Miracle-dz/UniFAR-FTUs huggingface dataset:
Titanium Wear Particles Exacerbate S. epidermidis-Induced Implant-Related Osteolysis and Decrease Efficacy of Antibiotic Therapy

Total joint arthroplasty (TJA) surgeries are common orthopedic procedures, but bacterial infection remains a concern. The aim of this study was to assess interactions between wear particles (WPs) and immune cells in vitro and to investigate if WPs affect the severity, or response to antibiotic therapy, of a Staphylococcus epidermidis orthopedic device-related infection (ODRI) in a rodent model. Biofilms grown on WPs were challenged with rifampin and cefazolin (100 µg/mL) to determine antibiotic efficacy. Neutrophils or peripheral blood mononuclear cells (PBMCs) were incubated with or without S. epidermidis and WPs, and myeloperoxidase (MPO) and cytokine release were analyzed, respectively. In the ODRI rodent model, rats (n = 36) had a sterile or S. epidermidis-inoculated screw implanted in the presence or absence of WPs, and a subgroup was treated with antibiotics. Bone changes were monitored using microCT scanning. The presence of WPs decreased antibiotic efficacy against biofilm-resident bacteria and promoted MPO and pro-inflammatory cytokine production in vitro. WPs exacerbated osteolytic responses to S. epidermidis infection and markedly reduced antibiotic efficacy in vivo. Overall, this work shows that the presence of titanium WPs reduces antibiotic efficacy in vitro and in vivo, induces proinflammatory cytokine release, and exacerbates S. epidermidis-induced osteolysis.
Synthesis of Hyaluronic Acid-Conjugated Fe3O4@CeO2 Composite Nanoparticles for a Target-Oriented Multifunctional Drug Delivery System

This study is based on the principle that superparamagnetic iron oxide nanoparticles (Fe3O4) can be used to target a specific area given that their magnetic properties emerge when an external magnetic field is applied. Cerium oxide (CeO2), which causes oxidative stress by generating reactive oxygen species (ROS) in the environment of tumor cells, was synthesized on the surface of superparamagnetic iron oxide nanoparticles to produce nanoparticles that selectively kill cancer cells. In addition, hyaluronic acid (HA) was coated on the cerium’s surface to target CD44-overexpressing tumor cells, and natZr was chelated on the Fe3O4@CeO2 surface to show the usefulness of labeling the radioisotope 89Zr (T1/2 = 3.3 d). The synthesis of Fe3O4@CeO2 was confirmed by Fourier Transform-Infrared Spectroscopy (FT-IR), X-ray Diffraction (XRD) and Field Emission-Transmission Electron Microscope (FE-TEM). The coating of HA was confirmed by FT-IR, X-ray Photoelectron. Spectroscopy (XPS), FE-TEM, Energy-Dispersive X-ray Spectroscopy (EDS) and Thermogravimetric Analysis (TGA)/Differential Scanning Calorimetry (DSC). The sizes of the prepared nanoparticles were confirmed through FE-TEM and Field Emission-Scanning Electron (FE-SEM) (sizes of 15 to 30 nm), and it was confirmed that natZr was introduced onto the surface of the nanoparticles using EDS. The particle size of the dispersed material was limited through Dynamic Light Scattering (DLS) to about 148 nm in aqueous solution, which was suitable for the (enhanced permeation and retention) EPR effect. It was confirmed that the HA-coated nanoparticles have good dispersibility. Finally, a cytotoxicity evaluation confirmed the ability of CeO2 to generate ROS and target the delivery of HA. In conclusion, Fe3O4@CeO2 can effectively inhibit cancer cells through the activity of cerium oxide in the body when synthesized in nano-sized superparamagnetic coral iron that has magnetic properties. Subsequently, by labeling the radioactive isotope 89Zr, it is possible to create a theranostic drug delivery system that can be used for cancer diagnosis.
Pulsed Electric Field and Salvia officinalis L. Leaves: A Successful Combination for the Extraction of High Value Added Compounds

