bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of intravitreal injection of siRNA against caspase-2 on retinal and optic nerve degeneration in air blast induced ocular trauma
Authors: Thomas Chloe N. , Bernardo-Colón Alexandra , Courtie Ella , Essex Gareth , Rex Tonia S. , Blanch Richard J. , Ahmed Zubair 
Published: August 2021

AbstractOcular repeated air blast injuries occur from low overpressure blast wave exposure, which are often repeated and in quick succession. We have shown that caspase-2 caused the death of retinal ganglion cells (RGC) after blunt ocular trauma. Here, we investigated if caspase-2 also mediates RGC apoptosis in a mouse model of air blast induced indirect traumatic optic neuropathy (b-ITON). C57BL/6 mice were exposed to repeated blasts of overpressure air (3 × 2 × 15 psi) and intravitreal injections of siRNA against caspase-2 (siCASP2) or against a control enhanced green fluorescent protein (siEGFP) at either 5 h after the first 2 × 15 psi (“post-blast”) or 48 h before the first blast exposure (“pre-blast”) and repeated every 7 days. RGC counts were unaffected by the b-ITON or intravitreal injections, despite increased degenerating ON axons, even in siCASP2 “post-blast” injection groups. Degenerating ON axons remained at sham levels after b-ITON and intravitreal siCASP2 “pre-blast” injections, but with less degenerating axons in siCASP2 compared to siEGFP-treated eyes. Intravitreal injections “post-blast” caused greater vitreous inflammation, potentiated by siCASP2, with less in “pre-blast” injected eyes, which was abrogated by siCASP2. We conclude that intravitreal injection timing after ocular trauma induced variable retinal and ON pathology, undermining our candidate neuroprotective therapy, siCASP2.
A panel of DNA methylation biomarkers for detection and improving diagnostic efficiency of lung cancer
Authors: Wei Bing , Wu Fengxin , Xing Wenqun , Sun Haibo , Yan Chi , Zhao Chengzhi , Wang Dongqing , Chen Xiaobing , Chen Yanli , Li Mingming , Ma Jie 
Published: August 2021

AbstractLung cancer remains the leading cause of cancer deaths worldwide. Although low-dose spiral computed tomography (LDCT) screening is used for the detection of lung cancer in a high-risk population, false-positive results of LDCT remain a clinical problem. Here, we developed a blood test of a novel panel of three established lung cancer methylation biomarkers for lung cancer detection. Short stature homeobox 2 gene (SHOX2), ras association domain family 1A gene (RASSF1A), and prostaglandin E receptor 4 gene (PTGER4) methylation was analyzed in a training cohort of 351 individuals (197 controls, 154 cases) and validated from an independent cohort of 149 subjects (89 controls, 60 cases). The novel panel biomarkers distinguished between malignant and benign lung disease at high sensitivity and specificity: 87.0% sensitivity [95% CI 80.2–91.5%], 98.0% specificity [95% CI 94.9–99.4%]. Sensitivity in adenocarcinoma, squamous cell carcinoma, small cell lung cancer, and other lung cancer was 89.0%, 87.5%, 85.7%, and 77.8%, respectively. Notably, cancer patients in stage I and II showed high diagnostic sensitivity at 82.5% and 90.5%, respectively. Moreover, the diagnostic efficiency did not show bias toward age, gender, smoking, and the presence of other (nonlung) cancers. The performance of the panel in the validation cohort confirmed the diagnostic value. These findings clearly showed that this panel of DNA methylation biomarkers was effective in detecting lung cancer noninvasively and may provide clinical utility in stand-alone or in combination with current imaging techniques to improve the diagnosis of lung cancer.
Identities, concentrations, and sources of pesticide exposure in pollen collected by managed bees during blueberry pollination
Authors: Graham Kelsey K. , Milbrath Meghan O. , Zhang Yajun , Soehnlen Annuet , Baert Nicolas , McArt Scott , Isaacs Rufus 
Published: August 2021

