Minimum Value Research Articles

Longitudinal association of grip strength with cardiovascular and all-cause mortality in older urban Lithuanian population.

Ageing populations experience greater risks associated with health and survival. It increases the relevance of identifying variables associated with mortality. Grip strength (GS) has been identified as an important biomarker for all cause and cardiovascular mortality, however, its prognostic value has not been studied in Lithuania. The aim of the present study is to evaluate the relationship of GS to vital status in a representative sample of the Lithuanian 45-72-year-old urban population during the period of 12 years of follow-up and to explore associations of GS with all-cause mortality and mortality from cardiovascular diseases (CVD). Within the framework of the international study Health, Alcohol and Psychosocial Factors in Eastern Europe (HAPIEE) 7,115 men and women 45-72 years of age were examined in the baseline survey (2006 to 2008). Data from the Official Lithuanian Mortality Register were used to evaluate CVD and all-cause mortality from follow-up till 2020. Cox proportional hazards regression was used, and four models for all-cause and CVD mortality were assessed. The mean GS was significantly higher among survivors' men and women as compared to individuals deceased from CVD and other causes of death. In survivor men and women groups, minimal values of GS in all terciles were higher as compared to all three deceased groups. In both men and women groups, the lowest GS (1st tercile) was associated with a significantly higher risk of all-cause and CVD mortality as compared to the highest levels of GS (3rd tercile) in three Cox regression models. In both men and women were found to have a 1.34- and 1.35-fold higher risk of all-cause mortality, respectively, at lower GS, but no significant difference in the risk of CVD mortality. When GS was treated in all models as decrement per 1kg and decrement per 1 SD, in both men and women, the risk of all-cause mortality significantly increased with decreasing of GS. The mean GS was significantly higher among survivors' men and women as compared to deceased from CVD and other causes of death. Risk of all-cause mortality significantly increased with decreasing of GS.

Advanced confidence methods in deep learning

The typical aim of classification tasks is to maximize the accuracy of the predicted label for a given input. This accuracy increases with the confidence, which is the maximal value of the output units, and when the accuracy equals confidence, calibration is achieved. Herein, several methods are proposed to enhance the accuracy of inputs with similar confidence, extending significantly beyond calibration. Using the first gap between the maximal and second maximal output values, the accuracy of the inputs with similar confidence is enhanced. The extension of the confidence or confidence gap to their minimal value among a set of augmented inputs further enhances the accuracy of inputs with similar confidence. Enhanced accuracies are demonstrated on EfficientNet-B0 trained on ImageNet and CIFAR-100, and VGG-16 trained on CIFAR-100. The results suggest improved applications for high-accuracy classification tasks that require manual operation for a given fraction of low-accuracy inputs.

Hybrid inorganic-organic capsules encapsulated FeNi with enhanced corrosion resistance and microwave absorption

Responding to the critical need for stable microwave absorption (MA) devices in complex and extreme environments, dual-function electromagnetic wave absorbers have garnered significant attention. Addressing this, we developed a novel FeNi-based composite encapsulated within superhydrophobic SiO2@Perfluorooctyltriethoxysilane (F13)/Methyltrimethoxysilane (MTMS) hybrid capsules, approximately 28-nm-thick, utilizing a straightforward one-pot strategy. This innovative FeNi@SiO2@F13/MTMS composite surpassed pure FeNi, exhibiting an effective MA bandwidth of 8.16 GHz and a minimal reflection loss value of −24.03 dB at 2.00 mm. It also boasted the largest radar scattering cross-section reduction value recorded at 21.09 dB m2. The enhanced MA properties were primarily attributed to the SiO2@F13/MTMS, which significantly improved the substrate's impedance matching. In addition to facilitating multiple scattering and reflection, it also triggered dipole and interfacial polarization. Moreover, the SiO2@F13/MTMS layer was instrumental in reducing the corrosion current density of FeNi from 3.512 mA cm2 to 5.59 nA cm2, thereby demonstrating exceptional corrosion resistance. This remarkable improvement was largely due to the surface hydrophobicity and the barrier effect provided by the hybrid capsules. Our exploration into corrosion-resistant and efficient MA materials provides valuable insights for the rational design of bifunctional components tailored for specific applications.

