PractiCPP: a deep learning approach tailored for extremely imbalanced datasets in cell-penetrating peptide prediction.

MOTIVATION: Effective drug delivery systems are paramount in enhancing pharmaceutical outcomes, particularly through the use of cell-penetrating peptides (CPPs). These peptides are gaining prominence due to their ability to penetrate eukaryotic cells efficiently without inflicting significant damage to the cellular membrane, thereby ensuring optimal drug delivery. However, the identification and characterization of CPPs remain a challenge due to the laborious and time-consuming nature of conventional methods, despite advances in proteomics. Current computational models, however, are predominantly tailored for balanced datasets, an approach that falls short in real-world applications characterized by a scarcity of known positive CPP instances. RESULTS: To navigate this shortfall, we introduce PractiCPP, a novel deep-learning framework tailored for CPP prediction in highly imbalanced data scenarios. Uniquely designed with the integration of hard negative sampling and a sophisticated feature extraction and prediction module, PractiCPP facilitates an intricate understanding and learning from imbalanced data. Our extensive computational validations highlight PractiCPP's exceptional ability to outperform existing state-of-the-art methods, demonstrating remarkable accuracy, even in datasets with an extreme positive-to-negative ratio of 1:1000. Furthermore, through methodical embedding visualizations, we have established that models trained on balanced datasets are not conducive to practical, large-scale CPP identification, as they do not accurately reflect real-world complexities. In summary, PractiCPP potentially offers new perspectives in CPP prediction methodologies. Its design and validation, informed by real-world dataset constraints, suggest its utility as a valuable tool in supporting the acceleration of drug delivery advancements. AVAILABILITY AND IMPLEMENTATION: The source code of PractiCPP is available on Figshare at https://doi.org/10.6084/m9.figshare.25053878.v1.

META-DDIE: predicting drug-drug interaction events with few-shot learning.

Drug-drug interactions (DDIs) are one of the major concerns in pharmaceutical research, and a number of computational methods have been developed to predict whether two drugs interact or not. Recently, more attention has been paid to events caused by the DDIs, which is more useful for investigating the mechanism hidden behind the combined drug usage or adverse reactions. However, some rare events may only have few examples, hindering them from being precisely predicted. To address the above issues, we present a few-shot computational method named META-DDIE, which consists of a representation module and a comparing module, to predict DDI events. We collect drug chemical structures and DDIs from DrugBank, and categorize DDI events into hundreds of types using a standard pipeline. META-DDIE uses the structures of drugs as input and learns the interpretable representations of DDIs through the representation module. Then, the model uses the comparing module to predict whether two representations are similar, and finally predicts DDI events with few labeled examples. In the computational experiments, META-DDIE outperforms several baseline methods and especially enhances the predictive capability for rare events. Moreover, META-DDIE helps to identify the key factors that may cause DDI events and reveal the relationship among different events.

Improved drug-target interaction prediction with intermolecular graph transformer.

The identification of active binding drugs for target proteins (referred to as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery. Although recent deep learning-based approaches achieve better performance than molecular docking, existing models often neglect topological or spatial of intermolecular information, hindering prediction performance. We recognize this problem and propose a novel approach called the Intermolecular Graph Transformer (IGT) that employs a dedicated attention mechanism to model intermolecular information with a three-way Transformer-based architecture. IGT outperforms state-of-the-art (SoTA) approaches by 9.1% and 20.5% over the second best option for binding activity and binding pose prediction, respectively, and exhibits superior generalization ability to unseen receptor proteins than SoTA approaches. Furthermore, IGT exhibits promising drug screening ability against severe acute respiratory syndrome coronavirus 2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses. Source code and datasets are available at https://github.com/microsoft/IGT-Intermolecular-Graph-Transformer.

Bio-Inspired Trace Hydroxyl-Rich Electrolyte Additives for High-Rate and Stable Zn-Ion Batteries at Low Temperatures.

