Individual Segments Research Articles

Implications of Activity Rate Characterization and Fault-Based Partitioning on Probabilistic Seismic Hazard Assessment: A Case Study of Extensional Tectonic Regime in Western Anatolia, Türkiye

ABSTRACT Complex fault geometries with multiple sets of inclined active faults pose a challenge to the accurate representation of fault-based seismic sources in probabilistic seismic hazard assessment (PSHA). In the absence of slip rates associated with fault segments and the presence of sparse seismic and geodetic data, the estimation of segment-specific activity rate includes significant uncertainty. This study proposes a comprehensive procedure for defining the segment-specific activity rates and associated uncertainties in fault-based PSHA for extensional tectonic regimes and applies the procedure in the northern margin of Western Anatolian Extensional Province. The seismic sources are modeled as rupture systems with individual fault segments using the connections between available active fault traces, first-order geological complexities, earthquake catalog, and focal mechanism solutions. Alternatives to estimate and partition segment-specific activity rates based on annual slip rate, seismicity rate, and moment rate are explored. Each alternative is implemented in PSHA, and the results are compared in terms of peak ground acceleration (PGA) maps for a 475-year return period. A comparison of the 475 yr PGA maps showed that the activity rates based on annual slip rates translate into higher hazard estimates and a more uniform distribution of PGA; whereas, the activity rates based on seismicity and moment rate result in a PGA distribution that is more sensitive to the occurrence and location of previous large-magnitude events. The approach utilized to partition activity rates among parallel segments has a noticeable effect in the areas where highly asymmetric fault activity is inferred from morphology. Hence, alternative approaches for estimation and partition of activity rates are combined to model the epistemic uncertainty in segment-specific activity rates, and a repeatable procedure is developed to build fault-based seismic source models in the presence of complex fault geometries.

DIGITALIZATION OF IRRIGATION SYSTEMS FROM WATER MANAGEMENT MAPS

With the increasing intensity of evapotranspiration caused by the changing climate, there is a growing need for water. This is especially true in locations where water-intensive vegetables are grown in intensive agriculture. Historically, irrigation systems were built in many intensive agriculture areas in Czechia, but they fell out of use, and evidence of their location was lost. However, Water Management Maps, which were only issued in paper form and have never been fully digitized, can provide evidence about the location of these large-scale irrigation systems. In this paper, we present a method for digitizing irrigation systems using the segmentation and classification of individual segments in the ArcGIS environment. The resulting raster is converted to polygons and is blended with the Land Parcel Identification System layer, resulting in a layer of irrigated land. Two statistical analyses were performed on this layer: statistics of the areas corresponding to the individual source watercourses, and statistics of the type of source.

Whole-Brain Vascular Architecture Mapping Identifies Region-Specific Microvascular Profiles In Vivo.

The novel MR imaging technique of vascular architecture mapping allows in vivo characterization of local changes in cerebral microvasculature, but reference ranges for vascular architecture mapping parameters in healthy brain tissue are lacking, limiting its potential applicability as an MR imaging biomarker in clinical practice. We conducted whole-brain vascular architecture mapping in a large cohort to establish vascular architecture mapping parameter references ranges and identify region-specific cortical and subcortical microvascular profiles. This was a single-center examination of adult patients with unifocal, stable low-grade gliomas with multiband spin- and gradient-echo EPI sequence at 3T using parallel imaging. Voxelwise plotting of resulting values for gradient-echo (R2*) versus spin-echo (R2) relaxation rates during contrast agent bolus administration generates vessel vortex curves that allow the extraction of vascular architecture mapping parameters representative of, eg, vessel type, vessel radius, or CBV in the underlying voxel. Averaged whole-brain parametric maps were calculated for 9 parameters, and VOI analysis was conducted on the basis of a standardized brain atlas and individual cortical GM and WM segmentation. Prevalence of vascular risk factors among subjects (n = 106; mean age, 39.2 [SD, 12.5] years; 56 women) was similar to those in the German population. Compared with WM, we found cortical GM to have larger mean vascular calibers (5.80 [SD, 0.59] versus 4.25 [SD, 0.62] P < .001), increased blood volume fraction (20.40 [SD, 4.49] s-1 versus 11.05 [SD, 2.44] s-1; P < .001), and a dominance of venous vessels. Distinct microvascular profiles emerged for cortical GM, where vascular architecture mapping vessel type indicator differed, eg, between the thalamus and cortical GM (mean, -2.47 [SD, 4.02] s-2 versus -5.41 [SD, 2.84] s-2; P < .001). Intraclass correlation coefficient values indicated overall high test-retest reliability for vascular architecture mapping parameter mean values when comparing multiple scans per subject. Whole-brain vascular architecture mapping in the adult brain reveals region-specific microvascular profiles. The obtained parameter reference ranges for distinct anatomic and functional brain areas may be used for future vascular architecture mapping studies on cerebrovascular pathologies and might facilitate early discovery of microvascular changes, in, eg, neurodegeneration and neuro-oncology.

