Proper Model Research Articles

Toward an improved ensemble of multi-source daily precipitation via joint machine learning classification and regression

Accurate estimation of precipitation at local to global scales can considerably enhance our understanding of climate system dynamics. While numerous precipitation products are available as indispensable tools for investigating precipitation and its associated processes, none can consistently provide the lowest estimation error across environmental conditions. The multiple source precipitation ensemble (MSPE) methods have been considered a vital solution. A new MSPE framework is proposed here, which simultaneously uses machine learning (ML) classification and regression techniques within an automatic workflow (MSPEaml). Six precipitation products and their ensembles based on different MSPE strategies were evaluated at 2365 gauged and 800 randomly selected ungauged sites over China. Results revealed significant precision inconsistencies among the products primarily due to their different data sources and retrieval algorithms; while MSPEaml can effectively reduce the random and classification errors of estimated precipitation according to the Kling-Gupta efficiency and Heidke skill score. The improvements demonstrated the unique features of MSPEaml, particularly the necessity of the joint use of ML classifiers and regressors and assigning spatiotemporal dynamic weights for merging precipitation data. Moreover, MSPEaml can substantially improve its generalizability through a simple binning procedure, making it applicable under more complex conditions. The varying contributions of predictor variables (indicated by Shapely values) in different ML models identified the complexity of the MSPE issue and further the importance of designing proper ML models according to specific targets. The proposed MSPE framework is expected to be a suitable solution for assembling multiple precipitation data sources with different time periods and scales.

Quantifying Seagrass Density Using Sentinel-2 Data and Machine Learning

Seagrasses, rooted aquatic plants growing completely underwater, are extremely important for the coastal ecosystem. They are an important component of the total carbon burial in the ocean, they provide food, shelter, and nursery to many aquatic organisms in coastal ecosystems, and they improve water quality. Due to human activity, seagrass coverage has been rapidly declining, and there is an urgent need to monitor seagrasses consistently. Seagrass coverage has been closely monitored in the Chesapeake Bay since 1970 using air photos and ground samples. These efforts are costly and time-consuming. Many studies have used remote sensing data to identify seagrass bed outlines, but few have mapped seagrass bed density. This study used Sentinel-2 satellite data and machine learning in Google Earth Engine and the Chesapeake Bay Program field data to map seagrass density. We used seagrass density data from the Chincoteague and Sinepuxent Bay to train machine learning algorithms and evaluate their accuracies. Out of the four machine learning models tested (Naive Bayes (NB), Classification and Regression Trees (CART), Support Vector Machine (SVM), and Random Forest (RF)), the RF model outperformed the other three models with overall accuracies of 0.874 and Kappa coefficients of 0.777. The SVM and CART models performed similarly and NB performed the poorest. We tested two different approaches to assess the models’ accuracy. When we used all the available ground samples to train the models, whereby our analysis showed that model performance was associated with seagrass density class, and that higher seagrass density classes had better consumer accuracy, producer accuracy, and F1 scores. However, the association of model performance with seagrass density class disappeared when using the same training data size for each class. Very sparse and dense seagrass classes had replacedhigherbetter accuracies than the sparse and moderate seagrass density classes. This finding suggests that training data impacts machine learning model performance. The uneven training data size for different classes can result in biased assessment results. Selecting proper training data and machine learning models are equally important when using machine learning and remote sensing data to map seagrass density. In summary, this study demonstrates the potential to map seagrass density using satellite data.

