Miracle-dz/UniFAR-FTUs huggingface dataset:
Mycofabrication of Mycelium-Based Leather from Brown-Rot Fungi

Sustainable substitutes for leather can be made from mushroom mycelium, which is an environmentally friendly alternative to animal and synthetic leather. Mycelium-based leather is derived from Polyporales, in which lignocellulosic material is used as the substrate. The plasticizing and crosslinking of mycelial mats with various reagents might affect the leather properties and mycelial architecture. This study investigated the physicochemical and mechanical properties of mycelium-based leather (MBL) samples, including the hygroscopic nature, thermal stability, cell wall chemistry, density, micromorphology, tensile strength, elongation rate, and Young’s modulus. Micromorphological observations confirmed the mycelial networks and their binding performance, verifying their efficacy as a substitute leather. The most significant effects were observed after treatment with 20% polyethylene glycol, which resulted in an increase in Young’s modulus and tensile strength. Furthermore, the samples generally exhibited a high density (1.35, 1.46 g/cm3) and tensile strength (7.21 ± 0.93, 8.49 ± 0.90 MPa), resembling leather. The tear strength reached as low as 0.5–0.8 N/mm. However, the tensile and tear strength may be affected by leather processing and the tuning of mycelial growth. Nevertheless, high-density mycelia are shown to be suitable for the production of MBL, while mycofabrication and strain selection are sustainable for novel industrial applications of MBL.
Characterization of a Centrifugal Microfluidic Orthogonal Flow Platform

To bring to bear the power of centrifugal microfluidics on vertical flow immunoassays, control of flow orthogonally through nanoporous membranes is essential. The on-disc approach described here leverages the rapid print-cut-laminate (PCL) disc fabrication and prototyping method to create a permanent seal between disc materials and embedded nanoporous membranes. Rotational forces drive fluid flow, replacing capillary action, and complex pneumatic pumping systems. Adjacent microfluidic features form a flow path that directs fluid orthogonally (vertically) through these embedded membranes during assay execution. This method for membrane incorporation circumvents the need for solvents (e.g., acetone) to create the membrane-disc bond and sidesteps issues related to undesirable bypass flow. In other recently published work, we described an orthogonal flow (OF) platform that exploited embedded membranes for automation of enzyme-linked immunosorbent assays (ELISAs). Here, we more fully characterize flow patterns and cellulosic membrane behavior within the centrifugal orthogonal flow (cOF) format. Specifically, high-speed videography studies demonstrate that sample volume, membrane pore size, and ionic composition of the sample matrix significantly impact membrane behavior, and consequently fluid drainage profiles, especially when cellulosic membranes are used. Finally, prototype discs are used to demonstrate proof-of-principle for sandwich-type antigen capture and immunodetection within the cOF system.
Automatic Fish Age Determination across Different Otolith Image Labs Using Domain Adaptation

The age determination of fish is fundamental to marine resource management. This task is commonly done by analysis of otoliths performed manually by human experts. Otolith images from Greenland halibut acquired by the Institute of Marine Research (Norway) were recently used to train a convolutional neural network (CNN) for automatically predicting fish age, opening the way for requiring less human effort and availability of expertise by means of deep learning (DL). In this study, we demonstrate that applying a CNN model trained on images from one lab (in Norway) does not lead to a suitable performance when predicting fish ages from otolith images from another lab (in Iceland) for the same species. This is due to a problem known as dataset shift, where the source data, i.e., the dataset the model was trained on have different characteristics from the dataset at test stage, here denoted as target data. We further demonstrate that we can handle this problem by using domain adaptation, such that an existing model trained in the source domain is adapted to perform well in the target domain, without requiring extra annotation effort. We investigate four different approaches: (i) simple adaptation via image standardization, (ii) adversarial generative adaptation, (iii) adversarial discriminative adaptation and (iv) self-supervised adaptation. The results show that the performance varies substantially between the methods, with adversarial discriminative and self-supervised adaptations being the best approaches. Without using a domain adaptation approach, the root mean squared error (RMSE) and coefficient of variation (CV) on the Icelandic dataset are as high as 5.12 years and 28.6%, respectively, whereas by using the self-supervised domain adaptation, the RMSE and CV are reduced to 1.94 years and 11.1%. We conclude that careful consideration must be given before DL-based predictors are applied to perform large scale inference. Despite that, domain adaptation is a promising solution for handling problems of dataset shift across image labs.
Application of Common Time-Frequency Analysis Methods in Analyzing Characteristic Quantity of Digital Seismic Wave

