Different Types Of Changes Research Articles

Time progression and regional expression of brain oxidative stress induced by obstructive jaundice in rats

BackgroundObstructive jaundice induces oxidative changes in the brain parenchyma and plays significant role in clinical manifestations of hepatic encephalopathy. We aim to study the progression of the brain oxidative status over time and the differences of its pattern over the hemispheres, the brainstem and the cerebellum. We use an experimental model in rats and measuring the oxidative stress (OS) specific biomarkers protein malondialdehyde (PrMDA) and protein carbonyls (PrC = O).ResultsHyperbilirubinemia has been confirmed in all study groups as the result of common bile duct obstruction. We confirmed increase in both PrMDA and PrC = O biomarkers levels with different type of changes over time. We also confirmed that the oxidative process develops differently in each of the brain areas in study.ConclusionsThe present study confirms the progressive increase in OS in all brain areas studied using markers indicative of cumulative protein modification.

Evaluation of hypolipidemic activity of homeopathic drug Allium sativum 6C potency on different grades of dyslipidemia in Wistar albino rat models

BackgroundUnhealthy dietary patterns and lifestyle modification could exacerbate heart-related diseases, and the risk factors for cardivascular disease mortality includes dyslipidemia, hypertension, and diabetes mellitus. However, controlling cholesterol, triglycerides, and low-density lipoprotein could minimize the occurrence of risk factors. Although homeopathic preparation of Allium sativum is effective in treating various ailments, it's therapeutic usage by the homeopathic practitioners must be validated scientifically to ascertain its action. ObjectivesThe present study is designed to evaluate the hypolipidemic and anti-inflammatory activities of the homeopathic drug A. sativum 6C in mitigating dyslipidemic changes in different types of dyslipidemic rat models. MethodsAssessment of serum biochemical parameters, evaluation of liver cytokines, and immunoblot analysis of inflammatory proteins were performed in this study. ResultsAdministration of a homeopathic preparation of A. sativum 6C to different types of dyslipidemic rat model showed a significant improvement in the serum lipid profile, reduction in the activity of serum marker enzymes (AST, ALT, LDH, and ALP), and decrement in the levels of liver inflammatory cytokines (TNFα, IL-6, and IL-1β). Further, the expression of inflammatory proteins (CRP, P-Selectin, E- Selectin) down regulated in the liver of dyslipidemic animals upon administration of A. sativum 6C. However, among the various dyslipidemic models, the efficacy of A. sativum is more profound in the HCD and HFD-fed animals. Analyzing the GC–MS data of A. sativum 6C revealed that it contains some active ingredients (Undecane; 1-methylene 1H-Indene, and Oxazolam) that might be responsible for the anti-inflammatory properties of A. sativum 6C. ConclusionHomeopathic preparation of A. sativum 6C modulates the levels of serum biochemical parameters and inflammatory events in HCD-T and T2DM-T animals than in the STZ-T animals, mitigating metabolic dysfunction. Therefore, this study brings limelight to support the potency of the homeopathic drug A. sativum for therapeutic purposes in correcting metabolic dysfunction associated with the current lifestyle.

On the Use of Sentinel-2 NDVI Time Series and Google Earth Engine to Detect Land-Use/Land-Cover Changes in Fire-Affected Areas

This study aims to assess the potential of Sentinel-2 NDVI time series and Google Earth Engine to detect small land-use/land-cover changes (at the pixel level) in fire-disturbed environs. To capture both slow and fast changes, the investigations focused on the analysis of trends in NDVI time series, selected because they are extensively used for the assessment of post-fire dynamics mainly linked to the monitoring of vegetation recovery and fire resilience. The area considered for this study is the central–southern part of the Italian peninsula, in particular the regions of (i) Campania, (ii) Basilicata, (iii) Calabria, (iv) Toscana, (v) Umbria, and (vi) Lazio. For each fire considered, the study covered the period from the year after the event to the present. The multi-temporal analysis was performed using two main data processing steps (i) linear regression to extract NDVI trends and enhance changes over time and (ii) random forest classification to capture and categorize the various changes. The analysis allowed us to identify changes occurred in the selected case study areas and to understand and evaluate the trend indicators that mark a change in land use/land cover. In particular, different types of changes were identified: (i) woodland felling, (ii) remaking of paths and roads, and (ii) transition from wooded area to cultivated field. The reliability of the changes identified was assessed and confirmed by the high multi-temporal resolution offered by Google Earth. Results of this comparison highlighted that the overall accuracy of the classification was higher than 0.86.

