Semi-quantitative Approaches Research Articles

Unveiling gully erosion susceptibility: A semi-quantitative modeling approach integrated with field data in contrasting landscapes and climate regions

This study addresses the challenge of mapping gully erosion susceptibility, which is often hindered by limited observed data, the complexity of controlling factors, and the uncertainties associated with characterizing these factors. We utilized a semi-quantitative modeling approach that integrates field-based data and ten controlling factors in the Chemoga watershed of Ethiopia's Upper Blue Nile basin. The resulting gully erosion susceptibility map was compared with a random forest-based approach to assess the methodological applicability. Additionally, an independent dataset from adjacent watersheds was used to validate the approach. The findings revealed that certain landscape positions with specific elevation ranges and slope steepness were more susceptible to gully erosion due to factors such as rainfall, lithological formations, soil characteristics, and agricultural activities. Approximately 10% of the watershed area was affected by gully erosion, with varying susceptibility levels. The comparison between the semi-quantitative and random forest approaches demonstrates a total agreement of around 58%, with minimal differences in susceptibility classes. The study also highlights a strong agreement between simulated and observed susceptibility maps, with a 76% value for the simulation and a lower 48% agreement for the random forest approach. Furthermore, in the adjacent watershed, 65% of the area exhibits no discrepancies between observed and simulated maps. This suggests that the semi-quantitative approach is effective in extrapolating gully erosion susceptibility when detailed data is limited, offering a cost-effective and efficient solution. The study emphasizes the utility of the semi-quantitative modeling approach in mapping gully erosion susceptibility and its potential for practical applications in land management and intervention strategies.

Geosites of Northern Mountainous Adygeya (Southwestern Russia): A Novel Vision

Territorial inventories of geosites remain on the international agenda, and they can help in acquiring information for solving pure geological research tasks. New field investigations in the northern part of Mountainous Adygeya (geoheritage-rich territory in the western Greater Caucasus) permitted us to extend our knowledge of its geosites. Five geosites were described qualitatively and assessed semi-quantitatively, namely the Polkovnitskaya River Valley (ammonite-bearing concretions of Aptian glauconitic sandstones), the Little Khadzhokh River Valley (Aptian glauconitic sandstones with fossils and trace fossils), the Shakhan and Middle Khadzhokh River Valley (Upper Jurassic variegated siliciclastics, Hauterivian fluvial and deltaic sandstones, mixed ancient and modern clastic material), the Big Khadzhoh Waterfall (splendid waterfall and exposures of locally folded Oxfordian–Kimmeridgian limestones), and the Verblyud Mountain (small, camel-shaped cuesta’s remnant). They were ranked nationally (one geosite), regionally (three geosites), and locally (one geosite). Close examination of the considered geosites permitted to register pure geological peculiarities (changes in the dip direction between sedimentary packages), which indicate the tectonic activity across the Jurassic–Cretaceous transition. Additionally, complex accessibility patterns were established in some geosites, and these patterns should be addressed in further refinements of the semi-quantitative approaches to geosite assessment.

Municipal Solid Waste Management and Health Risks: Application of Solid Waste Safety Plan in Novi Sad, Serbia

Inadequate solid waste management (SWM) can cause environmental contamination and health risks. Minimizing the health risks from SWM practices requires the identification of linkages between potential sources of exposure, environmental transport pathways, and adverse health outcomes. A safety planning approach can represent an innovative tool for reducing such risks. In previous research, we introduced the solid waste safety plan (SWSP) concept, only focusing on the health risk ranking assessment matrix. Here, we demonstrate the application of the SWSP framework in a case study of the municipal landfill of Novi Sad, Serbia. We identify potential hazards and assessed the likelihood and severity for them, using a combination of quantitative and semi-quantitative approaches to estimate risk levels for each identified hazard. Hazards deemed high and very high risks for the community and workers include groundwater contamination from leachate, airborne contaminants from combustion and non-combustion processes, and worker injuries associated with waste combustion. Control measures for each are identified, together with a corresponding cost analysis. Adding a final top cover to the landfill and introducing a collection, transport, and treatment system for the biogas would address hazards presenting high health risks but are also among the highest costs. While we demonstrate the approach and utility of an SWSP in a research context, future work is needed to assess the use of the SWSP by local communities or SWM utilities.

