bluuebunny/crossref_metadata_2025 huggingface dataset:
Wave Theory of Seismic Resistance of Underground Pipelines
Authors: Sultanov Karim Sultanovich , Vatin Nikolai Ivanovich
Published: February 2021

The object of the research is an underground straight horizontal pipeline subjected to seismic impact. The research method was analytical. The results were compared with the experimental results of other authors and computer calculations. It was shown that the main disadvantage of the dynamic theory of seismic resistance of underground pipelines is the neglect of the dynamic stress state in soil under seismic wave propagation. The next drawback of the dynamic theory is an inaccurate, approximate accounting for the displacement of the soil medium to which the underground pipeline is embedded. The complete interaction process includes the stages of nonlinear changes in the interaction force (the friction force) by manifesting its peak value and the Coulomb friction. The contact layer of soil undergoes shear deformations until complete structural destruction of the soil contact layer. The interaction force is the friction force, and its peak value does not appear. The seismic resistance of underground pipelines should be considered based on the theory of propagating seismic waves in a soil medium and the interaction of seismic waves with underground pipelines, i.e., based on the wave theory of seismic resistance of underground pipelines. A one-dimensional coupled problem of seismic resistance of underground pipelines under seismic impacts was posed based on the wave theory. An algorithm and a program for the numerical solution of the stated wave problems were developed using the method of characteristics and the method of finite differences. An analysis of the laws of interaction of underground pipelines with soil under seismic influences shows that it is necessary to use in the calculations the laws of interaction that account for the complete interaction processes observed in experiments. The analysis of the obtained numerical solutions and the posed coupled problems of the wave theory of seismic resistance of underground pipelines show the occurrence mechanisms of longitudinal stresses in underground pipelines under seismic influences. The results of calculations stated that an account for the dynamic stress normal to the underground pipeline’s outer surface leads to multiple increases in longitudinal stress in the underground pipeline. This multiple increase is due to the transformation of the interaction force into an active frictional force, resulting from a greater strain in soil than the one in the underground pipeline. Based on the analysis results, a theory of the seismic wave propagation process in an underground pipeline and surrounding soil was proposed.
Embodied Energy Optimization of Buttressed Earth-Retaining Walls with Hybrid Simulated Annealing
Authors: Martínez-Muñoz David , Martí José V. , García José , Yepes Víctor
Published: February 2021

The importance of construction in the consumption of natural resources is leading structural design professionals to create more efficient structure designs that reduce emissions as well as the energy consumed. This paper presents an automated process to obtain low embodied energy buttressed earth-retaining wall optimum designs. Two objective functions were considered to compare the difference between a cost optimization and an embodied energy optimization. To reach the best design for every optimization criterion, a tuning of the algorithm parameters was carried out. This study used a hybrid simulated optimization algorithm to obtain the values of the geometry, the concrete resistances, and the amounts of concrete and materials to obtain an optimum buttressed earth-retaining wall low embodied energy design. The relation between all the geometric variables and the wall height was obtained by adjusting the linear and parabolic functions. A relationship was found between the two optimization criteria, and it can be concluded that cost and energy optimization are linked. This allows us to state that a cost reduction of €1 has an associated energy consumption reduction of 4.54 kWh. To achieve a low embodied energy design, it is recommended to reduce the distance between buttresses with respect to economic optimization. This decrease allows a reduction in the reinforcing steel needed to resist stem bending. The difference between the results of the geometric variables of the foundation for the two-optimization objectives reveals hardly any variation between them. This work gives technicians some rules to get optimum cost and embodied energy design. Furthermore, it compares designs obtained through these two optimization objectives with traditional design recommendations.
Research on Microstructure of Soft Clay under Various Artificial Ground Freezing Conditions Based on NMR
Authors: Kong Bowen , He Shaoheng , Xia Tangdai , Ding Zhi
Published: February 2021

Nuclear magnetic resonance (NMR) can help to collect soft clay pore size data. This paper uses NMR to study the effect of freezing duration, the gap time between two freezing actions and thawing temperature on the pore size distribution (PSD) in soft clay. It was found that most of the freezing and thawing progress was completed during the first two hours. A longer time gap between the first and second freezing cycles leads to greater structural damage. An appropriate thawing temperature results in less subsidence induced by the artificial ground freezing (AGF). A new method to distinguish water types for various freezing conditions is proposed. This freezing method to determine the cutoff value could fill the defect in the previous methods.
Modification of PLA Scaffold Surface for Medical Applications
Authors: Młotek Michał , Gadomska-Gajadhur Agnieszka , Sobczak Aleksandra , Kruk Aleksandra , Perron Michalina , Krawczyk Krzysztof
Published: February 2021

