Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepEraser: Deep Iterative Context Mining for Generic Text Eraser

In this work, we present DeepEraser, an effective deep network for generic text removal. DeepEraser utilizes a recurrent architecture that erases the text in an image via iterative operations. Our idea comes from the process of erasing pencil script, where the text area designated for removal is subject to continuous monitoring and the text is attenuated progressively, ensuring a thorough and clean erasure. Technically, at each iteration, an innovative erasing module is deployed, which not only explicitly aggregates the previous erasing progress but also mines additional semantic context to erase the target text. Through iterative refinements, the text regions are progressively replaced with more appropriate content and finally converge to a relatively accurate status. Furthermore, a custom mask generation strategy is introduced to improve the capability of DeepEraser for adaptive text removal, as opposed to indiscriminately removing all the text in an image. Our DeepEraser is notably compact with only 1.4M parameters and trained in an end-to-end manner. To verify its effectiveness, extensive experiments are conducted on several prevalent benchmarks, including SCUT-Syn, SCUT-EnsText, and Oxford Synthetic text dataset. The quantitative and qualitative results demonstrate the effectiveness of our DeepEraser over the state-of-the-art methods, as well as its strong generalization ability in custom mask text removal. The codes and pre-trained models are available at https://github.com/fh2019ustc/DeepEraser
Overview of my research

Complexity of a Boolean function is the amount of memory needed to represent the function. For example, a function of n variables requires O(2) bits to be represented in the form of a truth table. Other forms of representation, such as Boolean circuits can be much more succinct for certain classes of functions. Understanding the complexity of a particular representation for a particular class of functions is very important from both theoretical and applied perspectives. I came to this field through my interest in knowledge representation. In this field knowledge is often represented in a propositional form. This representation is nothing else than a Boolean function. There are two quite contradictory requirements to this Boolean function. On the one hand, the representation should be sufficiently concise to be recorded in a computer memory. On the other hand, this representation should allow efficient querying. There is no universally good solution. However, a few reasonable representations reconciliating the above requirements have been proposed. It is important to study upper and lower bounds of these representations to understand the classes of functions they can represent and the classes they cannot.
Laboratory Exercise

Over the past 20 years, the biological sciences have increasingly incorporated chemistry, physics, computer science, and mathematics to aid in the development and use of mathematical models. Such combined approaches have been used to address problems from protein structure–function relationships to the workings of complex biological systems. Computer simulations of molecular events can now be accomplished quickly and with standard computer technology. Also, simulation software is freely available for most computing platforms, and online support for the nov-ice user is ample. We have therefore created a molecular dynamics laboratory module to enhance undergraduate student understanding of molecular events underlying organismal phenotype. This module builds on a previously described project in which students use site-directed mutagenesis to investigate functions of conserved sequence features in members of a eukaryotic protein kinase family. In this report, we detail the laboratory activities of a MD module that provide a complement to phenotypic outcomes by providing a hypothesis-driven and quantiﬁable measure of predicted structural changes caused by targeted mutations. We also present examples of analyses students may perform. These laboratory activities can be integrated with genetics or biochemistry experiments as described, but could also be used independently in any course that would beneﬁt from a quantitative approach to protein structure– function relationships. V C 2013 by The International Union of Biochemistry and Molecular Biology, 41(6):402–408, 2013
A necessary and suﬃcient condition for bound on the sum of a list of real numbers and its applications

