Miracle-dz/UniFAR-FTUs huggingface dataset:
Impaired microglia-mediated synaptic pruning in the nucleus accumbens during adolescence results in persistent dysregulation of familiar, but not novel social interactions in sex-specific ways

Evolutionarily conserved, peer-directed social behaviors are essential to participate in many aspects of human society. These behaviors directly impact psychological, physiological, and behavioral maturation. Adolescence is an evolutionarily conserved period during which reward-related behaviors, including social behaviors, develop via developmental plasticity in the mesolimbic dopaminergic ‘reward’ circuitry of the brain. The nucleus accumbens (NAc) is an intermediate reward relay center that develops during adolescence and mediates both social behaviors and dopaminergic signaling. In several developing brain regions, synaptic pruning mediated by microglia, the resident immune cells of the brain, is important for normal behavioral development. In rats, we previously demonstrated that microglial synaptic pruning also mediates NAc and social development during sex-specific adolescent periods and via sex-specific synaptic pruning targets. In this report, we demonstrate that interrupting microglial pruning in NAc during adolescence persistently dysregulates social behavior towards a familiar, but not novel social partner in both sexes, via sex-specific behavioral expression. This leads us to infer that naturally occurring NAc pruning serves to reduce social behaviors primarily directed toward a familiar conspecific in both sexes, but in sex-specific ways.
Adenylnucleotide-mediated binding of the PII-like protein SbtB contributes to controlling activity of the cyanobacterial bicarbonate transporter SbtA

Cyanobacteria have evolved a remarkably powerful CO2 concentrating mechanism (CCM), enabling high photosynthetic rates in environments with limited inorganic carbon (Ci). Therefore, this CCM is a promising system for integration into higher plant chloroplasts to boost photosynthetic efficiency and yield. The CCM depends on active Ci uptake, facilitated by bicarbonate transporters and CO2 pumps, to elevate CO2 concentration around the active sites of the primary CO2 fixing enzyme, Rubisco, which is encapsulated in cytoplasmic micro-compartments (carboxysomes). The essential CCM proteins have been identified, but the molecular signals and regulators that coordinate function in response to light, Ci availability and other environmental cues are largely unknown. Here, we provide evidence, based on a novel in vitro binding system, for a role of the PII-like SbtB protein in regulating Ci uptake by the bicarbonate transporter, SbtA, in response to the cellular adenylate energy charge (AEC) through dynamic protein-protein interaction. Binding of the SbtA and SbtB proteins from two phylogenetically distant species, Cyanobium sp. PCC7001 and Synechococcus elongatus PCC7942, was inhibited by high ATP, and promoted by low [ATP]:[ADP or AMP] ratios in vitro, consistent with a sensory response to the AEC mediated through adenylnucleotide ligand-specific conformation changes in SbtB. In vivo, cell cultures of S. elongatus showed up to 70% SbtB-dependent down-regulation of SbtA bicarbonate uptake activity specifically in the light activation phase during transitions from dark to low light when low cellular AEC is expected to limit metabolic activity. This suggests SbtB may function as a curfew protein during prolonged low cellular AEC and photosynthetically unfavourable conditions to prevent energetically futile and physiologically disadvantageous activation of SbtA.
The functional specificity of CDC42 isoforms is caused by their distinct subcellular localization

