Improving medium-range streamflow forecasts over South Korea with a dual-encoder transformer model.

Accurate and reliable hydrological forecasts play a pivotal role in ensuring water security, facilitating flood preparedness, and supporting agriculture activities. This study investigates the potential of hydrological forecasting in South Korea, focusing on medium-range lead times ranging from 1 to 10 days. The methodology involves leveraging a Transformer neural network, a model entirely based on attention mechanisms. Specifically, our study introduces the Dualformer, a dual-encoder-based transformer model capable of accommodating two distinct datasets: historical and forecast meteorological data. The performance of this proposed model, along with its variants designed to test specific structural aspects, is evaluated in predicting daily streamflow across 473 grid cells covering extensive regions within the study area. Furthermore, the proposed model is assessed against the performance of a recently developed approach aiming for the same objective. These models are trained using historical meteorological variables and geographic characteristics, alongside the Global Ensemble Forecast System, version 12 (GEFSv12) reforecasts, in addition to historical runoff. The results indicate that our proposed model performs competitively, especially for relatively short lead times while effectively managing information from two distinct data sources. For instance, the mean Nash-Sutcliffe efficiency for 473 grids is 0.664 for the first one-day lead when using the Dualformer, whereas the benchmark model achieves a score of 0.535. Additionally, we observe an additional enhancement in Dualformer's performance when utilizing a larger dataset. Finally, we conclude this paper with a discussion regarding potential improvements to the forecast model through the incorporation of additional input and modeling structures.

Independent replication of advanced brain age in mild cognitive impairment and dementia: detection of future cognitive dysfunction.

We previously developed a novel machine-learning-based brain age model that was sensitive to amyloid. We aimed to independently validate it and to demonstrate its utility using independent clinical data. We recruited 650 participants from South Korean memory clinics to undergo magnetic resonance imaging and clinical assessments. We employed a pretrained brain age model that used data from an independent set of largely Caucasian individuals (n = 757) who had no or relatively low levels of amyloid as confirmed by positron emission tomography (PET). We investigated the association between brain age residual and cognitive decline. We found that our pretrained brain age model was able to reliably estimate brain age (mean absolute error = 5.68 years, r(650) = 0.47, age range = 49-89 year) in the sample with 71 participants with subjective cognitive decline (SCD), 375 with mild cognitive impairment (MCI), and 204 with dementia. Greater brain age was associated with greater amyloid and worse cognitive function [Odds Ratio, (95% Confidence Interval {CI}): 1.28 (1.06-1.55), p = 0.030 for amyloid PET positivity; 2.52 (1.76-3.61), p < 0.001 for dementia]. Baseline brain age residual was predictive of future cognitive worsening even after adjusting for apolipoprotein E e4 and amyloid status [Hazard Ratio, (95% CI): 1.94 (1.33-2.81), p = 0.001 for total 336 follow-up sample; 2.31 (1.44-3.71), p = 0.001 for 284 subsample with baseline Clinical Dementia Rating ≤ 0.5; 2.40 (1.43-4.03), p = 0.001 for 240 subsample with baseline SCD or MCI]. In independent data set, these results replicate our previous findings using this model, which was able to delineate significant differences in brain age according to the diagnostic stages of dementia as well as amyloid deposition status. Brain age models may offer benefits in discriminating and tracking cognitive impairment in older adults.

Extreme ultraviolet pellicle wrinkles influence on mask 3D effects: experimental demonstration.

Extreme ultraviolet (EUV) lithography uses reflective optics and a thick mask absorber, leading to mask 3D (M3D) effects. These M3D effects cause disparities in the amplitudes and phases of EUV mask diffractions, impacting mask imaging performance and reducing process yields. Our findings demonstrate that wrinkles in the EUV pellicle can exacerbate M3D effects. This imbalance results in critical dimension variation, image contrast loss, and pattern shift in mask images. Therefore, the use of a pellicle material with thermodynamic characteristics that minimize wrinkles when exposed to EUV rays is imperative.