AbstractBees are critical for crop pollination, but there is limited information on levels and sources of pesticide exposure in commercial agriculture. We collected pollen from foraging honey bees and bumble bees returning to colonies placed in blooming blueberry fields with different management approaches (conventional, organic, unmanaged) and located across different landscape settings to determine how these factors affect pesticide exposure. We also identified the pollen and analyzed whether pesticide exposure was correlated with corbicular load composition. Across 188 samples collected in 2 years, we detected 80 of the 259 pesticide active ingredients (AIs) screened for using a modified QuEChERS method. Detections included 28 fungicides, 26 insecticides, and 21 herbicides. All samples contained pesticides (mean = 22 AIs per pollen sample), with pollen collected from bees on conventional fields having significantly higher average concentrations (2019 mean = 882.0 ppb) than those on unmanaged fields (2019 mean = 279.6 ppb). Pollen collected by honey bees had more AIs than pollen collected by bumble bees (mean = 35 vs. 19 AIs detected at each farm, respectively), whereas samples from bumble bees had higher average concentrations, likely reflecting differences in foraging behavior. Blueberry pollen was more common in pollen samples collected by bumble bees (25.9% per sample) than honey bees (1.8%), though pesticide concentrations were only correlated with blueberry pollen for honey bees. Pollen collected at farms with more blueberry in the surrounding landscape had higher pesticide concentrations, mostly AIs applied for control of blueberry pathogens and pests during bloom. However, for honey bees, the majority of AIs detected at each farm are not registered for use on blueberry at any time (55.2% of AIs detected), including several highly toxic insecticides. These AIs therefore came from outside the fields and farms they are expected to pollinate. For bumble bees, the majority of AIs detected in their pollen are registered for use on blueberry during bloom (56.9% of AIs detected), though far fewer AIs were sprayed at the focal farm (16.7%). Our results highlight the need for integrated farm and landscape-scale stewardship of pesticides to reduce exposure to pollinators during crop pollination.
Intermediate ice scour disturbance is key to maintaining a peak in biodiversity within the shallows of the Western Antarctic Peninsula
Authors: Robinson B. J. O. , Barnes D. K. A. , Grange L. J. , Morley S. A. 
Published: August 2021

AbstractClimate-related disturbance regimes are changing rapidly with profound consequences for ecosystems. Disturbance is often perceived as detrimental to biodiversity; however, the literature is divided on how they influence each other. Disturbance events in nature are diverse, occurring across numerous interacting trophic levels and multiple spatial and temporal scales, leading to divergence between empirical and theoretical studies. The shallow Antarctic seafloor has one of the largest disturbance gradients on earth, due to iceberg scouring. Scour rates are changing rapidly along the Western Antarctic Peninsula because of climate change and with further changes predicted, the Antarctic benthos will likely undergo dramatic shifts in diversity. We investigated benthic macro and megafaunal richness across 10–100 m depth range, much of which, 40–100 m, has rarely been sampled. Macro and megafauna species richness peaked at 50–60 m depth, a depth dominated by a diverse range of sessile suspension feeders, with an intermediate level of iceberg disturbance. Our results show that a broad range of disturbance values are required to detect the predicted peak in biodiversity that is consistent with the Intermediate Disturbance Hypothesis, suggesting ice scour is key to maintaining high biodiversity in Antarctica’s shallows.
Biomimetic reduced graphene oxide coated collagen scaffold for in situ bone regeneration
Authors: Bahrami Sajad , Baheiraei Nafiseh , Shahrezaee Mostafa 
Published: August 2021

AbstractA variety of bone-related diseases and injures and limitations of traditional regeneration methods require new tissue substitutes. Tissue engineering and regeneration combined with nanomedicine can provide different natural or synthetic and combined scaffolds with bone mimicking properties for implantation in the injured area. In this study, we synthesized collagen (Col) and reduced graphene oxide coated collagen (Col-rGO) scaffolds, and we evaluated their in vitro and in vivo effects on bone tissue repair. Col and Col-rGO scaffolds were synthesized by chemical crosslinking and freeze-drying methods. The surface topography, and the mechanical and chemical properties of scaffolds were characterized, showing three-dimensional (3D) porous scaffolds and successful coating of rGO on Col. The rGO coating enhanced the mechanical strength of Col-rGO scaffolds to a greater extent than Col scaffolds by 2.8 times. Furthermore, Col-rGO scaffolds confirmed that graphene addition induced no cytotoxic effects and enhanced the viability and proliferation of human bone marrow-derived mesenchymal stem cells (hBMSCs) with 3D adherence and expansion. Finally, scaffold implantation into rabbit cranial bone defects for 12 weeks showed increased bone formation, confirmed by Hematoxylin–Eosin (H&E) and alizarin red staining. Overall, the study showed that rGO coating improves Col scaffold properties and could be a promising implant for bone injuries.
Nutrient-related metabolite profiles explain differences in body composition and size in Nile tilapia (Oreochromis niloticus) from different lakes
Authors: Bayissa Tokuma Negisho , Geerardyn Michelle , Vanhauteghem Donna , Wakjira Mulugeta , Janssens Geert Paul Jules 
Published: August 2021

