Detailed Delineation Research Articles

Imaging Complex Structures of the Los Angeles Basin via Adjoint-State Travel-Time Tomography

ABSTRACT In this study, we present high-resolution seismic velocity models for the Los Angeles basin (LAB) and its adjacent area using adjoint-state travel-time tomography, fitting an extensive database of P- and S-wave travel times accumulated from 1980 to 2021. We select 151,193 first P-wave travel times and 149,997 first S-wave travel times from local earthquakes archived in the Southern California Earthquake Data Center to determine the velocity models, with earthquake locations updated at each iteration. With seismic stations spaced more than 3.5 km apart, our dataset has limited resolution in the uppermost 1–2 km. However, starting from three different initial models, our VP models, which are optimally imaged between 3 and 15 km, show similar velocity heterogeneity and provide a better fit to the observed first travel-time data compared to the Community Velocity Model-Harvard 15.1.0 and Community Velocity Model-Southern California Earthquake Center 4.26. Our models provide a detailed delineation of the subsurface structure beneath the LAB, revealing significant velocity variations across active faults, a 10-km-thick sequence of sedimentary rocks within the basin, and a distinct basin margin marked by transitions from low to high-velocities. In addition, these models highlight basement structures with elevated VP and VS located at depths of 9 to 12 km and beyond. Specifically, beneath the northeastern part of the basin, the models demonstrate improved accuracy and reliability in reflecting the linear relationship between VP and VS in mafic rocks. The accurate delineation of the basin’s structure provided by our models could also offer robust constraints for seismic response modeling and seismic hazard assessment in the region.

Highly accelerated non-contrast-enhanced time-resolved 4D MRA using stack-of-stars golden-angle radial acquisition with a self-calibrated low-rank subspace reconstruction.

To develop a highly accelerated non-contrast-enhanced 4D-MRA technique by combining stack-of-stars golden-angle radial acquisition with a modified self-calibrated low-rank subspace reconstruction. A low-rank subspace reconstruction framework was introduced in radial 4D MRA (SUPER 4D MRA) by combining stack-of-stars golden-angle radial acquisition with control-label k-space subtraction-based low-rank subspace modeling. Radial 4D MRA data were acquired and reconstructed using the proposed technique on 12 healthy volunteers and 1 patient with steno-occlusive disease. The performance of SUPER 4D MRA was compared with two temporally constrained reconstruction methods (golden-angle radial sparse parallel [GRASP] and GRASP-Pro) at different acceleration rates in terms of image quality and delineation of blood dynamics. SUPER 4D MRA outperformed the other two reconstruction methods, offering superior image quality with a clear background and detailed delineation of cerebrovascular structures as well as great temporal fidelity in blood flow dynamics. SUPER 4D MRA maintained excellent performance even at higher acceleration rates. SUPER 4D MRA is a promising technique for highly accelerating 4D MRA acquisition without comprising both temporal fidelity and image quality.

Dynahead-YOLO-Otsu: an efficient DCNN-based landslide semantic segmentation method using remote sensing images

Recent advancements in deep convolutional neural networks (DCNNs) have significantly improved landslides identification using remote sensing images. Pixel-wise semantic segmentation (PSS) and object-oriented detection (OOD) are two dominant approaches, wherein PSS are better as providing detailed delineation of landslide shapes. However, PSS are limited by the difficulty in labelling training data and low segmentation speed compared to OOD. In this paper, we propose an efficient DCNN-based landslide semantic segmentation method, the so-called Dynahead-YOLO-Otsu, to perform a PSS based on the OOD results. This is achieved by locating potential landslide regions in advance using the ODD-based Dynahead-YOLO model, which enhances the capacity for detecting landslides with variable proportions and complex background in the images. The preliminary results are then processed using the Otsu binarization algorithm to cluster pixels belonging to landslides from the images of potential regions for semantic segmentation. To validate the performance, we tested the proposed method using an open-source dataset containing 950 landslide images. We compared the results with three up-to-date DCNN-and PSS- based approaches, namely DeepLab v3+, PSPnet, and Unet. Results demonstrate that the proposed method achieves comparable Recall (71.80%) and F1 scores (75.80%), with an average improvement of 22% and 16% in Precision and IoU, respectively.