Calorimetric Studies and Thermodynamic Modeling of Ag-Mg-Ti Liquid Alloys.

Due to the absence of thermodynamic data concerning the Ag-Mg-Ti system in the existing literature, this study aims to fill this gap by offering the outcomes of calorimetric investigations conducted on ternary liquid solutions of these alloys. The measurements were performed using the drop calorimetry method at temperatures of 1294 K and 1297 K for the liquid solutions with the following constant mole fraction ratio: xAg/xMg = 9/1, 7/3, 1/1, 3/7 [(Ag0.9Mg0.1)1-xTix, (Ag0.7Mg0.3)1-xTix, (Ag0.5Mg0.5)1-xTix, (Ag0.3Mg0.7)1-xTix)], and xAg/xTi = 19/1 [(Ag0.95Ti0.05)1-xMgx]. The results show that the mixing enthalpy change is characterized by negative deviations from the ideal solutions and the observed minimal value equals -13.444 kJ/mol for the Ag0.95Ti0.05 alloy and xMg = 0.4182. Next, based on the thermodynamic properties of binary systems described by the Redlich-Kister model and the determined experimental data from the calorimetric measurements, the ternary optimized parameters for the Ag-Mg-Ti liquid phase were calculated by the Muggianu model. Homemade software (TerGexHm 1.0) was used to optimize the calorimetric data using the least squares method. Next, the partial and molar thermodynamic functions were calculated and are presented in the tables and figures. Moreover, this work presents, for comparative purposes, the values of the enthalpy of mixing of liquid Ag-Mg-Ti alloys, which were calculated using Toop's model. It was found that the best agreement between the modeled and experimental data was observed for the cross-sections xAg/xTi = 19/1 [(Ag0.95Ti0.05)1-xMgx] and xAg/xMg = 9/1 [(Ag0.9Mg0.1)1-xTix]. The results of the experiments presented in this paper are the first step in the investigation and future evaluation of the thermodynamics of phases and the calculation of the phase diagram of the silver-magnesium-titanium system.

Biogeochemical modelling to assess the effect of bioclogging on multiple electron acceptor-mediated petroleum hydrocarbon bioremediation in vadose zone.

Bioremediation is an economically viable and sustainable clean-up strategy. Hydrodynamic, as well as transport characteristics of the porous medium, can evolve over the period as a result of biological clean-up activities. The present study proposes a 2-D numerical framework to simulate the effect of bioclogging on multiple electron acceptor-mediated petroleum hydrocarbon bioremediation in the vadose zone. For modelling, a spill of BTEX (benzene, toluene, ethylbenzene and xylene) is assumed near source zone. The developed model results are validated using three previously published datasets on flow, transport and biodegradation in the vadose zone. Simulations are performed for three types of soil, including clay, sand and loam. The analysis shows that sand has a maximum infiltration rate and clay has a minimum. Hydraulic conductivity and saturation profile peaks reach their minimal value at a shallower depth (around four times) when bioclogging is present compared to when it is absent. The migration depth and concentration of BTEX are observed to be restricted to a shallower depth in aquifers with the presence of microbial clogging. The outcome shows that electron acceptor consumption is more (around sevenfold for oxygen, fourfold for nitrate and threefold for sulphate) in the presence of bioclogging at the shallower zone. Zeroth order spatial moment and sensitivity analyses show that biological clogging, number of electron acceptors and inhibition constant substantially affect BTEX bioremediation in the vadose zone.

Merging Counter-Propagation and Back-Propagation Algorithms: Overcoming the Limitations of Counter-Propagation Neural Network Models.

