bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cytotoxic unsaturated electrophilic compounds commonly target the ubiquitin proteasome system
Authors: Selvaraju Karthik , Mofers Arjan , Pellegrini Paola , Salomonsson Johannes , Ahlner Alexandra , Morad Vivian , Hillert Ellin-Kristina , Espinosa Belen , Arnér Elias S. J. , Jensen Lasse , Malmström Jonas , Turkina Maria V. , D’Arcy Padraig , Walters Michael A. , Sunnerhagen Maria , Linder Stig 
Published: July 2019

AbstractA large number of natural products have been advocated as anticancer agents. Many of these compounds contain functional groups characterized by chemical reactivity. It is not clear whether distinct mechanisms of action can be attributed to such compounds. We used a chemical library screening approach to demonstrate that a substantial fraction (~20%) of cytotoxic synthetic compounds containing Michael acceptor groups inhibit proteasome substrate processing and induce a cellular response characteristic of proteasome inhibition. Biochemical and structural analyses showed binding to and inhibition of proteasome-associated cysteine deubiquitinases, in particular ubiquitin specific peptidase 14 (USP14). The results suggested that compounds bind to a crevice close to the USP14 active site with modest affinity, followed by covalent binding. A subset of compounds was identified where cell death induction was closely associated with proteasome inhibition and that showed significant antineoplastic activity in a zebrafish embryo model. These findings suggest that proteasome inhibition is a relatively common mode of action by cytotoxic compounds containing Michael acceptor groups and help to explain previous reports on the antineoplastic effects of natural products containing such functional groups.
Comparison of biochars derived from different types of feedstock and their potential for heavy metal removal in multiple-metal solutions
Authors: Zhao JingJing , Shen Xin-Jie , Domene Xavier , Alcañiz Josep-Maria , Liao Xing , Palet Cristina 
Published: July 2019

AbstractThree different types of feedstocks and their biochars were used to remove Cr(III), Cd(II), Cu(II) and Pb(II) ions from a mixture of multiple heavy metals. The effect of the initial concentration of heavy metals in solution has been analysed, and kinetics modelling and a comparison of the adsorption capacity of such materials have been performed to elucidate the possible adsorption mechanisms. The results show that the adsorption capacity is dependent on the type of feedstock and on the pyrolysis conditions. The adsorption capacity of the biomass types is ranked as follows: FO (from sewage sludge)>> LO > ZO (both from agriculture biomass waste)>> CO (from wood biomass waste). Biochars, which are the product of the pyrolysis of feedstocks, clearly improve the adsorption efficiency in the case of those derived from wood and agricultural biomasses. Complexation and cation exchange have been found to be the two main adsorption mechanisms in systems containing multiple heavy metals, with cation exchange being the most significant. The pore structure of biomass/biochar cannot be neglected when investigating the adsorption mechanism of each material. All the disposal biomasses presented here are good alternatives for heavy metal removal from wastewaters.
Genome-scale metabolic model of the rat liver predicts effects of diet restriction
Authors: Baloni Priyanka , Sangar Vineet , Yurkovich James T. , Robinson Max , Taylor Scott , Karbowski Christine M. , Hamadeh Hisham K. , He Yudong D. , Price Nathan D. 
Published: July 2019

AbstractMapping network analysis in cells and tissues can provide insights into metabolic adaptations to changes in external environment, pathological conditions, and nutrient deprivation. Here, we reconstructed a genome-scale metabolic network of the rat liver that will allow for exploration of systems-level physiology. The resultingin silicomodel (iRatLiver) contains 1,882 reactions, 1,448 metabolites, and 994 metabolic genes. We then used this model to characterize the response of the liver’s energy metabolism to a controlled perturbation in diet. Transcriptomics data were collected from the livers of Sprague Dawley rats at 4 or 14 days of being subjected to 15%, 30%, or 60% diet restriction. These data were integrated with the iRatLiver model to generate condition-specific metabolic models, allowing us to explore network differences under each condition. We observed different pathway usage between early and late time points. Network analysis identified several highly connected “hub” genes (Pklr,Hadha,Tkt,Pgm1,Tpi1, andEno3) that showed differing trends between early and late time points. Taken together, our results suggest that the liver’s response varied with short- and long-term diet restriction. More broadly, we anticipate that the iRatLiver model can be exploited further to study metabolic changes in the liver under other conditions such as drug treatment, infection, and disease.
Repair Effects of Astragalus Polysaccharides with Different Molecular Weights on Oxidatively Damaged HK-2 Cells
Authors: Han Jin , Guo Da , Sun Xin-Yuan , Wang Jian-Min , Ouyang Jian-Ming , Gui Bao-Song 
Published: July 2019