Detailed delineation of the fetal brain in diffusion MRI via multi-task learning.

Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain inutero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in the fetal stage. However, because of the low data quality and rapid brain development, reliable analysis of fetal dMRI data requires dedicated computational methods that are currently unavailable. The lack of automated methods for fast, accurate, and reproducible data analysis has seriously limited our ability to tap the potential of fetal brain dMRI for medical and scientific applications. In this work, we developed and validated a unified computational framework to (1) segment the brain tissue into white matter, cortical/subcortical gray matter, and cerebrospinal fluid, (2) segment 31 distinct white matter tracts, and (3) parcellate the brain's cortex and delineate the deep gray nuclei and white matter structures into 96 anatomically meaningful regions. We utilized a set of manual, semi-automatic, and automatic approaches to annotate 97 fetal brains. Using these labels, we developed and validated a multi-task deep learning method to perform the three computations. Our evaluations show that the new method can accurately carry out all three tasks, achieving a mean Dice similarity coefficient of 0.865 on tissue segmentation, 0.825 on white matter tract segmentation, and 0.819 on parcellation. The proposed method can greatly advance the field of fetal neuroimaging as it can lead to substantial improvements in fetal brain tractography, tract-specific analysis, and structural connectivity assessment.

ERBB2/ ERBB3-mutated S100/ SOX10-positive unclassified high-grade uterine sarcoma: first detailed description of a novel entity.

With the increasing use of innovative next generation sequencing (NGS) platforms in routine diagnostic and research settings, the genetic landscape of uterine sarcomas has been dynamically evolving during the last two decades. Notably, the majority of recently recognized genotypes in uterine sarcomas represent gene fusions, while recurrent oncogene mutations of diagnostic and/ or therapeutic value have been rare. Recently, a distinctive aggressive uterine sarcoma expressing S100 and SOX10, but otherwise lacking diagnostic morphological, immunophenotypic and molecular features of other uterine malignancies has been presented in a scientific abstract form (USCAP, 2023), but detailed description and delineation of the entity is still missing. We herein describe two high-grade unclassified uterine sarcomas characterized by spindle to round cell morphology and diffuse expression of S100 and SOX10, originating in the uterine body and cervix of 53- and 45-year-old women and carrying an ERBB3 (p.Glu928Gly) and an ERBB2 (p.Val777Leu) mutation, respectively. Both tumors harbored in addition genomic HER2 amplification, ATRX mutation and CDKN2A deletion. Methylation studies revealed a methylome most similar to MPNST-like tumors, but distinct from melanoma, MPNST, clear cell sarcoma, and endometrial stromal sarcoma. Case 1 died of progressive peritoneal metastases after multiple trials of chemotherapy 47months after diagnosis. Case 2 is a recent case who presented with a cervical mass, which was biopsied. This study defines a novel heretofore unrecognized aggressive uterine sarcoma with unique phenotypic and genotypic features. Given the potential value of targeting HER2, recognizing this tumor type is mandatory for appropriate therapeutic strategies and for better future delineation of the entity.

Bi-direction and flexible multi-mode morphing wing based on antagonistic SMA wire actuators

This work evaluates the viability of a cutting-edge flexible wing prototype actuated by Shape Memory Alloy (SMA) wire actuators. Such flexible wings have garnered significant interest for their potential to enhance aerodynamic efficiency by mitigating noise and delaying flow separation. SMA actuators are particularly advantageous due to their superior power-to-weight ratio and adaptive response, making them increasingly favored in morphing aircraft applications. Our methodology begins with a detailed delineation of the fishbone camber morphing wing rib structure, followed by the construction of a multi-mode morphing wing segment through 3D-printed rib assembly. Comprehensive testing of the SMA wire actuators’ actuation capacity and efficiency was conducted to establish their operational parameters. Subsequent experimental analyses focused on the bi-directional and reciprocating morphing performance of the fishbone wing rib, which incorporates SMA wires on the upper and lower sides. These experiments confirmed the segment’s multi-mode morphing abilities. Aerodynamic assessments have demonstrated that our design substantially improves the Lift-to-Drag ratio (L/D) when compared to conventional rigid wings. Finally, two phases of flight tests demonstrated the feasibility of SMA as an aircraft actuator and the validity of flexible wing structures to adjust the aircraft attitude, respectively.

