Miracle-dz/UniFAR-FTUs huggingface dataset:
Drosophila FMRP recruits the miRISC to target mRNAs to repress translation

Fragile X syndrome (FXS) is the most common inherited form of intellectual disability and is caused by mutations in the gene encoding for the Fragile X messenger ribonucleoprotein (FMRP). FMRP is an evolutionarily conserved and neuronally enriched RNA binding protein (RBP) with functions in the control of processes including RNA editing, RNA transport, and protein translation. Specific target RNAs play critical roles in neurodevelopment including the regulation of neurite morphogenesis, synaptic plasticity, and cognitive function. The different biological functions of FMRP are modulated by its cooperative interaction with distinct sets of neuronal RNA and protein binding partners. Here, we focus on interactions between FMRP and components of the microRNA (miRNA) pathway. Using the Drosophila model system, we show that dFMRP can repress the translation of a reporter mRNA via a deadenylation-independent mechanism. This repression requires the activity of both AGO1 and GW182, conserved components of the miRNA-containing RISC (miRISC). Interestingly, we find that dFMRP can bind directly to a short stem loop structure in the reporter and that dFMRP binding is a prerequisite for repression by miR-958. Finally, we show that dFmr1 interacts genetically with GW182 to control neurite morphogenesis. Collectively, these data suggest the dFMRP can directly recruit the miRISC to nearby miRNA binding sites and then repress translation via the activity of the miRISC effector, GW182.
Collective signalling drives rapid jumping between cell states

Development can proceed in “fits and starts”, with rapid transitions between cell states involving concerted transcriptome-wide changes in gene expression. However, it is not clear how these transitions are regulated in complex cell populations, in which cells receive multiple inputs. It is also not clear to what extent these rapid transitions represent developmental commitment. Here we address these issues using Dictyostelium cells undergoing development in their physiological niche. A continuous single cell transcriptomics time series reveals a sharp “jump” in global gene expression marking functionally different cell states. By simultaneously live imaging the physiological dynamics of transcription and signalling over millimetre length scales, we show that the jump coincides with the onset of collective oscillations of cAMP, the positive feedback signal for multicellular development. Different jump genes respond to distinct dynamic features of signalling. The late gene expression changes of the jump are almost completely dependent on cAMP. In contrast, transcript changes at the onset of the jump require additional input. The spatial boundary marking the jump divides cells separated by only a few minutes of developmental time, with cells missing a jump then waiting several hours for the onset of the next wave of cAMP oscillations. This timing variability contrasts the prevailing developmental paradigm of a timed synchronous process and is associated with substantial pre-jump transcriptome variability. The coupling of collective signalling with gene expression is a potentially powerful strategy to drive robust cell state transitions in heterogeneous signalling environments. Based on the context of the jump, we also conclude that sharp gene expression transitions may not be sufficient for commitment.
Research on Construction Method of Operational Reliability Control Model for Space Manipulator Based on Particle Filter

The operational reliability of the space manipulator is closely related to the control method. However the existing control methods seldom consider the operational reliability from the system level. A method to construct the operational reliability system control model based on particle filter for the space manipulator is presented in this paper. Firstly, the definition of operational reliability and the degree of operational reliability are given and the state space equations of the control system are established as well. Secondly, based on the particle filter algorithm, a method to estimate the distribution of the end position error and calculate the degree of operational reliability with any form of noise distribution in real time is established. Furthermore, a performance model based on quality loss theory is built and a performance function is obtained to evaluate the quality of the control process. The adjustment value of the end position of the space manipulator can be calculated by using the performance function. Finally, a large number of simulation results show that the control method proposed in this paper can improve the task success rate effectively compared to the simulation results using traditional control methods and control methods based on Bayesian estimation.
Mechanical Dissociation of Tissues for Single Cell Analysis Using a Simple Motorized Device

The use of single cell analysis methods has grown rapidly in the last two decades and has led to rapid discoveries in cell biology and beyond. Single cell analysis requires complex systems like tissues to be dissociated, separating individual cells from extracellular tissue materials. This requires manual processing of tissues and materials through chopping, pipetting, and suspension with enzymes for degradation of the structural elements of the tissue. Manual processing can be time consuming and lead to variability between scientists. Automating this process through motorized dissociation could thus improve reproducibility of research and reduce time of cell manipulation prior to analysis. Here, we have designed a low-cost, customizable automatic tissue dissociator device that can be easily assembled by research groups for individual use. Our device allows for customizable programmed dissociation protocols for ease of use and reproducibility between researchers and can be placed into heat or cold environments based on the protocol need. We have found this device comparable in cell viability and reproducibility to manual dissociation, while significantly reducing time spent and even enhancing cells extracted from more fibrous tissues. Broad dissemination and use of this device could enhance single cell analysis reproducibility and provide a time-saving alternative to the currently used manual dissociation protocols.
Distributed Optimization for Overparameterized Problems: Achieving Optimal Dimension Independent Communication Complexity