Interobserver Agreement of visual and semi-quantitative methods in 99mTc-Methoxy-Isobuty-Isonitrile (MIBI) imaging for risk stratification of hypofunctional thyroid nodules.

99mTc-Methoxy-Isobuty-Isonitrile (MIBI) imaging is used for risk stratifications of hypofunctioning thyroid nodules (TNs). MIBI uptake in the nodular tissue is compared to the uptake in the paranodular thyroid tissue. MIBI imaging may be interpreted visually and/or semi-quantitatively. This study aimed to evaluate the interobserver agreement (IOA) of different methods of interpreting MIBI imaging (visual and semi-quantitative approaches). MIBI imaging data from 2018 to 2020 were collected. Four readers with varying work experience prospectively evaluated MIBI images (planar, SPECT/CT) visually and semi-quantitatively (Wash-Out Index (WOI)). After identifying the nodules on 99mTc-pertechnetate scintigram, the readers evaluated MIBI imaging data by using early, late, early-to-late, and SPECT late acquisitions. Region of interests (ROIs) were defined for semi-quantitative analysis and average counts were calculated using the WOI formula (by Campenni et al.) 1 2. IOA was assessed using Fleiss Kappa, Pearson correlation and Analysis of Variance (ANOVA). 23 patients with hypofunctioning nodules were included. Kappa analysis revealed an IOA of 0.57 for all readers for early imaging (moderate agreement); perfect matches were found in 57%. For late imaging, the IOA was 0.48 (moderate) for all, with perfect matches in 48%. The visual pattern (early-to-late) exhibited an IOA of 0.45 for all, with perfect matches in 57%. SPECT/CT evaluation showed an overall IOA of 0.44, with perfect matches in 48%. The semi-quantitative approach WOI yielded an overall result of 0.64 (good agreement) and perfect matches in 91%. The IOA for WOI was higher than for visual methods. The WOI is independent of the reader's experience level. Visual analysis requires a certain level of experience from the reader.

Hydromechanical modeling of evolving post-wildfire regional-scale landslide susceptibility

Post-wildfire mass wasting is a major problem throughout many regions worldwide. Recent dramatic increases in global wildfire activities coupled with a shift in wildfire-prone elevation to higher altitudes raise the need to better predict post-fire rainfall-triggered landslides. Despite its importance, only a limited number of studies have investigated landslide susceptibility in areas hit by wildfires using hydromechanical models. However, most of these studies follow either qualitative or semi-quantitative approaches without explicitly considering the fire's effects on the impacted area's physical behavior. This study aims to develop and employ a physics-based framework to generate susceptibility maps of rainfall-triggered shallow landslides in areas disturbed by wildfire. A coupled hydromechanical model considering unsaturated flow and root reinforcement is integrated into an infinite slope stability model to simulate the triggering of shallow landslides from rainfall. The impact of fire is considered through its effects on soil and land cover properties, near-surface processes, and canopy interception. The developed model is then integrated into a geographic information system (GIS) to characterize the regional distribution of landslide potential and its variability considering topography, geology, land cover, and burn severity. The proposed framework was tested for a study site in Southern California. The site was burned in the San Gabriel Complex Fire in June 2016 and experienced widespread landsliding almost three years later following an extreme rainstorm in January 2019. The proposed framework could successfully model the location of observed shallow landslides. The model also revealed a significantly higher likelihood for slope failure in areas burned at moderate to high severities as opposed to unburned and low-burn severity areas. The findings of this study can be employed to predict the timing and general locations of rainfall-triggered shallow landslides following wildfires.

