Miracle-dz/UniFAR-FTUs huggingface dataset:
PPARdelta signaling activation improves metabolic and contractile maturation of human pluripotent stem cell-derived cardiomyocytes

Pluripotent stem cell-derived cardiomyocytes (PSC-CMs) provide an unprecedented opportunity to study human heart development and disease. A major caveat however is that they remain functionally and structurally immature in culture, limiting their potential for disease modeling and regenerative approaches. Here, we address the question of how different metabolic pathways can be modulated in order to induce efficient hPSC-CM maturation. We show that PPAR signaling acts in an isoform-specific manner to balance glycolysis and fatty acid oxidation (FAO). PPARD activation or inhibition results in efficient respective up- or down-regulation of the gene regulatory networks underlying FAO in hPSC-CMs. PPARD induction further increases mitochondrial and peroxisome content, enhances mitochondrial cristae formation and augments FAO flux. Lastly PPARD activation results in enhanced myofibril organization and improved contractility. Transient lactate exposure, commonly used in hPSC-CM purification protocols, induces an independent program of cardiac maturation, but when combined with PPARD activation equally results in a metabolic switch to FAO. In summary, we identify multiple axes of metabolic modifications of hPSC-CMs and a role for PPARD signaling in inducing the metabolic switch to FAO in hPSC-CMs. Our findings provide new and easily implemented opportunities to generate mature hPSC-CMs for disease modeling and regenerative therapy.
Double nicking by RNA-directed Cascade-nCas3 for high-efficiency large-scale genome engineering

New CRISPR-based genome editing technologies are developed to continuedly drive advances in life sciences, which, however, are predominantly derived from systems of Type II CRISPR-Cas9 and Type V CRISPR-Cas12a for eukaryotes. Here we report a novel CRISPR-n(nickase)Cas3 genome editing tool established upon an endogenous Type I system of Zymomonas mobilis. We demonstrate that nCas3 variants can be created by alanine-substituting any catalytic residue of the Cas3 helicase domain. While nCas3 overproduction via plasmid shows severe cytotoxicity; an in situ nCas3 introduces targeted double-strand breaks, facilitating genome editing, without visible cell killing. By harnessing this CRISPR-nCas3, deletion of genes or genomic DNA stretches can be consistently accomplished with near-100% efficiencies, including simultaneous removal of two large genomic fragments. Our work describes the first establishment of a CRISPR-nCas3-based genome editing technology, thereby offering a simple, easy, yet useful approach to convert many endogenous Type I systems into advanced genome editing tools. We envision that many CRISPR-nCas3-based toolkits would be soon available for various industrially important non-model bacteria that carry active Type I systems to facilitate high-throughput prokaryotic engineering.
Strong attraction of male mosquitoes (Aedes aegypti) to humans

Female Aedes aegypti mosquitoes integrate multiple sensory cues to locate human hosts for blood meals. While male mosquitoes do not blood feed, male Ae. aegypti swarm around and land on humans in nature. Basrur et al. (2020) generated male Aedes aegypti lacking the fruitless gene and discovered that they gained strong attraction to humans, similar to female mosquitoes. The authors assume that host-seeking is a female-specific trait, which they confirmed through experiments. However, all experiments were performed under confined laboratory conditions which appear to inhibit swarming behavior. We used semi-field experiments to demonstrate robust attraction of male Ae. aegypti to humans. Human-baited traps captured up to 25% of released males within 15 min, whereas control traps without humans as bait failed to capture males. Rapid attraction to humans was further demonstrated through videography. Males swarmed around and landed on human subjects, with no activity recorded in paired unbaited controls. The absence of female Ae. aegypti in these experiments rules out a hypothesis by Basrur et al. (2020) that males are attracted not to the human, but to host-seeking females near humans. Finally, we confirm the lack of male attraction to humans in small laboratory cages, even when using recently field-collected males. Our direct observations of male mosquito attraction to humans refute a key assumption of Basrur et al. (2020) and raise questions around conditions under which fruitless prevents male host-seeking. Male mosquito attraction to humans is likely to be important for mating success in wild populations and its basis should be further explored.
A proteome‐scale analysis of vertebrate protein amino acid occurrence: Thermoadaptation shows a correlation with protein solvation but less so with dynamics

