Miracle-dz/UniFAR-FTUs huggingface dataset:
Bisphosphonate drugs have actions outside the skeleton and inhibit the mevalonate pathway in alveolar macrophages

Bisphosphonates drugs target the skeleton and are used globally for the treatment of common bone disorders. Nitrogen-containing bisphosphonates act by inhibiting the mevalonate pathway in bone-resorbing osteoclasts but, surprisingly, also appear to reduce the risk of death from pneumonia. We overturn the long-held belief that these drugs act only in the skeleton and show that a fluorescently-labelled bisphosphonate is internalised by alveolar macrophages and peritoneal macrophages in vivo. Furthermore, a single dose of a nitrogen-containing bisphosphonate (zoledronic acid) in mice was sufficient to inhibit the mevalonate pathway in tissue-resident macrophages, causing the build-up of a mevalonate metabolite and preventing protein prenylation. Importantly, one dose of bisphosphonate enhanced the immune response to bacterial endotoxin in the lung and increased the level of cytokines and chemokines in bronchoalveolar fluid. These studies suggest that bisphosphonates, as well as preventing bone loss, may boost immune responses to infection in the lung and provide a mechanistic basis to fully examine the potential of bisphosphonates to help combat respiratory infections that cause pneumonia.
Benchmarking artificial intelligence methods for end-to-end computational pathology

Artificial intelligence (AI) can extract subtle visual information from digitized histopathology slides and yield scientific insight on genotype-phenotype interactions as well as clinically actionable recommendations. Classical weakly supervised pipelines use an end-to-end approach with residual neural networks (ResNets), modern convolutional neural networks such as EfficientNet, or non-convolutional architectures such as vision transformers (ViT). In addition, multiple-instance learning (MIL) and clustering-constrained attention MIL (CLAM) are being used for pathology image analysis. However, it is unclear how these different approaches perform relative to each other. Here, we implement and systematically compare all five methods in six clinically relevant end-to-end prediction tasks using data from N=4848 patients with rigorous external validation. We show that histological tumor subtyping of renal cell carcinoma is an easy task which approaches successfully solved with an area under the receiver operating curve (AUROC) of above 0.9 without any significant differences between approaches. In contrast, we report significant performance differences for mutation prediction in colorectal, gastric and bladder cancer. Weakly supervised ResNet-and ViT-based workflows significantly outperformed other methods, in particular MIL and CLAM for mutation prediction. As a reason for this higher performance we identify the ability of ResNet and ViT to assign high prediction scores to highly informative image regions with plausible histopathological image features. We make all source codes publicly available at https://github.com/KatherLab/HIA, allowing easy application of all methods on any end-to-end problem in computational pathology.
DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells that must be tracked. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats and arbitrary image sizes as inputs. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data. Author Summary Time-lapse microscopy can generate large image datasets which track single-cell properties like gene expression or growth rate over time. Deep learning tools are very useful for analyzing these data and can identify the location of cells and track their position over time. In this work, we introduce a new version of our Deep Learning for Time-lapse Analysis (DeLTA) software, which includes the ability to robustly segment and track bacteria that are growing in two dimensions, such as on agarose pads or within microfluidic environments. This capability is essential for experiments where spatial and positional effects are important, such as conditions with microbial co-cultures, cell-to-cell interactions, or spatial patterning. The software also tracks pole age and can be used to analyze replicative aging. These new features join other improvements, such as the ability to work directly with many common microscope file formats. DeLTA 2.0 can reliably track hundreds of cells with low error rates, making it an ideal tool for high throughput analysis of microscopy data.
Estimating Intraclonal Heterogeneity and Subpopulation Changes from Perturbational Bulk Gene Expression Profiles in LINCS L1000 CMap by Premnas

Background The connectivity among signatures upon perturbations curated in the CMap library provides a valuable resource for understanding therapeutic pathways and biological processes associated with the drugs and diseases. Motivation However, due to the nature of bulk-level expression profiling by the L1000 assay, intraclonal heterogeneity and subpopulation compositional change that could contribute to the responses to perturbations are largely neglected, hampering the interpretability and reproducibility of the connections. Results In this work, we proposed a computational framework, Premnas, to estimate the abundance of undetermined subpopulations from L1000 profiles in CMap directly according to an ad hoc subpopulation representation learned from a well-normalized batch of single-cell RNA-seq datasets by the archetypal analysis. By recovering the information of subpopulation changes upon perturbation, the potentials of searching for drug cocktails and drug-resistant/susceptible subpopulations with CMap L1000 were further explored and examined. Conclusions The proposed framework enables a new perspective to understand the connectivity among cellular signatures and expands the scope of the CMAP and other similar perturbation datasets limited by the bulk profiling technology. The executable and source code of Premnas is freely available at https://github.com/jhhung/Premnas. Highlights A computational framework (i.e., Premnas) for learning subpopulation characteristics by the archetypal analysis and deconvoluting bulk expression profiles using the digital cytometry into subpopulation composition was proposed and validated. Intraclonal heterogeneity and subpopulation changes upon different perturbagen treatments in LINCS CMap L1000 datasets were estimated by Premnas. With Premnas, we introduced a new strategy of finding effective drug cocktails and further linked the drug-resistant subpopulation found in CMap L1000 to a known drug-resistant clone (i.e., the pre-adapted [PA] cell). To our best knowledge, this work is the first attempt to provide a new subpopulation perspective to CMap database. We believe Premnas can be applied to all the perturbation datasets, of which intraclonal/intratumoral heterogeneity was concealed by the bulk profiling and hereafter provides a new dimension of interpreting the connectivity.
Stress-dependent inhibition of cell polarity through unbalancing the GEF/GAP regulation of Cdc42