Materials used for medical applications (e.g., the cellular scaffold) should have not only the specific chemical composition, but the surface layer properties as well. For this reason, a method which enables an increase in the number of pores, wettability of the surface, and improvement the conditions of nutrient transportation into the membrane is being studied. The plasma of a dielectric barrier discharge was applied for the surface modification of polylactide obtained by dry or wet phase inversion. The plasma-modified surface was analyzed by contact angle measurements with water and diiodomethane. The surface free energy (SFE) was calculated by the Owens–Wendt method. The highest SFE and its polar component (67.6 mJ/m2 and 39.5 mJ/m2, respectively) were received when the process was conducted in an Ar + CO2 gas mixture with a discharge power of 20 W. The purpose of this research was to increase the wettability and porosity of the membrane’s surface. It can be concluded that the dielectric barrier discharge can effectively change the surface of the polylactide membranes, and that the structure of the modified membranes was not damaged during modification. The process of modification was easier for the membranes made by dry phase inversion. These materials had higher SFE values after the modification.
A Collaborative Control Method of Dual-Arm Robots Based on Deep Reinforcement Learning
Authors: Liu Luyu , Liu Qianyuan , Song Yong , Pang Bao , Yuan Xianfeng , Xu Qingyang
Published: February 2021

Collaborative control of a dual-arm robot refers to collision avoidance and working together to accomplish a task. To prevent the collision of two arms, the control strategy of a robot arm needs to avoid competition and to cooperate with the other one during motion planning. In this paper, a dual-arm deep deterministic policy gradient (DADDPG) algorithm is proposed based on deep reinforcement learning of multi-agent cooperation. Firstly, the construction method of a replay buffer in a hindsight experience replay algorithm is introduced. The modeling and training method of the multi-agent deep deterministic policy gradient algorithm is explained. Secondly, a control strategy is assigned to each robotic arm. The arms share their observations and actions. The dual-arm robot is trained based on a mechanism of “rewarding cooperation and punishing competition”. Finally, the effectiveness of the algorithm is verified in the Reach, Push, and Pick up simulation environment built in this study. The experiment results show that the robot trained by the DADDPG algorithm can achieve cooperative tasks. The algorithm can make the robots explore the action space autonomously and reduce the level of competition with each other. The collaborative robots have better adaptability to coordination tasks.
Effect of Genetic and Environmental Factors on the Impaction of Lower Third Molars
Authors: Trakinienė Giedrė , Smailienė Dalia , Lopatienė Kristina , Trakinis Tomas , Šidlauskas Antanas
Published: February 2021

The aim of this retrospective study was to evaluate the impact of genetic and environmental factors on the impaction of lower third molars using a classical twin study by evaluation of the third molar eruption space and M3 angulation. The study group consisted of 212 twins: 80 dizygotic and 132 monozygotic twins with digital panoramic radiograms and lateral cephalograms. The zygosity of twins was determined using 15 highly polymorphic DNA regions from the venous blood of twins. The results showed that differences between siblings in the dizygotic and monozygotic twin pairs were not statistically significant. The most fitting model for the angulations of lower third molars was AE (additive genetic and specific environmental effect), where the additive genetic factors had up to 88% influence and the specific environment up to 27%. Therefore, the ACE( additive genetic, common and specific environmental effect), model showed higher significance for the lower third molar eruption level where additive genetic estimates reached 71%, a specific environment contributed up to 15% and the common environment reached 32%. The conclusion was that a considerable proportion of the third molar impaction could be attributed to additive genetic effects and the common environment, whereas the specific environment had a lower, but significant impact.
Self-Attention Network for Human Pose Estimation
Authors: Xia Hailun , Zhang Tianyang
Published: February 2021

Estimating the positions of human joints from monocular single RGB images has been a challenging task in recent years. Despite great progress in human pose estimation with convolutional neural networks (CNNs), a central problem still exists: the relationships and constraints, such as symmetric relations of human structures, are not well exploited in previous CNN-based methods. Considering the effectiveness of combining local and nonlocal consistencies, we propose an end-to-end self-attention network (SAN) to alleviate this issue. In SANs, attention-driven and long-range dependency modeling are adopted between joints to compensate for local content and mine details from all feature locations. To enable an SAN for both 2D and 3D pose estimations, we also design a compatible, effective and general joint learning framework to mix up the usage of different dimension data. We evaluate the proposed network on challenging benchmark datasets. The experimental results show that our method has significantly achieved competitive results on Human3.6M, MPII and COCO datasets.
Persistent Organic Pollutants and Metals in Atmospheric Deposition Rates around the Port-Industrial Area of Civitavecchia, Italy
Authors: Settimo Gaetano , Soggiu Maria Eleonora , Inglessis Marco , Marsili Giovanni , Avino Pasquale
Published: February 2021

In recent years, studies on climate change have focused on reducing greenhouse gas emissions emitted by various civil and industrial processes. This study highlights the importance of characterizing the total deposition rates of airborne particles (bulk atmospheric deposition) in the surroundings of an industrial area along the north cost of the Lazio Region in Italy, to deepen knowledge about the potential impact of emissions from the coal-fired thermoelectric (CTE) power plant and other possible sources existing in the surrounding area. Four sampling sites were identified, and the monitoring plan was performed a yearlong with monthly collecting observation. The deposition samples were collected monthly and processed for determining organic (polychlorinated dibenzo-para-dioxins, PCDDs; polychlorinated dibenzofurans, PCDFs; dioxin-like polychlorinated biphenyls, DL-PCBs; polycyclic aromatic hydrocarbons, PAHs) and inorganic (metals) substances. The samples were collected monthly and sent for chemical characterization. In Europe and Italy, no reference values have been given for the deposition rates of chemicals, while some European countries have determined reference/guide values to which the authors will refer in this study. Therefore, the analytical results show that the deposition rates for PCDD/Fs and DL-PCBs are lower with respects guide values defined by Germany and Belgium; PAHs values are in line with those measured in other rural-type sites, while for metals the analytical results show a situation between rural and urban area. The approach used in this study can help to identify reference values for Italy in deposition rates, with the aim both to characterize the dynamic of pollution in area with multiple risk factors and to describe and protect human health from environmental exposures caused by the contamination of the food chain.
Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks
Authors: Grande Davide , Harris Catherine A. , Thomas Giles , Anderlini Enrico
Published: February 2021

