Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting subtle transcriptomic perturbations induced by lncRNAs knock-down in single-cell CRISPRi screening using a new sparse supervised autoencoder neural network

Single-cell CRISPR-based transcriptome screens are potent genetic tools for concomitantly assessing the expression profiles of cells targeted by a set of guides RNA (gRNA), and inferring target gene functions from the observed perturbations. However, due to various limitations, this approach lacks sensitivity in detecting weak perturbations and is essentially reliable when studying master regulators such as transcription factors. To overcome the challenge of detecting subtle gRNA induced transcriptomic perturbations and classifying the most responsive cells, we developed a new supervised autoencoder neural network method. Our Sparse supervised autoencoder (SSAE) neural network provides selection of both relevant features (genes) and actual perturbed cells. We applied this method on an in-house single-cell CRISPR-interference-based (CRISPRi) transcriptome screening (CROP-Seq) focusing on a subset of long non-coding RNAs (lncRNAs) regulated by hypoxia, a condition that promote tumor aggressiveness and drug resistance, in the context of lung adenocarcinoma (LUAD). The CROP-seq library of validated gRNA against a subset of lncRNAs and, as positive controls, HIF1A and HIF2A, the 2 main transcription factors of the hypoxic response, was transduced in A549 LUAD cells cultured in normoxia or exposed to hypoxic conditions during 3, 6 or 24 hours. We first validated the SSAE approach on HIF1A and HIF2 by confirming the specific effect of their knock-down during the temporal switch of the hypoxic response. Next, the SSAE method was able to detect stable short hypoxia-dependent transcriptomic signatures induced by the knock-down of some lncRNAs candidates, outperforming previously published machine learning approaches. This proof of concept demonstrates the relevance of the SSAE approach for deciphering weak perturbations in single-cell transcriptomic data readout as part of CRISPR-based screening.
Spontaneous Alignment of Myotubes through Myogenic Progenitor Cell Migration

In large volume muscle injuries, widespread damage to muscle fibers and the surrounding connective tissue prevents myogenic progenitor cells (MPCs) from initiating repair. There is a clinical need to rapidly fabricate large muscle tissue constructs for integration at the site of large volume muscle injuries. Most strategies for myotube alignment require microfabricated structures or prolonged orientation times. We utilize the MPC’s natural propensity to close gaps across an injury site to guide alignment on collagen I. When MPCs are exposed to an open boundary free of cells, they migrate unidirectionally into the cell-free region and align perpendicular to the original boundary direction. We study the utility of this phenomenon with biotin - streptavidin adhesion to position the cells on the substrate, and then demonstrate the robustness of this strategy with unmodified cells, creating a promising tool for MPC patterning without interrupting their natural function. We pre-position MPCs in straight-line patterns separated with small gaps. This temporary positioning initiates the migratory nature of the MPCs to align and form myotubes across the gaps, similar to how they migrate and align with a single open boundary. There is a directional component to the MPC migration perpendicular (90°) to the original biotin-streptavidin surface patterns. The expression of myosin heavy chain, the motor protein of muscle thick filaments, is confirmed through immunocytochemistry (ICC) in myotubes generated from MPCs in our patterning process, acting as a marker of skeletal muscle differentiation. The rapid and highly specific binding of biotin-streptavidin allows for quick formation of temporary patterns, with MPC alignment based on natural regenerative behavior rather than complex fabrication techniques. Impact Statement Positioning myogenic progenitor cells (MPCs) into straight-line patterns with intentional spacings initiates the migration of these cells to bridge these gaps, mimicking their behavior in response to small-scale injuries. By creating repetitions of patterned cells and spacings, we have demonstrated rapid migration and alignment of MPCs, which differentiate into a long-range 2D layer of aligned myotubes.
Detection and editing of the updated plastid- and mitochondrial-encoded proteomes for Arabidopsis with PeptideAtlas

