Insight into the spoilage heterogeneity of meat-borne bacteria isolates with high-producing collagenase

Chilled chicken is inevitably contaminated by microorganisms during slaughtering and processing,resulting in spoilage.Cutting parts of chilled chicken,especially wings,feet,and other skin-on products,are abundant in collagen,which may be the primary target for degradation by spoilage microorganisms.In this work,a total of 17 isolates of spoilage bacteria that could secrete both collagenase and lipase were determined by raw-chicken juice agar(RJA)method,and the results showed that 7 strains of Serratia,Aeromonas,and Pseudomonas could significantly decompose the collagen ingredients.The gelatin zymography showed that Serratia liquefaciens(F5)and(G7)had apparent degradation bands around 50 kDa,and Aeromonas veronii(G8)and Aeromonas salmonicida(H8)had a band around.65 and 95 kDa,respectively.The lipase and collagenase activities were detected isolate-by-isolate,with F5 showing the highest collagenase activity.For spoilage ability on meat in situ,F5 performed strongest in spoilage ability,indicated by the total viable counts,total volatile basic nitrogen content,sensory scores,lipase,and collagenase activity.This study provides a theoretical basis for spoilage heterogeneity of strains with high-producing collagenase in meat.

Reservoir heterogeneity analysis using multi-directional textural attributes from deep learning-based enhanced acoustic impedance inversion:A study from Poseidon,NW shelf Australia

Reservoir heterogeneities play a crucial role in governing reservoir performance and management.Traditionally,detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in the seismic data,which is a time-intensive task.Many researchers have utilized a robust Grey-level co-occurrence matrix(GLCM)-based texture attributes to map reservoir heterogeneity.However,these attributes take seismic data as input and might not be sensitive to lateral lithology variation.To incorporate the lithology information,we have developed an innovative impedance-based texture approach using GLCM workflow by integrating 3D acoustic impedance volume(a rock propertybased attribute)obtained from a deep convolution network-based impedance inversion.Our proposed workflow is anticipated to be more sensitive toward mapping lateral changes than the conventional amplitude-based texture approach,wherein seismic data is used as input.To evaluate the improvement,we applied the proposed workflow to the full-stack 3D seismic data from the Poseidon field,NW-shelf,Australia.This study demonstrates that a better demarcation of reservoir gas sands with improved lateral continuity is achievable with the presented approach compared to the conventional approach.In addition,we assess the implication of multi-stage faulting on facies distribution for effective reservoir characterization.This study also suggests a well-bounded potential reservoir facies distribution along the parallel fault lines.Thus,the proposed approach provides an efficient strategy by integrating the impedance information with texture attributes to improve the inference on reservoir heterogeneity,which can serve as a promising tool for identifying potential reservoir zones for both production benefits and fluid storage.

Individual dynamics and local heterogeneity provide a microscopic view of the epidemic spreading

The COVID-19 pandemic has caused severe global disasters,highlighting the importance of understanding the details and trends of epidemic transmission in order to introduce efficient intervention measures.While the widely used deterministic compartmental models have qualitatively presented continuous “analytical” insight and captured some transmission features,their treatment usually lacks spatiotemporal variation.Here,we propose a stochastic individual dynamical(SID)model to mimic the random and heterogeneous nature of epidemic propagation.The SID model provides a unifying framework for representing the spatiotemporal variations of epidemic development by tracking the movements of each individual.Using this model,we reproduce the infection curves for COVID-19 cases in different areas globally and find the local dynamics and heterogeneity at the individual level that affect the disease outbreak.The macroscopic trend of virus spreading is clearly illustrated from the microscopic perspective,enabling a quantitative assessment of different interventions.Seemingly,this model is also applicable to studying stochastic processes at the “meter scale”,e.g.,human society’s collective dynamics.

Editorial:Transcriptome analysis in tumor microenvironment and tumor heterogeneity

Cancer,a disease as intricate as it is devastating,continues to challenge the medical and scientific community[1].Its complex nature is epitomized by the tumor microenvironment and tumor heterogeneity.As we delve deeper into the realms of cancer research,the advent of transcriptome analysis has emerged as a powerful torchbearer,illuminating our understanding of these enigmatic facets of cancer biology[2].