Artificial neural networks (ANNs) are nowadays applied as the most efficient methods in the majority of machine learning approaches, including data-driven modeling for assessment of the toxicity of chemicals. We developed a combined neural network methodology that can be used in the scope of new approach methodologies (NAMs) assessing chemical or drug toxicity. Here, we present QSAR models for predicting the physical and biochemical properties of molecules of three different datasets: aqueous solubility, acute fish toxicity toward fat head minnow, and bio-concentration factors. A novel neural network modeling method is developed by combining two neural network algorithms, namely, the counter-propagation modeling strategy (CP-ANN) with the back-propagation-of-errors algorithm (BPE-ANN). The advantage is a short training time, robustness, and good interpretability through the initial CP-ANN part, while the extension with BPE-ANN improves the precision of predictions in the range between minimal and maximal property values of the training data, regardless of the number of neurons in both neural networks, either CP-ANN or BPE-ANN.

The Influence of a Commercial Few-Layer Graphene on the Photodegradation Resistance of a Waste Polyolefins Stream and Prime Polyolefin Blends

This work investigated the photostabilizing role of a commercially available few-layer graphene (FLG) in mixed polyolefins waste stream (MPWS), ensuring extended lifespan for outdoor applications. The investigation was conducted by analyzing carbonyl content increase, surface appearance, and the retention of mechanical properties of UV-exposed MPWS/FLG composites. Despite the likely predegraded condition of MPWS, approximately 60%, 70%, 80%, and 90% of the original ductility was retained in composites containing 1, 4, 7, and 10 wt.% FLG, respectively. Conversely, just 20% of the original ductility was retained in unfilled MPWS. Additionally, less crack density and lower carbonyl concentrations of the composites also highlighted the photoprotection effect of FLG. For prime polyolefin blends, only 0.5 wt.% or 1 wt.% FLG was sufficient to preserve the original surface finishing and protect the mechanical properties from photodegradation. Hence, it was observed that MPWS requires more FLG than prime polyolefin blends to get to comparable property retention. This could be attributed to the poor dispersion of FLG in MPWS and inevitable uncertainties such as the presence of impurities, pre-degradation, and polydispersity associated with MPWS. This study outlines a potential approach to revalorize MPWS that possess a minimal intrinsic value and would otherwise be destined for landfill disposal.

A COMPARATIVE STUDY OF CATBOOST AND ARTIFICIAL NEURAL NETWORKS IN ENHANCING TRIP GENERATION MODELLING FOR ILORIN CITY

Trip generation plays a crucial role in transportation planning, and the choice of an appropriate model is essential for predicting future travel patterns. This study focuses on comparing the suitability and performance of CatBoost and ANN for trip generation (production and attraction) modelling of Ilorin City. By incorporating Ilorin household and trip characteristics, population data, and maps, this study evaluates the performance of the models. The two models demonstrated high accuracy and performance. In terms of trip production, the CatBoost model displayed exceptional accuracy, attaining an R-squared value of 0.99999992016446, accompanied by an impressively low mean squared error (MSE) of 3.93870930136429e-05. In contrast, the neural network exhibited a slightly lower accuracy of 0.999873850524181, with an error value of 0.0581313408911228. Similarly, for trip attraction, the CatBoost model showcased remarkable accuracy and precision, achieving an accuracy score of 0.9999999999999994 and an extremely low error value of 2.26762031965784e-13. The neural network model demonstrated an accuracy of 0.99999999990335 and a negligible error value of 0.000000041994. These findings underscore the strong predictive capabilities of both models for trip production and attraction, with the CatBoost model notably excelling in achieving nearly flawless accuracy and minimal error values across both aspects in Ilorin. Further research can explore the application of other advanced machine-learning techniques and combine their strengths to enhance the accuracy and robustness of trip-generation models.