Splenic switch-off in [15O]H2O-positron emission tomography myocardial perfusion imaging using parametric blood flow images

BackgroundEvaluation of sufficient adenosine response constitutes a significant challenge in myocardial perfusion imaging (MPI). Splenic switch-off in MPI studies denotes a visually (qualitatively) reduced splenic radiotracer signal during adenosine stress and is considered indicative of sufficient cardiac vasodilation. In this study, we examined semi-quantitative and quantitative approaches to splenic switch-off assessment using [15O]H2O-PET with either summed activity images or calculated parametric splenic blood flow images. MethodsCohort 1: 90 clinical patients undergoing [15O]H2O MPI in whom adenosine response was considered clinically adequate were identified to characterize the corresponding splenic switch-off. Spleen stress/rest-ratio (SSR-ratio) was calculated as spleen stress signal intensity/spleen rest signal intensity on both summed activity and parametric blood flow images.Cohort 2: Twenty-five patients with repeat MPI due to suspected insufficient adenosine response were identified to observe if splenic switch-off on the initial MPI could predict the outcome of the repeat MPI.Cohort 3: Fifty-four patients who were considered adenosine responders on MPI and who had a coronary angiogram (CAG) follow-up within 3 months after MPI served as a separate validation group. ResultsSplenic switch-off was present in most patients with a clinically sufficient adenosine response (Cohort 1), illustrated by both visual (74.4%–86.7%), semi-quantitative (summed activity images) (85.6%), and quantitative (parametric blood flow images) (92.2%) evaluation, which corresponds to the distribution in patients with sufficient adenosine response and follow-up CAG (Cohort 3). In patients suspected of insufficient adenosine response on the initial MPI (Cohort 2), the repeat MPI only yielded different myocardial blood flow (MBF) results if the initial SSR-ratio was >0.90 on splenic parametric blood flow images. Conclusionquantitative splenic switch-off assessment on parametric blood flow images was superior to the semi-quantitative splenic switch-off approach. Patients with a suspected insufficient initial adenosine response and SSR-ratio >0.90 can benefit from a repeat MPI. Thus, the integration of quantitative splenic switch-off using parametric blood flow images in the evaluation of adenosine response may support future clinical decision-making.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiation of soft tissue sarcoma from benign lesions: a systematic review of literature.

To systematically review the literature assessing the role of Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in the differentiation of soft tissue sarcomas from benign lesions. A comprehensive literature search was performed with the following keywords: multiparametric magnetic resonance imaging, DCE-MR perfusion, soft tissue, sarcoma, and neoplasm. Original studies evaluating the role of DCE-MRI for differentiating benign soft-tissue lesions from soft-tissue sarcomas were included. Eighteen studies with a total of 965 imaging examinations were identified. Ten of twelve studies evaluating qualitative parameters reported improvement in discriminative power. One of the evaluated qualitative parameters was time-intensity curves (TIC), and malignant curves (TIC III, IV) were found in 74% of sarcomas versus 26.5% benign lesions. Six of seven studies that used the semiquantitative approach found it relatively beneficial. Four studies assessed quantitative parameters including Ktrans (contrast transit from the vascular compartment to the interstitial compartment), Kep (contrast return to the vascular compartment), and Ve (the volume fraction of the extracellular extravascular space) in addition to other parameters. All found Ktrans, and 3 studies found Kep to be significantly different between sarcomas and benign lesions. The values for Ve were variable. Additionally, eight studies assessed diffusion-weighted imaging (DWI), and 6 of them found it useful. Of different DCE-MRI approaches, qualitative parameters showed the best evidence in increasing the diagnostic performance of MRI. Semiquantitative and quantitative approaches seemed to improve the discriminative power of MRI, but which parameters and to what extent is still unclear and needs further investigation.

Influence of sex, age, ethnicity/race, and body mass index on the cerumen volatilome using two data analysis approaches: binary and semiquantitative.

