Reversible Mn2+/Mn4+ double redox in lithium-excess cathode materials.

There is an urgent need for low-cost, resource-friendly, high-energy-density cathode materials for lithium-ion batteries to satisfy the rapidly increasing need for electrical energy storage. To replace the nickel and cobalt, which are limited resources and are associated with safety problems, in current lithium-ion batteries, high-capacity cathodes based on manganese would be particularly desirable owing to the low cost and high abundance of the metal, and the intrinsic stability of the Mn4+ oxidation state. Here we present a strategy of combining high-valent cations and the partial substitution of fluorine for oxygen in a disordered-rocksalt structure to incorporate the reversible Mn2+/Mn4+ double redox couple into lithium-excess cathode materials. The lithium-rich cathodes thus produced have high capacity and energy density. The use of the Mn2+/Mn4+ redox reduces oxygen redox activity, thereby stabilizing the materials, and opens up new opportunities for the design of high-performance manganese-rich cathodes for advanced lithium-ion batteries.

Flood susceptibility mapping of Cheongju, South Korea based on the integration of environmental factors using various machine learning approaches.

Floods are natural occurrences that pose serious risks to human life and the environment, including significant property and infrastructure damage and subsequent socioeconomic challenges. Recent floods in Cheongju County, South Korea have been linked to river overflow. In this study, we created flood susceptibility maps of Cheongju, South Korea using machine learning techniques including support vector regression (SVR), boosted tree (BOOST), and long short-term memory (LSTM) algorithms, based on environmental factors. Potentially influential variables were selected based on flood data gathered through field surveys; these included the slope, aspect, length-slope factor, wind exposition index, terrain wetness index, plan curvature, normalized difference water index, geology, soil drainage, soil depth, soil texture, land use type, and forest density. To improve the robustness of the flood susceptibility model, the most influential factors were identified using the frequency ratio method. Implementing machine learning techniques like SVR and BOOST produced encouraging outcomes, achieving the area under the curve (AUC) of 83.16% and 86.70% for training, and 81.65% and 86.43% for testing, respectively. While, the LSTM algorithm showed superior flood susceptibility mapping performance, with an AUC value of 87.01% for training and 86.91% for testing, demonstrating its robust performance and reliability in accurately assessing flood susceptibility. The results of this study enhance our understanding of flood susceptibility in South Korea and demonstrate the potential of the proposed approach for informing and guiding crucial regional policy decisions, contributing to a more resilient and prepared future.

Glutamine increases stability of TPH1 mRNA via p38 mitogen-activated kinase in mouse mastocytoma cells.

Expression changes for tryptophan hydroxylase 1 (TPH1), the rate-limiting enzyme in serotonin synthesis, by environmental glutamine (GLN) were examined in mouse mastocytoma-derived P815-HTR cells. GLN-treated cells exhibited a robust increase in TPH1 mRNA after a 6 h exposure to GLN. 6-Diazo-5-oxo-L-norleucine (DON), a glutamine-utilizing glutaminase inhibitor, significantly inhibited the GLN-induction of TPH1 mRNA. Nuclear run-on assays and mRNA decay experiments demonstrated that the primary mechanism leading to increased TPH1 mRNA levels was not due to transcriptional changes, but rather due to increased TPH1 RNA stability induced by GLN. Treatment with GLN also led to activation of p38 MAP kinase, but not p42/44 MAPK. In addition, SB203580, a p38 MAP kinase specific inhibitor, completely abolished the GLN-mediated increase of TPH1 mRNA levels, suggesting the pathway stabilizing TPH1 mRNA might be mediated by the activated p38 MAP kinase pathway. Additionally, SB203580 significantly reduced the stability of TPH1 mRNA, and this reduction of the stability was not affected by GLN in the culture medium, implying a sequential signaling from GLN being mediated by p38 MAP kinase, resulting in alteration of TPH1 mRNA stability. TPH1 mRNA stability loss was also dependent on de novo protein synthesis as shown by treatment of cells with a transcriptional/translational blocker. We provide evidence that TPH1 mRNA levels are increased in response to increased exogenous GLN in mouse mastocytoma cells via a stabilization of TPH1 mRNA due to the activity of the p38 MAP kinase.

Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network.

Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods. However, the diverse characteristics of road networks, such as varying lengths, widths, materials, and geometries across different regions, pose a formidable obstacle for road extraction from RS imagery. The issue of road extraction can be defined as a task that involves capturing contextual and complex elements while also preserving boundary information and producing high-resolution road segmentation maps for RS data. The objective of the proposed Archimedes tuning process quantum dilated convolutional neural network for road Extraction (ATP-QDCNNRE) technology is to tackle the aforementioned issues by enhancing the efficacy of image segmentation outcomes that exploit remote sensing imagery, coupled with Archimedes optimization algorithm methods (AOA). The findings of this study demonstrate the enhanced road-extraction capabilities achieved by the ATP-QDCNNRE method when used with remote sensing imagery. The ATP-QDCNNRE method employs DL and a hyperparameter tuning process to generate high-resolution road segmentation maps. The basis of this approach lies in the QDCNN model, which incorporates quantum computing (QC) concepts and dilated convolutions to enhance the network's ability to capture both local and global contextual information. Dilated convolutions also enhance the receptive field while maintaining spatial resolution, allowing fine road features to be extracted. ATP-based hyperparameter modifications improve QDCNNRE road extraction. To evaluate the effectiveness of the ATP-QDCNNRE system, benchmark databases are used to assess its simulation results. The experimental results show that ATP-QDCNNRE performed with an intersection over union (IoU) of 75.28%, mean intersection over union (MIoU) of 95.19%, F1 of 90.85%, precision of 87.54%, and recall of 94.41% in the Massachusetts road dataset. These findings demonstrate the superior efficiency of this technique compared to more recent methods.

Cryo-EM structure of OSCA1.2 from Oryza sativa elucidates the mechanical basis of potential membrane hyperosmolality gating.

Sensing and responding to environmental water deficiency and osmotic stresses are essential for the growth, development, and survival of plants. Recently, an osmolality-sensing ion channel called OSCA1 was discovered that functions in sensing hyperosmolality in Arabidopsis Here, we report the cryo-electron microscopy (cryo-EM) structure and function of an OSCA1 homolog from rice (Oryza sativa; OsOSCA1.2), leading to a model of how it could mediate hyperosmolality sensing and transport pathway gating. The structure reveals a dimer; the molecular architecture of each subunit consists of 11 transmembrane (TM) helices and a cytosolic soluble domain that has homology to RNA recognition proteins. The TM domain is structurally related to the TMEM16 family of calcium-dependent ion channels and lipid scramblases. The cytosolic soluble domain possesses a distinct structural feature in the form of extended intracellular helical arms that are parallel to the plasma membrane. These helical arms are well positioned to potentially sense lateral tension on the inner leaflet of the lipid bilayer caused by changes in turgor pressure. Computational dynamic analysis suggests how this domain couples to the TM portion of the molecule to open a transport pathway. Hydrogen/deuterium exchange mass spectrometry (HDXMS) experimentally confirms the conformational dynamics of these coupled domains. These studies provide a framework to understand the structural basis of proposed hyperosmolality sensing in a staple crop plant, extend our knowledge of the anoctamin superfamily important for plants and fungi, and provide a structural mechanism for potentially translating membrane stress to transport regulation.

Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea.