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.
The Effect of Concentrated Microwave Field (CMF) on Selected Physical and Rheological Properties of Liquid Egg Products
Authors: Oziembłowski Maciej , Nawirska-Olszańska Agnieszka , Maksimowski Damian , Trenka Magdalena , Break Artur , Kulig Dominika , Miernik Anna
Published: February 2021

Concentrated microwave field (CMF) is the technology for preserving liquid food products, where the preservation factor is both a nonthermal effect and a moderate thermal effect. The term “concentrated microwave field” is traditionally used in Poland and is the equivalent to “single-mode-type microwave heating”. The aim of the research was to determine selected physical and rheological properties of liquid egg white, liquid yolk and liquid whole eggs (LWE) after CMF treatment. It was found that both the amount of energy supplied to the system and the intervals between CMF pulses had a statistically significant impact on the analyzed properties of liquid egg raw materials. In industrial practice, it would be recommended to use lower CMF treatment (at the level of 2.2–2.6 kJ) for the yolk, medium (3.2–3.6 kJ) for egg white and higher (4.0–4.4 kJ) for the liquid whole eggs. The “response surface” models presented in the paper may have a practical application in determining the influence of individual technological parameters on the final quality of various liquid food products.
EDC-Net: Edge Detection Capsule Network for 3D Point Clouds
Authors: Bazazian Dena , Parés M. Eulàlia
Published: February 2021

Edge features in point clouds are prominent due to the capability of describing an abstract shape of a set of points. Point clouds obtained by 3D scanner devices are often immense in terms of size. Edges are essential features in large scale point clouds since they are capable of describing the shapes in down-sampled point clouds while maintaining the principal information. In this paper, we tackle challenges of edge detection tasks in 3D point clouds. To this end, we propose a novel technique to detect edges of point clouds based on a capsule network architecture. In this approach, we define the edge detection task of point clouds as a semantic segmentation problem. We built a classifier through the capsules to predict edge and non-edge points in 3D point clouds. We applied a weakly-supervised learning approach in order to improve the performance of our proposed method and built in the capability of testing the technique in wider range of shapes. We provide several quantitative and qualitative experimental results to demonstrate the robustness of our proposed EDC-Net for edge detection in 3D point clouds. We performed a statistical analysis over the ABC and ShapeNet datasets. Our numerical results demonstrate the robust and efficient performance of EDC-Net.
Severity Classification of Parkinson’s Disease Based on Permutation-Variable Importance and Persistent Entropy
Authors: Tong Jigang , Zhang Jiachen , Dong Enzeng , Du Shengzhi
Published: February 2021

Parkinson’s disease (PD) is a neurodegenerative disease that causes chronic and progressive motor dysfunction. As PD progresses, patients show different symptoms at different stages of the disease. The severity assessment is inefficient and subjective when it comes to artificial diagnosis. However, abnormal gait was contingent and the subject selection was limited. Therefore, few-shot learning based on small sample sets is critical to solving the problem of insufficient sample data in PD patients. Using datasets from PhysioNet, this paper presents a method based on permutation-variable importance (PVI) and persistent entropy of topological imprints, and uses support vector machine (SVM) as a classifier to achieve the severity classification of PD patients. The method includes the following steps: (1) Take the data as gait cycles, and calculate the gait characteristics of each cycle. (2) Use the random forest (RF) method to obtain the leading factors differentiating the gait of patients at different severity levels. (3) Use time-delay embedding to map the data into a topological space, and use the topological data analysis based on permutation homology to obtain the persistent entropy. (4) Use the Borderline-SMOTE (BSM) method to balance the sample data. (5) Use the SVM to classify the samples for the severity levels of PD. An accuracy of 98.08% was achieved by 10-fold cross-validation, so our method can be used as an effective means of computer-aided diagnosis of PD, and has important practical value.
Using UAV to Detect Solar Module Fault Conditions of a Solar Power Farm with IR and Visual Image Analysis
Authors: Liao Kuo-Chien , Lu Jau-Huai
Published: February 2021

