitability for growing in alkaline soil and provide some guidance for their management under alkali stress. 【Method】Mixed alkali were used(NaHCO3﹕Na2CO3 with the mole ratio of 9﹕1) to simulate the typical alkali stress environment.The 35 sorghum cultivars were treated with 25 mmol.L-1 solution or distilled water and incubated in Petri dishes for 10 days at humidity of 60%,light/dark of 12 h/12 h,illumination of 34 μmol. m-2.s-1 and 28℃/25℃ of day/night during germination.The germination force was measured on the fourth day and the germination percentage,leaf length,root length,leaf fresh weight,root fresh weight,leaf dry weight,root dry weight and remaining seed dry weight were measured on the tenth day.Through the fuzzy mathematics method of membership function,principal component analysis(PCA) and clustering analysis(CA),the alkali resistance of different sorghum cultivars were evaluated. 【Result】The PCA results showed that root length,germination index and leaf dry weight were the most significant factors and are recommended as the main indexes for identifing alkali tolerance of sorghum at germination.The cultivars were ranked based on the membership function,and three groups on alkali tolerance were sorted from the 35 sorghum cultivars according to the CA.The analysis showed that seven of the 35 cultivars were strongly tolerant of alkaline conditions,e.g.Siza25,that twenty-two were had medium tolerance,e.g.Jiza 319 and Chiza 28,and that six were sensitive,e.g.Longza 9. 【Conclusion】Principal component analysis showed that root length,germination index and leaf dry weight were the most significant factors and these are recommended as the main indexes to identify alkali tolerance of sorghum at germination,and in plant breeding and evaluation projects should be supported by CA and membership function to provide rigorous assessment of cultivars tolerance of alkalinity.
HDAC1 SUMOylation promotes Argonaute directed transcriptional silencing in C. elegans

Eukaryotic cells use guided search to coordinately control dispersed genetic elements. The transitive effectors of these mechanisms, Argonaute proteins and their small-RNA co-factors, engage nascent RNAs and chromatin-associated proteins to direct transcriptional silencing. The small ubiquitin-like modifier (SUMO) has been shown to promote the induction and maintenance of silent chromatin (called heterochromatin) in yeast, plants, and animals. Here we show that Argonaute-directed transcriptional silencing in C. elegans requires SUMOylation of the type 1 histone deacetylase HDA-1. SUMOylation of HDA-1 promotes interactions with components of the nucleosome remodeling and deacetylase (NuRD) complex and with the nuclear Argonaute HRDE-1/WAGO-9. Our findings suggest how HDAC1 SUMOylation promotes the association of HDAC and other chromatin remodeling factors with a nuclear Argonaute in order to initiate de novo heterochromatin silencing.
Learning to Generate Personalized Query Auto-Completions via a Multi-View Multi-Task Attentive Approach

In this paper, we study the task of Query Auto-Completion (QAC), which is a very significant feature of modern search engines. In real industrial application, there always exist two major problems of QAC - weak personalization and unseen queries. To address these problems, we propose M2A, a multi-view multi-task attentive framework to learn personalized query auto-completion models. We propose a new Transformer-based hierarchical encoder to model different kinds of sequential behaviors, which can be seen as multiple distinct views of the user's searching history, and then a prefix-to-history attention mechanism is used to select the most relevant information to compose the final intention representation. To learn more informative representations, we propose to incorporate multi-task learning into the model training. Two different kinds of supervisory information provided by query logs are utilized at the same time by jointly training a CTR prediction model and a query generation model. To bridge the gap between the setting of research work and the real scenario, we release a new large-scale query log dataset - TaobaoQAC, which contains rich real prefix-to-query click behaviors. We conduct experiments on TaobaoQAC to demonstrate the effectiveness or our approach, and results show that M2A achieves superior performance compared with several strong baselines in both candidate ranking and query generation. We also conduct an online A/B testing and our approach has been deployed online.
ACOL-GAN: Learning Clustering Generative Adversarial Networks through Graph-Based Activity Regularization

In recent years, deep learning has achieved great success in many fields. As the most basic machine learning task, clustering has also become one of the research hotspots. However, clustering performance based on Variational Autoencoder is generally better than that based on Generative Adversarial Network, which is mainly because the former implements multi-modal learning and there are obvious boundaries between different categories, while the latter does not. In this paper, we propose a new clustering model named ACOL-GAN, which replaces the normal distribution that standard GAN relied on with sampling networks and adopts the Auto-clustering Output Layer as the output layer in discriminator. Due to Graph-based Activity Regularization terms, softmax nodes of parent-classes are specialized as the competition between each other during training. The experimental results show that ACOL-GAN achieved the state-of-the-art performance for clustering tasks on MNIST USPS and Fashion-MNIST, with the highest accuracy on Fashion-MNIST.
Multi-Class Data Description for Out-of-distribution Detection

