bluuebunny/crossref_metadata_2025 huggingface dataset:
Phage-Host Interaction Analysis by Flow Cytometry Allows for Rapid and Efficient Screening of Phages
Authors: Melo Luís D. R. , Monteiro Rodrigo , Pires Diana P. , Azeredo Joana
Published: January 2022

Recently, phages have become popular as an alternative to antibiotics. This increased demand for phage therapy needs rapid and efficient methods to screen phages infecting specific hosts. Existing methods are time-consuming, and for clinical purposes, novel, quick, and reliable screening methods are highly needed. Flow cytometry (FC) allows a quick differentiation and enumeration of bacterial cell populations and has been used to assess in vitro the activity of antimicrobial compounds. In this work, we propose FC as a rapid and reliable method to assess the susceptibility of a bacterial population to phage infection. For that, the interaction of phages vB_PaeM_CEB_DP1 and vB_PaeP_PE3 with Pseudomonas aeruginosa PAO1 was characterized by FC. Synchronous infection assays were performed, and samples were collected at different time points and stained with SYTO BC and PI before analysis. Part of the collected samples was used to characterize the expression of early, middle, and late genes by qPCR. Both FC and qPCR results were correlated with phage propagation assays. Results showed that SYTO BC median fluorescence intensity (MFI) values increased in the first 25 min of PE3 and DP1 infection. The increase of fluorescence is due to the expression of phage genes observed by qPCR. Since SYTO BC MFI values increase with gene expression, it allows the determination of host susceptibility to a phage in a short period of time, avoiding false positives caused by lysis from without. In conclusion, this method may allow for a quick and high-throughput real-time screening of different phages to a specific host, which can be crucial for a quick phage selection in clinical practice.
Factors Associated with Antimicrobial Stewardship Practices on California Dairies: One Year Post Senate Bill 27
Authors: Abdelfattah Essam M. , Ekong Pius S. , Okello Emmanuel , Williams Deniece R. , Karle Betsy M. , Lehenbauer Terry W. , Aly Sharif S.
Published: January 2022

Background: The current study is aimed at identifying the factors associated with antimicrobial drug (AMD) use and stewardship practices on conventional California (CA) dairies a year after CA Senate Bill 27. Methods: Responses from 113 out of 1282 dairies mailed a questionnaire in 2019 were analyzed to estimate the associations between management practices and six outcomes including producer familiarity with medically important antimicrobial drugs (MIADs), restricted use of MIADs previously available over the counter (OTC), use of alternatives to AMD, changes in on-farm management practices, changes in AMD costs, and animal health status in dairies. Results: Producers who reported having a veterinarian–client–patient relationship (VCPR) and tracking AMD withdrawal intervals had greater odds of being familiar with the MIADs. Producers who began or increased the use of preventive alternatives to AMD in 2019 had higher odds (OR = 3.23, p = 0.04) of decreased use of MIADs previously available OTC compared to those who did not. Changes in management practices to prevent disease outbreak and the use of diagnostics to guide treatment were associated with producer-reported improved animal health. In addition, our study identified record keeping (associated with familiarity with MIADs), use of alternatives to AMD (associated with management changes to prevent diseases and decreased AMD costs), and use of diagnostics in treatment decisions (associated with reported better animal health) as factors associated with AMD stewardship. Conclusions: Our survey findings can be incorporated in outreach education materials to promote antimicrobial stewardship practices in dairies.
Cyclic Tetrapeptides with Synergistic Antifungal Activity from the Fungus Aspergillus westerdijkiae Using LC-MS/MS-Based Molecular Networking
Authors: Han Junjie , Wang Hanying , Zhang Rui , Dai Huanqin , Chen Baosong , Wang Tao , Sun Jingzu , Wang Wenzhao , Song Fuhang , Li Erwei , Lyu Zhitang , Liu Hongwei
Published: January 2022

