Miracle-dz/UniFAR-FTUs huggingface dataset:
Overcoming Dimensional Collapse in Self-supervised Contrastive Learning for Medical Image Segmentation

Self-supervised learning (SSL) approaches have achieved great success when the amount of labeled data is limited. Within SSL, models learn robust feature representations by solving pretext tasks. One such pretext task is contrastive learning, which involves forming pairs of similar and dissimilar input samples, guiding the model to distinguish between them. In this work, we investigate the application of contrastive learning to the domain of medical image analysis. Our findings reveal that MoCo v2, a state-of-the-art contrastive learning method, encounters dimensional collapse when applied to medical images. This is attributed to the high degree of inter-image similarity shared between the medical images. To address this, we propose two key contributions: local feature learning and feature decorrelation. Local feature learning improves the ability of the model to focus on the local regions of the image, while feature decorrelation removes the linear dependence among the features. Our experimental findings demonstrate that our contributions significantly enhance the model's performance in the downstream task of medical segmentation, both in the linear evaluation and full fine-tuning settings. This work illustrates the importance of effectively adapting SSL techniques to the characteristics of medical imaging tasks. The source code will be made publicly available at: https://github.com/CAMMA-public/med-moco
GeneOH Diffusion: Towards Generalizable Hand-Object Interaction Denoising via Denoising Diffusion

In this work, we tackle the challenging problem of denoising hand-object interactions (HOI). Given an erroneous interaction sequence, the objective is to refine the incorrect hand trajectory to remove interaction artifacts for a perceptually realistic sequence. This challenge involves intricate interaction noise, including unnatural hand poses and incorrect hand-object relations, alongside the necessity for robust generalization to new interactions and diverse noise patterns. We tackle those challenges through a novel approach, GeneOH Diffusion, incorporating two key designs: an innovative contact-centric HOI representation named GeneOH and a new domain-generalizable denoising scheme. The contact-centric representation GeneOH informatively parameterizes the HOI process, facilitating enhanced generalization across various HOI scenarios. The new denoising scheme consists of a canonical denoising model trained to project noisy data samples from a whitened noise space to a clean data manifold and a"denoising via diffusion"strategy which can handle input trajectories with various noise patterns by first diffusing them to align with the whitened noise space and cleaning via the canonical denoiser. Extensive experiments on four benchmarks with significant domain variations demonstrate the superior effectiveness of our method. GeneOH Diffusion also shows promise for various downstream applications. Project website: https://meowuu7.github. io/GeneOH-Diffusion/.
Interference Produces False-Positive Pricing Experiments

It is standard practice in online retail to run pricing experiments by randomizing at the article-level, i.e. by changing prices of different products to identify treatment effects. Due to customers' cross-price substitution behavior, such experiments suffer from interference bias: the observed difference between treatment groups in the experiment is typically significantly larger than the global effect that could be expected after a roll-out decision of the tested pricing policy. We show in simulations that such bias can be as large as 100%, and report experimental data implying bias of similar magnitude. Finally, we discuss approaches for de-biased pricing experiments, suggesting observational methods as a potentially attractive alternative to clustering.
Exact non-Hermitian mobility edges and robust flat bands in two-dimensional Lieb lattices with imaginary quasiperiodic potentials

The mobility edge (ME) is a critical energy delineates the boundary between extended and localized states within the energy spectrum, and it plays a crucial role in understanding the metal-insulator transition in disordered or quasiperiodic systems. While there have been extensive studies on MEs in one-dimensional non-Hermitian (NH) quasiperiodic lattices recently, the investigation of exact NH MEs in two-dimensional (2D) cases remains rare. In the present study, we introduce a 2D dissipative Lieb lattice (DLL) model with imaginary quasiperiodic potentials applied solely to the vertices of the Lieb lattice. By mapping this DLL model to the 2D NH Aubry-Andr{\'e}-Harper (AAH) model, we analytically derive the exact ME and find it associated with the absolute eigenenergies. We find that the eigenvalues of extended states are purely imaginary when the quasiperiodic potential is strong enough. Additionally, we demonstrate that the introduction of imaginary quasiperiodic potentials does not disrupt the flat bands inherent in the system. Finally, we propose a theoretical framework for realizing our model using the Lindblad master equation. Our results pave the way for further investigation of exact NH MEs and flat bands in 2D dissipative quasiperiodic systems.
Linear Discriminant Regularized Regression