Despite differences in behaviors and living conditions, vertebrate organisms share the great majority of proteins, often with subtle differences in amino acid sequence. Here, we present a simple way to analyze the difference in amino acid occurrence by comparing highly homologous proteins on a subproteome level between several vertebrate model organisms. Specifically, we use this method to identify a pattern of amino acid conservation as well as a shift in amino acid occurrence between homeotherms (warm‐blooded species) and poikilotherms (cold‐blooded species). Importantly, this general analysis and a specific example further establish a broad correlation, if not likely connection between the thermal adaptation of protein sequences and two of their physical features: on average a change in their protein dynamics and, even more strongly, in their solvation. For poikilotherms, such as frog and fish, the lower body temperature is expected to increase the protein–protein interaction due to a decrease in protein internal dynamics. In order to counteract the tendency for enhanced binding caused by low temperatures, poikilotherms enhance the solvation of their proteins by favoring polar amino acids. This feature appears to dominate over possible changes in dynamics for some proteins. The results suggest that a general trend for amino acid choice is part of the mechanism for thermoadaptation of vertebrate organisms at the molecular level.
Gains and Losses Affect Learning Differentially at Low and High Attentional Load

Abstract Prospective gains and losses influence cognitive processing, but it is unresolved how they modulate flexible learning in changing environments. The prospect of gains might enhance flexible learning through prioritized processing of reward-predicting stimuli, but it is unclear how far this learning benefit extends when task demands increase. Similarly, experiencing losses might facilitate learning when they trigger attentional reorienting away from loss-inducing stimuli, but losses may also impair learning by increasing motivational costs or when negative outcomes are overgeneralized. To clarify these divergent views, we tested how varying magnitudes of gains and losses affect the flexible learning of feature values in environments that varied attentional load by increasing the number of interfering object features. With this task design, we found that larger prospective gains improved learning efficacy and learning speed, but only when attentional load was low. In contrast, expecting losses impaired learning efficacy, and this impairment was larger at higher attentional load. These findings functionally dissociate the contributions of gains and losses on flexible learning, suggesting they operate via separate control mechanisms. One mechanism is triggered by experiencing loss and reduces the ability to reduce distractor interference, impairs assigning credit to specific loss-inducing features, and decreases efficient exploration during learning. The second mechanism is triggered by experiencing gains, which enhances prioritizing reward-predicting stimulus features as long as the interference of distracting features is limited. Taken together, these results support a rational theory of cognitive control during learning, suggesting that experiencing losses and experiencing distractor interference impose costs for learning.
A Novel Cloud-Assisted Secure Deep Feature Classification Framework for Cancer Histopathology Images

The advancements in the Internet of Things (IoT) and cloud services have enabled the availability of smart e-healthcare services in a distant and distributed environment. However, this has also raised major privacy and efficiency concerns that need to be addressed. While sharing clinical data across the cloud that often consists of sensitive patient-related information, privacy is a major challenge. Adequate protection of patients’ privacy helps to increase public trust in medical research. Additionally, DL-based models are complex, and in a cloud-based approach, efficient data processing in such models is complicated. To address these challenges, we propose an efficient and secure cancer diagnostic framework for histopathological image classification by utilizing both differential privacy and secure multi-party computation. For efficient computation, instead of performing the whole operation on the cloud, we decouple the layers into two modules: one for feature extraction using the VGGNet module at the user side and the remaining layers for private prediction over the cloud. The efficacy of the framework is validated on two datasets composed of histopathological images of the canine mammary tumor and human breast cancer. The application of differential privacy preserving to the proposed model makes the model secure and capable of preserving the privacy of sensitive data from any adversary, without significantly compromising the model accuracy. Extensive experiments show that the proposed model efficiently achieves the trade-off between privacy and model performance.
RNNRepair: Automatic RNN Repair via Model-based Analysis

Deep neural networks are vulnerable to adversarial attacks. Due to their black-box nature, it is rather challenging to interpret and properly repair these incorrect behaviors. This paper focuses on interpreting and repairing the incorrect behaviors of Recurrent Neural Networks (RNNs). We propose a lightweight model-based approach ( RN-NRepair ) to help understand and repair incorrect behaviors of an RNN. Speciﬁcally, we build an inﬂuence model to characterize the stateful and statistical behaviors of an RNN over all the training data and to perform the inﬂuence analysis for the errors. Compared with the existing techniques on inﬂuence function, our method can efﬁciently estimate the inﬂuence of existing or newly added training samples for a given prediction at both sample level and segmentation level. Our empirical evaluation shows that the proposed inﬂuence model is able to extract accurate and understandable features. Based on the inﬂuence model, our proposed technique could effectively infer the in-ﬂuential instances from not only an entire testing sequence but also a segment within that sequence. Moreover, with the sample-level and segment-level inﬂuence relations, RNNRepair could further remediate two types of incorrect predictions at the sample level and segment level.
Solving "Limited" Task Allocation Problem for UAVs Based on Optimization Algorithms

