Patterns Of Selection Research Articles

Pattern selection mechanism from the equilibrium point and limit cycle.

The outbreak of infectious diseases often exhibits periodicity, and this periodic behavior can be mathematically represented as a limit cycle. However, the periodic behavior has rarely been considered in demonstrating the cluster phenomenon of infection induced by diffusion (the instability modes) in the SIR model. We investigate the emergence of Turing instability from a stable equilibrium and a limit cycle to illustrate the dynamical and biological mechanisms of pattern formation. We identify the Hopf bifurcation to demonstrate the existence of a stable limit cycle using First Lyapunov coefficient in our spatiotemporal diffusion-driven SIR model. The competition between different instability modes induces different types of patterns and eventually spot patterns emerge as stable patterns. We investigate the impact of susceptible, infected, and recovered individuals on the type of patterns. Interestingly, these instability modes play a vital role in selecting the pattern formations, which is directly related to the number of observed spot patterns. Subsequently, we explain the dynamical and biological mechanisms of spot patterns to develop an effective epidemic prevention strategy.

Unequal access to protection? Selection patterns over arrival cohorts of Syrians seeking refuge in Lebanon, Turkey, and Germany

IntroductionAn ample scholarly literature on voluntary migration has shown that migration is a highly selective process, resulting in migrant populations that often differ significantly from their respective population of origin in terms of their socio-demographic characteristics. The literature attributes these differences to either migrants' active choice and agency in the migration decision (i.e., self-selection), or to selectively applied external constraints. Although the socio-demographic make-up of forced migrant populations has received significant attention in public discourses in receiving countries such as Germany and Turkey, the literature on migrant selection largely focuses on voluntary migration and self-selection mechanisms. As a result, the selection mechanisms of forcibly displaced persons are less well-understood. Particularly in the context of forced migration, the conditions for migration fluctuate heavily within a relatively short time span, e.g., regarding immigration policies and border controls. In this study we contribute to that literature by exploring the changing conditions under which Syrians sought international humanitarian protection between 2013 and 2017 and linking them to the selection outcomes in three major receiving countries: Lebanon, Turkey, and Germany.MethodsBased on novel household survey data, we compare age, gender, socio-economic background, and family context of the Syrian populations in Lebanon, Turkey, and Germany by arrival cohort (2013–2017). In a narrative approach, we combine the cohort analysis of Syrians in Lebanon, Turkey, and Germany with contextual analyses of the (changing) frameworks governing refugee migration in transit and destination countries and descriptive analyses of changing risk levels along migration routes into Europe.ResultsOur analyses reveal that higher external barriers coincide with a stronger selection in migrants' socio-demographic make-up. In particular, riskier routes and higher entry barriers are associated with a lower share of female migrants, a lower share traveling with family members, and a higher socio-economic background.DiscussionIn this study, we describe differences in forced migrants' selection outcomes in countries of first refuge neighboring the origin country, relative to a reception country in the global north. By establishing legal and political frameworks as well as the accessibility of routes as external barriers to forced migration we expand on the existing theoretical approaches to selection effects and identify a need for policy intervention to ensure equitable access to humanitarian protection.

The evolution of sexual dimorphism in gene expression in response to a manipulation of mate competition.

Many genes are differentially expressed between males and females and patterns of sex-biased gene expression (SBGE) vary among species. Some of this variation is thought to have evolved in response to differences in mate competition among species that cause varying patterns of sex-specific selection. We used experimental evolution to test this by quantifying SBGE and sex-specific splicing in 15 Drosophila melanogaster populations that evolved for 104 generations in mating treatments that removed mate competition via enforced monogamy, or allowed mate competition in either small, simple, or larger, structurally more complex mating environments. Consistent with sex-specific selection affecting SBGE, initially sex-biased genes diverged in expression more among treatments than unbiased genes, and there was greater expression divergence for male- than female-biased genes. It has been suggested the transcriptome should be "feminized" under monogamy because of the removal of sexual selection on males; we did not observe this, likely because selection differs in additional ways between monogamy vs. polygamy. Significant divergence in average expression dimorphism between treatments was observed and, in some treatment comparisons, the direction of the divergence differed across different sex-bias categories. There was not a generalized reduction in expression dimorphism under enforced monogamy.