The present study aimed to evaluate the pulsed electric field (PEF)-assisted extraction of phytochemicals from Salvia officinalis L. leaves. The study parameters included a PEF pulse duration of 10 or 100 μs for 30 min, using different “green” extraction solvents: pure ethanol, pure water, and their mixtures at 25, 50, and 75% v/v concentrations. The resulting extracts were evaluated against reference extracts obtained without PEF. For estimation of the extraction efficiency, the content in total polyphenols, individual polyphenols, and volatile compounds, as well as the resistance to oxidation, were determined. The optimal PEF contribution on the total and individual polyphenols, rosmarinic acid, extractability (up to 73.2% and 403.1% increase, respectively) was obtained by 25% v/v aqueous ethanol solvent using a pulse duration of 100 μs. PEF was proven to also affect the final concentration and composition of volatile compounds of the extracts obtained.
Dark Side of Cancer Therapy: Cancer Treatment-Induced Cardiopulmonary Inflammation, Fibrosis, and Immune Modulation

Advancements in cancer therapy increased the cancer free survival rates and reduced the malignant related deaths. Therapeutic options for patients with thoracic cancers include surgical intervention and the application of chemotherapy with ionizing radiation. Despite these advances, cancer therapy-related cardiopulmonary dysfunction (CTRCPD) is one of the most undesirable side effects of cancer therapy and leads to limitations to cancer treatment. Chemoradiation therapy or immunotherapy promote acute and chronic cardiopulmonary damage by inducing reactive oxygen species, DNA damage, inflammation, fibrosis, deregulation of cellular immunity, cardiopulmonary failure, and non-malignant related deaths among cancer-free patients who received cancer therapy. CTRCPD is a complex entity with multiple factors involved in this pathogenesis. Although the mechanisms of cancer therapy-induced toxicities are multifactorial, damage to the cardiac and pulmonary tissue as well as subsequent fibrosis and organ failure seem to be the underlying events. The available biomarkers and treatment options are not sufficient and efficient to detect cancer therapy-induced early asymptomatic cell fate cardiopulmonary toxicity. Therefore, application of cutting-edge multi-omics technology, such us whole-exome sequencing, DNA methylation, whole-genome sequencing, metabolomics, protein mass spectrometry and single cell transcriptomics, and 10 X spatial genomics, are warranted to identify early and late toxicity, inflammation-induced carcinogenesis response biomarkers, and cancer relapse response biomarkers. In this review, we summarize the current state of knowledge on cancer therapy-induced cardiopulmonary complications and our current understanding of the pathological and molecular consequences of cancer therapy-induced cardiopulmonary fibrosis, inflammation, immune suppression, and tumor recurrence, and possible treatment options for cancer therapy-induced cardiopulmonary toxicity.
Insight into the Antioxidant Effect of Fermented and Non-Fermented Spirulina Water and Ethanol Extracts at the Proteome Level Using a Yeast Cell Model

Spirulina is rich in various antioxidants and nutraceuticals and it has proven to be effective in the treatment of various pathological conditions. This study explores the antioxidant effect of fermented and non-fermented Spirulina extracts on the proteome level using the yeast Saccharomyces cerevisiae as a model organism. Yeast cells were treated with fermented Spirulina water extract (SV), non-fermented Spirulina water extract (NFV), fermented Spirulina ethanol extract (SE), and non-fermented Spirulina ethanol extract (NFE). Cell lysates were prepared, and label-free quantitative proteome analysis was performed. In SV, when compared to NFV samples, the levels of most differentially expressed proteins were upregulated. Alternatively, SE compared to NFE samples showed a significant downregulation for the majority of the analyzed proteins involved in different cellular processes. Additionally, a higher downregulation of stress response related proteins was observed in SE compared to NFE samples, while their abundance in SV samples increased compared to NFV. This study provided a global view, on a proteome level, of how cells cope with exogenous antioxidants and remodel their cellular processes to maintain metabolic and redox balance. Furthermore, it combined for the first time the analysis of different extract effect, including the contribution of lactic acid fermentation to the cell activity.
On the local times of noise reinforced Bessel processes