Rupture segmentation on the East Anatolian fault (Turkey) controlled by along-strike variations in long-term slip rates in a structurally complex fault system

Abstract The East Anatolian fault in Turkey exhibits along-strike rupture segmentation, typically resulting in earthquakes with moment magnitude (Mw) up to 7.5 that are confined to individual segments. However, on 6 February 2023, a catastrophic Mw 7.8 earthquake struck near Kahramanmaraş (southeastern Turkey), defying previous expectations by rupturing multiple segments spanning over 300 km and overcoming multiple geometric complexities. We explore the mechanics of successive single- and multi-segment ruptures using numerical models of the seismic cycle calibrated to historical earthquake records and geodetic observations of the 2023 doublet. Our model successfully reproduces the observed historical rupture segmentation and the rare occurrence of multi-segment earthquakes. The segmentation pattern is influenced by variations in long-term slip rate along strike across the kinematically complex fault network between the Arabian and Anatolian plates. Our physics-based seismic cycle simulations shed light on the long-term variability of earthquake size that shapes seismic hazards.

ИЗУЧЕНИЕ БИОЛОГИЧЕСКОЙ ПОЛНОЦЕННОСТИ СПЕРМАТОЗОИДОВ В СПЕРМЕ С БАКТЕРИАЛЬНОЙ ОБСЕМЕНЕННОСТЬЮ

The aim of the study is to investigate the relationship between bacterial contamination of bull semen and the biological value of spermatozoa, as well as reproductive performance. The object of the study was Holstein bulls (n = 17). The material for the study was frozen-thawed semen of bulls in the amount of 61 samples. Microbiological studies of sperm doses were carried out using standard nutrient media in accordance with GOST 32222-2013. The total number of microorganisms was calculated by the number of grown colony-forming units (CFU/ml) in 1 cm3, with subsequent determination of their morphological pro-perties. Sperm motility and morphology were determined using the Argus CASA program. The state of DNA in spermatozoa was studied by the acridine orange test (AO test) using a fluorescence microscope. The study revealed a high positive correlation (r = +0.940**) between the number of microorganisms in sperm and pathology in individual sperm segments. Analysis of variance confirmed a statistically significant effect of the number of microorganisms on sperm morphology F = 123.2 (p=0.000). The correlation relationship between the fragmentation index and the content of microorganisms has a high value (r = +0.965**). Sperm motility negatively correlates (r = –0.768**) with the number of microorganisms (CFU/ml). In the presence of Mycoplasma spp in the samples, the number of cows with fruitful insemination was 35.5 % due to reduced motility, morphology and high fragmentation of sperm DNA (58 %). Sperm motility in samples containing Mycoplasma spp averages 24.5 %, while in samples without these microorganisms it is 48.9 %. Thus, it can be assumed that the presence of Mycoplasma spp in samples and high bacterial contamination rates lead to to lower reproductive performance in bulls.

Reconstruction of design principles of a late Gothic net vault using 3D laser scanning

Precise knowledge of the principles according to which vaults were designed and constructed and how they were implemented in construction not only forms a decisive basis for their preservation as monuments. These principles also provide information about the vaults shape finding and technology of their construction. The load-bearing behavior in its current state can also be traced back to certain premises. Using the example of the late Gothic net vault in the St. Peter and Paul church in Lutherstadt Eisleben, this article shows how design and construction principles of vaults can be precisely deduced with the help of known monitoring methods, tachymetry, and terrestrial three-dimensional (3D) laser scanning. The results of reverse engineering refer to the application of design principles that are relevant in the late Gothic period, such as the principal arch or the plan specifications for maintaining the heights of the ribbed intersections of figured vaults. Due to the artisanal nature of the building process at the time of their construction, individual segments of the vaults may well deviate more significantly from these rules. The fact that these apparent rule deviations may be responses to inadequacies at the project level, from planning to execution, is also explored.

