Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of the microscopic acid dissociation constant of piperacillin and identification of dissociated molecular forms

For amphoteric ß-lactam antibiotics, the acid dissociation constant (pK a) is a fundamental parameter to characterize physicochemical and biochemical properties of antibiotics and to predict persistence and removal of drugs. pK a of piperacillin (PIP) is determined by potentiometric titration with a glass electrode. Electrospray ionization mass spectrometry (ESI-MS) is creatively applied to verify the reasonable pK a value at every dissociation step. Two microscopic pK a values (3.37 ± 0.06 and 8.96 ± 0.10) are identified and attributed to the direct dissociation of the carboxylic acid functional group and one secondary amide group, respectively. Different from other ß-lactam antibiotics, PIP presents a dissociation pattern where direct dissociation is involved instead of protonation dissociation. Moreover, the degradation tendency of PIP in an alkaline solution may alter the dissociation pattern or dismiss the corresponding pK a of the amphoteric ß-lactam antibiotics. This work offers a reliable determination of the acid dissociation constant of PIP and a clear interpretation of the effect of stability of antibiotics on the dissociation process.
Restorative effects of Lactobacillus rhamnosus LR-32 on the gut microbiota, barrier integrity, and 5-HT metabolism in reducing feather-pecking behavior in laying hens with antibiotic-induced dysbiosis

The development of abnormal feather-pecking (FP) behavior, where laying hens display harmful pecks in conspecifics, is multifactorial and has been linked to the microbiota-gut-brain axis. Antibiotics affect the gut microbial composition, leading to gut-brain axis imbalance and behavior and physiology changes in many species. However, it is not clear whether intestinal dysbacteriosis can induce the development of damaging behavior, such as FP. The restorative effects of Lactobacillus rhamnosus LR-32 against intestinal dysbacteriosis-induced alternations need to be determined either. The current investigation aimed to induce intestinal dysbacteriosis in laying hens by supplementing their diet with the antibiotic lincomycin hydrochloride. The study revealed that antibiotic exposure resulted in decreased egg production performance and an increased tendency toward severe feather-pecking (SFP) behavior in laying hens. Moreover, intestinal and blood-brain barrier functions were impaired, and 5-HT metabolism was inhibited. However, treatment with Lactobacillus rhamnosus LR-32 following antibiotic exposure significantly alleviated the decline in egg production performance and reduced SFP behavior. Lactobacillus rhamnosus LR-32 supplementation restored the profile of the gut microbial community, and showed a strong positive effect by increasing the expression of tight junction proteins in the ileum and hypothalamus and promoting the expression of genes related to central 5-HT metabolism. The correlation analysis revealed that probiotic-enhanced bacteria were positively correlated, and probiotic-reduced bacteria were negatively correlated with tight junction-related gene expression, and 5-HT metabolism, and butyric acid levels. Overall, our findings indicate that dietary supplementation with Lactobacillus rhamnosus LR-32 can reduce antibiotic-induced FP in laying hens and is a promising treatment to improve the welfare of domestic birds.
Designed NiMoC@C and NiFeMo2C@C core-shell nanoparticles for oxygen evolution in alkaline media

Electrochemical water splitting is one of the most promising and clean ways to produce hydrogen as a fuel. Herein, we present a facile and versatile strategy for synthesizing non-precious transition binary and ternary metal-based catalysts encapsulated in a graphitic carbon shell. NiMoC@C and NiFeMo2C@C were prepared via a simple sol-gel based method for application in the Oxygen Evolution Reaction (OER). The conductive carbon layer surrounding the metals was introduced to improve electron transport throughout the catalyst structure. This multifunctional structure showed synergistic effects, possess a larger number of active sites and enhanced electrochemical durability. Structural analysis indicated that the metallic phases were encapsulated in the graphitic shell. Experimental results demonstrated that the optimal core-shell material NiFeMo2C@C exhibited the best catalytic performance for the OER in 0.5 M KOH, reaching a current density of 10 mA cm-2 at low overpotential of 292 mV for the OER, superior to the benchmark IrO2 nanoparticles. The good performances and stability of these OER electrocatalysts, alongside an easily scalable procedure makes these systems ideal for industrial purposes.
Neural correlates of cocaine-induced conditioned place preference in the posterior cerebellar cortex