Spatial and temporal distribution of palaeoclimatic records in the Maya Area

Palaeoclimatic research has been performed in the Maya Area (MA), using mainly lake sediment cores and speleothems. Most of the studies have been performed in the lowlands, leaving the highlands unexplored. Lake sediments records contain a diversity of proxies (e.g. Mineralogy, isotopes, pollen, charcoal, diatoms, chemicals, magnetic susceptibility, among others) and temporal resolution, making them frequently not easy to compare and leaving numerous gaps of information. Practically all stalagmites are focused on using δ18O as a proxy of effective rainfall during the Maya periods, having only some explored the role of palaeostorms and hurricanes as well as the paleoclimatology of the pre-Maya and modern periods. In this review paper, the location and temporal frame of palaeoenvironmental records of the MA and their proxies are presented, showing the zones and periods that possess environmental information and assessing their resolution. The comparison shows that more high-resolution records with a multi-proxy approach covering most of the Holocene are needed to understand the climate change in different zones of the MA. Finally, the geographic distribution of the diverse recorded hydroclimate responses based on the records is presented for three critical moments in the Maya History that have been associated with dry periods in the Great Maya Droughts hypothesis. This geographic perspective shows that dry events were not presented in all the MA during these moments although they were vastly recorded in both high- and lowlands. The geographic perspective also shows a negligible drought effect in the central lowlands for the Maya Hiatus period, where this cultural phenomenon was identified first. But signals of droughts are presented in other zones of the MA for this period. The distribution of the drought signal also shows that sites that thrived during the Maya Collapse period were in the regions that suffered the strongest droughts, whilst many sites that were abandoned were in regions rich in hydric resources. Explanations are reviewed for these contradictions. Finally, the works towards the development of mathematical models of the environmental variables are briefly reviewed, pointing out the lack of a proper computational model that has been fed by the palaeoclimatic data developed by the records in the MA.

Transformer-Based Video-Structure Multi-Instance Learning for Whole Slide Image Classification

Pathological images play a vital role in clinical cancer diagnosis. Computer-aided diagnosis utilized on digital Whole Slide Images (WSIs) has been widely studied. The major challenge of using deep learning models for WSI analysis is the huge size of WSI images and existing methods struggle between end-to-end learning and proper modeling of contextual information. Most state-of-the-art methods utilize a two-stage strategy, in which they use a pre-trained model to extract features of small patches cut from a WSI and then input these features into a classification model. These methods can not perform end-to-end learning and consider contextual information at the same time. To solve this problem, we propose a framework that models a WSI as a pathologist's observing video and utilizes Transformer to process video clips with a divide-and-conquer strategy, which helps achieve both context-awareness and end-to-end learning. Extensive experiments on three public WSI datasets show that our proposed method outperforms existing SOTA methods in both WSI classification and positive region detection.

A reanalysis of the LHS 1140 b atmosphere observed with the Hubble Space Telescope

ABSTRACT The super-Earth LHS 1140 b is an interesting target for atmospheric observations since it is close to the habitable zone of its star and falls in the gap of the radius distribution of small exoplanets, in the region thought to correspond to the transition between planets with and without atmospheres. Observations of the primary transit with WFC3 onboard of the Hubble Space Telescope (HST) revealed a modulation in the planet transmission spectrum compatible with the presence of water; however, this modulation may be also due to stellar activity-related phenomena. Here, we present a detailed analysis of the WFC3/HST observations to probe the nature of this modulation and to understand if it can be attributable to the presence of unocculted spots on the stellar surface. Our analysis strongly suggests that LHS1140 is a rather quiet star with subsolar [Fe/H] and enriched in α elements. Therefore, we rule out the possibility that the planetary spectrum is affected by the presence of spots and faculae. This analysis shows the importance of a proper modelling of the stellar spectrum when analysing transit observations. Finally, we modelled the planetary atmosphere of LHS1140 b to retrieve its atmospheric composition. However, the low resolution and the narrow spectral range of HST observations prevented us from definitively determining whether the spectral features are attributable to the presence of water or of other molecules in the planetary atmosphere.

