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Manufactured sand (MS), due to its wide availability and cost-effectiveness, is used as an alternative aggregate for quartz sand (QS) in ultra-high-performance concrete (UHPC) to prepare ultra-high-performance manufactured sand concrete (UHPMC). This study aims to assess the bond behavior of 600 MPa-grade, high-strength, hot-rolled ribbed bars (HRB 600) in UHPMC. Thirty specimens were designed for the pull-out tests, taking into account several feature parameters, including MS replacement ratio (0%, 50%, 100%), water–binder ratio (0.17, 0.19, 0.21), steel fiber content (0%, 1%, 2%), and anchorage length (2d, 3d, 4d, 5d). The effects of the feature parameters on the failure mode, bond stress–slip curves, bond strength, bond-slip mechanism, and constitutive model were analyzed and illustrated. The results reveal that the pull-out specimen of UHPMC exhibits three distinct failure modes: rebar pull-out failure, UHPMC splitting failure, and splitting-pull-out failure. The bond strength increases from 46.57 MPa to 56.92 MPa when the steel fiber content increases from 0% to 2%. Additionally, a decrease in anchoring length is beneficial for improving the bond strength; as the anchoring length increases from 2d to 5d, the bonding strength decreases by 35.84%. The bond strength increases with an increase in the MS replacement ratio. As for the water–binder ratio, the bond strength presents the highest value when the water–binder ratio is 0.17. In addition, a new bond-slip constitutive model applicable to UHPMC and HRB 600 rebar, considering the MS replacement ratio, the water–binder ratio, etc., is proposed, which presents favorable prediction accuracy. [ABSTRACT FROM AUTHOR]

Virus-encoded circular RNA (circRNA) participates in the immune response to viral infection, affects the human immune system, and can be used as a target for precision therapy and tumor biomarker. The coronaviruses SARS-CoV-1 and SARS-CoV-2 (SARS-CoV-1/2) that have emerged in recent years are highly contagious and have high mortality rates. In coronaviruses, little is known about the circRNA encoded by the SARS-CoV-1/2. Therefore, this study explores whether SARS-CoV-1/2 encodes circRNA and characteristics and functions of circRNA. Based on RNA-seq data of SARS-CoV-1 and SARS-CoV-2 infections, we used circRNA identification tools (circRNA_finder, find_circ and CIRI2) to identify circRNAs. The number of circRNAs encoded by SARS-CoV-1 and SARS-CoV-2 was identified as 151 and 470, respectively. It can be found that SARS-CoV-2 shows more prominent circRNA encoding ability than SARS-CoV-1. Expression analysis showed that only a few circRNAs encoded by SARS-CoV-1/2 showed high expression levels, and the positive strand produced more abundant circRNAs. Then, based on the identified SARS-CoV-1/2-encoded circRNAs, we performed circRNA identification and characterization using the previously developed CirRNAPL. Finally, target gene prediction and functional enrichment analysis were performed. It was found that viral circRNA is closely related to cancer and has a potential role in regulating host cell functions. This study studied the characteristics and functions of viral circRNA encoded by coronavirus SARS-CoV-1/2, providing a valuable resource for further research on the function and molecular mechanism of coronavirus circRNA. [ABSTRACT FROM AUTHOR]

The unique expression patterns of circRNAs linked to the advancement and prognosis of cancer underscore their considerable potential as valuable biomarkers. Repurposing existing drugs for new indications can significantly reduce the cost of cancer treatment. Computational prediction of circRNA-cancer and drug-cancer relationships is crucial for precise cancer therapy. However, prior computational methods fail to analyze the interaction between circRNAs, drugs, and cancer at the systematic level. It is essential to propose a method that uncover more valuable information for achieving cancer-centered multi-association prediction. In this paper, we present a novel computational method, AutoEdge-CCP, to unveil cancer-associated circRNAs and drugs. We abstract the complex relationships between circRNAs, drugs, and cancer into a multi-source heterogeneous network. In this network, each molecule is represented by two types information, one is the intrinsic attribute information of molecular features, and the other is the link information explicitly modeled by autoGNN, which searches information from both intra-layer and inter-layer of message passing neural network. The significant performance on multi-scenario applications and case studies establishes AutoEdge-CCP as a potent and promising association prediction tool. Author summary: CircRNAs serve as crucial biomarkers and drug targets in cancer therapy. Predicting cancer-associated circRNAs and drugs contributes to uncover intricate molecular mechanisms driving tumorigenesis, thus offering novel insights into cancer diagnosis, treatment, and research. However, prevailing predictive methods often neglect the comprehensive interactions within circRNAs, drugs, and cancer, leading to an incomplete understanding of their complex interplay. In response, we introduce AutoEdge-CCP, a framework that models circRNA-cancer-drug interactions within a multi-source heterogeneous network. Each molecule combines intrinsic attribute information describing molecular features with interaction information derived through autoGNN, revealing pivotal circRNAs and drugs associated with cancer. Experimental results across multi-scenario attest to AutoEdge-CCP's superior performance compared to competing methods, particularly in predicting novel circRNAs and drugs associated with cancer. Additionally, visualization of edge embeddings and case studies provide interpretable insights into the prediction outcomes. [ABSTRACT FROM AUTHOR]