Charge‐Programmable Photopolymers for 3D Electronics via Additive Manufacturing

AbstractCharge‐programmed 3D printing enables the fabrication of 3D electronics with lightweight and high precision via selective patterning of metals. This selective metal deposition is catalyzed by Pd nanoparticles that are specifically immobilized onto the charged surface and promises to fabricate a myriad of complex electronic devices with self‐sensing, actuation, and structural elements assembled in a designed 3D layout. However, the achievable property space and the material‐performance correlation of the charge‐programmed printing remain unexplored. Herein, a series of photo‐curable resins are designed for unveiling how the charge and crosslink densities synergistically impact the nanocatalyst‐guided selective deposition in catalytic efficiency and properties of the 3D printed charge‐programmed architectures, leading to high‐quality 3D patterning of solid and liquid metals. The findings offer a wide tunability of the structural properties of the printed electronics, ranging from stiff to extreme flexibility. Capitalizing on these results, the printing and successful application of an ultralight‐weight and deployable 3D multi‐layer antenna system operating at an ultrahigh‐frequency of 19 GHz are demonstrated.

Immunogram defines four cancer-immunity cycle phenotypes with distinct clonal selection patterns across solid tumors

BackgroundThe cancer-immunity cycle (CI cycle) provides a theoretical framework to illustrate the process of the anticancer immune response. Recently, the update of the CI cycle theory emphasizes the importance of tumor’s immunological phenotype. However, there is lack of immunological phenotype of pan-cancer based on CI cycle theory.MethodsHere, we applied a visualizing method termed ‘cancer immunogram’ to visualize the state of CI cycle of 8460 solid tumors from TCGA cohort. Unsupervised clustering of the cancer immunogram was performed using the nonnegative matrix factorization (NMF) analysis. We applied an evolutionary genomics approach (dN/dS ratio) to evaluate the clonal selection patterns of tumors with distinct immunogram subtypes.ResultsWe defined four major CI cycle patterns across 32 cancer types using a cancer immunogram approach. Immunogram-I was characterized by ‘hot’ and ‘exhausted’ features, indicating a favorable prognosis. Strikingly, immunogram-II, immunogram-III, and immunogram-IV represented distinct immunosuppressive patterns of ‘cold’ tumor. Immunogram-II was characterized by ‘cold’ and ‘radical’ features, which represented increased expression of immune inhibitor molecules and high levels of positive selection, indicating the worst prognosis. Immunogram-III was characterized by ‘cold’ and ‘recognizable’ features and upregulated expression of MHC I molecules. Immunogram-IV was characterized by ‘cold’ and ‘inert’ features, which represented overall immunosuppression, lower levels of immunoediting and positive selection, and accumulation of more tumor neoantigens. In particular, favorable overall survival was observed in metastatic urothelial cancer patients with immunogram-I and immunogram-IV after immune checkpoint inhibitor (ICI) therapy. Meanwhile, a higher response rate to ICI therapy was observed in metastatic gastric cancer patients with immunogram-I phenotype.ConclusionsOur findings provide new insight into the interaction between immunity and cancer evolution, which may contribute to optimizing immunotherapy strategies.

Functional Classification and Interaction Selectivity Landscape of the Human SH3 Domain Superfamily.