Abstract 107: Application of MSD on the lung cancer associated idiopathic pulmonary fibrosis

Abstract Idiopathic pulmonary fibrosis (IPF) is a progressive and irreversible chronic disease that kill ten thousand of people in China every year. The risk of IPF is highly correlated with smoke, air pollution, dust, virus infection and aging. Average survival period of patients diagnosed as IPF is around 2.8 years which is less than several types of cancers, therefore, IPF is also thought to be a lung cancer-like disease. In the past few decades, many animal models were created to mimic human IPF, however, induction materials, animal species and strain differences, sex, physiology structure and progress of disease make the choice of IPF animal models selection more difficult. In addition, lack of strong evidence of cytokines biomarker detection hampered the discovery of anti-IPF drugs. Meso Scale Discovery (MSD) instrument is an efficient tool to high throughput analyze the lowest level of several cytokines and other biomarkers in the same time and was used to detect the bleomycin (BLM) induced mouse lung fibrosis. Meanwhile, animal sex and dose effects of BLM were also tested to define the proper mouse model for anti-IPF drugs development. Our results indicated that 0.6-0.8 mg/kg of intratracheal injection of single dose BLM is sufficient to create the IPF model without sex differences by examining the lung weight, lung ratio, soluble hydroxyproline, HE staining and mason staining. However, male mice were more resistant to the lethal effect of BLM due to the larger size or bodyweight. Using MSD detection, several mouse cytokines were measured simultaneously and showed that IFN-γ, IL-5, TNF-α, and IL-10 were increased in the IPF mouse model and were recovered after pirfenidone treatment at day 14 which mimics the cytokines profile in IPF patients. In summary, the MSD detection and pathological evaluation prove that male mouse treated with 0.8 mg/kg BLM through intratracheal injection is a suitable mouse model for the development of anti-IPF dugs. Citation Format: Wei Bao, Ying-Ji Li, Jin-Fan Gu, Cong-Lin Yang, Yi-Da Wang, Tie-Jun Bing, Wen-Jen Yu. Application of MSD on the lung cancer associated idiopathic pulmonary fibrosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 107.

Genetic contribution to microglial activation in schizophrenia

Several lines of evidence indicate the involvement of neuroinflammatory processes in the pathophysiology of schizophrenia (SCZ). Microglia are brain resident immune cells responding toward invading pathogens and injury-related products, and additionally, have a critical role in improving neurogenesis and synaptic functions. Aberrant activation of microglia in SCZ is one of the leading hypotheses for disease pathogenesis, but due to the lack of proper human cell models, the role of microglia in SCZ is not well studied. We used monozygotic twins discordant for SCZ and healthy individuals to generate human induced pluripotent stem cell-derived microglia to assess the transcriptional and functional differences in microglia between healthy controls, affected twins and unaffected twins. The microglia from affected twins had increased expression of several common inflammation-related genes compared to healthy individuals. Microglia from affected twins had also reduced response to interleukin 1 beta (IL1β) treatment, but no significant differences in migration or phagocytotic activity. Ingenuity Pathway Analysis (IPA) showed abnormalities related to extracellular matrix signaling. RNA sequencing predicted downregulation of extracellular matrix structure constituent Gene Ontology (GO) terms and hepatic fibrosis pathway activation that were shared by microglia of both affected and unaffected twins, but the upregulation of major histocompatibility complex (MHC) class II receptors was observed only in affected twin microglia. Also, the microglia of affected twins had heterogeneous response to clozapine, minocycline, and sulforaphane treatments. Overall, despite the increased expression of inflammatory genes, we observed no clear functional signs of hyperactivation in microglia from patients with SCZ. We conclude that microglia of the patients with SCZ have gene expression aberrations related to inflammation response and extracellular matrix without contributing to increased microglial activation.

Molecular-Level Kinetic Model for Light Hydrocarbon Steam Cracking Based on the SU-BEM Framework.

In this work, a molecular-level kinetic model of ethane/propane steam cracking was developed by using a hybrid structural unit-bond electron matrix framework. The molecular-level simulation was conducted, creating a detailed feedstock composition, formulating the reaction rules, and automating the generation and visualization of reaction networks. Ordinary differential equations were automatically generated based on the Arrhenius equation, while the kinetic parameters were reduced via linear free energy relations (LFERs). Furthermore, proper mathematical models for mass transfer, heat transfer, and momentum transfer within the cracking furnace were integrated into the molecular-level kinetic model, enabling the simultaneous calculation of the transfer process and chemical kinetics in steam cracking. The model was validated by its precise prediction of product yields, outlet pressure, and outlet temperature, which were collected from an industrial gas-cracking furnace.