Trans-ethnic genomic informed risk assessment for Alzheimer's disease: An International Hundred K+ Cohorts Consortium study.

BACKGROUND: As a collaboration model between the International HundredK+ Cohorts Consortium (IHCC) and the Davos Alzheimer's Collaborative (DAC), our aim was to develop a trans-ethnic genomic informed risk assessment (GIRA) algorithm for Alzheimer's disease (AD). METHODS: The GIRA model was created to include polygenic risk score calculated from the AD genome-wide association study loci, the apolipoprotein E haplotypes, and non-genetic covariates including age, sex, and the first three principal components of population substructure. RESULTS: We validated the performance of the GIRA model in different populations. The proteomic study in the participant sites identified proteins related to female infertility and autoimmune thyroiditis and associated with the risk scores of AD. CONCLUSIONS: As the initial effort by the IHCC to leverage existing large-scale datasets in a collaborative setting with DAC, we developed a trans-ethnic GIRA for AD with the potential of identifying individuals at high risk of developing AD for future clinical applications.

Effect of wrinkles on extreme ultraviolet pellicle reflectivity and local critical dimension.

Extreme ultraviolet (EUV) pellicles must have an EUV reflectance (EUVR) below 0.04% to prevent the reduction of critical dimension (CD). However, pellicle wrinkles cause localized CD variation by locally amplifying the EUVR. This study demonstrates that wrinkles can increase the pellicle's EUVR by approximately four times, and the CD drop depends on the relative position of the reflected light from the wrinkle to the 0th- or 1st-order diffracted light. The CD decreases by 6 nm. Therefore, even if the pellicle satisfies the requirement for the EUVR, we need to tightly control the generation of wrinkles to suppress CD variation during the entire exposure process.

The bHLH Transcription Factors in Neural Development and Therapeutic Applications for Neurodegenerative Diseases.

The development of functional neural circuits in the central nervous system (CNS) requires the production of sufficient numbers of various types of neurons and glial cells, such as astrocytes and oligodendrocytes, at the appropriate periods and regions. Hence, severe neuronal loss of the circuits can cause neurodegenerative diseases such as Huntington's disease (HD), Parkinson's disease (PD), Alzheimer's disease (AD), and Amyotrophic Lateral Sclerosis (ALS). Treatment of such neurodegenerative diseases caused by neuronal loss includes some strategies of cell therapy employing stem cells (such as neural progenitor cells (NPCs)) and gene therapy through cell fate conversion. In this report, we review how bHLH acts as a regulator in neuronal differentiation, reprogramming, and cell fate determination. Moreover, several different researchers are conducting studies to determine the importance of bHLH factors to direct neuronal and glial cell fate specification and differentiation. Therefore, we also investigated the limitations and future directions of conversion or transdifferentiation using bHLH factors.

Cystitis Induces Altered CREB Expression Related with Micturition Reflex.

Background and objectives: Bladder stimulation upregulates neurotrophins associated with voiding reflex. Bacterial cystitis can be a stimulant that activates this system, resulting in a pathological state. Phosphorylated responsive element of binding protein (p-CREB) is a pivotal transcriptional factor in the neurotrophin signaling cascade. The goal of our study was to examine the change in expression of p-CREB in dorsal root ganglia (DRG) of rats after uropathogenic Escherichia coli infection of the bladder. Materials and methods: A total of 19 adult female Sprague−Dawley rats were induced with acute E. coli infection (n = 7), chronic E. coli infection (n = 6), or served as controls (n = 6). In each group, the profiles of p-CREB cell were counted in 6−10 sections of each of the DRG collected. DRG cells exhibiting intense nuclear staining were considered to be positive for p-CREB immunoreactivity (p-CREB-IR). Results: Overall, the immunoreactivity of p-CREB was examined in smaller cell profiles with nuclear staining or nuclear and cytoplasmic staining in the DRGs (L1−L6, S1). In the chronic cystitis group, p-CREB-IR in the L1−L6 and S1 DRG was significantly higher than the control group (p < 0.05). Further, p-CREB-IR in the L3−L6 and S1 DRG of the chronic cystitis group was significantly greater than that in the acute cystitis group (p < 0.05). In the control and acute cystitis groups, p-CREB-IR in the L4−L5 DRG was significantly lower than that found in the other DRG sections (p < 0.05). Conclusions: Altogether, acute or chronic E.coli cystitis changed the immunoreactivity of p-CREB in lumbosacral DRG cells. In particular, chronic E. coli infection triggered p-CREB overexpression in L1−L6 and S1 DRG, indicating subsequent pathologic changes.