SRC homology 3 (SH3) domains are critical interaction modules that orchestrate the assembly of protein complexes involved in diverse biological processes. They facilitate transient protein-protein interactions by selectively interacting with proline-rich motifs (PRMs). A database search revealed 298 SH3 domains in 221 human proteins. Multiple sequence alignment of human SH3 domains is useful for phylogenetic analysis and determination of their selectivity towards PRM-containing peptides (PRPs). However, a more precise functional classification of SH3 domains is achieved by constructing a phylogenetic tree only from PRM-binding residues and using existing SH3 domain-PRP structures and biochemical data to determine the specificity within each of the 10 families for particular PRPs. In addition, the C-terminal proline-rich domain of the RAS activator SOS1 covers 13 of the 14 recognized proline-rich consensus sequence motifs, encompassing differential PRP pattern selectivity among all SH3 families. To evaluate the binding capabilities and affinities, we conducted fluorescence dot blot and polarization experiments using 25 representative SH3 domains and various PRPs derived from SOS1. Our analysis has identified 45 interacting pairs, with binding affinities ranging from 0.2 to 125 micromolar, out of 300 tested and potential new SH3 domain-SOS1 interactions. Furthermore, it establishes a framework to bridge the gap between SH3 and PRP interactions and provides predictive insights into the potential interactions of SH3 domains with PRMs based on sequence specifications. This novel framework has the potential to enhance the understanding of protein networks mediated by SH3 domain-PRM interactions and be utilized as a general approach for other domain-peptide interactions.

Crop phenology reshapes the food‐safety landscape for roe deer in an agroecosystem

Abstract Understanding the behavioural adjustments of wildlife in anthropized landscapes is key for promoting sustainable human–wildlife coexistence. Little is known, however, about how synanthropic species navigate spatio‐temporal variation in the availability of food and cover that are shaped by human practices such as agriculture. Animal habitat use is predominantly driven by spatial and temporal variations in food and cover, as individuals respond to fluctuations in the trade‐off between food acquisition and risk avoidance. In agroecosystems, the availability of high‐quality forage and cover is dependent on agricultural practices (e.g., harvesting) and crop phenology, providing an ideal opportunity to evaluate how wildlife adjust their behaviour in a heterogeneous human‐dominated landscape. We investigated the influence of crop phenology on the behaviour of European roe deer (Capreolus capreolus) to infer the functional roles of crop types in the food‐cover landscape. We analysed the habitat selection and activity patterns of 105 GPS‐collared adult female roe deer using a unique data set combining field‐specific land cover data, region‐specific estimates of crop phenology and weekly harvesting data for three common crops in a French agroecosystem. We found very distinct habitat selection and activity patterns according to crop type, phenological stage and time of day. Wheat and artificial meadows were strongly selected at night‐time during the early and post‐harvest stages only, when roe deer were highly active, suggestive of feeding activity. On the contrary, roe deer strongly selected maize during the day when it was high enough to provide cover, when they were less active, indicating that it was primarily used for refuge. These patterns depended on the availability of more ‘natural’ cover, suggesting that mature maize may substitute for ‘natural’ cover when the latter is scarce. Synthesis and application: Our work highlights the importance of behavioural plasticity and habitat complementation in the persistence of this synanthropic species in agroecosystems. This behavioural adjustment may buffer the consequences of the reduction in natural habitats that accompanies intensification of agricultural production and has implications for understanding how agricultural practices shape the food‐safety trade‐off of wildlife living in these highly modified landscapes.

Numerical evaluation of cutting strategies for thin-walled parts

Static form errors due to in-process deflections is a major concern in flank milling of thin-walled parts. To increase both productivity and part geometric accuracy, there is a need to predict and control these form errors. In this work, a modelling framework for prediction of the cutting force-induced form errors, or thickness errors, during flank milling of a thin-walled workpiece is proposed. The modelled workpiece geometry is continuously updated to account for material removal and the reduced stiffness matrix is calculated for nodes in the engagement zone. The proposed modelling framework is able to predict the resulting thickness errors for a thin-walled plate which is cut on both sides. Several cutting strategies and cut patterns using constant z-level finishing are studied. The modelling framework is used to investigate the effect of different cut patterns, machining allowance, cutting tools and cutting parameters on the resulting thickness errors. The framework is experimentally validated for various cutting sequences and cutting parameters. The predicted thickness errors closely correspond to the experimental results. It is shown from numerical evaluations that the selection of an appropriate cut pattern is crucial in order to reduce the thickness error. Furthermore, it is shown that an increased machining allowance gives a decreased thickness error for thin-walled plates.