A novel sea-land segmentation network for enhanced coastline extraction using satellite remote sensing images

The extraction of coastlines from remote sensing images is vital for promoting sustainable development in coastal areas, conserving marine environments, strengthening disaster response capabilities, and supporting scientific research. However, current coastline detection approaches using remote sensing face challenges related to resolution, terrain, boundary, and data, requiring accurate solutions for reliability. Here, we introduce the Collaborative Supervision and Attention Fusion (CSAFNet) model for pixel-level sea-land segmentation, with a primary goal of improving the accuracy of coastline extraction. The model integrates the Edge Deep Supervision (EDS) module to enhance coastline edge detail fitting. Additionally, the Collaborative Semantic Deep Supervision (CSDS) module and Attention Fusion Module (AFM) collaborate to bridge the semantic gap between different hierarchical features, resulting in a more precise and detailed delineation of coastlines. Experimental validation on the publicly available SLSD 11The dataset was initially labeled as ”sea-land segmentation data” by the authors. For ease of discussion, we will adopt the practice of using the initial letters of each word as an abbreviation. dataset has demonstrated superiority over various advanced methods, with an impressive mIoU value of 96.72%. Through simple optimization, detailed and rich coastlines can be extracted, validating the feasibility of coastline extraction.

손해사정사제도의 실태분석 및 개선방안에 관한 연구

In modern society, compared to the past, the economic scale has grown, resulting in greater diversity in insurance coverage and an expansion of the market. Consequently, the causes of losses have become more complex. In this context, accurate determination of liability and indemnity, as well as the fair assessment of insurance claims, have become crucial tasks. As a result, the technical expertise and professionalism of claim-adjuster(CA) are continuously evolving. Claim adjustment primarily serves to realize the insured's interests for insurance claimants. It also plays a pivotal role in securing the competitiveness of insurance companies and providing a basis for determining insurance premiums, ultimately preventing ethical misconduct within the insurance industry. Therefore, this study examines the concept and current status of the loss assessment system, highlighting its issues. Specifically, the study explores the existing CA system in South Korea and relevant domestic laws. It also reviews international CA systems to provide insights. The research proposes several improvements to the CA system. Firstly, it suggests enhancing the independence of CA through a detailed delineation of their authority and the establishment of a legal protection framework. Secondly, it advocates for the enhancement of the professionalism of CA through the strengthening of qualification systems and the introduction of continuous education. Notably, it emphasizes the consolidation of loss assessor functions, focusing on strengthening examination subjects and recognition systems, introducing continuous education, and implementing license renewal procedures.

Advancements in acne detection: application of the CenterNet network in smart dermatology.

Acne detection is critical in dermatology, focusing on quality control of acne imagery, precise segmentation, and grading. Traditional research has been limited, typically concentrating on singular aspects of acne detection. We propose a multi-task acne detection method, employing a CenterNet-based training paradigm to develop an advanced detection system. This system collects acne images via smartphones and features multi-task capabilities for detecting image quality and identifying various acne types. It differentiates between noninflammatory acne, papules, pustules, nodules, and provides detailed delineation for cysts and post-acne scars. The implementation of this multi-task learning-based framework in clinical diagnostics demonstrated an 83% accuracy in lesion categorization, surpassing ResNet18 models by 12%. Furthermore, it achieved a 76% precision in lesion stratification, outperforming dermatologists by 16%. Our framework represents a advancement in acne detection, offering a comprehensive tool for classification, localization, counting, and precise segmentation. It not only enhances the accuracy of remote acne lesion identification by doctors but also clarifies grading logic and criteria, facilitating easier grading judgments.