The capability of reliably detecting out-of-distribution samples is one of the key factors in deploying a good classifier, as the test distribution always does not match with the training distribution in most real-world applications. In this work, we present a deep multi-class data description, termed as Deep-MCDD, which is effective to detect out-of-distribution (OOD) samples as well as classify in-distribution (ID) samples. Unlike the softmax classifier that only focuses on the linear decision boundary partitioning its latent space into multiple regions, our Deep-MCDD aims to find a spherical decision boundary for each class which determines whether a test sample belongs to the class or not. By integrating the concept of Gaussian discriminant analysis into deep neural networks, we propose a deep learning objective to learn class-conditional distributions that are explicitly modeled as separable Gaussian distributions. Thereby, we can define the confidence score by the distance of a test sample from each class-conditional distribution, and utilize it for identifying OOD samples. Our empirical evaluation on multi-class tabular and image datasets demonstrates that Deep-MCDD achieves the best performances in distinguishing OOD samples while showing the classification accuracy as high as the other competitors.
Incremental Mobile User Profiling: Reinforcement Learning with Spatial Knowledge Graph for Modeling Event Streams

We study the integration of reinforcement learning and spatial knowledge graph for incremental mobile user profiling, which aims to map mobile users to dynamically-updated profile vectors by incremental learning from a mixed-user event stream. After exploring many profiling methods, we identify a new imitation based criteria to better evaluate and optimize profiling accuracy. Considering the objective of teaching an autonomous agent to imitate a mobile user to plan next-visit based on the user's profile, the user profile is the most accurate when the agent can perfectly mimic the activity patterns of the user. We propose to formulate the problem into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of environment is a fused representation of a user and spatial entities (e.g., POIs, activity types, functional zones). An event that a user takes an action to visit a POI, will change the environment, resulting into a new state of user profiles and spatial entities, which helps the agent to predict next visit more accurately. After analyzing such interactions among events, users, and spatial entities, we identify (1)semantic connectivity among spatial entities, and, thus, introduce a spatial Knowledge Graph (KG) to characterize the semantics of user visits over connected locations, activities, and zones. Besides, we identify (2) mutual influence between users and the spatial KG, and, thus, develop a mutual-updating strategy between users and the spatial KG, mixed with temporal context, to quantify the state representation that evolves over time. Along these lines, we develop a reinforcement learning framework integrated with spatial KG. The proposed framework can achieve incremental learning in multi-user profiling given a mixed-user event stream. Finally, we apply our approach to human mobility activity prediction and present extensive experiments to demonstrate improved performances.
A Predictor Based on Parallel LSTM for Burst Network Traffic Flow

The network traffic prediction is a key step for service quality control in computer network. Aimed at the problem that the performance of the traditional prediction method significantly degrades for the burst short-term flow, this paper proposed a double LSTM architecture, one of which acts as the main flow predictor, another as the detector for the moment the burst flow starts. The two LSTM unit can exchange their internal state's information, and the predictor uses the detector's information to improve the accuracy of the prediction. To train the offline double LSTM architecture, a Depth-Backstep algorithm is put forward. To use the architecture to perform the online prediction, a pulse series is used as a simulant of the burst event. A simulation experiment is designed to test performance of the predictor. The results of the experiment show that the prediction accuracy of the double LSTM architecture is significantly improved, compared with the traditional single LSTM architecture.
Hi-COVIDNet: Deep Learning Approach to Predict Inbound COVID-19 Patients and Case Study in South Korea

The escalating crisis of COVID-19 has put people all over the world in danger. Owing to the high contagion rate of the virus, COVID-19 cases continue to increase globally. To further suppress the threat of the COVID-19 pandemic and minimize its damage, it is imperative that each country monitors inbound travelers. Moreover, given that resources for quarantine are often limited, they must be carefully allocated. In this paper, to aid in such allocation by predicting the number of inbound COVID-19 cases, we propose Hi-COVIDNet, which takes advantage of the geographic hierarchy. Hi-COVIDNet is based on a neural network with two-level components, namely, country-level and continent-level encoders, which understand the complex relationships among foreign countries and derive their respective contagion risk to the destination country. An in-depth case study in South Korea with real-world COVID-19 datasets confirmed the effectiveness and practicality of Hi-COVIDNet.
In and Out: Optimizing Overall Interaction in Probabilistic Graphs under Clustering Constraints

We study two novel clustering problems in which the pairwise interactions between entities are characterized by probability distributions and conditioned by external factors within the environment where the entities interact. This covers any scenario where a set of actions can alter the entities' interaction behavior. In particular, we consider the case where the interaction conditioning factors can be modeled as cluster memberships of entities in a graph and the goal is to partition a set of entities such as to maximize the overall vertex interactions or, equivalently, minimize the loss of interactions in the graph. We show that both problems are NP-hard and they are equivalent in terms of optimality. However, we focus on the minimization formulation as it enables the possibility of devising both practical and efficient approximation algorithms and heuristics. Experimental evaluation of our algorithms, on both synthetic and real network datasets, has shown evidence of their meaningfulness as well as superiority with respect to competing methods, both in terms of effectiveness and efficiency.