Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantification of Defensive Proteins in Skin Mucus of Atlantic salmon Using Minimally Invasive Sampling and High-Sensitivity ELISA

Simple Summary The external surfaces of fish, including the skin, are covered by mucus. Mucus is an important and multifunctional matrix of substantial complexity. The mucus is viscous and sticky, and adheres to the underlying epithelium, making the sampling of mucus challenging. To help define more standardized protocols for mucus sampling, we here compare three different sampling methods. The methods include scraping of mucus, wiping of mucus, and absorption of the liquid part of mucus. We compare the resulting damage to the fish skin and compare the content of two specific immune proteins in the three sample types. Using histological examination, we show that absorption leads to very limited damage to the skin epithelium while scraping causes substantial damage. Using Enzyme-Linked Immunosorbent Assay (ELISA) methods, we show that the mucus sample types contain similar amounts of antigen specific immunoglobulin M (IgM) and complement component 5 (C5), respectively. The levels of the IgM but not the C5 were moderately correlated between mucus and blood from the same fish, suggesting the importance of fish skin mucus for analyzing antigen-specific IgM after vaccination procedures. We conclude that absorption is an easily performed and minimally invasive sampling method that produces mucus samples with comparable contents of IgM and C5. Abstract Protocols used to collect fish skin mucus may inadvertently compromise the sampled fish or the resulting sample. Here, we used three methods (wiping, scraping, and absorption) to collect skin mucus from Atlantic salmon and compared their invasiveness on fish skin epithelium. We found that the absorption method was the least invasive. We also compared the abundance of antigen-specific immunoglobulin M subtype A antibodies (IgM-A Ab) and complement component 5 (C5) in mucus samples collected from vaccinated fish by the three methods. An enzyme-cascade-amplification strategy colorimetric immune assay was optimized and used to analyze IgM-A, and ELISA was used to analyze C5. The abundance of antigen-specific IgM-A in skin mucus was comparable between the three methods, but C5 was significantly lower in absorbed mucus in comparison to in the wiped or scraped mucus samples. Absorbed skin mucus samples collected from various body regions of salmon, levels of C5 were comparable, while specific IgM-A amounts varied between the regions. By comparing three mucus-absorbing materials (medical wipe, gauze, and cotton) for their ability to absorb and release IgM-A and C5, medical wipes proved to be ideal for IgM-A analysis, whereas gauze was the best for C5 analysis.
Hypoxia activated prodrug evofosfamide treatment in pancreatic ductal adenocarcinoma xenografts alters the tumor redox status to potentiate radiotherapy.

AIMS In hypoxic tumor microenvironments, the strongly reducing redox state converts evofosfamide (TH-302) to a reduced form and releases a cytotoxic bromo-isophosphoramide (Br-IPM) moiety. This drug therefore preferentially attacks hypoxic regions in tumors where other standard anti-cancer treatments such as chemotherapy and radiation therapy are often ineffective. Various combination therapies with evofosfamide have been proposed and tested in preclinical and clinical settings. However, the treatment effect of evofosfamide monotherapy on tumor hypoxia has not been fully understood, partly due to the lack of quantitative methods to assess tumor pO2 in vivo. Here, we use quantitative pO2 imaging by EPR to evaluate the change in tumor hypoxia in response to evofosfamide treatment using two pancreatic ductal adenocarcinoma xenograft models; MIA Paca-2 tumors responding to evofosfamide and Su.86. 86 tumors which do not respond. RESULTS EPR imaging showed oxygenation improved globally after evofosfamide treatment in hypoxic MIA Paca-2 tumors, in agreement with the ex vivo results obtained from hypoxia staining by pimonidazole and in apparent contrast to the decrease in Ktrans observed in DCE MRI. Innovations: The observation that evofosfamide not only kills the hypoxic region of the tumor but also improves oxygenation in the residual tumor regions provides a rationale for combination therapies using radiation and anti-proliferatives post evofosfamide for improved outcomes. CONCLUSION This study suggests reoxygenation after evofosfamide treatment is due to decreased oxygen demand rather than improved prefusion. Following the change in pO2 after treatment may therefore yield a way of monitoring treatment response.
Molecular detection of virulence genes in Salmonella spp. isolated from chicken faeces in Mafikeng, South Africa