Linking High-Resolution UAV-Based Remote Sensing Data to Long-Term Vegetation Sampling—A Novel Workflow to Study Slow Ecotone Dynamics

Unravelling slow ecosystem migration patterns requires a fundamental understanding of the broad-scale climatic drivers, which are further modulated by fine-scale heterogeneities just outside established ecosystem boundaries. While modern Unoccupied Aerial Vehicle (UAV) remote sensing approaches enable us to monitor local scale ecotone dynamics in unprecedented detail, they are often underutilised as a temporal snapshot of the conditions on site. In this study in the Southern Alps of New Zealand, we demonstrate how the combination of multispectral and thermal data, as well as LiDAR data (2019), supplemented by three decades (1991–2021) of treeline transect data can add great value to field monitoring campaigns by putting seedling regeneration patterns at treeline into a spatially explicit context. Orthorectification and mosaicking of RGB and multispectral imagery produced spatially extensive maps of the subalpine area (~4 ha) with low spatial offset (Craigieburn: 6.14 ± 4.03 cm; Mt Faust: 5.11 ± 2.88 cm, mean ± standard error). The seven multispectral bands enabled a highly detailed delineation of six ground cover classes at treeline. Subalpine shrubs were detected with high accuracy (up to 90%), and a clear identification of the closed forest canopy (Fuscospora cliffortioides, >95%) was achieved. Two thermal imaging flights revealed the effect of existing vegetation classes on ground-level thermal conditions. UAV LiDAR data acquisition at the Craigieburn site allowed us to model vegetation height profiles for ~6000 previously classified objects and calculate annual fine-scale variation in the local solar radiation budget (20 cm resolution). At the heart of the proposed framework, an easy-to-use extrapolation procedure was used for the vegetation monitoring datasets with minimal georeferencing effort. The proposed method can satisfy the rapidly increasing demand for high spatiotemporal resolution mapping and shed further light on current treeline recruitment bottlenecks. This low-budget framework can readily be expanded to other ecotones, allowing us to gain further insights into slow ecotone dynamics in a drastically changing climate.

Enhancing reginal wall abnormality detection accuracy: Integrating machine learning, optical flow algorithms, and temporal convolutional networks in multi-view echocardiography.

Regional Wall Motion Abnormality (RWMA) serves as an early indicator of myocardial infarction (MI), the global leader in mortality. Accurate and early detection of RWMA is vital for the successful treatment of MI. Current automated echocardiography analyses typically concentrate on peak values from left ventricular (LV) displacement curves, based on LV contour annotations or key frames during the heart's systolic or diastolic phases within a single echocardiographic cycle. This approach may overlook the rich motion field features available in multi-cycle cardiac data, which could enhance RWMA detection. In this research, we put forward an innovative approach to detect RWMA by harnessing motion information across multiple echocardiographic cycles and multi-views. Our methodology synergizes U-Net-based segmentation with optical flow algorithms for detailed cardiac structure delineation, and Temporal Convolutional Networks (ConvNet) to extract nuanced motion features. We utilize a variety of machine learning and deep learning classifiers on both A2C and A4C views echocardiograms to enhance detection accuracy. A three-phase algorithm-originating from the HMC-QU dataset-incorporates U-Net for segmentation, followed by optical flow for cardiac wall motion field features. Temporal ConvNet, inspired by the Temporal Segment Network (TSN), is then applied to interpret these motion field features, independent of traditional cardiac parameter curves or specific key phase frame inputs. Employing five-fold cross-validation, our SVM classifier demonstrated high performance, with a sensitivity of 93.13%, specificity of 83.61%, precision of 88.52%, and an F1 score of 90.39%. When compared with other studies using the HMC-QU datasets, these Fig s stand out, underlining our method's effectiveness. The classifier also attained an overall accuracy of 89.25% and Area Under the Curve (AUC) of 95%, reinforcing its potential for reliable RWMA detection in echocardiographic analysis. This research not only demonstrates a novel technique but also contributes a more comprehensive and precise tool for early myocardial infarction diagnosis.

Thin-slice Two-dimensional T2-weighted Imaging with Deep Learning-based Reconstruction: Improved Lesion Detection in the Brain of Patients with Multiple Sclerosis.