Human cerumen analysis is an innovative and non-invasive trend in diagnosing diseases. Recently, new cerumen volatile-based methods using binary (volatile presence/absence) and semiquantitative (volatile intensity) data approaches have shown great potential in detecting biomarkers for cancer, chronic and rare diseases, and xenobiotic exposures. However, to date, the impacts of demographic factors such as body mass index (BMI), sex, age, and ethnicity/race in cerumen data have not been widely described, which can hamper interpretation in biomarker discovery investigations. This study examined the effects of such factors in cerumen, defining the baseline volatile organic metabolites (VOMs) across different physiological groups. Cerumen samples from seventy volunteers were analyzed using headspace/gas chromatography-mass spectrometry (HS/GC-MS) and multivariate statistical analysis using binary and semiquantitative data approaches. In the binary data approach, several VOMs exhibited patterns of high occurrence in some specific demographic groups. However, no pattern of discrimination that could be attributed to demographic factors was observed. In the semiquantitative approach, the relative abundance of cerumen VOMs was more impacted by sex and BMI than age and ethnicity/race. In summary, we describe how cerumen VOM occurrence and abundance are affected by patient phenotype, which can pave the way for more personalized medicine in future cerumen volatile-based methods.

Knowledge management system as an efficient and sustainable strategy to minimize uncertainty in the process of risk assessment

Risk assessors could adopt qualitative, semi-quantitative, or quantitative approaches to analyze various risks; the combination of these approaches alleviates the shortcomings of risk assessment techniques, namely uncertainty, knowledge dimension, and time dynamics when techniques are used alone. The knowledge dimension plays a pivotal role in these shortcomings, as knowledge reduces uncertainty (United States Environmental Protection Agency [EPA], n.d.-a) and the timely knowledge update of global trends and emerging risks is expected to resolve the issue of time dynamics (another cause of uncertainty) by reassessing risks and characterizing risk data over a time interval (Wassénius & Crona, 2022). However, substantial research and development are required to generate adequate modeling and analytical methods to deal with different and complex systems. Based on the literature review and industry best practices, the study develops a risk assessment knowledge management system framework that focuses on the root of the shortcomings of risk assessment techniques, namely the knowledge dimension; this strategy is efficient and sustainable by indirectly addressing the unresolved issues of uncertainty and time dynamics through the knowledge dimension. The conceptual framework minimizes the uncertainty (the root of risk) in the decision-making process of selecting the appropriate risk assessment tools and effectively implementing them.

HRMS-based suspect screening of pharmaceuticals and their transformation products in multiple environmental compartments: An alternative to target analysis?

The comprehensive monitoring of pharmaceutically active compounds (PhACs) in the environment is challenging given the myriad of substances continuously discharged, the increasing number of new compounds being produced (and released), or the variety of the associated human metabolites and transformation products (TPs). Approaches such as high-resolution mass spectrometry (HRMS)-based suspect analysis have emerged to overcome the drawbacks of classical target analytical methods, e.g., restricted chemical coverage. In this study, we assess the readiness of HRMS-based suspect screening to replace or rather complement target methodologies by comparing the performance of both approaches in terms of i) detection of PhACs in various environmental samples (water, sediments, biofilm, fish plasma, muscle and liver) in a field study; ii) PhACs (semi)quantification and iii) prediction of their environmental risks. Our findings revealed that target strategies alone significantly underestimate the variety of PhACs potentially impacting the environment. However, relying solely on suspect strategies can misjudge the presence and risk of low-level but potentially risky PhACs. Additionally, semiquantitative approaches, despite slightly overestimating concentrations, can provide a realistic overview of PhACs concentrations. Hence, it is recommended to adopt a combined strategy that first evaluates suspected threats and subsequently includes the relevant ones in the established target methodologies.

