bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of urinary biomarkers for prediction of diabetic kidney disease: a propensity score matching analysis
Authors: Qin Yongzhang , Zhang Shuang , Shen Xiaofang , Zhang Shunming , Wang Jingyu , Zuo Minxia , Cui Xiao , Gao Zhongai , Yang Juhong , Zhu Hong , Chang Baocheng 
Published: December 2019

Background: The aim of this study was to evaluate the diagnostic value of six urinary biomarkers for prediction of diabetic kidney disease (DKD). Methods: The cross-sectional study recruited 1053 hospitalized patients with type 2 diabetes mellitus (T2DM), who were categorized into the diabetes mellitus (DM) with normoalbuminuria (NA) group ( n = 753) and DKD group ( n = 300) according to 24-h urinary albumin excretion rate (24-h UAE). Data on the levels of six studied urinary biomarkers [transferrin (TF), immunoglobulin G (IgG), retinol-binding protein (RBP), β-galactosidase (GAL), N-acetyl-beta-glucosaminidase (NAG), and β2-microglobulin (β2MG)] were obtained. The propensity score matching (PSM) method was applied to eliminate the influences of confounding variables. Results: Patients with DKD had higher levels of all six urinary biomarkers. All indicators demonstrated significantly increased risk of DKD, except for GAL and β2MG. Single RBP yielded the greatest area under the curve (AUC) value of 0.920 compared with the other five markers, followed by TF (0.867) and IgG (0.867). However, GAL, NAG, and β2MG were shown to have a weak prognostic ability. The diagnostic values of the different combinations were not superior to the single RBP. Conclusions: RBP, TF, and IgG could be used as reliable or good predictors of DKD. The combined use of these biomarkers did not improve DKD detection.
Availability of Lifesaving Maternal and Child Health Commodities and Associated Factors in Public and Private Health Facilities of Addis Ababa, Ethiopia
Authors: Damtew Dagim , Worku Fikru , Tesfaye Yonas , Jemal Awol 
Published: December 2019

Background: Inaccessible, unaffordable, and poor quality care are the key underlying reasons for the high burden of maternal and child morbidity and mortality in low- and middle-income countries. Objective: To assess the availability of lifesaving maternal and child health (MCH) commodities and associated factors in public and private health facilities of Addis Ababa, Ethiopia, 2016. Methods: Institutional-based, descriptive cross-sectional study was carried out in the selected health facilities (29 publics and 6 private) in Addis Ababa. The data were collected through pretested, structured questionnaire, and in-depth interviews. For the quantitative analysis, data were analyzed using SPSS version 20 statistical software, SPSS Inc. Descriptive statistics were used to summarize the variables, and the Spearman correlation test was run to determine the predictors of the outcome variables. For the qualitative data, the data were handled manually and transformed into categories related to the topics and coded on paper individually in order to identify themes and patterns for thematic analysis. Result: The overall availability of the lifesaving MCH commodities in the health facilities was 74.3%. There is a moderate, positive association between the availability of lifesaving MCH commodities with the adequacy of budget ( rs = 0.485, P < .001), use of more than 1 selection criteria during selection ( rs = 0.407, P = .015), and training given to health facilities on logistics management ( rs = 0.490, P = .003). Conclusion: The availability of the lifesaving MCH commodities in the health facilities was within the range of fairly high to high. Adequacy of budget, use of more than 1 selection criteria during selection, and training given on logistics management were the predictors of the availability of the commodities.
The immune landscape and response to immune checkpoint blockade therapy in lymphoma
Authors: Kline Justin , Godfrey James , Ansell Stephen M. 
Published: December 2019