Effect of robot-assisted gait training on motor dysfunction in Parkinson's patients:A systematic review and meta-analysis.

Robot-assisted gait training (RAGT) has been reported to treat motor dysfunction in patients with Parkinson's disease (PD) in the last few years. However, the benefits of RAGT for treating motor dysfunction in PD are still unclear. To investigate the efficacy of RAGT for motor dysfunction in PD patients. We searched PubMed, Web of Science, Cochrane Library, Embase, CNKI, Wanfang, Chinese Biomedical Literature Database (CBM), and Chinese VIP Database for randomized controlled trials investigating RAGT to improve motor dysfunction in PD from the databases' inception dates until September 1, 2022. The following outcome indexes were employed to evaluate motor dysfunction: the Berg Balance Scale (BBS), Activities-specific Balance Confidence Scale (ABC), 10-Meter Walk Test gait speed (10-MWT), gait speed, stride length, cadence Unified Parkinson Disease Rating Scale Part III (UPDRS III), 6-Minute Walk Test (6MWT), and the Timed Up and Go test (TUG). The meta-analysis was performed using the proper randomeffect model or fixed-effect model to evaluate the difference in efficacy between the RAGT and the control groups. The Cochrane Risk of Bias Tool was used for the included studies and Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) was used to interpret the certainty of the results. The results consisted of 17 studies comprising a total of 670 participants. Six hundred and seven PD patients with motor dysfunction were included: 335 in the RAGT group and 335 in the control group. This meta-analysis results established that when compared with the control group, robot-assisted gait training improved the BBS results of PD patients (MD: 2.80, 95%CI: 2.11-3.49, P< 0.00001), ABC score (MD: 7.30, 95%CI: 5.08-9.52, P< 0.00001), 10-MWT (MD: 0.06, 95%CI: 0.03-0.10, P= 0.0009), gait speed (MD: 3.67, 95%CI: 2.58-4.76, P< 0.00001), stride length (MD: 5.53, 95%CI: 3.64-7.42, P< 0.00001), cadence (MD: 4.52, 95%CI: 0.94-8.10, P= 0.01), UPDRS III (MD: -2.16, 95%CI: -2.48--1.83, P< 0.00001), 6MWT (MD: 13.87, 95%CI: 11.92-15.82, P< 0.00001). However, RAGT did not significantly improve the TUG test result of patients with PD (MD =-0.56, 95% CI: -1.12-0.00, P= 0.05). No safety concerns or adverse reactions among robot-assisted gait training patients were observed. Even though RAGT can improve balance function, walking function, and gait performance and has demonstrated positive results in several studies, there is currently insufficient compelling evidence to suggest that it can improve all aspects of lower motor function.

Efficient cooling system design by using different shaped nanoparticles and rotating cylinders in channel system for photovoltaic thermal management

Research focusing on improving the efficiency and performance of renewable energy systems requires the development of cooling systems (CSs) with effective solutions, including photovoltaic (PV) thermal management. The current work proposes a unique cooling method that uses double rotating cylinders (RCs) and different shaped alumina nanoparticles (NP; spherical, blade, brick, and cylindrical) in water for conductive panel’s cooling channel. The numerical study is performed for different values of Reynolds number (Re: 100–600), rotational Re (Rew: 0–20), size of the cylinders (Rc: 0.01H–0.2H), and solid particle volume fractions (svf: 0–0.03). It is observed that vortex size and numbers behind the cylinders can be controlled by varying Re and Rew. A 16 °C temperature drop of the panel is achieved at the highest Re at svf = 0.03. When rotations are active, the average Nu increases 99% and 37% for water and nanofluid (NF) at Rew = 20 and Re = 250 while temperature drops of 15 °C and 7.5 °C are obtained. The highest performance coefficient (PEC) is obtained as PEC = 1.38 when NF is used at Rew = 15. Large cylinders results in lower PEC values. When rotations are not active, the average Nu rises by about 26.8%, 13.5%, 12.8%, and 3.5% for cylindrical, blade, brick, and spherical-shaped particles while average cell temperature drops by about 5.5 °C, 4.5 °C, 4.3 °C, and 1 °C. The highest PEC value is obtained as 1.23 when cylindrical particles at the highest loading are used for stationary cylinders. Proper orthogonal-based model is considered for estimation of spatially varying panel temperature.