The small G-protein CDC42 is an evolutionary conserved polarity protein and a key regulator of numerous polarized cell functions, including directed cell migration. In vertebrates, alternative splicing gives rise to two CDC42 proteins: the ubiquitously expressed isoform (CDC42u) and the brain isoform (CDC42b), whose specific roles are not fully elucidated. The two isoforms only differ in their carboxy-terminal sequence, which includes the CAAX motif essential for CDC42 interaction with membrane. Here we show that these divergent sequences do not directly affect the range of CDC42’s potential binding partners, but indirectly influence CDC42-driven signaling by controlling the specific subcellular localization of the two isoforms. In astrocytes and neural precursors, which naturally express both variants, CDC42u is mainly cytosolic and associates with the leading-edge plasma membrane of migrating cells where it recruits the Par6-PKCζ complex to fulfill its polarity function. In contrast, CDC42b mainly localizes to intracellular membrane compartments, where it interacts with N-WASP. CDC42b does not participate in cell polarization but embodies the major isoform regulating endocytosis. Both CDC42 isoforms act in concert by contributing their specific functions to promote chemotaxis of neural precursors, demonstrating that the expression pattern of the two isoforms is decisive for the tissue-specific behavior of cells.
Drug Target Identification in Triple Negative Breast Cancer Stem Cell Pathways: a computational study of gene regulatory pathways using Boolean networks

Triple-negative breast cancer (TNBC) is an aggressive form of breast cancer associated with an early age of onset, greater propensity towards metastasis, and poorer clinical outcomes. It accounts for 10% to 20% of newly diagnosed breast cancer cases and disproportionately affects individuals from the African American race. While TNBC is sensitive to chemotherapy, it is also prone to relapse. This is because chemotherapy successfully targets the primary TNBC tumor cell but often fails to target the subpopulation of TNBC stem cells. TNBC stem cells display cancerous traits such as cell cycle progression, survival, proliferation, apoptosis inhibition, and epithelial-mesenchymal transition. To study the cancer initiating behavior of the TNBC stem cells, we studied their underlying signaling pathways using Boolean networks(BN). BNs are effective in capturing the causal interactions taking place in signaling pathways. We built the BN from the pathway literature and used it to evaluate the efficacies of eleven targeted inhibitory drugs in suppressing cancer-promoting genes. We simulated the BN when the pathways had single or multiple mutations, with a maximum of three mutations at a time. Our findings indicated that STAT3, GLI, and NF-κB are the most optimal targets for inhibition. These genes are known regulators of the cancer-promoting genes in the pathway,hence our model agrees with the existing biological literature. Therefore inhibiting these three genes has the potential to prevent TNBC relapse. Additionally, our studies found that drug efficacies decreased as mutations increased in the pathway. Furthermore, we noticed that combinations of drugs performed better than single drugs.
Non-Gaussian Tensor Programs

Does it matter whether one randomly initializes a neural network (NN) from Gaussian, uniform, or other distributions? We show the answer is ”yes” in some parameter tensors (the so-called matrix-like parameters) but ”no” in others when the NN is wide. This is a specific instance of a more general universality principle for Tensor Programs (TP) that informs precisely when the limit of a program depends on the distribution of its initial matrices and vectors. To obtain this principle, we develop the theory of non-Gaussian Tensor Programs. As corollaries, we obtain all previous consequences of the TP framework (such as NNGP/NTK correspondence, Free Indepedence Principle, Dynamical Dichotomy Theorem, and µ -parametrization) for NNs with non-Gaussian weights. 1
Convective Coupling and Interannual and Intraseasonal Coupled Variabilities in the Tropics

Abstract Recent analyses point at close structural similarities and intimate dynamical connections among the members of a wide spectrum of variabilities in the global Tropics. This suggests that these oscillations with widely different timescales may have a common dynamical origin, although diverse dynamical evolution. The present work explores such a dynamical framework for the genesis of tropical variabilities. The highlight of the dynamical framework is a parameterization of air–sea interaction, which the authors call convective coupling. In the proposed parameterization the effect of sea surface temperature (SST) on the atmosphere is only through the modulation of the column precipitation. The changed convective heating modifies the low-level flow, which in turn changes the oceanic variables, thus closing the cycle. Thus the forcing due to convective coupling behaves like a (second order) threshold process, with (SST modulated) convection acting like a switch. Since many of the observed variabilities .
Online Reinforcement Learning for Mixed Policy Scopes