Comparison of semiquantitative methodologies using Raman mapping for corrosion products on iron artifacts

The quantitative determination of corrosion products is essential for evaluating the corrosion state of archeological iron artifacts. This study explored two semiquantitative approaches using Raman mapping technique combined with chemometrics to quantify a binary mixture of magnetite and goethite. The first approach involved establishing quantification models using Raman spectra treated with Principal Components Regression (PCR) and Partial Least Squares (PLS) algorithms. Both the PCR and PLS model showed good predictive ability as indicated by correlation coefficients and root mean square errors (PLS: Rc2=0.9979, Rp2=0.9970, RMSEC=1.93, RMSEP =2.68, RMSECV=3.25). The second approach was based on spectral fitting using non-negative least squares (NNLS) algorithm. This method demonstrated a fair accuracy between the calculated and actual compositions. The absolute value of relative errors was 0.99%∼10.08% for Fe3O4 and 0.81%∼9.71% for α-FeOOH respectively, for compositions greater than 20%. These methods were then applied to quantify the corrosion products on an iron bar excavated from the Nanhai (South China Sea) No. I shipwreck. Compared with XRD quantitative results, Raman results showed that the spectral fitting method was superior to the established PCA and PLS quantification models in both qualitative determination and quantitative accuracy. Compositional maps depicting the distribution of different phases were also generated using spectral fitting method. It was concluded that Raman mapping has significant potential as an accurate quantitative method for the detection of iron corrosion products, and that the spectral fitting method is more suitable for determining iron corrosion compared to the PCR and PLS quantification models in this study.

Measuring mechanical properties with high-speed atomic force microscopy.

High-speed atomic force microscopy (HS-AFM) is now a widely used technique to study the dynamics of single biomolecules and complex structures. In the past, it has mainly been used to capture surface topography as structural analysis, leading to important discoveries not attainable by other methods. Similar to conventional AFM, the scope of HS-AFM was recently expanded to encompass quantities beyond topography, such as the measurement of mechanical properties. This review delves into various methodologies for assessing mechanical properties, ranging from semi-quantitative approaches to precise force measurements and their corresponding sample responses. We will focus on the application to single proteins such as bridging integrator-1, ion channels such as Piezo1, complex structures such as microtubules and supramolecular fibers. In all these examples, the unique combination of quantifiable force application and high spatiotemporal resolution allows to unravel mechanisms that cannot be investigated by conventional means.

Dynamic 18F-FDG-PET kinetic parameters for epileptogenic zone localization in drug-resistant epilepsy

Objective: Precisely localizing the seizure onset zone remains a challenging task in drug-resistant epilepsy (DRE) patients especially given its critical role in successful surgery and effective management. This study aimed to investigate the kinetic parameters of regional 18F-fluorodeoxyglucose (FDG) uptake in DRE patients, aiming to identify the kinetic parameters best enabling the identification of the epileptogenic region.Methods: Consecutive DRE patients with clinically mandated interictal 18F-FDG PET/CT were recruited from October 2019 to September 2020 for pre-surgical evaluation. Immediately after injecting 18F-FDG of 112–179 MBq, dynamic data were acquired for 90 min. The motion correction and resampling to the Montreal atlas was performed in order to generate a transformation matrix. 116 volume of interests (VOIs) and regional time-activity curves (TACs) were generated by employing the automated anatomical labeling (AAL) template using PMOD software. Kinetic parameters of FDG unidirectional blood-brain clearance (K1), efflux (k2), phosphorylation (k3), and net metabolic flux (Ki) were derived using irreversible 2-tissue-compartment model with an image-derived input function (IDIF). The kinetic parameters values obtained from all regions were ranked and compared with the presumed epileptogenic zone (EZ).Results: Eleven DRE patients (5 males, 6 females, mean age 35.1 ± 10.2 years) were analyzed. We found that the region with the lowest values of Ki provided correct lateralization in 7/7 (100%) of patient with temporal lobe epilepsy (TLE) and the region with the lowest Ki and k3 parameters showed concordance with the EZ in 100% and 71.4% of patients, respectively.Conclusion: The present parametric approach to the evaluation of FDG-PET may be more sensitive than semi-quantitative approaches for the detection of pathophysiology in the EZ of patients with medically unresponsive TLE in addition to the routine clinical investigations.

