Explore the vast range of titles available.

Accurate rainfall information underpins land‐surface water budgets, extreme‐weather analyses, and climate‐model evaluation. Yet in many regions, rain gauge networks are sparse, making conventional calibration of bottom up rainfall products difficult. To address this, we propose a self calibration framework that removes the need for a dedicated calibration phase. Our proposed approach systematically identifies bottom‐up model parameters without relying on region‐specific tuning by exploiting the K‐means, Gaussian Mixture Model|Gaussian Mixture Models (GMM), Agglomerative Clustering (AC) and Growing Neural Gas (GNG) algorithms. To demonstrate its effectiveness, we apply this framework to soil moisture (SM) to RAIN by using Net Water Flux (SM2RAIN‐NWF), a bottom‐up rainfall estimation model that leverages SM variations to infer rainfall. This self‐calibration strategy is particularly relevant, as it reduces dependence on traditional rain gauge data, making it well‐suited for large or data‐limited regions. In this study, we test this framework by comparing four clustering algorithms (K‐means, GNG, GMM, and AC) against International Soil Moisture Network observations using hold‐out and Leave‐One‐Out Cross‐Validation approaches. This validation confirms the framework's robustness and the superiority of K‐means and GNG. The K‐means method provides high stability, with key performance metrics (Correlation Coefficient (R) and Probability of Detection) showing minimal change from the baseline. The GNG method demonstrates that cluster parameters can significantly outperform site‐specific calibration, with correlation (R) gains exceeding +17% in key soil depths (5 < SM_Depth ≤ 10 cm). With no need for reference rainfall, the method is ideal for data sparse or ungauged regions and supports scalable climate monitoring, reanalysis, and prediction.

This paper presents a pseudo-static gain cell (PS-GC) with extended retention time for an embedded dynamic random-access memory (eDRAM) macro for analog processing-in-memory (PIM). The proposed eDRAM cell consists of a two-transistor (2T) gain cell with a pseudo-static leakage compensation that maintains stored data without charge loss issue. Hence, the PS-GC can offer unlimited retention time in the same manner as static RAM (SRAM). Due to the extended retention time, bulky capacitors in conventional eDRAM are no longer needed, thereby, improving the area efficiency of eDRAM-based analog PIMs. The active leakage compensation of the PS-GC can effectively hold stored data even in a deep-submicron process that show significant leakage current. Therefore, the PS-GC can accelerate write-access time and read-access time without concern of increased leakage current. The proposed gain cell and its 64 × 64 eDRAM macro were implemented in a 28 nm CMOS process. The bitcell of the proposed gain cell has 0.79- and 0.58-times the area of those of 6T SRAM and 8T STAM, respectively. The post-layout simulation results demonstrate that the eDRAM maintains the pseudo-static operation with unlimited retention time successfully under wide range variations of process, voltage and temperature. At the operating frequency of 667 MHz, the eDRAM macro achieved an operating voltage range from 0.9 to 1.2 V and operating temperature range from −25 to 85 °C regardless of the process variation. The post-layout simulated write-access time and read-access time were below 0.3 ns at an operating temperature of 85 °C. The PS-GC consumes a static power of 2.2 nW/bit at an operating temperature of 25 °C.

N6A-methyladenosine (m6A) post-transcriptional modification, the most abundant internal RNA modification, is catalyzed by the METTL3-14 methyltransferase complex. Recently, attention has been drawn to the METTL3-14 complex regarding its significant roles in the pathogenesis of acute myeloid leukemia (AML), attracting the potential of novel therapeutic targets for the disease. Herein, we report the identification and characterization of eltrombopag as a selective allosteric inhibitor of the METTL3-14 complex. Eltrombopag exhibited selective inhibitory activity in the most active catalytic form of the METTL3-14 complex by direct binding, and the mechanism of inhibition was confirmed as a noncompetitive inhibition by interacting at a putative allosteric binding site in METTL3, which was predicted by cavity search and molecular docking studies. At a cellular level, eltrombopag displayed anti-proliferative effects in the relevant AML cell line, MOLM-13, in correlation with a reduction in m6A levels. Molecular mechanism studies of eltrombopag using m6A-seq analysis provided further evidence of its cellular function by determining the hypomethylation of leukemogenic genes in eltrombopag-treated MOLM-13 cells and the overlapping of the pattern with those of METTL3-knockdown MOLM-13 cells. In conclusion, eltrombopag was first disclosed as a functional METTL3-14 allosteric inhibitor in AML cells, which could be utilized for the further development of novel anti-AML therapy.

