Change Detection Research Articles

Nonenzymatic dual glucose sensing on boronic acid modified zeolitic imidazolate framework-67 nanoparticles for diabetes management.

For early diabetes identification and management, the progression of an uncomplicated and exceedingly responsive glucose testing technology is crucial. In this study, we present a new sensor incorporating a composite of metal organic framework (MOF) based on cobalt, coated with boronic acid to facilitate selective glucose binding. Additionally, we successfully employed a highly sensitive electro-optical immunosensor for the detection of subtle changes in concentration of the diabetes biomarker glycated haemoglobin (HbA1c), using zeolitic imidazolate framework-67 (ZIF-67) coated with polydopamine which further modified with boronic acid. Utilizing the polymerization characteristics of dopamine and the NH2 groups, a bonding structure is formed between ZIF-67 and 4-carboxyphenylboronic acid. ZIF-67 composite served as an effective substrate for immobilising 4-carboxyphenylboronic acid binding agent, ensuring precise and highly selective glucose identification. The sensing response was evaluated through both electrochemical and optical methods, confirming its efficacy. Under optimized experimental condition, the ZIF-67 based sensor demonstrated a broad detection range of 50-500mg dL-1, a low limit of detection (LOD) of 9.87mg dL-1 and a high correlation coefficient of 0.98. Furthermore, the 4-carboxyphenylboronic acid-conjugated ZIF-67-based sensor platform exhibited remarkable sensitivity and selectivity in optical-based detection for glycated haemoglobin within the clinical range of 4.7-11.3%, achieving a LOD of 3.7%. These findings highlight the potential of the 4-carboxyphenylboronic acid-conjugated ZIF-67-based electro-optical sensor as a highly sensitive platform for diabetes detection.

Prototyping a wearable and stretchable graphene-on-PDMS sensor for strain detection on human body physiological and joint movements.

In the era of wearable electronic devices, which are quite popular nowadays, our research is focused on flexible as well as stretchable strain sensors, which are gaining humongous popularity because of recent advances in nanocomposites and their microstructures. Sensors that are stretchable and flexible based on graphene can be a prospective 'gateway' over the considerable biomedical speciality. The scientific community still faces a great problem in developing versatile and user-friendly graphene-based wearable strain sensors that satisfy the prerequisites of susceptible, ample range of sensing, and recoverable structural deformations. In this paper, we report the fabrication, development, detailed experimental analysis and electronic interfacing of a robust but simple PDMS/graphene/PDMS (PGP) multilayer strain sensor by drop casting conductive graphene ink as the sensing material onto a PDMS substrate. Electrochemical exfoliation of graphite leads to the production of abundant, fast and economical graphene. The PGP sensor selective to strain has a broad strain range of ⁓60%, with a maximum gauge factor of 850, detection of human physiological motion and personalized health monitoring, and the versatility to detect stretching with great sensitivity, recovery and repeatability. Additionally, recoverable structural deformation is demonstrated by the PGP strain sensors, and the sensor response is quite rapid for various ranges of frequency disturbances. The structural designation of graphene's overlap and crack structure is responsible for the resistance variations that give rise to the remarkable strain detection properties of this sensor. The comprehensive detection of resistance change resulting from different human body joints and physiological movements demonstrates that the PGP strain sensor is an effective choice for advanced biomedical and therapeutic electronic device utility.

Sedimentation field-flow fractionation for rapid phenotypic antimicrobial susceptibility testing: a pilot study.