Linear Discriminant Analysis (LDA) is an important classification approach. Its simple linear form makes it easy to interpret and it is capable to handle multi-class responses. It is closely related to other classical multivariate statistical techniques, such as Fisher's discriminant analysis, canonical correlation analysis and linear regression. In this paper we strengthen its connection to multivariate response regression by characterizing the explicit relationship between the discriminant directions and the regression coefficient matrix. This key characterization leads to a new regression-based multi-class classification procedure that is flexible enough to deploy any existing structured, regularized, and even non-parametric, regression methods. Moreover, our new formulation is generically easy to analyze compared to existing regression-based LDA procedures. In particular, we provide complete theoretical guarantees for using the widely used $\ell_1$-regularization that has not yet been fully analyzed in the LDA context. Our theoretical findings are corroborated by extensive simulation studies and real data analysis.
Consensus methodology to investigate appropriate referral criteria for inpatients to be offered a transfer of care service as they are discharged home

Hamde Nazar Gregory Maniatopoulos Efi Mantzourani Neil Watson 1School of Pharmacy, Newcastle University, Newcastle upon Tyne, UK; 2Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK; 3School of Pharmacy and Pharmaceutical Sciences, Cardiff University, Cardiff, Wales, UK; 4Pharmacy Services, Royal Victoria Infirmary NHS Hospital Trust, Newcastle upon Tyne, UK Introduction An emerging clinical role for community pharmacists is to become actively involved in the follow-up care of patients who are discharged back into the community (transfer of care services) by promoting better medication adherence and by contributing to the safe, effective, and efficient use of medication. Recent research highlights how this extended role of community pharmacists could help to reduce drug-related adverse events, unnecessary health provider visits, hospitalizations, and readmissions while strengthening integrated primary care delivery across the healthcare system. Risk prediction models have been developed to effectively target the offering of postdischarge interventions to reduce patient hospital readmission. These tools aim to standardize the offer and provision of services that could otherwise be dependent upon professional and clinical expertise, knowledge and bias. Despite over 30 years of work to develop and optimize a risk prediction tool, there is limited consensus on the fundamental patient parameters that are most useful to target delivery of readmission-reducing interventions. A consensus methodological approach was adopted in this study to address a gap in the literature by identifying appropriate referral criteria of hospital inpatients for follow-up care in the primary care setting.
Dependency Annotation of Ottoman Turkish with Multilingual BERT

This study introduces a pretrained large language model-based annotation methodology of the first dependency treebank in Ottoman Turkish. Our experimental results show that, through iteratively i) pseudo-annotating data using a multilingual BERT-based parsing model, ii) manually correcting the pseudo-annotations, and iii) fine-tuning the parsing model with the corrected annotations, we speed up and simplify the challenging dependency annotation process. The resulting treebank, that will be a part of the Universal Dependencies (UD) project, will facilitate automated analysis of Ottoman Turkish documents, unlocking the linguistic richness embedded in this historical heritage.
Large Language Models on Wikipedia-Style Survey Generation: an Evaluation in NLP Concepts

Educational materials such as survey articles in specialized fields like computer science traditionally require tremendous expert inputs and are therefore expensive to create and update. Recently, Large Language Models (LLMs) have achieved significant success across various general tasks. However, their effectiveness and limitations in the education domain are yet to be fully explored. In this work, we examine the proficiency of LLMs in generating succinct survey articles specific to the niche field of NLP in computer science, focusing on a curated list of 99 topics. Automated benchmarks reveal that GPT-4 surpasses its predecessors like GPT-3.5, PaLM2, and LLaMa2 in comparison to the established ground truth. We compare both human and GPT-based evaluation scores and provide in-depth analysis. While our findings suggest that GPT-created surveys are more contemporary and accessible than human-authored ones, certain limitations were observed. Notably, GPT-4, despite often delivering outstanding content, occasionally exhibited lapses like missing details or factual errors. At last, we compared the rating behavior between humans and GPT-4 and found systematic bias in using GPT evaluation.
Discovery of free glycated amines and glycated urea as potential markers of diabetic nephropathy