High-rate and stable Zn-ion batteries working at low temperatures are highly desirable for practical applications, but are challenged by sluggish kinetics and severe corrosion. Herein, inspired by frost-resistant plants, we report trace hydroxyl-rich electrolyte additives that implement a dual remodeling effect for high-performance low-temperature Zn-ion batteries. The additive with high Zn absorbability not only remodels Zn2+ primary solvent shell by alternating H2 O molecules, but also forms a shielding layer thus remodeling the Zn surface, which effectively enhances fast Zn2+ de-solvation reaction kinetics and prohibits Zn anode corrosion. Taking trace α-D-glucose (αDG) as a demonstration, the electrolyte obtains a low freezing point of -55.3 °C, and the Zn//Zn cell can stably cycle for 2000 h at 5 mA cm-2 under -25 °C, with a high cumulative capacity of 5000 mAh cm-2 . A full battery that stably operates for 10000 cycles at -50 °C is also demonstrated.

Research progress of additional pathogenic mutations in chronic neutrophilic leukemia.

Chronic neutrophilic leukemia (CNL) is a rare type of myeloproliferative neoplasm (MPN). Due to its nonspecific clinical symptoms and lack of specific molecular markers, it was previously difficult to distinguish it from other diseases with increased neutrophils. However, the discovery of the CSF3R mutation in CNL 10 years ago and the update of the diagnostic criteria by the World Health Organization (WHO) in 2016 brought CNL into a new era of molecular diagnosis. Next-generation sequencing (NGS) technology has led to the identification of numerous mutant genes in CNL. While CSF3R is commonly recognized as the driver mutation of CNL, other mutations have also been detected in CNL using NGS, including mutations in other signaling pathway genes (CBL, JAK2, NARS, PTPN11) and chromatin modification genes (ASXL1, SETBP1, EZH2), DNA methylation genes (DNMT3A, TET2), myeloid-related transcription factor genes (RUNX1, GATA2), and splicing and RNA metabolism genes (SRSF2, U2AF1). The coexistence of these mutated genes and CSF3R mutations, as well as the different evolutionary sequences of clones, deepens the complexity of CNL molecular biology. The purpose of this review is to summarize the genetic research findings of CNL in the last decade, focusing on the common mutated genes in CNL and their clinical significance, as well as the clonal evolution pattern and sequence of mutation acquisition in CNL, to provide a basis for the appropriate management of CNL patients.

Astrocyte senescence-like response related to peripheral nerve injury-induced neuropathic pain.

BACKGROUND: Peripheral nerve damage causes neuroinflammation, which plays a critical role in establishing and maintaining neuropathic pain (NeP). The mechanisms contributing to neuroinflammation remain poorly elucidated, and pharmacological strategies for NeP are limited. Thus, in this study, we planned to explore the possible link between astrocyte senescence and NeP disorders following chronic sciatic nerve injury. METHODS: An NeP animal model was established by inducing chronic constrictive injury (CCI) to the sciatic nerve in adult rats. A senolytic drug combination of dasatinib and quercetin was gavaged daily from the first postoperative day until the end of the study. Paw mechanical withdrawal threshold (PMWT) and paw thermal withdrawal latency (PTWL) were evaluated to assess behaviors in response to pain in the experimental rats. Senescence-associated ß-galactosidase staining, western blot analysis, and immunofluorescence were applied to examine the levels of proinflammatory factors and severity of the senescence-like response in the spinal cord. Lipopolysaccharide (LPS) was administered to induce senescence of spinal astrocytes in primary cultures in vitro, to explore the potential impacts of senescence on the secretion of proinflammatory factors. Furthermore, single-cell RNA sequencing (scRNA-seq) was conducted to identify senescence-related molecular responses in spinal astrocytes under neuropathic pain. RESULTS: Following sciatic nerve CCI, rats exhibited reduced PMWT and PTWL, increased levels of spinal proinflammatory factors, and an enhanced degree of senescence in spinal astrocytes. Treatment with dasatinib and quercetin effectively attenuated spinal neuroinflammation and mitigated the hypersensitivities of the rats subjected to sciatic nerve CCI. Mechanistically, the dasatinib-quercetin combination reversed senescence in LPS-stimulated primary cultured astrocytes and decreased the levels of proinflammatory factors. The scRNA-seq data revealed four potential senescence-related genes in the spinal astrocyte population, and the expression of clusterin (CLU) protein was validated via in vitro experiments. CONCLUSION: The findings indicate the potential role of astrocyte senescence in neuroinflammation following peripheral nerve injury, and suggest that targeting CLU activation in astrocytes might provide a novel therapeutic strategy to treat NeP.