Landslides are a geological hazard that can pose a serious threat to human health and the environment of highlands or mountain slopes. Landslide susceptibility mapping is an essential tool for predicting and mitigating landslides. This study aimed to investigate the application of deep learning algorithms based on convolutional neural networks (CNNs) with metaheuristic optimization algorithms, namely the grey wolf optimizer (GWO) and imperialist competitive algorithm (ICA), to landslide susceptibility mapping. The study area was Icheon City, South Korea, for which an accurate landslide inventory dataset was available. The landslide inventory map was prepared and randomly divided into datasets of 70% for training and 30% for validation. Additionally, 18 landslide-related factors, including geo-environmental and topo-hydrological factors, were considered as predictive variables. The models were compared using area under the curve (AUC) values in receiver operating characteristic (ROC) curve analysis. The validation results showed that optimized models based on CNN-GWO (AUC = 0.876, RMSE = 0.08) and CNN-ICA (AUC = 0.852, RMSE = 0.09) outperformed the standalone CNN model (AUC = 0.847, RMSE = 0.12). Nevertheless, the CNN model outperformed previous research that used a machine learning algorithm alone. Thus, the deep learning algorithm with optimization algorithms proposed in this study can generate more suitable models for landslide susceptibility mapping in the study area due to its improved accuracy.

Acremonamide, a Cyclic Pentadepsipeptide with Wound-Healing Properties Isolated from a Marine-Derived Fungus of the Genus Acremonium.

Acremonamide (1) was isolated from a marine-derived fungus belonging to the genus Acremonium. The chemical structure of 1 was established using MS, UV, and NMR spectroscopic data analyses. Acremonamide (1) was found to contain N-Me-Phe, N-Me-Ala, Val, Phe, and 2-hydroxyisovaleric acid. The absolute configurations of the four aforementioned amino acids were determined through acid hydrolysis followed by the advanced Marfey's method, whereas the absolute configuration of 2-hydroxyisovaleric acid was determined through GC-MS analysis after formation of the O-pentafluoropropionylated derivative of the (-)-menthyl ester of 2-hydroxyisovaleric acid. As an intrinsic biological activity, acremonamide (1) did not exert cytotoxicity to cancer and noncancer cells and increased the migration and invasion. Based on these activities, the wound healing properties of acremonamide (1) were confirmed in vitro and in vivo.

γ-Secretase Partitioning into Lipid Bilayers Remodels Membrane Microdomains after Direct Insertion.

γ-Secretase is a multisubunit complex that catalyzes intramembranous cleavage of transmembrane proteins. The lipid environment forms membrane microdomains that serve as spatio-temporal platforms for proteins to function properly. Despite substantial advances in the regulation of γ-secretase, the effect of the local membrane lipid microenvironment on the regulation of γ-secretase is poorly understood. Here, we characterized and quantified the partitioning of γ-secretase and its substrates, the amyloid precursor protein (APP) and Notch, into lipid bilayers using solid-supported model membranes. Notch substrate is preferentially localized in the liquid-disordered (Ld) lipid domains, whereas APP and γ-secretase partition as single or higher complex in both phases but highly favor the ordered phase, especially after recruiting lipids from the ordered phase, indicating that the activity and specificity of γ-secretase against these two substrates are modulated by membrane lateral organization. Moreover, time-elapse measurements reveal that γ-secretase can recruit specific membrane components from the cholesterol-rich Lo phase and thus creates a favorable lipid environment for substrate recognition and therefore activity. This work offers insight into how γ-secretase and lipid modulate each other and control its activity and specificity.

Earthquake Probability Assessment for the Indian Subcontinent Using Deep Learning.

Earthquake prediction is a popular topic among earth scientists; however, this task is challenging and exhibits uncertainty therefore, probability assessment is indispensable in the current period. During the last decades, the volume of seismic data has increased exponentially, adding scalability issues to probability assessment models. Several machine learning methods, such as deep learning, have been applied to large-scale images, video, and text processing; however, they have been rarely utilized in earthquake probability assessment. Therefore, the present research leveraged advances in deep learning techniques to generate scalable earthquake probability mapping. To achieve this objective, this research used a convolutional neural network (CNN). Nine indicators, namely, proximity to faults, fault density, lithology with an amplification factor value, slope angle, elevation, magnitude density, epicenter density, distance from the epicenter, and peak ground acceleration (PGA) density, served as inputs. Meanwhile, 0 and 1 were used as outputs corresponding to non-earthquake and earthquake parameters, respectively. The proposed classification model was tested at the country level on datasets gathered to update the probability map for the Indian subcontinent using statistical measures, such as overall accuracy (OA), F1 score, recall, and precision. The OA values of the model based on the training and testing datasets were 96% and 92%, respectively. The proposed model also achieved precision, recall, and F1 score values of 0.88, 0.99, and 0.93, respectively, for the positive (earthquake) class based on the testing dataset. The model predicted two classes and observed very-high (712,375 km2) and high probability (591,240.5 km2) areas consisting of 19.8% and 16.43% of the abovementioned zones, respectively. Results indicated that the proposed model is superior to the traditional methods for earthquake probability assessment in terms of accuracy. Aside from facilitating the prediction of the pixel values for probability assessment, the proposed model can also help urban-planners and disaster managers make appropriate decisions regarding future plans and earthquake management.