The role of protein glycation in the pathogenesis of diabetes is well established. Akin to proteins, free amino acids and other small-molecule amines are also susceptible to glycation in hyperglycemic conditions and may have a role in the pathogenesis of the disease. However, the information on glycation of free amino acids and other small- molecule amines is relatively obscure. In the quest to discover small molecule glycated amines in the plasma, we have synthesized glycated amino acids, glycated creatine, and glycated urea, and by using a high-resolution accurate mass spectrometer, a mass spectral library was developed comprising the precursor and predominant fragment masses of glycated amines. Using this information, we report the discovery of glycation of free lysine, arginine, and leucine/isoleucine from the plasma of diabetic patients. This has great physiological significance as glycation of these amino acids may create their deficiency and affect vital physiological processes such as protein synthesis, cell signaling and insulin secretion. Also, these glycated amino acids could serve as potential markers of diabetes and its complications. On the other hand, accumulation of creatinine and urea in the plasma act as biomarkers of diabetic nephropathy diagnosis. For the first time, we report the detection of glycated urea in diabetes, which is confirmed by matching the precursor and fragment masses with the in vitro synthesized glycated urea by using 12C6 and 13C6-glucose. Further, we quantified glycated urea detected in two forms, monoglycated urea (MGU) and diglycated urea (DGU), by a targeted mass spectrometric approach in the plasma of healthy, diabetes, and diabetic nephropathy subjects. Both MGU and DGU showed a positive correlation with clinical parameters such as blood glucose and HbA1c. Given that urea gets converted to glycated urea in hyperglycemic conditions, it is crucial to quantify MGU and DGU along with the urea for the diagnosis of diabetic nephropathy.
Aedes albopictus is not an arbovirus aficionado – Impacts of sylvatic flavivirus infection in vectors and hosts on mosquito engorgement on non-human primates

The contact structure between vertebrate hosts and arthropod vectors plays a key role in the spread of arthropod-borne viruses (arboviruses); thus, it is important to determine whether arbovirus infection of either host or vector alters vector feeding behavior. Here we leveraged a study of the replication dynamics of two arboviruses isolated from their ancestral cycles in paleotropical forests, sylvatic dengue-2 (DENV-2) and Zika (ZIKV), in one non-human primate (NHP) species from the paleotropics (cynomolgus macaques, Macaca fascicularis) and one from the neotropics (squirrel monkeys, Saimiri boliviensis) to test the effect of both vector and host infection with each virus on completion of blood feeding (engorgement) of the mosquito Aedes albopictus. Although mosquitoes were starved and given no choice of hosts, engorgement rates varied dramatically, from 0% to 100%. While neither vector nor host infection systematically affected engorgement, NHP species and body temperature at the time of feeding did. We also interrogated the effect of repeated mosquito bites on cytokine expression and found that epidermal growth factor (EGF) and macrophage migration inhibitory factor (MIF) concentrations were dynamically associated with exposure to mosquito bites. This study highlights the importance of incorporating individual-level heterogeneity of vector biting in arbovirus transmission models.
Random sampling of ligand arrangements on a one-dimensional lattice

We introduce a transfer-matrix-based sequential sampling scheme for generating random samples of ligand arrangements on one-dimensional templates. The number of ligand types is arbitrary, the binding constants can have positional dependence, and cooperativity parameters are included. From the random arrangements, any (linear or non-linear) observable can be calculated using sample averaging. We provide a publically available software with a computational time that scales linearly with the lattice size. As an example application, we study the competitive binding of three ligand types (the sequence-specific binder netropsin, YOYO-1 and ethidium bromide) to a DNA molecule.
Single-cell multimodal profiling of monocytes reveals diverse phenotypes and alterations linked to cardiovascular disease risks