The Biological Implication of Semicarbazide-Sensitive Amine Oxidase (SSAO) Upregulation in Rat Systemic Inflammatory Response under Simulated Aerospace Environment.

The progress of space science and technology has ushered in a new era for humanity's exploration of outer space. Recent studies have indicated that the aerospace special environment including microgravity and space radiation poses a significant risk to the health of astronauts, which involves multiple pathophysiological effects on the human body as well on tissues and organs. It has been an important research topic to study the molecular mechanism of body damage and further explore countermeasures against the physiological and pathological changes caused by the space environment. In this study, we used the rat model to study the biological effects of the tissue damage and related molecular pathway under either simulated microgravity or heavy ion radiation or combined stimulation. Our study disclosed that ureaplasma-sensitive amino oxidase (SSAO) upregulation is closely related to the systematic inflammatory response (IL-6, TNF-α) in rats under a simulated aerospace environment. In particular, the space environment leads to significant changes in the level of inflammatory genes in heart tissues, thus altering the expression and activity of SSAO and causing inflammatory responses. The detailed molecular mechanisms have been further validated in the genetic engineering cell line model. Overall, this work clearly shows the biological implication of SSAO upregulation in microgravity and radiation-mediated inflammatory response, providing a scientific basis or potential target for further in-depth investigation of the pathological damage and protection strategy under a space environment.

Prognostic significance of fibrinogen and neutrophil/lymphocyte ratio score and D-dimer/Albumin ratio for prognosis in patients with aneurysmal subarachnoid hemorrhage.

BACKGROUND: Recent research indicates that systemic inflammation significantly affects the overall prognosis of individuals with aneurysmal subarachnoid hemorrhage. To delve deeper into this issue, a retrospective study was undertaken. The study aimed to investigate the relationship between fibrinogen and neutrophil/lymphocyte ratio scores, D-dimer/Albumin ratios, and the Glasgow Outcome Scale at 6 months post-discharge for patients with aSAH. METHODS: A retrospective analysis was conducted on 321 patients who experienced aneurysmal subarachnoid hemorrhage. These patients were monitored using the Glasgow Outcome Scale six months after being discharged from Huizhou Central People's Hospital. Patients with GOS scores between 1 and 3 were classified as having a poor prognosis, while those with scores ranging from 4 to 5 were considered to have a good prognosis. To create distinct sets, patients were randomly divided into both training and validation groups. The best cut-off value for the D-dimer/Albumin ratio was established through ROC curves, and the scores for fibrinogen and the neutrophil/lymphocyte ratio were calculated. Utilizing multivariate logistic regression analysis, independent risk factors linked to an unfavorable prognosis in aSAH patients were identified. A nomogram model was developed and validated based on these findings, providing an improved approach for evaluating the prognostic influence of risk factors. To gauge the model's predictive performance, several analytical tools such as ROC curves, calibration curves, and decision curve analysis were employed. This comprehensive approach ensured a thorough assessment of the prognostic prediction capabilities of the model. RESULTS: Multivariate regression analysis revealed that Age (OR=3.87, 95%CI=1.54-9.73, p=0.004), Pneumonia (OR=3.54, 95%CI=1.41-8.86, p=0.007), WFNS (OR=3.24, 95%CI=1.23-8.54, p=0.017), DAR (OR=2.88, 95%CI=1.13-7.34, p=0.027), and F-NLR (OR=3.12, 95%CI=1.22-7.97, p=0.017) were identified as independent risk factors influencing the prognosis of patients with aSAH. Additionally, the area under the ROC curve was 0.866 (95%CI=0.805-0.927) for the training set and 0.924 (95%CI=0.849-0.999) for the validation set. The calibration curve analysis demonstrated a minor error of 0.02 for the training set and 0.051 for the validation set. Furthermore, both the training set and validation set displayed significant clinical benefits according to the DCA curves, underscoring the meaningful utility of the developed nomogram. CONCLUSIONS: Fibrinogen and neutrophil/lymphocyte ratio scores, and the D-dimer/Albumin ratio emerged as significant independent risk factors for prognosticating the outcomes of patients with aSAH. Leveraging these factors, a robust nomogram model was meticulously developed, showcasing its impressive precision in prognostic predictions. These results underscore the promising clinical applicability of these biomarkers as effective prognostic indicators for individuals afflicted by aSAH.