Vulnerability assessment of the pink shrimp small-scale fishery to climate change in southwestern Atlantic brackish coastal lagoons (Uruguay)

Assessing vulnerability using semi-quantitative approaches provide an opportunity to analyze conditions of exposure, sensitivity and adaptive capacity of socio-ecological systems concerning climate change drivers and anthropogenic factors. We assessed the vulnerability of the small-scale pink shrimp fishery of Castillos and Rocha lagoons, two coastal Uruguayan ecosystems of the southwest Atlantic, considering social factors and climate change drivers which affect atmospheric-oceanographic conditions regionally and locally. Using a risk approach, we evaluated the ecological system's exposure to environmental drivers. Then we estimated the sensitivity and adaptive capacity of fishers and the local community (social system). High vulnerability found for this fishery results from a combination of large-scale environmental drivers impacting the ecological system, and social factors causing high sensitivity and low adaptive capacity of the social system. High risk level with adverse ecosystem effects triggered by climate change drivers acting on the distribution of this migratory crustacean in the southwest Atlantic region could promote adverse consequences on harvest duration and shrimp catch levels in the Uruguayan coastal lagoons. Fishers and their communities showed high sensitivity to a set of (demographic, economic, governance, cultural and technological) factors. The social system of both lagoons showed a low adaptive capacity to prevent social impacts or to mitigate climate change hazards. A broad set of social indicators was identified in Rocha lagoon to enhance fishers and community resilience, while the indicator set was narrower in Castillos lagoon. Lastly, weak governance, dormant fisheries laws, non-compliance of fisheries regulations and lack of climate early warning monitoring hamper the adaptability of these small-scale fisher communities under global change scenarios. Comparative evaluation of spatial, environmental and social settings between lagoons can offer lessons for upscaling the assessment and adaptation measures to other small-scale fisheries regionally and globally.

Revisiting Assessment of Computational Methods for Hi-C Data Analysis.

The performances of algorithms for Hi-C data preprocessing, the identification of topologically associating domains, and the detection of chromatin interactions and promoter-enhancer interactions have been mostly evaluated using semi-quantitative or synthetic data approaches, without utilizing the most recent methods, since 2017. In this study, we comprehensively evaluated 24 popular state-of-the-art methods for the complete end-to-end pipeline of Hi-C data analysis, using manually curated or experimentally validated benchmark datasets, including a CRISPR dataset for promoter-enhancer interaction validation. Our results indicate that, although no single method exhibited superior performance in all situations, HiC-Pro, DomainCaller, and Fit-Hi-C2 showed relatively balanced performances of most evaluation metrics for preprocessing, topologically associating domain identification, and chromatin interaction/promoter-enhancer interaction detection, respectively. The comprehensive comparison presented in this manuscript provides a reference for researchers to choose Hi-C analysis tools that best suit their needs.

Aeromagnetic and structural characterization of dyke swarms in southeast Brazil: Evidence for Cenozoic reactivation of the Guapiara lineament, Ponta Grossa Arch

The Guapiara Lineament located on the northern edge of the Ponta Grossa Arch, Southeastern São Paulo state, Brazil, it is an important morphostructural feature defined by dykes oriented in NW-SE direction. The main objective of this work is applying new techniques for enhancing magnetic anomalies of the Guapiara Lineament and other structures, and using aeromagnetic data as support for the analysis of structure reactivation. Aerogeophysical techniques included qualitative and semiquantitative approaches (Euler Deconvolution and Radial Average Spectrum), we used the vertical and horizontal gradients, as well as deep magnetic sources and shallow magnetic sources filters, for an in-depth magnetic-structural characterization that was further correlated with the outcropping geology. We have also applied upward continuation (2000m) in the anomalous magnetic field map which was the basis for applying the Total Horizontal Gradient of the Analytical Signal (TDY), Analytic Signal Amplitude (ASA), Tilt angle (TDR), Total Horizontal Derivative of the Tilt Angle (TDR-THDR) and Tilt Angle of the Horizontal Gradient (TAHG) to improve our interpretations of the regional framework. The magnetic trends of the Guapiara Lineament are superimposed on the NE-SW (e.g.: Cubatão Lineament) and E-W (e.g.: São Sebastião Lineament) anomalies, which are considered structural inheritances of the Proterozoic basement. We report a set of reactivated N40–70W and E-W strike-slip faults showing the same orientation as the mapped magnetic lineaments. Moreover, they seem to be associated to deformational features in the dyke swarm, which was interpreted as evidence of Cenozoic tectonic reactivation.