Arabidopsis thaliana Col-0 has plastid and mitochondrial genomes encoding for over one hundred proteins and several ORFs. Public databases (e.g. Araport11) have redundancy and discrepancies in gene identifiers for these organelle-encoded proteins. RNA editing results in changes to specific amino acid residues or creation of start and stop codons for many of these proteins, but the impact of such RNA editing at the protein level is largely unexplored due to the complexities of detection. This study first assembled the non-redundant set of identifiers, their correct protein sequences, and 452 predicted non-synonymous editing sites of which 56 are edited at lower frequency. Accumulation of edited and/or unedited proteoforms was then determined by searching ∼259 million raw MSMS spectra from ProteomeXchange as part of Arabidopsis PeptideAtlas (www.peptideatlas.org/builds/arabidopsis/). All mitochondrial proteins and all except three plastid-encoded proteins (NDHG/NDH6, PSBM, RPS16), but none of the ORFs, were identified; we suggest that all ORFs and RPS16 are pseudogenes. Detection frequencies for each edit site and type of edit (e.g. S to L/F) were determined at the protein level, cross-referenced against the metadata (e.g. tissue), and evaluated for technical challenges of detection.167 predicted edit sites were detected at the proteome level. Minor frequency sites were indeed also edited at low frequency at the protein level. However, except for sites RPL5-22 and CCB382-124, proteins only accumulate in edited form (>98 –100% edited) even if RNA editing levels are well below 100%. This study establishes that RNA editing for major editing sites is required for stable protein accumulation.
A fluorescent assay for cryptic transcription in Saccharomyces cerevisiae reveals novel insights into factors that stabilize chromatin structure on newly replicated chromatin

The disruption of chromatin structure can result in transcription initiating from cryptic promoters. A well-characterized, chromatin-destabilizing stress is the passage of RNA polymerase, and numerous factors function to stabilize chromatin on transcribed genes, suppressing cryptic transcription from sites within gene bodies. DNA replication is also inherently disruptive to chromatin, and multiple replication-coupled histone chaperones suppress cryptic transcription. However, these factors also have documented roles in transcription, and thus whether DNA replication per se can activate cryptic promoters has not been directly examined. In this study, we tested the hypothesis that, in the absence of chromatin-stabilizing factors, DNA replication can promote cryptic transcription in S. cerevisiae. Using a novel fluorescent reporter assay, we show that multiple factors, including Asf1, Rtt106, Spt6, and Spt16, suppress transcription from a cryptic promoter, but are entirely or partially dispensable in G1-arrested cells, suggesting a requirement for DNA replication in chromatin disruption. Additionally, for the first time, we demonstrate modest cryptic transcription following the depletion of Rlf2/Cac1, a CAF-1 chromatin assembly complex component. Collectively, these results suggest that transcription fidelity is dependent on numerous factors that function to assemble chromatin on nascent DNA.
Potency of CRISPR-Cas Antifungals Is Enhanced by Cotargeting DNA Repair and Growth Regulatory Machinery at the Genetic Level

Fungal pathogens are virulent and resistant to antibiotic treatment. Due to their adaptability, specificity, and efficiency, CRISPR-Cas systems can be harnessed for neutralizing these fungal pathogens. However, the conventional design of CRISPR-Cas antimicrobials, based on induction of DNA double-strand-breaks (DSBs), is potentially less effective in fungi due to robust eukaryotic DNA repair machinery. Here, we report a novel design principle to formulate more effective CRISPR-Cas antifungals by co-targeting essential genes with DNA repair defensive genes that remove the fungi’s ability to repair the DSB sites of essential genes. By evaluating this design on the model fungus Saccharomyces cerevisiae, we demonstrated that essential and defensive gene co-targeting is more effective than either essential or defensive gene targeting alone. The top-performing CRISPR-Cas antifungals performed as effectively as the antibiotic Geneticin. Fast growth kinetics of S. cerevisiae induced resistance to CRISPR-Cas antifungals where genetic mutations mostly occurred in defensive genes and guide RNA sequences. Significance The emergence of virulent, resistant, and rapidly evolving fungal pathogens poses a significant threat to public health, agriculture, and the environment. Targeting cellular processes with standard small-molecule intervention may be effective but requires long development times and is prone to antibiotic resistance. To overcome the current limitation of antibiotic development and treatment, this study harnesses CRISPR-Cas systems as antifungals by capitalizing on their adaptability, specificity, and efficiency in target design. Simultaneous co-targeting of both essential and defensive genes is shown to be a novel design principle for formulating effective CRISPR-Cas antimicrobials that can be rapidly tuned to adapt to inevitable escapee events.
Time course of EEG complexity reflects attentional engagement during listening to speech in noise