An Artificial Intelligence Application for Post-Earthquake Damage Mapping in Palu, Central Sulawesi, Indonesia.

A Mw 7.4 earthquake hit Donggala County, Central Sulawesi Province, Indonesia, on 28 September 2018, triggering a tsunami and liquefaction in Palu City and Donggala. Around 2101 fatalities ensued and 68,451 houses were damaged by the earthquake. In light of this devastating event, a post-earthquake map is required to establish the first step in the evacuation and mitigation plan. In this study, remote sensing imagery from the Landsat-8 and Sentinel-2 satellites was used. Pre- and post-earthquake satellite images were classified using artificial neural network (ANN) and support vector machine (SVM) classifiers and processed using a decorrelation method to generate the post-earthquake damage map. The affected areas were compared to the field data, the percentage conformity between the ANN and SVM results was analyzed, and four post-earthquake damage maps were generated. Based on the conformity analysis, the Landsat-8 imagery (85.83%) was superior to that of Sentinel-2 (63.88%). The resulting post-earthquake damage map can be used to assess the distribution of seismic damage following the Palu earthquake and may be used to mitigate damage in the event of future earthquakes.

Structures of P-glycoprotein reveal its conformational flexibility and an epitope on the nucleotide-binding domain.

P-glycoprotein (P-gp) is one of the best-known mediators of drug efflux-based multidrug resistance in many cancers. This validated therapeutic target is a prototypic, plasma membrane resident ATP-Binding Cassette transporter that pumps xenobiotic compounds out of cells. The large, polyspecific drug-binding pocket of P-gp recognizes a variety of structurally unrelated compounds. The transport of these drugs across the membrane is coincident with changes in the size and shape of this pocket during the course of the transport cycle. Here, we present the crystal structures of three inward-facing conformations of mouse P-gp derived from two different crystal forms. One structure has a nanobody bound to the C-terminal side of the first nucleotide-binding domain. This nanobody strongly inhibits the ATP hydrolysis activity of mouse P-gp by hindering the formation of a dimeric complex between the ATP-binding domains, which is essential for nucleotide hydrolysis. Together, these inward-facing conformational snapshots of P-gp demonstrate a range of flexibility exhibited by this transporter, which is likely an essential feature for the binding and transport of large, diverse substrates. The nanobody-bound structure also reveals a unique epitope on P-gp.

Risk factors for recurrence of papillary thyroid carcinoma with clinically node-positive lateral neck.