Assessment and Visualization of Ki67 Heterogeneity in Breast Cancers through Digital Image Analysis

The Ki67 index (KI) is a standard clinical marker for tumor proliferation;however, its application is hindered by intratumoral heterogeneity. In this study, we used digital image analysis to comprehensively analyze Ki67 heterogeneity and distribution patterns in breast carcinoma. Using Smart Pathology software, we digitized and analyzed 42 excised breast carcinoma Ki67 slides. Boxplots, histograms, and heat maps were generated to illustrate the KI distribution. We found that 30% of cases (13/42) exhibited discrepancies between global and hotspot KI when using a 14% KI threshold for classification. Patients with higher global or hotspot KI values displayed greater heterogenicity. Ki67 distribution patterns were categorized as randomly distributed (52%, 22/42), peripheral (43%, 18/42), and centered (5%, 2/42). Our sampling simulator indicated analyzing more than 10 high-power fields was typically required to accurately estimate global KI, with sampling size being correlated with heterogeneity. In conclusion, using digital image analysis in whole-slide images allows for comprehensive Ki67 profile assessment, shedding light on heterogeneity and distribution patterns. This spatial information can facilitate KI surveys of breast cancer and other malignancies.

Effects of individual heterogeneity on social contagions

Despite having significant effects on social contagions,individual heterogeneity has frequently been overlooked in earlier studies.To better understand the complexity of social contagions,a non-Markovian model incorporating heterogeneous social influence and adoption thresholds is introduced.For theoretical analysis,a generalized edge-based compartmental theory which considers the heterogeneities of social influence and adoption thresholds is developed.Focusing on the final adoption size,the critical propagation probability,and the phase transition type,social contagions for adoption thresholds that follow normal distributions with various standard deviations,follow various distributions,and correlate with degrees are investigated.When thresholds follow normal distributions,a larger standard deviation results in a larger final adoption size when the information propagation probability is relatively low.However,when the information propagation probability is relatively high,a larger standard deviation results in a smaller final adoption size.When thresholds follow various distributions,crossover phenomena in phase transition are observed when investigating the relationship of the final adoption size versus the average adoption threshold for some threshold distributions.When thresholds are correlated with degrees,similar crossover phenomena occur when investigating the relationship of the final adoption size versus the degree correlation index.Additionally,we find that increasing the heterogeneity of social influence suppresses the effects of adoption threshold heterogeneity on social contagions in three cases.Our theory predictions agree well with the simulation results.

A geochemical perspective on the genesis of Cenozoic basic volcanism in northeastern Turkey:an overview of metasomatism and heterogeneity of the sub-continental lithospheric mantle in a post-collisional setting

The post-collisional Cenozoic basic volcanic rocks in NE Turkey show temporal variations in whole-rock lithophile element and highly siderophile element(HSE)systematics that are mainly associated with the nature of sub-continental lithospheric mantle(SCLM)sources and parental melt generation.So far,the traditional whole-rock lithophile geochemical data of these basic volcanic rocks have provided important constraints on the nature of SCLM sources.Integrated lithophile element and HSE geochemical data of these basic volcanic rocks also reveal the heterogeneity of the SCLM source,which is principally related to variable metasomatism resulting from previous subduction(s)and post-collisional mantle-crust interactions in an extensional setting.Lithophile element geochemical features suggest that the parental magmas have derived from metasomatized spinel-to garnet-bearing SCLM sources for Eocene and Miocene basic volcanic rocks with subduction signatures whereas originated from spinel-to garnet-bearing SCLM sources for Mio-Pliocene and Plio-Quaternary basaltic volcanic rocks without the subduction signature.Lithophile element and HSE geo-chemistry also reveal that Eocene and Miocene basic vol-canic rocks were affected by more pronounced crustal contamination than the basaltic volcanic rocks of Mio-Pliocene and Quaternary.Furthermore,the integrated lithophile element and HSE compositions of these basic volcanic rocks,together with the regional asymmetric lithospheric delamination model,reveal that the compositional variation(especially due to metasomatism)was significant temporally in the heterogeneity of the SCLM sources from which parental magmas formed during the Cenozoic era.

