Miracle-dz/UniFAR-FTUs huggingface dataset:
An Automated Pipeline for Tumour-Infiltrating Lymphocyte Scoring in Breast Cancer

Tumour-infiltrating lymphocytes (TILs) are considered as a valuable prognostic markers in both triple-negative and human epidermal growth factor receptor 2 (HER2) positive breast cancer. In this study, we introduce an innovative deep learning pipeline based on the Efficient-UNet architecture to predict the TILs score for breast cancer whole-slide images (WSIs). We first segment tumour and stromal regions in order to compute a tumour bulk mask. We then detect TILs within the tumour-associated stroma, generating a TILs score by closely mirroring the pathologist's workflow. Our method exhibits state-of-the-art performance in segmenting tumour/stroma areas and TILs detection, as demonstrated by internal cross-validation on the TiGER Challenge training dataset and evaluation on the final leaderboards. Additionally, our TILs score proves competitive in predicting survival outcomes within the same challenge, underscoring the clinical relevance and potential of our automated TILs scoring pipeline as a breast cancer prognostic tool.
Strategy and Action Plan for Mire and Peatland Conservation in Central Europe: Central European Peatland Project (CEPP)

Peatlands are vital economic and ecological resources which contribute to biological, landscape and cultural diversity. They comprise characteristic assemblages of species which can exhibit intense patterning of plant and animal communities. Peatlands are also the best ecosystem for sequestering carbon (with current stores far exceeding those held in rainforests). Guidelines for Global Action on Peatlands were agreed at the last Conference of Parties to the Ramsar Convention. These guidelines note that “There is a wide range of threats to peatlands that require urgent national and/or international action”. To help counter these threats, the Ramsar Convention is working with its sister conventions, on biodiversity and climate change. The Ramsar Secretariat is pleased to see this report, which provides a significant step forward by presenting a summary of current knowledge and a Strategy and an Action Plan for Central Europe; a region that still harbours large peatland areas and types which are virtually extinct elsewhere. But changes in land management, ownership and the nature of economic exploitation are now placing peatland in this region under increasing threat. This present publication will help inform all concerned to achieve better governance, and thereby management and conservation, for Peatlands in Central Europe.
Differentiable VQ-VAE's for Robust White Matter Streamline Encodings

Given the complex geometry of white matter streamlines, Autoencoders have been proposed as a dimension-reduction tool to simplify the analysis streamlines in a low-dimensional latent spaces. However, despite these recent successes, the majority of encoder architectures only perform dimension reduction on single streamlines as opposed to a full bundle of streamlines. This is a severe limitation of the encoder architecture that completely disregards the global geometric structure of streamlines at the expense of individual fibers. Moreover, the latent space may not be well structured which leads to doubt into their interpretability. In this paper we propose a novel Differentiable Vector Quantized Variational Autoencoder, which are engineered to ingest entire bundles of streamlines as single data-point and provides reliable trustworthy encodings that can then be later used to analyze streamlines in the latent space. Comparisons with several state of the art Autoencoders demonstrate superior performance in both encoding and synthesis.
Non-absolute integrable function spaces on metric measure spaces

Kuelbs-Steadman spaces are introduced in this article on a separable metric space with finite diameter and finite positive Borel measure. Kuelbs-Steadman spaces of the Lipschitz type are also discussed. Various inclusion properties are also discussed. In the sequel, we introduce HK-Sobolev spaces on metric mesure space which coincides with HK-Sobolev space in the Euclidean case. In application, we discuss the boundedness of Hardy-Littlewood maximal operator on Kuelbs-Steadman spaces and HK-Sobolev spaces over a metric measure space.
Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency

The growing number of AI-driven applications in mobile devices has led to solutions that integrate deep learning models with the available edge-cloud resources. Due to multiple benefits such as reduction in on-device energy consumption, improved latency, improved network usage, and certain privacy improvements, split learning, where deep learning models are split away from the mobile device and computed in a distributed manner, has become an extensively explored topic. Incorporating compression-aware methods (where learning adapts to compression level of the communicated data) has made split learning even more advantageous. This method could even offer a viable alternative to traditional methods, such as federated learning techniques. In this work, we develop an adaptive compression-aware split learning method ('deprune') to improve and train deep learning models so that they are much more network-efficient, which would make them ideal to deploy in weaker devices with the help of edge-cloud resources. This method is also extended ('prune') to very quickly train deep learning models through a transfer learning approach, which trades off little accuracy for much more network-efficient inference abilities. We show that the 'deprune' method can reduce network usage by 4x when compared with a split-learning approach (that does not use our method) without loss of accuracy, while also improving accuracy over compression-aware split-learning by 4 percent. Lastly, we show that the 'prune' method can reduce the training time for certain models by up to 6x without affecting the accuracy when compared against a compression-aware split-learning approach.
Superconformal anomalies from superconformal Chern-Simons polynomials

We consider the 4-dimensional $\mathcal{N}=1$ Lie superconformal algebra and search for completely"symmetric"(in the graded sense) 3-index invariant tensors. The solution we find is unique and we show that the corresponding invariant polynomial cubic in the generalized curvatures of superconformal gravity vanishes. Consequently, the associated Chern-Simons polynomial is a non-trivial anomaly cocycle. We explicitly compute this cocycle to all orders in the independent fields of superconformal gravity and establish that it is BRST equivalent to the so-called superconformal $a$-anomaly. We briefly discuss the possibility that the superconformal $c$-anomaly also admits a similar Chern-Simons formulation.
Generative Modeling of Residuals for Real-Time Risk-Sensitive Safety with Discrete-Time Control Barrier Functions

A key source of brittleness for robotic systems is the presence of model uncertainty and external disturbances. Most existing approaches to robust control either seek to bound the worst-case disturbance (which results in conservative behavior), or to learn a deterministic dynamics model (which is unable to capture uncertain dynamics or disturbances). This work proposes a different approach: training a state-conditioned generative model to represent the distribution of error residuals between the nominal dynamics and the actual system. In particular we introduce the Online Risk-Informed Optimization controller (ORIO), which uses Discrete-Time Control Barrier Functions, combined with a learned, generative disturbance model, to ensure the safety of the system up to some level of risk. We demonstrate our approach in both simulations and hardware, and show our method can learn a disturbance model that is accurate enough to enable risk-sensitive control of a quadrotor flying aggressively with an unmodelled slung load. We use a conditional variational autoencoder (CVAE) to learn a state-conditioned dynamics residual distribution, and find that the resulting probabilistic safety controller, which can be run at 100Hz on an embedded computer, exhibits less conservative behavior while retaining theoretical safety properties.
Detecting Suspicious Commenter Mob Behaviors on YouTube Using Graph2Vec

YouTube, a widely popular online platform, has transformed the dynamics of con-tent consumption and interaction for users worldwide. With its extensive range of content crea-tors and viewers, YouTube serves as a hub for video sharing, entertainment, and information dissemination. However, the exponential growth of users and their active engagement on the platform has raised concerns regarding suspicious commenter behaviors, particularly in the com-ment section. This paper presents a social network analysis-based methodology for detecting suspicious commenter mob-like behaviors among YouTube channels and the similarities therein. The method aims to characterize channels based on the level of such behavior and identify com-mon patterns across them. To evaluate the effectiveness of the proposed model, we conducted an analysis of 20 YouTube channels, consisting of 7,782 videos, 294,199 commenters, and 596,982 comments. These channels were specifically selected for propagating false views about the U.S. Military. The analysis revealed significant similarities among the channels, shedding light on the prevalence of suspicious commenter behavior. By understanding these similarities, we contribute to a better understanding of the dynamics of suspicious behavior on YouTube channels, which can inform strategies for addressing and mitigating such behavior.
Citation Recommendation on Scholarly Legal Articles