Dementia key gene identification with multi-layered SNP-gene-disease network.

MOTIVATION: Recently, various approaches for diagnosing and treating dementia have received significant attention, especially in identifying key genes that are crucial for dementia. If the mutations of such key genes could be tracked, it would be possible to predict the time of onset of dementia and significantly aid in developing drugs to treat dementia. However, gene finding involves tremendous cost, time and effort. To alleviate these problems, research on utilizing computational biology to decrease the search space of candidate genes is actively conducted.In this study, we propose a framework in which diseases, genes and single-nucleotide polymorphisms are represented by a layered network, and key genes are predicted by a machine learning algorithm. The algorithm utilizes a network-based semi-supervised learning model that can be applied to layered data structures. RESULTS: The proposed method was applied to a dataset extracted from public databases related to diseases and genes with data collected from 186 patients. A portion of key genes obtained using the proposed method was verified in silico through PubMed literature, and the remaining genes were left as possible candidate genes. AVAILABILITY AND IMPLEMENTATION: The code for the framework will be available at http://www.alphaminers.net/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Establishment of import tolerance for the insecticide thiacloprid in strawberry.

To promote exports, import tolerance (IT) of thiacloprid in strawberry was proposed using the Organization for Economic Cooperation and Development (OECD) maximum residue limit (MRL) calculator after conducting three different field trials. The pre-harvest interval of residual pattern and degradation dynamics of thiacloprid in strawberry were determined using ultra-performance liquid chromatography-tandem mass spectrometry. Samples were extracted with acetonitrile and a mixture of salts and dilution was performed for purification. A six-point matrix-matched calibration curve was constructed which provided excellent linearity with coefficient of determination (R2 ) of 0.9998 or more. Detection and quantification limits were 0.003 and 0.01 mg/kg, respectively. The method was validated in quintuplicate at three different concentrations, which resulted in acceptable recovery ranging from 80.86% to 101.71% with relative standard deviation of 6.50 or less among the three field sites. The developed method was applied to the field-treated sample harvested at different intervals. In the pre-harvest interval trial, the amount of thiacloprid residues ranged from 0.24 to 0.70 mg/kg in field site 1 (Nonsan), 0.16 to 0.50 mg/kg in field site 2 (Sunchang), and 0.36 to 0.50 mg/kg in field site 3 (Sacheon). By contrast, in the degradation trial, the observed residues were 0.03-0.81 mg/kg in field site 1 and 0.02-0.48 mg/kg in field site 2. Consequently, the IT of thiacloprid in strawberry using the OECD MRL calculator was proposed as 2 mg/kg, which is exactly the same as the MRL established by the Republic of Korea. In conclusion, the residue study proposes 2.0 mg/kg as the MRL of thiacloprid in strawberries.

Congenital Renal Arteriovenous Malformation: Diagnostic Clues and Methods.