Distraction caused by auditory noise poses a considerable challenge to the quality of information encoding in speech comprehension. The aim of this study was to explore the temporal dynamics and complexity of electroencephalog-raphy (EEG) microstates in relation to attentional engage-ment over time, contributing to the understanding of speech perception in noise. We examined three listening condi-tions: speech perception with background noise, focused attention on the background noise, and intentional disre-gard of the background noise. Our findings revealed an increase in complexity during the transition of microstates and a slower microstate recurrence when individuals directed their attention to speech compared to tasks without speech. Additionally, a two-stage time course for both microstate complexity and alpha-to-theta power ratio was observed. Specifically, in the early epochs, a lower level was observed, which gradually increased and eventually reached a steady level in the later epochs. The findings suggest that the ini-tial stage is primarily driven by sensory processes and infor-mation gathering, while the second stage involves higher-level cognitive engagement, including mnemonic binding and memory encoding.
On gravitational Phase Transitions, T-duality and Symmetry Breaking in AdS/CFT

In the present thesis we have the objective of testing and extending the domain of applicability of the holographic correspondence, a.k. a. AdS/CFT. This long term motivation has been made concrete in three different problem; there are also some additional specific goals for each them. The first is the role of higher curvature gravity corrections on thermal phase transitions between AdS and dS geometries. Apart from the purely gravitational interest, this research may eventually be helpful to clarify the correspondence in the case of dS geometries. Our main result is a phase transition from AdS boundary leading to the formation of dS cosmological horizon in Lanczos-Gauss-Bonnet gravity. In the presence of the higher curvature corrections no matter fields are required to match both sides of the bubble due to the Lanczos-Gauss-Bonnet term in the action. In the second we have made use of Non-Abelian T-duality (NATD) to generate new Supergravity solutions as well as to understand the inter- play of NATD with AdS/CFT. We are interested in deformations of Klebanov-Witten (KW) background AdS 5 xT 1,1 flowing to an AdS 3 factor in the IR. We generate new examples of them applying Non-Abelian T-duality on previously found solutions. After it, we compare the holographic observables of the generated solutions with those of the known deformations. The new geometries are smooth, supersymmetric and seem to be dual to long linear quiver gauge theories. Finally, we have carried the extension of the AdS/CFT correspondence, in particular the holographic renormalization procedure, to reproduce the Ward identities of symmetry breaking in a 1+1 holographic superconductor. It is established that alternative quantization is necessary in the three-dimensional bulk case, and after properly performed, the field theoretic higher dimensional Ward identities are recovered. This extension may be find application in AdS/CMT.
Pseudo Outlier Exposure for Out-of-Distribution Detection using Pretrained Transformers