Seismic wave is a non-stationary signal,which can not be analyzed comprehensively by classical Fourier transform because of the lack of local information.To non-stationary signal,time-frequency analysis is a powerful analysis tool.In this article,the short time Fourier transform,S transform,Choi-Williams distribution and Zhao-Atlas-Mark distribution are introduced briefly.Their application results and merits demerits in distinguishing seismic waves are summarized by using the four methods to numerical examples and real seismic signals.
Solving Inverse Electrocardiographic Mapping Using Machine Learning and Deep Learning Frameworks

Electrocardiographic imaging (ECGi) reconstructs electrograms at the heart’s surface using the potentials recorded at the body’s surface. This is called the inverse problem of electrocardiography. This study aimed to improve on the current solution methods using machine learning and deep learning frameworks. Electrocardiograms were simultaneously recorded from pigs’ ventricles and their body surfaces. The Fully Connected Neural network (FCN), Long Short-term Memory (LSTM), Convolutional Neural Network (CNN) methods were used for constructing the model. A method is developed to align the data across different pigs. We evaluated the method using leave-one-out cross-validation. For the best result, the overall median of the correlation coefficient of the predicted ECG wave was 0.74. This study demonstrated that a neural network can be used to solve the inverse problem of ECGi with relatively small datasets, with an accuracy compatible with current standard methods.
Finite Element Analysis of Frames with Reinforced Concrete Encased Steel Composite Columns

Structural frame systems that consists of concrete-encased-steel-embedded composite columns and reinforced concrete beams are typically used in mid-rise to tall buildings. In order to understand their overall structural behavior, a total of 12 frame models with high and low ductility features were constructed and analyzed using LS-DYNA software. Two of these models were validated using the results of previously tested frames. The remaining 10 models were studied to predict the behavior of frames with varying concrete strengths, reinforcement configurations, and structural steel sections under vertical and lateral loads. The results were investigated in terms of cracks and failure patterns, load-deflection relationships, energy dissipation, and stiffness degradation. The analytical results indicated that the high ductile frame models showed slightly better lateral load carrying performances compared to low ductility frame models. Moreover, the analytical studies demonstrated that the existence of structural steel in a column, regardless of its cross-sectional shape, was the most important parameter in improving the lateral load carrying capacity of a frame.
Towards a Stochastic Model to Simulate Grapevine Architecture: A Case Study on Digitized Riesling Vines Considering Effects of Elevated CO2

Modeling plant growth, in particular with functional-structural plant models, can provide tools to study impacts of changing environments in silico. Simulation studies can be used as pilot studies for reducing the on-field experimental effort when predictive capabilities are given. Robust model calibration leads to less fragile predictions, while introducing uncertainties in predictions allows accounting for natural variability, resulting in stochastic plant growth models. In this study, stochastic model components that can be implemented into the functional-structural plant model Virtual Riesling are developed relying on Bayesian model calibration with the goal to enhance the model towards a fully stochastic model. In this first step, model development targeting phenology, in particular budburst variability, phytomer development rate and internode growth are presented in detail. Multi-objective optimization is applied to estimate a single set of cardinal temperatures, which is used in phenology and growth modeling based on a development days approach. Measurements from two seasons of grapevines grown in a vineyard with free-air carbon dioxide enrichment (FACE) are used; thus, model building and selection are coupled with an investigation as to whether including effects of elevated CO2 conditions to be expected in 2050 would improve the models. The results show how natural variability complicates the detection of possible treatment effects, but demonstrate that Bayesian calibration in combination with mixed models can realistically recover natural shoot growth variability in predictions. We expect these and further stochastic model extensions to result in more realistic virtual plant simulations to study effects, which are used to conduct in silico studies of canopy microclimate and its effects on grape health and quality.
Fast Experimental Magnetic Model Identification for Synchronous Reluctance Motor Drives