The gene regulatory structure of cells involves not only the regulatory relationship between two genes, but also the cooperative associations of multiple genes. However, most gene regulatory network inference methods for single cell only focus on and infer the regulatory relationships of pairs of genes, ignoring the global regulatory structure which is crucial to identify the regulations in the complex biological systems. Here, we proposed a graph-based Deep learning model for Regulatory networks Inference among Genes (DeepRIG) from single-cell RNA-seq data. To learn the global regulatory structure, DeepRIG builds a prior regulatory graph by transforming the gene expression of data into the co-expression mode. Then it utilizes a graph autoencoder model to embed the global regulatory information contained in the graph into gene latent embeddings and to reconstruct the gene regulatory network. Extensive benchmarking results demonstrate that DeepRIG can accurately reconstruct the gene regulatory networks and outperform existing methods on multiple simulated networks and real-cell regulatory networks. Additionally, we applied DeepRIG to the samples of human peripheral blood mononuclear cells and triple-negative breast cancer, and presented that DeepRIG can provide accurate cell-type-specific gene regulatory networks inference and identify novel regulators of progression and inhibition. Author summary: Although many methods have been proposed to infer the gene regulatory network of a single cell, they only focus on the regulatory relationships of pairs of genes and ignore the global regulatory structure. Here, we present a deep learning-based model to learn the global regulatory structure and reconstruct the gene regulatory networks from single-cell RNA sequencing data with a graph view. We utilize the weighted gene co-expression analysis to build a prior regulatory graph of gene and a graph autoencoder to deconstruct the latent regulatory structure among genes. We performed extensive experiments on varieties of single-cell RNA sequencing datasets and compared our method with 9 stat-of-the-art gene regulatory network inference method. The results show that our method can significantly improve the accuracy of gene regulatory network inference and can be applied to identify key regulators in a wide range of scenarios. [ABSTRACT FROM AUTHOR]

By integrating biological and bioinformatics data, Turek et al. (2023) [[21]] performed a genome assembly on the B10v3 cucumber genome and presented a well-organized and annotated reference genome of cucumber, contributing to better understanding of the cucumber genome. Using genome-wide classification and functional annotation, Cenci et al. (2022) [[18]] effectively characterized a large gene family across multiple species and established a classification framework for gene functional transfer, enabling a better understanding of the evolutionary history of one gene. In addition, several papers explored functional genomics of plant genomes, such as gene functional annotation and the development of functional genomics tools. Comparative genomics and functional genomics are two basic branches of plant genomics. [Extracted from the article]

Emerging evidence indicates that circular RNAs (circRNAs) can provide new insights and potential therapeutic targets for disease diagnosis and treatment. However, traditional biological experiments are expensive and time-consuming. Recently, deep learning with a more powerful ability for representation learning enables it to be a promising technology for predicting disease-associated circRNAs. In this review, we mainly introduce the most popular databases related to circRNA, and summarize three types of deep learning-based circRNA-disease associations prediction methods: feature-generation-based, type-discrimination and hybrid-based methods. We further evaluate seven representative models on benchmark with ground truth for both balance and imbalance classification tasks. In addition, we discuss the advantages and limitations of each type of method and highlight suggested applications for future research. [ABSTRACT FROM AUTHOR]