Smallholder farmers’ behavioral preferences under the impact of climate change: A comparative analysis of two agricultural areas in China

Farmers’ behaviors to deal with climate change can be divided into two categories: mitigation and adaptation. Mitigation behaviors can reduce the rate of climate change, while adaptation behaviors can reduce the vulnerability to climate change. In this study, we focused on the factors influencing farmers’ behavioral preferences and explored the differences in their behaviors in response to climate change in different types of regions. A structural equation model was constructed to describe the relationship between trust, risk perception, psychological distance and risk severity, and farmer behaviors. The results indicate that the factors affecting climate adaptation vary greatly in different regions. In agricultural areas, risk salience, psychological distance and mitigation behaviors had an important influence on farmer adaptation behavior, while risk perception only affects farmers’ mitigation behaviors. Trust can not only predict farmers’ adaptability, but also explain farmers’ choice of mitigation behaviors. For farmers in farming and pastoral areas, belief, risk severity level and trust have positive driving effects on both adaptation and mitigation behaviors. The findings provide suggestions for the development of public policy and risk management approaches to deal with climate change, which could encourage active behavior among farmers.

Accelerated aging-related transcriptome alterations in neurovascular unit cells in the brain of Alzheimer’s disease

Alzheimer’s disease (AD) is the most common cause of dementia with no effective therapies. Aging is a dominant risk factor for AD. The neurovascular unit (NVU) plays an important role in maintaining homeostasis of the brain microenvironment. The accelerated aging of NVU cells may directly impair NVU function and contribute to AD pathogenesis. However, the expression patterns of aging-related genes (AGs) in NVU cells of AD remain unclear. In this study, we performed single-nucleus transcriptome analysis of 61,768 nuclei from prefrontal cortical samples of patients with AD and normal control (NC) subjects. Eight main cell types were identified, including astrocytes, microglia, excitatory neurons, inhibitory neurons, oligodendrocytes, oligodendrocyte precursor cells, pericytes, and endothelial cells. Transcriptomic analysis identified the expression patterns of AGs in NVU cells of AD. Gene set enrichment analysis confirmed the key aging-associated cellular pathways enriched in microglia and oligodendrocytes. These aging-related transcriptomic changes in NVU were cross-validated using bulk transcriptome data. The least absolute shrinkage and selection operator regression method was used to select the crucial AGs most associated with AD: IGF1R, MXI1, RB1, PPARA, NFE2L2, STAT5B, FOS, PRKCD, YWHAZ, HTT, MAPK9, HSPA9, SDHC, PRKDC, and PDPK1. This 15-gene model performed well in discriminating AD from NC samples. Among them, IGF1R, MXI1, PPARA, YWHAZ, and MAPK9 strongly correlated with pathologic progression in AD, were identified as critical regulators of AD. Although most AGs showed similar trends of expression changes in different types of NVU cells in AD, certain AGs were expressed in a cell-specific manner. Our comprehensive analysis of brain NVU from patients with AD reveals previously unknown molecular changes associated with aging that may underlie the functional dysregulation of NVU, providing important insights for exploring potential cell-specific therapeutic targets to restore brain homeostasis in AD.

Changes in the Work Schedule of Nurses Related to the COVID-19 Pandemic and Its Relationship with Sleep and Turnover Intention.