Lithographic Multicolor Patterning on Hybrid Perovskites for Nano-Optoelectronic Applications.

Ultrathin hybrid perovskites, with exotic properties and two-dimensional geometry, exhibit great potential in nanoscale optical and optoelectronic devices. However, it is still challenging for them to be compatible with high-resolution patterning technology toward miniaturization and integration applications, as they can be readily damaged by the organic solvents used in standard lithography processes. Here, a flexible three-step method is developed to make high-resolution multicolor patterning on hybrid perovskite, particularly achieved on a single nanosheet. The process includes first synthesis of precursor PbI2 , then e-beam lithography and final conversion to target perovskite. The patterns with linewidth around 150 nm can be achieved, which can be applied in miniature optoelectronic devices and high-resolution displays. As an example, the channel length of perovskite photodetectors can be down to 126 nm. Through deterministic vapor-phase anion exchange, a perovskite nanosheet can not only gradually alter the color of the same pattern in a wide wavelength range, but also display different colors simultaneously. The authors are optimistic that the method can be applied for unlimited perovskite types and device configurations for their high-integrated miniature applications.

Regulation of FOXO3 on neuronal ferroptosis after intracerebral hemorrhage via modulating NOX4 transcription.

INTRODUCTION: Intracerebral hemorrhage (ICH) is known to trigger neuronal ferroptosis while forkhead box O3 (FOXO3) is implicated in ICH. This study aimed to determine the specific effect of FOXO3 on neuronal ferroptosis after ICH. METHODS: The ICH mouse model was established through the injection of bacterial collagenase type IV and the cell model was established in Hemin-induced HT-22 cells. Subsequently, neurological functions, brain water content, and histopathological changes in mice were assessed. HT-22 cell activity was examined via cell counting kit-8 (CCK-8) method, and the levels of FOXO3, NADPH oxidase 4 (NOX4), and glutathione peroxidase 4 (GPX4) in brain tissues and HT-22 cells were measured. Fe2+ concentration and the levels of reactive oxygen species (ROS), malondialdehyde (MDA), and glutathione (GSH) in the tissues and cells were examined. The binding relationship between FOXO3 and the NOX4 promoter region was determined via chromatin-immunoprecipitation (Ch-IP). Rescue experiments were designed to probe the role of NOX4 in the regulation of FOXO3 on neuronal ferroptosis. RESULTS: FOXO3 was highly-expressed in ICH models while silencing FOXO3 alleviated brain damage, edema, and inflammatory infiltration in ICH mice. Meanwhile, silencing FOXO3 enhanced cell activity, diminished ROS and MDA activities and Fe2+ concentration, and elevated GSH and GPX4 levels in the tissues or cells. FOXO3 could bind to the NOX4 promoter and upregulate NOX4 transcription. NOX4 overexpression partially neutralized the repressive role of silencing FOXO3 in neuronal ferroptosis. CONCLUSION: Silencing FOXO3 attenuated ICH-induced neuronal ferroptosis via down-regulating NOX4 transcription levels, thus ameliorating post-ICH brain damage.

Enantioselective Total Synthesis of (+)-Peniciketals A and B: Two Architecturally Complex Spiroketals.

The enantioselective total syntheses of (+)-peniciketals A and B, two members of a family of architecturally complex spiroketals, have been achieved. Key synthetic transformations comprise Type I Anion Relay Chemistry (ARC) to construct the benzannulated [6,6]-spiroketal skeleton, a Negishi cross-coupling/olefin cross-metathesis reaction sequence to generate the trans-enone structure, and a late-stage large fragment union exploiting our recently developed photoisomerization/cyclization tactic.

A multimodal deep learning framework for predicting drug-drug interaction events.