There is evidence that coinfection of cervicovaginal high‐risk human papillomavirus (HR‐HPV) and bacteria is common in women of childbearing age. However, the relationship between bacterial vaginosis (BV) and persistent HR‐HPV infection in women of childbearing age and the underlying mechanisms remain unclear. In this study, we determined whether BV affects persistent HR‐HPV infection in women aged 20–45 years and explored the possible mechanisms of their interactions. From January 1 to April 30, 2020, we recruited women aged 20–45 years with and without BV at a ratio of 1:2 from Fujian Maternity and Child Health Hospital. All women were followed up at 0, 12, and 24 months. A BV assay, HR‐HPV genotyping and cervical cytology were performed at each follow‐up. At 0 months, additional vaginal secretions and cervical exfoliated cells were collected for 16S ribosomal RNA sequencing, bacterial metabolite determination, and POU5F1B, C‐myc, TLR4, NF‐κB, and hTERT quantification. A total of 920 women were included. The abundance of Prevotella (p = 0.016) and Gardnerella (p = 0.027) were higher, whereas the abundance of Lactobacillus was lower (p = 0.001) in women with persistent HR‐HPV infection and high‐grade squamous intraepithelial lesions (HSIL). The abundance of Prevotella (p = 0.025) and Gardnerella (p = 0.018) increased in the vaginas of women with persistent HPV16 infection, whereas only the abundance of Prevotella (p = 0.026) was increased in women with persistent HPV18 infection. The abundance of Prevotella in the vagina was significantly positively correlated with the expression levels of TLR4, NF‐κB, C‐myc, and hTERT in host cervical cells (p < 0.05). Our findings suggest that overgrowth of Prevotella in the vagina may influence the occurrence of persistent HR‐HPV infection‐related cervical lesions through host NF‐κB and C‐myc signaling. [ABSTRACT FROM AUTHOR]

Modeling-based anti-cancer drug sensitivity prediction has been extensively studied in recent years. While most drug sensitivity prediction models only use gene expression data, the remarkable impacts of gene mutation, methylation, and copy number variation on drug sensitivity are neglected. Drug sensitivity prediction can both help protect patients from some adverse drug reactions and improve the efficacy of treatment. Genomics data are extremely useful for drug sensitivity prediction task. This article reviews the role of drug sensitivity prediction, describes a variety of methods for predicting drug sensitivity. Moreover, the research significance of drug sensitivity prediction, as well as existing problems are well discussed. [ABSTRACT FROM AUTHOR]

Purpose: This study assessed the predictive value of the preoperative systemic immune-inflammatory index (SII) for lymph node metastasis (LNM) in endometrial cancer (EC) patients. Methods: We retrospectively included 392 EC patients between January 2013 and January 2019. Data on clinical indicators including age, body mass index (BMI), menopause, serum inflammatory immune index, serum tumor markers, history of diabetes and hypertension, stage, histological type, and myometrial invasion (MI) were collected. The association between clinical indicators and LNM was evaluated. Results: The results indicated that neutrophil (NE), monocyte (MO) counts, SII, cancer antigen 125 (CA125), cancer antigen 153 (CA153), cancer antigen 199 (CA199), and the expression of estrogen receptor (ER) and Ki67 were higher in EC patients with LNM than in those without LNM (P< 0.05). Lymph vascular space invasion (LVSI) was also associated with LNM (P< 0.05). Consequently, the SII, CA125, CA153 and LVSI were found to be independent risk factors for LNM, and a nomogram including these indicators was performed. The ROC curve analysis showed that compared with a single index, the combination of the SII, CA125, CA153 and LVSI significantly improved the efficiency of diagnosing LNM in EC patients (AUC=0.865, P < 0.001). Moreover, the SII was significantly associated with age, menopause, and FIGO stage (P < 0.05). Further logistic regression analysis suggested that elevated serum SII was an independent risk factor for MI and progression to a higher pathological grade in young premenopausal EC patients. In addition, elevated SII was an independent risk factor for advanced EC progression in age ≥ 55 or postmenopausal EC patients. Conclusion: An elevated SII is an independent risk factor for LNM in patients with EC. In addition, the SII can be used as a predictor of MI and higher pathological grade in young premenopausal EC patients. [ABSTRACT FROM AUTHOR]

