bluuebunny/crossref_metadata_2025_split huggingface dataset:
Black pepper and tarragon essential oils suppress the lipolytic potential and the type II secretion system of P. psychrophila KM02
Authors: Tomaś Natalia , Myszka Kamila , Wolko Łukasz 
Published: March 2022

AbstractGiven the increasing consumer demand for raw, nonprocessed, safe, and long shelf-life fish and seafood products, research concerning the application of natural antimicrobials as alternatives to preservatives is of great interest. The aim of the following paper was to evaluate the effect of essential oils (EOs) from black pepper (BPEO) and tarragon (TEO), and their bioactive compounds: limonene (LIM), β-caryophyllene (CAR), methyl eugenol (ME), and β-phellandrene (PHE) on the lipolytic activity and type II secretion system (T2SS) of Pseudomonas psychrophila KM02 (KM02) fish isolates grown in vitro and in fish model conditions. Spectrophotometric analysis with the p-NPP reagent showed inhibition of lipolysis from 11 to 46%. These results were confirmed by RT-qPCR, as the expression levels of lipA, lipB, and genes encoding T2SS were also considerably decreased. The supplementation of marinade with BPEO and TEO contributed to KM02 growth inhibition during vacuum packaging of salmon fillets relative to control samples. Whole-genome sequencing (WGS) provided insight into the spoilage potential of KM02, proving its importance as a spoilage microorganism whose metabolic activity should be inhibited to maintain the quality and safety of fresh fish in the food market.
The gap between perceived mental health needs and actual service utilization in Australian adolescents
Authors: Islam Md Irteja , Yunus Fakir Md , Isha Samia Naz , Kabir Enamul , Khanam Rasheda , Martiniuk Alexandra 
Published: March 2022

AbstractDespite being highly prevalent, adolescent mental health problems are undertreated. To better understand the mental health treatment gap, we assessed the prevalence and correlates of help-seeking, including perceived need for care and access to that care. Data were drawn from Young Minds Matter (YMM) survey—the second Australian child and adolescents survey of mental health and wellbeing. Parent-reported data and self-reported child data were combined into one dataset to analyse 2464 Australian adolescents aged 13–17 years. We employed bivariate and multivariate logistic regression models to assess the correlation between independent variables (professionally assessed with mental disorders only, self-reported self-harm/suicidality only and both) and their distribution over outcome variables (perceived need and service use). Mental disorders include depression, anxiety, ADHD and conduct disorder. Our study revealed 15.0%, 4.6% and 7.7% had professionally assessed with mental disorders only, self-reported self-harm/suicidality only and both, respectively. Overall, 47.4% and 27.5% of adolescents respectively perceived need for care and used services in the past-12-months. While among those only who perceived the need, only 53% of adolescents used any services. Professionally assessed with mental disorders only, self-reported self-harm/suicidality only and both were associated with higher likelihood of perceived need and service use (p < 0.001 for all). However, adolescents who self-reported self-harm/suicidality only were not found to be significantly associated with service use among those who perceived the need for care. Adolescents who perceived the need for mental health care but did not seek care represent a treatment gap. Our results suggest the importance of reducing the wide treatment gap that exists between need and care.
A greedy regression algorithm with coarse weights offers novel advantages
Authors: Jeffries Clark D. , Ford John R. , Tilson Jeffrey L. , Perkins Diana O. , Bost Darius M. , Filer Dayne L. , Wilhelmsen Kirk C. 
Published: March 2022

AbstractRegularized regression analysis is a mature analytic approach to identify weighted sums of variables predicting outcomes. We present a novel Coarse Approximation Linear Function (CALF) to frugally select important predictors and build simple but powerful predictive models. CALF is a linear regression strategy applied to normalized data that uses nonzero weights + 1 or − 1. Qualitative (linearly invariant) metrics to be optimized can be (for binary response) Welch (Student) t-test p-value or area under curve (AUC) of receiver operating characteristic, or (for real response) Pearson correlation. Predictor weighting is critically important when developing risk prediction models. While counterintuitive, it is a fact that qualitative metrics can favor CALF with ± 1 weights over algorithms producing real number weights. Moreover, while regression methods may be expected to change most or all weight values upon even small changes in input data (e.g., discarding a single subject of hundreds) CALF weights generally do not so change. Similarly, some regression methods applied to collinear or nearly collinear variables yield unpredictable magnitude or the direction (in p-space) of the weights as a vector. In contrast, with CALF if some predictors are linearly dependent or nearly so, CALF simply chooses at most one (the most informative, if any) and ignores the others, thus avoiding the inclusion of two or more collinear variables in the model.
Observations on the stress related variations of soil radon concentration in the Gulf of Corinth, Greece
Authors: Karastathis Vassilios K. , Eleftheriou George , Kafatos Menas , Tsinganos Kanaris , Tselentis G-Akis , Mouzakiotis Evangelos , Ouzounov Dimitar 
Published: March 2022