Abstract
The clinical development of effective cancer immunotherapies, along with advances in genomic analysis, has led to the identification of tumor environmental features that predict for sensitivity to immune checkpoint blockade therapy (CBT). Early-phase clinical trial results have demonstrated the remarkable effectiveness of CBT in specific lymphoma subtypes, including classical Hodgkin lymphoma and primary mediastinal B-cell lymphoma. Conversely, CBT has been relatively disappointing in follicular lymphoma and diffuse large B-cell lymphoma. These clinical observations, coupled with important scientific discoveries, have uncovered salient features of the lymphoma microenvironment that correlate with immunotherapy response in patients. For example, classical Hodgkin lymphoma is characterized by an inflammatory environment, genetic alterations that facilitate escape from immune attack, and sensitivity to PD-1 blockade therapy. On the other hand, for lymphomas in which measures of immune surveillance are lacking, including follicular lymphoma and most diffuse large B-cell lymphomas, anti-PD-1 therapy has been less effective. An improved understanding of the immune landscapes of these lymphomas is needed to define subsets that might benefit from CBT. In this article, we describe the immune environments associated with major B-cell lymphomas with an emphasis on the immune escape pathways orchestrated by these diseases. We also discuss how oncogenic alterations in lymphoma cells may affect the cellular composition of the immune environment and ultimately, vulnerability to CBT. Finally, we highlight key areas for future investigation, including the need for the development of biomarkers that predict for sensitivity to CBT in lymphoma patients.
Predictors of non-cystic fibrosis bronchiectasis in Indigenous adult residents of central Australia: results of a case–control study
Authors: Einsiedel Lloyd , Pham Hai , Au Virginia , Hatami Saba , Wilson Kim , Spelman Tim , Jersmann Hubertus 
Published: December 2019

The human T-cell leukaemia virus type 1 (HTLV-1) is associated with pulmonary inflammation. Indigenous Australians in central Australia have a very high prevalence of HTLV-1 infection and we hypothesised that this might contribute to high rates of bronchiectasis in this population.80 Indigenous adults with confirmed bronchiectasis, each matched by age, sex and language to two controls without bronchiectasis, were recruited. Case notes and chest imaging were reviewed, HTLV-1 serology and the number of peripheral blood leukocytes (PBLs) infected with HTLV-1 (pro-viral load (PVL)) were determined, and radiological abnormality scores were calculated. Participants were followed for a mean±sdof 1.14±0.86 years and causes of death were determined.Median (interquartile range) HTLV-1 PVL for cases was 8-fold higher than controls (cases 213.8 (19.7–3776.3) copies per 105PBLsversuscontrols 26.6 (0.9–361) copies per 105PBLs; p=0.002). Radiological abnormality scores were higher for cases with HTLV-1 PVL ≥1000 copies per 105PBLs and no cause of bronchiectasis other than HTLV-1 infection. Major predictors of bronchiectasis were prior severe lower respiratory tract infection (adjusted OR (aOR) 17.83, 95% CI 4.51–70.49; p<0.001) and an HTLV-1 PVL ≥1000 copies per 105PBLs (aOR 12.41, 95% CI 3.84–40.15; p<0.001). Bronchiectasis (aOR 4.27, 95% CI 2.04–8.94; p<0.001) and HTLV-1 PVL ≥1000 copies per 105PBLs (aOR 3.69, 95% CI 1.11–12.27; p=0.033) predicted death.High HTLV-1 PVLs are associated with bronchiectasis and with more extensive radiological abnormalities, which may result from HTLV-1-mediated airway inflammation.
Mold resistance of bamboo treated with copper complexes-grafted silica gel and its microdistribution in treated bamboo
Authors: Yang Shoulu , Luo Sha , Huang Anxiang , Luo Yang , Li Dan , Wu Yiqiang , Ji Ning 
Published: December 2019