The increase in antibiotic resistance is a major public health issue. The development of rapid antimicrobial susceptibility testing (AST) methods is becoming a priority to ensure early and appropriate antibiotic therapy. To evaluate sedimentation field-flow fractionation (SdFFF) as a method for performing AST in less than 3 h. SdFFF is based on the detection of early biophysical changes in bacteria, using a chromatographic-type technology. One hundred clinical Escherichia coli strains were studied. A calibrated bacterial suspension was incubated for 2 h at 37°C in the absence (untreated) or presence (treated) of five antibiotics used at EUCAST breakpoint concentrations. Bacterial suspensions were then injected into the SdFFF machine. For each E. coli isolate, retention times and elution profiles of antibiotic-treated bacteria were compared with retention times and elution profiles of untreated bacteria. Algorithms comparing retention times and elution profiles were used to determine if the strain was susceptible or resistant. Performance evaluation was done according to CLSI and the ISO standard 20776-2:2021 with broth microdilution used as the reference method. AST results from SdFFF were obtained in less than 3 h. SdFFF showed high categorical agreement (99.8%), sensitivity (99.5%) and specificity (100.0%) with broth microdilution. Results for each antimicrobial were also in agreement with the ISO 20776-2 recommendations, with sensitivity and specificity of ≥95.0%. This study showed that SdFFF can be used as a rapid, accurate and reliable phenotypic AST method with a turnaround time of less than 3 h.

210 Sleeping beauties: A daily time budget for individually housed research pigs at 6-d of age

Abstract The pre-wean period of development is difficult to manage and can impact the success of subsequent production stages. Behavior observations can provide insight into the health and welfare status of animals and allow for efficient and timely management interventions. A validated time budget can be utilized as a baseline for further comparisons and the detection of changes in behavior over time. In this project, 24-h of video data for 21 individually housed research pigs at 6 d of age was evaluated using a validated behavior ethogram. The behavior ethogram contained 5 main categories (inactive, consumption, exploratory, social, and other) and 23 behavior labels. The ethogram was applied using a validated sampling strategy, which included 5-min continuous sampling intervals at the start of each hour for active behaviors and 5-min instantaneous sampling intervals for inactive behaviors. The data from each behavior observation was quantified into durations and presented as percentage of observations spent performing a specific behavior label within the observation period. The daily time budget revealed the following time occupation across the 5 main behavior categories: inactive, 98.60% (lying, 85.43%; not lying, 13.14%); consumption, 0.30%; exploratory, 0.97%; social, 0.01%, and other, 0.12%. As expected for very young pigs, most of the day was inactive, with almost 21 h lying. The most performed behavior was “lying socially”, and this behavior represented 38.48% of the overall daily time budget. The remaining 46.95% of time spent lying was spent “lying sternal”, “lying lateral”, or “lying on an object” in the pen. The pigs are housed individually, but they are able to see, hear, and smell a neighboring pig through a plexiglass barrier. The behavior observation “lying socially” is defined as follows: “The piglet is lying (sternal or lateral) touching the plexiglass barrier. The piglet in the adjacent pen is lying directly on the other side of the barrier, and the piglets would be touching each other if the barrier was not in place.” This observation provides insight into the importance of social resting for young pigs.

Land use and land cover change in locally supported wildlife management areas in the Nyerere-Selous ecosystem, Tanzania

ABSTRACT Worldwide, there is conversion of natural habitats to other land cover types. These are explained by changes in land use and climate change. In Tanzania, environmental degradation happens also within and around protected areas. To mitigate the effects, wildlife management areas (WMAs) surrounding the borders of protected areas in Tanzania have been promoted. They are based on the concept of community based natural resources management (CBNRM). These WMAs have attracted adverse attention because there is a lack of active local participation and of equitable benefits for local people. Unsustainable activities have been reported in these areas. To determine the extent of these activities in WMAs in the Nyerere-Selous ecosystem in Southeastern Tanzania, the researchers did a change detection analysis using Landsat images. Analysis showed that the conditions of the vegetation within WMAs were considerably improving. Although almost all other categories of land use/cover had increased, socio-economic activities had decreased by 25% since the establishment of WMAs in 2003. This decrease is a positive sign towards attaining the goals of CBNRM.