Chickens have been implicated in most Salmonella disease outbreaks because they act as carriers of the pathogen in their gut. There are over 2500 serotypes of Salmonella that have been reported worldwide and 2000 of these serovars can be found in chickens. The main objective of this study was to determine the Salmonella serotypes found in poultry farms around Mafikeng district, South Africa. Salmonella was identified according to the guidelines of the International Organization for Standardization (ISO) (ISO 6579:2002) standard techniques. Faecal samples were collected and analysed for Salmonella using conventional cultural methods and polymerase chain reaction targeting the 16S Ribosomal Deoxyribonucleic acid (rDNA) gene for Salmonella identification. Out of 130 presumptive Salmonella isolates determined by urease and triple sugar iron tests, only 46 isolates were identified as Salmonella serotypes of which S. Typhimurium was the most frequent with 18 (39.1%), followed by S. Heidelberg with 9 (19.6%), S. bongori with 7 (15.2%), S. Enteritidis with 6 (13.0%) and both S. Paratyphi B and S. Newport with 3 (6.5%) each. Seven virulence genes including invA 100%, spy 39%, hilA 9%, misL 30%, sdfI 13%, orfL 11% and spiC 9% were detected from these Salmonella isolates in this study. The presence of these virulence genes indicates high pathogenicity potential of these isolates which is a serious public health concern because of zoonotic potential of Salmonella.
Mining scientific advice reports on cell‐based products: Insight into the nonclinical development program

The field of cell‐based therapies for human diseases is currently evolving from promising treatment options to established therapeutic concepts. The design of the nonclinical development program for cell‐based products, intended to provide a rationale for treatment and to gain insight into the safety profile, is challenging because of limitations caused by species‐specificity. The elements of the nonclinical package for cell‐based products were evaluated using advice reports from the European Medicines Agency database from 2013 to 2018 to identify the approach followed for nonclinical development of these products.
A High-Performance Transmitarray Antenna with Thin Metasurface for 5G Communication Based on PSO (Particle Swarm Optimization)

A 5G metasurface (MS) transmitarray (TA) feed by compact-antenna array with the performance of high gain and side-lobe level (SLL) reduction is presented. The proposed MS has two identical metallic layers etched on both sides of the dielectric substrate and four fixed vias connecting two metallic layers that works at 28 GHz to increase the transmission phase shift range. The proposed planar TA consisting of unit cells with different dimensional information can simulate the function as an optical lens according to the Fermat’s principle, so the quasi-spherical wave emitted by the compact Potter horn antenna at the virtual focal point will transform to the quasi-plane wave by the phase-adjustments. Then, the particle swarm optimization (PSO) is introduced to optimize the phase distribution on the TA to decrease the SLL further. It is found that the optimized TA could achieve 27 dB gain at 28 GHz, 11.8% 3 dB gain bandwidth, −30 dB SLL, and aperture efficiency of 23% at the operating bandwidth of 27.5–29.5 GHz, which performs better than the nonoptimized one. The advanced particularities of this optimized TA including low cost, low profile, and easy to configure make it great potential in paving the way to 5G communication and radar system.
They are what you eat: Shaping of viral populations through nutrition and consequences for virulence

Humans have coexisted with viral pathogens for tens of thousands of years, influencing both their emergence and evolution. However, the pervasiveness of the Western diet and disparities in food access and security have altered how we as hosts interact with our viral pathogens. Malnutrition, the state of having insufficient, excess, or imbalanced sources of energy, is well known to attenuate immune responses. Could nutrition also actively shape how viruses evolve? Malnourishment is a global, intersectional issue, and it may soon force a revision of our understanding of how viruses evolve within their hosts (Fig 1) [1].
Biomarker evidence for distal tubular damage but cortical sparing in hospitalized diabetic patients with acute kidney injury (AKI) while on SGLT2 inhibitors