Combination therapy refers to the use of multiple treatments – such as surgery, medication, and behavioral therapy - to cure a single disease, and has become a cornerstone for treating various conditions including cancer, HIV, and depression. All possible combinations of treatments lead to a collection of treatment regimens (i.e., policies) with mixed scopes, or what physicians could observe and which actions they should take depending on the context. In this paper, we investigate the online reinforcement learning setting for optimizing the policy space with mixed scopes. In particular, we develop novel online algorithms that achieve sublinear regret compared to an optimal agent deployed in the environment. The regret bound has a dependency on the maximal cardinality of the induced state-action space associated with mixed scopes. We further introduce a canonical representation for an arbitrary subset of interventional distributions given a causal diagram, which leads to a non-trivial, minimal representation of the model parameters.
Learning Bipartite Graphs: Heavy Tails and Multiple Components

We investigate the problem of learning an undirected, weighted bipartite graph under the Gaussian Markov random ﬁeld model, for which we present an optimization formulation along with an efﬁcient algorithm based on the projected gradient descent. Motivated by practical applications, where outliers or heavy-tailed events are present, we extend the proposed learning scheme to the case in which the data follow a multivariate Student-t distribution. As a result, the optimization program is no longer convex, but a veriﬁably convergent iterative algorithm is proposed based on the majorization-minimization framework. Finally, we propose an efﬁcient and provably convergent algorithm for learning k -component bipartite graphs that leverages rank constraints of the underlying graph Laplacian matrix. The proposed estimators outperform state-of-the-art methods for bipartite graph learning, as evidenced by real-world experiments using ﬁnancial time series data.
SignRFF: Sign Random Fourier Features

The industry practice has been moving to embedding based retrieval (EBR). For example, in many applications, the embedding vectors are trained by some form of two-tower models. During serving phase, candidates (embedding vectors) are retrieved according to the rankings of cosine similarities either exhaustively or by approximate near neighbor (ANN) search algorithms. For those applications, it is natural to apply “sign random projections” (SignRP) or variants, on the trained embedding vectors to facilitate efficient data storage and cosine distance computations. SignRP is also one of the standard indexing schemes for conducting approximate near neighbor search. In the literature, SignRP has been popular and, to an extent, becomes the default method for “locality sensitive hashing” (LSH). In this paper, we propose “sign random Fourier features” (SignRFF) as an alternative to SignRP. The original method of random Fourier features (RFF) is a standard technique for approximating the Gaussian kernel (as opposed to the linear cosine kernel), in the literature of large-scale machine learning. Basically, RFF applies a simple nonlinear transformation on the samples generated by random projections (RP). Thus, in the pipeline of EBR, it is straightforward to replace SignRP by SignRFF. This paper explains, in a principled manner, why it makes sense to do so. In this paper, a new analytical measure called Ranking Efficiency (RE) is developed, which in retrospect is closely related to the “two-sample mean” t -test statistic for binomial variables. RE provides a systematic and unified framework for comparing different LSH methods. We compare our proposed SignRP with SignRP, KLSH (kernel LSH), as well SQ-RFF (which is another 1-bit coding scheme for RFF). According to the RE expression, SignRFF consistently outperforms KLSH (for Gaussian kernel) and SQ-RFF. SignRFF
Efficient and Effective Optimal Transport-Based Biclustering

Bipartite graphs can be used to model a wide variety of dyadic information such as user-rating, document-term, and gene-disorder pairs. Biclustering is an extension of clustering to the underlying bipartite graph induced from this kind of data. In this paper, we leverage optimal transport (OT) which has gained momentum in the machine learning community to propose a novel and scalable biclustering model that generalizes several classical biclustering approaches. We perform extensive experimentation to show the validity of our approach compared to other OT biclustering algorithms along both dimensions of the dyadic datasets.
GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs

Despite the remarkable success of graph neural networks (GNNs) for graph representation learning, they are generally built on the (unreliable) i.i. d. assumption across training and testing data. However, real-world graph data are universally comprised of outliers in training set and out-of-distribution (OOD) testing samples from unseen domains, which solicits effective models for i) debiased learning and ii) OOD detection, towards general trustworthy purpose. In this paper, we first mathematically formulate the two challenging problems for graph data and take an initiative on tackling them under a unified probabilistic model. Specifically, we model the graph generative process to characterize the distribution shifts of graph data together with an additionally introduced latent environment variable as an indicator. We then define a variational distribution, i.e., a recognition model, to infer the environment during training of GNN. By instantiating the generative models as two-component mixtures, we derive a tractable learning objective and theoretically justify that the model can i) automatically identify and down-weight outliers in the training procedure, and ii) induce an effective OOD detector simultaneously. Experiments on diverse datasets with different types of OOD data prove that our model consistently outperforms strong baselines for both debiasing and OOD detection tasks. The source code has been made publicly available at https://github.com/Emiyalzn/GraphDE .
OPEN: Orthogonal Propagation with Ego-Network Modeling

To alleviate the unfavorable effect of noisy topology in Graph Neural networks (GNNs), some efforts perform the local topology reﬁnement through the pairwise propagation weight learning and the multi-channel extension. Unfortunately, most of them suffer a common and fatal drawback: irrelevant propagation to one node and in multi-channels. These two kinds of irrelevances make propagation weights in multi-channels free to be determined by the labeled data, and thus the GNNs are exposed to overﬁtting. To tackle this issue, a novel Orthogonal Propagation with Ego-Network modeling (OPEN) is proposed by modeling relevances between propagations. Speciﬁcally, the relevance between propagations to one node is modeled by whole ego-network modeling, while the relevance between propagations in multi-channels is modeled via diversity requirement. By interpreting the
Sharing Knowledge for Meta-learning with Feature Descriptions

Language is an important tool for humans to share knowledge. We propose a meta-learning method that shares knowledge across supervised learning tasks using feature descriptions written in natural language, which have not been used in the existing meta-learning methods. The proposed method improves the predictive performance on unseen tasks with a limited number of labeled data by meta-learning from various tasks. With the feature descriptions, we can ﬁnd relationships across tasks even when their feature spaces are different. The feature descriptions are encoded using a language model pretrained with a large corpus, which enables us to incorporate human knowledge stored in the corpus into meta-learning. In our experiments, we demonstrate that the proposed method achieves better predictive performance than the existing meta-learning methods using a wide variety of real-world datasets provided by the statistical ofﬁce of the EU and Japan.
Real-Valued Backpropagation is Unsuitable for Complex-Valued Neural Networks

Recently complex-valued neural networks have received increasing attention due to successful applications in various tasks and the potential advantages of better theoretical properties and richer representational capacity. However, the training dynamics of complex networks compared to real networks remains an open problem. In this paper, we investigate the dynamics of deep complex networks during real-valued backpropagation in the infinite-width limit via neural tangent kernel (NTK). We first extend the Tensor Program to the complex domain, to show that the dynamics of any basic complex network architecture is governed by its NTK under real-valued backpropagation. Then we propose a way to investigate the comparison of training dynamics between complex and real networks by studying their NTKs. As a result, we surprisingly prove that for most complex activation functions, the commonly used real-valued backpropagation reduces the training dynamics of complex networks to that of ordinary real networks as the widths tend to infinity, thus eliminating the characteristics of complex-valued neural networks. Finally, the experiments validate our theoretical findings numerically.
Learning Latent Seasonal-Trend Representations for Time Series Forecasting

Forecasting complex time series is ubiquitous and vital in a range of applications but challenging. Recent advances endeavor to achieve progress by incorporating various deep learning techniques (e.g., RNN and Transformer) into sequential models. However, clear patterns are still hard to extract since time series are often composed of several intricately entangled components. Motivated by the success of disentangled variational autoencoder in computer vision and classical time series decomposition, we plan to infer a couple of representations that depict seasonal and trend components of time series. To achieve this goal, we propose LaST, which, based on variational inference, aims to disentangle the seasonal-trend representations in the latent space. Furthermore, LaST supervises and disassociates representations from the perspectives of themselves and input reconstruction, and introduces a series of auxiliary objectives. Extensive experiments prove that LaST achieves state-of-the-art performance on time series forecasting task against the most advanced representation learning and end-to-end forecasting models. For reproducibility, our implementation is publicly available on Github 1 .
Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery

Conditional independence (CI) testing is frequently used in data analysis and machine learning for various scientific fields and it forms the basis of constraint-based causal discovery. Oftentimes, CI testing relies on strong, rather unrealistic assumptions. One of these assumptions is homoskedasticity, in other words, a constant conditional variance is assumed. We frame heteroskedasticity in a structural causal model framework and present an adaptation of the partial correlation CI test that works well in the presence of heteroskedastic noise, given that expert knowledge about the heteroskedastic relationships is available. Further, we provide theoretical consistency results for the proposed CI test which carry over to causal discovery under certain assumptions. Numerical causal discovery experiments demonstrate that the adapted partial correlation CI test outperforms the standard test in the presence of heteroskedasticity and is on par for the homoskedastic case. Finally, we discuss the general challenges and limits as to how expert knowledge about heteroskedasticity can be accounted for in causal discovery.
Values and Mathematics: Overt and Covert

This paper argues that mathematics is imbued with values reflecting its production from human imagination and dialogue. Epistemological, ontological, aesthetic and ethical values are specified, both overt and covert. Within the culture of mathematics, the overt values of truth, beauty, purity, universalism, objectivism, rationalism and utility are identified. In contrast, hidden within mathematics and its culture are the covert values of objectism and ethics, including the specific ethical values of separatism, openness, fairness and democracy. Some of these values emerge from a dialogical view of the nature of mathematics, and certainly from the dialogical nature of culture. It is acknowledged that the choice of certain perspectives, such as absolutism, lead to the appearance of mathematics as largely value-free through hiding value-assumptions behind the initial choice of philosophy. By uncovering the values of mathematics I show that it more closely resembles other components of human culture than is usually recognised.
Bayesian Optimistic Optimization: Optimistic Exploration for Model-based Reinforcement Learning

Reinforcement learning (RL) is a general framework for modeling sequential decision making problems, at the core of which lies the dilemma of exploitation and exploration. An agent failing to explore systematically will inevitably fail to learn efficiently. Optimism in the face of uncertainty (OFU) is a conventionally successful strategy for efficient exploration. An agent following the OFU principle explores actively and efficiently. However, when applied to model-based RL, it involves specifying a confidence set of the underlying model and solving a series of nonlinear constrained optimization, which can be computationally intractable. This paper proposes an algorithm, Bayesian optimistic optimization (BOO), which adopts a dynamic weighting technique for enforcing the constraint rather than explicitly solving a constrained optimization problem. BOO is a general algorithm proved to be sample-efficient for models in a finite-dimensional reproducing kernel Hilbert space. We also develop techniques for effective optimization and show through some simulation experiments that BOO is competitive with the existing algorithms.
Identification of the conserved iol gene cluster involved in rhizosphere competence in Pseudomonas

The Pseudomonas genus has shown great potential as a sustainable solution to support agriculture through its plant-growth promoting and biocontrol activities. However, their efficacy as bioinoculants is limited by unpredictable colonization in natural conditions. Our study identifies the iol locus, a gene cluster in Pseudomonas involved in inositol catabolism, as a feature enriched among superior root colonizers in natural soil. Further characterization revealed that the iol locus increases competitiveness by inducing swimming motility and fluorescent siderophore production in response to inositol, a plant-derived compound. Public data analyses indicate that the iol locus is broadly conserved in the Pseudomonas genus and linked to diverse host-microbe interactions. Our findings suggest the iol locus as a potential target for developing more effective bioinoculants, given its conservation and association with diverse host-microbe interactions.