Citation recommendation is the task of finding appropriate citations based on a given piece of text. The proposed datasets for this task consist mainly of several scientific fields, lacking some core ones, such as law. Furthermore, citation recommendation is used within the legal domain to identify supporting arguments, utilizing non-scholarly legal articles. In order to alleviate the limitations of existing studies, we gather the first scholarly legal dataset for the task of citation recommendation. Also, we conduct experiments with state-of-the-art models and compare their performance on this dataset. The study suggests that, while BM25 is a strong benchmark for the legal citation recommendation task, the most effective method involves implementing a two-step process that entails pre-fetching with BM25+, followed by re-ranking with SciNCL, which enhances the performance of the baseline from 0.26 to 0.30 MAP@10. Moreover, fine-tuning leads to considerable performance increases in pre-trained models, which shows the importance of including legal articles in the training data of these models.
Nucleon isovector form factors from domain-wall lattice QCD at the physical mass

The current status of lattice-QCD numerical calculations by joint LHP, RBC, and UKQCD collaborations of nucleon isovector vector- and axialvector-current form factors using a 2+1-flavor dynamical domain-wall fermions lattice QCD ensemble generated jointly by RBC and UKQCD collaborations are presented. The lattice spacing is set at about 0.1141(3) fm, and the lattice spatial extent is 48 spacings or about 5.4750(14) fm. The strange and degenerate up and down quark mass values are set at their essentially physical values to provide the physical $\Omega$ mass and a degenerate pion mass of 0.1392(2) GeV. Our nucleon mass estimate is about 0.947(6) GeV. Possible excited-state contaminations in the calculated vector- and axialvector-current form factors are hidden below larger statistical noises. The numerical details of the form-factor shape parameters, such as the mean squared radii, the anomalous magnetic moment, or the pseudoscalar coupling extracted from the form factors, are described, along with comparisons of different approaches used to extract them.
New binary black hole mergers in the LIGO-Virgo O3b data

We report the detection of 5 new candidate binary black hole (BBH) merger signals in the publicly released data from the second half of the third observing run (O3b) of advanced LIGO and advanced Virgo. The LIGO-Virgo-KAGRA (LVK) collaboration reported 35 compact binary coalescences (CBCs) in their analysis of the O3b data [1], with 30 BBH mergers having coincidence in the Hanford and Livingston detectors. We confirm 17 of these for a total of 22 detections in our analysis of the Hanford-Livingston coincident O3b data. We identify candidates using a search pipeline employing aligned-spin quadrupole-only waveforms. Our pipeline is similar to the one used in our O3a coincident analysis [2], except for a few improvements in the veto procedure and the ranking statistic, and we continue to use an astrophysical probability of one half as our detection threshold, following the approach of the LVK catalogs. Most of the new candidates reported in this work are placed in the upper and lower-mass gap of the black hole (BH) mass distribution. One BBH event also shows a sign of spin-orbit precession with negatively aligned spins. We also identify a possible neutron star-black hole (NSBH) merger. We expect these events to help inform the black hole mass and spin distributions inferred in a full population analysis.
Superlens-Assisted Laser Nanostructuring of Long Period Optical Fiber Gratings (LPGs) for Enhanced Refractive Index Sensing

We present an innovative method to enhance Long Period Optical Fiber Gratings (LPGs) for refractive index sensing using microsphere-assisted superlens laser nanostructuring. This technique involves self-assembling a silica microsphere monolayer on LPGs' outer surface, followed by pulsed laser irradiation to generate nanoholes (300-500 nm) forming nanohole-structured LPGs (NS-LPGs). In experiments, two nanohole densities were compared for their impact on sensing performance in sucrose and glycerin solutions. The nanostructured NS-LPGs showed improved sensitivity by 16.08% and 19.57% compared to regular LPGs, with higher nanohole density yielding greater enhancement. Importantly, the permanent nanohole structures ensure durability in harsh environments, surpassing conventional surface-coating-based LPGs. Further improvements can be achieved by refining nanostructuring density and controlling nanohole size and depth. Our work represents a notable advancement in LPG sensor engineering, prioritizing surface nanostructuring over nano-coating, promising enhanced refractive index sensing applications.
$\mathcal{O}\left(m\alpha^2 (Z\alpha)^6\right)$ contribution to Lamb shift from radiative corrections to the Wichmann-Kroll potential