Background and objectives: Renal arteriovenous malformation (AVM) is a rare disease and is difficult to be diagnosed by conventional methods because of its rarity. In this study, we investigated the diagnostic clues, and made an algorithm for the better diagnosis of renal AVM. Materials and Methods: We reviewed 13 patients who were diagnosed with AVM by using renal angiography from 1986 to 2020 at our institutes. We evaluated clinical features, diagnostic tools, treatment modalities, and outcomes after the treatment of patients. Results: All patients were female, and the mean age was 36.9 years (range 19 to 54 years). Twelve (92.3%) patients complained of gross hematuria. Four (30.8%) patients showed symptoms in relation with pregnancy and delivery. Angiographic findings demonstrated cirsoid type in 10 patients and aneurysmal type in 3 patients. Among the 11 patients who underwent computed tomography, AVMs were detected in 3 (27.3%) patients. Renal duplex Doppler was performed in 6 patients, and all of these patients were diagnosed with AVM, demonstrating a vascular turbulence or blood-rich area. Twelve patients were initially treated with transarterial embolization. Nephrectomy was performed in two patients due to persistent bleeding with hypovolemic shock. Conclusions: We should consider possible AVMs in patients who were not detected by conventional work up for hematuria, especially in mid-aged, pregnant, or recently delivered women. Renal duplex Doppler might be the optimal diagnostic modality in these patients. Our diagnostic algorithm could be aid to diagnosis and treatment for renal AVM patients.

Urothelial Cell Carcinoma of the Renal Pelvis Misdiagnosed as Ureteropelvic Junction Obstruction: A Case Report.

Urothelial tumors are typically a disease affecting elderly individuals and are rare in young patients. Moreover, upper urinary tract urothelial carcinoma is extremely rare in the young age group. In this study, we present a case of urothelial cell carcinoma of the renal pelvis and ureter in a young man without risk factors of urothelial carcinoma, which was misdiagnosed as ureteropelvic junction obstruction and treated with a laparoscopic pyeloplasty.

Tubulocystic Renal Cell Carcinoma Is Not an Indolent Tumor: A Case Report of Recurrences in the Retroperitoneum and Contralateral Kidney.

Tubulocystic renal cell carcinoma (RCC) is a rare subtype of RCC that was recently included in the 2016 World Health Organization classification of tumors of the kidney. Most of these tumors exhibit indolent behavior with low metastatic potential. However, here we report a case of recurrent tubulocystic RCC with aggressive features in the retroperitoneum and contralateral kidney treated with targeted agents and radiofrequency ablation.

Rapid Recurrence of Giant Multilocular Prostatic Cystadenoma after Laparoscopic Excision for Primary Case: A Case Report.

Giant multilocular prostatic cystadenoma is a rare benign tumor of the prostate gland that presents as a large retroperitoneal pelvic mass. The mass is usually located between the urinary bladder and rectum, and results in obstructive voiding symptoms and a change in bowel habits. Complete surgical excision is the treatment of choice. We present a case of rapid recurrent giant multilocular prostatic cystadenoma after laparoscopic excision for primary case. A previously healthy 54-year-old man presented with acute urinary retention. Prostate MRI showed a large cystic mass approximately 13 cm in size, multiple septa and lobulation in the prostate, and no visible solid lesions. Laparoscopic marsupialization of giant multilocular prostatic cystadenoma cysts was performed. One year later, the patient presented with local recurrence. Repeated laparoscopic complete resection was performed without any complications and further recurrence. Giant multilocular prostatic cystadenoma has the risk of recurrence in case of incomplete resection. Surgical treatment should be performed with the goal of complete removal following the same principles as cancer surgery.

A Case of Incidental Schwannoma Mimicking Necrotic Metastatic Lymph Node from Bladder Cancer.

Background and Objectives: Retroperitoneal schwannoma is a very rare case of schwannoma which commonly occurs in the other part of the body. However, it is difficult to distinguish schwannoma from other tumors before pathological examination because they do not show specific characteristics on imaging study such as ultrasound, computed tomography (CT), and magnetic resonance image (MRI). Case summary: A 60-year-old male showed a retroperitoneal cystic tumor which is found incidentally during evaluation of coexisted bladder tumor. Neurogenic tumor was suspicious for the retroperitoneal tumor through pre-operative imaging study. Finally, a schwannoma was diagnosed by immunohistochemical examination after complete surgical excision laparoscopically. Conclusion: As imaging technology is developed, there may be more chances to differentiate schwannoma from other neoplasm. However, still surgical resection and histopathological examination is feasible for diagnosis of schwannoma.