Introduction Addictive drugs are potent neuropharmacological agents capable of inducing long-lasting changes in learning and memory neurocircuitry. With repeated use, contexts and cues associated with consumption can acquire motivational and reinforcing properties of abused drugs, triggering drug craving and relapse. Neuroplasticity underlying drug-induced memories takes place in prefrontal-limbic-striatal networks. Recent evidence suggests that the cerebellum is also involved in the circuitry responsible for drug-induced conditioning. In rodents, preference for cocaine-associated olfactory cues has been shown to correlate with increased activity at the apical part of the granular cell layer in the posterior vermis (lobules VIII and IX). It is important to determine if the cerebellum’s role in drug conditioning is a general phenomenon or is limited to a particular sensory modality. Methods The present study evaluated the role of the posterior cerebellum (lobules VIII and IX), together with the medial prefrontal cortex (mPFC), ventral tegmental area (VTA), and nucleus accumbens (NAc) using a cocaine-induced conditioned place preference procedure with tactile cues. Cocaine CPP was tested using ascending (3, 6, 12, and 24 mg/kg) doses of cocaine in mice. Results Compared to control groups (Unpaired and Saline animals), Paired mice were able to show a preference for the cues associated with cocaine. Increased activation (cFos expression) of the posterior cerebellum was found in cocaine CPP groups and showed a positive correlation with CPP levels. Such increases in cFos activity in the posterior cerebellum significantly correlated with cFos expression in the mPFC. Discussion Our data suggest that the dorsal region of the cerebellum could be an important part of the network that mediates cocaine-conditioned behavior.
Comparison of feature extraction methods in dimensionality reduction

This technical note compares a number of feature extraction methods to determine which method enables higher accuracy of the performed classifications for dimensionality reduction in hyperspectral datasets. Two hyperspectral images were transformed into 10-, 15-, and 20-feature spaces using four unsupervised feature extraction methods (i.e., principal component analysis, maximum noise fraction, locally linear embedding (LLE), and independent component analysis) and one supervised feature extraction method (i.e., nonparametric weighted feature extraction, NWFE). A supervised classifier (i.e., support vector machine) processed a small number of training data and the feature spaces. The classification maps were compared with test samples, and then the classification accuracy of the feature extraction method was evaluated by kappa coefficient. With a 95% confidence interval of hypothesis testing, a 10-feature space could provide sufficient dimension for supervised classification and maximum noise fraction; and LLE outperformed the other feature extraction methods. Because NWFE might be limited by the small number of training samples, its classification performance was lower than those of the other feature extraction methods.
This construction is too hot to handle

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Zero CTE polyimides for athermal optical membranes

Polyimides are attractive mirror candidate materials due to their low mass, solar radiation resistance, and cryogenic flexibility. However, polyimides exhibit high coefficients of thermal expansion (CTE) values (40-70 ppm/K), inducing image distortion from CTE mismatch. Additionally, the temperature of large aperture (10 m) membranes is not uniformly controlled in space, further increasing image distortion from anisotropic deformations. The CTE of the MSRS Novastrat polyimide line was adjusted to exhibit CTEs between -16 ppm/K and 53 ppm/K, including 0 ppm/K, 10 ppm/K, 17 ppm/K, and 25 ppm/K corresponding with CTE matches of graphite/epoxy, carbon steel, copper, and aluminum (respectively). The development of these CTE-matched membranes is presented, as well as the effect of the CTE adjustment on the mechanical properties.
IMPLICIT LARGE EDDY SIMULATION OF INCOMPRESSIBLE FLOW

In a prepared urban scale model of airflow within a complex geometry area the method of Implicit Large Eddy Simulation (ILES) is used. A staggered grid is used for a discretization of the equations describing the incompressible flow. The use of ILES comes from an application of the high resolution method for convective (advection) terms of the momentum equation. To assess the applicability of the ILES method for this purpose, we are in the stage of testing it on simple and well described cases of turbulent flows. The first example of 3D turbulent flow is the Taylor-Green vortex – originally a simple free flow containing organized vortices with a subsequent transition to turbulence. In this contribution there will be shown several characteristics of the turbulent flow and given their comparison with results described in literature (energy spectra, kinetic energy dissipation rate etc.).
Theoretical Study on Interior Ballistics of High Velocity Gun Based on Liquid Traveling Charge

A new firing concept which can be used in high velocity gun charge was presented based on liquid traveling.The differential principle,i.e.the speed difference between the different components of the combined projectile,was used to realize the continuous injection of liquid propellant toward the in-bore space.Under the action of the high temperature gas,the sprayed-out liquid propellants burn,and the generated gas fill the pressure drop of the bottom of the projectile.The utilization rate of gun working volume is effectively improved.The kinetic equations of the combined projectile and the interior ballistic equations of combined firing concept were deduced.The combined firing concept of a large-diameter gun based on liquid traveling charge was obtained through numerical calculation and comparing multi-schemes.Under the condition of invariable maximum bore pressure,invariable projectile weight and travel,the initial velocity of projectile can be increased by 26%,and the utilization rate of gun working volume is increased by about 44%.
Multiple imputing simulated missing data