Conceptual shaking table tests for resilient segmental bridge columns with resettable sliding joints

The ever-increasing demand for accelerated construction of urban bridges or viaducts calls for more prefabrication with various joint schemes, while satisfying the need for seismic resilience. On the other hand, segmentation into components of various shapes and morphologies is common for multifunctional structures found in living organisms and ancient Chinese timber structures, which also inspires the development of resilient segmental bridge column design. To provide further insights into the cost-effective resilient design of precast bridge columns, this paper suggests the use of resettable sliding joints (RSJ) comprising lubricated gently-inclined joint interface with partially debonded tendon arrangement, essentially forming a hybrid sliding and rocking seismic isolation system of precast segmental structures. The deformability of the proposed design is high. Similitude analysis was conducted considering the rigid body behavior of individual segments under seismic input. It presents the conceptual shaking table testing of four 1:12 prestressed segmental column models fabricated with special three-dimensional (3D) printed plastic molds and interface treatment. A motion capture system was installed for accurate 3D displacement data acquisition and five typical major near-fault earthquake records were selected. Among all specimens, the RSJ segmental column could reach a high damping ratio of about 20 % during the tests, and it outperformed the others in many aspects: (i) a tolerable maximum top drift of about 1 %; (ii) a moderate percentage of transient rocking of about 20 %; (iii) an evenly distributed sliding displacement among all sliding joints; and (iv) negligible residual sliding drift at sliding joints. These superior attributes of RSJ segmental column are consistent under excitation of consecutive rounds of different major earthquake records, indicating high robustness as well as high seismic resilience in the design of RSJ segmental columns.

Att-Mask R-CNN: an individual tree crown instance segmentation method based on fused attention mechanism

Tree detection and canopy area measurement are important and difficult tasks in forest inventory, which are important for understanding forest stand structure. This study utilized remotely piloted aircraft (RPA) aerial photography technology to collect remote sensing images of forests in Xiong County, China, creating a dataset comprising 1200 images of six tree species. Based on this dataset, the paper proposes an optimized model, Att-Mask R-CNN, for canopy detection and segmentation. Att-Mask R-CNN outperforms the original models (Mask R-CNN and MS R-CNN) by achieving 65.29% mean average precision for detection, 80.44% mean intersection over union for segmentation, and 90.67% overall recognition rate for the six tree species. In addition, a pixel statistics method based on segmentation masks is introduced for estimating the vertical projected area of individual tree crowns, and comparisons between the measured and predicted vertical projected area of the crowns of six tree species (100 trees of each class) show an overall goodness-of-fit R2 of 85% and a relative root-mean-square error rRMSE of 12.81%. By using remote sensing images from RPAs and optimizing existing deep learning models, the detection and segmentation of individual tree canopies can be achieved, resulting in a more accurate understanding of forest structure, which provides scientific support for forest management and resource monitoring.

Everyday artificial intelligence unveiled: Societal awareness of technological transformation

Research background: As Artificial Intelligence (AI) weaves into the fabric of daily life, its societal and economic implications underscore the urgency of embracing an environment conducive to its informed adoption. This requires a sophisticated understanding of the societal perception and adaptability to AI, emphasizing the importance of developing comprehensive AI literacy. Purpose of the article: This study inquiries into the sociodemographic underpinnings of AI literacy, aiming to demystify how knowledge about AI's capabilities in everyday tasks varies across individual population segments. It allows us to define the basic determinants that influence the differences in the individual population structures. It also reveals the potential risks associated with the use of AI. Methods: This study investigates the awareness of Artificial Intelligence (AI) in daily lives of the Czech population, focusing on the influence of socio-demographic factors. Utilizing computer-assisted web interviewing, we surveyed 1,041 respondents in April 2023, ensuring representativeness by applying quotas for age, gender, education, region, and residential area size. Our investigation spanned AI applications in sectors like customer service, music playlist recommendation, email sorting, healthcare, online shopping, and home devices. Findings &amp; value added: Findings taken from descriptive statistics reveal variable AI awareness levels across different domains, with younger demographics exhibiting notably lower awareness in several areas. Regression analysis highlighted that awareness is significantly associated with gender, age, and education level. Regression analysis showed that males, younger age groups and those with higher levels of education were more likely to correctly answer majority of questions about the role of AI in everyday life. These insights are crucial for stakeholders aiming to enhance AI literacy, tailor communication strategies, and develop digital platforms, offering guidance for policymakers and market analysts in optimizing AI-related initiatives.