In recent years, solar energy has been regarded as one of the most important sustainable energy sources. Under the rapid and large-scale construction of solar farms, the maintenance and inspection of the health conditions of solar modules in a large solar farm become an important issue. This article proposes a method for detecting solar cell faults with unmanned aerial vehicle (UAV) equipped with a thermal imager and a visible light camera, and providing a fast and reliable detection method. The detection process includes a new concept of real-time monitoring of the detected area and analysis of the health of solar panels. An image process is proposed that may quickly and accurately detect the abnormality of a solar module. The whole process includes grayscale conversion, filtering, 3-D temperature representation, probability density function, and cumulative density function analysis. Ten cases in real fields have been studied with this process, including large scale solar farms and small size solar modules installed on buildings. Results show that the cumulative density function is a convenient way to determine the health status of the solar panel and may provide maintenance personnel a basis for determining whether replacement of solar cells is necessary for improving the overall power generation efficiency and simplify the maintenance process. It is worth noting that image recognition can increase the clarity of IR images and the cumulative chart can judge the defect rate of the cell. These two methods were combined to provide an instant, fast and accurate defect judgment.
Application of Dynamically Constrained Interpolation Methodology in Simulating National-Scale Spatial Distribution of PM2.5 Concentrations in China
Authors: Li Ning , Xu Junli , Lv Xianqing
Published: February 2021

Numerous studies have revealed that the sparse spatiotemporal distributions of ground-level PM2.5 measurements affect the accuracy of PM2.5 simulation, especially in large geographical regions. However, the high precision and stability of ground-level PM2.5 measurements make their role irreplaceable in PM2.5 simulations. This article applies a dynamically constrained interpolation methodology (DCIM) to evaluate sparse PM2.5 measurements captured at scattered monitoring sites for national-scale PM2.5 simulations and spatial distributions. The DCIM takes a PM2.5 transport model as a dynamic constraint and provides the characteristics of the spatiotemporal variations of key model parameters using the adjoint method to improve the accuracy of PM2.5 simulations. From the perspective of interpolation accuracy and effect, kriging interpolation and orthogonal polynomial fitting using Chebyshev basis functions (COPF), which have been proved to have high PM2.5 simulation accuracy, were adopted to make a comparative assessment of DCIM performance and accuracy. Results of the cross validation confirm the feasibility of the DCIM. A comparison between the final interpolated values and observations show that the DCIM is better for national-scale simulations than kriging or COPF. Furthermore, the DCIM presents smoother spatially interpolated distributions of the PM2.5 simulations with smaller simulation errors than the other two methods. Admittedly, the sparse PM2.5 measurements in a highly polluted region have a certain degree of influence on the interpolated distribution accuracy and rationality. To some extent, adding the right amount of observations can improve the effectiveness of the DCIM around existing monitoring sites. Compared with the kriging interpolation and COPF, the results show that the DCIM used in this study would be more helpful for providing reasonable information for monitoring PM2.5 pollution in China.
A Multi-Fidelity Framework for Wildland Fire Behavior Simulations over Complex Terrain
Authors: Vanella Marcos , McGrattan Kevin , McDermott Randall , Forney Glenn , Mell William , Gissi Emanuele , Fiorucci Paolo
Published: February 2021

A method for the large-eddy simulation (LES) of wildfire spread over complex terrain is presented. In this scheme, a cut-cell immersed boundary method (CC-IBM) is used to render the complex terrain, defined by a tessellation, on a rectilinear Cartesian grid. Discretization of scalar transport equations for chemical species is done via a finite volume scheme on cut-cells defined by the intersection of the terrain geometry and the Cartesian cells. Momentum transport and heat transfer close to the immersed terrain are handled using dynamic wall models and a direct forcing immersed boundary method. A new “open” convective inflow/outflow method for specifying atmospheric wind boundary conditions is presented. Additionally, three basic approaches have been explored to model fire spread: (1) Representing the vegetation as a collection of Lagrangian particles, (2) representing the vegetation as a semi-porous boundary, and (3) representing the fire spread using a level set method, in which the fire spreads as a function of terrain slope, vegetation type, and wind speed. Several test and validation cases are reported to demonstrate the capabilities of this novel wildfire simulation methodology.
A Pilot Study for Metabolic Profiling of Obesity-Associated Microbial Gut Dysbiosis in Male Wistar Rats
Authors: Hernandez-Baixauli Julia , Puigbò Pere , Torrell Helena , Palacios-Jordan Hector , Ripoll Vicent J. Ribas , Caimari Antoni , Del Bas Josep M , Baselga-Escudero Laura , Mulero Miquel
Published: February 2021

Obesity is one of the most incident and concerning disease worldwide. Definite strategies to prevent obesity and related complications remain elusive. Among the risk factors of the onset of obesity, gut microbiota might play an important role in the pathogenesis of the disease, and it has received extensive attention because it affects the host metabolism. In this study, we aimed to define a metabolic profile of the segregated obesity-associated gut dysbiosis risk factor. The study of the metabolome, in an obesity-associated gut dysbiosis model, provides a relevant way for the discrimination on the different biomarkers in the obesity onset. Thus, we developed a model of this obesity risk factors through the transference of gut microbiota from obese to non-obese male Wistar rats and performed a subsequent metabolic analysis in the receptor rats. Our results showed alterations in the lipid metabolism in plasma and in the phenylalanine metabolism in urine. In consequence, we have identified metabolic changes characterized by: (1) an increase in DG:34:2 in plasma, a decrease in hippurate, (2) an increase in 3-HPPA, and (3) an increase in o-coumaric acid. Hereby, we propose these metabolites as a metabolic profile associated to a segregated dysbiosis state related to obesity disease.
Severe Genotype, Pancreatic Insufficiency and Low Dose of Pancreatic Enzymes Associate with Abnormal Serum Sterol Profile in Cystic Fibrosis
Authors: Drzymała-Czyż Sławomira , Krzyżanowska-Jankowska Patrycja , Dziedzic Krzysztof , Lisowska Aleksandra , Kurek Szymon , Goździk-Spychalska Joanna , Kononets Victoria , Woźniak Dagmara , Mądry Edyta , Walkowiak Jarosław
Published: February 2021

