Miracle-dz/UniFAR-FTUs huggingface dataset:
Endothelial mTOR maintains hematopoiesis during aging

Aging leads to a decline in hematopoietic stem and progenitor cell (HSPC) function. We recently discovered that aging of bone marrow endothelial cells (BMECs) leads to an altered crosstalk between the BMEC niche and HSPCs, that instructs young HSPCs to behave as aged HSPCs. Here, we demonstrate aging leads to a decrease in mTOR signaling within BMECs that potentially underlies the age-related impairment of their niche activity. Our findings reveal that pharmacological inhibition of mTOR using Rapamycin has deleterious effects on hematopoiesis. To formally determine whether endothelial-specific inhibition of mTOR can influence hematopoietic aging, we conditionally deleted mTOR in ECs (mTOR(ECKO)) of young mice and observed that their HSPCs displayed attributes of an aged hematopoietic system. Transcriptional profiling of HSPCs from mTOR(ECKO) mice revealed that their transcriptome resembled aged HSPCs. Notably, during serial transplantations, exposure of wild type HSPCs to an mTOR(ECKO) microenvironment was sufficient to recapitulate aging-associated phenotypes, confirming the instructive role of EC-derived signals in governing HSPC aging. Summary Ramalingam et al. demonstrate that pharmacological inhibition of mTOR adversely impacts aging hematopoiesis. The authors demonstrate that aging results in decreased mTOR signaling within the bone marrow endothelium and endothelial-specific inhibition of mTOR causes hematopoietic defects observed during physiological aging.
A single bacterial genus maintains root development in a complex microbiome

Plants grow within a complex web of species interacting with each other and with the plant. Many of these interactions are governed by a wide repertoire of chemical signals, and the resulting chemical landscape of the rhizosphere can strongly affect root health and development. To understand how microbe-microbe interactions influence root development in Arabidopsis, we established a model system for plant-microbe-microbe-environment interactions. We inoculated seedlings with a 185-member bacterial synthetic community (SynCom), manipulated the abiotic environment, and measured bacterial colonization of the plant. This enabled classification of the SynCom into four modules of co-occurring strains. We deconstructed the SynCom based on these modules, identifying microbe-microbe interactions that determine root phenotypes. These interactions primarily involve a single bacterial genus, Variovorax, which completely reverts severe root growth inhibition (RGI) induced by a wide diversity of bacterial strains as well as by the entire 185-member community. We demonstrate that Variovorax manipulate plant hormone levels to balance this ecologically realistic root community’s effects on root development. We identify a novel auxin degradation operon in the Variovorax genome that is necessary and sufficient for RGI reversion. Therefore, metabolic signal interference shapes bacteria-plant communication networks and is essential for maintaining the root’s developmental program. Optimizing the feedbacks that shape chemical interaction networks in the rhizosphere provides a promising new ecological strategy towards the development of more resilient and productive crops.
Sparse long-range connections in visual cortex for cost-efficient small-world networks

The brain performs visual object recognition using much shallower hierarchical stages than artificial deep neural networks employ. However, the mechanism underlying this cost-efficient function is elusive. Here, we show that cortical long-range connectivity(LRC) may enable this parsimonious organization of circuits for balancing cost and performance. Using model network simulations based on data in tree shrews, we found that sparse LRCs, when added to local connections, organize a small-world network that dramatically enhances object recognition of shallow feedforward networks. We found that optimization of the ratio between LRCs and local connections maximizes the small-worldness and task performance of the network, by minimizing the total length of wiring needed for integration of the global information. We also found that the effect of LRCs varies by network size, which explains the existence of species-specific LRCs in mammalian visual cortex of various sizes. Our results demonstrate a biological strategy to achieve cost-efficient brain circuits. Highlights Long-range connections (LRCs) enhance the object recognition of shallow networks Sparse LRCs added to dense local connections organize a small-world type network Small-worldness of networks modulates the balance between performance and wiring cost Distinct LRCs in various species are due to the size-dependent effect of LRCs Significance statement The hierarchical depth of the visual pathway in the brain is constrained by biological factors, whereas artificial deep neural networks consist of super-deep structures (i.e., as deep as computational power allows). Here, we show that long-range horizontal connections (LRCs) observed in mammalian visual cortex may enable shallow biological networks to perform cognitive tasks that require deeper artificial structures, by implementing cost-efficient organization of circuitry. Using model simulations based on anatomical data, we found that sparse LRCs, when added to dense local circuits, organize “small-world” type networks and that this dramatically enhances image classification performance by integrating both local and global components of visual stimulus. Our findings show a biological strategy of brain circuitry to balance sensory performance and wiring cost in the networks. One sentence summary Cortical long-range connections organize a small-world type network to achieve cost-efficient functional circuits under biological constraints
The cell-cell adhesion protein JAM3 determines nuclear deformability by regulating microtubule organization

