Miracle-dz/UniFAR-FTUs huggingface dataset:
Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy

Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For instance, identifying the species and its antibiotic susceptibility is vital for effective bacterial infection treatment. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to discriminate four species of bacteria relevant to human health. The classification is performed on living bacteria and does not require fixation or staining, meaning that the bacterial species can be determined as the bacteria reproduce in a microfluidic device, enabling parallel determination of susceptibility to antibiotics. We assess the performance of convolutional neural networks and vision transformers, where the best model attained a class-average accuracy exceeding 98%. Our successful proof-of-principle results suggest that the methods should be challenged with data covering more species and clinically relevant isolates for future clinical use. Author Summary Bacterial infections are a leading cause of premature death worldwide, and growing antibiotic resistance is making treatment increasingly challenging. To effectively treat a patient with a bacterial infection, it is essential to quickly detect and identify the bacterial species and determine its susceptibility to different antibiotics. Prompt and effective treatment is crucial for the patient’s survival. A microfluidic device functions as a miniature “lab-on-chip” for manipulating and analyzing tiny amounts of fluids, such as blood or urine samples from patients. Microfluidic chips with chambers and channels have been designed for quickly testing bacterial susceptibility to different antibiotics by analyzing bacterial growth. Identifying bacterial species has previously relied on killing the bacteria and applying species-specific fluorescent probes. We introduce deep learning models as a fast and cost-effective method for identifying bacteria species directly from phase-contrast microscopy images of living bacteria simultaneously as growth is analyzed. We envision this method being employed concurrently with antibiotic susceptibility tests in future applications, significantly enhancing bacterial infection treatments.
KIS counteracts PTBP2 and regulates alternative exon usage in neurons

Alternative RNA splicing is an essential and dynamic process to control neuronal differentiation and synapse maturation, and dysregulation of this process has been associated with neurodegenerative diseases. Recent studies have revealed the importance of RNA-binding proteins in the regulation of neuronal splicing programs. However, the molecular mechanisms involved in the control of these splicing regulators are still unclear. Here we show that KIS, a brain-enriched kinase with a domain shared by splicing factors, controls exon usage in differentiated neurons at a genome-wide level. KIS phosphorylates the splicing regulator PTBP2 complex and markedly counteracts its role in exon exclusion. At the molecular level, phosphorylation of unstructured domains within PTBP2 causes its dissociation from key co-regulators and hinders its RNA-binding capacity. Taken together, our data provide new insights into the post-translational control of splicing regulators and uncover an essential role of KIS in setting alternative exon usage in neurons.
Heterogeneity-Preserving Discriminative Feature Selection for Subtype Discovery

The discovery of subtypes is pivotal for disease diagnosis and targeted therapy, considering the diverse responses of different cells or patients to specific treatments. Exploring the heterogeneity within disease or cell states provides insights into disease progression mechanisms and cell differentiation. The advent of high-throughput technologies has enabled the generation and analysis of various molecular data types, such as single-cell RNA-seq, proteomic, and imaging datasets, at large scales. While presenting opportunities for subtype discovery, these datasets pose challenges in finding relevant signatures due to their high dimensionality. Feature selection, a crucial step in the analysis pipeline, involves choosing signatures that reduce the feature size for more efficient downstream computational analysis. Numerous existing methods focus on selecting signatures that differentiate known diseases or cell states, yet they often fall short in identifying features that preserve heterogeneity and reveal subtypes. To identify features that can capture the diversity within each class while also maintaining the discrimination of known disease states, we employed deep metric learning-based feature embedding to conduct a detailed exploration of the statistical properties of features essential in preserving heterogeneity. Our analysis revealed that features with a significant difference in interquartile range (IQR) between classes possess crucial subtype information. Guided by this insight, we developed a robust statistical method, termed PHet (Preserving Heterogeneity) that performs iterative subsampling differential analysis of IQR and Fisher’s method between classes, identifying a minimal set of heterogeneity-preserving discriminative features to optimize subtype clustering quality. Validation using public single-cell RNA-seq and microarray datasets showcased PHet’s effectiveness in preserving sample heterogeneity while maintaining discrimination of known disease/cell states, surpassing the performance of previous outlier-based methods. Furthermore, analysis of a single-cell RNA-seq dataset from mouse tracheal epithelial cells revealed, through PHet-based features, the presence of two distinct basal cell subtypes undergoing differentiation toward a luminal secretory phenotype. Notably, one of these subtypes exhibited high expression of BPIFA1. Interestingly, previous studies have linked BPIFA1 secretion to the emergence of secretory cells during mucociliary differentiation of airway epithelial cells. PHet successfully pinpointed the basal cell subtype associated with this phenomenon, a distinction that pre-annotated markers and dispersion-based features failed to make due to their admixed feature expression profiles. These findings underscore the potential of our method to deepen our understanding of the mechanisms underlying diseases and cell differentiation and contribute significantly to personalized medicine.
Antagonism and selective modulation of the human glucocorticoid receptor both reduce recruitment of p300/CBP and the Mediator complex