Boundaries, Structure, and Integrity of the Occupation Layer at the Gordinskoye I (Guryakar) Fortified Settlement, Udmurtia (9th–13th Centuries), According to Multispectral Imaging

Science-based studies at Gordinskoye I (Guryakar) fortified settlement in northern Udmurtia (9th–13th centuries AD) were carried out. The occupation layer had been largely destroyed by plowing. The statistical analysis of multispectral images and segmentation of the generated images of vegetation distribution make it possible to assess the integrity of the occupation layer. The preliminary conclusion about the presence of its parts, varying in thickness, is based on the analysis of the configuration of areas of segmented multispectral images and the correspondence of diverse segments to relief features. Assessment of archaeological context and of the preservation of occupation layer (superficially disrupted, replaced, or transported) is possible only with the use of geophysical and soil studies and targeted excavations. The available reference data allow us to interpret individual segments present solely on the flat surface of the promontory. The assessment of the parameters of the occupation layer within the entire survey area is possible through the extrapolation of properties of vegetation segments with known characteristics. Based on the totality of data, it can be assumed that the built-up area of Guryakar was limited by the outer fortification line. A thick occupation layer is localized on the promontory, within two inner structural parts of the site. Two other parts of Guryakar, situated between the residence area and the outer fortification line, were probably used for utility purposes and manufacture. The deposition of the medieval occupation layer was less intense there. Additional markers of the fortifications, delimiting the boundaries of the settlement’s structural parts, are vertically oriented linear areas of the transported layer on slopes of the promontory.

Individual Tree Identification and Segmentation in Pinus spp. Stands through Portable LiDAR

Forest inventories are essential for sustainable forest management. In inventories at the tree level, all the information is linked to individuals: species, diameter, height, or spatial distribution, for example. Currently, the implementation of Portable LiDAR (PLS) is being studied, aiming to digitalize forest environments and increase the reliability of forest observations. Performing automatic individual tree identification (ITD) and segmentation (ITS) is essential for the operational implementation of PLS in forestry. Multiple algorithms have been developed for performing these tasks in LiDAR point clouds. Their performance varies according to the LiDAR system and the characteristics of the stand. In this study, the performance of several ITD and ITS algorithms is analyzed in very high-density PLS point clouds in Pinus species stands with a varying presence of understory, shrubs, and branches. The results showed that ITD methods based on finding trunks are more suitable for tree identification in regular stands with no understory. In the ITS process, the methods evaluated are highly conditioned by the presence of understory and branches. The results of this comparison help to identify the most suitable algorithm to be applied to these types of stands, and hence, they might enhance the operability of PLS systems.

CP-Net: Instance-aware part segmentation network for biological cell parsing

Instance segmentation of biological cells is important in medical image analysis for identifying and segmenting individual cells, and quantitative measurement of subcellular structures requires further cell-level subcellular part segmentation. Subcellular structure measurements are critical for cell phenotyping and quality analysis. For these purposes, instance-aware part segmentation network is first introduced to distinguish individual cells and segment subcellular structures for each detected cell. This approach is demonstrated on human sperm cells since the World Health Organization has established quantitative standards for sperm quality assessment. Specifically, a novel Cell Parsing Net (CP-Net) is proposed for accurate instance-level cell parsing. An attention-based feature fusion module is designed to alleviate contour misalignments for cells with an irregular shape by using instance masks as spatial cues instead of as strict constraints to differentiate various instances. A coarse-to-fine segmentation module is developed to effectively segment tiny subcellular structures within a cell through hierarchical segmentation from whole to part instead of directly segmenting each cell part. Moreover, a sperm parsing dataset is built including 320 annotated sperm images with five semantic subcellular part labels. Extensive experiments on the collected dataset demonstrate that the proposed CP-Net outperforms state-of-the-art instance-aware part segmentation networks.