MFMENet: multi-scale features mutual enhancement network for change detection in remote sensing images

ABSTRACT Change detection, an important task in remote sensing image analysis, has been extensively studied in recent years. However, change detection still faces problems such as difficulty in detecting small targets and incomplete edge detection. Furthermore, pseudo changes such as seasonal changes can also lead to many false detections. In response to these challenges, we propose the Multi-scale Features Mutual Enhancement Network (MFMENet), a simple yet efficient network. MFMENet maximizes feature utilization through mutual guidance and supplementation, enhancing the detection capabilities for small targets and edges. First, we use a lightweight feature extraction network to extract features, which mitigates the information loss caused by continuous downsampling of an overly deep network structure. Then, we design a Context Adaptive Interaction Module (CAIM) to realize the complementarity of feature information at different levels. This facilitates shallow features in gaining more semantic information and deep features in acquiring more texture information, thereby enhancing the model’s capability to capture more comprehensive edge features while effectively mitigating interference from pseudo changes. Finally, we introduce a Feature Aggregation Comparison Module (FACM), which uses a combination of aggregation and comparison methods to refine and enhance features. FACM can not only highlight the changed features but also retain more details, improving the model’s detection ability of small targets and edge details. The full utilization of features and effective mutual enhancement of information ensure the improvement of MFMENet’s performance in small target and edge detection. Extensive experiments on three publicly available datasets (LEVIR, DSIFN, and CDD) demonstrate that our approach achieves superior performance with fewer parameters compared to state-of-the-art methods in recent years. In comparison to these baseline methods, our proposed approach achieves improvements of 0.98%, 12.24%, and 2.03% in the IOU metric on the LEVIR, DSIFN, and CDD datasets, respectively, while utilizing only 1.1 M parameters.

Long‐term flood exposure assessment using satellite‐based land use change detection and inundation simulation: A 30‐year case study of the Bangkok Metropolitan Region

AbstractThe Bangkok Metropolitan Region (BMR), located in the Chao Phraya River basin delta, is particularly vulnerable to floods, with susceptibility heightened by geographical aspects and rapid urbanization. This study aimed to assess spatiotemporal flood exposure and allow proper flood‐risk recognition among all stakeholders through a three‐phase flood exposure assessment. First, land use and land cover (LULC) changes were identified based on a 30‐year Landsat time series. Second, built‐up areas that overlapped with past flood inundation maps were designated as flood exposure areas. Third, a rainfall‐runoff inundation (RRI) model simulated the 2011 Thailand Flood, the largest on record, by analyzing inundation depth implications across three decades. The findings revealed a dramatic increase in the use of built‐up areas and the associated flood exposure. In 1992, built‐up areas accounted for approximately 20% of the total area, sharply increasing to nearly 45% by 2022, according to the LULC classification. The flood exposure increased from 648.83 km2 in 1992 to 1681.26 km2 by 2022, demonstrating a linear trend. Notably, the catastrophic 2011 flood did not inhibit urbanization in flood‐prone areas, highlighting the need for robust policies, such as the segmentation of flood‐risk zones, to mitigate future exposure in the region.

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects.

Abstract TC-02: When to do a window trial

Abstract The window of opportunity in the short time between diagnosis and surgery for primary breast cancer has been exploited in trials of many endocrine agents. In these cases the therapeutic effect is antiproliferative and immunohistochemical detection of changes in Ki67 are considered a valid biomarker of likely long term efficacy. Trials have also been extended to assessment of inhibitors of growth factor receptors (eg lapatinib) and COX2 inhibitors. In these circumstances the mode of action is not purely inhibition of proliferation such that while changes in Ki67 may be of interest they should not be taken as necessarily reflecting the presence or absence of efficacy. It is also important to recognise that it may take longer than the time window available to get to a pharmacodynamic steady state. This is reflected by data from studies of tamoxifen and of GnRH analogues in premenopausal women. As well as being a window for assessing the biologic effectiveness of new agents it has been used as a means of assessing the prognosis of patients taking an established drug such as an aromatase inhibitor (AI) in the POETIC and an AI or tamoxifen in the ADAPT trial. Citation Format: M. Dowsett, S. Hurvitz. When to do a window trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr TC-02.

Auditory change detection and visual selective attention: association between MMN and N2pc.