Exogenous glucocorticoids are frequently used to treat inflammatory disorders and as adjuncts for treatment of solid cancers. However, their use is associated with severe side effects and therapy resistance. Novel glucocorticoid receptor (GR) ligands with a patient-validated reduced side effect profile have not yet reached the clinic. GR is a member of the nuclear receptor family of transcription factors and heavily relies on interactions with coregulator proteins for its transcriptional activity. To elucidate the role of the GR interactome in the differential transcriptional activity of GR following treatment with agonists, antagonists, or lead selective GR agonists and modulators (SEGRAMs), we generated comprehensive interactome maps by high-confidence proximity proteomics in lung epithelial carcinoma cells. We found that the GR antagonist RU486 and the SEGRAM Dagrocorat both reduced GR interaction with CREB-binding protein (CBP)/p300 and the Mediator complex when compared to the full GR agonist Dexamethasone. Our data offer new insights into the role of differential coregulator recruitment in shaping ligand-specific GR-mediated transcriptional responses. In Brief Glucocorticoids are commonly prescribed for the treatment of inflammatory disorders but are associated with severe side effects. Novel glucocorticoid receptor (GR) ligands with strong anti-inflammatory effects but reduced side effects are still sought after. Despite decades-long GR research, there is still an incomplete understanding of the molecular mechanisms driving context-specific GR activity. Using proximity labeling proteomics, we identified CREB-binding protein (CBP), p300 and the Mediator complex as potential crucial GR coregulators driving ligand-induced changes in GR’s transcriptional activity. Highlights Glucocorticoids (GCs), potent anti-inflammatory agents, can elicit side effects More selective GCs, causing less side effects, are currently still unavailable Lack of fundamental insights on context-specific actions of the GC receptor (GR) We mapped ligand-specific GR interactomes using proximity labeling proteomics p300/CBP and Mediator undergo ligand-dependent changes in interaction with GR
Hyperphosphorylated tau Inflicts Intracellular Stress Responses That Are Mitigated by Apomorphine

Background Abnormal phosphorylation of the microtubule-binding protein tau in the brain is a key pathological marker for Alzheimer’s disease and additional neurodegenerative tauopathies. However, how hyperphosphorylated tau causes cellular dysfunction or death that underlie neurodegeneration remains an unsolved question critical for the understanding of disease mechanism and the design of efficacious drugs. Methods Using a recombinant hyperphosphorylated tau protein (p-tau) synthesized by the PIMAX approach, we examined how cells responded to the cytotoxic tau and explored means to enhance cellular resistance to tau attack. Results Upon p-tau uptake, the intracellular calcium levels rose promptly. Gene expression analyses revealed that p-tau potently triggered endoplasmic reticulum (ER) stress, Unfolded Protein Response (UPR), ER stress-associated apoptosis, and pro-inflammation in cells. Proteomics studies showed that p-tau diminished heme oxygenase-1 (HO-1), an ER stress associated anti-inflammation and anti-oxidative stress regulator, while stimulated the accumulation of MIOS and other proteins. P-tau-induced ER stress-associated apoptosis and pro-inflammation are ameliorated by apomorphine, a brain-permeable prescription drug widely used to treat Parkinson’s disease symptoms, and by overexpression of HO-1. Conclusion Our results reveal probable cellular functions targeted by hyperphosphorylated tau. Some of these dysfunctions and stress responses have been linked to neurodegeneration in Alzheimer’s disease. The observations that the ill effects of p-tau can be mitigated by a small compound and by overexpressing HO-1 that is otherwise diminished in the treated cells inform new directions of Alzheimer’s disease drug discovery.
Prolonged Cell Cycle Arrest in Response to DNA damage in Yeast Requires the Maintenance of DNA Damage Signaling and the Spindle Assembly Checkpoint