The translational network for metabolic disease - from protein interaction to disease co-occurrence.

BACKGROUND: The recent advances in human disease network have provided insights into establishing the relationships between the genotypes and phenotypes of diseases. In spite of the great progress, it yet remains as only a map of topologies between diseases, but not being able to be a pragmatic diagnostic/prognostic tool in medicine. It can further evolve from a map to a translational tool if it equips with a function of scoring that measures the likelihoods of the association between diseases. Then, a physician, when practicing on a patient, can suggest several diseases that are highly likely to co-occur with a primary disease according to the scores. In this study, we propose a method of implementing 'n-of-1 utility' (n potential diseases of one patient) to human disease network-the translational disease network. RESULTS: We first construct a disease network by introducing the notion of walk in graph theory to protein-protein interaction network, and then provide a scoring algorithm quantifying the likelihoods of disease co-occurrence given a primary disease. Metabolic diseases, that are highly prevalent but have found only a few associations in previous studies, are chosen as entries of the network. CONCLUSIONS: The proposed method substantially increased connectivity between metabolic diseases and provided scores of co-occurring diseases. The increase in connectivity turned the disease network info-richer. The result lifted the AUC of random guessing up to 0.72 and appeared to be concordant with the existing literatures on disease comorbidity.

Effect of Molding Condition on Lap Joint of Glass Fiber Reinforced Plastic.

In this study, materials with each different fiber content ratio and fiber orientation state were used in the study for the decision of molding condition of GFRP and the condition of lap joint. Clarify joint strength and lap joint efficiency during high temperature and compression press lap joint molding of GFRP and research data regarding the lap length of the joint part was presented. Thus, the purpose of this study is to contribute to the substitution of existing products as well as usage development in the non-automobile field and also to find out precise dynamic characteristics as designing data of structures. In press lap joint molding of GFRP, tensile stress and lap joint connection efficiency was increased according to the increase of lap length L. However, as the increase of compression ratio and fiber content ratio per unit area was higher in impact test, it has caused the deterioration of lap joint efficiency after joint molding of GFRP.

Crystal structure of inositol 1,3,4,5,6-pentakisphosphate 2-kinase from Cryptococcus neoformans.

The fungal pathogen Cryptococcus neoformans is a causative agent of meningoencephalitis in humans. For its pathogenicity, the inositol polyphosphate biosynthetic pathway plays critical roles. Recently, Ipk1 from C. neoformans (CnIpk1) was identified as an inositol 1,3,4,5,6-pentakisphosphate 2-kinase that catalyzes the phosphorylation of IP5 to form IP6, a substrate for subsequent reaction to produce inositol pyrophosphates, such as PP-IP5/IP7. Furthermore, it was shown that deletion of IPK1 significantly reduces the virulence of C. neoformans, indicating that Ipk1 is a major virulence contributor. In this study, we determined a crystal structure of the apo-form of CnIpk1 at 2.35Å resolution, the first structure for a fungal Ipk1, using a single-wavelength anomalous dispersion method. Even with a low sequence similarity of 26-28%, its overall structure resembles two other Ipk1 orthologs from Arabidopsis thaliana (AtIpk1) and Mus musculus (MmIpk1), and the most crucial residues in the active site are conserved. Unlike AtIpk1 and MmIpk1, however, metal-binding sites for structural stabilization and conformational variations are absent in CnIpk1. The binding environments for substrate IP5 could be inferred by the two different binding sites for sulfate ion in CnIpk1. Taken together, these observations suggest structural similarities and discrepancies for fungal Ipk1 among members of the Ipk1 family and provide structural information for the possible development of drug design for treatment of cryptococcosis.

Proteome characterization of copper stress responses in the roots of sorghum.