For real-world language applications, detecting an out-of-distribution (OOD) sample is helpful to alert users or reject such unreliable samples. However, modern over-parameterized language models often produce overconfident predictions for both in-distribution (ID) and OOD samples. In particular, language models suffer from OOD samples with a similar semantic representation to ID samples since these OOD samples lie near the ID manifold. A rejection network can be trained with ID and diverse outlier samples to detect test OOD samples, but explicitly collecting auxiliary OOD datasets brings an additional burden for data collection. In this paper, we propose a simple but effective method called Pseudo Outlier Exposure (POE) that constructs a surrogate OOD dataset by sequentially masking tokens related to ID classes. The surrogate OOD sample introduced by POE shows a similar representation to ID data, which is most effective in training a rejection network. Our method does not require any external OOD data and can be easily implemented within off-the-shelf Transformers. A comprehensive comparison with state-of-the-art algorithms demonstrates POE's competitiveness on several text classification benchmarks.
Exploring Robust Overfitting for Pre-trained Language Models

We identify the robust overfitting issue for pre-trained language models by showing that the robust test loss increases as the epoch grows. Through comprehensive exploration of the robust loss on the training set, we attribute robust overfitting to the model’s memorization of the adversarial training data. We attempt to mitigate robust overfitting by combining regularization methods with adversarial training. Following the philosophy to prevent the model from memorizing the adversarial data, we find that flooding, a regularization method with loss scaling, can mitigate robust overfitting for pre-trained language models. Eventually, we investigate the effect of flooding levels and evaluate the models’ adversarial robustness under textual adversarial attacks. Extensive experiments demonstrate that our method can mitigate robust overfitting upon three top adversarial training methods and further promote adversarial robustness.
Reinforced Active Learning for Low-Resource, Domain-Specific, Multi-Label Text Classification

Text classification datasets from specialised or technical domains are in high demand, especially in industrial applications. However, due to the high cost of annotation such datasets are usually expensive to create. While Active Learning (AL) can reduce the labeling cost, required AL strategies are often only tested on general knowledge domains and tend to use information sources that are not consistent across tasks. We propose Reinforced Active Learning (RAL) to train a Reinforcement Learning policy that utilizes many different aspects of the data and the task in order to select the most informative unlabeled subset dynamically over the course of the AL procedure. We demonstrate the superior performance of the proposed RAL framework compared to strong AL base-lines across four intricate multi-class, multi-label text classification datasets taken from specialised domains. In addition, we experiment with a unique data augmentation approach to further reduce the number of samples RAL needs to annotate.
Triplet-Free Knowledge-Guided Response Generation

Generating vivid and informative responses (e.g., comments for social posts and utterances for dialogues) is challenging without giving relevant knowledge. Prior works focus on constructing the “latent” knowledge first and then learning how to “ground” it based on pseudo (context, knowledge, response) triplets. However, the retrieval between real responses and their latent knowledge is difficult in nature. In this paper, instead of focusing on how to ground knowledge given the responses, we take a different perspective to optimize the final responses for given guided knowledge directly. This allows us to re-formulate the entire problem in a simplified yet more scalable way. Specifically, we pretrain a response language model (LM) to measure the relevance and consistency between any context and response, then use search engines to collect the top-ranked passages to serve as the guiding knowledge without explicitly optimizing the “best” latent knowledge that corresponds to a given response. The final response generation model is trained through reinforcement learning by taking both the response LM prior and knowledge-injection rate as rewards. For better evaluations, we construct a new Chinese benchmark, “IceKC”, using fresh multimodal online social posts. Both automatic evaluations and human evaluations show our zero-resource approach performs significantly better than prior works. 1
End-to-End Task-Oriented Dialogue Systems Based on Schema

This paper presents a schema-aware end-to-end neural network model for handling task-oriented dialogues based on a dynamic set of slots within a schema. Contrary to existing studies that proposed end-to-end approaches for task-oriented dialogue systems by relying on a unified schema across domains, we de-sign our approach to support a domain covering multiple services where diverse schemas are available. To enable better generalizability among services and domains with different schemas, we supply the schema’s context information including slot descriptions and value constraints to the model. The experimental results on a well-known Schema-Guided Dialogue (SGD) dataset demonstrated the performance improvement by the proposed model compared to state-of-the-art baselines in terms of end-to-end modeling, dialogue state tracking task, and generalization on new services and domains using a limited number of dialogues. 1
AMR-TST: Abstract Meaning Representation-based Text Style Transfer