Background: This study aimed to investigate whether different types of changes in the work schedule of nurses working rotating shifts during the COVID-19 pandemic were associated with sleep duration, sleep quality, and turnover intention. Methods: Cross-sectional questionnaire data from 694 nurses participating in the SUrvey of Shift work, Sleep and Health (SUSSH) were collected between the first and the second wave of the COVID-19 pandemic in Norway. A total of 89.9% were female, and mean age was 44.6 years (SD = 8.6 years). Changes in the shift work schedule related to the pandemic comprised reports of more long workdays (>8 h), less days off between work periods, more night shifts, more quick returns (i.e., 11 h or less between two consecutive shifts), more day shifts, and more evening shifts compared to no change in the respective shift characteristics. Change in sleep duration, sleep quality, and turnover intention as well as demographics were also assessed. Logistic regression analyses were performed to investigate whether changes in the specific work schedules were associated with sleep duration, sleep quality, and turnover intention, controlling for sex, age, cohabitation, children living in household, percentage of full time equivalent and other changes in the work schedule. Results: A total of 17% reported experiencing one or more changes in their work schedule during the pandemic. Experiencing any change in the work schedule predicted worse sleep quality (OR = 2.68, p < 0.001), reduced sleep duration (OR = 4.56, p < 0.001), and higher turnover intention (OR = 1.96, p = 0.006) compared to experiencing no change in work schedule. Among the specific changes in work schedules, experiencing an increase in quick returns had the highest odds ratio for worse sleep quality (OR = 10.34, p = 0.007) and higher turnover intention (OR = 8.49, p = 0.014) compared to those who reported no change in quick returns. Nurses experiencing an increase in long workdays were more likely to report higher turnover intention (OR = 4.37, p = 0.003) compared to those experiencing no change in long workdays. Conclusions: Change in work schedule related to the pandemic was associated with worse sleep quality, reduced sleep duration, and higher turnover intention. Increase in quick returns emerged as especially problematic in terms of sleep quality and turnover intention, along with long workdays, which were associated with higher turnover intention.

Contemporaneous Update and Enforcement of ABAC Policies.

Access control policies are dynamic in nature, and therefore require frequent updates to synchronize with the latest organizational security requirements. As these updates are handled, it is important that all user access requests be answered contemporaneously and correctly without any interruption or delay. In this paper, considering the context of Attribute Based Access Control (ABAC), we propose an approach that is capable of immediately materializing any update to the policy and ensuring that it is taken into account for any subsequent access requests. One possibility is to update the policy based on the incoming changes through ABAC policy mining techniques. However, it turns out that no existing mining approach can offer correct enforcement of policies when access requests are entertained during the updates. We provide a formal proof for this surprising result and then propose an approach called δwOP that does not suffer from this problem. Essentially, δwOP keeps track of the needed information from updates and uses this in conjunction with the existing ABAC policy rules to make access decisions. We present the complexity analysis as well as a comprehensive experimental evaluation to demonstrate the efficacy of the proposed approach for different types of changes.

CIAFP

Object-oriented (OO) code has many dependencies among the classes and different types of changes that often have an impact during the maintenance of the software. In this paper, we proposed a technique for change impact analysis (CIA) with fault prediction (FP) using machine learning techniques for OO software. In proposed method an intermediate OO program representation is proposed using a graph which detects the difference between the original program and the modified program. For fault prediction, class level metrics are extracted from KC1 data set and 14 machine learning algorithms are trained on dataset. The genetic algorithm (GA) and correlation are used to extract significant features from the dataset. Trained prediction models are evaluated using classification accuracy (Acc). Our proposed approach consider various types of changes possible in the program, and test cases are selected to test the modified code during regression testing and finds the fault in classes. Among the 14 machine learning algorithms random forest (RF) giving best accuracy than other algorithms.

Metabolomic profiles as a new understanding of disease processes

Aim. This review will demonstrate possibilities of using metabolomic profiling to identify biomarkers of various internal organs diseases.Key points. A new diagnostic direction is associated with high-sensitive spectral analysis of biomarker molecules. This review will discuss some of the latest advances with an emphasis on the use of metabolomics to identify major metabolic changes in various diseases. The possibility of finding diagnostic markers in diseases of the gastrointestinal tract, respiratory and cardiovascular systems, in oncology, endocrinology, neurology are discussed. These results define new potential therapeutic strategies, making metabolomics useful for a wide range of biomedical and pharmaceutical research.Conclusion. Metabolomic profile changes in different types of diseases will help to improve understanding of the pathogenesis. New therapeutic approaches may be developed. They will take into account individual characteristics of the patient, identified by using current molecular technologies. The results of metabolomic studies can be used to monitor treatment outcomes.

Optimizing cultural intelligence development by considering different types of change

Increasing globalization and cultural diversity have accentuated the importance of developing cross-cultural competencies like cultural intelligence (CQ). Typically, scholars and professionals have viewed and operationalized CQ development as a quantitative change in mean CQ scores over time. This work argues that research and practice will benefit from expanding the operationalization of CQ development to include CQ reconceptualization and CQ measurement recalibration because these types of change are also integral to the CQ development process. This work highlights that by not examining the presence of CQ reconceptualization and CQ measurement recalibration before examining changes in mean CQ scores over time, scholars and practitioners could fail to recognize the presence of CQ development when it happens, create a threat to the substantive interpretation offindings, and further contribute to the inconsistent and conflicting research results. Methodologies for assessing different types of change in CQ development are discussed with implications for improving cross-cultural competence research and practice. The main contribution of this work lies in providing ways for improving the rigor of CQ development studies to enhance the quality of CQ development research and practice.