BACKGROUND: Papillary thyroid carcinoma (PTC) with clinically node-positive lateral neck is more likely to recur after surgery than node-negative PTC. The present study investigated the risk factors for recurrence in PTC patients with clinically node-positive lateral neck. MATERIALS AND METHODS: This study involved 136 patients with pathologically confirmed PTC and a clinically lymph node (LN)-positive lateral neck but no initial distant metastasis who underwent total thyroidectomy with therapeutic central and lateral neck dissection. Clinicopathologic characteristics, intraoperative findings, postoperative thyroglobulin (Tg) levels, and post-treatment recurrences were examined. Univariate and multivariate analyses were performed to identify factors associated with recurrence-free survival (RFS). RESULTS: During a median follow-up of 62 months (range 33-90 months), 27 (19.9 %) patients had locoregional or distant recurrences. Univariate analyses showed that primary tumor size (p = 0.049), recurrent laryngeal nerve invasion (p = 0.035), the maximal size of metastatic LN foci (≥1.5 cm; p = 0.012), extranodal extension (p = 0.025), total LN ratio (≥0.26; p = 0.008), American Thyroid Association (ATA) risk categories (p < 0.001), and stimulated serum Tg level (≥4.4; p < 0.001) at the time of radioactive iodine ablation therapy just after thyroidectomy were significant predictors of RFS. Multivariate analyses showed that the maximal size of metastatic foci (p = 0.037), ATA risk categories (p < 0.001), and stimulated Tg level (p < 0.001) were independent predictors of RFS. CONCLUSIONS: Maximal size of metastatic foci, ATA risk categories, and stimulated serum Tg levels are predictive of recurrence after surgery. Careful follow-up of patients with these risk factors is therefore recommended.

Intraoperative diagnosis of central compartment lymph node metastasis predicts recurrence of patients with papillary thyroid carcinoma and clinically node-negative lateral neck and may guide extent of initial surgery.

BACKGROUND: Although lymph node (LN) metastasis (LNM) of papillary thyroid carcinoma (PTC) is common, routine prophylactic LN dissection (LND) is still controversial. The purpose of this study was to investigate risk factors for recurrence of PTC with clinically node-negative lateral neck to determine the utility of intraoperative LN biopsy. MATERIALS AND METHODS: This study involved 185 patients with pathologically confirmed PTC and clinically node-negative lateral neck. All patients underwent thyroidectomy with or without ipsilateral or bilateral central LND after intraoperative central LN biopsy. Routine lateral neck LND was not performed. Clinicopathologic and intraoperative findings and post-treatment recurrences were recorded. Univariate and multivariate analyses with Cox-proportional hazards model were used to identify factors associated with recurrence. RESULTS: During a follow-up of 50-96 months, six (3.2 %) patients had recurrences in lateral cervical LNs at a median 28 months (range 7-57 months) after surgery. Overall, 2- and 5-year RFS rates were 98.4 and 96.7 %, respectively. Univariate analyses revealed that tumor size (P = 0.005), bilaterality (P = 0.033), T4 disease (P < 0.001), and intraoperative diagnosis of central LNM (P = 0.001) were significantly predictive of recurrence. Multivariate analyses showed that T4 disease (P = 0.049) and intraoperative diagnosis of central LNM (P = 0.027) were independently predictive of recurrence. CONCLUSIONS: Prophylactic lateral neck LND is not advocated for PTC with clinically node-negative lateral neck. Intraoperative LN biopsy may help identify patients at risk for recurrence and those who would benefit from LND.

Short-term hearing results using ossicular replacement prostheses of hydroxyapatite versus titanium.

The aim of this study was to compare the short-term audiologic results of the ossiculoplasty using partial or total ossicular replacement prostheses (PORP or TORP) made of hydroxyapatite (HA) and titanium (Ti). Fifty-two consecutive patients who underwent ossiculoplasty due to chronic otitis media with or without cholesteatoma were enrolled. Patients were assessed at 6 months postoperatively to establish short-term audiologic results. Preoperative and postoperative air-conduction and bone-conduction thresholds were measured at five frequencies: 0.25, 0.5, 1, 2, and 3 kHz. Postoperative air-bone gap (ABG) of less than 20 dB was considered as successful. Success rates for HA PORP versus Ti PORP and HA TORP versus Ti TORP were compared. Postoperative ABGs at each frequency according to the types of prostheses were also compared. All types of prostheses showed a significant decrease of ABG postoperatively. Regarding PORP, the success rate of postoperative ABG of less than 20 dB was non-significantly higher in Ti group (89%) than in HA group (72%). Regarding TORP, HA group showed non-significantly higher success rate (100%) than Ti group (67%). Comparing postoperative ABGs at each frequency, there was no significant difference in ABGs between HA and Ti groups. There was no difference between the two materials. Both HA and Ti gave good functional results and operator can choose any types of prostheses by personal preference and affordability. Further randomized controlled long-term trials with large sample sizes are needed to establish the difference exactly.