Background: Several factors could lead to lipid disturbances observed in cystic fibrosis (CF). This study aimed to assess sterol homeostasis in CF and define potential exogenous and endogenous determinants of lipid dysregulation. Methods: The study involved 55 CF patients and 45 healthy subjects (HS). Sterol concentrations (μg/dL) were measured by gas chromatography/mass spectrometry. CF was characterised by lung function, pancreatic status, liver disease and diabetes coexistence, Pseudomonas aeruginosa colonisation and BMI. CFTR genotypes were classified as severe or other. Results: Campesterol and β-sitosterol concentrations were lower (p = 0.0028 and p < 0.0001, respectively) and lathosterol levels (reflecting endogenous cholesterol biosynthesis) were higher (p = 0.0016) in CF patients than in HS. Campesterol and β-sitosterol concentrations were lower in patients with a severe CFTR genotype, pancreatic insufficiency and lower pancreatic enzyme dose (lipase units/gram of fat). In multiple regression analyses, β-sitosterol and campesterol concentrations were predicted by genotype and pancreatic insufficiency, whereas cholesterol and its fractions were predicted by phytosterol concentrations, age, dose of pancreatic enzymes, nutritional status and genotype. Conclusions: Independent determinants of lipid status suggest that malabsorption and pancreatic enzyme supplementation play a significant role in sterol abnormalities. The measurement of campesterol and β-sitosterol concentrations in CF patients may serve for the assessment of the effectiveness of pancreatic enzyme replacement therapy and/or compliance, but further research is required.
Forefront Users’ Experience Evaluation by Employing Together Virtual Reality and Electroencephalography: A Case Study on Cognitive Effects of Scents
Authors: Mancini Marco , Cherubino Patrizia , Cartocci Giulia , Martinez Ana , Borghini Gianluca , Guastamacchia Elena , di Flumeri Gianluca , Rossi Dario , Modica Enrica , Menicocci Stefano , Lupo Viviana , Trettel Arianna , Babiloni Fabio
Published: February 2021

Scents have the ability to affect peoples’ mental states and task performance with to different extents. It has been widely demonstrated that the lemon scent, included in most all-purpose cleaners, elicits stimulation and activation, while the lavender scent elicits relaxation and sedative effects. The present study aimed at investigating and fostering a novel approach to evaluate users’ experience with respect to scents’ effects through the joint employment of Virtual Reality and users’ neurophysiological monitoring, in particular Electroencephalography. In particular, this study, involving 42 participants, aimed to compare the effects of lemon and lavender scents on the deployment of cognitive resources during a daily life experience consisting in a train journey carried out in virtual reality. Our findings showed a significant higher request of cognitive resources during the processing of an informative message for subjects exposed to the lavender scent with respect to the lemon exposure. No differences were found between lemon and lavender conditions on the self-reported items of pleasantness and involvement; as this study demonstrated, the employment of the lavender scent preserves the quality of the customer experience to the same extent as the more widely used lemon scent.
Unfavorable Dynamics of Platelet Reactivity during Clopidogrel Treatment Predict Severe Course and Poor Clinical Outcome of Ischemic Stroke
Authors: Wiśniewski Adam , Sikora Joanna , Karczmarska-Wódzka Aleksandra , Bugieda Joanna , Filipska Karolina , Ślusarz Robert
Published: February 2021

Background: Previous studies have revealed that high platelet reactivity while on clopidogrel may affect the severe course and worse prognosis of ischemic stroke. However, the above findings were based on a single measurement of platelet function. We aimed to investigate whether the dynamics of platelet reactivity over time would more accurately determine its actual impact on clinical outcome. Methods: We enrolled 74 ischemic stroke subjects, taking a dose of 75 mg a day of clopidogrel to this prospective, single-center, and observational study. The determination of platelet function was based on the impedance aggregometry 6–12 h after the first dose of clopidogrel and 48 h later. We defined a favorable dynamics of platelet reactivity as a decrease in values at least equal to the median obtained in the entire study. The clinical condition was assessed by the National Institutes of Health Stroke Scale on the first, third, and ninetieth days and the functional status by modified Rankin Scale, respectively. Results: A favorable dynamics of platelet reactivity was associated with the mild clinical condition and favorable functional status, both early and late. Early neurological deterioration was related to unfavorable dynamics of platelet reactivity over time. In multivariate regression models, we found that unfavorable dynamics of platelet reactivity, alone and combined with a high baseline value of platelet reactivity, is an independent predictor of a severe clinical condition, the risk of deterioration, and poor early and late prognosis. Conclusion: We highlighted that dynamics of platelet reactivity over time predict the clinical course and prognosis of stroke better than a single value.
Phytochemical Analysis, In Vitro Anticholinesterase, Antioxidant Activity and In Vivo Nootropic Effect of Ferula ammoniacum (Dorema ammoniacum) D. Don. in Scopolamine-Induced Memory Impairment in Mice
Authors: Nazir Nausheen , Nisar Mohammad , Zahoor Muhammad , Uddin Faheem , Ullah Saeed , Ullah Riaz , Ansari Siddique Akber , Mahmood Hafiz Majid , Bari Ahmed , Alobaid Abdulrehman
Published: February 2021