AbstractThis study investigated the repair effects of three Astragalus polysaccharides (APSs) with different molecular weights (Mws) on injured human renal proximal tubular epithelial (HK-2) cells to reveal the effect of Mw of polysaccharide on cell repair. A damage model was established by injuring HK-2 cells with 2.6 mM oxalate, and APS0, APS1, and APS2 with Mw of 11.03, 4.72, and 2.61 KDa were used to repair the damaged cells. After repair by APSs, the morphology of damaged HK-2 cells gradually returned to normal, the destruction of intercellular junctions recovered, intracellular reactive oxygen species production amount decreased, and their mitochondrial membrane potential increased. In addition, the cell cycle progression gradually normalized, lysosome integrity increased, and cell apoptotic rates obviously declined in the repaired cells. All three APSs could promote the expression of Keap1, Nrf2, SOD1, and CAT. In addition, the expression levels of inflammation markers containing MCP-1 and IL-6 decreased after APS repair. We deduced that APSs exert their repair function by activating the Nrf2–Keap1 signaling pathway and inhibiting inflammation. Among the APSs, APS1 with a moderate Mw provided the strongest repair effect. APSs may have a preventive effect on kidney stones.
Role of corticotropin-releasing factor on bladder function in rats with psychological stress
Authors: Seki Masaya , Zha Xin-Min , Inamura So , Taga Minekatsu , Matsuta Yosuke , Aoki Yoshitaka , Ito Hideaki , Yokoyama Osamu 
Published: July 2019

AbstractStress-related peptide corticotropin-releasing factor (CRF) and CRF-related peptides are distributed in the peripheral viscera such as the bladder. We investigated the contribution of psychological stress (PS) and CRF on bladder function. Male rats received sham stress (SS) or PS using a communication box method for 120 min every day for 7 days. One group of rats received the intraperitoneal CRF-R1 antagonist antalarmin for 7 days during stress exposure. Mean voided volume per micturition was significantly lower in PS rats compared to SS rats, which was antagonized by antalarmin treatment. Increases in plasma and bladder CRF, and mRNA expressions of bladder CRF, CRF-R1, and M2/3 muscarinic receptors, were found in PS rats. CRF did not influence bladder contraction in itself; however, stress increased the response of muscarinic contraction of bladder strips. These changes were antagonized by antalarmin treatment. In conclusion, PS reinforces M3 receptor-mediated contractions via CRF-R1, resulting in bladder storage dysfunction.
Proteomic study of the membrane components of signalling cascades of Botrytis cinerea controlled by phosphorylation
Authors: Escobar-Niño Almudena , Liñeiro Eva , Amil Francisco , Carrasco Rafael , Chiva Cristina , Fuentes Carlos , Blanco-Ulate Barbara , Cantoral Fernández Jesús M. , Sabidó Eduard , Fernández-Acero Francisco Javier 
Published: July 2019

