Miracle-dz/UniFAR-FTUs huggingface dataset:
Degradome analysis to identify direct protein substrates of small-molecule degraders

Targeted protein degradation (TPD) has emerged as a powerful strategy to selectively eliminate cellular proteins using small-molecule degraders, offering therapeutic promise for targeting proteins that are otherwise undruggable. However, a remaining challenge is to unambiguously identify primary TPD targets that are distinct from secondary downstream effects in the proteome. Here we introduce an approach that combines stable isotope labeling and click-chemistry for selective quantification of protein degradation by mass spectrometry, excluding confounding effects of altered transcription and translation induced by target depletion. We show that the approach efficiently operates at the time scale of TPD (hours) and we demonstrate its utility by analyzing the Cyclin K degraders dCeMM2 and dCeMM4, which induce widespread transcriptional downregulation, and the GSPT1 degrader CC-885, an inhibitor of protein translation. Additionally, we apply it to characterize compound 1, a previously uncharacterized degrader, and identify the zinc-finger protein FIZ1 as a degraded target.
Efficient enzyme-free isolation of brain-derived extracellular vesicles

Extracellular vesicles (EVs) have gained significant attention as pathology mediators and potential diagnostic tools for neurodegenerative diseases. However, isolation of brain-derived EVs (BDEVs) from tissue remains challenging, often involving enzymatic digestion steps that may compromise the integrity of EV proteins and overall functionality. Here, we describe that collagenase digestion, commonly used for BDEV isolation, produces undesired protein cleavage of EV-associated proteins in brain tissue homogenates and cell-derived EVs. In order to avoid this effect, we studied the possibility of isolating BDEVs with a reduced amount of collagenase or without any protease. Characterization of the isolated BDEVs revealed their characteristic morphology and size distribution with both approaches. However, we revealed that even minor enzymatic digestion induces ‘artificial’ proteolytic processing in key BDEV markers, such as Flotillin-1, CD81, and the cellular prion protein (PrPC), whereas avoiding enzymatic treatment completely preserves their integrity. We found no differences in mRNA and protein content between non-enzymatically and enzymatically isolated BDEVs, suggesting that we are purifying the same BDEV populations with both approaches. Intriguingly, the lack of Golgi marker GM130 signal, often referred to as contamination contamination-negative marker in EV preparations, seems to result from enzymatic digestion rather than from its actual absence in BDEV samples. Overall, we show that non-enzymatic isolation of EVs from brain tissue is possible and avoids artificial pruning of proteins while achieving a high BDEV yield and purity. This protocol will help to understand the functions of BDEV in a near-physiological setting, thus opening new research approaches.
An algorithm to estimate building heights from Google street-view imagery using single view metrology across a representational state transfer system

Urban ecosystem studies require monitoring, controlling and planning to analyze building density, urban density, urban planning, atmospheric modeling and land use. In urban planning, there are many methods for building height estimation using optical remote sensing images. These methods however, highly depend on sun illumination and cloud-free weather. In contrast, high resolution synthetic aperture radar provides images independent from daytime and weather conditions, although, these images rely on special hardware and expensive acquisition. Most of the biggest cities around the world have been photographed by Google street view under different conditions. Thus, thousands of images from the principal streets of a city can be accessed online. The availability of this and similar rich city imagery such as StreetSide from Microsoft, represents huge opportunities in computer vision because these images can be used as input in many applications such as 3D modeling, segmentation, recognition and stereo correspondence. This paper proposes a novel algorithm to estimate building heights using public Google Street-View imagery. The objective of this work is to obtain thousands of geo-referenced images from Google Street-View using a representational state transfer system, and estimate their average height using single view metrology. Furthermore, the resulting measurements and image metadata are used to derive a layer of heights in a Google map available online. The experimental results show that the proposed algorithm can estimate an accurate average building height map of thousands of images using Google Street-View Imagery of any city.
In Silico Study of the Early Stages of Aggregation of β-Sheet Forming Antimicrobial Peptide GL13K

Antimicrobial peptides (AMPs) are of growing interest as potential candidates for antibiotics to which antimicrobial resistance increases slowly. In this article, we perform the first in silico study of the synthetic β sheet-forming AMP GL13K. Through atomistic simulations of single and multipeptide systems under different charge conditions, we are able to shine a light on the short timescales of early aggregation. We find that isolated peptide conformations are primarily dictated by sequence rather than charge, whereas changing charge has a significant impact on the conformational free energy landscape of multi-peptide systems. We demonstrate that the lack of charge-charge repulsion is a sufficient minimal model for experimentally observed aggregation. Overall, our work explores the molecular biophysical underpinnings of the first stages of aggregation of a unique AMP, laying necessary groundwork for its further development as an antibiotic candidate.
Zebrafish live imaging reveals only around 2% rather than 50% of motor neurons die through apoptosis during development