Cells evoke the DNA damage checkpoint (DDC) to inhibit mitosis in the presence of DNA double-strand breaks (DSBs) to allow more time for DNA repair. In budding yeast, a single irreparable DSB is sufficient to activate the DDC and induce cell cycle arrest prior to anaphase for about 12 to 15 hours, after which cells “adapt” to the damage by extinguishing the DDC and resuming the cell cycle. While activation of the DNA damage-dependent cell cycle arrest is well-understood, how it is maintained remains unclear. To address this, we conditionally depleted key DDC proteins after the DDC was fully activated and monitored changes in the maintenance of cell cycle arrest. Degradation of Ddc2ATRIP, Rad9, Rad24, or Rad53CHK2 results in premature resumption of the cell cycle, indicating that these DDC factors are required both to establish and to maintain the arrest. Dun1 is required for establishment, but not maintenance of arrest, whereas Chk1 is required for prolonged maintenance but not for initial establishment of the mitotic arrest. When the cells are challenged with 2 persistent DSBs, they remain permanently arrested. This permanent arrest is initially dependent on the continuous presence of Ddc2 and Rad53; however, after 15 hours both proteins become dispensable. Instead, the continued mitotic arrest is sustained by spindle-assembly checkpoint (SAC) proteins Mad1, Mad2, and Bub2 but not by Bub2’s binding partner Bfa1. These data suggest that prolonged cell cycle arrest in response to 2 DSBs is achieved by a handoff from the DDC to specific components of the SAC. Furthermore, the establishment and maintenance of DNA damage-induced cell cycle arrest requires overlapping but different sets of factors.
In vivo Ontogeny of human forebrain neural progenitor cell grafts in adult rats: an immunohistological study

A thorough understanding of the cell behaviors of the human neural grafts is fundamental to exploit them to achieve cell therapy for recovering brain functions. Here by using immunohistological staining, we trace the cell fate of the intrastriatal human neural progenitor cell (NPC) grafts up to 9 months in adult rats, with multiple examining time points to provide a unified working time frame for future transplantation study. Lots of Nestin+/Sox2+ human cells continuously migrate along the white matter tracts into distal brain parenchyma even long time after transplantation, providing a potential for curing diffuse brain damage. Further analysis reveals a significant heterogeneity of the long-term sustained neural stem cells (NSC)/NPCs that progressing throughout different stages, mimicking the neural development of human forebrain. More importantly, the initial GFAP expression in human grafts marks the NSC progression instead of terminal astrocyte differentiation. The distally migrating human cells continuously show the capability to produce new neurons, albeit at a low efficiency in the intact brain. Further investigations in neural disease models are needed. Such study would benefit neural cell therapy with regarding to the optimization of the transplantation strategy and choosing of acting mode by neural grafts (e.g. via cell replacement or ex vivo gene therapy).
The Loss of the H1.4 Linker Histone Impacts Nascent Transcription and Chromatin Accessibility