We investigate the effects of noise reinforcement on a Bessel process of dimension $d\in(0,2)$, and more specifically on the asymptotic behavior of its additive functionals. This leads us to introduce a local time process and its inverse. We identify the latter as an increasing self-similar (time-homogeneous) Markov process, and from this, several explicit results can be deduced.
Statically bounded-memory delayed sampling for probabilistic streams

Probabilistic programming languages aid developers performing Bayesian inference. These languages provide programming constructs and tools for probabilistic modeling and automated inference. Prior work introduced a probabilistic programming language, ProbZelus, to extend probabilistic programming functionality to unbounded streams of data. This work demonstrated that the delayed sampling inference algorithm could be extended to work in a streaming context. ProbZelus showed that while delayed sampling could be effectively deployed on some programs, depending on the probabilistic model under consideration, delayed sampling is not guaranteed to use a bounded amount of memory over the course of the execution of the program. In this paper, we the present conditions on a probabilistic program’s execution under which delayed sampling will execute in bounded memory. The two conditions are dataflow properties of the core operations of delayed sampling: the m-consumed property and the unseparated paths property. A program executes in bounded memory under delayed sampling if, and only if, it satisfies the m-consumed and unseparated paths properties. We propose a static analysis that abstracts over these properties to soundly ensure that any program that passes the analysis satisfies these properties, and thus executes in bounded memory under delayed sampling.
Reducing the LQG Cost With Minimal Communication

We study the linear quadratic Gaussian (LQG) control problem, in which the controller's observation of the system state is insufficient to attain a desired quadratic control cost. To achieve the desired LQG cost, we introduce a communication link from the observer (encoder) to the controller (decoder). We investigate the fundamental tradeoff between a desired LQG cost and the required communication (information) resources, measured with the conditional directed information. The optimization domain is all encoding–decoding policies, and our first result is the optimality of memoryless encoders that only transmit Gaussian measurements of the current system state. Additionally, it is shown that even if the controller's measurements are made available to the encoder it does not reduce the minimal directed information. The main result is a semidefinite programming (SDP) formulation for that optimization problem, which applies to general time-varying linear dynamical systems in the finite-horizon scenario, and to time-invariant systems at infinite horizon. For the latter scenario of time-invariant systems, we show that it is unnecessary to consider time-varying policies as a simple and optimal time-invariant policy can be directly constructed from the SDP solution. Our results extend a seminal work by Tanaka et al. to the scenario where the controller has access to a noisy measurement of the system state. We demonstrate the viability of this extra resource by illustrating that even low-quality measurements may have a significant impact on the required communication resources.
A General Gaussian Heatmap Labeling for Arbitrary-Oriented Object Detection

—Recently, many arbitrary-oriented object detection (AOOD) methods have been proposed and attracted widespread attention in many ﬁelds. However, most of them are based on anchor-boxes or standard Gaussian heatmaps. Such label assignment strategy may not only fail to reﬂect the shape and direction characteristics of arbitrary-oriented objects, but also have high parameter-tuning efforts. In this paper, a novel AOOD method called General Gaussian Heatmap Labeling (GGHL) is proposed. Speciﬁcally, an anchor-free object-adaptation label assignment (OLA) strategy is presented to deﬁne the positive candidates based on two-dimensional (2-D) oriented Gaussian heatmaps, which reﬂect the shape and direction features of arbitrary-oriented objects. Based on OLA, an oriented-bounding-box (OBB) representation component (ORC) is developed to indicate OBBs and adjust the Gaussian center prior weights to ﬁt the characteristics of different objects adaptively through neural network learning. Moreover, a joint-optimization loss (JOL) with area normalization and dynamic conﬁdence weighting is designed to reﬁne the misalign optimal results of different subtasks. Extensive experiments on public datasets demonstrate that the proposed GGHL improves the AOOD performance with low parameter-tuning and time costs. Furthermore, it is generally applicable to most AOOD methods to improve their performance including lightweight models on embedded platforms.
Geometric Syzygy Conjecture in Positive Characteristic