An Individual Tree Detection and Segmentation Method from TLS and MLS Point Clouds Based on Improved Seed Points

Individual Tree Detection and Segmentation (ITDS) is a key step in accurately extracting forest structural parameters from LiDAR (Light Detection and Ranging) data. However, most ITDS algorithms face challenges with over-segmentation, under-segmentation, and the omission of small trees in high-density forests. In this study, we developed a bottom–up framework for ITDS based on seed points. The proposed method is based on density-based spatial clustering of applications with noise (DBSCAN) to initially detect the trunks and filter the clusters by a set threshold. Then, the K-Nearest Neighbor (KNN) algorithm is used to reclassify the non-core clustered point cloud after threshold filtering. Furthermore, the Random Sample Consensus (RANSAC) cylinder fitting algorithm is used to correct the trunk detection results. Finally, we calculate the centroid of the trunk point clouds as seed points to achieve individual tree segmentation (ITS). In this paper, we use terrestrial laser scanning (TLS) data from natural forests in Germany and mobile laser scanning (MLS) data from planted forests in China to explore the effects of seed points on the accuracy of ITS methods; we then evaluate the efficiency of the method from three aspects: trunk detection, overall segmentation and small tree segmentation. We show the following: (1) the proposed method addresses the issues of missing segmentation and misrecognition of DBSCAN in trunk detection. Compared to using DBSCAN directly, recall (r), precision (p), and F-score (F) increased by 6.0%, 6.5%, and 0.07, respectively; (2) seed points significantly improved the accuracy of ITS methods; (3) the proposed ITDS framework achieved overall r, p, and F of 95.2%, 97.4%, and 0.96, respectively. This work demonstrates excellent accuracy in high-density forests and is able to accurately segment small trees under tall trees.

Mechanical and structural properties of articular cartilage and subchondral bone in human osteoarthritic knees.

Knee osteoarthritis (OA), characterized by multiple joint tissue degenerations, remains a significant clinical challenge. Recent evidence suggests that crosstalk within the osteochondral unit may drive OA progression. Although structural-biomechanical properties of bone and cartilage have been studied, potential interaction within the osteochondral unit in the context of OA has yet to be investigated. We performed comprehensive structural and biomechanical quantification of the cartilage, subchondral bone plate (SBP), and subchondral trabecular bone (STB) using 101 osteochondral cores collected from tibial plateaus of 12 control human cadavers (CT, 5 male/7 female) and 19 patients undergoing total knee replacement (OA, 6 male/13 female). For each sample, we quantified SBP microstructure, plate-and-rod morphological properties of the STB using individual trabecula segmentation, and morphological and compositional properties of the articular cartilage. We also performed indentation testing on each compartment of the osteochondral unit to extract the respective structural-mechanical properties. Cartilage thickness was lower in moderate and severe OA regions, while Osteoarthritis Research Society International score was higher only in severe OA regions. GAG content did not change in any OA region. Aggregate and shear moduli were lower only in severe OA regions, while permeability was lower only in moderate OA regions. In the SBP, thickness and tissue mineral density were higher in moderate and severe OA regions. Tissue modulus of STB was lower in moderate OA regions despite a thicker and more mineralized SBP; this deterioration was not observed in severe OA regions. Regression analysis revealed strong correlations between cartilage and STB properties in CT; these correlations were also found in moderate OA regions but were not observed in severe OA regions. In summary, our findings comprehensively characterize the human OA osteochondral unit. Importantly, uncoupling cartilage and subchondral bone structural-mechanical properties may be a hallmark of OA.