Let x 1 , . , x n be a list of real numbers, let s := P ni =1 x i , and let h : N → R be a function. We gave a necessary and suﬃcient condition for s > h ( n ) (respectively, s < h ( n )). Let G = ( V, E ) be a graph, let { H 1 , . , H n } and { V 1 , . , V n } be a decomposition and a partition of G , respectively. G is generalized periodic or partition-transitive if for each pair of integers ( i, j ) , 1 ≤ i ≤ j ≤ n , there exists an automorphism θ i,j of G such that θ i,j ( H i + k ) = H j + k or θ i,j ( V i + k ) = V j + k for all k , 1 ≤ k ≤ n , respectively, where subscripts are taken modulo n . Let f : E → R and g : V → R be mappings, and let the weight of f or g on G be Σ e ∈ E f ( e ) or Σ v ∈ V g ( v ), respectively. Suppose that parameters λ and ξ of G can be expressed as the minimum or maximum weight of speciﬁed f and g , respectively. Then our conditions imply a necessary and suﬃcient condition for λ ( G 1 ) = h ( n ) (respectively, ξ ( G 2 ) = h ( n )), where G 1 is generalized periodic and G 2 is partition-transitive. For example, cr( ⊙ ( T n )) = h ( n ), where cr( ⊙ ( T n )) is the crossing number of a periodic graph ⊙ ( T n ). As applications, we determined the crossing number of the circulant C (4 n ; { 1 , 4 } ), the paired domination number of C 5 (cid:3) C n and the upper total domination number of C 4 (cid:3) C n .
WRF: Weighted Rouge-F1 Metric for Entity Recognition

The continuous progress in Named Entity Recognition allows the identification of complex entities in multiple domains. The traditionally used metrics like precision, recall, and F1-score can only reflect the classification quality of the underlying NER model to a limited extent. Existing metrics do not distinguish between a non-recognition of an entity and a misclassification of an entity. Additionally, the dealing with redundant entities remains unaddressed. We propose WRF, a Weighted Rouge F1 metric for Entity Recognition, to solve the mentioned gaps in currently available metrics. We successfully employ the WRF metric for automotive entity recognition, followed by a comprehensive qualitative and quantitative analysis of the obtained results.
Estimating Directed Connectivity with OPM-MEG: A Feasibility Study

We investigated the feasibility of using directed connectivity analysis for magnetoencephalography data recorded via Optically Pumped Magnetometers (OPM-MEG). Ten healthy adult participants were scanned twice in an OPM-MEG system, and Beamformer source localisation was employed to obtain source time series within 62 cortical brain regions. Multivariate transfer entropy (mTE) was used to quantify directed connectivity, with lags ranging from 1 to 10 time points. Our findings showed that out-degree connectivity (node to network) in somatomotor and attention networks was higher than in other networks. In contrast, in-degree connectivity (network to node) was not different between networks. Longer time lags were associated with shorter physical distances between nodes. Transfer entropy networks demonstrated a small-world network topology, reinforcing the biological plausibility of our findings. Brain connections with higher transfer entropy values exhibited a stronger Phase Lag Index and Pearson’s correlation coefficient than non-significant transfer entropy connections, suggesting a relationship between directed and non-directed connectivity measures. All results were consistent across the two scans. OPM-MEG research is likely to expand significantly, and based on our preliminary findings, we propose that directed connectivity measures may provide valuable insights into the underlying mechanisms of brain communication.
Attenuated replication and damaging effects of SARS-CoV-2 Omicron variants in an intestinal epithelial barrier model

Many COVID-19 patients suffer from gastrointestinal symptoms and impaired intestinal barrier function may play a key role in Long COVID. Despite its importance, the impact of SARS-CoV-2 on intestinal epithelia is poorly understood. To address this, we established an intestinal barrier model integrating epithelial Caco-2 cells, mucus-secreting HT29 cells and human Raji cells. This gut epithelial model allows efficient differentiation of Caco-2 cells into microfold-like cells, faithfully mimics intestinal barrier function, and is highly permissive to SARS-CoV-2 infection. Early strains of SARS-CoV-2 and the Delta variant replicated with high efficiency, severely disrupted barrier function, and depleted tight junction proteins, such as claudin-1, occludin and ZO-1. In comparison, Omicron subvariants also depleted ZO-1 from tight junctions but had fewer damaging effects on mucosal integrity and barrier function. Remdesivir and the TMPRSS2 inhibitor Camostat prevented SARS-CoV-2 replication and thus epithelial barrier damage, while the Cathepsin inhibitor E64d was ineffective. Our results support that SARS-CoV-2 disrupts intestinal barrier function but further suggest that circulating Omicron variants are less damaging than earlier viral strains.
A simulated annealing algorithm for randomizing weighted networks