While the auditory and visual systems each provide distinct information to our brain, they also work together to process and prioritize input to address ever-changing conditions. Previous studies highlighted the trade-off between auditory change detection and visual selective attention; however, the relationship between them is still unclear. Here, we recorded electroencephalography signals from 106 healthy adults in three experiments. Our findings revealed a positive correlation at the population level between the amplitudes of event-related potential indices associated with auditory change detection (mismatch negativity) and visual selective attention (posterior contralateral N2) when elicited in separate tasks. This correlation persisted even when participants performed a visual task while disregarding simultaneous auditory stimuli. Interestingly, as visual attention demand increased, participants whose posterior contralateral N2 amplitude increased the most exhibited the largest reduction in mismatch negativity, suggesting a within-subject trade-off between the two processes. Taken together, our results suggest an intimate relationship and potential shared mechanism between auditory change detection and visual selective attention. We liken this to a total capacity limit that varies between individuals, which could drive correlated individual differences in auditory change detection and visual selective attention, and also within-subject competition between the two, with task-based modulation of visual attention causing within-participant decrease in auditory change detection sensitivity.

CA-Markov prediction modeling for the assessment of land use/land cover change in two sub-basins of the Tocantins-Araguaia River Basin.

Due to the anthropogenic pressures of expansion areas for livestock and agricultural production in the Brazilian Cerrado, it is of paramount importance to understand the dynamics of land use/land cover (LULC) changes in this region. Thus, we investigated LULC changes in two sub-basins of the Tocantins-Araguaia River basin from 1997 to 2015 and consequently projected future changes for the timespan between 2030 and 2050. The Formoso sub-basin experienced significant expansion of agricultural and pasture areas, whereas the Sono sub-basin limited farmland expansion (more stable native vegetation) due to substantial protected areas, trends that were also observed for future projections (2030 and 2050). Pastureland in the Formoso sub-basin increased by 5.8%, while the Sono sub-basin saw significant gains in cultivated land, according to change detection analyses during the 1997-2015 period. High stability probabilities of no change (> 70%) for grassland areas in the Sono River sub-basin and pasturelands in the Formoso River sub-basin were computed. The CA-Markov model demonstrated a high consistency level with actual LULC classes for both sub-basins, as indicated by an overall Kappa coefficient above 0.8. Future projections for 2030 and 2050 show a substantial expansion of agriculture and pasture in both sub-basins, driven by specific factors such as soil organic carbon stocks, distance from rural settlements, and proximity to rivers. Short- and mid-term simulations indicate substantial expansion of agriculture and pasture in both basins, with potential adverse impacts on water erosion. Consequently, developing policies for soil management and sustainable land use planning is essential for agroecosystem sustainability, promoting a balanced approach to economic development while addressing climate change and anthropogenic challenges.

Comparative analysis of SAOCOM and Sentinel-1 data for surface soil moisture retrieval using a change detection method in a semiarid region (Douro River’s basin, Spain)

The growing interest in low-frequency SAR for soil parameter retrieval has led to the development of new active L-band satellites, that will provide novel surface soil moisture products and retrieval possibilities; however, due to data unavailability so far, limited applications have investigated the use of change detection models using L-band satellite SAR data. Since July 2020, high revisit time, high-resolution acquisitions by the Satélite Argentino de Observación COn Microondas (SAOCOM) Argentinian-Italian constellation have become accessible over Europe. Therefore, this research presents an investigation of the potential of multi-temporal L-band SAOCOM-1 for monitoring soil moisture variations underneath low and sparse agricultural vegetation. Moreover, it proposes a procedure for the mitigation of roughness contribution, by exploiting the entropy parameter derived from the dual-polarimetric decomposition. L-band sensitivity to soil moisture has been jointly evaluated in respect of Sentinel-1 C-band data by (1) comparing the temporal profiles of the backscattering coefficient, γ0, at VV and VH polarization, with the support of decomposition parameters (entropy and ᾱ), NDVI and precipitation data; (2) regression analysis with in situ soil moisture measurements, obtained by the REMEDHUS network in the Douro River basin (Spain); 3) evaluating the soil moisture retrievals obtained at C- and L- band using a change detection method. Finally, the effectiveness of the roughness normalization procedure for SAOCOM data has been validated using in situ data. L-band co-polarized γ0 has proved to be the best configuration for soil moisture inversion, being relatively insensitive to vegetation, as demonstrated by decomposition results and trend interpretation. Overall, regressions detected an R2 22% higher at L-band than C-band, with values up to 0.74 for VV (R̄2=0.32) and up to 0.47 for the VH band (R̄2=0.14). Co-polarized data obtained R2 on average 62.1% and 74.7% higher for SAOCOM and Sentinel-1. The retrieval models show an ubRMSD of 7.1% for SAOCOM data and 8.3% for Sentinel-1. The application of the proposed roughness normalization procedure to SAOCOM led to an ubRMSD of 6.7% improving the retrieved soil moisture trend by 7.9%. This exploratory analysis demonstrated SAOCOM data potential for soil moisture mapping and would serve as a foundation for more advanced retrieval procedures.