Abstract Background Inhibitors of sodium-glucose co-transporter-2 (SGLT2i) were found to improve renal outcome in diabetic patients in large prospective randomized trials. Yet, SGLT2i may acutely reduce kidney function through volume depletion, altered glomerular hemodynamics or intensified medullary hypoxia leading to acute tubular injury (ATI). The aim or this study was to prospectively assess the pathophysiology of acute kidney injury (AKI) in patients hospitalized while on SGLT2i, differing ATI from pre-renal causes using renal biomarkers. Methods Serum and urine Neutrophil Gelatinase-Associated Lipocalin (NGAL) and Kidney Ischemia Molecule (KIM)-1, markers of distal and proximal tubular injury, respectively, were determined in 46 diabetic patients who were on SGLT2i upon hospitalization with an acute illness. Results Serum and urine NGAL, but not KIM-1, were significantly increased in 21 of the patients who presented with AKI upon admission, as compared with 25 patients that maintained kidney function. Both serum and urinary NGAL correlated with the degree of impaired renal function, which in many cases was likely the result of additional acute renal perturbations, such as sepsis. Conclusions Increased urinary and serum NGAL indicates that ATI, principally affecting distal tubular segments, may develop in some of the patients hospitalized with an acute illness and AKI while on SGLT2i. It is suggested that intensified medullary hypoxia by SGLT2i might be detrimental in this injury. By contrast, concomitantly unaltered KIM-1 might reflect improved cortical oxygenation by SGLT2i, and may explain an overall reduced risk of AKI with SGLT1i in large series. The independent potential of SGLT2i to inflict medullary hypoxic damage should be explored further.
Understanding the Link between Lipid Diversity and the Biophysical Properties of the Neuronal Plasma Membrane.

Cell membranes contain incredible diversity in the chemical structures of their individual lipid species and the ratios in which these lipids are combined to make membranes. Nevertheless, our current understanding of how each of these components affects the properties of the cell membrane remains elusive, in part due to the difficulties in studying the dynamics of membranes at high spatiotemporal resolution. In this work, we use coarse-grained molecular dynamics simulations to investigate how individual lipid species contribute to the biophysical properties of the neuronal plasma membrane. We progress through eight membranes of increasing chemical complexity, ranging from a simple POPC/CHOL membrane to a previously published neuronal plasma membrane [Ingólfsson, H. I. , et al. (2017) Biophys. J. 113 (10), 2271-2280] containing 49 distinct lipid species. Our results show how subtle chemical changes can affect the properties of the membrane and highlight the lipid species that give the neuronal plasma membrane its unique biophysical properties. This work has potential far-reaching implications for furthering our understanding of cell membranes.
Modern Variable Selection in Action: Comment on the Papers by HTT and BPV

Let me begin by congratulating the authors of these two papers, hereafter HTT and BPV, for their superb contributions to the comparisons of methods for variable selection problems in high dimensional regression. The methods considered are truly some of today’s leading contenders for coping with the size and complexity of big data problems of so much current importance. Not surprisingly, there is no clear winner here because the terrain of comparisons is so vast and complex, and no single method can dominate across all situations. The considered setups vary greatly in terms of the number of observations n, the number of predictors p, the number and relative sizes of the underlying nonzero regression coefficients, predictor correlation structures and signal-to-noise ratios (SNRs). And even these only scratch the surface of the infinite possibilities. Further, there is the additional issue as to which performance measure is most important. Is the goal of an analysis exact variable selection or prediction or both? And what about computational speed and scalability? All these considerations would naturally depend on the practical application at hand. The methods compared by HTT and BPV have been unleashed by extraordinary developments in computational speed, and so it is tempting to distinguish them primarily by their novel implementation algorithms. In particular, the recent integer optimization related algorithms for variable selection differ in fundamental ways from the now widely adopted coordinate ascent algorithms for the lasso related methods. Undoubtedly, the impressive improvements in computational speed unleashed by these algorithms are critical for the feasibility of practical applications. However, the more fundamental story behind the performance differences has to do with the differences between the criteria that their algorithms are seeking to optimize. In an important sense, they are being guided by different solutions to the general variable selection problem. Focusing first on the paper of HTT, its main thrust appears to have been kindled by the computational breakthrough of Bertsimas, King and Mazumder (2016) (hereafter BKM), which had proposed a mixed integer opti-
Asymptotically exact scattering theory of active particles with anti-alignment interactions