Scientific discovery in connectomics relies on the use of network null models. To systematically evaluate the prominence of brain network features, empirical measures are compared against null statistics computed in randomized networks. Modern imaging and tracing technologies provide an increasingly rich repertoire of biologically meaningful edge weights. Despite the prevalence of weighted graph analysis in connectomics, randomization models that only preserve binary node degree remain most widely used. Here, to adapt network null models to weighted network inference, we propose a simulated annealing procedure for generating strength sequence-preserving randomized networks. This model outperforms other commonly used rewiring algorithms in preserving weighted degree (strength). We show that these results generalize to directed networks as well as a wide range of real-world networks, making them generically applicable in neuroscience and in other scientific disciplines. Furthermore, we introduce morphospace representation as a tool for the assessment of null network ensemble variability and feature preservation. Finally, we show how the choice of a network null model can yield fundamentally different inferences about established organizational features of the brain such as the rich-club phenomenon and lay out best practices for the use of rewiring algorithms in brain network inference. Collectively, this work provides a simple but powerful inferential method to meet the challenges of analyzing richly detailed next-generation connectomics datasets.
Shape Non-rigid Kinematics (SNK): A Zero-Shot Method for Non-Rigid Shape Matching via Unsupervised Functional Map Regularized Reconstruction

We present Shape Non-rigid Kinematics (SNK), a novel zero-shot method for non-rigid shape matching that eliminates the need for extensive training or ground truth data. SNK operates on a single pair of shapes, and employs a reconstruction-based strategy using an encoder-decoder architecture, which deforms the source shape to closely match the target shape. During the process, an unsupervised functional map is predicted and converted into a point-to-point map, serving as a supervisory mechanism for the reconstruction. To aid in training, we have designed a new decoder architecture that generates smooth, realistic deformations. SNK demonstrates competitive results on traditional benchmarks, simplifying the shape-matching process without compromising accuracy. Our code can be found online: https://github.com/pvnieo/SNK
Patterns of Intra-Clustering Analysis Reveal Hidden Oncogenic Relations

The identification of genes that can initiate and progress carcinogenesis through mutations is a challenging issue due to the sparsity of mutations and the high mutational heterogeneity between tumors of the same cancer type. While current methods mostly rely on finding recurrently mutated genes, this work aims to recover infrequently mutated genes that can contribute to cancer, by exploiting clustering analysis. To that end, we constructed a Network Graph of 8303 patients and 198 genes from single-point-mutation data, retrieved from the The Cancer Genome Atlas (TCGA), to find patient-gene groups with the parallel use of two separate methodologies: (a) one based on Barber’s modularity index, and (b) one based on network dynamics. A systematic analysis was applied over 23 statistically meaningful groups of 2037 patients spanning 22 cancer types, derived from both methodologies, using the Fisher’s exact test followed by the Benjamini-Hochberg false discovery rate method. This procedure recovered 32 known significantly mutated genes and 4 putative driver genes that are not identified by standard driver-gene identification algorithms used in the TCGA consortium analysis. This study has also revealed 14 statistically significant patterns not reported in the current literature, resulting in a total of 18 hidden oncogenic relations. Notably, among the results, skin cutaneous melanoma is related to IL7R. However, current literature suggests that only IL7R overexpression is linked to skin cancers. To the best of our knowledge, this is the first reported pan-cancer intra-clustering analysis. Significance Driver genes can initiate and progress carcinogenesis through mutations. Here, we aim to recover infrequently mutated driver genes by constructing a Network Graph of 8303 patients and 198 genes from single-point mutation data to obtain patient-gene groups with the parallel use of two separate methodologies. Robust statistical analysis over the resulting modules recovered 18 hidden oncogenic relations: 4 putative driver genes and 14 statistically significant mutational patterns. To the best of our knowledge, this is the first reported pan-cancer intra-clustering analysis.
A multi-looping chromatin signature predicts dysregulated gene expression in neurons with familial Alzheimer’s disease mutations