The Drosophila melanogaster Y-linked gene, WDY, is required for sperm to swim in the female reproductive tract

Unique patterns of inheritance and selection on Y chromosomes have led to the evolution of specialized gene functions. We report CRISPR mutants in Drosophila of the Y-linked gene, WDY, which is required for male fertility. We demonstrate that the sperm tails of WDY mutants beat approximately half as fast as those of wild-type and that mutant sperm do not propel themselves within the male ejaculatory duct or female reproductive tract. Therefore, although mature sperm are produced by WDY mutant males, and are transferred to females, those sperm fail to enter the female sperm storage organs. We report genotype-dependent and regional differences in sperm motility that appear to break the correlation between sperm tail beating and propulsion. Furthermore, we identify a significant change in hydrophobicity at a residue at a putative calcium-binding site in WDY orthologs at the split between the melanogaster and obscura species groups, when WDY first became Y-linked. This suggests that a major functional change in WDY coincided with its appearance on the Y chromosome. Finally, we show that mutants for another Y-linked gene, PRY, also show a sperm storage defect that may explain their subfertility. Overall, we provide direct evidence for the long-held presumption that protein-coding genes on the Drosophila Y regulate sperm motility.

A bearing fault diagnosis method with improved symplectic geometry mode decomposition and feature selection

A rolling bearing fault diagnosis method based on improved symplectic geometry mode decomposition (SGMD) and feature selection was proposed to solve the problem of low fault identification due to the influence of noise on early bearing fault features. First, the SGMD SGMD is improved to enhance its robustness in decomposing signals with noise, then the time domain, frequency domain, and time-frequency features of each symplectic geometric component are extracted as feature vectors. Second, a comprehensive feature selection strategy is proposed to select the optimal subset of features that are conducive to fault classification. Finally, considering the problem of low classification accuracy of a single machine learning model, the AdaBoost-WSO-SVM model is constructed for fault classification using the AdaBoost algorithm of integrated learning. Experimental decomposition of complex signals with noise indicates that the improved SGMD is more effective compared to traditional SGMD. Subsequently, multiple experiments were conducted using the bearing datasets from Case Western Reserve University (CWRU) and Jiangnan University (JNU). The experimental results reveal that, after comprehensive feature selection and ensemble learning pattern recognition experiments on the CWRU dataset, the average accuracy of fault diagnosis can reach 99.67%. On the JNU dataset, the proposed fault diagnosis method achieves an average accuracy of 95.03%. This suggests that, compared to other feature selection methods and classification models, the proposed approach in this paper exhibits higher accuracy and generalization capabilities.

Beyond age‐structured single‐species management: Optimal harvest selectivity in the face of predator–prey interactions

AbstractIn the single‐species literature, it is widely acknowledged that conserving young fish for future harvesting is beneficial. This finding holds great significance in fisheries economics and has garnered substantial attention over the years. In this study, a full‐blown age‐structured predator–prey model is developed and used to demonstrate that multispecies considerations may shift the optimal selection of predators towards smaller individuals, providing valuable counteractive insights. These new results offer a fresh perspective highly relevant to regulation and choice of selectivity patterns.

Selecting Indispensable Edge Patterns With Adaptive Sampling and Double Local Analysis for Data Description

Support vector data description (SVDD) inspires us in data analysis, adversarial training, and machine unlearning. However, collecting support vectors requires pricey computation, while the alternative boundary selection with O(N2) is still a challenge. The authors propose an indispensable edge pattern selection method (IEPS) for data description with direct SVDD model building. IEPS suggests a double local analysis to select the global edge patterns. Edge patterns belong to a subset of the target problem of SVDD and its variants, and neighbor analysis becomes pivotal. While an excessive number of participating data result in redundant computations, an insufficient number may impede data separability or compromise the model's quality. Consequently, a data-adaptive sampling strategy has been devised to ascertain an optimal ratio of retained data for edge pattern selection. Extensive experiments indicate that IEPS keeps indispensable edge patterns for data description while reducing the interference in the norm vector generation to guarantee the effectiveness for clustering analysis.