Decentralized optimization are playing an important role in applications such as training large machine learning models, among others. Despite its superior practical performance, there has been some lack of fundamental understanding about its theoretical properties. In this work, we address the following open research question: To train an overparameterized model over a set of distributed nodes, what is the minimum communication overhead (in terms of the bits got exchanged) that the system needs to sustain, while still achieving (near) zero training loss? We show that for a class of overparameterized models where the number of parameters D is much larger than the total data samples N , the best possible communication complexity is ⌦ ( N ) , which is independent of the problem dimension D . Further, for a few speciﬁc overparameterized models (i.e., the linear regression, and certain multi-layer neural network with one wide layer), we develop a set of algorithms which uses certain linear compression followed by adaptive quantization, and show that they achieve dimension independent, near-optimal communication complexity. To our knowledge, this is the ﬁrst time that dimension independent communication complexity has been shown for distributed optimization.
A homemade atomic force microscope based on a quartz tuning fork for undergraduate instruction

Atomic force microscopes are a key tool in nanotechnology that overcome the limitations of optical microscopes and provide imaging capabilities with nanoscale resolution. We have developed an atomic force microscope that uses an inexpensive quartz tuning fork as a micro cantilever. Because of its ease of operation and its open structure, it can be easily customized by students. Due to its low costs, it is possible that every student in the course has access to one setup, allowing all students to obtain deep insights into nanotechnology and to understand the principles of atomic force microscopy.
Evolution and genetics of accessory gland transcriptome divergence between Drosophila melanogaster and D. simulans.

Studies of allele-specific expression in interspecific hybrids have provided important insights into gene-regulatory divergence and hybrid incompatibilities. Many such investigations in Drosophila have used transcriptome data from complex mixtures of many tissues or from gonads, however, regulatory divergence may vary widely among species, sexes, and tissues. Thus, we lack sufficiently broad sampling to be confident about the general biological principles of regulatory divergence. Here we seek to fill some of these gaps in the literature by characterizing regulatory evolution and hybrid misexpression in a somatic male sex organ, the accessory gland, in F1 hybrids between Drosophila melanogaster and D. simulans. The accessory gland produces seminal fluid proteins, which play an important role in male and female fertility and may be subject to adaptive divergence due to male-male or male-female interactions. We find that trans differences are relatively more abundant than cis, in contrast to most of the interspecific hybrid literature, though large effect-size trans differences are rare. Seminal fluid protein genes have significantly elevated levels of expression divergence and tend to be regulated through both cis and trans divergence. We find limited misexpression (over- or underexpression relative to both parents) in this organ compared to most other Drosophila studies. As in previous studies, male-biased genes are overrepresented among misexpressed genes and are much more likely to be underexpressed. ATAC-Seq data show that chromatin accessibility is correlated with expression differences among species and hybrid allele-specific expression. This work identifies unique regulatory evolution and hybrid misexpression properties of the accessory gland and suggests the importance of tissue-specific allele-specific expression studies.
Predicting Label Distribution from Multi-label Ranking

Label distribution can provide richer information about label polysemy than logical labels in multi-label learning. There are currently two strategies including LDL (label distribution learning) and LE (label enhancement) to predict label distributions. LDL requires experts to annotate instances with label distributions and learn a predictive mapping on such a training set. LE requires experts to annotate instances with logical labels and generates label distributions from them. However, LDL requires costly annotation, and the performance of the LE is unstable. In this paper, we study the problem of predicting label distribution from multi-label ranking which is a compromise w.r. t. annotation cost but has good guarantees for performance. On the one hand, we theoretically investigate the relation between multi-label ranking and label distribution. We deﬁne the notion of EAE (expected approximation error) to quantify the quality of an annotation, give the bounds of EAE for multi-label ranking, and derive the optimal range of label distribution corresponding to a particular multi-label ranking. On the other hand, we propose a framework of label distribution predicting from multi-label ranking via conditional Dirichlet mixtures. This framework integrates the processes of recovering and learning label distributions end-to-end and allows us to easily encode our knowledge about current tasks by a scoring function. Finally, we implement extensive experiments to validate our proposal.
Molecular choreography of primer synthesis by the eukaryotic Pol α-primase