Background: Ferula ammoniacum (D. Don) is one of the endemic medicinal plants that is traditionally used to treat a number of diseases. Although the plant has been used to enhance memory, the investigational evidence supporting the nootropic effect was unsubstantial. Hence, the rationale for this study was to assess the potential beneficial effect of F. ammoniacum seed extracts on learning and memory in mice. Methods: The powdered plant samples (aerial parts) were subjected to extraction ad fractionation. Among the extracts, crude and ethyl acetate extracts were screened for major phytochemicals through HPLC analysis. All the extracts were evaluated for the in vitro anticholinesterase (AChE and BChE) and antioxidant potentials. Among the extracts the active fraction was further assessed for improving learning and memory in mice using behavioural tests like Y-maze and novel object recognition test (NORT) using standard protocols. After behavioural tests, all the animals were sacrificed and brains tissues were assessed for the ex vivo anticholinesterase and antioxidant potentials. Results: Phytochemicals like chlorogenic acid, quercetin, mandelic acid, phloroglucinol, hydroxy benzoic acid, malic acid, epigallocatechin gallate, ellagic acid, rutin, and pyrogallol were identified in crude methanolic extract (Fa.Met) and ethyl acetate fraction (Fa.EtAc) through HPLC. Fa.EtAc and Fa.Chf extracts more potently inhibited AChE and BChE with IC50 values of 40 and 43 µg/mL, and 41 and 42 µg/mL, respectively. Similarly highest free radical scavenging potential was exhibited by Fa.EtAc fraction against DPPH (IC50 = 100 µg/mL) and ABTS (IC50 = 120 µg/mL). The extract doses, 100 and 200 mg/kg body weight significantly (p < 0.01) improved the short-term memory by increasing the percent spontaneous alternation in the Y-maze test along with increasing discrimination index in the NORT that clearly indicated the enhancement in the recognition memory of mice. Conclusion: The extracts more potently scavenged the tested free radicals, exhibited anticholinesterase activities, improved the learning abilities and reduced the memory impairment induced by scopolamine in mice model thus suggesting that these extracts could be effectively used for the management of oxidative stress, neurodegenerative diseases and memory loss.
Chronic Mild Unpredictable Stress and High-Fat Diet Given during Adolescence Impact Both Cognitive and Noncognitive Behaviors in Young Adult Mice
Authors: Lippi Stephen L. P.
Published: February 2021

Stress and diet are intricately linked, and they often interact in a negative fashion. Increases in stress can lead to poor food choices; adolescence is a period that is often accompanied by increased levels of stress. Stress and poor dietary choices can affect learning and memory; it is important to understand their combined effects when occurring during crucial developmental periods. Here, we present evidence that chronic mild unpredictable stress (CMUS) and high-fat diet (HFD) impact both cognitive and noncognitive behaviors when assessed after four weeks of manipulation in four-week old mice. CMUS mice had increased anxiety in the open field test (OFT) (p = 0.01) and spent more time in the open arms of the elevated zero maze (EZM) (p < 0.01). HFD administration was shown to interact with CMUS to impair spatial memory in the Morris Water Maze (MWM) (p < 0.05). Stress and diet also led to disturbances in non-cognitive behaviors: CMUS led to significantly more burrowing (p < 0.05) and HFD administration led to the poorer nest construction (p < 0.05). These findings allow for researchers to assess how modifying lifestyle factors (including diet and stress) during adolescence can serve as a potential strategy to improve cognition in young adulthood.
PD-L1 Expression Correlated with p53 Expression in Pediatric Glioblastoma Multiforme
Authors: Litak Jakub , Grajkowska Wiesława , Szumiło Justyna , Krukow Paweł , Maciejewski Ryszard , Roliński Jacek , Grochowski Cezary
Published: February 2021

High-grade gliomas are infrequent in the pediatric population compared to adults, nevertheless, mortality and morbidity caused by malignant gliomas in this group of patients remain significant. PD-L1 and PD-1 Immune checkpoints (IC) molecules maintain immunological balance between activation and suppression. Eighteen patients with a histopathological diagnosis of pediatric glioblastoma multiforme (GBM, WHO IV) were studied. In total, PD-L1 expression was detected in 8 patients (44%). The molecular aspect of IC and immunotherapy targeted on PD-1/PD-L1 axis in pediatric population may be a promising adjuvant therapy in pediatric glioblastoma multiform treatment, however, this subject requires further investigation.
CAR-T Therapy, the End of a Chapter or the Beginning of a New One?
Authors: Mostafa Kamel Yasser
Published: February 2021