A Hybrid Method for Individual Tree Detection in Broadleaf Forests Based on UAV-LiDAR Data and Multistage 3D Structure Analysis

Individual tree detection and segmentation in broadleaf forests have always been great challenges due to the overlapping crowns, irregular crown shapes, and multiple peaks in large crowns. Unmanned aerial vehicle (UAV)-borne light detection and ranging (LiDAR) is a powerful tool for acquiring high-density point clouds that can be used for both trunk detection and crown segmentation. A hybrid method that combines trunk detection and crown segmentation is proposed to detect individual trees in broadleaf forests based on UAV-LiDAR data. A trunk point distribution indicator-based approach is first applied to detect potential trunk positions. The treetops extracted from a canopy height model (CHM) and the crown segments obtained by applying a marker-controlled watershed segmentation to the CHM are used to identify potentially false trunk positions. Finally, the three-dimensional structures of trunks and branches are analyzed at each potentially false trunk position to distinguish between true and false trunk positions. The method was evaluated on three plots in subtropical urban broadleaf forests with varying proportions of evergreen trees. The F-score in three plots ranged from 0.723 to 0.829, which are higher values than the F-scores derived by a treetop detection method (0.518–0.588) and a point cloud-based individual tree segmentation method (0.479–0.514). The influences of the CHM resolution (0.25 and 0.1 m) and the data acquisition season (leaf-off and leaf-on) on the final individual tree detection result were also evaluated. The results indicated that using the CHM with a 0.25 m resolution resulted in under-segmentation of crowns and higher F-scores. The data acquisition season had a small influence on the individual tree detection result when using the hybrid method. The proposed hybrid method needs to specify parameters based on prior knowledge of the forest. In addition, the hybrid method was evaluated in small-scale urban broadleaf forests. Further research should evaluate the hybrid method in natural forests over large areas, which differ in forest structures compared to urban forests.

Diabetic technology in India: Status, barriers, and future prospects

&lt;p&gt;This article provides a comprehensive review of diabetic technology in India. Researchers are persistently integrating novel technologies and enhanced medical knowledge to offer further avenues for improving the well-being of individuals with diabetes. Continuous patient engagement is essential by using highly effective technologies that address pertinent issues, as certain problems can be resolved with precision. India has a demographically large population, characterized by a substantial segment of individuals with diabetes. The estimated prevalence of diabetes in India is approximately 8%, with type 2 diabetes accounting for half of these occurrences. India’s per capita expenditure on health constantly falls behind that of other developing countries. Refined statistical data indicate that India’s healthcare expenditure is &amp;lt;50% of the average expenditure observed in the Organization for Economic Co-operation and Development (OECD) countries.&lt;/p&gt;

The role of the innovation ecosystem as a tool for innovation development

The development of various industrial sectors is one ofthe priority tasks of the state, the solution of which is toimprove the quality and life expectancy of the population.According to the authors of the article, an innovationecosystem should be understood as an open dynamicsystem that provides collaborative forms of interaction between actors, taking into account the non-hierarchical relationships that have developed within the institutional ecosystem of enterprises, regions, and countries acting inthe direction of sustainable advanced innovativedevelopment based on modern technological concepts.Modern innovation ecosystems are created and functionto overcome individual limitations, maximise resourceuse, and distribute risks and costs to better achievecommon goals. This approach differs from the traditionalones in that it takes into account the current level ofeconomic development, taking into account the processesof digital transformation of all spheres of activity andprovides an integrated approach to the formation of aninnovation ecosystem. The article concludes that industry is currentlydeveloping as a traditional innovation system. The analysis of different views and main approaches tounderstanding innovation ecosystems allowed to identifythe advantages of ecosystems in comparison withtraditional innovation systems and to justify the need todevelop industry as an innovation ecosystem. A moredetailed study was carried out on the basis of market datafor various types of equipment. These conclusions wereconfirmed when analysing the prerequisites for theformation of an innovation ecosystem based on theresults of the analysis of the current state of theequipment market. A number of systemic problems canbe identified that are common to all stages of productionand circulation of various products: limited financialresources of manufacturers for development (researchand development, sales and promotion of products);scientific and technological lagging behind the level ofworld leaders; difficulty in commercialisingdevelopments. These problems have a complex impact on thedevelopment of the industry as a whole and its individual product segments. Eliminating these constraints will helpcreate favourable conditions for the machinery marketand unlock its potential.

