Miracle-dz/UniFAR-FTUs huggingface dataset:
Corncob biochar supplementation improves nutrient digestibility, fattening performance and carcass characteristics of fattening sheep

Abstract Background Corncob is a cheap source for biochar in developing countries. No studies reported on use of biochar as sheep fattening supplement. Objectives The goal of this study is to determine the effect of corncob biochar supplementation on growth performance and carcass characteristics of sheep. Methods Twenty‐four Horro yearling rams (18.6 ± 0.938 kg live weight) were randomly distributed to four groups, control (300 g/day concentrate + local hay ad libitum), BC1.5 (control +1.5 g/day biochar), BC3 (control +3 g/day biochar) and BC4.5 (control +4.5/day biochar). The experimental concentrate consisted of 200 g/day wheat bran and 100 g/day Noug cake. The study consisted of a growth trial of 111 days followed by a digestibility trial of 10 days. Carcass characteristics for all rams were recorded at the end of the trial. Results Dry matter intake, crude protein intake, dry matter digestibility and acid detergent fibre digestibility of BC1.5 were significantly higher than the control. Corncob biochar supplementation significantly improved daily weight gain and feed conversion ratio of the rams. Supplementation sheep with corncob biochar improved significantly dressing percentage, hindquarters weight, forequarters weight, ribs weight, brisket weigh and rib eye muscle area. There was a significant effect of the dietary treatment on weight of internal organs; however, they were all within the normal range of sheep. Conclusions Corncob biochar supplementation would be recommended at a level of 1.5 g/day to improve growth performance and carcass characteristics of sheep.
Large membrane array scattering

The scattering of a time harmonic acoustic wave by an array of N identical baffled membranes is considered in the limit as N→∞. The method of matched asymptotic expansions is used to construct an inner and outer expansion; the inner expansion contains the physics of the local periodic structure and the outer expansion has the proper behavior in the far field. The matching process is used to determine a set of unknown coefficients in the analysis and the result is a far‐field pattern for the structure that blends the physics of both expansions. In particular, the far‐field pattern is highly localized about the Bragg or mode angles of the inner representation. The maximum value of this function is directly proportional to the reflection coefficient determined numerically from the inner problem. Several numerical examples are presented illustrating these features. Moreover, it is observed that the asymptotic theory agrees quite well with the exact answer (obtained by using a finite difference scheme) when N=3.
Protective Capacity of Monoclonal Antibodies against Acinetobacter baumannii K9 Capsular Polysaccharide

MAbs detected A. baumannii in infected lung tissue, effectively opsonized bacteria, and protected model animals from infection. ABSTRACT Acinetobacter baumannii is an antibiotic-resistant opportunistic pathogen, one of the main causes of hospital infections. There is an urgent need for the development of therapy strategies which are not based on antibiotics. Hybridoma technology was used to obtain monoclonal antibodies. The antibodies were characterized by enzyme immunoassay and fluorescence microscopy according to their ability to opsonize A. baumannii and to protect model animals from infection upon intraperitoneal and pulmonary injection. Monoclonal antibodies (MAbs), IgG, against the K9 capsular polysaccharide (CPS) of A. baumannii were prepared using a glycoconjugate, synthesized by squaric-acid chemistry, consisting of two CPS K9 monomer units and a carrier protein. The MAbs were highly specific, stained the bacterial surface, allowed detection of A. baumannii in infected lung tissue, effectively opsonized the bacteria at nanogram concentrations (up to 1.5 ng/mL for CPS-407), and demonstrated a high ability to protect an organism against bacterial infection upon intraperitoneal and lung injection. In intraperitoneal infection of a mouse model with A. baumannii K9, the CPS-407 antibody protected at a dose of 25 μg/mouse. When bacteria were injected into the lung, MAb therapy prevented infection of the body and led to a significant reduction of the bacterial load in infected tissues. IMPORTANCE MAbs detected A. baumannii in infected lung tissue, effectively opsonized bacteria, and protected model animals from infection.
Knockout of Targeted Plasmid-Borne β-Lactamase Genes in an Extended-Spectrum-β-Lactamase-Producing Escherichia coli Strain: Impact on Resistance and Proteomic Profile