A common problem that is encountered while conducting clinical trials are patients that do not complete the trial, thus a dataset with missing values arises. One way of coping with this, is by multiple imputing the missing values. There are several multiple imputation methods and one of them is predictive mean matching (PMM) imputation, a method used to impute continuous monotone missing data. We compare the predictive mean matching with other more naive methods like the last observation carried forward (LOCF) and baseline carried forward (BCF), methods that are widely used in longitudinal clinical trials. We simulate complete data in order to have estimates, to compare with the estimates from imputed datasets. The simulation is done by studying the correlation structure and mean vector of an existing clinical trial, from which we simulate a complete dataset. From this dataset we create monotone missing data pattern based on different missing data mechanisms. The missing values are later imputed, with the methods mentioned above. Thus we are able to compare simulated missing and imputed data with simulated complete data. We compare the results with point estimates and with relative widths of 95% confidence intervals of the point estimates.
Calculation of Expected Mean Squares by the Abbreviated Doolittle and Square Root Methods

The method of least squares, fitting constants, is commonly used as an approximate solution for the analysis of variance when the model contains more than one random component. Techniques are available for obtaining the expected mean squares for balanced designs and for completely nested (hierarchal) experiments. For unbalanced designs containing crossed (factorial) variates, the expected mean squares are generally difficult to obtain. The expected mean squares for only a few relatively simple crossed or mixed (crossed and nested) experiments are readily available. This paper provides a single unified procedure for obtaining the expected mean squares from the forward solution of the Abbreviated Doolittle or Square Root Methods. This procedure encompasses any experimental design, balanced or unbalanced, for crossed, nested, or mixed classifications containing any type of effect (fixed, finite, or random).
Terahertz-range interrogated grating-based two-axis optical fiber inclinometer

Abstract. We report a two-axis fiber inclinometer fabricated using an ultraweak terahertz-range fiber Bragg grating. Three sensing grating structures were inscribed along a single-mode optical fiber using a femtosecond laser, bound together into a sensing array using thermoformed plastic, and fixed to a two-axis rotational stage. Inclination tests were performed in which the fiber was deflected from 0 to 1.7 deg. These tests were repeated at eight azimuthal angles in increments of 45 deg (from 0 to 315 deg). The standard deviation of the largest inclination angle error was 0.048 deg and the stability of the inclination angle was 0.030 deg.
Performance and acceptability of the Stressful Life Events Screening Questionnaire in a chronic pain population: a mixed-methods study

Supplemental Digital Content is Available in the Text. The Stressful Life Events Screening Questionnaire demonstrates satisfactory sensitivity, specificity, temporal stability, and acceptability in a chronic pain clinic and can help guide clinical practice within these settings. Abstract Introduction: Pain-related fear, anxiety, and avoidance may play key roles in the chronification of pain and related disability. For practitioners, knowledge about the source or drivers of these fears, including patients' exposure to potentially traumatic events (PTEs) and related posttraumatic stress symptoms, could be particularly helpful in guiding their treatment approach. Objectives: We aimed to investigate whether the use of a brief screening for PTEs could help inform chronic pain treatment. Methods: The performance and acceptability of the Stressful Life Events Screening Questionnaire (SLESQ) was assessed among 567 adult patients (59% women, mean age 48.1 years) meeting at a hospital outpatient pain clinic. The sensitivity, specificity, and 20 months temporal stability of the SLESQ, assessing exposure to 14 specific trauma types followed by a 15th item capturing exposure to “other events,” were assessed through digital administration and follow-up interviews with 55 participants. The qualitative responses of 158 participants reporting exposure to “other events” were reviewed and assessed based on fulfillment of the A Criterion for traumatic events in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. The acceptability of the SLESQ was assessed in clinical interviews with 12 participants. Results: The SLESQ demonstrated acceptable sensitivity (70.0%), high specificity (94.9%), and moderate temporal stability (κ = 0.66, P < 0.001). Participants' qualitative elaborations of “other events” were largely (76.3%) consistent with Criterion A events. The screening was well accepted and welcomed. Conclusion: The results indicate that the use of a brief screening for potential trauma may be helpful to guide clinical practice in chronic pain settings.
Interaction between Six Waxy Components in Summer Black Grapes (Vitis vinifera) and Mancozeb and Its Effect on the Residue of Mancozeb