The eukaryotic polymerase α (Pol α) is a dual-function DNA polymerase/primase complex that synthesizes an RNA-DNA hybrid primer of 20-30 nucleotides for DNA replication. Pol α is composed of Pol1, Pol12, Primase 1 (Pri1), and Pri2, with Pol1 and Pri1 containing the DNA polymerase activity and RNA primase activity, respectively, whereas Pol12 and Pri2 serve a structural role. It has been unclear how Pol α hands over an RNA primer made by Pri1 to Pol1 for DNA primer extension, and how the primer length is defined, perhaps due to the difficulty in studying the highly mobile structure. Here we report a comprehensive cryo-EM analysis of the intact 4-subunit yeast Pol α in the apo, primer initiation, primer elongation, RNA primer hand-off from Pri1 to Pol1, and DNA extension states in a 3.5 Å - 5.6 Å resolution range. We found that Pol α is a three-lobed flexible structure. Pri2 functions as a flexible hinge that holds together the catalytic Pol1-core, and the noncatalytic Pol1 CTD that binds to Pol 12 to form a stable platform upon which the other components are organized. In the apo state, Pol1-core is sequestered on the Pol12−Pol1-CTD platform, and Pri1 is mobile perhaps in search of a template. Upon binding a ssDNA template, a large conformation change is induced that enables Pri1 to perform RNA synthesis, and positions Pol1-core to accept the future RNA primed site 50 Å upstream of where Pri1 binds. We reveal in detail the critical point at which Pol1-core takes over the 3’-end of the RNA from Pri1. DNA primer extension appears limited by the spiral motion of Pol1-core while Pri2-CTD stably holds onto the 5’ end of the RNA primer. Since both Pri1 and Pol1-core are attached via two linkers to the platform, primer growth will produce stress within this “two-point” attachment that may limit the length of the RNA-DNA hybrid primer. Hence, this study reveals the large and dynamic series of movements that Pol α undergoes to synthesize a primer for DNA replication.
Fused Orthogonal Alternating Least Squares for Tensor Clustering

We introduce a multi-mode tensor clustering method that implements a fused version of the alternating least squares algorithm (Fused-Orth-ALS) for simultaneous tensor factorization and clustering. The statistical convergence rates of recovery and clustering are established when the data are a noise contaminated tensor with a latent low rank CP decomposition structure. Specifically, we show that a modified alternating least squares algorithm can provably recover the true latent low rank factorization structure when the data form an asymmetric tensor with perturbation. Clustering consistency is also established. Finally, we illustrate the accuracy and computational efficient implementation of the Fused-Orth-ALS algorithm by using both simulations and real datasets
MetricFormer: A Unified Perspective of Correlation Exploring in Similarity Learning

Similarity learning can be significantly advanced by informative relationships among different samples and features. The current methods try to excavate the multiple correlations in different aspects, but cannot integrate them into a unified framework. In this paper, we provide to consider the multiple correlations from a unified perspective and propose a new method called MetricFormer, which can effectively capture and model the multiple correlations with an elaborate metric transformer. In MetricFormer, the feature decoupling block is adopted to learn an ensemble of distinct and diverse features with different discriminative characteristics. After that, we apply the batch-wise correlation block into the batch dimension of each mini-batch to implicitly explore sample relationships. Finally, the feature-wise correlation block is performed to discover the intrinsic structural pattern of the ensemble of features and obtain the aggregated feature embedding for similarity measuring. With three kinds of transformer blocks, we can learn more representative features through the proposed MetricFormer. Moreover, our proposed method can be flexibly integrated with any metric learning framework. Extensive experiments on three widely-used datasets demonstrate the superiority of our proposed method over state-of-the-art methods.
HandMeThat: Human-Robot Communication in Physical and Social Environments

We introduce HandMeThat, a benchmark for a holistic evaluation of instruction understanding and following in physical and social environments. While previous datasets primarily focused on language grounding and planning, HandMeThat considers the resolution of human instructions with ambiguities based on the physical (object states and relations) and social (human actions and goals) information. HandMeThat contains 10,000 episodes of human-robot interactions. In each episode, the robot first observes a trajectory of human actions towards her internal goal. Next, the robot receives a human instruction and should take actions to accomplish the subgoal set through the instruction. In this paper, we present a textual interface for our benchmark, where the robot interacts with a virtual environment through textual commands. We evaluate several baseline models on HandMeThat, and show that both offline and online reinforcement learning algorithms perform poorly on HandMeThat, suggesting significant room for future work on physical and social human-robot communications and interactions.
Stochastic Window Transformer for Image Restoration