Experimental and theoretical investigation of the effect of alkali (Li, Na and K) doping on the properties of nickel oxide thin films: Comparative study

Pure and 6% alkali-doped NiO thin films (alkali A = Li, Na, K) were prepared by a sol-gel spin coating method and deposited on glass substrates. XRD analysis showed that the prepared films belonged to a cubic structure with (111) plane as preferential growth orientation for undoped and K-doped samples and (200) for Li and Na doping. An optical study based on (UV-Visible) showed that the band gap tends to decrease with alkali doping and achieves a minimal value with Na doping. The Urbach energy increases systematically with the decrease of the optical band gap. The resistivity measurements showed that alkali doping led to a significant decrease in the resistivity value. The lowest value was achieved for the 6% Na-doped sample. Structural, optical and elastic properties of pure and 6% A-doped NiO were performed using the first principal method based on density functional theory. The optimization of the geometry of the studied samples revealed that the lattice parameters changed after doping. The band structure and density of states calculations showed that undoped and alkali doped samples exhibited an indirect band gap and the doped samples had comparatively narrower band gaps. The elastic constants Cij, Bulk modulus B, Shear modulus G, Young modulus, and Poison ratio of doped and alkali-doped NiO were further investigated.

Evaluation of three protocols for direct susceptibility testing for Gram-negative rods from flagged positive blood culture bottles.

Bloodstream infections are associated with high mortality that can be reduced by targeted antibiotic therapy in the early stages of infection. Direct antibiotic susceptibility testing (AST) from flagged positive blood cultures may facilitate the administration of early effective antimicrobials much before the routine AST. Clinical and Laboratory Standards Institute-recommended direct AST can be performed with a limited number of antibiotic disks only. On the other hand, using an automated system for direct AST will not only allow effective laboratory workflow with reduced turnaround time but also provide the minimum inhibitory concentration values of tested antibiotics. However, using expensive automated systems for direct AST may not be feasible for resource-limited laboratories. Therefore, in this study, we aimed to evaluate the CLSI-recommended method and two other direct AST protocols (one with an automated system and the other with disk diffusion) for Gram-negative rods from flagged positive blood cultures.

MAIAC AOD profiling over the Persian Gulf: A seasonal-independent machine learning approach

Aerosol Optical Depth (AOD) across various altitudes is crucial for gaining a comprehensive understanding of aerosol dynamics. However, current methodologies utilizing passive remote sensing and active sensors have limitations in providing precise vertical coverage. In our methodology, we introduce a Seasonal-Independent model employing Machine Learning (ML) algorithms to retrieve AOD values at both 1.5 km and 3 km layers. The propose approach is assessed the performance of various ML algorithms, including XGBoost, Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Regression (SVR). Remarkably, our study successfully overcame seasonal constraints, yielding impressive R2 values of 0.94, 0.93, 0.93, and 0.87 for the 1.5 km layer, and 0.83, 0.79, 0.82, and 0.78 for the 3 km layer across the mentioned models for 2017, 2018 and 2019 data. Evaluating the proposed Seasonal-Independent XGBoost model against CALIOP AOD values for the 2020 data, we observed substantial agreement with R2 values of 0.93 and 0.81, and minimal RMSE values of 0.002 and 0.004 for the AODs at 1.5 km and 3 km, respectively. Furthermore, a comparative analysis of trends between estimated and CALIOP AODs revealed a strong resemblance in both altitude layers.

Artificial intelligence-based model for predicting the minimum inhibitory concentration of antibacterial peptides against ESKAPEE pathogens.

In response to environmental threats, pathogens make several changes in their genome, leading to antimicrobial resistance (AMR). Due to AMR, the pathogens do not respond to antibiotics. Amongst drug-resistant pathogens, the ESKAPEE group of bacteria poses a major threat to humans, and therefore World Health Organization has given them the highest priority status. Antibacterial peptides (ABPs) are a family of peptides found in nature that play a crucial role in the innate immune systems of organisms. These ABPs offer several advantages over widely used antibiotics. As a result, they have recently received a lot of attention as potential replacements for currently available antibiotics. But it is expensive and time-consuming to identify ABPs from natural sources. Thus, wet lab researchers employ various tools to screen promising ABPs rapidly. However, the main limitation of the existing tools is that they do not provide the minimum inhibitory concentration values against the ESKAPEE pathogens for the identified ABP. To address this, in the current work, we developed ESKAPEE-MICpred, a two-input model that utilizes transfer learning and ensemble learning techniques. The concept of ensemble learning was realized by combining the decisions provided by deep learning algorithms, whereas the concept of transfer learning was realized by utilizing pretrained amino acid embeddings. The proposed model has been deployed as a web server at https://eskapee-micpred.anvil.app/ to aid the scientific community.