Brain MRI with high spatial resolution allows for a more detailed delineation of multiple sclerosis (MS) lesions. The recently developed deep learning-based reconstruction (DLR) technique enables image denoising with sharp edges and reduced artifacts, which improves the image quality of thin-slice 2D MRI. We, therefore, assessed the diagnostic value of 1 mm-slice-thickness 2D T2-weighted imaging (T2WI) with DLR (1 mm T2WI with DLR) compared with conventional MRI for identifying MS lesions. Conventional MRI (5 mm T2WI, 2D and 3D fluid-attenuated inversion recovery) and 1 mm T2WI with DLR (imaging time: 7 minutes) were performed in 42 MS patients. For lesion detection, two neuroradiologists counted the MS lesions in two reading sessions (conventional MRI interpretation with 5 mm T2WI and MRI interpretations with 1 mm T2WI with DLR). The numbers of lesions per region category (cerebral hemisphere, basal ganglia, brain stem, cerebellar hemisphere) were then compared between the two reading sessions. For the detection of MS lesions by 2 neuroradiologists, the total number of detected MS lesions was significantly higher for MRI interpretation with 1 mm T2WI with DLR than for conventional MRI interpretation with 5 mm T2WI (765 lesions vs. 870 lesions at radiologist A, < 0.05). In particular, of the 33 lesions in the brain stem, radiologist A detected 21 (63.6%) additional lesions by 1 mm T2WI with DLR. Using the DLR technique, whole-brain 1 mm T2WI can be performed in about 7 minutes, which is feasible for routine clinical practice. MRI with 1 mm T2WI with DLR enabled increased MS lesion detection, particularly in the brain stem.

Familial Glucocorticoid Deficiency: the changing landscape of an eponymous syndrome.

Familial Glucocorticoid Deficiency encompasses a broad spectrum of monogenic recessive disorders that theoretically solely abrogate cortisol biosynthesis. In reality, delineating clear genotype-phenotype correlations in this disorder is made complicated by marked phenotypic heterogeneity even within kindreds harbouring identical variants. Phenotypes range from isolated glucocorticoid insufficiency to cortisol deficiency plus a variety of superimposed features including salt-wasting and hypoaldosteronism, primary hypothyroidism, hypogonadism and growth defects. Furthermore, mutation type, domain topology and perceived enzyme activity do not always predict disease severity. Given the high burden of disease and implications of a positive diagnosis, genetic testing is crucial in the management of patients warranting detailed delineation of genomic variants including viable functional studies.

Deep image prior cine MR fingerprinting with B1 + spin history correction.

To develop a deep image prior (DIP) reconstruction for B1 + -corrected 2D cine MR fingerprinting (MRF). The proposed method combines low-rank (LR) modeling with a DIP to generate cardiac phase-resolved parameter maps without motion correction, employing self-supervised training to enforce consistency with undersampled spiral k-space data. Two implementations were tested: one approach (DIP) for cine T1 , T2 , and M0 mapping, and a second approach (DIP with effective B1 + estimation [DIP-B1]) that also generated an effective B1 + map to correct for errors due to RF transmit inhomogeneities, through-plane motion, and blood flow. Cine MRF data were acquired in 14 healthy subjects and four reconstructions were compared: LR, low-rank motion-corrected (LRMC), DIP, and DIP-B1. Results were compared to diastolic ECG-triggered MRF, MOLLI, and T2 -prep bSSFP. Additionally, bright-blood and dark-blood images calculated from cine MRF maps were used to quantify ventricular function and compared to reference cine measurements. DIP and DIP-B1 outperformed other cine MRF reconstructions with improved noise suppression and delineation of high-resolution details. Within-segment variability in the myocardium (reported as the coefficient of variation for T1 /T2 ) was lowest for DIP-B1 (2.3/8.3%) followed by DIP (2.7/8.7%), LRMC (3.5/10.5%), and LR (15.3/39.6%). Spatial homogeneity improved with DIP-B1 having the lowest intersegment variability (2.6/4.1%). The mean bias in ejection fraction was -1.1% compared to reference cine scans. A DIP reconstruction for 2D cine MRF enabled cardiac phase-resolved mapping of T1 , T2 , M0 , and the effective B1 + with improved noise suppression and precision compared to LR and LRMC.

Cell-cell communication in kidney fibrosis.