Thanks to the powerful representation capabilities, transformers have made impressive progress in image restoration. However, existing transformers-based methods do not carefully consider the particularities of image restoration. In general, image restoration requires that an ideal approach should be translation-invariant to the degradation, i.e., the undesirable degradation should be removed irrespective of its position within the image. Furthermore, the local relationships also play a vital role, which should be faithfully exploited for recovering clean images. Nevertheless, most transformers either adopt local attention with the fixed local window strategy or global attention, which unfortunately breaks the translation invariance and causes huge loss of local relationships. To address these issues, we propose an elegant stochastic window strategy for transformers. Specifically, we first introduce the window partition with stochastic shift to replace the original fixed window partition for training. Then, we design a new layer expectation propagation algorithm to efficiently approximate the expectation of the induced stochastic transformer for testing. Our stochastic window transformer not only enjoys powerful representation but also maintains the desired property of translation invariance and locality. Experiments validate the stochastic window strategy consistently improves performance on various image restoration tasks (derain-ing, denoising and deblurring) by significant margins. The code is available at https://github.com/jiexiaou/Stoformer .
Latent Planning via Expansive Tree Search

Planning enables autonomous agents to solve complex decision-making problems by evaluating predictions of the future. However, classical planning algorithms often become infeasible in real-world settings where state spaces are high-dimensional and transition dynamics unknown. The idea behind latent planning is to simplify the decision-making task by mapping it to a lower-dimensional embedding space. Common latent planning strategies are based on trajectory optimization techniques such as shooting or collocation, which are prone to failure in long-horizon and highly non-convex settings. In this work, we study long-horizon goal-reaching scenarios from visual inputs and formulate latent planning as an explorative tree search. Inspired by classical sampling-based motion planning algorithms, we design a method which iteratively grows and optimizes a tree representation of visited areas of the latent space. To encourage fast exploration, the sampling of new states is biased towards sparsely represented regions within the estimated data support. Our method, called Expansive Latent Space Trees (ELAST), relies on self-supervised training via contrastive learning to obtain (a) a latent state representation and (b) a latent transition density model. We embed ELAST into a model-predictive control scheme and demonstrate significant performance improvements compared to existing baselines given challenging visual control tasks in simulation, including the navigation for a deformable object.
Self-Supervised Pretraining for Large-Scale Point Clouds

Pretraining on large unlabeled datasets has been proven to improve the down-stream task performance on many computer vision tasks, such as 2D object detection and video classification. However, for large-scale 3D scenes, such as outdoor LiDAR point clouds, pretraining is not widely used. Due to the special data characteristics of large 3D point clouds, approaches for 2D pretraining frameworks tend to not generalize well to this domain. In this paper, we propose a new self-supervised pretraining method that targets large-scale 3D scenes. We pretrain commonly used point-based and voxel-based model architectures and show the transfer learning performance on 3D object detection and semantic segmentation. We demonstrate the effectiveness of our approach on both dense 3D indoor point clouds and sparse outdoor LiDAR point clouds.
Sketching based Representations for Robust Image Classification with Provable Guarantees

How do we provably represent images succinctly so that their essential latent attributes are correctly captured by the representation to as high level of detail as possible? While today’s deep networks (such as CNNs) produce image embeddings they do not have any provable properties and seem to work in mysterious non-interpretable ways. In this work we theoretically study synthetic images that are composed of a union or intersection of several mathematically speciﬁed shapes using thresholded polynomial functions (for e.g. ellipses, rectangles). We show how to produce a succinct sketch of such an image so that the sketch “smoothly” maps to the latent-coefﬁcients producing the different shapes in the image. We prove several important properties such as: easy reconstruction of the image from the sketch, similarity preservation (similar shapes produce similar sketches), being able to index sketches so that other similar images and parts of other images can be retrieved, being able to store the sketches into a dictionary of concepts and shapes so parts of the same or different images that refer to the same shape can point to the same entry in this dictionary of common shape attributes.
Robust Learning against Relational Adversaries

Test-time adversarial attacks have posed serious challenges to the robustness of machine-learning models, and in many settings the adversarial perturbation needs not be bounded by small ℓ p -norms. Motivated by attacks in program analysis and security tasks, we investigate relational adversaries , a broad class of attackers who create adversarial examples in a reflexive-transitive closure of a logical relation. We analyze the conditions for robustness against relational adversaries and investigate different levels of robustness-accuracy trade-off due to various patterns in a relation. Inspired by the insights, we propose normalize-and-predict , a learning framework that leverages input normalization to achieve provable robustness. The framework solves the pain points of adversarial training against relational adversaries and can be combined with adversarial training for the benefits of both approaches. Guided by our theoretical findings, we apply our framework to source code authorship attribution and malware detection. Results of both tasks show our learning framework significantly improves the robustness of models against relational adversaries. In the process, it outperforms adversarial training, the most noteworthy defense mechanism, by a wide margin.