2-Keto-3-deoxy-galactonate (KDGal) serves as a pivotal metabolic intermediate within both the fungal d -galacturonate pathway, which is integral to pectin catabolism, and the bacterial DeLey-Doudoroff pathway for d -galactose catabolism. The presence of KDGal enantiomers, l -KDGal and d -KDGal, varies across these pathways. Fungal pathways generate l -KDGal through the reduction and dehydration of d -galacturonate, whereas bacterial pathways produce d -KDGal through the oxidation and dehydration of d -galactose. Two distinct catabolic routes further metabolize KDGal: a nonphosphorolytic pathway that employs aldolase and a phosphorolytic pathway involving kinase and aldolase. Recent findings have revealed that l -KDGal, identified in the bacterial catabolism of 3,6-anhydro- l -galactose, a major component of red seaweeds, is also catabolized by Escherichia coli , which is traditionally known to be catabolized by specific fungal species, such as Trichoderma reesei . Furthermore, the potential industrial applications of KDGal and its derivatives, such as pyruvate and d - and l -glyceraldehyde, are underscored by their significant biological functions. This review comprehensively outlines the catabolism of l -KDGal and d -KDGal across different biological systems, highlights stereospecific methods for discriminating between enantiomers, and explores industrial application prospects for producing KDGal enantiomers. Key points • KDGal is a metabolic intermediate in fungal and bacterial pathways • Stereospecific enzymes can be used to identify the enantiomeric nature of KDGal • KDGal can be used to induce pectin catabolism or produce functional materials

This paper introduces an n-type pseudo-static gain cell (PS-nGC) embedded within dynamic random-access memory (eDRAM) for high-speed processing-in-memory (PIM) applications. The PS-nGC leverages a two-transistor (2T) gain cell and employs an n-type pseudo-static leakage compensation (n-type PSLC) circuit to significantly extend the eDRAM’s retention time. The implementation of a homogeneous NMOS-based 2T gain cell not only reduces write access times but also benefits from a boosted write wordline technique. In a comparison with the previous pseudo-static gain cell design, the proposed PS-nGC exhibits improvements in write and read access times, achieving 3.27 times and 1.81 times reductions in write access time and read access time, respectively. Furthermore, the PS-nGC demonstrates versatility by accommodating a wide supply voltage range, spanning from 0.7 to 1.2 V, while maintaining an operating frequency of 667 MHz. Fabricated using a 28 nm complementary metal oxide semiconductor (CMOS) process, the prototype features an efficient active area, occupying a mere 0.284 µm2 per bitcell for the 4 kb eDRAM macro. Under various operational conditions, including different processes, voltages, and temperatures, the proposed PS-nGC of eDRAM consistently provides speedy and reliable read and write operations.

The seismically deficient column details in existing reinforced concrete buildings affect the overall behavior of the building depending on the failure type of the column. The purpose of this study is to develop and validate a machine-learning-based prediction model for the column failure modes (shear, flexure–shear, and flexure failure modes). For this purpose, artificial neural network (ANN), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) models were used considering previously collected experimental data. Using four machine learning methodologies, we developed a classification learning model that can predict the column failure modes in terms of the input variables using the concrete compressive strength, steel yield strength, axial load ratio, height-to-dept aspect ratio, longitudinal reinforcement ratio, and transverse reinforcement ratio. The performance of each machine learning model was compared and verified by calculating the accuracy, precision, recall, F1-Score, and ROC. Based on the performance measurements of the classification model, the RF model has the highest average value for the classification model performance measurements among the considered learning methods and can conservatively predict the shear failure mode. Thus, the RF model can rapidly predict the column failure modes with the simple column details. Additionally, it was demonstrated that the predicted failure modes from the selected model were exactly same as the failure mode determined from a code-defined equation (traditional method).