With the rapid development of science and technology, unmanned technology has been widely used in many fields. One of the most important applications is in the field of civil and military UAVs. In the field of military UAVs (unmanned aerial vehicles), UAVs usually have to complete a series of tasks. In this series of tasks, there are often some key tasks. Key tasks play an important role, which is highly related to the feasibility of the whole action or task; mission failure sometimes causes incalculable damage. When assigning tasks to UAVs, it is necessary to ensure the accurate implementation of key tasks, so as to ensure the orderly implementation of the overall task. This paper not only successfully solved the previous problems but also comprehensively considered the minimization of resource consumption and the maximization of task revenue in the process of UAV task allocation. On the basis of considering the key system, considering the constraints and multiobjective problems in the UAV task allocation process, the violence allocation algorithm, constraint optimization evolutionary algorithm, PSO algorithm, and greedy algorithm combined with a constraint evolutionary algorithm are improved and optimized; it has been proven that they can solve the above difficulties. At the same time, several comparison experiments have been carried out; the performance and conclusion of the above four algorithms in the “limited” UAV task allocation scheme are analyzed in the experimental part.
Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

The Internet of Things (IoT) has emerged as a technology capable of connecting heterogeneous nodes/objects, such as people, devices, infrastructure, and makes our daily lives simpler, safer, and fruitful. Being part of a large network of heterogeneous devices, these nodes are typically resource-constrained and became the weakest link to the cyber attacker. Classical encryption techniques have been employed to ensure the data security of the IoT network. However, high-level encryption techniques cannot be employed in IoT devices due to the limitation of resources. In addition, node security is still a challenge for network engineers. Thus, we need to explore a complete solution for IoT networks that can ensure nodes and data security. The rule-based approaches and shallow and deep machine learning algorithms– branches of Artificial Intelligence (AI)– can be employed as countermeasures along with the existing network security protocols. This paper presented a comprehensive layer-wise survey on IoT security threats, and the AI-based security models to impede security threats. Finally, open challenges and future research directions are addressed for the safeguard of the IoT network.
Poisson-Randomised DirBN: Large Mutation is Needed in Dirichlet Belief Networks

The Dirichlet Belief Network (DirBN) was recently proposed as a promising deep generative model to learn interpretable deep latent distributions for objects. However, its current representation capability is limited since its latent distributions across different layers is prone to form similar patterns and can thus hardly use multi-layer structure to form ﬂexible distributions. In this work, we propose Poisson-randomised Dirichlet Belief Networks (Pois-DirBN), which allows large mutations for the latent distributions across layers to enlarge the representation capability. Based on our key idea of inserting Poisson random variables in the layer-wise connection, Pois-DirBN ﬁrst introduces a component-wise propagation mechanism to enable latent distributions to have large variations across different layers. Then, we develop a layer-wise Gibbs sampling algorithm to infer the latent distributions, leading to a larger number of effective layers compared to DirBN. In addition, we integrate out latent distributions and form a multi-stochastic deep integer network, which provides an alternative view on Pois-DirBN. We apply Pois-DirBN to relational modelling and validate its effectiveness through improved link prediction performance and more interpretable latent distribution visuali-sations. The code can be downloaded at https: //github.com/xuhuifan/Pois_DirBN .
Discriminative Complementary-Label Learning with Weighted Loss

Complementary-label learning (CLL) deals with the weak supervision scenario where each training instance is associated with one complementary label, which speciﬁes the class label that the instance does not belong to. Given the training instance x , existing CLL approaches aim at modeling the generative relationship between the complementary label ¯ y , i.e. P (¯ y | x ) , and the ground-truth label y , i.e. P ( y | x ) . Nonetheless, as the ground-truth label is not directly accessible for complementarily labeled training instance, strong generative assumptions may not hold for real-world CLL tasks. In this paper, we derive a simple and theoretically-sound discriminative model towards P (¯ y | x ) , which naturally leads to a risk estimator with estimation error bound at O (1 / √ n ) convergence rate. Accordingly, a practical CLL approach is proposed by further introducing weighted loss to the empirical risk to maximize the predictive gap between potential ground-truth label and complementary label. Extensive experiments clearly validate the effectiveness of the proposed discriminative complementary-label learning approach.
Approximately Optimal Control of Nonlinear Dynamic Stochastic Problems with Learning: The OPTCON Algorithm

OPTCON is an algorithm for the optimal control of nonlinear stochastic systems which is particularly applicable to economic models. It delivers approximate numerical solutions to optimum control (dynamic optimization) problems with a quadratic objective function for nonlinear economic models with additive and multiplicative (parameter) uncertainties. The algorithm was first programmed in C# and then in MATLAB. It allows for deterministic and stochastic control, the latter with open loop (OPTCON1), passive learning (open-loop feedback, OPTCON2), and active learning (closed-loop, dual, or adaptive control, OPTCON3) information patterns. The mathematical aspects of the algorithm with open-loop feedback and closed-loop information patterns are presented in more detail in this paper.
Towards Better Robust Generalization with Shift Consistency Regularization