Mammalian genomes fold into tens of thousands of long-range loops, but their functional role and physiologic relevance remain poorly understood. Here, using human post-mitotic neurons with rare familial Alzheimer’s disease (FAD) mutations, we identify hundreds of reproducibly dysregulated genes and thousands of miswired loops prior to amyloid accumulation and tau phosphorylation. Single loops do not predict expression changes; however, the severity and direction of change in mRNA levels and single-cell burst frequency strongly correlate with the number of FAD-gained or -lost promoter-enhancer loops. Classic architectural proteins CTCF and cohesin do not change occupancy in FAD-mutant neurons. Instead, we unexpectedly find TAATTA motifs amenable to binding by DLX homeodomain transcription factors and changing noncoding RNAPolII signal at FAD-dynamic promoter-enhancer loops. DLX1/5/6 mRNA levels are strongly upregulated in FAD-mutant neurons coincident with a shift in excitatory-to-inhibitory gene expression and miswiring of multi-loops connecting enhancers to neural subtype genes. DLX1 overexpression is sufficient for loop miswiring in wildtype neurons, including lost and gained loops at enhancers with tandem TAATTA arrays and singular TAATTA motifs, respectively. Our data uncover a genome structure-function relationship between multi-loop miswiring and dysregulated excitatory and inhibitory transcriptional programs during lineage commitment of human neurons homozygously-engineered with rare FAD mutations.
A 2D Gabor-wavelet baseline model out-performs a 3D surface model in scene-responsive cortex

Understanding 3D representations of spatial information, particularly in naturalistic scenes, remains a significant challenge in vision science. This is largely because of conceptual difficulties in disentangling higher-level 3D information from co-occurring features and cues (e.g., the 3D shape of a scene image is necessarily defined by spatial frequency and orientation information). Recent work has employed newer models and analysis techniques that attempt to mitigate these in-principle difficulties. For example, one such study reported 3D-surface features were uniquely present in areas OPA, PPA, and MPA/RSC (areas typically referred to as ‘scene- selective’), above and beyond a Gabor-wavelet baseline (“2D”) model. Here, we tested whether these findings generalized to a new stimulus set that, on average, dissociated static Gabor- wavelet baseline (“2D”) features from 3D scene-surface features. Surprisingly, we found evidence that a Gabor-wavelet baseline model better fit voxel responses in areas OPA, PPA and MPA/RSC compared to a model with 3D-surface information. This raises the question of whether previous findings of greater 3D information could have been due to a baseline condition that didn’t model some potentially critical low-level features (e.g., motion). Our findings also emphasize that much of the information in “scene-selective” regions—potentially even information about 3D surfaces—may be in the form of spatial frequency and orientation information often considered 2D or low-level, and they highlight continued fundamental conceptual challenges in disentangling the contributions of low-level vs. high-level features in visual cortex.
Cyclase-associated protein 1 (CAP1) represses MRTF-SRF-dependent gene expression in the mouse cerebral cortex

Serum response factor (SRF) is a ubiquitously expressed transcription factor essential for brain development and function. SRF activity is controlled by two competing classes of coactivators, myocardin-related transcription factors (MRTF) and ternary complex factors (TCF), which introduce specificity into gene expression programs. To date, only few brain studies investigated upstream regulatory mechanisms, which mainly focused on TCF. Since an inhibitory function of monomeric actin towards MRTF-SRF signaling is well-established, we hypothesized a regulatory role for the key actin regulator ADF/cofilin. Surprisingly, ADF/cofilin was largely dispensable for neuronal MRTF-SRF activity. Instead, reporter assays combined with pharmacological and genetic approaches in isolated mouse neurons identified cyclase-associated protein 1 (CAP1) as an important regulator of this pathway. CAP1 promotes cytosolic MRTF retention and represses neuronal MRTF-SRF signaling via an actin-dependent mechanism that requires two specific protein domains. Further, deep RNA sequencing and mass spectrometry in mutant mice proved CAP1’s in vivo relevance for this pathway in the cerebral cortex, and led to the identification of neuronal MRTF-SRF target genes. Together, we identified CAP1 as a novel and crucial repressor of neuronal MRTF-SRF signaling.
Kir6.2-KATP channels alter glycolytic flux to modulate cortical activity, arousal, and sleep-wake homeostasis