Influenza, one of the most contagious and infectious diseases, is predominantly transmitted through aerosols, leading to the development of filter-based protective equipment. Though the currently available filters are effective at removing submicron-sized particulates, filter materials with enhanced virus-capture efficiency are still in demand. Coating or chemically modifying filters with molecules capable of binding influenza viruses has received attention as a promising approach for the production of virus-capturing filters. For this purpose, tannic acid (TA), a plant-derived polyphenol, is a promising molecule for filter functionalization because of its antiviral activities and ability to serve as a cost-efficient adhesive for various materials. This study demonstrates the facile preparation of TA-functionalized high-efficiency particulate air (HEPA) filter materials and their efficiency in influenza virus capture. Polypropylene HEPA filter fabrics were coated with TA via a dipping/washing process. The TA-functionalized HEPA filter (TA-HF) exhibits a high in-solution virus capture efficiency of up to 2,723 pfu/mm 2 within 10 min, which is almost two orders of magnitude higher than that of non-functionalized filters. This result suggests that the TA-HF is a potent anti-influenza filter that can be used in protective equipment to prevent the spread of pathogenic viruses.

Age-related hearing loss (ARHL), also known as presbycusis, is a chronic disorder characterized by impairment of the transduction of acoustic signals. This study analysed the prevalence and demographic characteristics of ARHL in the Korean population. We used the data from the Korea National Health and Nutrition Examination Survey (KNHANES) from 2009 to 2012 and analysed the association between age and hearing impairment. A total of 16,799 adults were selected for the current study. Physical examinations, blood tests, otoscopic examinations, and hearing tests were performed. The demographic variables included age, gender, obesity, economic status, education level, noise exposure history, and underlying diseases. Among 16,799 participants, the prevalence of unilateral hearing loss was 8% (1,349 people), and bilateral hearing loss was 5.9% (989 people). Men were 53.4% more likely to have hearing loss than women. Age and underlying diseases, like hypertension, diabetes, and abdominal obesity, were significantly associated with hearing loss (P < 0.0001). Further, mental health factors, such as cognitive function, depression, and suicidal ideation, were related to hearing loss. The prevalence of hearing loss increased with advancing years, especially in the high frequency of 6 kHz, with a sharply increase in patients aged 65 and over. The analysis of auditory performance in the Korean population confirmed the association of high-frequency hearing loss with advancing age. A threshold of 6 kHz should be included to correctly diagnose hearing impairment in elderly patients. Patients with ARHL should be provided with suitable aural rehabilitation that includes active high-frequency control.

This study investigated the potential of mucoadhesive polymeric nanoparticles coated with polydopamine (Dopa-NPs) for inner ear drug delivery. Dopa, inspired by mussel adhesion proteins, leveraged the mucoadhesive properties of NPs and enhanced their retention within the cochlea. Dopa-NPs were compared with non-adhesive uncoated control NPs to reveal their safety and drug delivery efficiency. The safety evaluation demonstrated no toxicity during in vitro test using HEI-OC1 cells and in vivo test using mouse with auditory function assessment. When coumarin-encapsulated NPs were administered through intratympanic injection to mice, Dopa-NPs provided intense fluorescence in the inner ear compared to non-adhesive control NPs. Furthermore, dexamethasone (Dex)-encapsulated Dopa-NPs exhibited significantly higher drug concentrations in the cochlea than dexamethasone sodium phosphate and uncoated NPs. Finally, in vivo test using an ototoxicity-induced animal model showed that Dopa-NPs improved hearing protection, as indicated by the auditory brainstem response (ABR) test. In conclusion, mucoadhesive Dopa-NPs exhibit enhanced drug delivery efficiency and safety, offering a promising strategy for inner ear drug delivery and chemotherapy. Graphical abstract

Immunity-related GTPase B10 (IRGB10) is a crucial member of the interferon (IFN)-inducible GTPases and plays a vital role in host defense mechanisms. Following infection, IRGB10 is induced by IFNs and functions by liberating pathogenic ligands to activate the inflammasome through direct disruption of the pathogen membrane. Despite extensive investigation into the significance of the cell-autonomous immune response, the precise molecular mechanism underlying IRGB10–mediated microbial membrane disruption remains elusive. Herein, we present two structures of different forms of IRGB10, the nucleotide-free and GppNHp-bound forms. Based on these structures, we identified that IRGB10 exists as a monomer in nucleotide-free and GTP binding states. Additionally, we identified that GTP hydrolysis is critical for dimer formation and further oligomerization of IRGB10. Building upon these observations, we propose a mechanistic model to elucidate the working mechanism of IRGB10 during pathogen membrane disruption.