Clinical predictors of Alzheimer's disease-like brain atrophy in individuals with memory complaints.

The definition and assessment methods for subjective cognitive decline (SCD) vary among studies. We aimed to investigate which features or assessment methods of SCD best predict Alzheimer's disease (AD)-related structural atrophy patterns. We assessed 104 individuals aged 55+ with memory complaints but normal cognitive screening. Our research questions were as follows: To improve the prediction of AD related morphological changes, (1) Would the use of a standardized cognitive screening scale be beneficial? (2) Is conducting a thorough neuropsychological evaluation necessary instead of relying solely on cognitive screening tests? (3) Should we apply SCD-plus research criteria, and if so, which criterion would be the most effective? (4) Is it necessary to consider medical and psychiatric comorbidities, vitamin deficiencies, vascular burden on MRI, and family history? We utilized Freesurfer to analyze cortical thickness and regional brain volume meta-scores linked to AD or predicting its development. We employed multiple linear regression models for each variable, with morphology as the dependent variable. AD-like morphology was associated with subjective complaints in males, individuals with advanced age, and higher education. Later age of onset for complaints, complaints specifically related to memory, excessive deep white matter vascular lesions, and using medications that have negative implications for cognitive health (according to the Beers criteria) were predictive of AD-related morphology. The subjective cognitive memory questionnaire scores were found to be a better predictor of reduced volumes than a single-question assessment. It is important to note that not all SCD-plus criteria were evaluated in this study, particularly the APOE genotype, amyloid, and tau status, due to resource limitations. The detection of AD-related structural changes is impacted by demographics and assessment methods. Standardizing SCD assessment methods can enhance predictive accuracy.

Is the change deforestation? Using time-series analysis of satellite data to disentangle deforestation from other forest degradation causes

Protecting natural ecosystems requires monitoring approaches that work as early warning systems to avoid degradation and protect biodiversity. However, separating forest disturbance causes in change-detection pipelines is challenging due to the complex interplay of multiple drivers affecting vegetation. This study aims to detect deforestation in highly heterogeneous ecosystems. We used Landsat NDVI time-series data for testing three unsupervised change detection methods: 1) the non-parametric phenological anomaly detection (npphen), 2) the continuous change detection and classification (CCDC), and 3) the pruned exact linear time (PELT) algorithms. We used visual interpretation of Google Earth Pro high-resolution data (< 10 m) to depict deforestation, and natural-induced changes, like forest browning and fires, evaluating the performance of the unsupervised methods. Additionally, a Random Forest model trained with the outputs from detection algorithms together with elevation and radar vegetation index data were utilised to depict deforestation in a second step. While PELT slightly outperformed other methods for tracking general vegetation changes, with overall accuracies (OA) ranging from 0.78 to 0.99, depending on the vegetation type, it also showed the slowest deforestation tracking response. CCDC presented the fastest response and an OA between 0.78 and 0.95. Additionally, we observed a mean OA of only 0.47 when separating deforestation from other changes using only the unsupervised models. On the other hand, deforestation was accurately detected (OA = 0.93; kappa = 0.83) when using CCDC outputs within a secondary supervised classification, agreeing with selected citizen-based complaints from the Environmental Superintendence. The relatively fast response in deforestation tracking using CCDC makes it a viable alternative for near real-time monitoring. Commonly used unsupervised detection methods may be coupled with supervised techniques to depict vegetation change sources robustly. This application constitutes a step forward for managing and monitoring vegetation areas in highly complex and dynamic landscapes, like Mediterranean ecosystems.