The critical situation regarding antibiotic resistance requires a more in-depth effort for understanding underlying mechanisms involved in antibiotic resistance, beyond just detecting resistance genes. The methodology presented in this work provides a framework for knocking out selected resistance factors, to study the adjustments of the bacterium in response to a particular antibiotic stress, elucidating the genetic response and proteins that are mobilized. ABSTRACT Resistance to β-lactams is known to be multifactorial, although the underlying mechanisms are not well established. The aim of our study was to develop a system for assessing the phenotypic and proteomic responses of bacteria to antibiotic stress as a result of the loss of selected antimicrobial resistance genes. We applied homologous recombination to knock out plasmid-borne β-lactamase genes (blaOXA-1, blaTEM-1, and blaCTX-M15) in Escherichia coli CCUG 73778, generating knockout clone variants lacking the respective deleted β-lactamases. Quantitative proteomic analyses were performed on the knockout variants and the wild-type strain, using bottom-up liquid chromatography tandem mass spectrometry (LC-MS/MS), after exposure to different concentrations of cefadroxil. Loss of the blaCTX-M-15 gene had the greatest impact on the resulting protein expression dynamics, while losses of blaOXA-1 and blaTEM-1 affected fewer proteins’ expression levels. Proteins involved in antibiotic resistance, cell membrane integrity, stress, and gene expression and unknown function proteins exhibited differential expression. The present study provides a framework for studying protein expression in response to antibiotic exposure and identifying the genomic, proteomic, and phenotypic impacts of resistance gene loss. IMPORTANCE The critical situation regarding antibiotic resistance requires a more in-depth effort for understanding underlying mechanisms involved in antibiotic resistance, beyond just detecting resistance genes. The methodology presented in this work provides a framework for knocking out selected resistance factors, to study the adjustments of the bacterium in response to a particular antibiotic stress, elucidating the genetic response and proteins that are mobilized. The protocol uses MS-based determination of the proteins that are expressed in response to an antibiotic, enabling the selection of strong candidates representing putative resistance factors or mechanisms and providing a basis for future studies to understand their implications in antibiotic resistance. This allows us to better understand how the cell responds to the presence of the antibiotic when a specific gene is lost and, consequently, identify alternative targets for possible future treatment development.
Alternative intraoperative optical imaging modalities for fluorescence angiography in gastrointestinal surgery: spectral imaging and imaging photoplethysmography

Abstract Introduction Intraoperative near-infrared fluorescence angiography with indocyanine green (ICG-FA) is a well-established modality in gastrointestinal surgery. Its main drawback is the application of a fluorescent agent with possible side effects for patients. The goal of this review paper is the presentation of alternative, non-invasive optical imaging methods and their comparison with ICG-FA. Material and methods The principles of ICG-FA, spectral imaging, imaging photoplethysmography (iPPG), and their applications in gastrointestinal surgery are described based on selected published works. Results The main applications of the three modalities are the evaluation of tissue perfusion, the identification of risk structures, and tissue segmentation or classification. While the ICG-FA images are mainly evaluated visually, leading to subjective interpretations, quantitative physiological parameters and tissue segmentation are provided in spectral imaging and iPPG. The combination of ICG-FA and spectral imaging is a promising method. Conclusions Non-invasive spectral imaging and iPPG have shown promising results in gastrointestinal surgery. They can overcome the main drawbacks of ICG-FA, i.e. the use of contrast agents, the lack of quantitative analysis, repeatability, and a difficult standardization of the acquisition. Further technical improvements and clinical evaluations are necessary to establish them in daily clinical routine.
Dynamic coupling between shallow-water sloshing and horizontal vehicle motion