We study the syzygies of canonical curves of even genus g = 2k over an algebraically closed field F with char(F) = p > 0. For p ≥ g+4 2 = k + 2, we prove a structure theorem for the last syzygy space. As a corollary, this gives a new proof of Green’s Conjecture for general canonical curves of even genus in positive characteristic. Assuming p > 2k, we prove Geometric Syzygy Conjecture for a general smooth curve of even genus g = 2k.
Pushing on Text Readability Assessment: A Transformer Meets Handcrafted Linguistic Features

We report two essential improvements in readability assessment: 1. three novel features in advanced semantics and 2. the timely evidence that traditional ML models (e.g. Random Forest, using handcrafted features) can combine with transformers (e.g. RoBERTa) to augment model performance. First, we explore suitable transformers and traditional ML models. Then, we extract 255 handcrafted linguistic features using self-developed extraction software. Finally, we assemble those to create several hybrid models, achieving state-of-the-art (SOTA) accuracy on popular datasets in readability assessment. The use of handcrafted features help model performance on smaller datasets. Notably, our RoBERTA-RF-T1 hybrid achieves the near-perfect classification accuracy of 99%, a 20.3% increase from the previous SOTA.
Small data problems in political research: a critical replication study

In an often-cited 2019 paper on the use of machine learning in political research, Anastasopoulos&Whitford (A&W) propose a text classification method for tweets related to organizational reputation. The aim of their paper was to provide a 'guide to practice' for public administration scholars and practitioners on the use of machine learning. In the current paper we follow up on that work with a replication of A&W's experiments and additional analyses on model stability and the effects of preprocessing, both in relation to the small data size. We show that (1) the small data causes the classification model to be highly sensitive to variations in the random train-test split, and that (2) the applied preprocessing causes the data to be extremely sparse, with the majority of items in the data having at most two non-zero lexical features. With additional experiments in which we vary the steps of the preprocessing pipeline, we show that the small data size keeps causing problems, irrespective of the preprocessing choices. Based on our findings, we argue that A&W's conclusions regarding the automated classification of organizational reputation tweets -- either substantive or methodological -- can not be maintained and require a larger data set for training and more careful validation.
Universal Payment Channels: An Interoperability Platform for Digital Currencies

With the innovation of distributed ledger technology (DLT), often known as blockchain technology, there has been significant growth of digital tokens in the form of cryptocurrencies, stablecoins, and central bank digital currencies. As the number of DLT networks increases, each with varying design characteristics, the likelihood that transacting parties are on the same network decreases. Thus, it is crucial to facilitate payments that are universal across networks, scalable to massive loads, and highly available. We envision a future payment network that may be built on top of DLT networks without being subject to their limitations on interoperability, scalability, and availability faced by DLT payment solutions today. Specifically, we propose a hub-and-spoke payment route, referred to here as Universal Payment Channels (UPC), that can be used to support digital token transfers of funds across different networks through payment channels. We further discuss the potential use cases of the UPC technology to support, and not complicate, an already robust digital payment ecosystem. Finally, through the paper, we share some future directions of the UPC technology.
Wasserstein Patch Prior for Image Superresolution