Pumping-induced Well Hydraulics and Groundwater Budget in a Leaky Aquifer System with Vertical Heterogeneity in Aquitard Hydraulic Properties

In groundwater hydrology,aquitard heterogeneity is often less considered compared to aquifers,despite its significant impact on groundwater hydraulics and groundwater resources evaluation.A semi-analytical solution is derived for pumping-induced well hydraulics and groundwater budget with consideration of vertical heterogeneity in aquitard hydraulic conductivity(K)and specific storage(S_(s)).The proposed new solution is innovative in its partitioning of the aquitard into multiple homogeneous sub-layers to enable consideration of various forms of vertically heterogeneous K or S_(s).Two scenarios of analytical investigations are explored:one is the presence of aquitard interlayers with distinct K or S_(s) values,a common field-scale occurrence;another is an exponentially depth-decaying aquitard S_(s),a regional-scale phenomenon supported by statistical analysis.Analytical investigations reveal that a low-K interlayer can significantly increase aquifer drawdown and enhance aquifer/aquitard depletion;a high-S_(s) interlayer can noticeably reduce aquifer drawdown and increase aquitard depletion.Locations of low-K or high-S_(s) interlayers also significantly impact well hydraulics and groundwater budget.In the context of an exponentially depth-decaying aquitard S_(s),a larger decay exponent can enhance aquifer drawdown.When using current models with a vertically homogeneous aquitard,half the sum of the geometric and harmonic means of exponentially depth-decaying aquitard S_(s) should be used to calculate aquitard depletion and unconfined aquifer leakage.

Effect of Modulus Heterogeneity on the Equilibrium Shape and Stress Field ofαPrecipitate in Ti-6Al-4V

For media with inclusions(e.g.,precipitates,voids,reinforcements,and others),the difference in lattice parameter and the elastic modulus between the matrix and inclusions cause stress concentration at the interfaces.These stress fields depend on the inclusions’size,shape,and distribution and will respond instantly to the evolving microstructure.This study develops a phase-field model concerningmodulus heterogeneity.The effect of modulus heterogeneity on the growth process and equilibrium state of theαplate in Ti-6Al-4V during precipitation is evaluated.Theαprecipitate exhibits strong anisotropy in shape upon cooling due to the interplay of the elastic strain and interfacial energy.The calculated orientation of the habit plane using the homogeneous modulus ofαphase shows the smallest deviation fromthat of the habit plane observed in the experiment,compared to the case where the homogeneous modulus ofβphase is adopted.In addition,the equilibrium volume ofαphase within the systemusing homogeneousβmodulus exhibits the largest dependency on the applied stresses.The stress fields across theα/βinterface are further calculated under the assumption of modulus heterogeneity and compared to those using homogeneous modulus of eitherαorβphase.This study provides an essential theoretical basis for developing mechanics models concerning systems with heterogeneous structures.

The impact of heterogeneity and pore network characteristics on single and multi-phase fluid propagation in complex porous media:An X-ray computed tomography study

This study investigates the impact of pore network characteristics on fluid flow through complex and heterogeneous porous media,providing insights into the factors affecting fluid propagation in such systems.Specifically,high-resolution or micro X-ray computed tomography(CT)imaging techniques were utilized to examine outcrop stromatolite samples of the Lagoa Salgada,considered flow analogous to the Brazilian Pre-salt carbonate reservoirs.The petrophysical results comprised two distinct stromatolite depositional facies,the columnar and the fine-grained facies.By generating pore network model(PNM),the study quantified the relationship between key features of the porous system,including pore and throat radius,throat length,coordination number,shape factor,and pore volume.The study found that the less dense pore network of the columnar sample is typically characterized by larger pores and wider and longer throats but with a weaker connection of throats to pores.Both facies exhibited less variability in the radius of the pores and throats in comparison to throat length.Additionally,a series of core flooding experiments coupled with medical CT scanning was designed and conducted in the plug samples to assess flow propagation and saturation fields.The study revealed that the heterogeneity and presence of disconnected or dead-end pores significantly impacted the flow patterns and saturation.Two-phase flow patterns and oil saturation distribution reveal a preferential and heterogeneous displacement that mainly swept displaced fluid in some regions of plugs and bypassed it in others.The relation between saturation profiles,porosity profiles,and the number of fluid flow patterns for the samples was evident.Only for the columnar plug sample was the enhancement in recovery factor after shifting to lower salinity water injection(SB)observed.