Abstract Meaning Representation (AMR) is a semantic representation that can enhance nat-ural language generation (NLG) by providing a logical semantic input. In this paper, we pro-pose the AMR-TST, an AMR-based text style transfer (TST) technique. The AMR-TST converts the source text to an AMR graph and generates the transferred text based on the AMR graph modified by a TST policy named style rewriting. Our method combines both the ex-plainability and diversity of explicit and implicit TST methods. The experiments show that the proposed method achieves state-of-the-art results compared with other baseline models in automatic and human evaluations. The generated transferred text in qualitative evaluation proves the AMR-TST have significant advantages in keeping semantic features and reducing hallucinations. To the best of our knowledge, this work is the first to apply the AMR method focusing on node-level features to the TST task.
ECOLA: Enhancing Temporal Knowledge Embeddings with Contextualized Language Representations

Since conventional knowledge embedding models cannot take full advantage of the abundant textual information, there have been extensive research efforts in enhancing knowledge embedding using texts. However, existing enhancement approaches cannot apply to temporal knowledge graphs (tKGs), which contain time-dependent event knowledge with complex temporal dynamics. Specifically, existing enhancement approaches often assume knowledge embedding is time-independent. In contrast, the entity embedding in tKG models usually evolves, which poses the challenge of aligning temporally relevant texts with entities. To this end, we propose to study enhancing temporal knowledge embedding with textual data in this paper. As an approach to this task, we propose Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations (ECOLA), which takes the temporal aspect into account and injects textual information into temporal knowledge embedding. To evaluate ECOLA, we introduce three new datasets for training and evaluating ECOLA. Extensive experiments show that ECOLA significantly enhances temporal KG embedding models with up to 287% relative improvements regarding Hits@1 on the link prediction task. The code and models are publicly available on https://anonymous.4open. science/r/ECOLA.
Prosody-TTS: Improving Prosody with Masked Autoencoder and Conditional Diffusion Model For Expressive Text-to-Speech

Expressive text-to-speech aims to generate high-quality samples with rich and diverse prosody, which is hampered by dual challenges : 1) prosodic attributes in highly dynamic voices are difficult to capture and model without intonation; and 2) highly multimodal prosodic representations cannot be well learned by simple regression (e.g., MSE) objectives, which causes blurry and over-smoothing predictions. This paper proposes Prosody-TTS, a two-stage pipeline that enhances prosody modeling and sampling by introducing several components: 1) a self-supervised masked autoencoder to model the prosodic representation without relying on text transcriptions or local prosody attributes, which ensures to cover diverse speaking voices with superior generalization; and 2) a diffusion model to sample diverse prosodic patterns within the latent space, which prevents TTS models from generating samples with dull prosodic performance. Experimental results show that Prosody-TTS achieves new state-of-the-art in text-to-speech with natural and expressive synthesis. Both subjective and objective evaluation demonstrate that it exhibits superior audio quality and prosody naturalness with rich and diverse prosodic attributes. 1
Uncovering Hidden Consequences of Pre-training Objectives in Sequence-to-Sequence Models

Some variants of self-supervised denoising objectives for pre-training encoder-decoder language models have been reported to have a negligible impact on downstream performance. Yet the design of these pre-training objectives leads to behavioural differences that can be uncovered with specific manipulations. We re-produce a recently proposed zero-shot control method and find that it is only successful on a subset of models. To understand what causes the difference in its effectiveness, we perform a set of controlled experiments, varying only the pre-training objective, and find unexpected interactions between the pre-training method and downstream controllability of models after fine-tuning. Our results show that different pre-training objectives have consequences that may not be visible in standard downstream evaluation, but which should be taken into account when developing models with controllability in mind.