Kidney fibrosis is a common outcome of a wide variety of chronic kidney diseases, in which virtually all kinds of renal resident and infiltrating cells are involved. As such, well-orchestrated intercellular communication is of vital importance in coordinating complex actions during renal fibrogenesis. Cell-cell communication in multicellular organisms is traditionally assumed to be mediated by direct cell contact or soluble factors, including growth factors, cytokines and chemokines, through autocrine, paracrine, endocrine and juxtacrine signaling mechanisms. Growing evidence also demonstrates that extracellular vesicles, lipid bilayer-encircled particles naturally released from almost all types of cells, can act as a vehicle to transfer a diverse array of biomolecules including proteins, mRNA, miRNA and lipids to mediate cell-cell communication. We recently described a new mode of intercellular communication via building a special extracellular niche by insoluble matricellular proteins. Kidney cells, upon injury, produce and secrete different matricellular proteins, which incorporate into the local extracellular matrix network, and regulate the behavior, trajectory and fate of neighboring cells in a spatially confined fashion. This extracellular niche-mediated cell-cell communication is unique in that it restrains the crosstalk between cells within a particular locality. Detailed delineation of this unique manner of intercellular communication will help to elucidate the mechanism of kidney fibrosis and could offer novel insights in developing therapeutic intervention.

Amblytropidia paranaensis n. sp., a new species of grasshopper (Orthoptera: Acrididae: Gomphocerinae) from Paran State, Brazil, including new records, a distribution map and a key to the Brazilians species.

A new species of Amblytropidia Stl, 1873 (Orthoptera, Acrididae, Gomphocerinae), named Amblytropidia paranaensis n. sp., is delineated from the Trs Barras do Paran municipality, Paran State, Brazil. Detailed morphological delineations, accompanied by illustrations highlighting key diagnostic traits of both male and female external genitalia, are provided. Amblytropidia paranaensis n. sp. is distinguished from its congeners by distinctive body coloration, particularly evident in the head and thorax regions, as well as larger-sized anchorae compared to anterior projections, and uniquely trilobated and sclerotized lophi. Moreover, this study offers insights into the species habitat preferences, documented occurrences, a distribution map, and an updated taxonomy key specific to Brazilian Amblytropidia species.

Assessment of glenoid bone loss and other osseous shoulder pathologies comparing MR-based CT-like images with conventional CT

ObjectivesTo evaluate and compare the diagnostic performance of CT-like images based on a 3D T1-weighted spoiled gradient-echo sequence (T1 GRE), an ultra-short echo time sequence (UTE), and a 3D T1-weighted spoiled multi-echo gradient-echo sequence (FRACTURE) with conventional CT in patients with suspected osseous shoulder pathologies.Materials and methodsPatients with suspected traumatic dislocation of the shoulder (n = 46, mean age 40 ± 14.5 years, 19 women) were prospectively recruited and received 3-T MR imaging including 3D T1 GRE, UTE, and 3D FRACTURE sequences. CT was performed in patients with acute fractures and served as standard of reference (n = 25). Agreement of morphological features between the modalities was analyzed including the glenoid bone loss, Hill-Sachs interval, glenoid track, and the anterior straight-line length. Agreement between the modalities was assessed using Bland-Altman plots, Student’s t-test, and Pearson’s correlation coefficient. Inter- and intrareader assessment was evaluated with weighted Cohen’s κ and intraclass correlation coefficient.ResultsAll osseous pathologies were detected accurately on all three CT-like sequences (n = 25, κ = 1.00). No significant difference in the percentage of glenoid bone loss was found between CT (mean ± standard deviation, 20.3% ± 8.0) and CT-like MR images (FRACTURE 20.6% ± 7.9, T1 GRE 20.4% ± 7.6, UTE 20.3% ± 7.7, p > 0.05). When comparing the different measurements on CT-like images, measurements performed using the UTE images correlated best with CT.ConclusionAssessment of bony Bankart lesions and other osseous pathologies was feasible and accurate using CT-like images based on 3-T MRI compared with conventional CT. Compared to the T1 GRE and FRACTURE sequence, the UTE measurements correlated best with CT.Clinical relevance statementIn an acute trauma setting, CT-like images based on a T1 GRE, UTE, or FRACTURE sequence might be a useful alternative to conventional CT scan sparing associated costs as well as radiation exposure.Key Points• No significant differences were found for the assessment of the glenoid bone loss when comparing measurements of CT-like MR images with measurements of conventional CT images.• Compared to the T1 GRE and FRACTURE sequence, the UTE measurements correlated best with CT whereas the FRACTURE sequence appeared to be the most robust regarding motion artifacts.• The T1 GRE sequence had the highest resolution with high bone contrast and detailed delineation of even small fractures but was more susceptible to motion artifacts.