AbstractOur observations indicate a characteristic pattern in the long-term variation of soil radon concentrations, which seems to be consistent with the expected variation of regional stress in relation to seismicity. However, it seems that the major changes in radon level begin before the rock rapture, i.e. before the earthquake occurs. These conclusions have emerged after long-term observations with continuous and thorough real-time gamma-radiation monitoring in the seismically active area of the Gulf of Corinth, Greece. The recordings acquired close to a hot spring were of very high quality, implying that the deep hydraulic flow can possibly play a key role in the pre-earthquake variation of radon level. We were able to observe outstanding examples of radon level variations before significant seismic events in the Gulf of Corinth that cannot be attributed to other external factors such as atmospheric phenomena.
Exploring carbohydrate binding module fusions and Fab fragments in a cellulose-based lateral flow immunoassay for detection of cystatin C
Authors: Natarajan Satheesh , Joseph Jayaraj , França Prazeres Duarte Miguel 
Published: March 2022

AbstractThis paper presents a lateral flow assay (LFA) for the quantitative, fluorescence-based detection of the kidney biomarker cystatin C that features conjugates of capture antibodies and fusions of carbohydrate binding modules (CBM) with ZZ domains anchored on cellulose deposited over nitrocellulose (NC). The ZZ-CBM3 fusion provides a biomolecular interface between the cellulose layer and the Fc portion of the capture antibodies. By resorting to detection Fab fragments that lack the Fc portion we overcome the observed interference of full-length detection antibodies with the ZZ-CBM3 fusion at the test lines. Using the new LFA architecture, a linear concentration–response relationship was observed in the 0–10 ng/mL cystatin C concentration range, which is compatible with the clinically normal (5–120 ng/mL) and abnormal (> 250 ng/mL) levels of cystatin C, as long as proper dilutions are made. An inter assay CoV of 0.72% was obtained. Finally, mock urine samples characteristic of normal (100 ng/mL) and kidney tubular disease (4000 ng/mL) patients were successfully analyzed. Overall, we demonstrate an innovative LFA architecture that combines NC strips with layered cellulose, ZZ-CBM3 fusions and fluorescently labeled Fab fragments.
Coupled online sequential extreme learning machine model with ant colony optimization algorithm for wheat yield prediction
Authors: Ali Mumtaz , Deo Ravinesh C. , Xiang Yong , Prasad Ramendra , Li Jianxin , Farooque Aitazaz , Yaseen Zaher Mundher 
Published: March 2022

AbstractInadequate agricultural planning compounded by inaccurate predictions results in an inflated local market rate and prompts higher importation of wheat. To tackle this problem, this research has designed two-phase universal machine learning (ML) model to predict wheat yield (Wpred), utilizing 27 agricultural counties’ data within the Agro-ecological zone. The universal model, online sequential extreme learning machines coupled with ant colony optimization (ACO-OSELM) is developed, by incorporating the significant annual yield data lagged at (t − 1) as the model’s predictor to generate future yield at 6 test stations. In the first phase, ACO is adopted to search for suitable, statistically relevant data stations for model training, and the corresponding test station by virtue of a feature selection strategy. An annual wheat yield time-series input dataset is constructed utilizing data from each selected training station (1981–2013) and applied against 6 test stations (with each case modelled with 26 station data as the input) to evaluate the hybrid ACO-OSELM model. The partial autocorrelation function is implemented to deduce statistically significant lagged data, and OSELM is applied to generate Wpred. The two-phase hybrid ACO-OSELM model is tested within the 6 agricultural districts (represented as stations) of Punjab province, Pakistan and the results are benchmarked with extreme learning machine (ELM) and random forest (RF) integrated with ACO (i.e., hybrid ACO-ELM and hybrid ACO-RF models, respectively). The performance of the ACO-OSELM model was proven to be good in comparison to ACO-ELM and ACO-RF models. The hybrid ACO-OSELM model revealed its potential to be implemented as a decision-making system for crop yield prediction in areas where a significant association with the historical agricultural crop is well-established.
Half-integer anomalous currents in 2D materials from a QFT viewpoint
Authors: Dudal David , Matusalem Filipe , Mizher Ana Júlia , Rocha Alexandre Reily , Villavicencio Cristian 
Published: March 2022