AbstractBamboo is readily discolored by mold fungi, which greatly limits its applications. An effective antifungal agent, copper(II) chloride (CuCl2)-grafted silica gel, was prepared by a sol–gel process using tetraethoxysilane (TEOS)/3-aminopropyltriethoxysilane (APTES) mixtures. The elemental composition and the chemical combinations of homogeneous sol mixture (HSM) and bamboo were determined via Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy with energy-dispersive X-ray spectrometry (SEM–EDS). The mold resistance of bamboo treated with HSM, alkaline copper quat (ACQ), chromated copper arsenate (CCA), and purified water was characterized by an indoor mold test. The micro-morphology of bamboo treated with HSM was investigated using scanning electron microscopy (SEM). HSM penetrated into the bamboo vessels, and formed xerogels, which was able to coordinate copper(II) cations. SEM–EDS investigations suggest that Si–O–Cu linkages may be formed through an exchange reaction between silanol groups and copper complexes. The bamboo samples treated with HSM showed highly efficient mold resistance due to a good penetration of HSM. Furthermore, no fungal hyphae were found in the structure of HSM-treated bamboo after a 5-week mold test. The copper complexes grafted to silica gel developed in this work provide an efficient antifungal agent for a wide range of potential applications in bamboo protection.
Experimental Demonstration of Electromagnetically Induced Transparency in a Conductively Coupled Flexible Metamaterial with Cheap Aluminum Foil
Authors: Hu Jie , Lang Tingting , Xu Weihang , Liu Jianjun , Hong Zhi 
Published: December 2019

AbstractWe propose a conductively coupled terahertz metallic metamaterial exhibiting analog of electromagnetically induced transparency (EIT), in which the bright and dark mode antennae interact via surface currents rather than near-field coupling. Aluminum foil, which is very cheap and often used in food package, is used to fabricate our metamaterials. Thus, our metamaterials are also flexible metamaterials. In our design, aluminum bar resonators and aluminum split ring resonators (SRRs) are connected (rather than separated) in the form of a fork-shaped structure. We conduct a numerical simulation and an experiment to analyze the mechanism of the proposed metamaterial. The surface current due to LSP resonance (bright mode) flows along different paths, and a potential difference is generated at the split gaps of the SRRs. Thus, an LC resonance (dark mode) is induced, and the bright mode is suppressed, resulting in EIT. The EIT-like phenomenon exhibited by the metamaterial is induced by surface conducting currents, which may provide new ideas for the design of EIT metamaterials. Moreover, the process of fabricating microstructures on flexible substrates can provide a reference for producing flexible microstructures in the future.
Joint between-sample normalization and differential expression detection through ℓ0-regularized regression
Authors: Liu Kefei , Shen Li , Jiang Hui 
Published: December 2019

Abstract
Background
A fundamental problem in RNA-seq data analysis is to identify genes or exons that are differentially expressed with varying experimental conditions based on the read counts. The relativeness of RNA-seq measurements makes the between-sample normalization of read counts an essential step in differential expression (DE) analysis. In most existing methods, the normalization step is performed prior to the DE analysis. Recently, Jiang and Zhan proposed a statistical method which introduces sample-specific normalization parameters into a joint model, which allows for simultaneous normalization and differential expression analysis from log-transformed RNA-seq data. Furthermore, an ℓ0 penalty is used to yield a sparse solution which selects a subset of DE genes. The experimental conditions are restricted to be categorical in their work.

Results
In this paper, we generalize Jiang and Zhan’s method to handle experimental conditions that are measured in continuous variables. As a result, genes with expression levels associated with a single or multiple covariates can be detected. As the problem being high-dimensional, non-differentiable and non-convex, we develop an efficient algorithm for model fitting.

Conclusions
Experiments on synthetic data demonstrate that the proposed method outperforms existing methods in terms of detection accuracy when a large fraction of genes are differentially expressed in an asymmetric manner, and the performance gain becomes more substantial for larger sample sizes. We also apply our method to a real prostate cancer RNA-seq dataset to identify genes associated with pre-operative prostate-specific antigen (PSA) levels in patients.
Statistical modeling of STR capillary electrophoresis signal
Authors: Karkar Slim , Alfonse Lauren E. , Grgicak Catherine M. , Lun Desmond S. 
Published: December 2019