The Chromatosome superstructure, comprised of core histone containing nucleosomes and linker histones, act in concert as physical barriers to genetic material in the mammalian nucleus to trans-acting factors. Appropriate arrangement, composition, and post-translational modification of the chromatosome is highly regulated and necessary for appropriate gene expression. These proteins act to radically condense the genetic material and linker H1 histone is essential for the further condensation of the chromatin fiber. However, the regulatory role of H1 in gene expression and chromatin organization is complicated by cell type specific expression and compensation of multiple H1 variants. Leveraging the UL3 osteosarcoma cell line which displays biased expression of H1 variants, and CRISPR/Cas9, we generated H1.4-deficient clones. Loss of H1.4 results in consistent changes to chromatin accessibility concomitant with changes to histone tail modifications, as well as a set of differentially expressed genes shared among ΔH1.4 genetic clones. We identified immune and inflammation immediate early genes as enriched in differentially expressed genes, skewed towards AP-1 regulated targets. Our data show that H1.4 is critical for the regulation of stress response pathways. Key Points for NAR (3 bullet points summarizing the manuscript’s contribution to the field) H1.4 is essential for appropriate expression of over 6,000 nascent transcripts in UL3 cells. Loss of H1.4 results in widespread changes in chromatin accessibility at enhancers and transcribed regions as well as heterochromatin and quiescent chromatin. Immediate early genes, and especially AP-1 family members, are highly sensitive to H1.4 loss and their binding sites coincide with losses in chromatin accessibility
Motor adaptation is facilitated by sleep-associated modulation of beta oscillations

Motor adaptation reflects the ability of the sensorimotor system in the brain to flexibly deal with changes in the environment to generate effective motor behaviour. How and whether sleep contributes to the consolidation of motor adaptation remains elusive. In this study, a new motor task was employed which utilizes a highly automatized motor skill to explore the effect of sleep on motor adaptation. We hypothesized that sleep-mediated beta band (13-30Hz) desynchronization would enhance motor adaptation performance. Healthy human experts in typing on the regular computer keyboard, were trained to type on a vertically mirrored keyboard while we recorded electroencephalography (EEG). Their typing performance was assessed after either a full night of sleep with polysomnography, or a similar period of daytime wakefulness. We show that motor adaptation performance improved after overnight sleep but not after daytime wakefulness. Beta oscillatory power as well as the spectral slope of the EEG (1-45Hz) were reduced during mirrored typing compared to regular typing. Notably, sleep caused an even further decrease in the power of beta oscillations. Additionally, overnight changes in the EEG spectral slope during mirrored typing was associated with the overnight changes in adaptive performance. Finally, an increase in fast sleep spindle density (13-15Hz) during the night following motor adaptation training, predicted successful motor adaptation. These findings suggest that sleep plays a crucial role in modulating
Global substrate identification and high throughput in vitro dephosphorylation reactions uncover PP1 and PP2A-B55 specificity principles

Phosphoprotein phosphatases (PPPs) dephosphorylate Serine (Ser)/Threonine (Thr) residues to regulate major signaling pathways and cellular transitions. Despite the central role of PPPs the substrates in most cellular processes and the determinants of phosphatase specificity are poorly understood. This is because methods to investigate this at scale are lacking. Here we develop a novel in vitro assay, MRBLE:Dephos, that allows multiplexing of dephosphorylation reactions to determine phosphatase preferences. Using MRBLE:Dephos, we establish amino acid preferences of the residues surrounding the dephosphorylation site for PP1 and PP2A- B55, which reveals common and unique preferences for the two phosphatases. To compare the MRBLE:Dephos results to cellular substrates, we focused on mitotic exit that requires extensive dephosphorylation by PP1 and PP2A-B55. We use specific inhibition of PP1 and PP2A-B55 in mitotic exit lysates coupled with quantitative phosphoproteomics to identify more than 2000 regulated phosphorylation sites. Importantly, the sites dephosphorylated during mitotic exit reveal key signatures that are consistent with the MRBLE:Dephos results. We use these insights to specifically alter INCENP dephosphorylation kinetics at mitotic exit, resulting in defective cytokinesis thus underscoring the biological relevance of our determined specificity principles. Finally, we provide a comprehensive characterization of PP1 binding motifs and demostrate how binding of phosphatases to substrates shapes dephosphorylation specificity. Collectively, we develop novel approaches to advance our ability to investigate protein phosphatases and use these to provide a framework for understanding mitotic exit regulation by dephosphorylation.
Tumor-derived biomarkers beyond antigen expression enhance efficacy of CD276/B7H3 antibody-drug conjugate in metastatic prostate cancer