Formula omitted] and its interpretations

In this paper, we unify the study of classical and non-classical algebra-valued models of set theory, by studying variations of the interpretation functions for = and ∈. Although, these variations coincide with the standard interpretation in Boolean-valued constructions, nonetheless they extend the scope of validity of ZF to new algebra-valued models. This paper presents, for the first time, non-trivial paraconsistent models of full ZF. Moreover, due to the validity of Leibniz's law in these structures, we will show how to construct proper models of set theory by quotienting these algebra-valued models with respect to equality, modulo the filter of the designated truth-values.

Maser polarization through anisotropic pumping

Context. Polarized emission from masers is an excellent tool to study magnetic fields in maser sources. The linear polarization of the majority of masers is understood as an interplay of maser saturation and anisotropic pumping. However, for the latter mechanism, no quantitative modeling has been presented yet. Aims. We aim to construct a comprehensive model of maser polarization, including quantitative modeling of both anisotropic pumping and the effects of maser saturation on the polarization of masers. Methods. We extended regular (isotropic) maser excitation modeling with a dimension that describes the molecular population alignments, as well as including the linear polarization dimension to the radiative transfer. The results of the excitation analysis yielded the anisotropic pumping and decay parameters, which were subsequently used in one-dimensional proper maser polarization radiative transfer modeling. Results. We present the anisotropic pumping parameters for a variety of transitions from class I CH3OH masers, H2O masers, and SiO masers. SiO masers are highly anisotropically pumped due to them occurring in the vicinity of a late-type star, which irradiates the maser region with a strong directional radiation field. Class I CH3OH masers and H2O masers occur in association with shocks, and they are modestly anisotropically pumped due to the anisotropy of the excitation region. Conclusions. Our modeling constitutes the first quantitative constraints on the anisotropic pumping of masers. We find that anisotropic pumping can explain the high polarization yields of SiO masers, as well as the modest polarization of unsaturated class I CH3OH masers. The common 22 GHz H2O maser has a relatively weak anisotropic pumping; in contrast, we predict that the 183 GHz H2O maser is strongly anisotropically pumped. Finally, we outline a mechanism through which non-Zeeman circular polarization is produced, when the magnetic field changes direction along the propagation through an anisotropically pumped maser.

Biaxial Testing of EPDM Rubbers for Automotive Applications Using a Uniaxial Testing Machine

The FE-based structural design of components made of rubber materials requires an experimental characterization to calibrate a proper hyperelastic material model. A strain energy density (SED) parameter is typically adopted to describe the hyperelastic behavior of isotropic rubbers. This can be written as a function of the strain tensors, the strain invariants or the stretch ratios and proper material parameters. The latter must be calibrated on experimental data generated by testing rubber materials under different loading conditions, typically two among uniaxial tension, pure shear and biaxial tension. In this context, the mechanical behavior of Ethylene–Propylene–Diene Monomer (EPDM) rubbers, both in the dense and expanded configurations, which are typically employed to manufacture automotive seals, has been investigated. First, uniaxial tension tests have been performed. Afterwards, a new fixture has been developed to perform equi-biaxial tension tests using a uniaxial testing machine. The fixture has been adopted to test EPDM cruciform specimens under equi-biaxial tension loading. Finally, both uniaxial and equi-biaxial data have been employed to fit a Mooney–Rivlin material model for each tested EPDM rubber, which has been validated by comparing the results of FE simulations with the experimental ones.