AbstractProtein phosphorylation and membrane proteins play an important role in the infection of plants by phytopathogenic fungi, given their involvement in signal transduction cascades. Botrytis cinerea is a well-studied necrotrophic fungus taken as a model organism in fungal plant pathology, given its broad host range and adverse economic impact. To elucidate relevant events during infection, several proteomics analyses have been performed in B. cinerea, but they cover only 10% of the total proteins predicted in the genome database of this fungus. To increase coverage, we analysed by LC-MS/MS the first-reported overlapped proteome in phytopathogenic fungi, the “phosphomembranome” of B. cinerea, combining the two most important signal transduction subproteomes. Of the 1112 membrane-associated phosphoproteins identified, 64 and 243 were classified as exclusively identified or overexpressed under glucose and deproteinized tomato cell wall conditions, respectively. Seven proteins were found under both conditions, but these presented a specific phosphorylation pattern, so they were considered as exclusively identified or overexpressed proteins. From bioinformatics analysis, those differences in the membrane-associated phosphoproteins composition were associated with various processes, including pyruvate metabolism, unfolded protein response, oxidative stress response, autophagy and cell death. Our results suggest these proteins play a significant role in the B. cinerea pathogenic cycle.
Detecting Retinal Nerve Fibre Layer Segmentation Errors on Spectral Domain-Optical Coherence Tomography with a Deep Learning Algorithm
Authors: Jammal Alessandro A. , Thompson Atalie C. , Ogata Nara G. , Mariottoni Eduardo B. , Urata Carla N. , Costa Vital P. , Medeiros Felipe A. 
Published: July 2019

AbstractIn this study we developed a deep learning (DL) algorithm that detects errors in retinal never fibre layer (RNFL) segmentation on spectral-domain optical coherence tomography (SDOCT) B-scans using human grades as the reference standard. A dataset of 25,250 SDOCT B-scans reviewed for segmentation errors by human graders was randomly divided into validation plus training (50%) and test (50%) sets. The performance of the DL algorithm was evaluated in the test sample by outputting a probability of having a segmentation error for each B-scan. The ability of the algorithm to detect segmentation errors was evaluated with the area under the receiver operating characteristic (ROC) curve. Mean DL probabilities of segmentation error in the test sample were 0.90 ± 0.17 vs. 0.12 ± 0.22 (P < 0.001) for scans with and without segmentation errors, respectively. The DL algorithm had an area under the ROC curve of 0.979 (95% CI: 0.974 to 0.984) and an overall accuracy of 92.4%. For the B-scans with severe segmentation errors in the test sample, the DL algorithm was 98.9% sensitive. This algorithm can help clinicians and researchers review images for artifacts in SDOCT tests in a timely manner and avoid inaccurate diagnostic interpretations.
Exploration of different classes of metrics to characterize motor variability during repetitive symmetric and asymmetric lifting tasks
Authors: Sedighi Alireza , Nussbaum Maury A. 
Published: July 2019

AbstractThe substantial kinematic degrees-of-freedom available in human movement lead to inherent variations in a repetitive movement, or motor variability (MV). Growing evidence suggests that characterizing MV permits a better understanding of potential injury mechanisms. Several diverse methods, though, have been used to quantify MV, but limited evidence exists regarding the merits of these methods in the occupational context. In this work, we explored different classes of methods for characterizing MV during symmetric and asymmetric box lifting tasks. Kinematic MV of both the whole-body center-of-mass (COM) and the box were quantified, using metrics derived from a linear method (Standard Deviation), a non-linear method (Sample Entropy; an index of movement regularity), and a novel application of an equifinality method (Goal Equivalent Manifold; an index related to the set of effective motor solutions). Our results suggest that individuals manipulate regularity and the set of effective motor solutions to overcome unwanted motor noises related to the COM. These results, together with earlier evidence, imply that individuals may prioritize stability over variability with increasing task difficulty. Task performance also appeared to deteriorate with decreasing variability and regularity of the COM. We conclude that diverse metrics of MV may be complimentary to reveal differences in MV.
Large-Scale Channel Migration in the Sittang River Estuary
Authors: Shimozono T. , Tajima Y. , Akamatsu S. , Matsuba Y. , Kawasaki A. 
Published: July 2019