Which came first, the gentrification or the Airbnb? Identifying spatial patterns of neighbourhood change using Airbnb data

Over time, neighbourhoods experience different types of change. Neighbourhood change is a spatiotemporal process that involves analysing how attributes of a location change over time. In this article, we explore the capability of short-term property rental data to identify neighbourhoods where change is occurring. The article focuses on the relationship between data from the Airbnb platform and gentrification. Through a study in Dublin, Ireland, Airbnb data was analysed using state-of-the-art Emerging Hotspot Analysis to identify locations with high potential to experience gentrification and areas that have undergone recent development. The locations identified by the method are correlated with gentrification and neighbourhood change described in other literature. The spatiotemporal approach reveals more about locational changes in the city than other methods, which focus only on one component, such as space or time. The study highlights the importance of access to real estate market data, such as data about the short-term rental market, to extract spatiotemporal changes occurring in neighbourhoods. The results contribute to the discourse on the impacts of short-term rental and Airbnb on cities and can be used by policymakers, observers, and citizens to motivate the distribution of services. The question as to whether gentrification or Airbnb came first remains an open one.

Investigating the Potential of Artificial Intelligence Powered Interfaces to Support Different Types of Memory for People with Dementia.

There has been a growing interest in HCI to understand the specific technological needs of people with dementia and supporting them in self-managing daily activities. One of the most difficult challenges to address is supporting the fluctuating accessibility needs of people with dementia, which vary with the specific type of dementia and the progression of the condition. Researchers have identified auto-personalized interfaces, and more recently, Artificial Intelligence or AI-driven personalization as a potential solution to making commercial technology accessible in a scalable manner for users with fluctuating ability. However, there is a lack of understanding on the perceptions of people with dementia around AI as an aid to their everyday technology use and its role in their overall self-management systems, which include other non-AI technology, and human assistance. In this paper, we present future directions for the design of AI-based systems to personalize an interface for dementia-related changes in different types of memory, along with expectations for AI interactions with the user with dementia.

Assessing the impacts of Grain-for-Green Programme on ecosystem services in Jinghe River basin, China

Land-use/land-cover (LULC) change could have huge impacts on a wide range of ecosystem services, such as the quality, types, processes, temporal, and spatial patterns of ecosystem, leading to dramatic changes in different types of ecosystem services. China has implemented Grain-for-Green Programme (GFGP) to alleviate soil erosion, restore and improve ecosystem services. We selected Jinghe River basin, one of the main GFGP pilot areas in the central part of the Loess Plateau in China as study area. InVEST models were adopted to quantify three of the most important ecosystem services (water yield, sediment control and carbon storage) from 2000 to 2015, during which an important GFGP process happens. Results showed that compared with 2000, the area of farmland decreased by 466 km2, the area of degraded grassland decreased by 394 km2, while the area of forestland increased by 339 km2 (including shrub land) in Jinghe River basin. The average annual water yield and sediment export from 2000 to 2015 is 1.11 × 109 m3 and 9.67 × 107 t, which is 36.2% and 60.7% lower than the average of the previous 50 years. Unsurprisingly, carbon storage has increased 5907559.28 mg, about 2.4% more compared to 2000. There are trade-offs between water yield and sediment control as well as carbon storage, and there are synergies between sediment control and carbon storage. Our quantitative assessment has revealed the complex spatial and temporal trade-offs and synergies among competing ecosystem services during the GFGP process. In addition, the results of t-tests in the sub-basin showed that the water yield and sediment control of grassland restoration area and forestland restoration area are better than those of farmland. The water yield of grassland restoration area is better than that of forestland restoration area. But it is difficult to judge the sediment export in forestland restoration area and grassland restoration area. Therefore, considering the trade-offs between water yield and sediment control, grasses are better vegetation type of GFGP compared to trees for the Loess Plateau which has relatively limited precipitation. Our results showed that the control of the erosion by adding vegetation triggers to a diminution of water yield, especially in arid and semi-arid areas. Decision making regards to future GFGP and ecological restoration projects should consider the complex and competing trade-offs and synergies in a wide range of ecosystem services, and optimize the planting structure and scale of vegetation in order to achieve long-term, secure, and sustainable provision of vital ecosystem services.