The heterogeneity of tumor-associated macrophages and strategies to target it

Tumor-associated macrophages(TAMs)are emerging as targets for tumor therapy because of their primary role in promoting tumor progression.Several studies have been conducted to target TAMs by reducing their infiltration,depleting their numbers,and reversing their phenotypes to suppress tumor progression,leading to the development of drugs in preclinical and clinical trials.However,the heterogeneous characteristics of TAMs,including their ontogenetic and functional heterogeneity,limit their targeting.Therefore,in-depth exploration of the heterogeneity of TAMs,combined with immune checkpoint therapy or other therapeutic modalities could improve the efficiency of tumor treatment.This review focuses on the heterogeneous ontogeny and function of TAMs,as well as the current development of tumor therapies targeting TAMs and combination strategies.

Quantifying the impact of earthquakes and geological factors on spatial heterogeneity of debris-flow prone areas:A case study in the Hengduan Mountains

Understanding the spatial heterogeneity of debris-flow-prone areas holds significant implications for regional risk management, particularly in seismically active regions with geological faults. Despite the significance of this knowledge, a comprehensive quantification of the influence of regional topographical and geological factors on the spatial heterogeneity of debris-flow-prone areas has been lacking. This study selected the Hengduan Mountains, an earthquake-prone region characterized by diverse surface conditions and complex landforms, as a representative study area. An improved units zoning and objective factors identification methodology was employed in earthquake and fault analysis to assess the impact of seismic activity and geological factors on spatial heterogeneity of debrisflow prone areas. Results showed that the application of GIS technology with hydrodynamic intensity and geographical units analysis can effectively analyze debris-flow prone areas. Meanwhile, earthquake and fault zones obviously increase the density of debrisflow prone catchments and make them unevenly distributed. The number of debris-flow prone areas shows a nonlinear variation with the gradual increase of geomorphic factor value. Specifically, the area with 1000 m-2500 m elevation difference, 25°-30° average slope, and 0.13-0.15 land use index is the most favorable conditions for debris-flow occurrence;The average annual rainfall from 600 to 1150 mm and landslides gradient from 16° to 35° are the main causal factors to trigger debris flow. Our study sheds light on the quantification of spatial heterogeneity in debris flow-prone areas in earthquake-prone regions, which can offer crucial support for post-debris flow risk management strategies.

Influence of Transportation Accessibility on Urban-rural Income Disparity and Its Spatial Heterogeneity

Transportation accessibility has been treated as an important means of reducing the urban-rural income disparity.However,only a few studies have examined the effects of different types of transportation accessibility on urban-rural income disparity and their spatial heterogeneity.Based on data from 285 prefecture-level(and above)Chinese cities in 2000,2005,2010,2015,and 2020,this study uses spatial econometric models to examine how highway accessibility and railway accessibility influence the urban-rural income disparity and to identify their spatial heterogeneity.The result reveals that highway accessibility and railway accessibility have‘coreperiphery’ring-like circle structures.The urban-rural income disparity exhibits strong spatial clustering effects.Both highway accessibility and railway accessibility are negatively associated with urban-rural income disparity,and the former having a greater effect size.Moreover,there is a substitution effect between highway accessibility and railway accessibility in the whole sample.Furthermore,these associations differ in geographic regions.In the central region,highway accessibility is more important in reducing the urban-rural income disparity,but its effect is weakened with the increase of railway accessibility.In the western region,railway accessibility has a larger effect on narrowing the urban-rural income disparity,and this effect is strengthened by the increase of highway accessibility.We conclude that improving transportation accessibility is conducive to reducing the urban-rural income disparity but its effect is spatial heterogenetic.Highways and railways should be developed in a coordinated manner to promote an integrated transport network system.