We consider a model of Non-Brownian self-propelled particles with anti-alignment interactions where particles try to avoid each other by attempting to turn into opposite directions. The particles undergo apparent Brownian motion, even though the particle's equations are fully deterministic. We show that the deterministic interactions lead to internal, dynamical noise. Starting from the N-particle Liouville equation, a kinetic equation for the one-particle distribution function is obtained. We show that the usual mean-field assumption of molecular chaos leads to qualitatively wrong predictions such as an infinite coefficient of self-diffusion. Going beyond mean-field and applying the refined assumption of ``one-sided molecular chaos'', we analytically calculate the scattering of particles in the limit of low density and obtain explicit expressions for the dynamical noise of an effective one-particle Langevin-equation and the corresponding self-diffusion. In this calculation, the so-called superposition principle of traditional kinetic theory was modified to handle a system with non-Hamiltonian dynamics involving phase-space compression. The predicted theoretical expressions for the relaxation of hydrodynamic modes and the self-diffusion coefficient are in excellent, quantitative agreement with agent-based simulations at small density and small anti-alignment strength. At large particle densities, a given particle is constantly approached and abandoned by different collision partners. Modelling this switching of partners by a random telegraph process and exactly solving a self-consistent integral equation, we obtain explicit expressions for the noise correlations of the effective one-particle Langevin-equation. Comparison with agent-based simulation show very good agreement.
GRAFT EFFICIENCY OF CO-CULTURED SPERMATOGONIAL CELLS USING SPERM ASSAY IN EPIDIDYMAL LUMEN OF RECIPIENT MICE

Received: 1/Jan/2006, Accepted: 20/Feb/2006 Introduction: Transplantation of germ cells restores the male fertility. Nevertheless, a lot of questions remain incompletely resolved. The aim of this study was to evaluate in vitro colonization efficiency of germ cells and sperm production capacity of spermatogonial cells before and after culture by sperm number assay in epididymis of recip ient mice. Materials and Methods: We developed a Sertoli cell feeder in a co-culture system wi th spermatogonial cells and the cells were co-cultured for 2 months. The cells were isolated from mouse neonates. Colony assay was performed during culture using light microscopy. The transplanted cells were traced using BrdU incorporation. Sperm parameters were assessed 2 months after transplantation. Results: Our fi ndings showed that spermatogonial cells created colonies during culture. Transplantation of fresh spermatogonial cells a t a concentration of 2×105 cells/ml did not show significant differences. However, after transplantation of 2×105 cells/ml cultured for 2 weeks, the number of epididymal sperms in recipients increased significantly in groups with more fresh cells. Conclusion: Epididymal sperm number in recipient mice can be increased by enrichment of type A spermatogonial cells using an in vitro co-culture system. Other important factors include the source of donor cells and the number of transplanted cells.
GlobalNER: Incorporating Non-local Information into Named Entity Recognition

Nowadays, many Natural Language Processing (NLP) tasks see the demand for incorporating knowledge external to the local information to further improve the performance. However, there is little related work on Named Entity Recognition (NER), which is one of the foundations of NLP. Specifically, no studies were conducted on the query generation and re-ranking for retrieving the related information for the purpose of improving NER. This work demonstrates the effectiveness of a DNN-based query generation method and a mention-aware re-ranking architecture based on BERTScore particularly for NER. In the end, a state-of-the-art performance of 61.56 micro-f1 score on WNUT17 dataset is achieved.
Recruitment into the Alzheimer Prevention Trials (APT) Webstudy for a Trial-Ready Cohort for Preclinical and Prodromal Alzheimer’s Disease (TRC-PAD)