Background: China's Fujian Cervical Pilot Project (FCPP) transitioned cervical cancer screening from high-risk human papillomavirus (HR-HPV) nongenotyping to genotyping. We investigated the clinical impact of this introduction, comparing performance indicators between HR-HPV genotyping combined with cytology screening (HR-HPV genotyping period) and the previous HR-HPV nongenotyping combined with cytology screening (HR-HPV nongenotyping period). Methods: A retrospective population-based cohort study was performed using data from the FCPP for China. We obtained data for the HR-HPV nongenotyping period from 1 January 2012 to 31 December 2013, and for the HR-HPV genotyping period from 1 January 2014 to 31 December 2016. Propensity score matching was used to match women from the two periods. Multivariable Cox regression was used to assess factors associated with cervical intraepithelial neoplasia of grade 2 or worse (CIN2+). The primary outcome was the incidence of CIN2+ in women aged ⩾25 years. Performance was assessed and included consistency, reach, effectiveness, adoption, implementation and cost. Results: Compared with HR-HPV nongenotyping period, in the HR-HPV genotyping period, more CIN2+ cases were identified at the initial screening (3.06% versus 2.32%; p < 0.001); the rate of colposcopy referral was higher (10.87% versus 6.64%; p < 0.001); and the hazard ratio of CIN2+ diagnosis was 1.64 (95% confidence interval, 1.43–1.88; p < 0.001) after controlling for health insurance status and age. The total costs of the first round of screening (US$66,609 versus US$65,226; p = 0.293) were similar during the two periods. Higher screening coverage (25.95% versus 25.19%; p = 0.007), higher compliance with age recommendations (92.70% versus 91.69%; p = 0.001), lower over-screening (4.92% versus 10.15%; p < 0.001), and reduced unqualified samples (cytology: 1.48% versus 1.73%, p = 0.099; HR-HPV: 0.57% versus 1.34%, p < 0.001) were observed in the HR-HPV genotyping period. Conclusions: Introduction of an HR-HPV genotyping assay in China could detect more CIN2+ lesions at earlier stages and improve programmatic indicators. Evidence suggests that the introduction of HR-HPV genotyping is likely to accelerate the elimination of cervical cancer in China. [ABSTRACT FROM AUTHOR]

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Background: The diagnosis of postpartum pelvic organ prolapse (POP) relies on symptoms combined with pelvic organ prolapse-quantification (POP-Q) and lacks serological indicators. The objective of this study was to assess serum elastin, type I collagen, miRNA-30d, and miRNA-181a in the early postpartum period to identify hematologic predictors of POP. Material and Methods: The study included 1013 42- to 60-day-postpartum women who had delivered at Quanzhou Women's and Children's Hospital from October 1, 2016, to October 31, 2017. This study was performed in accordance with the Declaration of Helsinki. The pregnancy and childbirth characteristics and pelvic floor function were evaluated. Forty cases with and without POP were matched, and serum elastin and type I collagen were determined by enzyme-linked immunosorbent assay (ELISA). Reverse-transcription polymerase chain reaction (RT-PCR) was used to detect miRNA-30d and miRNA-181a in 15 pairs. Results: Of the 1013 women recruited, 699 (69.00%) were diagnosed with POP. The mean age was 29.00 years old, and the mean body mass index (BMI) was 22.6 kg/m2. In the univariate analysis, age ≥ 35 years (OR, 1.449; 95% CI, 0.965, 2.298), postpartum BMI ≥ 24 (OR, 4.402; 95% CI, 2.657, 6.148), neonatal weight ≥ 4 kg (OR, 4.832; 95% CI, 1.373, 17.290) and vaginal delivery (OR, 2.751; 95% CI, 1.855, 4.081) were risk factors for postpartum POP. There were no significant differences in the concentrations of serum elastin and type I collagen between the groups (P=0.52; P=0.26). There were significant differences in the concentrations of miRNA-30d and miRNA-181a between the groups (P=0.004; P=0.003). Conclusion: miRNA-30d and miRNA-181a tended to be increased in women with POP and could be potential clinical predictors. [ABSTRACT FROM AUTHOR]

Purpose: We investigated the relationship between metabolic syndrome (MetS) and its components and erythrocyte parameters such as red blood cell count (RBC) and hemoglobin (Hb) levels, and their association with the risk of deep myometrial invasion in endometrioid endometrial carcinoma (EEC). Patients and Methods: A total of 473 women diagnosed with EEC between January 2012 and December 2018 were included in the study. MetS was diagnosed using a modified version of the Chinese Diabetes Society 2004 criteria. Demographic and clinical characteristics were stratified by MetS and compared. Logistic regression analyses were performed to calculate the odds ratios (ORs) of myometrial invasion. Results: The prevalence of MetS was 18.8% (89/473). RBC and Hb levels were significantly higher in EEC patients with MetS. RBC and Hb values increased linearly with the number of MetS components. The OR of the MetS risk increased across the RBC quartiles (OR=2.212, 95% confidence interval [Cl]=1.125-4.350, P˂0.05). Among EEC patients with MetS, the OR of deep myometrial invasion increased with the decrease of the RBC quartiles (P=0.037). Similarly, in EEC patients with obesity and hyperglycemia or with ≥ 2 MetS criteria, the lowest RBC level (Q1) was a risk factor for deep myometrial invasion compared with the reference RBC group (Q4, P˂0.05). However, this trend was not evident in EEC patients without MetS and its components. Conclusion: EEC with MetS and its components had higher RBC and Hb levels. The association between the erythrocyte parameters and myometrial invasion differed with MetS. RBC was identified as a risk factor of myometrial invasion in EEC patients with MetS and its components. [ABSTRACT FROM AUTHOR]

