Hybrid reconstruction of the physical model with the deep learning that improves structured illumination microscopy

Structured illumination microscopy(SIM)has been widely used in live-cell superresolution(SR)imaging.However,conventional physical model-based SIM SR reconstruction algorithms are prone to artifacts in handling raw images with low signal-to-noise ratios(SNRs).Deep-learning(DL)-based methods can address this challenge but may lead to degradation and hallucinations.By combining the physical inversion model with a total deep variation(TDV)regularization,we propose a hybrid restoration method(TDV-SIM)that outperforms conventional or DL methods in suppressing artifacts and hallucinations while maintaining resolutions.We demonstrate the performance superiority of TDV-SIM in restoring actin filaments,endoplasmic reticulum,and mitochondrial cristae from extremely low SNR raw images.Thus TDV-SIM represents the ideal method for prolonged live-cell SR imaging with minimal exposure and photodamage.Overall,TDV-SIM proves the power of integrating model-based reconstruction methods with DL ones,possibly leading to the rapid exploration of similar strategies in high-fidelity reconstructions of other microscopy methods.

Toward Selective Synthesis of Protein Olympiadanes via Orthogonal Active Templates in One Step

Unlike small molecules,the topological complexity of macromolecules remains largely unexplored due to the huge synthetic challenge.Herein,we report the development of orthogonal active templates for concise and selective synthesis of protein[n]heterocatenanes toward protein olympiadanes.An active template(AT-Snoop)was first developed based on the isopeptide-bond-forming RrgA domain with comparable efficiency and excellent orthogonality to the previously reported active template(AT-Spy)based on the CnaB2 domain.Their combination facilitated the selective synthesis of protein[n]catenanes from multiple components in one step and the resulting structures were verified by sodium dodecyl sulfate-polyacrylamide gel electrophoresis,size exclusion chromatography,liquid chromatography-mass spectrometry,and proteolytic digestion experiments.The results offered a promising solution to tackling the daunting challenge of precision synthesis of protein olympiadane with five distinct ring components.Not only did the success provide new tools for proteintopology engin eering but alsospurred and fueled the future exploitation of topology-related functional benefits in protein science.

Active Template Synthesis of Protein[n]Catenanes Using Engineered Peptide–Peptide Ligation Tools

The expansion of protein topological diversity requires new and efficient synthetic tools.Herein,we report the second and third generations of the SpyStapler-mediated SpyTag/BDTag ligation system for the efficient synthesis of 4-arm star proteins and the repurposing of the third generation as an active template to enable the synthesis of higher-order protein[n]catenanes(n=3,4,and 5).SpyStapler003 has a higher affinity to its cognate SpyTag and BDTag reactive pairs relative to the original SpyStapler.Hence,it can overcome much more profound steric hindrance in protein ligation and improve the efficiency of the resulting active template tool to facilitate the construction of radial protein[n]catenanes.Various proteins of interest,such as dihydrofolate reductase and the nanobody KN035,can be modularly incorporated into the[n]catenanes with intact activity.Combination of passive and active template strategies gives rise to linear protein[4]catenanes,which further expands the current topological diversity.Moreover,higher-order protein catenation not only leads to enhanced thermal stability and proteolytic resistance but also higher affinity of the nanobody via multivalent effects.Our study provides tools useful for bioconjugation and new topological protein scaffolds for the multivalent display of enzymes and antibodies.

Differences in action potential propagation speed and axon initial segment plasticity between neurons from Sprague-Dawley rats and C57BL/6 mice