Monocytes are a critical innate immune system cell type that serves homeostatic and immunoregulatory functions. The Cell surface expression of CD14 and CD16 has historically identified them, however, recent single-cell studies have uncovered that they are much more heterogeneous than previously realized. We utilized cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-cell RNA sequencing (scRNA-seq) to describe the comprehensive transcriptional and phenotypic landscape of 437,126 monocytes. This high-dimensional multimodal approach identified vast phenotypic diversity and functionally distinct subsets, including IFN-responsive, MHCIIhi, monocyte-platelet aggregates, and non-classical, as well as several subpopulations of classical monocytes. Using flow cytometry, we validated the existence of MHCII+CD275+ MHCIIhi, CD42b+ monocyte-platelet aggregates, CD16+CD99- non-classical monocytes, and CD99+ classical monocytes. Each subpopulation exhibited unique functions, developmental trajectories, transcriptional regulation, and tissue distribution. Moreover, we revealed alterations associated with cardiovascular disease (CVD) risk factors, including race, smoking, and hyperlipidemia, and the effect of hyperlipidemia was recapitulated in mouse models of elevated cholesterol. This integrative and cross-species comparative analysis provides a unique resource to compare alterations in monocytes in pathological conditions and offers insights into monocyte-driven mechanisms in CVD and the potential for targeted therapies. Summary Multimodal profiling provides a comprehensive phenotypic and transcriptional understanding of monocytes in health and cardiovascular disease risk states.
AIBP controls TLR4 inflammarafts and mitochondrial dysfunction in a mouse model of Alzheimer’s disease

Microglia-driven neuroinflammation plays an important role in the development of Alzheimer’s disease (AD). Microglia activation is accompanied by the formation and chronic maintenance of TLR4 inflammarafts, defined as enlarged and cholesterol-rich lipid rafts serving as an assembly platform for TLR4 dimers and complexes of other inflammatory receptors. The secreted apoA-I binding protein (APOA1BP or AIBP) binds TLR4 and selectively targets cholesterol depletion machinery to TLR4 inflammaraft expressing inflammatory, but not homeostatic microglia. Here we demonstrated that amyloid-beta (Aβ) induced formation of TLR4 inflammarafts in microglia in vitro and in the brain of APP/PS1 mice. Mitochondria in Apoa1bp-/- APP/PS1 microglia were hyperbranched and cupped, which was accompanied by increased ROS and the dilated ER. The size and number of Aβ plaques and neuronal cell death were significantly increased, and the animal survival was decreased in Apoa1bp-/- APP/PS1 compared to APP/PS1 female mice. These results suggest that AIBP exerts control of TLR4 inflammarafts and mitochondrial dynamics in microglia and plays a protective role in AD associated oxidative stress and neurodegeneration.
Gene expression plasticity of the mammalian brain circadian clock in response to photoperiod