We report avalanche photodiodes fabricated from Alx In1−xAs ySb1− y wafers grown by molecular beam epitaxy using the digital alloy technique. A series of AlxIn 1−xAs ySb1− y (x = 0.3, 0.4, 0.5, 0.6, 0.7) p-i-n structures have been grown on GaSb substrate. Dark current, avalanche multiplication, excess noise, and external quantum efficiencies have been characterized. Very low excess noise, as characterized by k ∼ 0.01–0.05, has been achieved. [ABSTRACT FROM PUBLISHER]

InP-based high-power and high-speed modified unitraveling carrier photodiodes heterogeneously integrated on silicon-on-insulator waveguides are demonstrated. Internal responsivity up to 0.95A/W and bandwidth up to 48 GHz have been achieved. The maximum RF output power of a $20 \times 35$ μm2 photodiode was 16.6, 15.8, and 13.5 dBm at 10, 20, and 30 GHz, respectively. The maximum output RF power of a $10 \times 35$ μm2 photodiode was 12 dBm at 40 GHz. Using the same integration technology, we show that balanced waveguide photodiodes reach 0.78-A/W internal responsivity, 14-GHz bandwidth, and >20-dB common-mode rejection ratio. In the differential mode, the unsaturated RF output power was 17.2 dBm at 10 GHz and 15.2 dBm at 20 GHz. [ABSTRACT FROM PUBLISHER]

We report Al0.8Ga0.2As recessed-window single-photon avalanche photodiodes with high internal single-photon detection efficiency and low dark count probability. External quantum efficiency was increased by a factor of 2 at \lambda = 405 nm. Annealing in arsine with hydrogen carrier gas reduced the dark count probability by a factor of 100, to \sim 10^{-6} /gate with a \sim 5$ ns gate, at room temperature. The activation energies of primary carrier traps, which give rise to afterpulsing, are extracted in a temperature range from 150 to 200 K. [ABSTRACT FROM PUBLISHER]

An antireflection coating was created for InP-based pin photodiodes using natural lithography with 100 nm-diameter SiO2 spheres. The surface showed a normal-incidence reflection of <5% for wavelengths from 900 to 2500 nm. Photodiodes with surface texturing showed an enhancement in quantum efficiency with no dark current degradation. [ABSTRACT FROM AUTHOR]