Monitoring of Plant Ecological Units Cover Dynamics in a Semiarid Landscape from Past to Future Using Multi-Layer Perceptron and Markov Chain Model

Anthropogenic activities and natural disturbances cause changes in natural ecosystems, leading to altered Plant Ecological Units (PEUs). Despite a long history of land use and land cover change detection, the creation of change detection maps of PEUs remains problematic, especially in arid and semiarid landscape. This study aimed to determine and describe the changes in PEUs patterns in the past and present, and also predict and monitor future PEUs dynamics using the multi-layer perceptron-Markov chain (MLP-MC) model in a semiarid landscape in Central Zagros, Iran. Analysis of PEUs classification maps formed the basis for the identification of the main drivers in PEUs changes. First, an optimal time-series dataset of Landsat images were selected to derive PEUs classification maps in three periods, each separated by 16 years. Then, PEUs multi-temporal maps classified for period 1 (years 1986–1988) period 2 (years 2002–2004), and period 3 (years 2018–2020) were employed to analyze and predict PEUs dynamics. The dominant transitions were identified, and the transition potential was determined by developing twelve sub-models in the final change prediction process. Transitions were modeled using a Multi-Layer Perceptron (MLP) algorithm. To predict the PEU map for period 3, two PEUs classification maps of period 1 and period 2 were used using the MLP-MC method. The classified map and the predicted map of period 3 were used to evaluate and validate the predicted results. Finally, based on the results, transitions of future PEUs were predicted for the year 2036. The MLP-MC model proved to be a powerful model that can predict future PEUs dynamics that are the result of current human and managerial activities. The findings of this study demonstrate that the impact of anthropogenic processes and management activities will become visible in the natural environment and ecosystem in less than a decade.

Analysis of Land Use and Land Cover Change Detection for Indore District of Malwa Plateau Region Using Supervised Machine Learning

The main aim of this work is to find out LULC classes for the Indore region. For the last seven years, Indore has been crowned the cleanest city in India according to the latest ranking of 2023. In the Indian state of Madhya Pradesh, the city and district of Indore are situated on the southern edge of the Malwa plateau. The municipalities of the Indore district majorly cover Manpur, Depalpur, Goutampura, Mhow, Hatod, Betma, Rau and Sanwer, with an area of approximately 3900 square kilometers. In contrast, Indore covers a land area of approximately 530 square kilometres. It is in charge of the nation’s slow but steady industrialization and population growth found in the last three decades. The city was much smaller and had a far smaller population thirty years ago. However, to evaluate the changes and decide on Indore city's future planning, Indore has taken over responsibility for the city's economic activities in recent years. New appropriate patterns should be recommended based on local factors like topography characteristics. The satellite image with multiple spectrums was used for the investigation. ArcGIS is based on pixel-by-pixel supervised classification using the maximum likelihood approach of Landsat satellite images from 2003 and 2023. The ground area, agriculture, and urbanization are linked to the LULC features. The various categories of classified land use and land cover features include the area of built-up regions, water bodies, cropland, forest, and barren land taken to predict the broad changes. Remotely sensed Landsat 5 images of 2003 and 8 images of 2023 were utilized to identify changes to accomplish this goal. This work clarifies the comparison of LULC classes for the Indore region.