The shape, size, and architecture of the nucleus determines the output of transcriptional programmes. As such, the ability of the nucleus to resist deformation and maintain its shape is essential for homeostasis. Conversely, changes in nuclear shape can alter transcription and cell state. The ability of cells to deform their nuclei is also essential for cells to invade confined spaces. But how cells set the extent of nuclear deformability in response to their environment is unclear. Here we show that the cell-cell adhesion protein JAM3 regulates nuclear shape. In epithelial cells, JAM3 is required for maintenance of nuclear shape by organizing microtubule polymers and promoting LMNA stabilization in the nuclear membrane. Depletion of JAM3 in normal epithelial cells leads to dysmorphic nuclei, which leads to differentiation into a mesenchymal-like state. Inhibiting the actions of kinesins in JAM3 depleted cells restores nuclear morphology and prevents differentiation into the mesenchymal-like state. Critically, JAM3 expression is predictive of disease progression. Thus JAM3 is a molecule which allows cells to control cell fates in response to the presence of neighbouring cells by tuning the extent of nuclear deformability.
Cellular mRNA signals in human kidney tumors

The cellular transcriptome may provide clues into the differentiation state and origin of human cancer, as tumor cells may retain patterns of gene expression similar to the cell they derive from. Here, we studied the differentiation state and cellular origin of human kidney tumors, by assessing mRNA signals in 1,300 childhood and adult renal tumors, spanning seven different tumor types. Using single cell mRNA reference maps of normal tissues generated by the Human Cell Atlas project, we measured the abundance of reference "cellular signals" in each tumor. Quantifying global differentiation states, we found that, irrespective of tumor type, childhood tumors exhibited fetal cellular signals, thus replacing the long-held presumption of "fetalness" with a precise, quantitative readout of immaturity. By contrast, in adult cancers our assessment refuted the suggestion of dedifferentiation towards a fetal state in the overwhelming majority of cases, with the exception of lethal variants of clear cell renal cell carcinoma. Examining the specific cellular phenotype of each tumor type revealed an intimate connection between the different mesenchymal populations of the developing kidney and childhood renal tumors, whereas adult tumors mostly represented specific mature tubular cell types. RNA signals of each tumor type were remarkably uniform and specific, indicating a possible therapeutic and diagnostic utility. We demonstrated this utility with a case study of a cryptic renal tumor. Whilst not classifiable by clinical pathological work-up, mRNA signals revealed the diagnosis. Our findings provide a cellular definition of human renal tumors through an approach that is broadly applicable to human cancer.
Regulation of yefM/yoeB toxin antitoxin system is independent of ppGpp and inorganic polyphosphate in Escherichia coli

Bacterial persistence is a phenomenon wherein small proportion of a bacterial population attains transient antibiotic tolerance likely by virtue of metabolic minimization. Type II Toxin–Antitoxin systems (TAs), small overlapping bicistronic negative auto-regulons, were recently shown to induce the persistence state. Maisonneuve et al., 2013 reported that TAs are activated by a regulatory cascade consisting of stochastic accumulation of ppGpp leading to accumulation of inorganic polyphosphate (polyP). PolyP supposedly is essential for Lon protease-dependent-degradation of antitoxins resulting in activation of toxins and induction of persistence phenotype. In contrast, using semi-quantitative primer extension, we show that transcriptional up-regulation of yefM/yoeB loci, one of the well characterized TAs of Escherichia coli, is independent of ppGpp and polyP. Similarly, we show that chromosome-encoded YoeB-dependent target mRNA cleavage is independent of polyP. Our results and meta-analysis of literature we conclude that the regulation of yefM/yoeB TAs is independent of ppGpp and polyP.
A REVIEW ON SCREENING METHODS OF BREAST CANCER, CANCER BIOMARKERS AND PHYTOCONSTITUENTS AGAINST BREAST CANCER

Â Â A cancer biomarker refers to a substance or process that is used as an indicative the presence of cancer in the body. A biomarker may be a molecule secreted by a tumor or a specific response of the body to the presence of cancer. Biomarkers are utilized in three elementary ways as a diagnostic tool, prognostic tool, and predictive tool. The screening tests employed are clinical and breast exams, mammography, hereditary screening, ultrasound, and attractive reverberation imaging. Biomarkers involved in breast cancer are human epidermal growth factor-2, estrogen receptor, progesterone receptor, Cyclin D1, and Cyclin E. Specific bioactive phytoconstituents used as anticancer include curcumin, genistein, resveratrol, isothiocyanates, silymarin, diallyl sulfisoxazole, lycopene, apigenin, and gingerol. Hence, this review indicates the study of screening methods, various biomarkers in breast cancer and phytoconstituents against breast cancer.
Inferring intrinsic and extrinsic noise from a dual fluorescent reporter