Sphingosine 1-phosphate receptors are essential mediators of eyelid closure during embryonic development.

The fetal development of the mammalian eyelid involves the expansion of the epithelium over the developing cornea, fusion into a continuous sheet covering the eye, and a splitting event several weeks later that results in the formation of the upper and lower eyelids. Recent studies have revealed a significant number of molecular signaling components that are essential mediators of eyelid development. Receptor-mediated sphingosine 1-phosphate (S1P) signaling is known to influence diverse biological processes, but its involvement in eyelid development has not been reported. Here, we show that two S1P receptors, S1P2 and S1P3, are collectively essential mediators of eyelid closure during murine development. Homozygous deletion of the gene encoding either receptor has no apparent effect on eyelid development, but double-null embryos are born with an "eyes open at birth" defect due to a delay in epithelial sheet extension. Both receptors are expressed in the advancing epithelial sheet during the critical period of extension. Fibroblasts derived from double-null embryos have a deficient response to epidermal growth factor, suggesting that S1P2 and S1P3 modulate this essential signaling pathway during eyelid closure.

Stereotyped fetal brain disorganization is induced by hypoxia and requires lysophosphatidic acid receptor 1 (LPA1) signaling.

Fetal hypoxia is a common risk factor that has been associated with a range of CNS disorders including epilepsy, schizophrenia, and autism. Cellular and molecular mechanisms through which hypoxia may damage the developing brain are incompletely understood but are likely to involve disruption of the laminar organization of the cerebral cortex. Lysophosphatidic acid (LPA) is a bioactive lipid capable of cortical influences via one or more of six cognate G protein-coupled receptors, LPA(1-6), several of which are enriched in fetal neural progenitor cells (NPCs). Here we report that fetal hypoxia induces cortical disruption via increased LPA(1) signaling involving stereotyped effects on NPCs: N-cadherin disruption, displacement of mitotic NPCs, and impaired neuronal migration, as assessed both ex vivo and in vivo. Importantly, genetic removal or pharmacological inhibition of LPA(1) prevented the occurrence of these hypoxia-induced phenomena. Hypoxia resulted in overactivation of LPA(1) through selective inhibition of G protein-coupled receptor kinase 2 expression and activation of downstream pathways including G(αi) and Ras-related C3 botulinum toxin substrate 1. These data identify stereotyped and selective hypoxia-induced cerebral cortical disruption requiring LPA(1) signaling, inhibition of which can reduce or prevent disease-associated sequelae, and may take us closer to therapeutic treatment of fetal hypoxia-induced CNS disorders and possibly other forms of hypoxic injury.

FTY720 (fingolimod) efficacy in an animal model of multiple sclerosis requires astrocyte sphingosine 1-phosphate receptor 1 (S1P1) modulation.

Sphingosine 1-phosphate (S1P), a lysophospholipid, has gained relevance to multiple sclerosis through the discovery of FTY720 (fingolimod), recently approved as an oral treatment for relapsing forms of multiple sclerosis. Its mechanism of action is thought to be immunological through an active phosphorylated metabolite, FTY720-P, that resembles S1P and alters lymphocyte trafficking through receptor subtype S1P(1). However, previously reported expression and in vitro studies of S1P receptors suggested that direct CNS effects of FTY720 might theoretically occur through receptor modulation on neurons and glia. To identify CNS cells functionally contributing to FTY720 activity, genetic approaches were combined with cellular and molecular analyses. These studies relied on the functional assessment, based on clinical score, of conditional null mouse mutants lacking S1P(1) in CNS cell lineages and challenged by experimental autoimmune encephalomyelitis (EAE), an animal model of multiple sclerosis. All conditional null mutants displayed WT lymphocyte trafficking that responded normally to FTY720. In marked contrast, EAE was attenuated and FTY720 efficacy was lost in CNS mutants lacking S1P(1) on GFAP-expressing astrocytes but not on neurons. In situ hybridization studies confirmed that astrocyte loss of S1P(1) was the key alteration in functionally affected mutants. Reductions in EAE clinical scores were paralleled by reductions in demyelination, axonal loss, and astrogliosis. Receptor rescue and pharmacological experiments supported the loss of S1P(1) on astrocytes through functional antagonism by FTY720-P as a primary FTY720 mechanism. These data identify nonimmunological CNS mechanisms of FTY720 efficacy and implicate S1P signaling pathways within the CNS as targets for multiple sclerosis therapies.