It is widely accepted that neurons will die through apoptosis if they cannot receive enough growth factors during development of vertebrates; however, there is still no real-time observation showing this dying process in live animals. Here, we generated sensor zebrafish achieving live imaging of motor neuron apoptosis at single-cell resolution. Using these sensor zebrafish, we observed for the first time that in an apoptotic motor neuron, caspase-3 activation occurred quickly within 5-6 min and at the same time between the cell body and axon. Interestingly, we found that only around 2% of motor neurons died during zebrafish development, which is much lower than the generally believed 50% cell death occurred in embryonic stage of vertebrates. Our data also showed that most of the apoptotic bodies of these dead motor neurons were not cleared by macrophages. These sensor zebrafish can serve as powerful tools to study motor neuron apoptosis in vivo.
Astrocytic tau inclusions lead to microglial abnormalities, but leave neuronal networks intact

Astrocytic tau inclusions are commonly found in the aging brain and primary tauopathies, such as progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). The functional consequences of these histopathological lesions, however, are unknown due to the lack of specific animal models. We have developed a mouse model of astrocytic tau pathology to study downstream effects on microglia and neuronal networks. We have designed an adeno-associated viral (AAV) vector expressing aggregation-prone human truncated tau (amino acids 151-391/4R) and red fluorophore mCherry in equal ratio under the astrocytic GFAP promotor. Injection of AAV-GFAP-htTau into the hippocampus of wild-type mice led to expression of human truncated tau in astrocytes and accumulation of soluble phosphorylated tau (p214, p231) but no detectable cognitive impairment. In vivo multiphoton imaging revealed alterations in microglial morphology in the vicinity of truncated tau positive astrocytes in the cortex. No alterations in firing patterns of excitatory cortical neurons surrounded by astrocytes overexpressing truncated tau were detected. These results suggest that early stages of astrocytic tau pathology lead to changes in microglial function, but not to functional impairment of neuronal networks.
In silico λ-dynamics predicts protein binding specificities to modified RNAs

RNA modifications shape gene expression through a smorgasbord of chemical changes to canonical RNA bases. Although numbering in the hundreds, only a few RNA modifications are well characterized, in part due to the absence of methods to identify modification sites. Antibodies remain a common tool to identify modified RNA and infer modification sites through straightforward applications. However, specificity issues can result in off-target binding and confound conclusions. This work utilizes in silico λ-dynamics to efficiently estimate binding free energy differences of modification-targeting antibodies between a variety of naturally occurring RNA modifications. Crystal structures of inosine and N6-methyladenosine (m6A) targeting antibodies bound to their modified ribonucleosides were determined and served as structural starting points. λ-Dynamics was utilized to predict RNA modifications that permit or inhibit binding to these antibodies. In vitro RNA-antibody binding assays supported the accuracy of these in silico results. High agreement between experimental and computed binding propensities demonstrated that λ-dynamics can serve as a predictive screen for antibody specificity against libraries of RNA modifications. More importantly, this strategy is an innovative way to elucidate how hundreds of known RNA modifications interact with biological molecules without the limitations imposed by in vitro or in vivo methodologies.
Resolution of ribosomal stalling by ABCF ATPases YfmR and YkpA/YbiT

Efficiency of protein synthesis on the ribosome is strongly affected by the amino acid composition of the assembled amino acid chain. Challenging sequences include proline-rich motifs as well as highly positively and negatively charged amino acid stretches. Members of the F subfamily of ABC ATPases (ABCFs) have been long hypothesised to promote translation of such problematic motifs. In this study we have applied genetics and reporter-based assays to characterise the four housekeeping ABCF ATPases of Bacillus subtilis: YdiF, YfmM, YfmR/Uup and YkpA/YbiT. We show that YfmR cooperates with the translation factor EF-P that promotes translation of Pro-rich motifs. Simultaneous loss of both YfmR and EF-P results in a dramatic growth defect. Surprisingly, this growth defect can be largely suppressed though overexpression of an EF-P variant lacking the otherwise crucial 5-amino-pentanolylated residue K32. Using in vivo reporter assays, we show that overexpression of YfmR can alleviate ribosomal stalling on Asp-Pro motifs. Finally, we demonstrate that YkpA/YbiT promotes translation of positively and negatively charged motifs but is inactive in resolving ribosomal stalls on proline-rich stretches. Collectively, our results provide insights into the function of ABCF translation factors in modulating protein synthesis in B. subtilis.
A Comprehensive Resource for Exploring Antiphage Defense: DefenseFinder Webservice, Wiki and Databases