Abstract
Background
In order to isolate an individual’s genotype from a sample of biological material, most laboratories use PCR and Capillary Electrophoresis (CE) to construct a genetic profile based on polymorphic loci known as Short Tandem Repeats (STRs). The resulting profile consists of CE signal which contains information about the length and number of STR units amplified. For samples collected from the environment, interpretation of the signal can be challenging given that information regarding the quality and quantity of the DNA is often limited. The signal can be further compounded by the presence of noise and PCR artifacts such as stutter which can mask or mimic biological alleles. Because manual interpretation methods cannot comprehensively account for such nuances, it would be valuable to develop a signal model that can effectively characterize the various components of STR signal independent of a priori knowledge of the quantity or quality of DNA.

Results
First, we seek to mathematically characterize the quality of the profile by measuring changes in the signal with respect to amplicon size. Next, we examine the noise, allele, and stutter components of the signal and develop distinct models for each. Using cross-validation and model selection, we identify a model that can be effectively utilized for downstream interpretation. Finally, we show an implementation of the model in NOCIt, a software system that calculates the a posteriori probability distribution on the number of contributors.

Conclusion
The model was selected using a large, diverse set of DNA samples obtained from 144 different laboratory conditions; with DNA amounts ranging from a single copy of DNA to hundreds of copies, and the quality of the profiles ranging from pristine to highly degraded. Implemented in NOCIt, the model enables a probabilisitc approach to estimating the number of contributors to complex, environmental samples.
DeepEP: a deep learning framework for identifying essential proteins
Authors: Zeng Min , Li Min , Wu Fang-Xiang , Li Yaohang , Pan Yi 
Published: December 2019

AbstractBackgroundEssential proteins are crucial for cellular life and thus, identification of essential proteins is an important topic and a challenging problem for researchers. Recently lots of computational approaches have been proposed to handle this problem. However, traditional centrality methods cannot fully represent the topological features of biological networks. In addition, identifying essential proteins is an imbalanced learning problem; but few current shallow machine learning-based methods are designed to handle the imbalanced characteristics.ResultsWe develop DeepEP based on a deep learning framework that uses the node2vec technique, multi-scale convolutional neural networks and a sampling technique to identify essential proteins. In DeepEP, the node2vec technique is applied to automatically learn topological and semantic features for each protein in protein-protein interaction (PPI) network. Gene expression profiles are treated as images and multi-scale convolutional neural networks are applied to extract their patterns. In addition, DeepEP uses a sampling method to alleviate the imbalanced characteristics. The sampling method samples the same number of the majority and minority samples in a training epoch, which is not biased to any class in training process. The experimental results show that DeepEP outperforms traditional centrality methods. Moreover, DeepEP is better than shallow machine learning-based methods. Detailed analyses show that the dense vectors which are generated by node2vec technique contribute a lot to the improved performance. It is clear that the node2vec technique effectively captures the topological and semantic properties of PPI network. The sampling method also improves the performance of identifying essential proteins.ConclusionWe demonstrate that DeepEP improves the prediction performance by integrating multiple deep learning techniques and a sampling method. DeepEP is more effective than existing methods.
Transcriptomic and microstructural analyses in Liriodendron tulipifera Linn. reveal candidate genes involved in nectary development and nectar secretion
Authors: Liu Huanhuan , Ma Jikai , Li Huogen 
Published: December 2019

Abstract
Background
Nectar is a major floral attractant and reward for insects that ensures pollination. Liriodendron, a genus of the Magnoliaceae family, includes only two relict species, L. chinense and L. tulipifera, which are considered “basal angiosperms” according to plant evolutionary history. The flowers of Liriodendron plants are insect pollinated and secrete nectar to attract pollinators. To date, the morphology and anatomy of nectaries, the mechanism of nectar secretion and the molecular mechanism of nectary development in Liriodendron remain poorly understood.