The coupled motion between shallow-water sloshing in a moving vehicle and the vehicle dynamics is considered, with the vehicle dynamics restricted to horizontal motion. The paper is motivated by Cooker's experiments and theory for water waves in a suspended container. A new derivation of the coupled problem in the Eulerian fluid representation is given. However, it is found that transformation to a Lagrangian representation leads to a formulation which has nice properties for numerical simulation. In the Lagrangian representation, a simple and fast numerical algorithm with excellent energy conservation over long times, based on the Störmer–Verlet method, is implemented. Numerical simulations of the coupled dynamics in both the linear and non-linear case are presented.
Strain-Specific Gifsy-1 Prophage Genes Are Determinants for Expression of the RNA Repair Operon during the SOS Response in Salmonella enterica Serovar Typhimurium

The transcriptional activator RtcR and the RNA repair proteins whose expression it regulates, RtcA and RtcB, are widely conserved in Proteobacteria. In Salmonella Typhimurium 14028s, genotoxic stress activates RtcR to direct RpoN-dependent expression of the rsr-yrlBA-rtcBA operon. ABSTRACT The adaptation of Salmonella enterica serovar Typhimurium to stress conditions involves expression of genes within the regulon of the alternative sigma factor RpoN (σ54). RpoN-dependent transcription requires an activated bacterial enhancer binding protein (bEBP) that hydrolyzes ATP to remodel the RpoN-holoenzyme-promoter complex for transcription initiation. The bEBP RtcR in S. Typhimurium strain 14028s is activated by genotoxic stress to direct RpoN-dependent expression of the RNA repair operon rsr-yrlBA-rtcBA. The molecular signal for RtcR activation is an oligoribonucleotide with a 3′-terminal 2′,3′-cyclic phosphate. We show in S. Typhimurium 14028s that the molecular signal is not a direct product of nucleic acid damage, but signal generation is dependent on a RecA-controlled SOS-response pathway, specifically, induction of prophage Gifsy-1. A genome-wide mutant screen and utilization of Gifsy prophage-cured strains indicated that the nucleoid-associated protein Fis and the Gifsy-1 prophage significantly impact RtcR activation. Directed-deletion analysis and genetic mapping by transduction demonstrated that a three-gene region (STM14_3218-3220) in Gifsy-1, which is variable between S. Typhimurium strains, is required for RtcR activation in strain 14028s and that the absence of STM14_3218-3220 in the Gifsy-1 prophages of S. Typhimurium strains LT2 and 4/74, which renders these strains unable to activate RtcR during genotoxic stress, can be rescued by complementation in cis by the region encompassing STM14_3218-3220. Thus, even though RtcR and the RNA repair operon are highly conserved in Salmonella enterica serovars, RtcR-dependent expression of the RNA repair operon in S. Typhimurium is controlled by a variable region of a prophage present in only some strains. IMPORTANCE The transcriptional activator RtcR and the RNA repair proteins whose expression it regulates, RtcA and RtcB, are widely conserved in Proteobacteria. In Salmonella Typhimurium 14028s, genotoxic stress activates RtcR to direct RpoN-dependent expression of the rsr-yrlBA-rtcBA operon. This work identifies key elements of a RecA-dependent pathway that generates the signal for RtcR activation in strain 14028s. This signaling pathway requires the presence of a specific region within the prophage Gifsy-1, yet this region is absent in most other wild-type Salmonella strains. Thus, we show that the activity of a widely conserved regulatory protein can be controlled by prophages with narrow phylogenetic distributions. This work highlights an underappreciated phenomenon where bacterial physiological functions are altered due to genetic rearrangement of prophages.
Learning Optimal Phase-Shifts of Holographic Metasurface Transceivers

Holographic metasurface transceivers (HMT) is an emerging technology for enhancing the coverage and rate of wireless communication systems. However, acquiring accurate channel state information in HMT-assisted wireless communication systems is critical for achieving these goals. In this paper, we propose an algorithm for learning the optimal phase-shifts at a HMT for the far-field channel model. Our proposed algorithm exploits the structure of the channel gains in the far-field regions and learns the optimal phase-shifts in presence of noise in the received signals. We prove that the probability that the optimal phase-shifts estimated by our proposed algorithm deviate from the true values decays exponentially in the number of pilot signals. Extensive numerical simulations validate the theoretical guarantees and also demonstrate significant gains as compared to the state-of-the-art policies.
Cost-optimal Seeding Strategy During a Botanical Pandemic in Domesticated Fields