Xenon Antiaggregant Effects.

In in vitro model of short-term therapeutic inhalation of Xe/O2 mixture, xenon in millimolar concentrations led to a pronounced decrease in induced platelet aggregation in the platelet-enriched blood plasma. The maximum and statistically significant decrease occurred in response to induction by collagen (by ≈30%, p≤0.01) and ADP (by ≈25%, p≤0.01). A slightly weaker but statistically significant reduction in aggregation appeared in response to ristocetin (by ≈12%, p≤0.01) and epinephrine (by ≈9%, p≤0.01). It should be noted that the spontaneous aggregation exceeded the reference values in the control group. Nevertheless, even at minimal absolute values, spontaneous platelet aggregation decreased by 2 times in response to xenon (p≤0.01). The reasons for the decrease of spontaneous and induced aggregation are xenon accumulation in the lipid bilayer of the membrane with subsequent nonspecific (mechanical) disassociation of membrane platelet structures and specific block of its distinct from neuronal NMDA receptor.

Characterization and therapeutic potential of chitosan‐zinc oxide nanostructured particles synthesized using crab shell derived biopolymer

AbstractThe development of novel drug candidates for treating microbial infections and cancer turns into a challenging task. The biopolymer‐based nanomaterials have gained popularity for their effective roles in the pharmaceutical sector. The present investigation aims to valorize crab shell wastes to extract chitosan. The obtained (chitosan) biomass was utilized to synthesize chitosan‐zinc oxide nanoparticles (Ch‐ZnO‐NPs). The nanomaterial characterization was achieved by a variety of physicochemical techniques including UV, Fourier transform infrared spectroscopy (FT‐IR), X‐ray diffraction (XRD), elemental analysis, and High‐resolution transmission electron microscopic (HR‐TEM) examination. The synthesized nanomaterials have a particle size range of 35–80 nm and are more stable with a zeta potential value of (−40.78) mv. Additionally, the designed nanomaterials were utilized to evaluate the biological efficiency. The minimal inhibitory concentration value of Ch‐ZnO‐NPs was observed as 10 μg/mL. The designed nanomaterials significantly reduced the growth of Vibrio alginolyticus and Bacillus cereus and inhibited biofilm formation. The synthesized Ch‐ZnO‐NPs unveiled cytotoxic effects on A549 cells with an IC50 (half‐maximal inhibitory concentration) of 20 μg/mL. Furthermore, substantial increase in cell death was noticed in A549 cells treated with Ch‐ZnO‐NPs. Consequently, this investigation concludes that Ch‐ZnO‐NPs may act as a biomedicine.

Minimal Important Clinical Difference Values Are Not Uniformly Valid in the Active Duty Military Population Recovering from Shoulder Surgery

There are multiple methods for calculating the minimal clinically important difference (MCID) threshold, and previous reports highlight heterogeneity and limitations of anchor-based and distribution-based analyses. The Warfighter Readiness Survey assesses the perception of a military population's fitness to deploy and may be used as a functional index in anchor-based MCID calculations. The purpose of the current study in a physically demanding population undergoing shoulder surgery was to compare the yields of two different anchor-based methods of calculating MCID for a battery of PROMs, a standard receiver operator curve (ROC) -based MCIDs and baseline-adjusted ROC MCIDs. All service members enrolled prospectively in a multicenter database with prior shoulder surgery that completed pre- and postoperative PROMs at a minimum of 12 months were included. The PROMs battery included Single Assessment Numeric Evaluation (SANE), American Shoulder and Elbow Surgeons score (ASES), Patient Reported Outcome Management Information System (PROMIS) Physical Function (PF), PROMIS Pain Interference (PI), and the Warfighter Readiness survey. Standard anchor-based and baseline-adjusted ROC MCIDs were employed to determine if the calculated MCIDs were both statistically and theoretically valid (95% confidence interval either completely negative or positive). There were 117 patients (136 operations) identified, comprised of 83% males with a mean age of 35.7 ± 10.4 years and 47% arthroscopic labral repair/capsulorrhaphy. Using the standard, anchor-based ROC MCID calculation, the area under the curve (AUC) for SANE, ASES, PROMIS PF, and PROMIS PI were greater than 0.5 (statistically valid). For ASES, PROMIS PF, and PROMIS PI, the calculated MCID 95% CI all crossed 0 (theoretically invalid). Using the baseline-adjusted ROC MCID calculation, the MCID estimates for SANE, ASES, and PROMIS PI were both statistically and theoretically valid if the baseline score was less than 70.5, 69, and 65.7. When MCIDs were calculated and anchored to the results of standard, anchor-based MCID, a standard ROC analysis did not yield statistically or theoretically valid results across a battery of PROMs commonly used to assess outcomes after shoulder surgery in the active duty military population. Conversely, a baseline-adjusted ROC method was more effective at discerning changes across a battery of PROMs among the same cohort.