AbstractThis study investigated how metabolite analysis can explain differences in tissue composition and size in fish from different habitats. We, therefore, studied Nile tilapia (Oreochromis niloticus) from three Ethiopian lakes (Gilgel Gibe, Ziway, and Langano) using dried bloodspot (DBS) analysis of carnitine esters and free amino acids. A total of sixty (N = 60) Nile tilapia samples were collected comprising twenty (n = 20) fish from each lake. The proximate composition of the targeted tissues (muscle, skin, gill, gut, and liver) were analyzed. The DBS samples were analyzed for acylcarnitine and free amino acid profiles using quantitative electrospray tandem mass spectrometry. Metabolite ratios were calculated from relevant biochemical pathways that could identify relative changes in nutrient metabolism. The mean weight of Nile tilapia sampled from each lake showed weight variation among the lakes, fish from Lake Ziway were largest (178 g), followed by Gilgel Gibe reservoir (134 g) and Lake Langano (118 g). Fish from Gilgel Gibe showed significantly higher fat composition in all tissues (P < 0.05) except the liver in which no significant variation was observed. The source of fish affected the tissue fat composition. Marked differences were observed in Nile tilapia metabolic activity between the lakes. For instance, the lower body weight and condition of the fish in Lake Langano coincided with several metabolite ratios pointing to a low flow of glucogenic substrate to the citric acid cycle. The low propionyl to acetylcarnitine ratio (C3:C2) in Gilgel Gibe fish is indicating that more of the available acetyl CoA is not led into the citric acid cycle, but instead will be used for fat synthesis. The metabolic markers for lipogenesis and metabolic rate could explain the high-fat concentration in several parts of the body composition of fish from Gilgel Gibe. Our results show that nutrition-related blood metabolite ratios are useful to understand the underlying metabolic events leading to the habitat-dependent differences in the growth of Nile tilapia, and by extension, other species.
A machine learning approach for the factorization of psychometric data with application to the Delis Kaplan Executive Function System
Authors: Camilleri J. A. , Eickhoff S. B. , Weis S. , Chen J. , Amunts J. , Sotiras A. , Genon S. 
Published: August 2021

AbstractWhile a replicability crisis has shaken psychological sciences, the replicability of multivariate approaches for psychometric data factorization has received little attention. In particular, Exploratory Factor Analysis (EFA) is frequently promoted as the gold standard in psychological sciences. However, the application of EFA to executive functioning, a core concept in psychology and cognitive neuroscience, has led to divergent conceptual models. This heterogeneity severely limits the generalizability and replicability of findings. To tackle this issue, in this study, we propose to capitalize on a machine learning approach, OPNMF (Orthonormal Projective Non-Negative Factorization), and leverage internal cross-validation to promote generalizability to an independent dataset. We examined its application on the scores of 334 adults at the Delis–Kaplan Executive Function System (D-KEFS), while comparing to standard EFA and Principal Component Analysis (PCA). We further evaluated the replicability of the derived factorization across specific gender and age subsamples. Overall, OPNMF and PCA both converge towards a two-factor model as the best data-fit model. The derived factorization suggests a division betweenlow-levelandhigh-levelexecutive functioning measures, a model further supported in subsamples. In contrast, EFA, highlighted a five-factor model which reflects the segregation of the D-KEFS battery into its main tasks while still clustering higher-level tasks together. However, this model was poorly supported in the subsamples. Thus, the parsimonious two-factors model revealed by OPNMF encompasses the more complex factorization yielded by EFA while enjoying higher generalizability. Hence, OPNMF provides a conceptually meaningful, technically robust, and generalizable factorization for psychometric tools.
Plasma fibrinogen, d-dimer, and fibrin degradation product as biomarkers of rheumatoid arthritis
Authors: Xue Li , Tao Li , Li Xueyi , Wang Yan , Wang Biao , Zhang Yanping , Gao Ning , Dong Yanying , Xu Nan , Xiong Chaoliang , Zhou Ting , Liu Zeshi , Liu Hailong , He Juntao , Li Ke , Geng Yan , Li Ming 
Published: August 2021