Copper (Cu) is a important micronutrient for plants, but it is extremely toxic to plants at high concentration and can inactivate and disturb protein structures. To explore the Cu stress-induced tolerance mechanism, the present study was conducted on the roots of sorghum seedlings exposed to 50 and 100 µM CuSO4 for 5 days. Accumulation of Cu increased in roots when the seedlings were treated with the highest concentration of Cu2+ ions (100 µM). Elevated Cu concentration provoked notable reduction of Fe, Zn, Ca, and Mn uptake in the roots of sorghum seedlings. In the proteome analysis, high-throughput two-dimensional polyacrylamide gel electrophoresis combined with MALDI-TOF-TOF MS was performed to explore the molecular responses of Cu-induced sorghum seedling roots. In two-dimensional silver-stained gels, 422 protein spots were identified in the 2-D gel whereas twenty-one protein spots (≥1.5-fold) were used to analyze mass spectrometry from Cu-induced sorghum roots. Among the 21 differentially expressed proteins, 10 proteins were increased, while 11 proteins were decreased due to the intake of Cu ions by roots of sorghum. Abundance of most of the identified proteins from the roots that function in stress response and metabolism was remarkably enhanced, while proteins involved in transcription and regulation were severely reduced. Taken together, these results imply insights into a potential molecular mechanism towards Cu stress in C4 plant, sorghum.

Network mirroring for drug repositioning.

BACKGROUND: Although drug discoveries can provide meaningful insights and significant enhancements in pharmaceutical field, the longevity and cost that it takes can be extensive where the success rate is low. In order to circumvent the problem, there has been increased interest in 'Drug Repositioning' where one searches for already approved drugs that have high potential of efficacy when applied to other diseases. To increase the success rate for drug repositioning, one considers stepwise screening and experiments based on biological reactions. Given the amount of drugs and diseases, however, the one-by-one procedure may be time consuming and expensive. METHODS: In this study, we propose a machine learning based approach for efficiently selecting candidate diseases and drugs. We assume that if two diseases are similar, then a drug for one disease can be effective against the other disease too. For the procedure, we first construct two disease networks; one with disease-protein association and the other with disease-drug information. If two networks are dissimilar, in a sense that the edge distribution of a disease node differ, it indicates high potential for repositioning new candidate drugs for that disease. The Kullback-Leibler divergence is employed to measure difference of connections in two constructed disease networks. Lastly, we perform repositioning of drugs to the top 20% ranked diseases. RESULTS: The results showed that F-measure of the proposed method was 0.75, outperforming 0.5 of greedy searching for the entire diseases. For the utility of the proposed method, it was applied to dementia and verified 75% accuracy for repositioned drugs assuming that there are not any known drugs to be used for dementia. CONCLUSION: This research has novelty in that it discovers drugs with high potential of repositioning based on disease networks with the quantitative measure. Through the study, it is expected to produce profound insights for possibility of undiscovered drug repositioning.

Disease causality extraction based on lexical semantics and document-clause frequency from biomedical literature.

BACKGROUND: Recently, research on human disease network has succeeded and has become an aid in figuring out the relationship between various diseases. In most disease networks, however, the relationship between diseases has been simply represented as an association. This representation results in the difficulty of identifying prior diseases and their influence on posterior diseases. In this paper, we propose a causal disease network that implements disease causality through text mining on biomedical literature. METHODS: To identify the causality between diseases, the proposed method includes two schemes: the first is the lexicon-based causality term strength, which provides the causal strength on a variety of causality terms based on lexicon analysis. The second is the frequency-based causality strength, which determines the direction and strength of causality based on document and clause frequencies in the literature. RESULTS: We applied the proposed method to 6,617,833 PubMed literature, and chose 195 diseases to construct a causal disease network. From all possible pairs of disease nodes in the network, 1011 causal pairs of 149 diseases were extracted. The resulting network was compared with that of a previous study. In terms of both coverage and quality, the proposed method showed outperforming results; it determined 2.7 times more causalities and showed higher correlation with associated diseases than the existing method. CONCLUSIONS: This research has novelty in which the proposed method circumvents the limitations of time and cost in applying all possible causalities in biological experiments and it is a more advanced text mining technique by defining the concepts of causality term strength.