The accurate magnetic model is mandatory for high-performance control of high anisotropy synchronous machines. This paper presents a time-efficient and accurate magnetic model identification based on triangle current injection while the machine under the test is driven at a constant speed by a prime mover. The current injection pattern allows scanning the whole range of current, reducing the identification time compared to the standard constant-speed method (CSM) with the same level of accuracy. The ohmic voltage drop and inverter nonlinearities are compensated by using the average voltage of motor and generator modes. The synchronous reluctance machine is used as a case study for validation through the comparison between the experimental results obtained by the proposed method and the CSM against finite element simulation. Moreover, the temperature variation of the machine winding is measured showing no considerable changes during the identification test.
Effect of Paper-Bagging on Apple Skin Patterning Associated with MdMYB10 Promoter Methylation

Paper-bagging is an efficient method to maximize apple skin color, but a relationship between this technique and fruit skin patterning has not been demonstrated. Here, the ‘Fuji’ fruit with red-striped skin changed to red-blushed skin under re-exposure to light after bag treatment. Higher expression of MdMYB10, a transcription factor that regulates anthocyanin biosynthesis in apples, correlated with increased anthocyanin concentration in bag removal fruit. At the mature stage, a comparison of methylation status in the MdMYB10 promoter revealed that the methylation level in the region from −2585 to −2117 bp was reduced in bag removal fruit, especially for CHG context. It can be regulated by the downregulated expression of DNA methyltransferases such as MdMET, MdCMT, and MdDRM. Our results suggest that the bag removal treatment in this cultivar causes a change in skin patterning from striped to blushed pigmentation by inducing DNA demethylation of MdMYB10.
Evaluation of the modified Wells score in predicting venous thromboembolic disease in patients with tuberculosis or HIV in a South African setting

Background There is paucity of data on the modified Wells score (MWS) utility on patients with venous thromboembolism (VTE) in a South African setting where there is a high burden of HIV and tuberculosis (TB). This study analyses the performance of this score in HIV/TB-infected patients compared with non-infected patients. Objectives To assess the performance of the MWS as an additional risk factor for VTE in hospitalised patients with a high burden of HIV/TB infections. Method This study was a retrospective cross-sectional cohort analysis of the utility of the MWS in 156 HIV/TB-infected and non-infected adult patients diagnosed with VTE on compression ultrasonography (CUS) or computed tomography pulmonary angiography (CTPA) in a medical inpatient setting over six months. Patients with HIV and/or TB were assessed as having an additional risk factor (1 point for each), and this was compared with the MWS. A McNeymar’s paired sample chi-squared test was used to compare the sensitivity of this score against the MWS. Results Of the 156 patients with VTE who were enrolled, HIV was the commonest risk factor (42.31%) with TB accounting for 10.90% of cases. When the MWS adjusted for HIV/TB was used, the sensitivity increased from 25% to 100% for the HIV–/TB+ category, it increased from 77.36% to 98.11% in the HIV+/TB– category and it increased from 84.62% to 92.95% in the HIV+/TB+ category. These differences were statistically significant at P < 0.05 in all categories. Conclusion The MWS performs better when the infectivity of HIV/TB is included as an additional risk factor in the score.
Adherence to Medication in Older Adults with Type 2 Diabetes Living in Lubuskie Voivodeship in Poland: Association with Frailty Syndrome