Transient response mitigation using type-2 fuzzy controller optimized by grey wolf optimizer in converter high voltage direct current

Long high voltage direct current (HVDC) transmission link is commonly used to transmit electrical energy via land or under-sea cable. The long HVDC avoids reactive power losses (RPL) and power stability problems (PSP). On the contrary, the RPL and PSP phenomena occur in long high voltage alternative current-link (HVAC) caused by the high reactive component in the HVAC-link. However, the HVDC produces a high and slow transient current response (TCR) on the high value of the up-ramp rate. Interval type-2 fuzzy (IT2F) control on converter-side HVDC is proposed to mitigate this TCR problem. The IT2F is optimized by grey wolf optimizer (GWO) to adjust input-output IT2F parameters optimally. The performance of IT2F-GWO is assessed by the minimum value of integral time squared error (ITSE), peak overshoot, and settling time of the TCR. The IT2FC-GWO performance is validated by the performance of IT2F control that is optimized by genetic algorithm (IT2F-GA) and proportional integral (PI) controller. Simulation results show that the IT2F-GWO performs better with small ITSE, low peak overshoot, and shorter settling times than competing controllers.

Подземные источники балки Пещерная озера Баскунчак: гидрохимические особенности и эмиссия парниковых газов

For the first time, full-scale measurements of methane and carbon dioxide emissions by the chamber method were carried out in summer for groundwater sources discharged in the valley of the Peshernaya Gulch creek, located in the north-west of Lake Baskunchak. pH values, concentrations of CH4, basic ions, gross Fe and Mn were determined in the water of the sources; concentrations of CH4, H2S, organic matter, Eh and pH values, density and humidity were determined in various layers of bottom sediments (up to 27 cm). A detailed description of the identified groundwater sources was carried out, their primary morphometric and morphological characteristics were measured, as well as the flow rate. It was found that more than 10 ascending karst sources of groundwater are discharged in the Peshchernaya beam, 5 of which were active during the research period and mainly confined to the left screw of the beam. The active sources of the left screw of the beam are characterized by a small flow rate and are sodium chloride brines with a mineralization of 93.9–107.1 g/l. The bottom sediments accumulating in the sinkholes of underground sources are characterized by a neutral acid-base environment and restorative conditions. The concentrations of CH4 and ∑H2S in the sediments of the sources varied, respectively, in the range of 0.08–0.77 µg/g vl.o. and 0.023–2.63 mg/g vl.o. with minimal values for both gases in the lower less reduced layer with a low content of Sorg. The concentration of methane in the water of active sources varied from 26.3 up to 38.4 µl/l and was on average 2 times higher than in the waters of a stream fed by them and an inactive source. The specific methane flux from the water surface of active sources varied in the range of 0.6–1.5 mg/(m2∙h), which is 2 orders of magnitude lower than the specific CO2 flux of 46.4–106.1 mg/(m2∙h). The isotopic composition of carbon (δ13C) CH4 and CO2 measured in gas bubbles rising from the bottom of one of the ascending sources indicates the modern biochemical genesis of these greenhouse gases.