Cdc42 rules cell polarity and growth in fission yeast. It is negatively and positively regulated by GTPase-activating proteins (GAPs) and by Guanine nucleotide Exchange factors (GEFs), respectively. Active Cdc42-GTP localizes to the poles, where it associates with numerous proteins constituting the polarity module. However, little is known about its down-regulation. We describe here that oxidative stress causes Sty1 kinase-dependent Cdc42 inactivation at cell poles. Both the amount of active Cdc42 at poles and cell length inversely correlate with Sty1 activity, explaining the elongated morphology of Δsty1 cells. We have created stress-blinded cell poles by either eliminating two Cdc42 GAPs or through the constitutive tethering of a GEF to the cell tips, and biochemically demonstrate that Rga3 is a direct substrate of Sty1. We propose that stress-activated Sty1 promotes GTP hydrolysis and prevents GEF activity at the cell tips, thus leading to the inhibition of Cdc42 and polarized growth cessation.
GFP-Tagged Protein Detection by Electron Microscopy Using a GBP-APEX Tool in Drosophila

In cell biology, detection of protein subcellular localizations is often achieved by optical microscopy techniques and more rarely by electron microscopy (EM) despite the greater resolution offered by EM. One of the possible reasons was that protein detection by EM required specific antibodies whereas this need could be circumvented by using fluorescently-tagged proteins in optical microscopy approaches. Recently, the description of a genetically encodable EM tag, the engineered ascorbate peroxidase (APEX), whose activity can be monitored by electron-dense DAB precipitates, has widened the possibilities of specific protein detection in EM. However, this technique still requires the generation of new molecular constructions. Thus, we decided to develop a versatile method that would take advantage of the numerous GFP-tagged proteins already existing and create a tool combining a nanobody anti-GFP (GBP) with APEX. This GBP-APEX tool allows a simple and efficient detection of any GFP fusion proteins without the needs of specific antibodies nor the generation of additional constructions. We have shown the feasibility and efficiency of this method to detect various proteins in Drosophila ovarian follicles such as nuclear proteins, proteins associated with endocytic vesicles, plasma membranes or nuclear envelopes. Lastly, we expressed this tool in Drosophila with the UAS/GAL4 system that enables spatiotemporal control of the protein detection.
Prostaglandin E2 Dilates Intracerebral Arterioles When Applied to Capillaries: Implications for Small Vessel Diseases

Prostaglandin E2 (PGE2) has been widely proposed to mediate neurovascular coupling by dilating brain parenchymal arterioles through activation of prostanoid EP4 receptors. However, our previous report that direct application of PGE2 induces an EP1-mediated constriction strongly argues against its direct action on arterioles during neurovascular coupling, the mechanisms sustaining functional hyperemia. Recent advances have highlighted the role of capillaries in sensing neuronal activity and propagating vasodilatory signals to the upstream penetrating parenchymal arteriole. Here, we examined the effect of capillary stimulation with PGE2 on upstream arteriolar diameter using an ex vivo capillary-parenchymal arteriole preparation and in vivo cerebral blood flow measurements with two-photon laser-scanning microscopy. We found that PGE2 caused upstream arteriolar dilation when applied onto capillaries with an EC50 of 70 nM. The response was inhibited by EP1 receptor antagonist and was greatly reduced, but not abolished, by blocking the strong inward-rectifier K+ channel. We further observed a blunted dilatory response to capillary stimulation with PGE2 in a genetic mouse model of cerebral small vessel disease with impaired functional hyperemia. This evidence casts previous findings in a different light, indicating that capillaries are the locus of PGE2 action to induce upstream arteriolar dilation in the control of brain blood flow, thereby providing a paradigm-shifting view that nonetheless remains coherent with the broad contours of a substantial body of existing literature.
Frailty Status Among the Elderly of Different Genders and the Death Risk: A Follow-Up Study