Purpose: Diabetic patients aged 65 years or older are more likely to be frail than non-diabetic older adults. Adherence to therapeutic recommendations in the elderly suffering from diabetes and co-existent frailty syndrome may prevent complications such as micro- or macroangiopathy, as well as significantly affect prevention and reversibility of frailty. The study aimed at assessing the impact of frailty syndrome (FS) on the level of adherence to medication in elderly patients with type 2 diabetes (DM2). Patients and Methods: The research was carried out among 175 DM2 patients (87; 49.71% women and 88; 50.29% men) whose average age amounted to 70.25 ± 6.7. Standardized research instruments included Tilburg frailty indicator (TFI) to assess FS and adherence in chronic disease scale questionnaire (ACDS) to measure adherence to medications. Results: The group of 101 (57.71%) patients displayed medium, 39 (22.29%)—low, and 35 (20.00%)—high adherence. As many as 140 of them (80.00%) were diagnosed with frailty syndrome. The median of the average result of TFI was significantly higher in the low adherence group (p ˂ 0.001) (Mdn = 9, Q1–Q3; 7–10 pt.) than in the medium (Mdn = 6, Q1–Q3; 5–9 pt.) or high adherence (Mdn = 6.00, Q1–Q3; 4.5–8 pt.) ones. The independent predictors of the chance to be qualified to the non-adherence group included three indicators: TFI (OR 1.558, 95% CI 1.245–1.95), male gender (OR 2.954, 95% CI 1.044–8.353), and the number of all medications taken daily (each extra pill decreased the chance of being qualified to the non-adherence group by 15.3% (95% CI 0.728–0.954). Conclusion: Frailty syndrome in elderly DM2 patients influenced medical adherence in this group. The low adhesion group had higher overall TFI scores and separately higher scores in the physical and psychological domains compared to the medium and high adhesion groups.
Low Salt Treatment Results in Plant Growth Enhancement in Tomato Seedlings

Climate change together with excessive fertilization and poor water quality can affect soil quality and salinization. In plants, high salinity causes osmotic stress, ionic toxicity, and oxidative stress. Consequently, salt stress limits plant development, growth, productivity, and yield. Tomatoes are a very common agricultural product, and some cultivars can partially tolerate salinity. However, most studies are focused on salt excess, which does not necessarily extrapolate on how plants develop in soils with low concentrations of salts. Thus, this study characterizes plant growth and the development of different salt concentrations from 25 to 200 mM in Solanum lycopersicum cv. Moneymaker. Tomato seedlings grown in Murashige and Skoog medium supplied with different NaCl concentrations (0, 25, 50, 75, 100, 125, 150, 175, and 200 mM) showed that low salt concentrations (25 and 50 mM) have a positive impact on lateral root development. This was further observed in physiological parameters such as shoot length, primary root length, and proliferation of lateral roots versus controls. Interestingly, no significant changes in Na+ concentration were observed in 25 mM NaCl in roots or shoots versus controls. Overall, our results suggest that non-toxic salt concentrations can have a positive impact on plant development.
Syndicated Lending, Competition and Relative Performance Evaluation

Relative performance evaluation (RPE) intensifies competitive pressure by tying executive compensation to the profits of rivals. We show that these contracts make loan syndication harder by reducing banks’ willingness to participate in loans underwritten by banks named in their RPE contracts. Lead arranger banks which are more frequently named in RPE hold larger shares of the loans they syndicate, and their borrowers face higher spreads. These banks, in turn, lose market share to banks less likely to be named in RPE. Our results highlight the tension between the normal benefits of competition versus the need for cooperation in loan syndication. Thomas Schneider Old Dominion University Norfolk, VA 23529 tischnei@odu.edu Philip Strahan Carroll School of Management 324B Fulton Hall Boston College Chestnut Hill, MA 02467 and NBER philip.strahan@bc.edu Jun Yang Notre Dame South Bend, IN 46556 jyang23@nd.edu
Impacts of Phase Noise on the Anti-Jamming Performance of Power Inversion Algorithm