AbstractThis study aimed to assess the association of coagulation-related indicators such as plasma fibrinogen (FIB), d-dimer, and fibrin degradation product (FDP) in rheumatoid arthritis (RA) with the disease activity. Data from 105 RA patients and 102 age- and gender-matched healthy controls were collected in the retrospective study. Disease activity score in 28 joints based on C-reactive protein (DAS28-CRP) was used to divide RA patients into low activity group (DAS28-CRP ≤ 2.7) and active group (DAS28-CRP > 2.7). Receiver operating characteristic (ROC) curve was applied to determine area under the curve (AUC). The association between plasma FIB, d-dimer, and FDP and DAS28-CRP was evaluated by spearman correlation. Logistical regression analysis was used to identify the independent variables associated with RA disease activity. RA patients showed higher levels of plasma FIB, d-dimer, and FDP than the controls (P < 0.01). Plasma FIB, d-dimer, and FDP were also increased in active groups of RA patients than those in inactive groups (P < 0.001). ROC curve analyses revealed that the AUC of d-dimer was higher than erythrocyte sedimentation rate (ESR) and rheumatoid factor (RF), and that of FDP was higher than RF in RA patients. In addition, the optimal cut-off value of plasma FIB, d-dimer, and FDP for RA diagnosis was 286 mg/dL, 470 μg/L, and 1.45 mg/L, respectively. Spearman analysis showed that plasma FIB, d-dimer, and FDP were positively related with DAS28-CRP (P < 0.001) in RA patients. Logistical regression analysis showed that d-dimer (odds ratio 2.862, 95% confidence interval 1.851–5.426, P < 0.001) was an independent variable associated with RA disease activity. FIB, d-dimer, and FDP were increased in RA patients and positively correlated with the disease activity of RA. d-dimer may act as a novel inflammatory indice for indicating disease activity in RA patients.
Trace proteinuria as a risk factor for cancer death in a general population
Authors: Matsui Masaru , Tsuruya Kazuhiko , Yoshida Hisako , Iseki Kunitoshi , Fujimoto Shouichi , Konta Tsuneo , Moriyama Toshiki , Yamagata Kunihiro , Narita Ichiei , Kasahara Masato , Shibagaki Yugo , Kondo Masahide , Asahi Koichi , Watanabe Tsuyoshi 
Published: August 2021

AbstractGrowing evidence has demonstrated an association between nondialysis chronic kidney disease and cancer incidence, although the association between trace proteinuria and cancer death remains unclear. The aim of this study was to investigate the association between trace proteinuria and cancer death in a community-based population in Japan. This was a prospective cohort study of 377,202 adults who participated in the Japanese Specific Health Check and Guidance System from 2008 to 2011. Exposure was dipstick proteinuria categorized as − (negative), ± (trace), 1 + (mild), or ≥ 2 + (moderate to heavy). Outcome was cancer death based on information from the national database of death certificates. Adjusted Cox hazard regression model was used to evaluate the associations between trace proteinuria and cancer death. During median follow-up of 3.7 years, 3056 cancer deaths occurred, corresponding to overall cancer death rate of 21.7/10,000 person-years. In the fully adjusted model, risk of cancer death increased significantly in each successive category of proteinuria: hazard ratio (HR) (95% confidence interval [95% CI]) for risk of cancer death was 1.16 (1.03–1.31), 1.47 (1.27–1.70), and 1.61 (1.33–1.96) for trace, mild, and moderate to heavy proteinuria, respectively. Sensitivity analyses revealed a similar association between trace proteinuria and cancer death, and participants with trace proteinuria had greater risk of mortality from hematological cancers (HR: 1.59 [95% CI: 1.09–2.31]). Both mild to heavy and trace proteinuria were significantly associated with risk of mortality from cancer in a general population.
Tightly linked Rps12 and Rps13 genes provide broad-spectrum Phytophthora resistance in soybean
Authors: Sahoo Dipak K. , Das Anindya , Huang Xiaoqiu , Cianzio Silvia , Bhattacharyya Madan K. 
Published: August 2021