Riverside Landslide Susceptibility Overview: Leveraging Artificial Neural Networks and Machine Learning in Accordance with the United Nations (UN) Sustainable Development Goals

Riverside landslides present a significant geohazard globally, posing threats to infrastructure and human lives. In line with the United Nations’ Sustainable Development Goals (SDGs), which aim to address global challenges, professionals in the field have developed diverse methodologies to analyze, assess, and predict the occurrence of landslides, including quantitative, qualitative, and semi-quantitative approaches. With the advent of computer programs, quantitative techniques have gained prominence, with computational intelligence and knowledge-based methods like artificial neural networks (ANNs) achieving remarkable success in landslide susceptibility assessments. This article offers a comprehensive review of the literature concerning the utilization of ANNs for landslide susceptibility assessment, focusing specifically on riverside areas, in alignment with the SDGs. Through a systematic search and analysis of various references, it has become evident that ANNs have emerged as the preferred method for these assessments, surpassing traditional approaches. The application of ANNs aligns with the SDGs, particularly Goal 11: Sustainable Cities and Communities, which emphasizes the importance of inclusive, safe, resilient, and sustainable urban environments. By effectively assessing riverside landslide susceptibility using ANNs, communities can better manage risks and enhance the resilience of cities and communities to geohazards. While the number of ANN-based studies in landslide susceptibility modeling has grown in recent years, the overarching objective remains consistent: researchers strive to develop more accurate and detailed procedures. By leveraging the power of ANNs and incorporating relevant SDGs, this survey focuses on the most commonly employed neural network methods for riverside landslide susceptibility mapping, contributing to the overall SDG agenda of promoting sustainable development, resilience, and disaster risk reduction. Through the integration of ANNs in riverside landslide susceptibility assessments, in line with the SDGs, this review aims to advance our knowledge and understanding of this field. By providing insights into the effectiveness of ANNs and their alignment with the SDGs, this research contributes to the development of improved risk management strategies, sustainable urban planning, and resilient communities in the face of riverside landslides.

Vulnerability assessment of species caught by the shrimp trawl fishery in northeastern Brazil

In Brazil, there are no official fisheries statistics to quantitatively assess the status of target and bycatch species. Since food security is at stake, appropriate tools are needed to maintain fisheries sustainability. One widely used “data-poor” method, despite the critics of its subjectivity, is Productivity and Susceptibility Analysis (PSA). In this study, we use PSA to assess the impact of a northeastern Brazil shrimp fishery which is socioeconomically relevant for the local population but is known to have adverse ecological impacts. The vulnerability status (high, medium, or low) of 30 species were determined by the PSA and compared with two other semi-quantitative approaches that assess stock or extinction risk. The results obtained from the PSA and other methods were partially convergent, highlighting six bycatch and one target species for which management measures or data collection should be prioritised. This study showed that PSA, when compared with other semi-quantitative methods, can constitute a valuable preliminary assessment for assessing data-poor fisheries. The integrated use of various methods is advisable in diagnosing fishery resources. However, it is essential to understand the peculiarities of each technique to appreciate its applicability and feasibility.