Chimeric antigen receptor-T (CAR-T) therapy targeting CD19 has revolutionised the treatment of advanced acute lymphoblastic leukaemia (ALL) and diffuse large B-cell lymphoma (DLBCL). The ability to specifically target the cancer cells has shown high positive results as reported in the registration studies. The success of CAR-T therapy in the first two indications led to the initiation of a large number of studies testing CAR-T therapy in different haematologic tumours such as acute myelogenous leukaemia (AML), Hodgkin’s disease (HD), chronic lymphocytic leukaemia (CLL), multiple myeloma (MM), as well as different solid tumours. Unfortunately, relapses occurred in patients treated with CAR-T therapy, calling for the development of effective subsequent therapies. Likewise, this novel mechanism of action was also accompanied by a different toxicity profile, such as cytokine release syndrome (CRS). Patients’ access to the treatment is still limited by its cost. Notwithstanding, this did not prohibit further development of this new therapy to treat other malignancies. This research activity of CAR-T therapy moves it from being used as an end-stage treatment for ALL and DLBCL to a new therapeutic option for a wide range of patients with different haematologic and solid tumours.
Pathogenetic Features and Current Management of Glioblastoma
Authors: Nguyen Hong-My , Guz-Montgomery Kirsten , Lowe Devin B. , Saha Dipongkor
Published: February 2021

Glioblastoma (GBM) is the most common form of primary malignant brain tumor with a devastatingly poor prognosis. The disease does not discriminate, affecting adults and children of both sexes, and has an average overall survival of 12–15 months, despite advances in diagnosis and rigorous treatment with chemotherapy, radiation therapy, and surgical resection. In addition, most survivors will eventually experience tumor recurrence that only imparts survival of a few months. GBM is highly heterogenous, invasive, vascularized, and almost always inaccessible for treatment. Based on all these outstanding obstacles, there have been tremendous efforts to develop alternative treatment options that allow for more efficient targeting of the tumor including small molecule drugs and immunotherapies. A number of other strategies in development include therapies based on nanoparticles, light, extracellular vesicles, and micro-RNA, and vessel co-option. Advances in these potential approaches shed a promising outlook on the future of GBM treatment. In this review, we briefly discuss the current understanding of adult GBM’s pathogenetic features that promote treatment resistance. We also outline novel and promising targeted agents currently under development for GBM patients during the last few years with their current clinical status.
The FDA-Approved Antiviral Raltegravir Inhibits Fascin1-Dependent Invasion of Colorectal Tumor Cells In Vitro and In Vivo
Authors: Alburquerque-González Begoña , Bernabé-García Ángel , Bernabé-García Manuel , Ruiz-Sanz Javier , López-Calderón Fernando Feliciano , Gonnelli Leonardo , Banci Lucia , Peña-García Jorge , Luque Irene , Nicolás Francisco José , Cayuela-Fuentes María Luisa , Luchinat Enrico , Pérez-Sánchez Horacio , Montoro-García Silvia , Conesa-Zamora Pablo
Published: February 2021

Background: Fascin1 is the key actin-bundling protein involved in cancer invasion and metastasis whose expression is associated with bad prognosis in tumor from different origins. Methods: In the present study, virtual screening (VS) was performed for the search of Fascin1 inhibitors and RAL, an FDA-approved inhibitor of human immunodeficiency virus-1 (HIV-1) integrase, was identified as a potential Fascin1 inhibitor. Biophysical techniques including nuclear magnetic resonance (NMR) and differential scanning fluorimetry (DSF) were carried out in order to confirm RAL as a Fascin1 blocker. The effect of RAL on actin-bundling activity Fascin1 was assessed by transmission electron microscopy (TEM), immunofluorescence, migration, and invasion assays on two human colorectal adenocarcinoma cell lines: HCT-116 and DLD-1. In addition, the anti-metastatic potential of RAL was in vivo evaluated by using the zebrafish animal model. Results: NMR and DSF confirmed in silico predictions and TEM demonstrated the RAL-induced disorganization of the actin structure compared to control conditions. The protrusion of lamellipodia in cancer cell line overexpressing Fascin1 (HCT-116) was abolished in the presence of this drug. By following the addition of RAL, migration of HCT-116 and DLD-1 cell lines was significantly inhibited. Finally, using endogenous and exogenous models of Fascin1 expression, the invasive capacity of colorectal tumor cells was notably impaired in the presence of RAL in vivo assays; without undesirable cytotoxic effects. Conclusion: The current data show the in vitro and in vivo efficacy of the antiretroviral drug RAL in inhibiting human colorectal cancer cells invasion and metastasis in a Fascin1-dependent manner.
Complete Removal of the Lesion as a Guidance in the Management of Patients with Breast Ductal Carcinoma In Situ
Authors: Nicosia Luca , di Giulio Giuseppe , Bozzini Anna Carla , Fanizza Marianna , Ballati Francesco , Rotili Anna , Lazzeroni Matteo , Latronico Antuono , Abbate Francesca , Renne Giuseppe , Addante Francesca , Lucioni Marco , Cassano Enrico , Mastropasqua Mauro Giuseppe
Published: February 2021