AbstractAn estuary is a dynamic environment where marine and fluvial processes meet to form complex and transient morphology. The estuary morphology is largely determined by net sediment transport by two-way tidal flows, but the hydrodynamics also depends on the morphology of the tidal channels. The estuary inherently accommodates cyclic processes that are internally generated through hydro-morphodynamic interactions. In addition, the estuary evolves in response to changes in external forces by natural and anthropogenic factors. Morphological changes under the different controls often hinder the comprehension of the evolutionary processes of estuaries. Here we explored morphological changes in the Sittang River estuary, Myanmar, which has great morphological dynamism from extreme tidal energy and large sediment inputs, through field surveys and satellite imagery analysis. We identify an autocyclic process in a sedimentary system driving large-scale channel migration in decadal to multidecadal cycles. We show that drastic changes of the estuary morphology occasionally occur with rapid bank erosion through modulation of the cyclic channel migration under conflicting tidal and fluvial forces. This extreme case with minimal human intervention highlights channel migration as a key process in morphological evolution of tide-dominated estuaries undergoing active infilling.
Visual and kinesthetic modes affect motor imagery classification in untrained subjects
Authors: Chholak Parth , Niso Guiomar , Maksimenko Vladimir A. , Kurkin Semen A. , Frolov Nikita S. , Pitsik Elena N. , Hramov Alexander E. , Pisarchik Alexander N. 
Published: July 2019

AbstractThe understanding of neurophysiological mechanisms responsible for motor imagery (MI) is essential for the development of brain-computer interfaces (BCI) and bioprosthetics. Our magnetoencephalographic (MEG) experiments with voluntary participants confirm the existence of two types of motor imagery, kinesthetic imagery (KI) and visual imagery (VI), distinguished by activation and inhibition of different brain areas in motor-related α- and β-frequency regions. Although the brain activity corresponding to MI is usually observed in specially trained subjects or athletes, we show that it is also possible to identify particular features of MI in untrained subjects. Similar to real movement, KI implies muscular sensation when performing an imaginary moving action that leads to event-related desynchronization (ERD) of motor-associated brain rhythms. By contrast, VI refers to visualization of the corresponding action that results in event-related synchronization (ERS) of α- and β-wave activity. A notable difference between KI and VI groups occurs in the frontal brain area. In particular, the analysis of evoked responses shows that in all KI subjects the activity in the frontal cortex is suppressed during MI, while in the VI subjects the frontal cortex is always active. The accuracy in classification of left-arm and right-arm MI using artificial intelligence is similar for KI and VI. Since untrained subjects usually demonstrate the VI imagery mode, the possibility to increase the accuracy for VI is in demand for BCIs. The application of artificial neural networks allows us to classify MI in raising right and left arms with average accuracy of 70% for both KI and VI using appropriate filtration of input signals. The same average accuracy is achieved by optimizing MEG channels and reducing their number to only 13.
Border Trap Extraction with Capacitance- Equivalent Thickness to Reflect the Quantum Mechanical Effect on Atomic Layer Deposition High-k/In0.53Ga0.47As on 300-mm Si Substrate
Authors: Rahman Md. Mamunur , Kim Jun-Gyu , Kim Dae-Hyun , Kim Tae-Woo 
Published: July 2019

AbstractThis study presents a model to calculate the border trap density (Nbt) of atomic layer deposition high-k onto In0.53Ga0.47As on a 300-mm (001) Si substrate. This model considers the quantum confinement effect and band nonparabolicity. Capacitance-equivalent thickness (CET) was used to reflect the distance of the charge centroid from the oxide–semiconductor interface. The border trap values based on CET were found to be approximately 65% lower than the extracted values based on physical thickness in the In0.53Ga0.47As material. In an investigation of two different post-metal annealing effects on border traps, the border trap was more effectively passivated by N2-based forming gas annealing (FGA) compared with rapid thermal annealing (RTA), whereas a lower interface state density was observed in RTA-annealed samples compared with FGA-annealed samples. Nbt extraction at different bias voltages demonstrated that the applied frequencies travel deep into the oxide and interact with more traps as more the Fermi level passes the conduction band, thus creating tunneling with the carriers.
Selective response of the nucleus taeniae of the amygdala to a naturalistic social stimulus in visually naive domestic chicks
Authors: Mayer Uwe , Rosa-Salva Orsola , Loveland Jasmine L. , Vallortigara Giorgio 
Published: July 2019