MOTIVATION: Drug-drug interactions (DDIs) are one of the major concerns in pharmaceutical research. Many machine learning based methods have been proposed for the DDI prediction, but most of them predict whether two drugs interact or not. The studies revealed that DDIs could cause different subsequent events, and predicting DDI-associated events is more useful for investigating the mechanism hidden behind the combined drug usage or adverse reactions. RESULTS: In this article, we collect DDIs from DrugBank database, and extract 65 categories of DDI events by dependency analysis and events trimming. We propose a multimodal deep learning framework named DDIMDL that combines diverse drug features with deep learning to build a model for predicting DDI-associated events. DDIMDL first constructs deep neural network (DNN)-based sub-models, respectively, using four types of drug features: chemical substructures, targets, enzymes and pathways, and then adopts a joint DNN framework to combine the sub-models to learn cross-modality representations of drug-drug pairs and predict DDI events. In computational experiments, DDIMDL produces high-accuracy performances and has high efficiency. Moreover, DDIMDL outperforms state-of-the-art DDI event prediction methods and baseline methods. Among all the features of drugs, the chemical substructures seem to be the most informative. With the combination of substructures, targets and enzymes, DDIMDL achieves an accuracy of 0.8852 and an area under the precision-recall curve of 0.9208. AVAILABILITY AND IMPLEMENTATION: The source code and data are available at https://github.com/YifanDengWHU/DDIMDL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Evolution of Anion Relay Chemistry: Construction of Architecturally Complex Natural Products.

Multicomponent union tactics in which three or more fragments are rapidly connected are highly prized in the construction of architecturally complex natural products. Anion Relay Chemistry (ARC), a multicomponent union tactic, has just such potential to elaborate structurally diverse scaffolds in a single operation with excellent stereochemical control. Conceptually, the ARC tactic can be divided into two main classes: "Through-Bond," by the relay of negative charge through the bonding system of a molecule; and "Through-Space," by the migration of negative charge across space by a transfer agent. "Through-Space" Anion Relay Chemistry, the focus of this Account, can be further subdivided into two types: Type I ARC, originated from the Tietze-Schaumann-Smith coupling reaction, which for the first time permits controllable Brook rearrangements to construct unsymmetrical adducts, and as such has been successfully employed in the total syntheses of diverse natural products, including the mycoticins, bryostatin 1, spongistatins, rimocidin, indolizidine alkaloids, and enigmazole A; and Type II ARC, central to which is the design of novel bifunctional linchpins that enable rapid assembly of linear and cyclic fragments with diverse architectural features, ranging from polyols, spiroketals, and polyenes to polypropionate scaffolds. Recently, the Type II ARC tactic has been exploited as the key construction tactic in the total syntheses of the spirastrellolides, the cryptocarya acetates, secu'amamine A, mandelalide A, and nahuoic acid Ci (Bii). This Account will present the evolution of both the Type I and Type II Anion Relay tactics, in conjunction with some prominent applications.

Total Syntheses of (+)-Peniciketals A-B and (-)-Diocollettines A Exploiting a Photoisomerization/Cyclization Union Protocol.

A late-stage photoisomerization/cyclization union tactic, in conjunction with Type I Anion Relay Chemistry (ARC), permits enantioselective total syntheses and then biological evaluation of (+)-peniciketals A and B. The photochemical protocol was further showcased by an efficient three-step construction of the architecturally complex polycyclic skeleton found in (-)-diocollettines A. The mechanism and diastereoselectivity of the photochemical protocol have also been explored by both experiment and density functional theory calculations.

Prevalence and Risk Factors of Anxiety and Depression in Patients with Postherpetic Neuralgia: A Retrospective Study.

OBJECTIVES: Pain and psychological disorders are the 2 most commonly occurring symptom clusters in patients with postherpetic neuralgia (PHN). This study aimed to investigate the risk factors for anxiety and depressive disorders in patients with PHN. METHODS: Retrospectively, we examined the potential risk factors of anxiety and depression among patients with PHN from the clinic medical records of the Third Affiliated Hospital of Sun Yat-Sen University from 2017 to 2019. The Chinese version of the Hospital Anxiety and Depression Scale was used to assess anxiety and depression. Patients were retrospectively allocated to 2 groups - PHN with and without anxiety/depression - and compared to identify the differential patient characteristics. RESULTS: Cases of 661 patients who were diagnosed with PHN were included. Anxiety and depression developed in 69.0% (456/661) and 65.8% (435/661) of the enrolled patients with PHN, respectively. Results of univariate regression analyses showed that female sex, magnitude of pain intensity, time from onset of rash and extent of spread of rashes were significantly associated with anxiety and depression in patients with PHN. Multivariate analysis revealed that both anxiety and depression states significantly correlated with female sex, magnitude of pain intensity, and extent of spread of rashes. CONCLUSIONS: Anxiety and depression were not uncommon in patients with PHN. Women with PHN who experience severe pain and develop extensive rashes have a high risk of developing anxiety and depressive disorders.