Seasonal daylength, or circadian photoperiod, is a pervasive environmental signal that profoundly influences physiology and behavior. In mammals, the central circadian clock resides in the suprachiasmatic nuclei (SCN) of the hypothalamus where it receives retinal input and synchronizes, or entrains, organismal physiology and behavior to the prevailing light cycle. The process of entrainment induces sustained plasticity in the SCN, but the molecular mechanisms underlying SCN plasticity are incompletely understood. Entrainment to different photoperiods persistently alters the timing, waveform, period, and light resetting properties of the SCN clock and its driven rhythms. To elucidate novel molecular mechanisms of photoperiod plasticity, we performed RNAseq on whole SCN dissected from mice raised in Long (LD 16:8) and Short (LD 8:16) photoperiods. Fewer rhythmic genes were detected in Long photoperiod and in general the timing of gene expression rhythms was advanced 4-6 hours. However, a few genes showed significant delays, including Gem. There were significant changes in the expression clock-associated gene Timeless and in SCN genes related to light responses, neuropeptides, GABA, ion channels, and serotonin. Particularly striking were differences in the expression of the neuropeptide signaling genes Prokr2 and Cck, as well as convergent regulation of the expression of three SCN light response genes, Dusp4, Rasd1, and Gem. Transcriptional modulation of Dusp4 and Rasd1, and phase regulation of Gem, are compelling candidate molecular mechanisms for plasticity in the SCN light response through their modulation of the critical NMDAR-MAPK/ERK-CREB/CRE light signaling pathway in SCN neurons. Modulation of Prokr2 and Cck may critically support SCN neural network reconfiguration during photoperiodic entrainment. Our findings identify the SCN light response and neuropeptide signaling gene sets as rich substrates for elucidating novel mechanisms of photoperiod plasticity.
Revisiting the high-dimensional geometry of population responses in visual cortex

Recent advances in large-scale recording technology have spurred exciting new inquiries into the high-dimensional geometry of the neural code. However, characterizing this geometry from noisy neural responses, particularly in datasets with more neurons than trials, poses major statistical challenges. We address this problem by developing new tools for the accurate estimation of high-dimensional signal geometry. We apply these tools to investigate the geometry of representations in mouse primary visual cortex. Previous work has argued that these representations exhibit a power law, in which the n’th principal component falls off as 1/n. Here we show that response geometry in V1 is better described by a broken power law, in which two different exponents govern the falloff of early and late modes of population activity. Our analysis reveals that later modes decay more rapidly than previously suggested, resulting in a substantially larger fraction of signal variance contained in the early modes of population activity. We examined the signal representations of the early population modes and found them to have higher fidelity than even the most reliable neurons. Intriguingly there are many population modes not captured by classic models of primary visual cortex indicating there is highly redundant yet poorly characterized tuning across neurons. Furthermore, inhibitory neurons tend to co-activate in response to stimuli that drive the early modes consistent with a role in sharpening population level tuning. Overall, our novel and broadly applicable approach overturns prior results and reveals striking structure in a population sensory representation. Significance Statement The nervous system encodes the visual environment across millions of neurons. Such high-dimensional signals are difficult to estimate—and consequently—to characterize. We address this challenge with a novel statistical method that revises past conceptions of the complexity of encoding in primary visual cortex. We discover population encoding is dominated by approximately ten features while additional features account for much less of the representation than previously thought. Many dominant features are not explained by classic models indicating highly redundant encoding of poorly characterized nonlinear image features. Interestingly, inhibitory neurons respond in unison to dominant features consistent with a role in sharpening population representation. Overall, we discover striking properties of population visual representation with novel, broadly applicable, statistical tools.
An axonal brake on striatal dopamine output by cholinergic interneurons

Depolarisation of distal axons is necessary for neurons to translate somatic action potentials into neurotransmitter release. Studies have shown that striatal cholinergic interneurons (ChIs) can directly drive ectopic action potentials in dopamine (DA) axons and trigger DA release. However, here we show that this action occurs within a broader context of axonal signal integration whereby activation of ChIs and depolarisation of DA axons by nicotinic receptors (nAChRs) limits the subsequent depolarisation and release of DA in response to ensuing activity. We demonstrate that activation of ChIs and nAChRs in ex vivo mouse striatum, even when it does not trigger DA release that is detectable by fast-scan cyclic voltammetry, limits DA release for ∼100 ms by depressing subsequent axonal depolarisation and calcium summation. This axonal brake on DA release is stronger in dorsal than ventral striatum, and is unrelated to DA depletion. In vivo, antagonism of nAChRs in dorsal striatum elevated extracellular DA levels and promoted conditioned place-preference, underscoring its physiological relevance. Our findings reveal that under physiological conditions in vivo, ChIs acting via nAChRs dynamically attenuate DA output driven by DA neuron activity, leading to a predominantly inverse relationship between ACh and DA signalling that varies continuously with ChI activity.