Power inversion (PI) is a known adaptive beamforming algorithm that is widely used in wireless communication systems for anti-jamming purposes. The PI algorithm is typically implemented in a digital domain, which requires the radio-frequency signals to be down-converted into base-band signals, and then sampled by ADCs. In practice, the down-conversion circuit will introduce phase noises into the base-band signals, which may degrade the performance of the algorithm. At present, the impacts of phase noise on the PI algorithm have not been studied, according to the open literature, which is, however, important for practical design. Therefore, in this paper, we present a theoretical analysis on the impacts, provide a new mathematical model of the PI algorithm, and offer a closed-form formula of the interference cancellation ratio (ICR) to quantify the relations between the algorithm performance and the phase noise level, as well as the number of auxiliary antennas. We find that the ICR in decibel decreases logarithmically linearly with the phase noise variance. In addition, the ICR improves with an increasing number of auxiliary antennas, but the increment is upper-bounded. The above findings are verified with both simulated and measured phase noise data.
Experiment Research on Thermal Contact Resistance of Normal Used Joints

Existence of joints can affect static and dynamic characteristic of machine tool and it has great effect on thermal characteristic also. On the basis of experimental research, thermal contact resistance of joints that was normal used on machine tool is studied. Relationship between pressure and thermal contact resistance is gained when the material mates, surface manufacturing quality and medium are varied. And also, some related questions are discussed and basic data are accumulated to study applied technology of joints’ thermal contact resistance.
Rotating Magnetic Field-Assisted Reactor Enhances Mechanisms of Phage Adsorption on Bacterial Cell Surface

Growing interest in bacteriophage research and use, especially as an alternative treatment option for multidrug-resistant bacterial infection, requires rapid development of production methods and strengthening of bacteriophage activities. Bacteriophage adsorption to host cells initiates the process of infection. The rotating magnetic field (RMF) is a promising biotechnological method for process intensification, especially for the intensification of micromixing and mass transfer. This study evaluates the use of RMF to enhance the infection process by influencing bacteriophage adsorption rate. The RMF exposition decreased the t50 and t75 of bacteriophages T4 on Escherichia coli cells and vb_SauM_A phages on Staphylococcus aureus cells. The T4 phage adsorption rate increased from 3.13 × 10−9 mL × min−1 to 1.64 × 10−8 mL × min−1. The adsorption rate of vb_SauM_A phages exposed to RMF increased from 4.94 × 10−9 mL × min−1 to 7.34 × 10−9 mL × min−1. Additionally, the phage T4 zeta potential changed under RMF from −11.1 ± 0.49 mV to −7.66 ± 0.29 for unexposed and RMF-exposed bacteriophages, respectively.
A Preliminary Metabolomic Study of Yorkshire Terrier Enteropathy

Perturbations of metabolite profiles in human and canine enteropathies have been reported before. However, data in dogs are scarce and inconsistent. Currently, the metabolite profile in Yorkshire Terrier enteropathy (YTE) and the impact of treatment is unknown. The objective of this study was to investigate the plasma metabolome of 13 Yorkshire Terriers with YTE and compare it to 20 healthy Yorkshire Terriers. Furthermore, we studied the impact of treatment on the metabolome. In this prospective observational study, plasma metabolite profiles were analyzed by flow injection analysis-tandem mass spectrometry (FIA-MS/MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) using a targeted metabolomics kit. Metabolite analysis revealed that YTE is accompanied by changes in lipid and bile acid metabolism. YTE was associated with a significant decrease of long-chain fatty acids (octadecenoic acid, eicosadienoic acid, eicosatrienoic acid) and lower levels of long-chain acylcarnitines (tetradecanoylcarnitine, hexadecanoylcarnitine, hexadecenoylcarnitine, octadecenoylcarnitine) compared with healthy controls. Furthermore, taurodeoxycholic acid, a secondary bile acid, was decreased in plasma from YTE patients. These changes might be breed-specific and might be involved in the pathogenesis of YTE. Interestingly, changes in metabolite levels were not recovered after treatment and differed considerably from healthy controls.