Metabolism plays an important role in the maintenance of vigilance states (e.g. wake, NREM, and REM). Brain lactate fluctuations are a biomarker of sleep. Increased interstitial fluid (ISF) lactate levels are necessary for arousal and wake-associated behaviors, while decreased ISF lactate is required for sleep. ATP-sensitive potassium (KATP) channels couple glucose-lactate metabolism with neuronal excitability. Therefore, we explored how deletion of neuronal KATP channel activity (Kir6.2-/- mice) affected the relationship between glycolytic flux, neuronal activity, and sleep/wake homeostasis. Kir6.2-/- mice shunt glucose towards glycolysis, reduce neurotransmitter synthesis, dampen cortical EEG activity, and decrease arousal. Kir6.2-/- mice spent more time awake at the onset of the light period due to altered ISF lactate dynamics. Together, we show that Kir6.2-KATP channels act as metabolic sensors to gate arousal by maintaining the metabolic stability of each vigilance state and providing the metabolic flexibility to transition between states. Highlights Glycolytic flux is necessary for neurotransmitter synthesis. In its absence, neuronal activity is compromised causing changes in arousal and vigilance states despite sufficient energy availability. With Kir6.2-KATP channel deficiency, the ability to both maintain and shift between different vigilance states is compromised due to changes in glucose utilization. Kir6.2-KATP channels are metabolic sensors under circadian control that gate arousal and sleep/wake transitions. Graphical Abstract
Whole genome assembly of the Little Millet (Panicum sumatrense) genome: A climate resilient crop species

Little millet (Panicum sumatrense) belongs to one of the largest genera, Panicum comprising of 500 species that are distributed especially in tropical and sub-tropical Asia as well as Africa. This is a climate resilient crop that has the ability to adapt to adverse growing conditions, especially drought. Added to this is the better nutritional profile of little millet, which has higher iron and fibre contents as compared with rice. These characteristics make it an important crop species that would be pivotal for ensuring food security. Generating genomic resources for the species is significant and will have implications in crop improvement. Therefore, in the present study we report the de novo whole genome sequence of P. sumatrense. Long read sequencing and Hi-C based scaffolding resulted in a total of 279 scaffolds with N50 of 7.8Mb. The genome was annotated to predict protein coding genes and orthologous groups were analysed after comparison with various plant genomes. This high-quality genome assembly can be a valuable resource as future reference for genomic studies in this crop and related crop species.
Is forest landscape restoration socially desirable? A discrete choice experiment applied to the Scandinavian transboundary Fulufjället National Park Area

Using an economic valuation approach, we assessed people's stated preferences for policy aimed at enhancing restoration of functional networks of naturally dynamic boreal forest habitats as a public good. Active landscape restoration can improve the functionality of boreal forest habitats as green infrastructure, which is essential for biodiversity conservation and delivery of multiple ecosystem services that production forests are poor at providing. In contrast, so far designation of protected areas in Fennoscandia has focused on remnant patches of near‐natural forests, and not on forest landscape restoration. We assessed citizens' preferences for forest landscape restoration in a transboundary region primarily managed for sustained‐yield wood production for the forest industry, and which hosts the transboundary Fulufjället National Parks in Sweden and Norway. We conducted a discrete choice experiment by asking Swedish and Norwegian citizens to choose among two options for the Fulufjället area, including (1) extension of passive protection on one side or both sides of the border, by additional area protection and restoration of forest naturalness, and (2) a status quo option. The scenario assumed that extension of the protected forest area would imply a compulsory tax administered bilaterally by the two countries. Just over half of the sample, in both countries expressed willingness to pay for forest landscape restoration. Alternatives that contemplated larger extensions of forest landscape restoration were assigned higher willingness‐to‐pay. Public awareness and support, combined with spatial planning, are necessary for forest landscape restoration to become a viable tool for biodiversity conservation in Scandinavia.