A video object segmentation-based fish individual recognition method for underwater complex environments

Currently, aquaculture methods tend to combine scale and intelligence, which saves manpower and improves the survival rate of seafood at the same time. High-precision and high-efficiency fish individual recognition can provide key technical support for fish disease detection, feeding habits, body condition, etc. In the realm of intelligent aquaculture, it provides robust data support for precision fish farming. However, the current research methods for individual fish recognition struggle to maintain the network model's focus on the fish body in real marine underwater complex environments (e.g., environmental background interference such as coral reefs, overlap between fish bodies, light noise, etc.), leading to unsatisfactory recognition results. To this end, this paper proposes a method for fish individual recognition in underwater complex environments based on video object segmentation, which consists of three parts, including a fish individual segmentation detection module, a fish individual recognition module, and an all-in-one visualization module. The work adopts a combination of deep learning methods and video object segmentation algorithms to solve the problem of low attention and poor detection accuracy of fish individuals in real underwater complex environments, which effectively improves the accuracy and efficiency of fish individual recognition, and analyzes and discusses the comparison of recognition effects using different weights. The results of the simulation experiments show that the key metric Rank1 value of the method achieves more than 96% accuracy on the public datasets DlouFish, WideFish, and the Fish-seg dataset produced in this paper, and improves over the state-of-the-art methods for fish individual recognition by 2.23%, 1.33%, and 1.25%, respectively.

Importance of T1-Mapping Sequence in Patients with Hypertrophic Cardiomyopathy without Foci of Non-Ischemic Myocardial Injury in Late Gadolinium Enhancement Sequence.

The aim of this study was to assess the importance of T1-mapping sequences in the diagnosis of hypertrophic cardiomyopathy (HCM) in patients without foci of non-ischemic myocardial injury in classic cardiac magnetic resonance (CMR) sequences. Two groups were compared: 28 patients with HCM, without any foci of myocardial injury in the late gadolinium enhancement (LGE) sequence (HCM group), and 28 patients without cardiomyopathy (CON group). Classic CMR sequences and T1-mapping sequences were performed. The following parameters were assessed: T1 time of the whole left ventricular myocardium, T1 time of myocardium in the basal, middle and apical layers of the left ventricle, and T1 time in individual segments of the left ventricular myocardium. Myocardial extracellular volume (ECV) was assessed similarly. ECV was significantly higher in the HCM group than in the CON group, for the whole left ventricular myocardium, for the basal and apical layers of the left ventricle, and for segments 1-3, 8, and 13-16 of the left ventricle. Regression analysis showed that a higher left-ventricular mass index (LVMI), a higher body mass index and older age are factors independently associated with a higher ECV of the whole myocardium but only in the group with LVMI ≥ 131.84 g/m2. In patients with HCM without foci of non-ischemic myocardial injury, higher ECV values of the left ventricular myocardium are observed.

Fractures in women with type 2 diabetes are associated with marked deficits in cortical parameters and trabecular plates.

The basis for increased fracture risk in type 2 diabetes (T2DM) is not well understood. In this multi-ethnic, population-based study (n = 565), we investigated bone microstructure, trabecular plate/rod morphology, and mineralization in women with T2DM (n = 175) with and without fracture using a second-generation HRpQCT and individual trabecula segmentation and mineralization (ITS; ITM). Covariate-adjusted aBMD was 3.0%-6.5% higher at all sites (all p<.005) in T2DM vs controls. By HRpQCT, T2DM had higher covariate-adjusted trabecular vBMD (5.3%-6.4%) and number (3.8%-5.1%) and greater cortical area at the radius and tibia. Covariate-adjusted cortical porosity was 10.0% higher at the tibia only in T2DM vs controls, but failure load did not differ. Among women with T2DM, those with adult atraumatic fracture (n = 59) had 5.2%-8.5% lower adjusted aBMD at all sites by DXA compared with those without fracture (n = 103). By HRpQCT, those with fracture had lower adjusted total vBMD and smaller cortical area (10.2%-16.1%), lower cortical thickness (10.5-15.8%) and lower cortical vBMD associated with 18.1 and 17.2% lower failure load at the radius and tibia, respectively (all p<.05); plate volume and thickness were 5.7% and 4.7% lower, respectively, (p<.05) while rod volume fraction was 12.8% higher in the fracture group at the tibia only. Sodium glucose cotransporter 2 inhibitor users (SGLT2i; n = 19), tended to have lower radial rod tissue mineral density by ITS (p=.06). GLP1 agonist users (n = 19) had trabecular deficits at both sites and higher cortical porosity and larger pores at the distal tibia. In summary, T2DM is associated with increased cortical porosity while those with T2DM and fracture have more marked cortical deficits and fewer trabecular plates associated with lower failure load.