Island biogeography theory and the habitat heterogeneity jointly explain global patterns of Rhododendron diversity

Mountain biodiversity is of great importance to biogeography and ecology.However,it is unclear what ecological and evolutionary processes best explain the generation and maintenance of its high levels of species diversity.In this study,we determined which of six common hypotheses(e.g.,climate hypotheses,habitat heterogeneity hypothesis and island biogeography theory)best explain global patterns of species diversity in Rhododendron.We found that Rhododendron diversity patterns were most strongly explained by proxies of island biogeography theory(i.e.,mountain area)and habitat heterogeneity(i.e.,elevation range).When we examined other relationships important to island biogeography theory,we found that the planimetric area and the volume of mountains were positively correlated with the Rhododendron diversity,whereas the‘mountains-to-mainland’distance was negatively correlated with Rhododendron diversity and shared species.Our findings demonstrate that Rhododendron diversity can be explained by island biogeography theory and habitat heterogeneity,and mountains can be regarded as islands which supported island biogeography theory.

Age-specific heterogeneity of genetic susceptibility to cardiovascular disease might have opposite outcomes depending on the presence of prediabetes

Examining age-specific heterogeneity of susceptibility to cardiovascular disease is also essential in individuals without prediabetes to determine its relative size and direction compared to those with prediabetes.Of particular interest,age-specific heterogeneity in genetic susceptibility may exhibit opposite directions depending on the presence or absence of prediabetes.

Inversion of walkaway VSP data in the presence of lateral velocity heterogeneity

Multi-azimuth walkaway vertical seismic profiling is an established technique for the estimation of in situ slowness surfaces and inferring anisotropy parameters.Normally,this technique requires the assumption of lateral homogeneity,which makes the horizontal slowness components at depths of downhole receivers equal to those measured at the surface.Any violations of this assumption,such as lateral heterogeneity or nonzero dip of intermediate interfaces,lead to distortions in reconstructed slowness surfaces and,consequently,to errors in estimated anisotropic parameters.In this work,we relax the assumption of lateral homogeneity and discuss how to correct vertical seismic profile data for weak lateral heterogeneity.We describe a procedure of downward continuation of recorded traveltimes that accounts for the presence of both vertical inhomogeneity and weak lateral heterogeneity,which produces correct slowness surfaces at depths of downhole receivers,noticing that sufficiently dense receiver coverage along a borehole is required to separate influences of vertical and lateral heterogeneity on measured traveltimes and obtain accurate estimates of the slowness surfaces.Once the slowness surfaces are found and a desired type of anisotropic model to be inverted is selected,the corresponding anisotropic parameters,providing the best fit to the estimated slownesses,can be obtained.We invert the slowness surfaces of P-waves for parameters of the simplest anisotropic model describing dipping fractures(transversely isotropic medium with a tilted symmetry axis).Five parameters of this model,namely,the P-wave velocity V0 in the direction of the symmetry axis,Thomsen's anisotropic coefficients e and d,the tilt n,and the azimuth b of the symmetry axis,can be estimated in a stable manner when maximum source offset is greater than half of receiver depth.

Prediction of Porous Media Fluid Flow with Spatial Heterogeneity Using Criss-Cross Physics-Informed Convolutional Neural Networks

Recent advances in deep neural networks have shed new light on physics,engineering,and scientific computing.Reconciling the data-centered viewpoint with physical simulation is one of the research hotspots.The physicsinformedneural network(PINN)is currently the most general framework,which is more popular due to theconvenience of constructing NNs and excellent generalization ability.The automatic differentiation(AD)-basedPINN model is suitable for the homogeneous scientific problem;however,it is unclear how AD can enforce fluxcontinuity across boundaries between cells of different properties where spatial heterogeneity is represented bygrid cells with different physical properties.In this work,we propose a criss-cross physics-informed convolutionalneural network(CC-PINN)learning architecture,aiming to learn the solution of parametric PDEs with spatialheterogeneity of physical properties.To achieve the seamless enforcement of flux continuity and integration ofphysicalmeaning into CNN,a predefined 2D convolutional layer is proposed to accurately express transmissibilitybetween adjacent cells.The efficacy of the proposedmethodwas evaluated through predictions of several petroleumreservoir problems with spatial heterogeneity and compared against state-of-the-art(PINN)through numericalanalysis as a benchmark,which demonstrated the superiority of the proposed method over the PINN.