AbstractCharge carriers in Dirac/Weyl semi-metals exhibit a relativistic-like behavior. In this work we propose a novel type of intrinsic half-integer Quantum Hall effect in 2D materials, thereby also offering a topological protection mechanism for the current. Its existence is rooted in the 2D parity anomaly, without any need for a perpendicular magnetic field. We conjecture that it may occur in disturbed honeycomb lattices where both spin degeneracy and time reversal symmetry are broken. These configurations harbor two distinct gap-opening mechanisms that, when occurring simultaneously, drive slightly different gaps in each valley, causing a net anomalous conductivity when the chemical potential is tuned to be between the distinct gaps. Some examples of promising material setups that fulfill the prerequisites of our proposal are also listed to motivate looking for the effect at the numerical and experimental level.
Prognostic significance of AP-2α/γ targets as cancer therapeutics
Authors: Kołat Damian , Kałuzińska Żaneta , Bednarek Andrzej K. , Płuciennik Elżbieta 
Published: March 2022

AbstractIdentifying genes with prognostic importance could improve cancer treatment. An increasing number of reports suggest the existence of successful strategies based on seemingly “untargetable” transcription factors. In addition to embryogenesis, AP-2 transcription factors are known to play crucial roles in cancer development. Members of this family can be used as prognostic factors in oncological patients, and AP-2α/γ transcription factors were previously investigated in our pan-cancer comparative study using their target genes. The present study investigates tumors that were previously found similar with an emphasis on the possible role of AP-2 factors in specific cancer types. The RData workspace was loaded back to R environment and 3D trajectories were built via Monocle3. The genes that met the requirement of specificity were listed using top_markers(), separately for mutual and unique targets. Furthermore, the candidate genes had to meet the following requirements: correlation with AP-2 factor (through Correlation AnalyzeR) and validated prognostic importance (using GEPIA2 and subsequently KM-plotter or LOGpc). Eventually, the ROC analysis was applied to confirm their predictive value; co-dependence of expression was visualized via BoxPlotR. Some similar tumors were differentiated by AP-2α/γ targets with prognostic value. Requirements were met by only fifteen genes (EMX2, COL7A1, GRIA1, KRT1, KRT14, SLC12A5, SEZ6L, PTPRN, SCG5, DPP6, NTSR1, ARX, COL4A3, PPEF1 and TMEM59L); of these, the last four were excluded based on ROC curves. All the above genes were confronted with the literature, with an emphasis on the possible role played by AP-2 factors in specific cancers. Following ROC analysis, the genes were verified using immunohistochemistry data and progression-related signatures. Staining differences were observed, as well as co-dependence on the expression of e.g. CTNNB1, ERBB2, KRAS, SMAD4, EGFR or MKI67. In conclusion, prognostic value of targets suggested AP-2α/γ as candidates for novel cancer treatment. It was also revealed that AP-2 targets are related to tumor progression and that some mutual target genes could be inversely regulated.
Stent-based delivery of AAV2 vectors encoding oxidation-resistant apoA1
Authors: Hooshdaran Bahman , Pressly Benjamin B. , Alferiev Ivan S. , Smith Jonathan D. , Zoltick Philip W. , Tschabrunn Cory M. , Wilensky Robert L. , Gorman Robert C. , Levy Robert J. , Fishbein Ilia 
Published: March 2022