In recent years, a vast number of novel antiphage defense mechanisms were uncovered. To facilitate the exploration of mechanistic, ecological, and evolutionary aspects related to antiphage defense systems, we released DefenseFinder in 2021 (Tesson et al., 2022). DefenseFinder is a bioinformatic program designed for the systematic identification of all known antiphage defense mechanisms. The initial release of DefenseFinder v1.0. 0 included 60 systems. Over the past three years, the number of antiphage systems incorporated into DefenseFinder has grown to 152. The increasing number of known systems makes it a challenge to enter the field and makes the interpretation of detections of antiphage systems difficult. Moreover, the rapid development of sequence-based predictions of structures offers novel possibilities of analysis and should be easily available. To overcome these challenges, we present a hub of resources on defense systems including 1) a search tool (DefenseFinder as a web service) 2) a knowledge base, and 3) precomputed databases (results of DefenseFinder ran on RefSeq database, predicted structure database computed with AlphaFold). These pages can be freely accessed for users as a starting point on their journey to better understand a given system. We anticipate that these resources will foster the use of bioinformatics in the study of antiphage systems and will serve the community of researchers who study antiphage systems. This resource is available at: https://defensefinder.mdmlab. fr.
Telencephalic eversion in embryos and early larvae of four teleost species

The telencephalon of ray-finned fishes undergoes eversion, which is very different to the evagination that occurs in most other vertebrates. Ventricle morphogenesis is key in order to build an everted telencephalon. Thus, here we use the apical marker zona occludens 1 (ZO1) to understand ventricle morphology, extension of the tela choroidea and the eversion process during early telencephalon development of four teleost species: giant danio (Devario aequipinnatus), blind cavefish (Astyanax mexicanus), medaka (Oryzias latipes) and paradise fish (Macroposus opercularis). In addition, by using immunohistochemistry against tubulin and calcium binding proteins, we analyse the general morphology of the telencephalon, showing changes in the location and extension of the olfactory bulb and other telencephalic regions from 2 to 5 days of development. We also analyse the impact of abnormal eye and telencephalon morphogenesis on eversion, showing that cyclops mutants do undergo eversion despite very dramatic abnormal eye morphology. We discuss how the formation of the telencephalic ventricle in teleost fish, with its characteristic shape, is crucial event during eversion.
Unleashing the Power of NLP Models for Semantic Consistency Checking of Automation Rules

Trigger-Action Platforms (TAPs) empower users to automate tasks involving smart devices, allowing them to either create rules from scratch or access a catalog of existing user-defined rules. Users can explore the catalog and find rules based on their interests, relying on the so-called User-defined descriptions (UDDs) provided by the rules’ creators. However, TAPs lack a mechanism to verify or regulate these descriptions, resulting in potential inaccuracies or errors. This poses challenges for users when seeking relevant rules, as descriptions may present misleading or irrelevant information. In this paper
A Survivorship Care Preferences A pilot study of young adults with cancer

ATTEMPTS HAVE BEEN MADE TO RAISE AWARENESS of adolescent and young adult (AYA) patients aged 15–39 years diagnosed with cancer because of their unique health issues (Smith et al., 2016). AYA patients experience cancer-related health disparities in terms of lagging survival rates for common cancers (Kent et al., 2015; Smith et al., 2016), underrepresentation on therapeutic trials (Freyer & Seibel, 2015; Harlan et al., 2011), and heightened risk for comorbid conditions (Nass et al., 2015). The survivorship period, after therapy ends, brings new challenges because of the late complications caused by previous curative chemotherapy and radiation therapy. These include infertility (Cardonick, 2016), second cancers (Nass et al., 2015; Smith et al., 2016), cardiovascular disease (Chao et al., 2016), musculoskeletal problems (Daniel et al., 2015), psychosocial distress (Tai et al., 2014), and cognitive diﬃcul-ties (John, Sender, & Bota, 2016). Because AYA survivors are at a particularly critical juncture in their lives, impairment of cognitive function and medical interruptions can have devastating eﬀects on these patients’ education (Parsons et al., 2012; Patterson, McDonald, Zebrack, & Medlow, 2015; Tai et al., 2012), attainment of employment (Parsons et al., 2012; Patterson et al., 2015; Tai et al., 2012), and ability to start a family (Bellizzi et al., 2012; Smith et al., 2016). Although AYA survivors deserve focused attention, this group is challenging
STA-STF based Recurrent Neural Network for Next Location Prediction