The Alzheimer Prevention Trials (APT) Webstudy is the first stage in establishing a Trial-ready Cohort for Preclinical and Prodromal Alzheimer’s disease (TRC-PAD). This paper describes recruitment approaches for the APT Webstudy. To remotely enroll a cohort of individuals into a web-based longitudinal observational study. Participants are followed quarterly with brief cognitive and functional assessments, and referred to Sites for in-clinic testing and biomarker confirmation prior to enrolling in the Trial-ready Cohort (TRC). Participants are referred to the APT Webstudy from existing registries of individuals interested in brain health and Alzheimer’s disease research, as well as through central and site recruitment efforts. The study team utilizes Urchin Tracking Modules (UTM) codes to better understand the impact of electronic recruitment methods. A remotely enrolled online study. Volunteers who are at least 50 years old and interested in Alzheimer’s research. Demographics and recruitment source of participant where measured by UTM. 30,650 participants consented to the APT Webstudy as of April 2020, with 69.7% resulting from referrals from online registries. Emails sent by the registry to participants were the most effective means of recruitment. Participants are distributed across the US, and the demographics of the APT Webstudy reflect the referral registries, with 73.1% female, 85.0% highly educated, and 92.5% Caucasian. We have demonstrated the feasibility of enrolling a remote web-based study utilizing existing registries as a primary referral source. The next priority of the study team is to engage in recruitment initiatives that will improve the diversity of the cohort, towards the goal of clinical trials that better represent the US population.
The rCOS tool

The goal of the rCOS tool is to harness state of the art techniques from use case-and model driven-development of component-based systems on top of UML. It facilitates both the development process and the persistence of formal veriﬁcation artefacts in the model: use cases are speciﬁed in multiple views, using Class, Sequence and State Diagrams to capture their behaviour. Functionality is speciﬁed in rCOS pre-/post-conditions based on UTP. Provably correct reﬁnement steps transform the Requirements Model into a component-based Design Model. Consistency of the diﬀerent views is ensured, for example by checking that the State Diagram accepts the protocol speciﬁed in the Sequence Diagram. For component composition, protocol compatibility is veriﬁed. To that end, either third party tools like the FDR2 model checker are invoked, or annotations to code skeletons for additional tools like the Java Modeling Language (JML) are generated. The tool is implemented on top of the Eclipse platform using a UML proﬁle, ensuring compatibility with other UML-based software engineering tools.
Logic Extraction for Explainable AI

In this paper, we investigate logic extraction as a means for building explainable AI. Blackbox AI system is used as an oracle that can label inputs with positive or negative label depending on its decision. We formulate generating explanations as the problem of learning Boolean formulae from examples obtained by actively querying such an oracle. This problem has exponential worst-case complexity in the general case as well as for many restrictions. In this paper, we focus on learning sparse Boolean formulae which depend on only a small (but unknown) subset of the overall vocabulary of atomic propositions. We propose two algorithms first, based on binary search in the Hamming space, and the second, based on random walk on the Boolean hypercube, to learn these sparse Boolean formulae with a given confidence. This assumption of sparsity is motivated by the problem of mining explanations for decisions made by artificially intelligent (AI) algorithms, where the explanation of individual decisions may depend on a small but unknown subset of all the inputs to the algorithm. We demonstrate the use of these algorithms in automatically generating explanations of these decisions. We show that the number of examples needed for both proposed algorithms only grows logarithmically with the size of the vocabulary of atomic propositions. We illustrate the practical effectiveness of our approach on a diverse set of case studies. In this paper, we summarize the results presented in our recent work [7, 5].