Canine Mammary Tumors: Classification, Biomarkers, Traditional and Personalized Therapies.

In recent years, many studies have focused their attention on the dog as a proper animal model for human cancer. In dogs, mammary tumors develop spontaneously, involving a complex interplay between tumor cells and the immune system and revealing several molecular and clinical similarities to human breast cancer. In this review, we summarized the major features of canine mammary tumor, risk factors, and the most important biomarkers used for diagnosis and treatment. Traditional therapy of mammary tumors in dogs includes surgery, which is the first choice, followed by chemotherapy, radiotherapy, or hormonal therapy. However, these therapeutic strategies may not always be sufficient on their own; advancements in understanding cancer mechanisms and the development of innovative treatments offer hope for improved outcomes for oncologic patients. There is still a growing interest in the use of personalized medicine, which should play an irreplaceable role in the research not only in human cancer therapy, but also in veterinary oncology. Moreover, immunotherapy may represent a novel and promising therapeutic option in canine mammary cancers. The study of novel therapeutic approaches is essential for future research in both human and veterinary oncology.

Observation and modeling of irradiance near water surface of a photovoltaic pond

Accurate evaluation of near ground solar radiation in photovoltaic (PV) covered areas is essential for controlling adverse environmental effects and comprehensively utilizing the earth’s surface resources. The irradiance near the water surface of a photovoltaic fishpond was measured with eighteen distributed pyranometers. A simplified penetration model is proposed to reveal the shielding effect of PV panels on direct and diffuse irradiance. The model can well capture the spatial–temporal variation of the global irradiance due to the shielding effect of PV arrays. Numerical results show that proper modeling of the shading effect on diffuse irradiance is essential for the calculation accuracy of the cumulative global irradiance loss for various weather types and sky-view restriction levels. The spatial variation range of global radiation penetration ratio under the PV panels is 10%–90%. The spatially-averaged monthly shielding effects show that the maximum (87%) and minimum (46%) relative irradiance loss ratios in the studied PV area occur in December and June, respectively.

Architected materials for artificial reefs to increase storm energy dissipation.

Increasing extreme weather events require a corresponding increase in coastal protection. We show that architected materials, which have macroscopic properties that differ from those of their constituent components, can increase wave energy dissipation by more than an order of magnitude over both natural and existing artificial reefs, while providing a biocompatible environment. We present a search that optimized their design through proper hydrodynamic modeling and experimental testing, validated their performance, and characterized sustainable materials for their construction.

Landslide extraction using a novel empirical method and binary semantic segmentation U-NET framework using sentinel-2 imagery

ABSTRACT Artificial intelligence (AI) has achieved a remarkable place in solving complex problems in almost all disciplines. Based on the recent notable performances of machine learning and deep learning techniques for rapid and automatic landslide identifcation, it is observed that availability of quality training data, proper model training and associated cost are crucial for developing such frameworks. Therefore, the primary objective of the study is to propose a novel empirical algorithm, DvD, for rapid landslide identification using Sentinel-2 imagery and comparatively evaluate its performance to a deep learning architecture popularly used in feature extraction problems, binary semantic segmentation U-NET (BSS-UNET) framework. The empirical method has been investigated over a dataset diverse in topography and land cover to evaluate its efficacy. The proposed BSS-UNET framework is trained on the landslide database provided by the Institute of Advance Research in Artificial Intelligence (IARAI) in Landslide4Sense 2022 challenge which achieved a high mIoU value of 0.78 with 84.23% precision, 65% recall and 73.32 F1-score. The DvD algorithm outperformed the BSS-UNET framework and achieved 0.80 mIoU when applied to the IARAI dataset. The proposed empirical method has the potential to serve as large-scale rapid landslide inventory preparation subject to the availability of cloud-free satellite imagery.