Background: Considering highly selected patients with ductal carcinoma in situ (DCIS), active surveillance is a valid alternative to surgery. Our study aimed to show the reliability of post-biopsy complete lesion removal, documented by mammogram, as additional criterion to select these patients. Methods: A total of 2173 vacuum-assisted breast biopsies (VABBs) documented as DCIS were reviewed. Surgery was performed in all cases. We retrospectively collected the reports of post-VABB complete lesion removal and the histological results of the biopsy and surgery. We calculated the rate of upgrade of DCIS identified on VABB upon excision for patients with post-biopsy complete lesion removal and for those showing residual lesion. Results: We observed 2173 cases of DCIS: 408 classified as low-grade, 1262 as intermediate-grade, and 503 as high-grade. The overall upgrading rate to invasive carcinoma was 15.2% (330/2173). The upgrade rate was 8.2% in patients showing mammographically documented complete removal of the lesion and 19% in patients without complete removal. Conclusion: The absence of mammographically documented residual lesion following VABB was found to be associated with a lower upgrading rate of DCIS to invasive carcinoma on surgical excision and should be considered when deciding the proper management DCIS diagnosis.
Exosomal HMGB1 Promoted Cancer Malignancy
Authors: Wang Jiaan-Der , Wang Ya-Yu , Lin Shih-Yi , Chang Cheng-Yi , Li Jian-Ri , Huang Shi-Wei , Chen Wen-Ying , Liao Su-Lan , Chen Chun-Jung
Published: February 2021

Reciprocal crosstalk between platelets and malignancies underscores the potential of antiplatelet therapy in cancer treatment. In this study, we found that human chronic myeloid leukemia K562 cell-differentiated megakaryocytes and murine platelets produced bioactive substances and these are released into the extracellular space, partly in their exosomal form. High-mobility group box 1 (HMGB1) is a type of exosomal cargo, and the antiplatelet drugs aspirin and dipyridamole interfered with its incorporation into the exosomes. Those released substances and exosomes, along with exogenous HMGB1, promoted cancer cell survival and protected cells from doxorubicin cytotoxicity. In a tumor-bearing model established using murine Lewis lung carcinoma (LLC) cells and C57BL/6 mice, the tumor suppressive effect of dipyridamole correlated well with decreased circulating white blood cells, soluble P-selectin, TGF-β1 (Transforming Growth Factor-β1), exosomes, and exosomal HMGB1, as well as tumor platelet infiltration. Exosome release inhibitor GW4869 exhibited suppressive effects as well. The suppressive effect of dipyridamole on cancer cell survival was paralleled by a reduction of HMGB1/receptor for advanced glycation end-products axis, and proliferation- and migration-related β-catenin, Yes-associated protein 1, Runt-related transcription factor 2, and TGF- β1/Smad signals. Therefore, exosomes and exosomal HMGB1 appear to have roles in platelet-driven cancer malignancy and represent targets of antiplatelet drugs in anticancer treatment.
Kraft Lignin Ethanolysis over Zeolites with Different Acidity and Pore Structures for Aromatics Production
Authors: Baxter Nathan Cody , Wang Yuxin , Huang Huijiang , Liao Yixin , Barnett Heath , Zhao Yujun , Wang Shengnian
Published: February 2021

To utilize its rich aromatics, lignin, a high-volume waste and environmental hazard, was depolymerized in supercritical ethanol over various zeolites types with different acidity and pore structures. Targeting at high yield/selectivity of aromatics such as phenols, microporous Beta, Y, and ZSM-5 zeolites were first examined in lignin ethanolysis, followed by zeolites with similar micropore size but different acidity. Further comparisons were made between zeolites with fin-like and worm-like mesoporous structures and their microporous counterparts. Despite depolymerization complexity and diversified ethanolysis products, strong acidity was found effective to cleave both C–O–C and C–C linkages of lignin while mild acidity works mainly in ether bond breakdown. However, when diffusion of gigantic molecules is severe, pore size, particularly mesopores, becomes more decisive on phenol selectivity. These findings provide important guidelines on future selection and design of zeolites with appropriate acidity and pore structure to promote lignin ethanolysis or other hydrocarbon cracking processes.
Hierarchical Ternary Sulfides as Effective Photocatalyst for Hydrogen Generation Through Water Splitting: A Review on the Performance of ZnIn2S4
Authors: Janani Ravichandran , Preethi V Raja , Singh Shubra , Rani Aishwarya , Chang Chang-Tang
Published: February 2021

One of the major aspects and advantages of solar energy conversion is the photocatalytic hydrogen generation using semiconductor materials for an eco-friendly technology. Designing a low-cost efficient material to overcome limited light absorption as well as rapid recombination of photogenerated charge carriers is essential to achieve considerable hydrogen generation. In recent years, sulfide based semiconductors have attracted scientific research interest due to their excellent solar response and narrow band gap. The present review focuses on the recent approaches in the development of hierarchical ternary sulfide based photocatalysts with a special focus on ZnIn2S4. We also observe how the electronic structure of ZnIn2S4 is beneficial for water splitting and the various strategies involved for improving the material efficiency for photocatalytic hydrogen generation. The review places emphasis on the latest advancement/new insights on ZnIn2S4 being used as an efficient material for hydrogen generation through photocatalytic water splitting. Recent progress on essential aspects which govern light absorption, charge separation and transport are also discussed in detail.