Multitemporal analysis of land subsidence induced by open-pit mining activity using improved combined scatterer interferometry with deep learning algorithm optimization.

Mine operational safety is an important aspect of maintaining the operational continuity of a mining area. In this study, we used the InSAR time series to analyze land surface changes using the ICOPS (improved combined scatterers with optimized point scatters) method. This ICOPS method combines persistent scatterers (PS) with distributed scatterers (DS) to increase surface deformation analysis's spatial coverage and quality. One of the improvements of this study is the use of machine learning in postprocessing, based on convolutional neural networks, to increase the reliability of results. This study used data from the Sentinel-1 SAR C-band satellite during the 2016-2022 observation period at the Musan mine, North Korea. In the InSAR surface deformation time analysis, the maximum average rate of land subsidence was approximately > 15.00 cm per year, with total surface deformation of 170 cm and 70 cm for the eastern dumping area and the western dumping area, respectively. Analyzing the mechanism of land surface changes also involved evaluating the geological conditions in the Musan mining area. Our research findings show that combining machine learning and statistical methods has great potential to enhance the understanding of mine surface deformation.

LPA receptors: subtypes and biological actions.

Lysophosphatidic acid (LPA) is a small, ubiquitous phospholipid that acts as an extracellular signaling molecule by binding to and activating at least five known G protein-coupled receptors (GPCRs): LPA(1)-LPA(5). They are encoded by distinct genes named LPAR1-LPAR5 in humans and Lpar1-Lpar5 in mice. The biological roles of LPA are diverse and include developmental, physiological, and pathophysiological effects. This diversity is mediated by broad and overlapping expression patterns and multiple downstream signaling pathways activated by cognate LPA receptors. Studies using cloned receptors and genetic knockout mice have been instrumental in uncovering the significance of this signaling system, notably involving basic cellular processes as well as multiple organ systems such as the nervous system. This has further provided valuable proof-of-concept data to support LPA receptors and LPA metabolic enzymes as targets for the treatment of medically important diseases that include neuropsychiatric disorders, neuropathic pain, infertility, cardiovascular disease, inflammation, fibrosis, and cancer.

Phthalides Isolated from the Endolichenic Arthrinium sp. EL000127 Exhibits Antiangiogenic Activity.

Endolichenic fungi (ELF) produce specialized metabolites that have various medicinal properties. Inhibition of tumor angiogenesis efficaciously suppresses many types of cancer. This study aimed to discover novel antiangiogenic agents from specialized metabolite extracts of ELF strains isolated from Korean lichens. The EtOAc extracts of 51 ELF strains were subjected to a screening pipeline consisting of cell viability, scratch wound healing, and Transwell migration assays. The EtOAc extract of Arthrinium sp. EL000127 showed the most potent inhibitory activity against the chemotactic migration of human umbilical vein endothelial cells (HUVEC). Targeted isolation on the major LC-MS peaks exhibited a previously known phthalide, 3-O-methylcyclopolic acid (1), and two unknown analogues of 1, 3-O-phenylethylcyclopolic acid (2) and 3-O-p-hydroxyphenylethylcyclopolic acid (3). The structures were characterized by MS and NMR analyses. All the isolates were acquired and applied to bioassays as racemates due to spontaneous racemization. Among the isolates, compound 3 effectively inhibits HUVEC motility by suppressing mRNA expressions of genes regulating epithelial cell survival and motility, which suggested that compound 3 is a potent antiangiogenic agent suitable for further exploration as a potential novel therapeutic against cancers.