Mancozeb, an antifungal typically used for the growth of fruits, has the characteristic of non-internal absorption, and has a risk of binding to the waxy components of fruits. This work investigated the interaction of pesticide molecules with the waxy layer on the grape surface and their effects on pesticide residues in grapes. The study observed significant changes in the compositions of the waxy layer on the grape surface after soaking in a mancozeb standard solution. The six substances—oleanolic acid, ursolic acid, lupeol, octacosanol, hexacosanal, and γ-sitosterol—with discernible content differences were chosen for molecular docking. Docking results were further visualized by an independent gradient model based on Hirshfeld partition (IGMH). Hydrogen bonds and van der Waals forces were found between mancozeb and the six waxy components. Moreover, the negative matrix effects caused by the presence or absence of wax for the determination of mancozeb were different through the QuEChERS-HPLC-MS method. Compared with the residue of mancozeb in grapes (5.97 mg/kg), the deposition of mancozeb in grapes after dewaxing was significantly lower (1.12 mg/kg), which further supports that mancozeb may interact with the wax layer compositions. This work not only provides insights into the study of the interaction between pesticides and small molecules but also provides theoretical guidelines for the investigation of the removal of pesticide residues on the surface of fruits.
Holographic Duality in Condensed Matter Physics

A pioneering treatise presenting how the new mathematical techniques of holographic duality unify seemingly unrelated fields of physics. This innovative development morphs quantum field theory, general relativity and the renormalisation group into a single computational framework and this book is the first to bring together a wide range of research in this rapidly developing field. Set within the context of condensed matter physics and using boxes highlighting the specific techniques required, it examines the holographic description of thermal properties of matter, Fermi liquids and superconductors, and hitherto unknown forms of macroscopically entangled quantum matter in terms of general relativity, stars and black holes. Showing that holographic duality can succeed where classic mathematical approaches fail, this text provides a thorough overview of this major breakthrough at the heart of modern physics. The inclusion of extensive introductory material using non-technical language and online Mathematica notebooks ensures the appeal to students and researchers alike.
Data-Driven Underwater Navigation workshop: AUV Close-Range Localization and Guidance Employing an Electro-Magnetic Beacon

AUV’s navigation is primarily based on acoustic sensors (e.g. sonars) and inertial navigation systems (INS). However, acoustic sensors provide a relatively low resolution and slow update rate images while INS systems suffer from drift and noise. Consequently, those sensors are insufficient to support tasks that require precise navigation and localization, such as autonomous intervention and docking. Electromagnetic (EM)-based navigation offers accurate navigation and localization at high rates; it is not affected by poor visibility, nor does it require line-of-sight between the sensor and the reference point. This study proposes a method for precise close-range localization of an AUV by employing a beacon and an onboard magnetometer. The localization solution was optimized by deep-learning method.
Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach

Knee osteoarthritis is a challenging problem affecting many adults around the world. There are currently no medications that cure knee osteoarthritis. The only way to control the progression of knee osteoarthritis is early detection. Currently, X-ray imaging is a central technique used for the prediction of osteoarthritis. However, the manual X-ray technique is prone to errors due to the lack of expertise of radiologists. Recent studies have described the use of automated systems based on machine learning for the effective prediction of osteoarthritis from X-ray images. However, most of these techniques still need to achieve higher predictive accuracy to detect osteoarthritis at an early stage. This paper suggests a method with higher predictive accuracy that can be employed in the real world for the early detection of knee osteoarthritis. In this paper, we suggest the use of transfer learning models based on sequential convolutional neural networks (CNNs), Visual Geometry Group 16 (VGG-16), and Residual Neural Network 50 (ResNet-50) for the early detection of osteoarthritis from knee X-ray images. In our analysis, we found that all the suggested models achieved a higher level of predictive accuracy, greater than 90%, in detecting osteoarthritis. However, the best-performing model was the pretrained VGG-16 model, which achieved a training accuracy of 99% and a testing accuracy of 92%.
Time-Accurate Flow Simulations Using a Finite-Volume Based Lattice Boltzmann Flow Solver with Dual Time Stepping Scheme

In this study, the Lattice Boltzmann Method (LBM) is implemented through a finite-volume approach to perform 2D, incompressible, and time-accurate fluid flow analyses on structured grids. Compared to the standard LBM (the so-called stream and collide scheme), the finite-volume approach followed in this study necessitates more computational effort, but the major limitations of the former on grid uniformity and Courant–Friedrichs–Lewy (CFL) number that is to be one are removed. Even though these improvements pave the way for the possibility of solving more practical fluid flow problems with the LBM, time-accurate simulations are still restricted due to the stability criteria dictated by high-aspect ratio grid cells that are usually required for adequate resolution of boundary layers and the stiffness due to the nature of the equation that are being solved. To overcome this limitation, a Dual Time Stepping (DTS) scheme, which iterates the solution in pseudo time using an Implicit-Explicit (IMEX) Runge–Kutta method while advancing the solution in physical time with an explicit scheme (backward difference formula), is developed and implemented. The accuracy of the resulting flow solver is evaluated using benchmark flow problems and overall second-order accuracy is demonstrated.