Botanical pandemics cause enormous economic damage and food shortages around the globe. However, since botanical pandemics are here to stay in the short-medium term, domesticated field owners can strategically seed their fields to optimize each session's economic profit. In this work, we propose a novel epidemiological-economic mathematical model that describes the economic profit from a field of plants during a botanical pandemic. We describe the epidemiological dynamics using a spatiotemporal extended susceptible-infected-recovered epidemiological model with a non-linear output economic model. We provide an algorithm to obtain an optimal grid-formed seeding strategy to maximize economic profit, given field and pathogen properties. We show that the recovery and basic infection rates have a similar economic influence. Unintuitively, we show that a larger farm does not promise higher economic profit. Our results demonstrate a significant benefit of using the proposed seeding strategy and shed more light on the dynamics of the botanical pandemic.
Development of a Novel Nonlinear Dynamic Cavitation Model and Its Numerical Validations

Aiming at modeling the cavitation bubble cluster, we propose a novel nonlinear dynamic cavitation model (NDCM) considering the second derivative term in Rayleigh-Plesset equation through strict mathematical derivation. There are two improvements of the new model: i) the empirical coefficients are eliminated by introduction of the nonuniform potential functions of {\psi}_v and {\psi}_c for growth and collapse processes respectively, and ii) only two model parameters are required, which both base on physical quantities - the Blake critical radius R_b and the average maximum growth radius R_m. The corresponding cavitation solver was developed by using OpenFOAM in which we implemented the modified momentum interpolation (MMI) method to ensure that the calculated results are independent of time step size. Three validation cases, namely numerical bubble cluster collapse, ultrasonic horn experiment, and hydrodynamic cavitation around slender body are employed. The results indicate that {\psi}_v and {\psi}_c can reveal the nonlinear characteristics for cavity accurately, and R_b and R_m can reflect the relevance between cavitation model and actual physical quantities. Moreover, it is discussed the potentiality of NDCM that is generally applied on the cavitating flow possessing with dispersed bubbly cloud.
Phase Transitions in Inorganic Halide Perovskites from Machine-Learned Potentials

The atomic scale dynamics of halide perovskites have a direct impact not only on their thermal stability but their optoelectronic properties. Progress in machine learned potentials has only recently enabled modeling the finite temperature behavior of these material using fully atomistic methods with near first-principles accuracy. Here, we systematically analyze the impact of heating and cooling rate, simulation size, model uncertainty, and the role of the underlying exchange-correlation functional on the phase behavior of CsPbX3 with X=Cl, Br, and I, including both the perovskite and the delta-phases. We show that rates below approximately 30 K/ns and system sizes of at least a few ten thousand atoms are indicated to achieve convergence with regard to these parameters. By controlling these factors and constructing models that are specific for different exchange-correlation functionals we then show that the semi-local functionals considered in this work (SCAN, vdW-DF-cx, PBEsol, and PBE) systematically underestimate the transition temperatures separating the perovskite phases while overestimating the lattice parameters. Among the considered functionals the vdW-DF-cx functional yields the closest agreement with experiment, followed by SCAN, PBEsol, and PBE. Our work provides guidelines for the systematic analysis of dynamics and phase transitions in inorganic halide perovskites and similar systems. It also serves as a benchmark for the further development of machine-learned potentials as well as exchange-correlation functionals.
Mimicking non-ideal instrument behavior for hologram processing using neural style translation