Forensic assessment of landslides induced by environmental changes in Sumedang, Indonesia, an important step for the future disaster prevention

Landslide forensics assessment was conducted in Cimanggung, Sumedang with the aim of identifying the causes and processes of landslides to guide the critical step of future disaster prevention. The methodology includes investigations on geology, lithology, hydrology, land cover, land-use changes, and rainfall triggering the landslide. Unmanned Aerial Vehicles were used to investigate detailed spatial geomorphology for determining landslide development. The forensic assessment of the landslide that occurred in Cimanggung, Sumedang, on January 9, 2021, revealed the role of environmental changes. The conversion of open land to residential areas affects the water balance. The surface burden to the site on the hilltop by houses and toe-side cutting at the bottom of the slope has occurred. Changes in land conditions and hydrology, along with lithology thickness and variations, are essential aspects of the landslide incident. During the high rainfall rate, there is more water runoff, and the flow could be concentrated in a paleo channel formed by thicker unconsolidated sediments. The combination of these aspects destabilized the slope causing it to slide. Water management, particularly drainage improvement, should be established to prevent excessive runoff and centralized infiltration. Detailed hazard zonation, site delineation, and rainwater storage could be developed to avoid future landslides.

Review on the Possibilities of Mapping Old-Growth Temperate Forests by Remote Sensing in Europe

AbstractOld-growth forests (OGF) provide valuable ecosystem services such as habitat provision, carbon sequestration or recreation maintaining biodiversity, carbon storage, or human well-being. Long-term human pressure caused OGFs in Europe to be rare and scattered. Their detailed extent and current status are largely unknown. This review aims to identify potential methods to map temperate old-growth forests (tOGF) by remote sensing (RS) technology, highlights the potentials and benefits, and identifies main knowledge gaps requesting further research. RS offers a wide range of data and methods to map forests and their properties, applicable from local to continental scale. We structured existing mapping approaches in three main groups. First, parameter-based approaches, which are based on forest parameters and usually applied on local to regional scale using detailed data, often from airborne laser scanning (ALS). Second, direct approaches, usually employing machine learning algorithms to generate information from RS data, with high potential for large-area mapping but so far lacking operational applications and related sound accuracy assessment. Finally, indirect approaches integrating various existing data sets to predict OGF existence. These approaches have also been used for large area mapping with a main drawback of missing physical evidence of the identified areas to really hold OGFs as compared to the likelihood of OGF existence. In conclusion, studies dealing with the mapping of OGF using remote sensing are quite limited, but there is a huge amount of knowledge from other forestry-related applications that is yet to be leveraged for OGF identification. We discuss two scenarios, where different data and approaches are suitable, recognizing that one single system cannot serve all potential needs. These may be hot spot identification, detailed area delineation, or status assessment. Further, we pledge for a combined method to overcome the identified limitations of the individual approaches.

A homozygous EVC mutation in a prenatal fetus with Ellis-van Creveld syndrome.

Ellis-van Creveld (EvC) syndrome, caused by variants in EVC, is a rare genetic skeletal dysplasia. Its clinical phenotype is highly diverse. EvC syndrome is rarely reported in prenatal stages because its presentation overlaps with other diseases. A Chinese pedigree diagnosed with EvC syndrome was enrolled in this study. Whole-exome sequencing (WES) was applied in the proband to screen potential genetic variant(s), and then Sanger sequencing was used to identify the variant in family members. Minigene experiments were applied. WES identified a homozygous variant (NM_153717.3:c.153_174 + 42del) in EVC which was inherited from the heterozygous parents and confirmed by Sanger sequencing. Further experiments demonstrated that this variant disrupts the canonical splicing site and produces a new splicing site at NM_153717.3: c.-164_174del, which ultimately leads to a 337 bp deletion at the 3' end of exon 1 and loss of the start codon. This is the first reported case of EvC syndrome based on a splicing variant and detailed delineation of the aberrant splicing effect in the fetus. Our study demonstrates the pathogenesis of this new variant, expands the spectrum of EVC mutations, and demonstrates that WES is a powerful tool in the clinical diagnosis of diseases with genetic heterogeneity.