Male and female selection effects on gender wage gaps in three countries

A vast literature on gender wage gaps has examined the importance of selection into employment. However, most analyses have focused only on female labour force participation and gaps at the median. The Great Recession questions this approach because of the major shift in male employment that it implied. This paper uses the methodology proposed by Arellano and Bonhomme (2017) to estimate a quantile selection model over the period 2007–2018. Using a tax and benefit microsimulation model, I compute an instrument capturing both male and female decisions to participate in the labour market: the potential out-of-work income. Since my instrument is crucially determined by the welfare state, I consider three countries with notably different benefit systems – the UK, France and Finland. My results imply different selection patterns across countries and a sizeable male selection in France and the UK. Correction for selection bias lowers the gender wage gap and reveals a substantial glass ceiling with different magnitudes. Findings suggest that disparities between these countries are driven by occupational segregation and public spending on families.

Anomaly detection using large-scale multimode industrial data: An integration method of nonstationary kernel and autoencoder

Kernel methods and neural networks (NNs) are two mainstream nonlinear data modeling methods and have been widely applied to industrial process monitoring. However, they both present imperfect properties, so the relevant applications are limited. On the one hand, kernels are not so reconstructable, scalable, and robust to hyperparameters that they suffer performance degradation for large-scale data modeling and monitoring. On the other hand, the high-dimensional parameter space of NNs that is sorted to parameter initialization presents severe anomaly detection performance inconsistency, which makes the industry cautious about using NNs. Motivated by these facts, we propose to integrate kernels and NNs, forming a new model structure that is scalable, reconstructable, and performance-consistent. Specifically, a novel autoencoder-based nonstationary pattern selection kernel (AE-NPSK) is proposed by (1) selecting from the training set the critical edges and interior data as the centers of the radial basis functions in the hidden layers and (2) adaptively adjusting the kernel width in the training procedure. Also, the new NN has strong performance consistency, which facilitates the search for optimal parameters. Finally, we test the performance of the proposed method on the challenging multimode processes. The results validate the efficacy of the proposed method.

SpatialHeatmap: visualizing spatial bulk and single-cell assays in anatomical images.

Visualizing spatial assay data in anatomical images is vital for understanding biological processes in cell, tissue, and organ organizations. Technologies requiring this functionality include traditional one-at-a-time assays, and bulk and single-cell omics experiments, including RNA-seq and proteomics. The spatialHeatmap software provides a series of powerful new methods for these needs, and allows users to work with adequately formatted anatomical images from public collections or custom images. It colors the spatial features (e.g. tissues) annotated in the images according to the measured or predicted abundance levels of biomolecules (e.g. mRNAs) using a color key. This core functionality of the package is called a spatial heatmap plot. Single-cell data can be co-visualized in composite plots that combine spatial heatmaps with embedding plots of high-dimensional data. The resulting spatial context information is essential for gaining insights into the tissue-level organization of single-cell data, or vice versa. Additional core functionalities include the automated identification of biomolecules with spatially selective abundance patterns and clusters of biomolecules sharing similar abundance profiles. To appeal to both non-expert and computational users, spatialHeatmap provides a graphical and a command-line interface, respectively. It is distributed as a free, open-source Bioconductor package (https://bioconductor.org/packages/spatialHeatmap) that users can install on personal computers, shared servers, or cloud systems.

Visual analysis of acupuncture point selection patterns and related mechanisms in acupuncture for hypertension.