Action potentials(APs)in neurons are generated at the axon initial segment(AIS).AP dynamics,including initiation and propagation,are intimately associated with neuronal excitability and neurotransmitter release kinetics.Most learning and memory studies at the single-neuron level have relied on the use of animal models,most notably rodents.Here,we studied AP initiation and propagation in cultured hippocampal neurons from Sprague-Dawley(SD)rats and C57BL/6(C57)mice with genetically encoded voltage indicator(GEVI)-based voltage imaging.Our data showed that APs traveled bidirectionally in neurons from both species;forward-propagating APs(fpAPs)had a different speed than backpropagating APs(bpAPs).Additionally,we observed distinct AP propagation characteristics in AISs emerging from the somatic envelope compared to those originating from dendrites.Compared with rat neurons,mouse neurons exhibited higher bpAP speed and lower fpAP speed,more distally located ankyrin G(AnkG)in AISs,and longer Nav1.2 lengths in AISs.Moreover,during AIS plasticity,AnkG and Nav1.2 showed distal shifts in location and shorter lengths of labeled AISs in rat neurons;in mouse neurons,however,they showed a longer AnkG-labeled length and more distal Nav1.2 location.Our findings suggest that hippocampal neurons in SD rats and C57 mice may have different AP propagation speeds,different AnkG and Nav1.2 patterns in the AIS,and different AIS plasticity properties,indicating that comparisons between these species must be carefully considered.

Neural Decoding of Visual Information Across Different Neural Recording Modalities and Approaches

Vision plays a peculiar role in intelligence.Visual information,forming a large part of the sensory information,is fed into the human brain to formulate various types of cognition and behaviours that make humans become intelligent agents.Recent advances have led to the development of brain-inspired algorithms and models for machine vision.One of the key components of these methods is the utilization of the computational principles underlying biological neurons.Additionally,advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual information.Thus,there is a high demand for mapping out functional models for reading out visual information from neural signals.Here,we briefly review recent progress on this issue with a focus on how machine learning techniques can help in the development of models for contending various types of neural signals,from fine-scale neural spikes and single-cell calcium imaging to coarse-scale electroencephalography(EEG)and functional magnetic resonance imaging recordings of brain signals.

Sememe knowledge computation:a review of recent advances in application and expansion of sememe knowledge bases

A sememe is defined as the minimum semantic unit of languages in linguistics.Sememe knowledge bases are built by manually annotating sememes for words and phrases.HowNet is the most well-known sememe knowledge base.It has been extensively utilized in many natural language processing tasks in the era of statistical natural language processing and proven to be effective and helpful to understanding and using languages.In the era of deep learning,although data are thought to be of vital importance,there are some studies working on incorporating sememe knowledge bases like HowNet into neural network models to enhance system performance.Some successful attempts have been made in the tasks including word representation learning,language modeling,semantic composition,etc.In addition,considering the high cost of manual annotation and update for sememe knowledge bases,some work has tried to use machine learning methods to automatically predict sememes for words and phrases to expand sememe knowledge bases.Besides,some studies try to extend HowNet to other languages by automatically predicting sememes for words and phrases in a new language.In this paper,we summarize recent studies on application and expansion of sememe knowledge bases and point out some future directions of research on sememes.

Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation

In fluorescence microscopy,computational algorithms have been developed to suppress noise,enhance contrast,and even enable super-resolution(SR).However,the local quality of the images may vary on multiple scales,and these differences can lead to misconceptions.Current mapping methods fail to finely estimate the local quality,challenging to associate the SR scale content.Here,we develop a rolling Fourier ring correlation(rFRC)method to evaluate the reconstruction uncertainties down to SR scale.To visually pinpoint regions with low reliability,a filtered rFRC is combined with a modified resolution-scaled error map(RSM),offering a comprehensive and concise map for further examination.We demonstrate their performances on various SR imaging modalities,and the resulting quantitative maps enable better SR images integrated from different reconstructions.Overall,we expect that our framework can become a routinely used tool for biologists in assessing their image datasets in general and inspire further advances in the rapidly developing field of computational imaging.