Change detection for multispectral images using modified semantic segmentation network

Change detection is a significant issue for understanding the changes occurring on the land surface. We propose a change detection approach based on a semantic segmentation network from multispectral (MS) images. Different from the traditional approaches that learn deep features from the change index or establish mapping relations from patches, the proposed approach employs the semantic segmentation network UNet++ for end-to-end change detection. Nevertheless, in UNet++, the deep feature is directly upsampled from the node in the lower level and does not involve much information from the nodes in the other levels. To cope with this problem and further enhance its robustness, the zigzag UNet++ (ZUNet++) is developed. In ZUNet++, the zigzag connection between nodes can be found, so the inputs of the node involve not only the upsampled deep feature but also the downsampled shallow feature, i.e., the network fuses multiple feature information. In addition, as few MS training datasets are available, we designed a strategy in which each MS image is transferred into several pseudo-RGB images; thus the network is trained by available RGB training sets and can be applied to the testing MS datasets. In the experiment, three real testing MS datasets that reflect different types of changes in Xi’an City are used. Experimental results show that, upon determining the appropriate parameter, the proposed ZUNet++ outperforms the other state-of-the-art approaches, demonstrating its feasibility and effectiveness.

The role of astrocytes in epileptic disorders.

Epilepsy affects about 1% of the population and approximately 30% of epileptic patients are resistant to current antiepileptic drugs. As a hallmark in epileptic tissue, many of the epileptic patients show changes in glia morphology and function. There are characteristic changes in different types of glia in different epilepsy models. Some of these changes such as astrogliosis are enough to provoke epileptic seizures. Astrogliosis is well known in mesial temporal lobe epilepsy (MTLE), the most common form of refractory epilepsy. A better understanding of astrocytes alterations could lead to novel and efficient pharmacological approaches for epilepsy. In this review, we present the alterations of astrocyte morphology and function and present some instances of targeting astrocytes in seizure and epilepsy.

The Fruit Proteome Response to the Ripening Stages in Three Tomato Genotypes.

The tomato is a horticultural crop that appears in various colors as it ripens. Differences in the proteome expression abundance of a tomato depend on its genotype and ripening stage. Thus, this study aimed to confirm the differences in changes in the proteome according to four ripening stages (green, breaker, turning, and mature) of three tomato genotypes, i.e., yellow, black, and red tomatoes, using a gel-based proteomic technique. The number of protein spots shown as two-dimensional electrophoresis (2-DE) gels differed according to tomato genotype and ripening stage. A total of 286 variant proteins were determined using matrix-assisted laser desorption-time of flight (MALDI-TOF) mass spectrometry (MS) analysis, confirming 233 identified protein functions. In three tomato genotypes in each ripening stage, grouping according to the Munich Information Center for Protein Sequences (MIPS) functional categories confirmed the variant proteins involved in the following: energy processes (21%); metabolism (20%); protein fate (15%); protein synthesis (10%); a protein with a binding function or cofactor requirement (8%); cell rescue, defense, and virulence (8%); cellular transport, transport facilitation, and transport routes (6%); the biogenesis of cellular components (5%); cell cycle and DNA processing (2%); others (5%). Among the identified protein spots in the function category, two proteins related to metabolism, four related to energy, four related to protein synthesis, and two related to interaction with the cellular environment showed significantly different changes according to the fruit color by the ripening stage. This study reveals the physiological changes in different types of tomatoes according to their ripening stage and provides information on the proteome for further improvement.

Dynamic Landscapes in the UK Driven by Pressures from Energy Production and Forestry—Results of the CORINE Land Cover Map 2018