Dual fluorescent reporter constructs, which measure gene expression from two identical promoters within the same cell, allow total gene expression noise to be decomposed into an extrinsic component, roughly associated with cell-to-cell fluctuations in cellular component concentrations, and intrinsic noise, roughly associated with inherent stochasticity of the biochemical reactions involved in gene expression [1]. A recent paper by Fu and Pachter presented frequentist statistical estimators for intrinsic and extrinsic noise using data from dual reporters [2]. For comparison, I here present results of a Bayesian analysis of this problem. I show that the orthodox estimators suffer from pathologies such as predicting negative values for a manifestly non-negative quantity, i.e. variance, and show that the Bayesian estimators do not suffer from such pathologies. In addition, I show that the Bayesian analysis automatically identifies that optimal estimates of intrinsic and extrinsic noise depend on a subtle combination of two statistics of the data, allowing for accuracies that are up to twice the accuracy of the orthodox estimators in some parameter regimes. I hope up this little worked out example contrasting orthodox statistical analysis based on ad hoc estimators with estimators resulting from a Bayesian analysis, will be educational for others in the field. I distribute a Mathematica Notebook with this paper that allows users to easily reproduce all results and figures of the paper.
Reliability of Electrical impedance tomography (EIT) as a novel non-invasive cystovolumetric technique for self monitoring– a comparative study to standard ultrasound methods

Hypothesis / aims of study Electrical Impedance Tomography (EIT) is proposed as an unobtrusive cystovolumetric technique to determine bladder volume continuously and a possible integration into a portable device which would enable self monitoring to patients with bladder dysfunction. For this technique, a set of electrodes are placed around the lower abdomen. A small AC current (5 mA at 50 KHz) is injected into the body and the generated surface voltages are measured between the remaining pairs of electrodes. These measurements contain information about the cross-sectional impedance distribution in the lower abdominal region. Previously, it has been shown that the measured impedance correlates linearly to bladder volume [1]. In this study, the feasibility of this promising method is analyzed by comparison to standard ultrasound (US) and bladder scan (BS) measurements, which are the gold standard for non-invasive bladder volumetry.
Inherent Dependency Displacement Bias of Transition-Based Algorithms

A wide variety of transition-based algorithms are currently used for dependency parsers. Empirical studies have shown that performance varies across different treebanks in such a way that one algorithm outperforms another on one treebank and the reverse is true for a different treebank. There is often no discernible reason for what causes one algorithm to be more suitable for a certain treebank and less so for another. In this paper we shed some light on this by introducing the concept of an algorithm’s inherent dependency displacement distribution. This characterises the bias of the algorithm in terms of dependency displacement, which quantify both distance and direction of syntactic relations. We show that the similarity of an algorithm’s inherent distribution to a treebank’s displacement distribution is clearly correlated to the algorithm’s parsing performance on that treebank, specificially with highly significant and substantial correlations for the predominant sentence lengths in Universal Dependency treebanks. We also obtain results which show a more discrete analysis of dependency displacement does not result in any meaningful correlations.
A first look at COVID-19 information and misinformation sharing on Twitter

Since December 2019, COVID-19 has been spreading rapidly across the world. Not surprisingly, conversation about COVID-19 is also increasing. This article is a first look at the amount of conversation taking place on social media, specifically Twitter, with respect to COVID-19, the themes of discussion, where the discussion is emerging from, myths shared about the virus, and how much of it is connected to other high and low quality information on the Internet through shared URL links. Our preliminary findings suggest that a meaningful spatio-temporal relationship exists between information flow and new cases of COVID-19, and while discussions about myths and links to poor quality information exist, their presence is less dominant than other crisis specific themes. This research is a first step toward understanding social media conversation about COVID-19.
Prediction Confidence from Neighbors

The inability of Machine Learning (ML) models to successfully extrapolate correct predictions from out-of-distribution (OoD) samples is a major hindrance to the application of ML in critical applications. Until the generalization ability of ML methods is improved it is necessary to keep humans in the loop. The need for human supervision can only be reduced if it is possible to determining a level of confidence in predictions, which can be used to either ask for human assistance or to abstain from making predictions. We show that feature space distance is a meaningful measure that can provide confidence in predictions. The distance between unseen samples and nearby training samples proves to be correlated to the prediction error of unseen samples. Depending on the acceptable degree of error, predictions can either be trusted or rejected based on the distance to training samples. %Additionally, a novelty threshold can be used to decide whether a sample is worth adding to the training set. This enables earlier and safer deployment of models in critical applications and is vital for deploying models under ever-changing conditions.
3D Sketch-Aware Semantic Scene Completion via Semi-Supervised Structure Prior