AbstractThe Phytophtora root and stem rot is a serious disease in soybean. It is caused by the oomycete pathogen Phytophthora sojae. Growing Phytophthora resistant cultivars is the major method of controlling this disease. Resistance is race- or gene-specific; a single gene confers immunity against only a subset of the P. sojae isolates. Unfortunately, rapid evolution of new Phytophthora sojae virulent pathotypes limits the effectiveness of an Rps (“resistance to Phytophthora sojae”) gene to 8–15 years. The current study was designed to investigate the effectiveness of Rps12 against a set of P. sojae isolates using recombinant inbred lines (RILs) that contain recombination break points in the Rps12 region. Our study revealed a unique Rps gene linked to the Rps12 locus. We named this novel gene as Rps13 that confers resistance against P. sojae isolate V13, which is virulent to recombinants that contains Rps12 but lack Rps13. The genetic distance between the two Rps genes is 4 cM. Our study revealed that two tightly linked functional Rps genes with distinct race-specificity provide broad-spectrum resistance in soybean. We report here the molecular markers for incorporating the broad-spectrum Phytophthora resistance conferred by the two Rps genes in commercial soybean cultivars.
Computer vision based individual fish identification using skin dot pattern
Authors: Cisar Petr , Bekkozhayeva Dinara , Movchan Oleksandr , Saberioon Mohammadmehdi , Schraml Rudolf 
Published: August 2021

AbstractPrecision fish farming is an emerging concept in aquaculture research and industry, which combines new technologies and data processing methods to enable data-based decision making in fish farming. The concept is based on the automated monitoring of fish, infrastructure, and the environment ideally by contactless methods. The identification of individual fish of the same species within the cultivated group is critical for individualized treatment, biomass estimation and fish state determination. A few studies have shown that fish body patterns can be used for individual identification, but no system for the automation of this exists. We introduced a methodology for fully automatic Atlantic salmon (Salmo salar) individual identification according to the dot patterns on the skin. The method was tested for 328 individuals, with identification accuracy of 100%. We also studied the long-term stability of the patterns (aging) for individual identification over a period of 6 months. The identification accuracy was 100% for 30 fish (out of water images). The methodology can be adapted to any fish species with dot skin patterns. We proved that the methodology can be used as a non-invasive substitute for invasive fish tagging. The non-invasive fish identification opens new posiblities to maintain the fish individually and not as a fish school which is impossible with current invasive fish tagging.
Nanobubbles activate anaerobic growth and metabolism of Pseudomonas aeruginosa
Authors: Ito Miu , Sugai Yuichi 
Published: August 2021

AbstractThe effect of nanobubbles on anaerobic growth and metabolism of Pseudomonas aeruginosa was investigated. P. aeruginosa grew earlier in the culture medium containing nanobubbles and the bacterial cell concentration in that culture medium was increased a few times higher compared to the medium without nanobubbles under anaerobic condition. Both gas and protein, which are the metabolites of P. aeruginosa, were remarkably produced in the culture medium containing nanobubbles whereas those metabolites were little detected in the medium without nanobubbles, indicating nanobubbles activated anaerobic growth and metabolism of P. aeruginosa. The carbon dioxide nanobubbles came to be positively charged by adsorbing cations and delivered ferrous ions, one of the trace essential elements for bacterial growth, to the microbial cells, which activated the growth and metabolism of P. aeruginosa. The oxygen nanobubbles activated the activities of P. aeruginosa as an oxygen source.
Polyaza functionalized graphene oxide nanomaterial based sensor for Escherichia coli detection in water matrices
Authors: Rose Lina , Mary X. Anitha , Johnson I. , Srinivasan Ganesh , Priya Lakshmi , Bhagavathsingh Jebasingh 
Published: August 2021

AbstractWater quality is widely discussed owing to its significance in public health due to the inability to access clean water. Waterborne diseases account for the presence of pathogens like Escherichia coli (E. coli) in drinking water in the environmental community. Owing to the rapid increase of such bacterial microorganisms, a cost-effective sensor setup has been developed. Herein, we demonstrate the amine-functionalized graphene oxide (fGO) based 2D nanomaterial used to graft E. coli on its surface. The comparative analysis of the deposition of nanosheets on the glass substrate and PDMS was executed. The impedance variations of GO-based nanosensor at various concentrations of E. coli were performed and their potential difference was recorded. It was observed that the impedance changes inversely with the bacterial concentrations and was fed to the Arduino microcontroller. The experimental setup was standardized for the range of 0.01 Hz to 100 kHz. The obtained analog data was programmed with a microcontroller and the bacterial concentration in colony-forming units was displayed. The real-time analysis showsthe low-level detection of E. coli in aquatic environments. Experiments were conducted using the developed nanosensor to test the efficiency in complex water matrices and whose behavior changes with various physical, chemical, and environmental factors.