AbstractIn-stent restenosis (ISR) complicates revascularization in the coronary and peripheral arteries. Apolipoprotein A1 (apoA1), the principal protein component of HDL possesses inherent anti-atherosclerotic and anti-restenotic properties. These beneficial traits are lost when wild type apoA1(WT) is subjected to oxidative modifications. We investigated whether local delivery of adeno-associated viral (AAV) vectors expressing oxidation-resistant apoA1(4WF) preserves apoA1 functionality. The efflux of 3H-cholesterol from macrophages to the media conditioned by endogenously produced apoA1(4WF) was 2.1-fold higher than for apoA1(WT) conditioned media in the presence of hypochlorous acid emulating conditions of oxidative stress. The proliferation of apoA1(WT)- and apoA1(4FW)-transduced rat aortic smooth muscle cells (SMC) was inhibited by 66% ± 10% and 65% ± 11%, respectively, in comparison with non-transduced SMC (p < 0.001). Conversely, the proliferation of apoA1(4FW)-transduced, but not apoA1(WT)-transduced rat blood outgrowth endothelial cells (BOEC) was increased 41% ± 5% (p < 0.001). Both apoA1 transduction conditions similarly inhibited basal and TNFα-induced reactive oxygen species in rat aortic endothelial cells (RAEC) and resulted in the reduced rat monocyte attachment to the TNFα-activated endothelium. AAV2-eGFP vectors immobilized reversibly on stainless steel mesh surfaces through the protein G/anti-AAV2 antibody coupling, efficiently transduced cells in culture modeling stent-based delivery. In vivo studies in normal pigs, deploying AAV2 gene delivery stents (GDS) preloaded with AAV2-eGFP in the coronary arteries demonstrated transduction of the stented arteries. However, implantation of GDS formulated with AAV2-apoA1(4WF) failed to prevent in-stent restenosis in the atherosclerotic vasculature of hypercholesterolemic diabetic pigs. It is concluded that stent delivery of AAV2-4WF while feasible, is not effective for mitigation of restenosis in the presence of severe atherosclerotic disease.
Characterization of aging tumor microenvironment with drawing implications in predicting the prognosis and immunotherapy response in low-grade gliomas
Authors: Zhou Zijian , Wei JinHong , Jiang Wenbo 
Published: March 2022

AbstractAging tumor microenvironment (aging TME) is emerging as a hot spot in cancer research for its significant roles in regulation of tumor progression and tumor immune response. The immune and stromal scores of low-grade gliomas (LGGs) from TCGA and CGGA databases were determined by using ESTIMATE algorithm. Differentially expressed genes (DEGs) between high and low immune/stromal score groups were identified. Subsequently, weighted gene co-expression network analysis (WGCNA) was conducted to screen out aging TME related signature (ATMERS). Based on the expression patterns of ATMERS, LGGs were classified into two clusters with distinct prognosis via consensus clustering method. Afterwards, the aging TME score for each sample was calculated via gene set variation analysis (GSVA). Furthermore, TME components were quantified by MCP counter and CIBERSORT algorithm. The potential response to immunotherapy was evaluated by Tumor Immune Dysfunction and Exclusion analysis. We found that LGG patients with high aging TME scores showed poor prognosis, exhibited an immunosuppressive phenotype and were less likely to respond to immunotherapy compared to those with low scores. The predictive performance of aging TME score was verified in three external datasets. Finally, the expression of ATMERS in LGGs was confirmed at protein level through the Human Protein Atlas website and western blot analysis. This novel aging TME-based scoring system provided a robust biomarker for predicting the prognosis and immunotherapy response in LGGs.
Association between chronic rhinosinusitis and pneumonia: a longitudinal follow-up study using a national health screening cohort
Authors: Wee Jee Hye , Min Chanyang , Jung Hahn Jin , Park Min Woo , Park Bumjung , Choi Hyo Geun 
Published: March 2022