While adversarial training becomes one of the most promising defending approaches against adversarial attacks for deep neural networks, the conventional wisdom through robust optimization may usually not guarantee good generalization for robustness. Concerning with robust generalization over unseen adversarial data, this paper investigates adversarial training from a novel perspective of shift consistency in latent space. We argue that the poor robust generalization of adversarial training is owing to the signiﬁcantly dispersed latent representations generated by training and test adversarial data, as the adversarial perturbations push the latent features of natural examples in the same class towards diverse directions. This is un-derpinned by the theoretical analysis of the robust generalization gap, which is upper-bounded by the standard one over the natural data and a term of feature inconsistent shift caused by adversarial perturbation – a measure of latent dispersion. To-wards better robust generalization, we propose a new regularization method – shift consistency regularization (SCR) – to steer the same-class latent features of both natural and adversarial data into a common direction during adversarial training. The effectiveness of SCR in adversarial training is evaluated through extensive experiments over different datasets, such as CIFAR-10, CIFAR-100, and SVHN, against several competitive methods.
Inverse Decision Modeling: Learning Interpretable Representations of Behavior

Decision analysis deals with modeling and enhancing decision processes. A principal challenge in improving behavior is in obtaining a transparent description of existing behavior in the first place. In this paper, we develop an expressive, unifying perspective on inverse decision modeling: a framework for learning parameterized representations of sequential decision behavior. First, we formalize the forward problem (as a normative standard), subsuming common classes of control behavior. Second, we use this to formalize the inverse problem (as a descriptive model), generalizing existing work on imitation/reward learning -- while opening up a much broader class of research problems in behavior representation. Finally, we instantiate this approach with an example (inverse bounded rational control), illustrating how this structure enables learning (interpretable) representations of (bounded) rationality -- while naturally capturing intuitive notions of suboptimal actions, biased beliefs, and imperfect knowledge of environments.
Product Customer Satisfaction Measurement Based on Multiple Online Consumer Review Features

With the development of the e-commerce industry, various brands of products with different qualities and functions continuously emerge, and the number of online shopping users is increasing every year. After purchase, users always leave product comments on the platform, which can be used to help consumers choose commodities and help the e-commerce companies better understand the popularity of their goods. At present, the e-commerce platform lacks an effective way to measure customer satisfaction based on various customer comments features. In this paper, our goal is to build a product customer satisfaction measurement by analyzing the relationship between the important attributes of reviews and star ratings. We first use an improved information gain algorithm to analyze the historical reviews and star rating data to find out the most informative words that the purchasers care about. Then, we make hypotheses about the relevant factors of the usefulness of reviews and verify them using linear regression. We finally establish a customer satisfaction measurement based on different review features. We conduct our experiments based on three products with different brands chosen from the Amazon online store. Based on our experiments, we discover that features such as length and extremeness of the comments will affect the review usefulness, and the consumer satisfaction measurement constructed using the exponential moving average method can effectively reflect the trend of user satisfaction over time. Our work can help companies acquire valuable suggestions to improve product features, increase sales, and help customers make wise purchases.
Reversed Lempel-Ziv Factorization with Suffix Trees

We present linear-time algorithms computing the reversed Lempel–Ziv factorization [Kolpakov and Kucherov, TCS’09] within the space bounds of two different suffix tree representations. We can adapt these algorithms to compute the longest previous non-overlapping reverse factor table [Crochemore et al., JDA’12] within the same space but pay a multiplicative logarithmic time penalty.
FOP: Factorizing Optimal Joint Policy of Maximum-Entropy Multi-Agent Reinforcement Learning

Value decomposition recently injects vigorous vitality into multi-agent actor-critic methods. However, existing decomposed actor-critic methods cannot guarantee the convergence of global optimum. In this paper, we present a novel multi-agent actor-critic method, FOP, which can factorize the optimal joint policy induced by maximum-entropy multi-agent reinforcement learning (MARL) into individual policies. The-oretically, we prove that factorized individual policies of FOP converge to the global optimum. Empirically, in the well-known matrix game and differential game, we verify that FOP can converge to the global optimum for both discrete and continuous action spaces. We also evaluate FOP on a set of StarCraft II micromanagement tasks, and demonstrate that FOP substantially outperforms state-of-the-art decomposed value-based and actor-critic methods.