Background: Frailty in the elderly population is currently a frontier and focus in the field of health and aging. The goal of this study was to explore the frailty status among the elderly of different genders and its influence on the risk of death during 11 years. Methods: Frailty index (FI) was used to evaluate the frailty status in the elderly based on the baseline data conducted in 2009; and death as outcome variables collected in 2020 were analyzed. The difference of the frailty level and mortality of different genders was compared. Cox regression and Kaplan–Meier curves were applied to evaluate the influence on the risk of death and the 11-year survival of the elderly at different level of frailty, respectively. Results: Totally, 1,246 elderly people were recruited. The mortality in men (43.7%, 227/519) was statistically higher than that in women (34.3%, 249/727) (x2 = 11.546, P = 0.001). Deficits accumulated exponentially with age, and at all ages, women accumulated more deficits than do men on average (B = 0.030 vs. 0.028, t = 4.137, P = 0.023). For any given level of frailty, the mortality rate is higher in men than in women, and the difference in mortality between genders reached the peak when FI value was 0.26. Cox regression analysis showed that FI value had a greater impact on the risk of death in older men (HR = 1.171, 95%CI: 1.139~1.249)than that in older women (HR = 1.119, 95%CI: 1.039~1.137). Survival analysis showed that the median 11-year survival time in women was longer than that in men (95.26 vs. 89.52 months, Log rank = 9.249, P = 0.002). Kaplan–Meier curves showed that the survival rate decreased with the increase of frailty, and at the same level of frailty, survival time in older women was longer than that in older men, except for severe frailty (FI ≥ 0.5). Conclusion: The frailty status and its influence on mortality are different among the older people of different genders; therefore, specific interventions for frailty should be conducted in the elderly population of different genders, as well as of different degrees of frailty.
A Potential Canary in the Coal Mine: A Critical Policy Analysis of the Illinois LSAMP During the COVID-19 Pandemic

The COVID-19 pandemic has continued to impact every industry and test problem-solving capabilities and innovation across the board; education is no exception. As institutions continue to adapt to the impacts of the current public health crisis, colleges and universities are also navigating federal policy prompted by the pandemic. Literature has shown the positive influence of organizations, such as the Louis Stokes Alliances for Minority Participation (LSAMP), and, we argue that they can take a more active intermediary approach, that of an Organizational Buffer, to best support their students during times of uncertainty. Current research highlights the disconnect between STEM education and policy, as well as how the pandemic is disproportionately impacting communities of color. The pervasiveness of whiteness within educational policy and the negative impacts of unequal distribution of resources on students of color in STEM highlight the need to center race in a theoretical framework and policy. The purpose of this study was to understand the policy and communication responses to the pandemic as they pertained to supporting student success in STEM. Using the Theory of Racialized Organizations, which is a qualitative case study approach that leverages diffractive readings, was implemented to understand whether educational policy and communication responses during this time have or perpetuated inequitable systems. Guided by the research question, in what ways do pandemic policies and communications bolster the success of underrepresented minoritized students (URM) majoring in STEM, our study found four versions of policymaking (i.e., Performative, Picking Winners and Losers, Stay in your Lane, and Time Burden) that emerged and did not support URM STEM students equitably and consistently. Based on these findings, we present implications for institutional responses, LSAMP-alliance support, and future research.
Long Non-coding RNA N1LR Protects Against Myocardial Ischemic/Reperfusion Injury Through Regulating the TGF-β Signaling Pathway

Long non-coding RNAs (lncRNAs) have been shown to play critical roles in various cell biological processes. However, the mechanism of lncRNAs in acute myocardial infarction (AMI) is not fully understood. Previous studies showed that lncRNA N1LR was down-regulated in ischemic cerebral stroke and its up-regulation was protective. The current study was designed to assess the protective effect of N1LR and further to explore potential mechanisms of N1LR in ischemic/reperfusion (I/R) injury after AMI. Male C57BL/6J mice and H9c2 cardiomyocytes were selected to construct in vivo and in vitro pathological models. In H9c2 cell line, N1LR expression was markedly decreased after H2O2 and CoCl2 treatments and N1LR overexpression alleviated apoptosis, inflammation reaction, and LDH release in cardiomyocytes treated with H2O2 and CoCl2. Mouse in vivo study showed that overexpression of N1LR enhanced cardiac function and suppressed inflammatory response and fibrosis. Mechanistically, we found that the expression of transforming growth factor (TGF)-β1 and smads were significantly decreased in the N1LR overexpression group exposed to H2O2. In a summary, our study indicated that N1LR can act as a protective factor against cardiac ischemic-reperfusion injury through regulating the TGF-β/Smads signaling pathway.
Tools to Image Germplasm Dynamics During Early Zebrafish Development