We derive an analytical expression for the contribution of the order $m\alpha^2 (Z\alpha)^6$ to the hydrogen Lamb shift which comes from the diagrams for radiative corrections to the Wichmann-Kroll potential. We use modern methods of multiloop calculations, based on IBP reduction, DRA method and differential equations.
High-Energy Neutrinos from Active Galactic Nuclei

Active Galactic Nuclei (AGN) are sources of high-energy gamma-rays and are considered to be promising candidates to be sources of high-energy cosmic rays and neutrinos as well. We present and discuss various models for ion acceleration and their interactions with matter and radiation leading to high-energy neutrino production. We consider neutrino production mechanisms in both jet-loud and jet-quiet AGN, focusing on disks and coronae in the vicinity of the central black hole, jet regions, and magnetized environments surrounding the AGN. The IceCube Collaboration has reported high-energy neutrino events that may come from both the jet-loud AGN TXS 0506+056 and the jet-quiet AGN NGC 1068. We discuss the implications of these observations themselves as well as the the origins of the all-sky neutrino intensity.
Early antigen receptor signaling in natural killer cells alters STAT4-dependent fate decisions via epigenetic remodeling

Natural Killer (NK) cells are innate cytotoxic lymphocytes that possess features of adaptive immunity, including antigen specificity and clonal expansion. NK cells rapidly respond to cytokines released during the innate phase of viral infection and are thought to migrate from circulation into infected organs to execute their early effector functions. However, recent evidence suggests that tissue-resident NK cells are among the first responders to viral infection. In this study, we observe that antigen receptor signaling precedes substantial proinflammatory cytokine signaling in a population of NK cells during mouse cytomegalovirus infection. Early antigen receptor signals epigenetically prime NK cells for optimal expansion during the later adaptive phase of the antiviral response. Mechanistically, receptor signaling increases chromatin accessibility at STAT4-binding genomic sites within differentiating NK cells. To promote adaptive programming of NK cells during infection, activating receptor-dependent epigenetic remodeling antagonizes IL-12 driven terminal maturation, poises NK cells for proliferation via sustained CDK6 expression, and antagonizes early apoptosis of short-lived effector cells via suppression of Bim. Thus, antigen receptor signaling alters an IL-12 dependent fate decision during the innate-to-adaptive transition of antiviral NK cells.
PINK1 regulated mitophagy is evident in skeletal muscles

PINK1, mutated in familial forms of Parkinson’s disease, initiates mitophagy following mitochondrial depolarization. However, it is difficult to monitor this pathway physiologically in mice as loss of PINK1 does not alter basal mitophagy levels in most tissues. To further characterize this pathway in vivo, we used mito-QC mice in which loss of PINK1 was combined with the mitochondrial-associated POLGD257A mutation. We focused on skeletal muscle as gene expression data indicates that this tissue has the highest PINK1 levels. We found that loss of PINK1 in oxidative hindlimb muscle significantly reduced mitophagy. Of interest, the presence of the POLGD257A mutation, while having a minor effect in most tissues, restored levels of muscle mitophagy caused by the loss of PINK1. Although our observations highlight that multiple mitophagy pathways operate within a single tissue, we identify skeletal muscle as a tissue of choice for the study of PINK1-dependant mitophagy under basal conditions.
Signals of microbial growth learned from single amplicon samples