In the evolving landscape of technology and smart devices, the focus on modeling spatial correlations, temporal dynamics, and friendship influence in point-of-interest (POI) check-ins has intensified. Existing works center on capturing user check-in behavior, emphasizing spatial and temporal dependencies of POIs. Markov chain-based methods address instance-level interactions, while recurrent neural network (RNN) approaches excel in handling variable-length check-in sequences. However, the former struggles with high-order POI transition dependency, and the latter cannot discern individual POI contributions in a historical check-in sequence. Additionally, RNNs propagate local and global information through a single bottleneck—hidden states. To address these limitations, a novel model is presented, enforcing contextual constraints on sequential data. The design incorporates spatial and temporal attention mechanisms over an RNN, highlighting the significance of POIs visited by users within specific time intervals and geographical distances during successive check-ins. The attention mechanism aids in identifying crucial POIs based on time difference and spatial distance in user check-in history for predicting the next POI. Periodicity and friendship influence are also considered in the model design. Experimental results on the BrightKite location-based social network demonstrate the proposed method's outperformance over existing state-of-the-art deep neural network methods for the next POI prediction and understanding of user transition behavior. Sensitivity analyses of parameters, including context windows for capturing sequential effects and estimating temporal and spatial attention, further validate the model's effectiveness. In parallel, this paper introduces a Space-Time Features-based Recurrent Neural Network (STF-RNN) for predicting individuals' next movements using mobility patterns from GPS device logs. Unlike traditional approaches, the STF-RNN automatically extracts internal representations of space and time features, enhancing efficiency in uncovering valuable insights into human behavior. Leveraging the sequence-representing ability of RNN structures, the model keeps track of user movement history, enabling the discovery of more meaningful dependencies and, consequently, improving performance. Collectively, these contributions advance the understanding and prediction capabilities in the realms of spatial dynamics, temporal dependencies, and human mobility patterns.
Design of a Multi-Dopamine-Modi ﬁ ed Polymer Ligand Optimally Suited for Interfacing Magnetic Nanoparticles with Biological Systems

: We have designed a set of multifunctional and multicoordinating polymer ligands that are optimally suited for surface functionalizing iron oxide and potentially other magnetic nanoparticles (NPs) and promoting their integration into biological systems. The amphiphilic polymers are prepared by coupling (via nucleophilic addition) several amine-terminated dopamine anchoring groups, poly(ethylene glycol) moieties, and reactive groups onto a poly(isobutylene-alt -maleic anhydride) (PIMA) chain. This design greatly bene ﬁ ts from the highly e ﬃ cient and reagent-free one-step reaction of maleic anhydride groups with amine-containing molecules. The availability of several dopamine groups in the same ligand greatly enhances the ligand a ﬃ nity, via multiple coordination, to the magnetic NPs, while the hydrophilic and reactive groups promote colloidal stability in bu ﬀ er media and allow subsequent conjugation with target biomolecules. Iron oxide nanoparticles ligand exchanged with these polymer ligands have a compact hydrodynamic size and exhibit enhanced long-term colloidal stability over the pH range of 4 − 12 and in the presence of excess electrolytes. Nanoparticles ligated with terminally reactive polymers have been easily coupled to target dyes and tested in live cell imaging with no measurable cytotoxicity. Finally, the resulting hydrophilic nanoparticles exhibit large and size-dependent r 2 relaxivity values.
Exploring correlation eﬀects and volume collapse during electride dimensionality change in Ca 2 N †

We investigate the role of interstitial electronic states in the metal-to-semiconductor transition and the origin of the volume collapse in Ca 2 N during the pressure-induced phase transitions accompanied by changes of electride subspace dimensionality. Our findings highlight the importance of correlation eﬀects in the electride subsystem as an essential component of the complex phase transformation mechanism. By employing a simplified model that incorporates the distortion of the local environment surrounding the interstitial quasi-atom (ISQ) which emerges under pressure and solving this model by Dynamical Mean Field Theory (DMFT), we successfully reproduced the evolution between the metallic and semiconducting phases and captured the remarkable volume collapse. Central to this observation is a significant enhancement of the localization of excess electrons and the emergence of antiferromagnetic pairing among them, leading to a spin-state transition with a notable reduction in the magnetic moment on the interstitial states.
On Estimating Uncertainty of Fingerprint Enhancement Models

The state-of-the-art models for ﬁngerprint enhancement are sophisticated deep neural network architectures that eliminate noise from ﬁngerprints by generating ﬁngerprints image with im-provedridge-valleyclarity. However, thesemodelsperformﬁngerprintenhancementlikeablack box and do not specify whether a model is expected to generate an erroneously enhanced ﬁnger-print image. Uncertainty estimation is a standard technique to interpret deep models. Generally, uncertainty in a deep model arises because of uncertainty in parameters of the model (termed as model uncertainty) or noise present in the data (termed as data uncertainty). Recent works showcase the usefulness of uncertainty estimation to interpret ﬁngerprint preprocessing models. Motivated by these works, this chapter presents a detailed analysis of the usefulness of estimating model uncertainty and data uncertainty of ﬁngerprint enhancement models. Furthermore, we also study the generalization ability of both these uncertainties on ﬁngerprint ROI segmentation. A detailed analysis of predicted uncertainties presents insights into the characteristics learnt by each of these uncertainties. Extensive experiments on several challenging ﬁngerprint databases demonstrate the signiﬁcance of estimating the uncertainty of ﬁngerprint enhancement models.