Structural and Functional Characterization of 4-Hydroxyphenylacetate 3-Hydroxylase from Escherichia coli.

The hydroxylation of phenols into polyphenols, which are valuable chemicals and pharmaceutical products, is a challenging reaction. The search for green synthetic processes has led to considering microorganisms and pure hydroxylases as catalysts for phenol hydroxylation. Herein, we report the structural and functional characterization of the flavin adenine dinucleotide (FAD)-dependent 4-hydroxyphenylacetate 3-monooxygenase from Escherichia coli, named HpaB. It is shown that this enzyme enjoys a relatively broad substrate specificity, which allows the conversion of a number of non-natural phenolic compounds, such as tyrosol, hydroxymandelic acid, coumaric acid, hydroxybenzoic acid and its methyl ester, and phenol, into the corresponding catechols. The reaction can be performed by using a simple chemical assay based on formate as the electron donor and the organometallic complex [Rh(bpy)Cp*(H2 O)]2+ (Cp*: 1,2,3,4,5-pentamethylcyclopentadiene, bpy: 2,2'-bipyridyl) as the catalyst for FAD reduction. The availability of a crystal structure of HpaB in complex with FAD at 1.8 Šresolution opens up the possibility of the rational tuning of the substrate specificity and activity of this interesting class of phenol hydroxylases.

Total Synthesis of (-)-Nahuoic Acid Ci (Bii).

A convergent total synthesis of (-)-nahuoic acid Ci(Bii) (3), a novel cis-decalin polyketide, has been achieved. Key synthetic transformations include Type II Anion Relay Chemistry (ARC) to construct the polyol chain, a Ti-catalyzed asymmetric Diels-Alder reaction to generate the cis-decalin skeleton, and a late-stage large fragment union exploiting a Micalizio alkoxide-directed alkyne-alkene coupling tactic.

Oxidative [1,2]-Brook Rearrangements Exploiting Single-Electron Transfer: Photoredox-Catalyzed Alkylations and Arylations.

Oxidative [1,2]-Brook rearrangements via hypervalent silicon intermediates induced by photoredox-catalyzed single-electron transfer have been achieved, permitting the formation of reactive radical species that can engage in alkylations and arylations.

Diverse Pathways in Catalytic Reactions of Propargyl Aryldiazoacetates: Selectivity between Three Reaction Sites.

Three catalyst-dependent divergent reaction pathways for reactions of propargyl aryldiazoacetates are disclosed. Transition metal catalysts including those of rhodium(II), palladium(0 and II), silver(I), mercury(II), copper(I and II), platinum(II), and cationic gold(I) are effective for reactions that proceed through dinitrogen extrusion, carbene/alkyne metathesis, and aromatic substitution to form fused indeno-furanones, and use of tetrakis(acetonitrile)copper(I) provides indeno-furanones in the highest product yields. A Lewis acid catalyzed pathway that forms furan-2-ones is uncovered with FeCl3, ZnBr2, and BF3·Et2O as catalysts that proceed through activation of the aryldiazoacetate ester for arylpropargyl cation dissociation followed by recombination through cation addition to the diazo carbon. Neutral gold catalysts selectively activate the triple bond of propargyl aryldiazoacetates, resulting in the formation of allenic aryldiazoesters that further undergo uncatalyzed rearrangement.

Erbium-doped amplification in circular photonic crystal fiber supporting orbital angular momentum modes.

We propose a new orbital angular momentum (OAM) erbium-doped fiber amplifier based on a circular photonic crystal fiber, which can support a total of 18 modes (14 OAM modes) over C-band. A correction factor is proposed to modify the overlap factor, with the aim of evaluating the performance of the amplifier more accurately. We found that the confined doping profile can help optimize the differential model gain (DMG). Numerical simulations suggest that the proposed OAM fiber amplifier can provide a gain larger than 20 dB for all 14 OAM modes, with the small DMG less than 0.2 dB and the noise figure lower than 3.5 dB across the C-band.