The goal of the Semantic Scene Completion (SSC) task is to simultaneously predict a completed 3D voxel representation of volumetric occupancy and semantic labels of objects in the scene from a single-view observation. Since the computational cost generally increases explosively along with the growth of voxel resolution, most current state-of-the-arts have to tailor their framework into a low-resolution representation with the sacrifice of detail prediction. Thus, voxel resolution becomes one of the crucial difficulties that lead to the performance bottleneck. In this paper, we propose to devise a new geometry-based strategy to embed depth information with low-resolution voxel representation, which could still be able to encode sufficient geometric information, e.g., room layout, object’s sizes and shapes, to infer the invisible areas of the scene with well structure-preserving details. To this end, we first propose a novel 3D sketch-aware feature embedding to explicitly encode geometric information effectively and efficiently. With the 3D sketch in hand, we further devise a simple yet effective semantic scene completion framework that incorporates a light-weight 3D Sketch Hallucination module to guide the inference of occupancy and the semantic labels via a semi-supervised structure prior learning strategy. We demonstrate that our proposed geometric embedding works better than the depth feature learning from habitual SSC frameworks. Our final model surpasses state- of-the-arts consistently on three public benchmarks, which only requires 3D volumes of 60 × 36 × 60 resolution for both input and output.
EvolveGraph: Heterogeneous Multi-Agent Multi-Modal Trajectory Prediction with Evolving Interaction Graphs

Multi-agent interacting systems are prevalent in the world, from pure physical systems to complicated social dynamic systems. In many applications, effective understanding of the environment and accurate trajectory prediction of interactive agents play a significant role in downstream tasks, such as decision and planning. In this paper, we propose a generic trajectory forecasting framework (named EvolveGraph) with explicit interaction modeling via a latent interaction graph among multiple heterogeneous, interactive agents. Considering the uncertainty and the possibility of different future behaviors, the model is designed to provide multi-modal prediction hypotheses. Since the interactions may be time-varying even with abrupt changes, and different modalities may have different interactions, we address the necessity and effectiveness of adaptively evolving the interaction graph and provide an effective solution. We also introduce a double-stage training pipeline which not only improves training efficiency and accelerates convergence, but also enhances model performance in terms of prediction error. The proposed framework is evaluated on multiple public benchmark datasets in various areas for trajectory prediction, where the agents cover on-road vehicles, pedestrians, cyclists and sports players. The experimental results illustrate that our approach achieves state-of-the-art performance in terms of prediction accuracy.
X-Linear Attention Networks for Image Captioning

Recent progress on fine-grained visual recognition and visual question answering has featured Bilinear Pooling, which effectively models the 2nd order interactions across multi-modal inputs. Nevertheless, there has not been evidence in support of building such interactions concurrently with attention mechanism for image captioning. In this paper, we introduce a unified attention block --- X-Linear attention block, that fully employs bilinear pooling to selectively capitalize on visual information or perform multi-modal reasoning. Technically, X-Linear attention block simultaneously exploits both the spatial and channel-wise bilinear attention distributions to capture the 2$^{nd}$ order interactions between the input single-modal or multi-modal features. Higher and even infinity order feature interactions are readily modeled through stacking multiple X-Linear attention blocks and equipping the block with Exponential Linear Unit (ELU) in a parameter-free fashion, respectively. Furthermore, we present X-Linear Attention Networks (dubbed as X-LAN) that novelly integrates X-Linear attention block(s) into image encoder and sentence decoder of image captioning model to leverage higher order intra- and inter-modal interactions. The experiments on COCO benchmark demonstrate that our X-LAN obtains to-date the best published CIDEr performance of 132.0% on COCO Karpathy test split. When further endowing Transformer with X-Linear attention blocks, CIDEr is boosted up to 132.8%. Source code is available at https://github.com/Panda-Peter/image-captioning.
A Survey of Bias in Machine Learning Through the Prism of Statistical Parity

Abstract Applications based on machine learning models have now become an indispensable part of the everyday life and the professional world. As a consequence, a critical question has recently arose among the population: Do algorithmic decisions convey any type of discrimination against specific groups of population or minorities? In this article, we show the importance of understanding how bias can be introduced into automatic decisions. We first present a mathematical framework for the fair learning problem, specifically in the binary classification setting. We then propose to quantify the presence of bias by using the standard disparate impact index on the real and well-known adult income dataset. Finally, we check the performance of different approaches aiming to reduce the bias in binary classification outcomes. Importantly, we show that some intuitive methods are ineffective with respect to the statistical parity criterion. This sheds light on the fact that trying to make fair machine learning models may be a particularly challenging task, in particular when the training observations contain some bias.