AbstractThis study was aimed to compare the risk of pneumonia between patients with chronic rhinosinusitis (CRS) and those without CRS (control) in a Korean population. The population aged 40 years or over was included from the Korean National Health Insurance Service-Health Screening Cohort. Participants with CRS (n = 6393) and controls (n = 25,572) were selected by 1:4 matching for age, sex, income, region of residence, and history of pneumonia for the previous 1 year. The index date (ID) of the controls was set as the treatment date of their matched CRS participants. The incidence of pneumonia after the ID was measured from 2003 to 2015. Simple and multiple linear regressions were performed to calculate estimated values (EVs) and 95% confidence intervals (CIs) for 1-y post-ID pneumonia, 2-y post-ID pneumonia, and 3-y post-ID pneumonia in CRS participants compared to controls. Statistical significance was noted in the 3-y post-ID period (EV = 0.017, 95% CI = 0.002–0.031, P = 0.030). In the subgroup analyses according to age and sex, statistical significance was seen in the younger age group (< 60 years old) in the 3-y post-ID period and in the female group in the 1-y and 3-y post-ID periods. This study revealed an increased risk for pneumonia following a diagnosis of CRS.
Improving the specificity of nucleic acid detection with endonuclease-actuated degradation
Authors: Zou Roger S. , Gavrilov Momcilo , Liu Yang , Rasoloson Dominique , Conte Madison , Hardick Justin , Shen Leo , Chen Siqi , Pekosz Andrew , Seydoux Geraldine , Manabe Yukari C. , Ha Taekjip 
Published: March 2022

AbstractNucleic acid detection is essential for numerous biomedical applications, but often requires complex protocols and/or suffers false-positive readouts. Here, we describe SENTINEL, an approach that combines isothermal amplification with a sequence-specific degradation method to detect nucleic acids with high sensitivity and sequence-specificity. Target single-stranded RNA or double-stranded DNA molecules are amplified by loop-mediated isothermal amplification (LAMP) and subsequently degraded by the combined action of lambda exonuclease and a sequence-specific DNA endonuclease (e.g., Cas9). By combining the sensitivity of LAMP with the precision of DNA endonucleases, the protocol achieves attomolar limits of detection while differentiating between sequences that differ by only one or two base pairs. The protocol requires less than an hour to complete using a 65 °C heat block and fluorometer, and detects SARS-CoV-2 virus particles in human saliva and nasopharyngeal swabs with high sensitivity.
Non-enzymatic catalytic asymmetric cyanation of acylsilanes
Authors: Nagano Tagui , Matsumoto Akira , Yoshizaki Ryotaro , Asano Keisuke , Matsubara Seijiro 
Published: March 2022

AbstractThe asymmetric cyanation of acylsilanes affords densely functionalized tetrasubstituted chiral carbon centers bearing silyl, cyano, and hydroxy groups, which are of particular interest in synthetic and medicinal chemistry. However, this method has been limited to a few enzymatic approaches, which employ only one substrate because of substrate specificity. Here we show the non-enzymatic catalytic asymmetric cyanation of acylsilanes using a chiral Lewis base as an enantioselective catalyst, trimethylsilyl cyanide as a cyanating reagent, and isopropyl alcohol as an additive to drive catalyst turnover. High enantio- and site-selectivities are achieved in a catalytic manner, and a variety of functional groups are installed in optically active acylsilane cyanohydrins, thus overcoming the limitations imposed by substrate specificity in conventional enzymatic methods. A handle for the synthetic application of the products is also established through the development of a catalyst for protecting acylsilane cyanohydrins, which are unstable and difficult to protect alcohols.
Cooperative circumnavigation control of multiple unicycle‐type robots with non‐identical input constraints
Authors: Yu Yangguang , Miao Zhiqiang , Wang Xiangke , Shen Lincheng 
Published: March 2022

AbstractFor the robot control problems, the input constraint is an issue that must be carefully considered. Considering the control inputs of the robots in the multi‐robot system are constrained by different saturations in reality, we address the cooperative circumnavigation control problem of multiple unicycle‐type robots with non‐identical and unknown input constraints in this paper. A distributed control law is designed to drive the robots constrained by different and unknown input saturations to achieve the cooperative circumnavigation around a moving target. The algorithm proposed in this paper largely reduces the requirement for the maneuverability of the robots to achieve the cooperative circumnavigation. As long as the robots owns the basic maneuverability of circumnavigating around the target, it is proved rigorously that the proposed control algorithm can achieve the desired cooperation circumnavigation of the multi‐robot system. The effectiveness of the proposed method is validated by a numerical simulation.