Antibody-drug conjugates (ADCs) are promising targeted cancer therapy; however, patient selection based solely on target antigen expression without consideration for cytotoxic payload vulnerabilities has plateaued clinical benefits. Biomarkers to capture patients who might benefit from specific ADCs have not been systematically determined for any cancer. We present a comprehensive therapeutic and biomarker analysis of a B7H3-ADC with pyrrolobenzodiazepine(PBD) payload in 26 treatment-resistant, metastatic prostate cancer(mPC) models. B7H3 is a tumor-specific surface protein widely expressed in mPC, and PBD is a DNA cross-linking agent. B7H3 expression was necessary but not sufficient for B7H3-PBD-ADC responsiveness. RB1 deficiency and/or replication stress, characteristics of poor prognosis, conferred sensitivity and were associated with complete tumor regression in both neuroendocrine (NEPC) and androgen receptor positive(ARPC) prostate cancer models, even with low B7H3 levels. Non-ARPC models, which are currently lacking efficacious treatment, demonstrated the highest replication stress and were most sensitive to treatment. In RB1 wild-type ARPC tumors, SLFN11 expression or select DNA repair mutations in SLFN11 non-expressors governed response. Significantly, wild-type TP53 predicted non-responsiveness (7/8 models). Overall, biomarker-focused selection of models led to high efficacy of in vivo treatment. These data enable a paradigm shift to biomarker-driven trial designs for maximizing clinical benefit of ADC therapies.
The application of medical professional English multimedia teaching in clinical education (Enseñanza multimedia del inglés médico profesional en la educación sanitaria)

ABSTRACT Studying foreign languages for medical students is an indispensable requirement in today’s job market. The purpose of the study is to establish the advantages and effectiveness of using multimedia tools in teaching medical English. The study included 133 second-year medical students (79 young men and 54 young women) aged 19 to 21 years. At the end of the study, the total score for the final test in the experimental group was 38.6% higher compared to the baseline. In the control group, it was 21.2% higher with a statistically significant intergroup difference. The final score for writing and grammar in the experimental group increased by 29.9 and 21.9% compared to the baseline and the control group, respectively. The use of multimedia tools in teaching English to medical students is highly effective and contributes to improved speaking, listening skills and long-term memorization of new information. Multimedia tools also increase the interest and motivation of medical students in learning English.
Application of Vision Transformers and 3D Convolutional Neural Networks for Sign Language Cluster Recognition

In this study the overview of the methods for sign language recognition was done and the existing datasets in this area were analyzed. It was shown how based on the real data to develop and test different approaches. The models based on the Vision Transformer (ViViT) and 3D Convolutions CNN (3dCNN) using different batch sizes were built and compared. It was also shown how to learn models on different data sizes and to search the compromise between accuracy, speed and overfitting of the models. Our research provides valuable insights into the strengths and limitations of different models of the task, not solved tasks and offer a direction and possible improvements of existed methods in this area by using vision transformers.
Efficiency Increasing of No-reference Image Quality Assessment in UAV Applications

Unmanned aerial vehicle (UAV) imaging is a dynamically developing field, where the effectiveness of imaging applications highly depends on quality of the acquired images. No-reference image quality assessment is widely used for quality control and image processing management. However, there is a lack of accuracy and adequacy of existing quality metrics for human visual perception. In this paper, we demonstrate that this problem persists for typical applications of UAV images. We present a methodology to improve the efficiency of visual quality assessment by existing metrics for images obtained from UAVs, and introduce a method of combining quality metrics with the optimal selection of the elementary metrics used in this combination. A combined metric is designed based on a neural network trained to utilize subjective assessments of visual quality. The metric was tested using the TID2013 image database and a set of real UAV images with embedded distortions. Verification results have demonstrated the robustness and accuracy of the proposed metric.