In vitro activity of selected antimicrobials against methicillin-resistant Staphylococcus pseudintermedius of canine origin in Poland.

Methicillin-resistant Staphylococcus pseudintermedius (MRSP) is an important veterinary pathogen. In general, only a few antimicrobials show in vitro activity against MRSP isolates. The objective of this study was to determine the in vitro activity of selected antimicrobials, including last-choice drugs, against clinical MRSP isolates of canine origin. The activity of 10 selected agents was evaluated against 41 clinical MRSP isolates. The disk diffusion method and minimal inhibitory concentration values were used for antimicrobial susceptibility testing (AST). The guidelines for staphylococci of canine or human origin were employed for the interpretation of the results. Among the examined MRSP isolates, resistance to enrofloxacin and clindamycin was the most prevalent (n=40; 97.6%). Resistance to doxycycline and gentamicin was observed in 83.0% (n=34) and 68.3% (n=28) of the isolates, respectively. Single isolates were resistant to chloramphenicol (n=5; 12.2%) and rifampicin (n=3; 7.3%), whereas all showed susceptibility to amikacin, vancomycin, mupirocin and linezolid. Predominantly, the results of AST obtained by both methods were consistent. Some discrepancies were observed for gentamicin; however, clinical breakpoints for staphylococci of human origin were used. Amikacin and chloramphenicol constitute potential treatment options in infections caused by MRSP and may be included in extended susceptibility testing in our geographical region. The determination of clinical breakpoints for some antimicrobials not incorporated in the recommendations should be a high priority in the veterinary diagnostics.

Selective Genotyping and Phenotyping for Optimization of Genomic Prediction Models for Populations with Different Diversity.

To overcome the different challenges to food security caused by a growing population and climate change, soybean (Glycine max (L.) Merr.) breeders are creating novel cultivars that have the potential to improve productivity while maintaining environmental sustainability. Genomic selection (GS) is an advanced approach that may accelerate the rate of genetic gain in breeding using genome-wide molecular markers. The accuracy of genomic selection can be affected by trait architecture and heritability, marker density, linkage disequilibrium, statistical models, and training set. The selection of a minimal and optimal marker set with high prediction accuracy can lower genotyping costs, computational time, and multicollinearity. Selective phenotyping could reduce the number of genotypes tested in the field while preserving the genetic diversity of the initial population. This study aimed to evaluate different methods of selective genotyping and phenotyping on the accuracy of genomic prediction for soybean yield. The evaluation was performed on three populations: recombinant inbred lines, multifamily diverse lines, and germplasm collection. Strategies adopted for marker selection were as follows: SNP (single nucleotide polymorphism) pruning, estimation of marker effects, randomly selected markers, and genome-wide association study. Reduction of the number of genotypes was performed by selecting a core set from the initial population based on marker data, yet maintaining the original population's genetic diversity. Prediction ability using all markers and genotypes was different among examined populations. The subsets obtained by the model-based strategy can be considered the most suitable for marker selection for all populations. The selective phenotyping based on makers in all cases had higher values of prediction ability compared to minimal values of prediction ability of multiple cycles of random selection, with the highest values of prediction obtained using AN approach and 75% population size. The obtained results indicate that selective genotyping and phenotyping hold great potential and can be integrated as tools for improving or retaining selection accuracy by reducing genotyping or phenotyping costs for genomic selection.

Infinite horizon LQG Graphon Mean Field Games: Explicit Nash values and local minima

In this study, we generalize the analysis of infinite horizon linear quadratic Gaussian (LQG) Mean Field Games within the framework of Graphon Mean Field Games (GMFG) introduced in Caines and Huang (2018) over finite horizons. Graphon Mean Field Games (GMFGs) are non-uniform generalizations of Mean Field Games where the non-uniformity of agents is characterized by the nodes on which they are located in a network. Under mild assumptions on the structure of the network and parameters of the game, we obtain for almost every node, an explicit analytical expression for the Nash values (i.e. the cost at equilibrium). With additional assumptions, we provide sufficient conditions for nodes to have locally minimal Nash values. We illustrate the results for the uniform attachment network.