Rational Design and Cellular Synthesis of Proteins with Unconventional Chemical Topology

Chemical topology refers to the three-dimensional arrangement(i.e.,connectivity and spatial relationship)of a molecule's constituent atoms and bonds.The molecular mechanism for translation defines the linear configuration of all nascent proteins.Nontrivial protein topology arises only upon post-translational processing events and often imparts functional benefits such as enhanced stability,making topology a unique dimension for protein engineering.Utilizing the assembly-reaction synergy,our group has developed several methods for the effective and convenient cellular synthesis of a variety of topological proteins,such as lasso proteins,protein rotaxanes,and protein catenanes.The work opens the access to new protein classes and paves the road toward illustrating the topological effects on structure-function relationship of proteins,which lays solid foundation for exploring topological proteins’practical application.

Identity-Preserving Adversarial Training for Robust Network Embedding

Network embedding,as an approach to learning low-dimensional representations of nodes,has been proved extremely useful in many applications,e.g.,node classification and link prediction.Unfortunately,existing network embed-ding models are vulnerable to random or adversarial perturbations,which may degrade the performance of network em-bedding when being applied to downstream tasks.To achieve robust network embedding,researchers introduce adversari-al training to regularize the embedding learning process by training on a mixture of adversarial examples and original ex-amples.However,existing methods generate adversarial examples heuristically,failing to guarantee the imperceptibility of generated adversarial examples,and thus limit the power of adversarial training.In this paper,we propose a novel method Identity-Preserving Adversarial Training(IPAT)for network embedding,which generates imperceptible adversarial exam-ples with explicit identity-preserving regularization.We formalize such identity-preserving regularization as a multi-class classification problem where each node represents a class,and we encourage each adversarial example to be discriminated as the class of its original node.Extensive experimental results on real-world datasets demonstrate that our proposed IPAT method significantly improves the robustness of network embedding models and the generalization of the learned node representations on various downstream tasks.

Enriching the Transfer Learning with Pre-Trained Lexicon Embedding for Low-Resource Neural Machine Translation

Most State-Of-The-Art(SOTA) Neural Machine Translation(NMT) systems today achieve outstanding results based only on large parallel corpora.The large-scale parallel corpora for high-resource languages is easily obtainable.However,the translation quality of NMT for morphologically rich languages is still unsatisfactory,mainly because of the data sparsity problem encountered in Low-Resource Languages(LRLs).In the low-resource NMT paradigm,Transfer Learning(TL) has been developed into one of the most efficient methods.It is difficult to train the model on high-resource languages to include the information in both parent and child models,as well as the initially trained model that only contains the lexicon features and word embeddings of the parent model instead of the child languages feature.In this work,we aim to address this issue by proposing the language-independent Hybrid Transfer Learning(HTL) method for LRLs by sharing lexicon embedding between parent and child languages without leveraging back translation or manually injecting noises.First,we train the High-Resource Languages(HRLs) as the parent model with its vocabularies.Then,we combine the parent and child language pairs using the oversampling method to train the hybrid model initialized by the previously parent model.Finally,we fine-tune the morphologically rich child model using a hybrid model.Besides,we explore some exciting discoveries on the original TL approach.Experimental results show that our model consistently outperforms five SOTA methods in two languages Azerbaijani(Az) and Uzbek(Uz).Meanwhile,our approach is practical and significantly better,achieving improvements of up to 4:94 and 4:84 BLEU points for low-resource child languages Az ! Zh and Uz ! Zh,respectively.

Towards a New Paradigm for Brain-inspired Computer Vision

Brain-inspired computer vision aims to learn from biological systems to develop advanced image processing techniques.However,its progress so far is not impressing.We recognize that a main obstacle comes from that the current paradigm for brain-inspired computer vision has not captured the fundamental nature of biological vision,i.e.,the biological vision is targeted for processing spatio-temporal patterns.Recently,a new paradigm for developing brain-inspired computer vision is emerging,which emphasizes on the spatio-temporal nature of visual signals and the brain-inspired models for processing this type of data.In this paper,we review some recent primary works towards this new paradigm,including the development of spike cameras which acquire spiking signals directly from visual scenes,and the development of computational models learned from neural systems that are specialized to process spatio-temporal patterns,including models for object detection,tracking,and recognition.We also discuss about the future directions to improve the paradigm.