During the first day of zebrafish development, ribonucleoprotein (RNP) complexes called germplasm form large aggregates that initially segregate asymmetrically during cleavage stages. After zygotic genome activation, the granules break into smaller fragments that associate with the nuclear membrane as perinuclear (germ) granules toward the end of gastrulation. The mechanisms underlying the highly dynamic behavior of germ granules are not well studied but thought to be facilitated by the cytoskeleton. Here, we present efficient mounting strategies using 3d-printed tools that generate wells on agarose-coated sample holders to allow high-resolution imaging of multiplexed embryos that are less than one day post-fertilization (dpf) on inverted (spinning disk confocal) as well as upright (lattice light-sheet and diSPIM) microscopes. In particular, our tools and methodology allow water dipping lenses to have direct access to mounted embryos, with no obstructions to the light path (e.g., through low melting agarose or methyl cellulose). Moreover, the multiplexed tight arrays of wells generated by our tools facilitate efficient mounting of early embryos (including cleavage stages) for live imaging. These methods and tools, together with new transgenic reporter lines, can facilitate the study of germ granule dynamics throughout their lifetime in detail, at high resolution and throughput, using live imaging technologies.
A High Cattle-Grazing Density Alters Circadian Rhythmicity of Temperature, Heart Rate, and Activity as Measured by Implantable Bio-Loggers

Six cows managed under extensive grazing conditions were used to study the effect of moving the animals to a higher grazing density on the circadian rhythms of temperature (T), heart rate (HR), and activity (ACT), which were recorded by implantable bio-loggers. Cows were maintained at a density of 1.5 livestock units per hectare (LSUs/ha; low density, LD) until they were moved to a grazing area at 128 LSUs/ha (high density, HD). Animals were implanted subcutaneously with a T, HR, and ACT bio-logger, which was programmed to record data at 5-min intervals. For each animal, cosinor rhythmometry (the study of circadian rhythms by fitting a sine wave to a time series) was applied to the data recorded over 5 days in LD and HD. Mean Midline Estimating Statistic of Rhythm (MESOR; the average value around which the variable oscillates), amplitude (difference between the peak and the mean value of a wave), and acrophase (timing of peak activity) were calculated and evaluated statistically. Differences between mean day and nighttime values, and mean LD and HD values were calculated. Cows presented cosinor curves that fit a 24-h rhythm (p < 0.001) in T, HR, and ACT at both densities. MESOR (T: 37.98 vs. 38.02°C; HR: 69.12 vs. 65.91 bpm; ACT: 49.39 vs. 40.41 mg, for LD and HD, respectively) and amplitude (T: 0.28 vs. 0.28°C; HR: 4.12 vs. 3.14 bpm; ACT: 18.14 vs. 11.28 mg, respectively) did not differ significantly between the two densities; however, significant (p < 0.05) differences between densities occurred in the acrophase of the three variables; specifically, the T acrophase was 2 h later at HD (22:45 h) than LD (20:45 h), and HR (LD: 19:51; HD: 16:49 h) and ACT acrophases 3 and 2 h earlier at HD than LD (LD: 14:47; HD: 12:49 h), respectively. T and ACT differed significantly (p < 0.01) between daytime (mean ± SE; 37.92 ± 0.19°C, 40.39 ± 4.74 mg) and nighttime (38.14 ± 0.17°C, 29.93 ± 5.66 mg). In conclusion, our study suggests that a high animal grazing density might exacerbate the social competence for valuable resources for animals, resulting in shifting the circadian rhythmicity of temperature, heart rate, and activity of the cows, advancing or delaying their acrophases.
The Screening Accuracy of a Visually Based Montreal Cognitive Assessment Tool for Older Adult Hearing Aid Users

Objectives This research aims to validate a modified visually based Montreal Cognitive Assessment for hearing-aid users (MoCA-HA). This population should be the target of cognitive screening due to high risk of developing dementia. Design Case-control study. Setting The participants were recruited from referral hearing-aid center and memory clinic in central London, United Kingdom. Participant 75 hearing-aid users were recruited. Of these, thirty were cognitively intact controls with hearing impairment (NC-HI); thirty had mild cognitive impairment with hearing impairment (MCI-HI); fifteen had dementia with hearing impairment (D-HI). Measurements The baseline characteristics and analysis of the MoCA-HA for the NC-HI were recorded. The MoCA-HA performance of the MCI-HI cohort and D-HI cohort were also studied. Results The cutpoint of <26 yields 93.3% sensitivity with 80% specificity in distinguishing MCI-HI from NC-HI. The specificity increased to 95.6% in screening for all cognitive impairment (MCI-HI and D-HI) from NC-HI. Conclusion The MoCA-HA has been validated with a cutpoint which is comparable to the traditional MoCA. This tool may help clinicians to early identify older adult hearing-aid users for appropriate cognitive evaluation.