Many recent superresolution methods are based on supervised learning. That means, that they require a large database of pairs of high- and low-resolution images as training data. However, for many applications, acquiring registered pairs of high and low resolution data or even imaging a large area with a high resolution is unrealistic. To overcome this problem, we introduce a Wasserstein patch prior for unsupervised superresolution of two- and three-dimensional images. In addition to the low-resolution observation, our method only requires one, possibly small, reference image which has a similar patch distribution as the high resolution ground truth. This assumption can e.g. be fulfilled when working with texture images or images of homogeneous material microstructures. The proposed regularizer penalizes the Wasserstein-2-distance of the patch distributions within the reconstruction and the reference image at different scales. We demonstrate the performance of the proposed method by applying it to two- and three-dimensional images of materials' microstructures.
Topic Model Robustness to Automatic Speech Recognition Errors in Podcast Transcripts

For a multilingual podcast streaming service, it is critical to be able to deliver relevant content to all users independent of language. Podcast content relevance is conventionally determined using various metadata sources. However, with the increasing quality of speech recognition in many languages, utilizing automatic transcriptions to provide better content recommendations becomes possible. In this work, we explore the robustness of a Latent Dirichlet Allocation topic model when applied to transcripts created by an automatic speech recognition engine. Specifically, we explore how increasing transcription noise influences topics obtained from transcriptions in Danish; a low resource language. First, we observe a baseline of cosine similarity scores between topic embeddings from automatic transcriptions and the descriptions of the podcasts written by the podcast creators. We then observe how the cosine similarities decrease as transcription noise increases and conclude that even when automatic speech recognition transcripts are erroneous, it is still possible to obtain high-quality topic embeddings from the transcriptions.
Delta Hedging with Transaction Costs: Dynamic Multiscale Strategy using Neural Nets

In most real scenarios the construction of a risk-neutral portfolio must be performed in discrete time and with transaction costs. Two human-imposed constraints are the risk-aversion and the profit-maximization, which together define a nonlinear optimization problem with a model-dependent solution. In this context, an optimal fixedfrequency hedging strategy can be determined a posteriori by maximizing a sharpe-ratio-simil path-dependent reward function. Sampling from Heston processes, a convolutional neural network was trained to infer which period is optimal using partial information, thus leading to a dynamic hedging strategy in which the portfolio is hedged at various frequencies, each weighted by the network’s probability estimate of that frequency being optimal.
Graph-based clustering of gamma-ray bursts

Aims. An attempt to classify gamma-ray bursts (GRBs) with a low level of supervision using the state-of-the-start approaches stemming from graph theory was undertaken. Methods. Graph-based classification methods, relying on different variants of the $k$-nearest neighbour graph, were applied to various GRB samples in the duration-hardness ratio parameter space to infer the optimal partitioning. Results. In most cases it is found that both two and three groups are feasible, with the outcome being more ambiguous with an increasing sample size. Conclusions. There is no clear indication of the presence of a third GRB class; however, such a possibility cannot be ruled out with the employed methodology. There are no hints at more than three classes though.
Accelerating LSM-Tree with the Dentry Management of File System

The log-structured merge tree (LSM-tree) gains wide popularity in building key-value (KV) stores. It employs logs to back up arriving KV pairs and maintains a few on-disk levels with exponentially increasing capacity limits, resembling a tiered tree-like structure. A level comprises SST files, each of which holds a sequence of sorted KV pairs. From time to time, LSM-tree redeploys KV pairs from a full level to the lower level by compaction, which merge-sorts and moves KV pairs among SST files, thereby incurring substantial disk I/Os. In this paper, we revisit the design of LSM-tree and find that organizing multiple KV pairs in an SST file entails the heavyweight redeployment of actual KV pairs in a compaction. Accordingly we revolutionize the organization of KV pairs by transforming an SST file of KV pairs to an SST directory, in which each KV pair makes into an independent KV file with the key and value as filename and main file contents, respectively. Moving KV pairs in a compaction converts to transferring directory entries (dentrys), which causes concretely fewer disk I/Os. This is the essence of our design named DeLSM. We build a prototype of DeLSM on LevelDB and evaluation results show that it significantly outperforms the state-of-the-art LSM-tree variants in different dimensions.