Abstract P6-12-03: Proinflammatory and estrogen signaling modulates the chemoresistance and metastasis of breast cancer cells through post-translational modifications of pioneering factor FOXA1

Abstract ER-positive breast cancers compose most breast cancers at the time of diagnosis and are primarily driven by mitogenic estrogen signaling. In ER-positive breast cancers, the pioneer transcription factor FOXA1 plays a critical role in the estrogen receptor (ER) function. It binds to condensed chromatin and promotes chromatin accessibility for subsequent ER binding upon estrogen stimulation. We have reported that TNFa-stimulated proinflammatory signaling relocates FOXA1 to a new set of latent enhancers, which initiates the binding of estrogen liganded ER and subsequent expression of a unique transcriptome with clinical significance. The redistribution of FOXA1 occurs within 40 mins of the TNFa treatment, which implies a rapid signaling cascade that arises from changes to either FOXA1’s post-translational modifications (PTMs) or its binding partners. To understand this genomic redistribution of FOXA1, we compared the post-translational modifications (PTMs) of FOXA1 from Vehicle, E2, TNFa, and E2+TNFa treated MCF-7 breast cancer cells. More than five acetylation and phosphorylation events have been identified around the DNA binding domain of FOXA1 by semi-quantitative and quantitative mass spectrometry approaches, and their abundance varies across treatments. To study these PTMs of FOXA1, we used CRISPR/Cas9 to create specific knock-in mutations to mimic or prevent acetylation events in MCF-7 cells. Specifically, we engineered MCF-7 cell lines where K270 was mutated to glutamine (K270Q) to mimic acetylation. And for comparison, we also created cell lines where K270 was mutated to arginine (K270R) to prevent acetylation of FOXA1. Our data, including FOXA1 ChIP-seq and RNA-seq, revealed the genomic redistribution of FOXA1 with these PTMs, which subsequently alters gene expression programs and promotes cell growth, migration, or chemoresistance. These results were confirmed in other ER+ cell lines (such as T47D cells) providing evidence for the generalizability of our findings. Taken together, our data suggest that inflammatory signaling signaling can reshape the enhancer landscape of FOXA1 through post-translational modifications, resulting in changes to estrogen signaling that have profound effects on breast cancer biology. Citation Format: Shen Li, Hector L. Franco, Hyunsoo Kim, Rosemary N. Plagens, Raul Mendez-Giraldez, Colby Tubbs, Venkat Malladi, Joseph Garay. Proinflammatory and estrogen signaling modulates the chemoresistance and metastasis of breast cancer cells through post-translational modifications of pioneering factor FOXA1 [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-12-03.

Multidimensional Profiling of Human Body Hairs Using Qualitative and Semi-Quantitative Approaches with SR-XRF, ATR-FTIR, DSC, and SEM-EDX

This study aimed to assess the potential of a multidimensional approach to differentiate body hairs based on their physico-chemical properties and whether body hairs can replace the use of scalp hair in studies linked to forensic and systemic intoxication. This is the first case report controlling for confounding variables to explore the utility of multidimensional profiling of body hair using synchrotron synchrotron microbeam X-ray fluorescence (SR-XRF) for longitudinal and hair morphological region mapping) and benchtop methods, including attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) (complemented with chemometrics analysis), energy dispersive X-ray analysis (EDX) (complemented with heatmap analysis), differential scanning calorimetry (DSC), and scanning electron microscopy (SEM) analysis (complemented by descriptive statistics) to profile different body hairs in terms of their elemental, biochemical, thermal, and cuticle properties. This multidimensional approach provided supportive information to emphasize the intricate and rather complex interplay between the organization and levels of elements and biomolecules within the crystalline and amorphous matrix of different body hairs responsible for the differences in physico-chemical properties between body hairs that are predominantly affected by the growth rate, follicle or apocrine gland activity, and external factors such as cosmetic use and exposure to environmental xenobiotics. The data from this study may have important implications for forensic science, toxicology and systemic intoxication, or other studies involving hair as a research matrix.