Long‐term soil change in the US Great Plains: An evaluation of the Haas Soil Archive

AbstractDiverse patterns of climate and edaphic factors challenge detection of soil property change in the US Great Plains. Because detectable soil change can take decades, insights into the trajectory of soil properties frequently require long‐term site monitoring and, where available, associated soil archives to enable comparisons with initial or baseline states. Unfortunately, few multi‐decadal soil change investigations have been conducted in this region. Here, we document effects of dryland cropping on a suite of soil properties by comparing matched historic (1947) and contemporary (2018) soil samples from the Haas Soil Archive at three sites in the US Great Plains: Moccasin, MT, Akron, CO, and Big Spring, TX. Current analytical methods were used to provide insight into changes in soil texture, pH, carbon, and micronutrients at 0‐ to 15.2‐cm and 15.2‐ to 30.5‐cm depths. Changes in direction and magnitude of soil properties over 71 years were site specific. Changes in textural class occurred at all sites, with Moccasin and Akron transitioning from loam to clay loam and Big Spring from sandy clay loam to sandy loam. The soil pH reaction class changed from slightly alkaline to moderately acid at Akron and slightly alkaline to moderately alkaline at Big Spring. At 0–15.2 cm, soil organic carbon decreased by 15% and 36% at Moccasin and Big Spring, respectively, but increased by 15% at Akron. Soil micronutrients generally declined at all sites. Weather‐related variables derived from air temperature and precipitation records were not correlated with soil change. Inferred factors contributing to soil change included on‐site management, inherent soil features, weather metrics not evaluated, or a combination thereof.

LiftoffTools: a toolkit for comparing gene annotations mapped between genome assemblies

In 2020 we published Liftoff, which was the first standalone tool specifically designed for transferring gene annotations between genome assemblies of the same or closely related species. While the gene content is expected to be very similar in closely related genomes, the differences may be biologically consequential, and a computational method to extract all gene-related differences should prove useful in the analysis of such genomes. Here we present LiftoffTools, a toolkit to automate the detection and analysis of gene sequence variants, synteny, and gene copy number changes.  We provide a description of the toolkit and an example of its use comparing genes mapped between two human genome assemblies.

[Expert recommendations for magnetic resonance imaging of muscle disorders].

Magnetic resonance (MRI) imaging of the skeletal muscles (muscle MRI for short) is increasingly being used in clinical routine for diagnosis and longitudinal assessment of muscle disorders. However, cross-centre standards for measurement protocol and radiological assessment are still lacking. The aim of this expert recommendation is to present standards for the application and interpretation of muscle MRI in hereditary and inflammatory muscle disorders. This work was developed in collaboration between neurologists, neuroradiologists, radiologists, neuropaediatricians, neuroscientists and MR physicists from different university hospitals in Germany. The recommendations are based on expert knowledge and afocused literature search. The indications for muscle MRI are explained, including the detection and monitoring of structural tissue changes and oedema in the muscle, as well as the identification of asuitable biopsy site. Recommendations for the examination procedure and selection of appropriate MRI sequences are given. Finally, steps for astructured radiological assessment are presented. The present work provides concrete recommendations for the indication, implementation and interpretation of muscle MRI in muscle disorders. Furthermore, it provides apossible basis for the standardisation of the measurement protocols at all clinical centres in Germany.

SELECTED PATHOLOGICAL CONDITIONS WITHIN THE CAVERNOUS SINUS

The neurosurgical literature shows that the effectiveness of the main sinus outflow in its main function depends on access to the anatomy and pathology in the presellar area and the development of neuroimaging methods. The pathology of the cavernous sinus is often revealed in imaging tests, which are often accompanied by other tests unrelated to the cause. Tumors located within the cavernous sinus pose a significant challenge for surgeons. Although it is often possible to decide to opt out, it is possible. Not only tumors can be located in the cavernous sinus and affect placement in any structure. The aim of this work is to raise awareness of pathological conditions within the cavernous sinus. Methods: Selected articles from Pubmed and specialist textbooks were analyzed in detail. We focused on selected pathological conditions occurring within the cavernous sinus, which a doctor may often encounter in his clinical work. Conclusion: The cavernous sinus is a place where tumors and other pathologies arise. The development of imaging diagnostics allows neurosurgeons to more precisely determine the place within the sinus that has changed, and access to it is quite a challenge. Numerous observations of clinical conditions are increasingly a source of knowledge that can provide a therapeutic trial, but also speed up the detection of potential changes.