Irregularities in metagenomic whole-genome shotgun (WGS) read coverage can arise in quickly replicating microbial populations. These irregularities, summarized as peak-to-trough ratios (PTRs), are correlated with growth rates. This study seeks to explore the presence of similar coverage irregularities in 16S amplicon datasets, where multicopy diverged 16S genes provide an opportunity to explore coverage at different positions on the bacterial chromosome. To this end, we propose a model of Operational Taxonomic Unit (OTU) observations under replication and sequence similarity, from which we derive a method for simultaneous copy number correction and dynamics estimation by gradient descent. We conduct a series of benchmarks on synthetic data, determining a set of heuristics for when such methods may be applied, and compare our method with WGS-based methods on a real dataset. We find no correlation between coPTR estimates and our method, suggesting further modifications to our method may be required.
Genetic modification of Candida maltosa, a nonpathogenic CTG species, reveals EFG1 function

Candida maltosa is closely related to important pathogenic Candida species, but it has been rarely isolated from humans. Therefore, through comparative studies, it could be a powerful model to understand the pathogenicity underpinnings of Candida species. To facilitate studying C. maltosa at a molecular level, we built a cohesive genomic sequence composed of 45 scaffolds, a substantial improvement from the thousands of contigs of the available draft. Comparison with C. albicans and C. tropicalis revealed a reduction in the total number of genes in C. maltosa. However, gene loss seems not to be associated to its avirulence given that most pathogenicity genes were also present in C. maltosa. To genetically edit C. maltosa we generated triple auxotrophic strains so that gene deletions can be performed as has been routinely done in pathogenic species. As a proof of concept, we generated gene knockouts of Efg1, a transcription regulator involved in filamentation and biofilm formation in pathogenic species. Although in C. maltosa Efg1 also played a role in these processes, it seems to rather be a repressor of filamentation. The genome assembly and auxotrophic mutants developed are a key step to start using C. maltosa for comparative studies at a molecular level.
Staphylococcal sRNA IsrR down-regulates methylthiotransferase MiaB under iron-deficient conditions

Staphylococcus aureus is a major contributor to bacterial-associated mortality, owing to its exceptional adaptability across diverse environments. Iron, vital for most organisms, poses toxicity risks in excess. To manage iron, S. aureus, like many pathogens, employs intricate systems. We have recently identified IsrR as a key regulatory RNA induced during iron starvation. Its role is to reduce the synthesis of non-essential iron-containing proteins under iron-depleted conditions. In this study, we unveil IsrR’s regulatory action on MiaB, an enzyme responsible for methylthio group addition to specific sites on transfer RNAs (tRNAs). We use predictive tools and reporter fusion assays to demonstrate IsrR’s binding to the Shine-Dalgarno sequence of miaB RNA, thereby impeding its translation. The effectiveness of IsrR hinges on the integrity of a specific C-rich region. As MiaB is non-essential and has iron-sulfur clusters, IsrR induction spares iron by downregulating miaB. This likely enhances S. aureus fitness and aids in navigating the host’s nutritional immune defenses. IMPORTANCE In many biotopes, including those found within an infected host, bacteria confront the challenge of iron deficiency. They employ various strategies to adapt to this scarcity of nutrients, one of which involves regulating iron-containing proteins through the action of small regulatory RNAs. Our study shows how IsrR, a small RNA from S. aureus, prevents the production of MiaB, a tRNA-modifying enzyme containing iron-sulfur clusters. Through this illustration, we shed light on a novel substrate for a small RNA-sparing iron, thereby unveiling a way by which bacteria conserve their iron resource.
Possible eddy markings in the Shinarump conglomerate of northeastern Utah

Spiral impressions in sandstone of the Shinarump conglomerate (Triassic) occur about 10 mi. N of Vernal on the southern flank of the Uinta Mountains, Utah. The impressions are 1-3 ft. in diameter and have been cut 1/4 to 1/2 in. deep into the sandstone bedding surface. The spirals may have originated from movement of a tree branch caught in a vortex or eddy current of a stream that deposited the sandstone.