As a kind of novel non-invasive marker for molecular detection, cell-free DNA (cfDNA) has potential value for the early diagnosis of diseases, prognosis assessment, and efficacy monitoring. The constant developments in molecular biology detection technologies have led to an increase in clinical studies on the use of cfDNA detection methods for patients, and many gratifying outcomes have been achieved. In this review, the contributions of bioinformatics tools to the study of cfDNA are well discussed. The focus of the review is on cfDNA identification signals, cfDNA identification methods, and the relationship of cfDNA with human diseases such as hepatic cancer, lung cancer, end-stage kidney disease, and ischemic stroke. The research significance and existing problems of using cfDNA as a biomarker for diseases are also discussed., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Recently, a large number of studies have indicated that circRNAs with covalently closed loops play important roles in biological processes and have potential as diagnostic biomarkers. Therefore, research on the circRNA-disease relationship is helpful in disease diagnosis and treatment. However, traditional biological verification methods require considerable labor and time costs. In this paper, we propose a new computational method (RGCNCDA) to predict circRNA-disease associations based on relational graph convolutional networks (R-GCNs). The method first integrates the circRNA similarity network, miRNA similarity network, disease similarity network and association networks among them to construct a global heterogeneous network. Then, it employs the random walk with restart (RWR) and principal component analysis (PCA) models to learn low-dimensional and high-order information from the global heterogeneous network as the topological features. Finally, a prediction model based on an R-GCN encoder and a DistMult decoder is built to predict the potential disease-associated circRNA. The predicted results demonstrate that RGCNCDA performs significantly better than the other six state-of-the-art methods in a 5-fold cross validation. Furthermore, the case study illustrates that RGCNCDA can effectively discover potential circRNA-disease associations., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Objective: According to the 2019 American Society for Colposcopy and Cervical Pathology (ASCCP) recommendations, women with a positive high-risk human papillomavirus (HR-HPV) diagnosis and low-grade cervical intraepithelial lesion (LSIL) cytology result should be referred for further colposcopy examination. However, this strategy results in over-treatment in several cases. In this study, we assessed the performance of extended HR-HPV genotyping in women with a simultaneous positive HR-HPV and LSIL diagnosis with the aim of improving the current triage strategy. Methods: This study was an observational analysis of women from the Fujian Province Cervical Lesion Screening Cohorts (FCLSCs). Women who were HR-HPV-positive and had a cytological examination of LSIL, which were followed up with colposcopy and biopsy, from 2015 to 2018 were included. The study endpoint was defined as the detection of histological cervical intraepithelial neoplasia grade 2 or worse (CIN2+). We combined HR-HPV genotypes according to the prevalence rate in histological CIN2+ and ranked them from high to low to establish HR-HPV genotyping models. Outcomes were assessed with respect to sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and colposcopy referral rate. Results: Overall, 56,788 women undergoing preliminary screening for HR-HPV genotyping were included in this study. Among them, 10,499 women positive for HR-HPV underwent a cytology examination, and 902 women with LSIL cytology diagnosed and subsequent biopsy results were included in the final evaluation. Among these patients, 25.1% (226/902) were found to have CIN2+ in histology. HPV-16, -58, -52, -18, -33, and -31 infections were the most common genotypes, and HPV-16, -18, -58, -33, and -31 (odds ratio [OR] = 5.41, 2.98, 1.38, 1.24, and 1.21, respectively) were associated with the potential for histological CIN2+, from the highest to lowest. In the detection of CIN2+ lesions in HR-HPV-positive LSIL women of different HR-HPV genotyping models, the extended HPV 16/18/31/33/52/58 genotyping model was found to have better efficacy with higher sensitivity (92.9%) and NPV (93.0%), but a significantly lower colposcopy referral rate (74.7%) than the ASCCP-recommended HR-HPV non-genotyping model. Conclusion: For HR-HPV-positive women with LSIL, the HPV 16/18/31/33/52/58 genotyping model can serve as an alternative approach to the ASCCP recommendations, potentially reducing the unnecessary colposcopy referral burden in China., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

The separation behaviors of Mg 2+ and Li + were investigated using molecular dynamics. Two functionalized graphene nanopore models (i.e., co&#95;5 and coo&#95;5) inspired by the characteristic structural features of Mg 2+ channels were used. Both nanopores exhibited a higher preference to Mg 2+ than to Li + , and the selectivity ratios were higher for coo&#95;5 than for co&#95;5 under all the studied transmembrane voltages. An evaluation of the effect of coordination on the ionic hydration microstructures for both nanopores showed that the positioning of the modified groups could better fit a hydrated Mg 2+ than a hydrated Li + , as if Mg 2+ was not dehydrated according to hydrogen bond analysis of the ionic hydration shells. This condition led to a lower resistance for Mg 2+ than for Li + when traveling through the nanopores. Moreover, a distinct increase in hydrogen bonds occurred with coo&#95;5 compared with co&#95;5 for hydrated Li + , which made it more difficult for Li + to pass through coo&#95;5. Thus, a higher Mg 2+ /Li + selectivity was found in for coo&#95;5 than for co&#95;5. These findings provide some design principles for developing artificial Mg 2+ channels, which have potential applications as Mg 2+ sensors and novel devices for Mg 2+ /Li + separation.

Natural lithography with 100-nm-diameter SiO(2) spheres followed by inductively coupled plasma etching was used to texture the surface of 4H-SiC for a wide-spectrum large-acceptance-angle anti-reflective layer. The surface showed low normal-incidence reflectance of < 5% over a wide spectrum from 250 nm to 550 nm. Photodiodes fabricated from the surface-textured SiC showed broader spectral and angular responsivity than SiC photodiodes with SiO(2) antireflective coating. The textured SiC photodiodes showed peak responsivity of 116 mA/W, large angle of acceptance angle (< 2% decrease in responsivity at 50° incident angle) and low dark current at 10V., (© 2011 Optical Society of America)

Gallium Phosphide (GaP) reach-through avalanche photodiodes (APDs) are reported. The APDs exhibited dark current less than a pico-ampere at unity gain. A quantum efficiency of 70% was achieved with a recessed window structure; this is almost two times higher than previous work., (© 2011 Optical Society of America)