Hypertension has become one of the most pathogenic diseases in the world. This paper summarizes and analyzes the acupuncture point combinations and treatment principles of acupuncture for hypertension in a systematic way by means of big data mining. The literature for this paper was obtained from CNKI, Wanfang, VIP, SinoMed and PubMed, Embase, Cochrane Library, Web of Science, and Ovid databases. Thedata were collected to obtain combinations of acupoints with strong associations through association rule analysis, complex networks for screening to obtain core acupoint nuclei, and cluster analysis to derive treatment principles. A total of 127 acupuncture prescriptions involving 66 acupoints were included in this study. Tai-chong (LR3), Qu-chi (LI11), Zu-san-li (ST36), Feng-chi (GB20), and He-gu (LI4) were the most commonly used acupoints. The large intestine meridian was the preferred meridian, and most of the extremity acupoints, especially the lower extremities, were selected clinically. The association rule reveals that Qu-chi (LI11) and Zu-san-li (ST36) are the dominant combination acupoints. 3 core association points obtained after complex network analysis, the 1st association, Bai-hui (DU20), Tai-xi (KI3), Gan-shu (BL18), Shen-shu (BL23); The 2nd association, Qu-chi (LI11), He-gu (LI4), San-yin-jiao (SP6), Zu-san-li (ST36), Feng-chi (GB20), Tai-chong (LR3); The 3rd association, Qi-hai (RN6), Guan-yuan (RN4), Zhong-wan (RN12), Zhao-hai (KI6), Tai-yang (EX-HN5), Lie-que (LU7), Yang-ling-quan (GB34), Xing-jian (LR2), Yin-ling-quan (SP9). Cluster analysis yielded the treatment principles of nourishing Yin and submerging Yang, pacifying the liver and submerging Yang, tonifying Qi and Blood, and calming the mind and restoring the pulse, improving clinical outcomes. By means of big data mining, we can provide reference for acupuncture point grouping and selection for clinical acupuncture treatment of hypertension.

Community assembly and microbial interactions in an alkaline vanadium tailing pond

Intensive development of vanadium-titanium mines leads to an increasing discharge of vanadium (V) into the environment, imposing potential risks to both environmental system and public health. Microorganisms play a key role in the biogeochemical cycling of V, influencing its transformation and distribution. In addition, the characterization of microbial community patterns serves to assess potential threats imposed by elevated V exposure. However, the impact of V on microbial community remains largely unknown in alkaline V tailing areas with a substantial amounts of V accumulation and nutrient-poor conditions. This study aims to explore the characteristics of microbial community in a wet tailing pond nearby a large-scale V mine. The results reveal V contamination in both water (0.60 mg/L) and sediment tailings (340 mg/kg) in the tailing pond. Microbial community diversity shows distinctive pattern between environmental metrices. Genera with the functional potential of metal reduction\\resistance, nitrogen metabolism, and carbon fixation have been identified. In this alkaline V tailing pond, V and pH are major drivers to induce community variation, particularly for functional bacteria. Stochastic processes primarily govern the assemblies of microbial community in the water samples, while deterministic process regulate the community assemblies of sediment tailings. Moreover, the co-occurrence network pattern unveils strong selective pattern for sediment tailings communities, where genera form a complex network structure exhibiting strong competition for limited resource. These findings reveal the patterns of microbial adaptions in wet vanadium tailing ponds, providing insightful guidelines to mitigate the negative impact of V tailing and develop sustainable management for mine-waste reservoir.

High-altitude adaptation is accompanied by strong signatures of purifying selection in the mitochondrial genomes of three Andean waterfowl.