Least Squares Model Averaging Based on Generalized Cross Validation

Frequentist model averaging has received much attention from econometricians and statisticians in recent years.A key problem with frequentist model average estimators is the choice of weights.This paper develops a new approach of choosing weights based on an approximation of generalized cross validation.The resultant least squares model average estimators are proved to be asymptotically optimal in the sense of achieving the lowest possible squared errors.Especially,the optimality is built under both discrete and continuous weigh sets.Compared with the existing approach based on Mallows criterion,the conditions required for the asymptotic optimality of the proposed method are more reasonable.Simulation studies and real data application show good performance of the proposed estimators.

DynamicRetriever:A Pre-trained Model-based IR System Without an Explicit Index

Web search provides a promising way for people to obtain information and has been extensively studied.With the surge of deep learning and large-scale pre-training techniques,various neural information retrieval models are proposed,and they have demonstrated the power for improving search(especially,the ranking)quality.All these existing search methods follow a common paradigm,i.e.,index-retrieve-rerank,where they first build an index of all documents based on document terms(i.e.,sparse inverted index)or representation vectors(i.e.,dense vector index),then retrieve and rerank retrieved documents based on the similarity between the query and documents via ranking models.In this paper,we explore a new paradigm of information retrieval without an explicit index but only with a pre-trained model.Instead,all of the knowledge of the documents is encoded into model parameters,which can be regarded as a differentiable indexer and optimized in an end-to-end manner.Specifically,we propose a pre-trained model-based information retrieval(IR)system called DynamicRetriever,which directly returns document identifiers for a given query.Under such a framework,we implement two variants to explore how to train the model from scratch and how to combine the advantages of dense retrieval models.Compared with existing search methods,the model-based IR system parameterizes the traditional static index with a pre-training model,which converts the document semantic mapping into a dynamic and updatable process.Extensive experiments conducted on the public search benchmark Microsoft machine reading comprehension(MS MARCO)verify the effectiveness and potential of our proposed new paradigm for information retrieval.

A Moonlighting Superpositively Charged SpyCatcher

Genetically encoded covalent peptide tagging tools such as the SpyTag/SpyCatcher reactive pair,have been demonstrated versatile and useful for protein modification.Herein,we present a superpositively charged SpyCatcher bearing a theoretical net charge of+21 capable of accomplishing multiple unrelated independent tasks to enrich this toolbox and cultivate new functions.The SpyCatcher(+21)possessed stimuli-responsive reactivity toward SpyTag and could serve as a potent and general platform for the delivery of proteins,including RNaseAinto HeLa cells.Remarkably,the delivered RNase A caused substantial proliferation inhibition toward HeLa cells.In addition,the superpositively charged SpyCatcher could form coacervate with plasmid DNA for further study of gene delivery and liquid–liquid phase separation.These findings demonstrate the robustness of the SpyTag/SpyCatcher structure against surface mutation and the prospect of applying supercharging technology on diverse functional proteins to create moonlighting proteins.

COPPER:a combinatorial optimization problem solver with processing-in-memory architecture

The combinatorial optimization problem(COP),which aims to find the optimal solution in discrete space,is fundamental in various fields.Unfortunately,many COPs are NP-complete,and require much more time to solve as the problem scale increases.Troubled by this,researchers may prefer fast methods even if they are not exact,so approximation algorithms,heuristic algorithms,and machine learning have been proposed.Some works proposed chaotic simulated annealing(CSA)based on the Hopfield neural network and did a good job.However,CSA is not something that current general-purpose processors can handle easily,and there is no special hardware for it.To efficiently perform CSA,we propose a software and hardware co-design.In software,we quantize the weight and output using appropriate bit widths,and then modify the calculations that are not suitable for hardware implementation.In hardware,we design a specialized processing-in-memory hardware architecture named COPPER based on the memristor.COPPER is capable of efficiently running the modified quantized CSA algorithm and supporting the pipeline further acceleration.The results show that COPPER can perform CSA remarkably well in both speed and energy.