AbstractThe detection of animate beings at the onset of life is important for phylogenetically distant species, such as birds and primates. Naïve chicks preferentially approach a stimulus resembling a conspecific (a stuffed fowl) over a less naturalistic one (a scrambled version of the stuffed fowl, presenting the same low-level visual features as the fowl in an unnatural configuration). The neuronal mechanisms underlying this behavior are mostly unknown. However, it has been hypothesized that innate social predispositions may involve subpallial brain areas including the amygdala. Here we asked whether a stuffed hen would activate areas of the arcopallium/amygdala complex, in particular the nucleus taeniae of the amygdala (TnA) or septum. We measured brain activity by visualizing the immediate early gene product c-Fos. After exposure to the hen, TnA showed higher density of c-Fos expressing neurons, compared to chicks that were exposed to the scrambled stimulus. A similar trend was present in the lower portion of the arcopallium, but not in the upper portion of the arcopallium or in the septum. This demonstrates that at birth the TnA is already engaged in responses to social visual stimuli, suggesting an important role for this nucleus in the early ontogenetic development of social behavior.
Genetic deletion of calcium/calmodulin-dependent protein kinase type II delta does not mitigate adverse myocardial remodeling in volume-overloaded hearts
Authors: Mohamed Belal A. , Elkenani Manar , Jakubiczka-Smorag Joanna , Buchholz Eric , Koszewa Sabrina , Lbik Dawid , Schnelle Moritz , Hasenfuss Gerd , Toischer Karl 
Published: July 2019

AbstractCalcium/calmodulin-dependent protein kinase type II delta (CaMKIIδ), the predominant CaMKII isoform expressed in the heart, has been implicated in the progression of myocardial infarction- and pressure overload-induced pathological remodeling. However, the role of CaMKIIδ in volume overload (VO) has not been explored. We have previously reported an activation of CaMKII during transition to HF in long-term VO. Here, we address whether CaMKIIδ is critically involved in the mortality, myocardial remodeling, and heart failure (HF) progression in response to VO. CaMKIIδ knockout (δ-KO) and wild-type (WT) littermates were exposed to aortocaval shunt-induced VO, and the progression of adverse myocardial remodeling was assessed by serial echocardiography, histological and molecular analyses. The mortality rates during 10 weeks of VO were similar in δ-KO and WT mice. Both genotypes displayed comparable eccentric myocardial hypertrophy, altered left ventricle geometry, perturbed systolic and diastolic functions after shunt. Additionally, cardiomyocytes hypertrophy, augmented myocyte apoptosis, and up-regulation of hypertrophic genes were also not significantly different in δ-KO versus WT hearts after shunt. Therefore, CaMKIIδ signaling seems to be dispensable for the progression of VO-induced maladaptive cardiac remodeling. Accordingly, we hypothesize that CaMKIIδ-inhibition as a therapeutic approach might not be helpful in the context of VO-triggered HF.
Predicting Protein-Protein Interactions from Matrix-Based Protein Sequence Using Convolution Neural Network and Feature-Selective Rotation Forest
Authors: Wang Lei , Wang Hai-Feng , Liu San-Rong , Yan Xin , Song Ke-Jian 
Published: July 2019

AbstractProtein is an essential component of the living organism. The prediction of protein-protein interactions (PPIs) has important implications for understanding the behavioral processes of life, preventing diseases, and developing new drugs. Although the development of high-throughput technology makes it possible to identify PPIs in large-scale biological experiments, it restricts the extensive use of experimental methods due to the constraints of time, cost, false positive rate and other conditions. Therefore, there is an urgent need for computational methods as a supplement to experimental methods to predict PPIs rapidly and accurately. In this paper, we propose a novel approach, namely CNN-FSRF, for predicting PPIs based on protein sequence by combining deep learning Convolution Neural Network (CNN) with Feature-Selective Rotation Forest (FSRF). The proposed method firstly converts the protein sequence into the Position-Specific Scoring Matrix (PSSM) containing biological evolution information, then uses CNN to objectively and efficiently extracts the deeply hidden features of the protein, and finally removes the redundant noise information by FSRF and gives the accurate prediction results. When performed on the PPIs datasets Yeast and Helicobacter pylori, CNN-FSRF achieved a prediction accuracy of 97.75% and 88.96%. To further evaluate the prediction performance, we compared CNN-FSRF with SVM and other existing methods. In addition, we also verified the performance of CNN-FSRF on independent datasets. Excellent experimental results indicate that CNN-FSRF can be used as a useful complement to biological experiments to identify protein interactions.