The CORINE Land Cover (CLC) map was established in 1985 and is now one of the most widely used products from the Copernicus Land Monitoring Service. As the world’s longest consistent operational land cover monitoring product, CLC maps have been produced for reference years 1990, 2000, 2006, 2012 and now for 2018. This paper presents the results from the CLC2018 mapping project in the UK and analyses the results of the land cover status layer and the change layer from the period 2012–2018. It sets this change in context with the change results from the period 2006–2012 and finds that the rate of change between the subsequent CORINE land cover maps is continuing to increase. Changes mapped for the period 2012–2018 covered 76,032 ha greater than the change mapped between 2006 and 2012, an increase of 26% of mapped change. The area of changes mapped covered an area equivalent to 1.16% of the total land area of the UK. The number of different types of changes also continue to diversify; however, the dominance of rotational forestry is consistent with the previous map. The process of urban land take has been highlighted in the results between 2012 and 2018 and is a trend identified in previous iterations of the CLC inventories. The largest gain is in industrial or commercial units (an increase of 14.4%). This growth is mainly attributed to renewable energy infrastructure. As well as the descriptive analysis, the results have been analysed to identify the likely pressures being experienced on the land in the UK. Although the CLC mapping approach is consistent, there have been improvements to the input EO data used to map the changes. For 2018, the Copernicus Sentinel-2 system offered a consistent and reliable image source for the first time. This increased the spatial resolution of the source datasets to 10 m, allowing for more accurate identification of small features and those with fine spatial textures such as suburban, road networks and windfarms. We also look forward to the development of CLC+, the new generation of CORINE land mapping, and the improvements it could make.

Unsupervised Multiple Change Detection in Remote Sensing Images via Generative Representation Learning Network

With abundant temporal, spectral, and spatial information, multispectral images are proficient for acquiring a superior comprehension of the Earth’s condition and its changes, which enables the achievement of multiple change detection (CD) tasks. However, high temporal, spatial, and spectral information of data brings obstacles to perform multiple change analysis due to the lack of effective feature extraction operation. In addition, the traditional multiple CD methods rely too much on manual participation. Here, a generative representation learning network (GRN) and a cyclic clustering technique are combined into a unified model, which is driven to learn spatial–temporal–spectral features for unsupervised multiple CD. GRN aims to efficiently extract and merge robust difference information with a recurrent learning mechanism for self-adaptive classification refinement, in which different types of changes can be identified and highlighted. Furthermore, a cyclic training strategy is designed to refine the clustering-friendly features, in which similar change types are gradually merged into the same classes. Meanwhile, the number of change types will be optimized through a self-adaptive way and eventually converge to its stable state, which is close to the real distribution. Experimental results on real multispectral datasets demonstrate the effectiveness and superiority of the proposed model on multiple CD.

The effect of heat treatment on the nutritional and antioxidant content of different milk types

Heat treatments may cause some chemical and physicochemical changes in milk, although milk is a heat-stable system. Heat treatments can cause different changes in different types of milk. This study aimed to compare the effects of pasteurization and boiling on goat and cow milk's macromolecular contents, glutathione levels, and superoxide dismutase activities. The protein level of both types of milk decreased with the pasteurization process, and boiling also reduced the protein level of goat milk. Both heat treatments reduced superoxide dismutase activity and glutathione levels in both types of milk. While the boiling process did not change the cow's milk lactose level, it increased the goat milk lactose level. It was determined that pasteurization reduced the lactose level in both types of milk. Pasteurization did not change the fat level in cow milk but decreased the fat level in goat milk. In conclusion, cow milk was less affected by these heat treatments, which can be attributed to having large fat globules, high lactose concentration, and high heat resistance protein content compared to goat milk.

Mechanism of strain-induced magnetic properties changes for metal magnetic memory technology on atomic scale

Magnetic non-destructive testing technology is widely used to detect stresses and defects in ferromagnetic materials based on the magneto-mechanical coupling effect. In the existing studies, calculated are the magnetic moment variations of the &lt;i&gt;α&lt;/i&gt;-Fe system under axial tension and compression by using first-principles study, and the magneto-mechanical coupling mechanism is preliminarily discussed at an atomic level for the magnetic testing technology. In this work, taking the more complex doping systems Fe-C and Fe-Mn for examples, under different loading conditions of tension, compression and shearing, the coupling mechanisms such as the magnetic moment changes in different types of atomic doping systems are discussed in detail. The results show that the &lt;i&gt;α&lt;/i&gt;-Fe and doping systems follow different changing laws of magnetic moments and energy under different types of strains. The detailed analyses of the density of states, the band structure, and the atomic magnetic moment show that doping elements change the morphology of band structure and the peak value of density of states by affecting the magnetic moment of Fe atoms, which leads the changing laws of magnetic moment and energy to be different from each other. In this work, discussed are the magneto-mechanical effects on the atomic level for ferromagnetic materials with different loading types, different doping elements and different element content. The results can be used as an important part of the multi-field coupling mechanism for magnetic testing technology.