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.

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.

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.

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.

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.

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.

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.

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.

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.

Generation, expression and utilization of single-domain antibodies for in vivo protein localization and manipulation in sea urchin embryos.

Single-domain antibodies, also known as nanobodies, are small antigen-binding fragments (~15kDa) that are derived from heavy chain only antibodies present in camelids (VHH, from camels and llamas), and cartilaginous fishes (VNAR, from sharks). Nanobody V-like domains are useful alternatives to conventional antibodies due to their small size, and high solubility and stability across many applications. In addition, phage display, ribosome display, and mRNA/cDNA display methods can be used for the efficient generation and optimization of binders in vitro. The resulting nanobodies can be genetically encoded, tagged, and expressed in cells for in vivo localization and functional studies of target proteins. Collectively, these properties make nanobodies ideal for use within echinoderm embryos. This chapter describes the optimization and imaging of genetically encoded nanobodies in the sea urchin embryo. Examples of live-cell antigen tagging (LCAT) and the manipulation of green fluorescent protein (GFP) are shown. We discuss the potentially transformative applications of nanobody technology for probing membrane protein trafficking, cytoskeleton re-organization, receptor signaling events, and gene regulation during echinoderm development.

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.

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.

Directing the Lithium-Sulfur Reaction Pathway via Sparingly Solvating Electrolytes for High Energy Density Batteries.

The lithium-sulfur battery has long been seen as a potential next generation battery chemistry for electric vehicles owing to the high theoretical specific energy and low cost of sulfur. However, even state-of-the-art lithium-sulfur batteries suffer from short lifetimes due to the migration of highly soluble polysulfide intermediates and exhibit less than desired energy density due to the required excess electrolyte. The use of sparingly solvating electrolytes in lithium-sulfur batteries is a promising approach to decouple electrolyte quantity from reaction mechanism, thus creating a pathway toward high energy density that deviates from the current catholyte approach. Herein, we demonstrate that sparingly solvating electrolytes based on compact, polar molecules with a 2:1 ratio of a functional group to lithium salt can fundamentally redirect the lithium-sulfur reaction pathway by inhibiting the traditional mechanism that is based on fully solvated intermediates. In contrast to the standard catholyte sulfur electrochemistry, sparingly solvating electrolytes promote intermediate- and short-chain polysulfide formation during the first third of discharge, before disproportionation results in crystalline lithium sulfide and a restricted fraction of soluble polysulfides which are further reduced during the remaining discharge. Moreover, operation at intermediate temperatures ca. 50 °C allows for minimal overpotentials and high utilization of sulfur at practical rates. This discovery opens the door to a new wave of scientific inquiry based on modifying the electrolyte local structure to tune and control the reaction pathway of many precipitation-dissolution chemistries, lithium-sulfur and beyond.

Sphingosine 1-phosphate receptor 2 (S1P2) attenuates reactive oxygen species formation and inhibits cell death: implications for otoprotective therapy.

Ototoxic drugs, such as platinum-based chemotherapeutics, often lead to permanent hearing loss through apoptosis of neuroepithelial hair cells and afferent neurons of the cochlea. There is no approved therapy for preventing or reversing this process. Our previous studies identified a G protein-coupled receptor (GPCR), S1P2, as a potential mediator of otoprotection. We therefore sought to identify a pharmacological approach to prevent cochlear degeneration via activation of S1P2. The cochleae of S1pr2(-/-) knockout mice were evaluated for accumulation of reactive oxygen species (ROS) with a nitro blue tetrazolium (NBT) assay. This showed that loss of S1P2 results in accumulation of ROS that precedes progressive cochlear degeneration as previously reported. These findings were supported by in vitro cell-based assays to evaluate cell viability, induction of apoptosis, and accumulation of ROS following activation of S1P2 in the presence of cisplatin. We show for the first time, that activation of S1P2 with a selective receptor agonist increases cell viability and reduces cisplatin-mediated cell death by reducing ROS. Cumulatively, these results suggest that S1P2 may serve as a therapeutic target for attenuating cisplatin-mediated ototoxicity.

High-Surface-Area Nitrogen-Doped Reduced Graphene Oxide for Electric Double-Layer Capacitors.

A two-step method consisting of solid-state microwave irradiation and heat treatment under NH3 gas was used to prepare nitrogen-doped reduced graphene oxide (N-RGO) with a high specific surface area (1007 m(2) g(-1) ), high electrical conductivity (1532 S m(-1) ), and low oxygen content (1.5 wt %) for electrical double-layer capacitor applications. The specific capacitance of N-RGO was 291 F g(-1) at a current density of 1 A g(-1) , and a capacitance of 261 F g(-1) was retained at 50 A g(-1) , which indicated a very good rate capability. N-RGO also showed excellent cycling stability and preserved 96 % of the initial specific capacitance after 100 000 cycles. Near-edge X-ray absorption fine-structure spectroscopy results provided evidenced for the recovery of π conjugation in the carbon networks with the removal of oxygenated groups and revealed chemical bonding of the nitrogen atoms in N-RGO. The good electrochemical performance of N-RGO is attributed to its high surface area, high electrical conductivity, and low oxygen content.

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.

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.

In vitro nanobody discovery for integral membrane protein targets.

Nanobodies (Nbs) or single-domain antibodies are among the smallest and most stable binder scaffolds known. In vitro display is a powerful antibody discovery technique used worldwide. We describe the first adaptation of in vitro mRNA/cDNA display for the rapid, automatable discovery of Nbs against desired targets, and use it to discover the first ever reported nanobody against the human full-length glucose transporter, GLUT-1. We envision our streamlined method as a bench-top platform technology, in combination with various molecular evolution techniques, for expedited Nb discovery.