Holographic cloud probes provide unprecedented information on cloud particle density, size and position. Each laser shot captures particles within a large volume, where images can be computationally refocused to determine particle size and location. However, processing these holograms with standard methods or machine learning (ML) models requires considerable computational resources, time and occasional human intervention. ML models are trained on simulated holograms obtained from the physical model of the probe since real holograms have no absolute truth labels. Using another processing method to produce labels would be subject to errors that the ML model would subsequently inherit. Models perform well on real holograms only when image corruption is performed on the simulated images during training, thereby mimicking non-ideal conditions in the actual probe. Optimizing image corruption requires a cumbersome manual labeling effort. Here we demonstrate the application of the neural style translation approach to the simulated holograms. With a pre-trained convolutional neural network, the simulated holograms are "stylized" to resemble the real ones obtained from the probe, while at the same time preserving the simulated image "content" (e.g. the particle locations and sizes). With an ML model trained to predict particle locations and shapes on the stylized data sets, we observed comparable performance on both simulated and real holograms, obviating the need to perform manual labeling. The described approach is not specific to holograms and could be applied in other domains for capturing noise and imperfections in observational instruments to make simulated data more like real world observations.
A New Noncoherent Gaussian Signaling Scheme for Low Probability of Detection Communications

We propose a novel, Gaussian signaling mechanism for low probability of detection (LPD) communication systems with either single or multiple antennas. The new scheme is designed to allow the noncoherent detection of Gaussian-distributed signals, enabling LPD communications using signals that follow the complex Gaussian distribution in the time and frequency domains. It is demonstrated via simulations that the proposed scheme achieves better performance than a comparable conventional scheme over the entire SNR region, with the advantage becoming more significant in scenarios with lower overhead.
Advances in the Filamentary Modelling of Electromagnetic Flux Compression

In this paper, we present an improved filamentary method of calculating the forces and pressures encountered during electromagnetic flux compression experiments in θ-pinch geometry, and which allows for both the independent movement of each filamentary row and the presence of nearby metallic components. Performance predictions provided by the new model are compared with both corresponding results from the best previous model and highly accurate experimental data.
ENERGY CONTENT AND PROPAGATION IN TRANSVERSE SOLAR ATMOSPHERIC WAVES

Recently, a significant amount of transverse wave energy has been estimated propagating along solar atmospheric magnetic fields. However, these estimates have been made with the classic bulk Alfvén wave model which assumes a homogeneous plasma. In this paper, the kinetic, magnetic, and total energy densities and the flux of energy are computed for transverse MHD waves in one-dimensional cylindrical flux tube models with a piecewise constant or continuous radial density profile. There are fundamental deviations from the properties for classic bulk Alfvén waves. (1) There is no local equipartition between kinetic and magnetic energy. (2) The flux of energy and the velocity of energy transfer have, in addition to a component parallel to the magnetic field, components in the planes normal to the magnetic field. (3) The energy densities and the flux of energy vary spatially, contrary to the case of classic bulk Alfvén waves. This last property has the important consequence that the energy flux computed with the well known expression for bulk Alfvén waves could overestimate the real flux by a factor in the range 10–50, depending on the flux tube equilibrium properties.
A Primer on the Data Cleaning Pipeline

The availability of both structured and unstructured databases, such as electronic health data, social media data, patent data, and surveys that are often updated in real time, among others, has grown rapidly over the past decade. With this expansion, the statistical and methodological questions around data integration, or rather merging multiple data sources, has also grown. Specifically, the science of the ``data cleaning pipeline'' contains four stages that allow an analyst to perform downstream tasks, predictive analyses, or statistical analyses on ``cleaned data. '' This article provides a review of this emerging field, introducing technical terminology and commonly used methods.
Unveiling and Mitigating Bias in Ride-Hailing Pricing for Equitable Policy Making

Ride-hailing services have skyrocketed in popularity due to the convenience they offer, but recent research has shown that their pricing strategies can have a disparate impact on some riders, such as those living in disadvantaged neighborhoods with a greater share of residents of color or residents below the poverty line. Since these communities tend to be more dependent on ride-hailing services due to lack of adequate public transportation, it is imperative to address this inequity. To this end, this paper presents the first thorough study on fair pricing for ride-hailing services by devising applicable fairness measures and corresponding fair pricing mechanisms. By providing discounts that may be subsidized by the government, our approach results in an increased number and more affordable rides for the disadvantaged community. Experiments on real-world Chicago taxi data confirm our theoretical findings which provide a basis for the government to establish fair ride-hailing policies.