Heterogeneity of mature oligodendrocytes in the central nervous system

Mature oligodendrocytes form myelin sheaths that are crucial for the insulation of axons and efficient signal transmission in the central nervous system.Recent evidence has challenged the classical view of the functionally static mature oligodendrocyte and revealed a gamut of dynamic functions such as the ability to modulate neuronal circuitry and provide metabolic support to axons.Despite the recognition of potential heterogeneity in mature oligodendrocyte function,a comprehensive summary of mature oligodendrocyte diversity is lacking.We delve into early 20th-century studies by Robertson and Río-Hortega that laid the foundation for the modern identification of regional and morphological heterogeneity in mature oligodendrocytes.Indeed,recent morphologic and functional studies call into question the long-assumed homogeneity of mature oligodendrocyte function through the identification of distinct subtypes with varying myelination preferences.Furthermore,modern molecular investigations,employing techniques such as single cell/nucleus RNA sequencing,consistently unveil at least six mature oligodendrocyte subpopulations in the human central nervous system that are highly transcriptomically diverse and vary with central nervous system region.Age and disease related mature oligodendrocyte variation denotes the impact of pathological conditions such as multiple sclerosis,Alzheimer's disease,and psychiatric disorders.Nevertheless,caution is warranted when subclassifying mature oligodendrocytes because of the simplification needed to make conclusions about cell identity from temporally confined investigations.Future studies leveraging advanced techniques like spatial transcriptomics and single-cell proteomics promise a more nuanced understanding of mature oligodendrocyte heterogeneity.Such research avenues that precisely evaluate mature oligodendrocyte heterogeneity with care to understand the mitigating influence of species,sex,central nervous system region,age,and disease,hold promise for the development of therapeutic interventions targeting varied central nervous system pathology.

Spatial Heterogeneity Modeling Using Machine Learning Based on a Hybrid of Random Forest and Convolutional Neural Network (CNN)

Spatial heterogeneity refers to the variation or differences in characteristics or features across different locations or areas in space. Spatial data refers to information that explicitly or indirectly belongs to a particular geographic region or location, also known as geo-spatial data or geographic information. Focusing on spatial heterogeneity, we present a hybrid machine learning model combining two competitive algorithms: the Random Forest Regressor and CNN. The model is fine-tuned using cross validation for hyper-parameter adjustment and performance evaluation, ensuring robustness and generalization. Our approach integrates Global Moran’s I for examining global autocorrelation, and local Moran’s I for assessing local spatial autocorrelation in the residuals. To validate our approach, we implemented the hybrid model on a real-world dataset and compared its performance with that of the traditional machine learning models. Results indicate superior performance with an R-squared of 0.90, outperforming RF 0.84 and CNN 0.74. This study contributed to a detailed understanding of spatial variations in data considering the geographical information (Longitude & Latitude) present in the dataset. Our results, also assessed using the Root Mean Squared Error (RMSE), indicated that the hybrid yielded lower errors, showing a deviation of 53.65% from the RF model and 63.24% from the CNN model. Additionally, the global Moran’s I index was observed to be 0.10. This study underscores that the hybrid was able to predict correctly the house prices both in clusters and in dispersed areas.

Optimal Insurance with Background Risk under the Ambiguity and Belief Heterogeneity Structure

In this paper, we discuss the optimal insurance in the presence of background risk while the insured is ambiguity averse and there exists belief heterogeneity between the insured and the insurer. We give the optimal insurance contract when maxing the insured’s expected utility of his/her remaining wealth under the smooth ambiguity model and the heterogeneous belief form satisfying the MHR condition. We calculate the insurance premium by using generalized Wang’s premium and also introduce a series of stochastic orders proposed by [1] to describe the relationships among the insurable risk, background risk and ambiguity parameter. We obtain the deductible insurance is the optimal insurance while they meet specific dependence structures.