Evidence from a variety of organisms points to convergent evolution on the mitochondria associated with a physiological response to oxygen deprivation or temperature stress, including mechanisms for high-altitude adaptation. Here, we examine whether demography and/or selection explains standing mitogenome nucleotide diversity in high-altitude adapted populations of three Andean waterfowl species: yellow-billed pintail (Anas georgica), speckled teal (Anas flavirostris), and cinnamon teal (Spatula cyanoptera). We compared a total of 60 mitogenomes from each of these three duck species (n = 20 per species) across low and high altitudes and tested whether part(s) or all of the mitogenome exhibited expected signatures of purifying selection within the high-altitude populations of these species. Historical effective population sizes (Ne) were inferred to be similar between high- and low-altitude populations of each species, suggesting that selection rather than genetic drift best explains the reduced genetic variation found in mitochondrial genes of high-altitude populations compared to low-altitude populations of the same species. Specifically, we provide evidence that establishment of these three Andean waterfowl species in the high-altitude environment, coincided at least in part with a persistent pattern of negative purifying selection acting on oxidative phosphorylation (OXPHOS) function of the mitochondria. Our results further reveal that the extent of gene-specific purifying selection has been greatest in the speckled teal, the species with the longest history of high-altitude occupancy.

Analysis of the Evolvement of Livelihood Patterns of Farm Households Relocated for Poverty Alleviation Programs in Ethnic Minority Areas of China

Exploring the evolution of livelihood patterns of farm households relocated for poverty alleviation in three prefectures of Guizhou Province, China, can provide a theoretical basis and practical value for achieving sustainable livelihoods of relocated farm households in ethnic minority areas. This study took three ethnic minority autonomous prefectures in Guizhou as the research area. The types and evolution pathways of farm households’ livelihood patterns were divided using 355 tracking and monitoring data from the four phases of relocated farm households’ livelihoods. The evolution characteristics of farm households’ livelihood patterns and changes in their evolution pathways from “before relocation” to 2021 were analyzed. An indicator system of influencing factors was established based on the sustainable livelihood framework. The influencing factors of the evolution path selection and transformation of livelihood patterns were identified using the binary and multiple logistic regression models. The main results are as follows: (1) Implementing the poverty alleviation relocation project shifted the livelihood activities of farm households from agricultural production to non-agricultural industries. The transformation trend of livelihood patterns shifted from agro-dominated, agricultural, agricultural-diversified and balanced types to highly diversified, deeply diversified and subsidy-dependent types. (2) The transformation mode of the evolution pathways of farm households’ livelihood patterns mainly included the stable transformation modes of “active→retention→active” and “active→retention→retention” and the fluctuating transformation modes of “active→retention→fallback”, “active→fallback→active” and “retention→fallback→active”. (3) Natural capital promoted farm households to choose fallback pathways, while material and human capital showed an inhibitory impact. In contrary to human capital, social capital promoted farm households to choose retention pathways. Financial and human capital promoted farm households to choose active pathways. (4) Per capita income, the household head age and relocation years positively affected the evolution pathways of the livelihood pattern from the fallback type to the retention type. The number of means of transport, per capita education level, transportation and communication cost, and the presence of civil servants in the household positively affected the evolution pathways of the livelihood pattern from the fallback type to the active type. Policy suggestions are as follows: Expanding local and nearby employment channels, strengthening vocational skills training for farm households, expanding financial access for farm households, improving policy support for ethnic characteristic industries, implementing bottom-line guarantee policies and exploring ethnic interactive activities for resettlement farm households.

News Frames Emotion: Analyzing How Audience Are Influenced by News Frames

Purpose: As a persistent pattern of selection, interpretation, emphasis and presentation, news frames is very fundamental in influencing individual emotions. News is the construct and presentation of the news media. The study examines how audience emotions are influenced by news frames.
 Materials and Methods: The study is anchored on the theory of media framing, which deals with how the media package and present information to the audience or how audiences feel about an issue. Using analytical review of literature, the paper is exploratory and explanatory in nature as it adopts the gathering of relevant literature data with the sole aim of interpreting these data in order to achieve the desired aim of the study.
 Findings: Arising from the analyses of data, the study submits that news frame as the construct and presentation of the news media is capable of influencing audience’s emotions. Individual’s perception and reaction to news is a product of the journalistic news frame. This is because news frames involve selecting and emphasizing words, expressions, and images and these are capable of influencing audience’s cognitive, attitude and emotional effects.
 Implications to Theory, Practice and Policy: However, audience emotions to news largely depend on the state of the audience’s selection, interpretation and perception processes of the news.