Endo-Surgical Force Feedback System Design for Virtual Reality Applications in Medical Planning

Virtual reality (VR) applications for surgical planning and mentoring are currently becoming more widespread. Within these fields of applications, haptic systems are crucial to help the understanding of the feedback provided by the tissue during the interaction with the surgical tool. The accuracy of the VR scenario is related to many aspects, among them the ability of the haptic device to provide the force feedback and thus the adoption of proper characterization and modeling of the soft tissue behavior during deformation. This paper presents the current development concerning a VR environment to be equipped with a force feedback haptic device to simulate and experience clamping in colorectal surgery. The reaction at the clamp handle is provided starting from a FEA of the contact between the surgical tool and the tissue, coupled with the kinematic analysis of the mechanism. Connecting the virtual scenario to a microcontroller board, the reaction force is applied to the the haptic device, shaped as a clamping tool handle, so that the user may experience the tissue impedence during clamping. By simulations made through Simscape, a concept is evaluated according to some simulation paths that involve common scenarios of the virtual reality experience (such as collision, partial or total clamping operation). The novelty of the research regards the system design oriented to increase the accuracy of the soft tissue material behaviour through a preliminary off-line dedicated FEA investigation. Through Simulink simulations, the system design is verified so that the VR system set-up may be carried out. The results highlighted: (a) a good correspondence among the theoretical study of the reference kinematism that the haptic device must reproduce; (b) the implementation of a bilinear material model, derived off-line from FEA, is suitable to capture hyperelastic and geometric non-linearities of the soft tissue; (c) the feasibility of the open loop haptic control in accordance to the explored kinematism. The final goal of the research is to propose a system design approach able to tune the VR experince for teaching and mentoring through a in-house set-up easy to be tailored.

A pair of primary colorectal cancer-derived and corresponding synchronous liver metastasis-derived organoid cell lines.

Proper preclinical models for the research of colorectal cancer (CRC) and CRC liver metastases (CLM) are a clear and unmet need. Patient-derived organoids have recently emerged as a robust preclinical model, but are not available to all scientific researchers. Here, we present paired 3D organoid cell lines of CWH22 (CRC-derived) and CLM22 (CLM-derived) with sound background information and the short tandem repeats are identical to those of the normal tissue. Morphological and immunohistochemical staining, along with whole-exome sequencing (WES), confirmed that the organoids exhibited the same differentiation, molecular expression, and mutation status as the corresponding tumor tissue. Both organoids possessed mutated APC/KRAS/SMAD4/CDKN1B/KMT2C genes and wild-type TP53 and PIK3CA; stably secreted the tumor markers CEA and CA19-9, and possessed sound proliferation rates in vitro, as well as subcutaneous tumorigenicity and liver metastatic abilities in vivo. IC50 assays confirmed that both cell lines were sensitive to 5-fluorouracil, oxaliplatin, SN-38, and sotorasib. WES and karyotype analyses revealed the genomic instability status as chromosome instability. The corresponding adherent cultured CWH22-2D/CLM22-2D cells were established and compared with commonly used CRC cell lines from the ATCC. Both organoids are publicly available to all researchers and will be useful tools for specific human CRC/CLM studies both in vitro and in vivo.

Estimation on strength parameters of sediments with hydrate layered distribution based on triaxial shearing tests

Proper models for predicting strength parameters of reservoirs are vital to the numerical simulation and risk analysis during gas hydrate exploration. However, unlike homogeneous hydrate reservoirs, strength parameters of interlayered sediments depend on hydrate distribution modes, which remains unclear. Herein, a series of triaxial shearing tests on sediments with hydrate layered distribution are conducted to investigate variations of strength parameters and effects of sublayers. The results indicate that failure strength of whole sediments mainly depends on the high hydrate-saturated sublayer, while the cohesion is more relevant to the low hydrate-saturated layer. Besides, prediction models are proposed to estimate the failure strength through data-fitting, strength-average, and hydrate-average methods. By comparing the accuracy and applicability of these three methods, the hydrate-average method is the best way to estimate failure strength for current use. This study can provide a feasibility reference for strength estimation and risk control during natural gas hydrate development.