Methods
In this study, we examined the nectary surface cells and change in starch in L. tulipifera by using scanning electron microscopy and periodic acid-Schiff techniques to select appropriate samples for subsequent research. Transcriptome sequencing was of the top and middle parts of immature nectaries and the middle part of mature and postsecretory nectaries in L. tulipifera was performed. We evaluated the expression profiles of 21 DEGs that are closely related to nectary development and nectar secretion for real-time quantitative PCR analysis.

Results
L. tulipifera nectaries are starch-storing nectaries and are located in the top and middle parts of L. tulipifera petals. After analyzing the RNA-seq data, we obtained 115.26 Gb of clean data in 12 libraries and mapped the results to the L. chinense reference genome with 71.02–79.77% efficiency. In total, 26,955 DEGs were identified by performing six pairwise comparisons. The flavonoid biosynthesis, phenylpropanoid biosynthesis, anthocyanin biosynthesis and starch and sucrose metabolism pathways were enriched and related to nectar secretion and pigment change. We identified 56 transcription factor families, and members of the TCP, Trihelix, C2H2, ERF, and MADS families changed dynamically during nectary development. Moreover, to further verify the accuracy of the RNA-seq results, we validated the expression profiles of 21 candidate genes.

Conclusions
We evaluated the nectary development and secretion processes comprehensively and identified many related candidate genes in L. tulipifera. These findings suggest that nectaries play important roles in flavonoid synthesis and petal color presentation.
Relationship between early proteinuria and long term outcome of kidney transplanted patients from different decades of donor age
Authors: Diena Davide , Messina Maria , De Biase Consuelo , Fop Fabrizio , Scardino Edoardo , Rossetti Maura M. , Barreca Antonella , Verri Aldo , Biancone Luigi 
Published: December 2019

AbstractBackgroundProteinuria after kidney transplantation portends a worse graft survival. However the magnitude of proteinuria related to patient and graft survival and its correlation with donor and recipient characteristics are poorly explored.MethodsThis study investigated the impact of post transplant proteinuria in the first year in 1127 kidney transplants analyzing the impact of different donor ages. Proteinuria cut off was set at 0.5 g/day.ResultsTransplants with proteinuria> 0.5 g/day correlated with poor graft and patient outcome in all donor age groups. In addition, 6-month-1-year proteinuria increase was significantly associated with graft outcome, especially with donors> 60 years old (p <  0.05; Odd Ratio 1.8). 1-year graft function (eGFR < or ≥ 44 ml/min) had similar impact to proteinuria (≥ 0.5 g/day) on graft failure (Hazard Ratio 2.77 vs Hazard Ratio 2.46). Low-grade proteinuria (0.2–0.5 g/day) demonstrated a trend for worse graft survival with increasing donor age. Also in kidney-paired analysis proteinuria ≥0.5 effect was more significant with donors> 50 years old (Odd Ratio 2.3).ConclusionsPost-transplant proteinuria was increasingly harmful with older donor age. Proteinuria ≥0.5 g/day correlates with worse outcomes in all transplanted patients. Prognostic value of proteinuria and eGFR for graft and patient survival was comparable and these two variables remain significant risk factors even in a multivariate model that take into consideration the most important clinical variables (donor age, rejection, delayed graft function and cytomegalovirus viremia among others).
Great auricular nerve schwannoma in neck region: a case report with the risk of medical disputes
Authors: Xu Chunling , Sun Qingjia 
Published: December 2019

AbstractBackgroundGreat auricular nerve schwannoma is extremely rare. Herein, we reported the first case of schwannoma arising from great auricular nerve trunk.Case presentationA 29 year-old female complained of a slowly-growing superfacial neck mass for 6 months. MRI revealed a high possibility of schwannoma. Although the patient underwent successfully surgical removal of the tumor, ipsilateral numbness of both auricle and peripheral skin developed due to traction of the nerve. Immunohistochemistry staining confirmed the diagnosis of schwannoma. And the patient has been followed regularly.ConclusionFor superficial cervical tumors, the cervical plexus cutaneous nerve should be considered if MRI and other imaging findings suggest neurogenic tumors.