Fungal natural products play a prominent role in the development of pharmaceuticalagents. Two new cyclic tetrapeptides (CTPs), westertide A (1) and B (2), with eight known compounds (3–10) were isolated from the fungus Aspergillus westerdijkiae guided by OSMAC (one strain-many compounds) and molecular networking strategies. The structures of new compounds were unambiguously determined by a combination of NMR and mass data analysis, and chemical methods. All of the isolates were evaluated for antimicrobial effects, synergistic antifungal activity, cytotoxic activity, and HDAC inhibitory activity. Compounds 1–2 showed synergistic antifungal activity against Candida albicans SC5314 with the presence of rapamycin and weak HDAC (histone deacetylase) inhibitory activity. These results indicate that molecular networking is an efficient approach for dereplication and identification of new CTPs. CTPs might be a good starting point for the development of synergistic antifungal agents.
Error Model and Frequency Modulation Characteristics Analysis of Laser Processing Platform for Micro Crystal Resonator
Authors: Hu Jian , Li Zhihang , Wu Yanlin , Yu Xindong , Li Gangyan
Published: January 2022

Laser processing platform for micro crystal resonator (MCR) frequency modulation is an important piece of equipment to apply laser etching technology to the MCR frequency modulation process. The positioning accuracy of the laser processing platform has an important influence on frequency modulation characteristics. In order to improve the precision and efficiency of the laser processing platform for MCR frequency modulation, the error model of the laser processing platform for MCR frequency modulation is established based on the multi-body system theory. According to the Monte Carlo simulation method, the influence of laser processing platform error for MCR on frequency modulation characteristics is analyzed. Considering the requirements of the MCR frequency modulation process, the laser processing platform for MCR frequency modulation is built, and the frequency deviation and frequency modulation performance are verified to meet the engineering requirements.
Sentence Boundary Extraction from Scientific Literature of Electric Double Layer Capacitor Domain: Tools and Techniques
Authors: Miah Md. Saef Ullah , Sulaiman Junaida , Sarwar Talha Bin , Naseer Ateeqa , Ashraf Fasiha , Zamli Kamal Zuhairi , Jose Rajan
Published: January 2022

Given the growth of scientific literature on the web, particularly material science, acquiring data precisely from the literature has become more significant. Material information systems, or chemical information systems, play an essential role in discovering data, materials, or synthesis processes using the existing scientific literature. Processing and understanding the natural language of scientific literature is the backbone of these systems, which depend heavily on appropriate textual content. Appropriate textual content means a complete, meaningful sentence from a large chunk of textual content. The process of detecting the beginning and end of a sentence and extracting them as correct sentences is called sentence boundary extraction. The accurate extraction of sentence boundaries from PDF documents is essential for readability and natural language processing. Therefore, this study provides a comparative analysis of different tools for extracting PDF documents into text, which are available as Python libraries or packages and are widely used by the research community. The main objective is to find the most suitable technique among the available techniques that can correctly extract sentences from PDF files as text. The performance of the used techniques Pypdf2, Pdfminer.six, Pymupdf, Pdftotext, Tika, and Grobid is presented in terms of precision, recall, f-1 score, run time, and memory consumption. NLTK, Spacy, and Gensim Natural Language Processing (NLP) tools are used to identify sentence boundaries. Of all the techniques studied, the Grobid PDF extraction package using the NLP tool Spacy achieved the highest f-1 score of 93% and consumed the least amount of memory at 46.13 MegaBytes.
Dist-YOLO: Fast Object Detection with Distance Estimation
Authors: Vajgl Marek , Hurtik Petr , Nejezchleba Tomáš
Published: January 2022

We present a scheme of how YOLO can be improved in order to predict the absolute distance of objects using only information from a monocular camera. It is fully integrated into the original architecture by extending the prediction vectors, sharing the backbone’s weights with the bounding box regressor, and updating the original loss function by a part responsible for distance estimation. We designed two ways of handling the distance, class-agnostic and class-aware, proving class-agnostic creates smaller prediction vectors than class-aware and achieves better results. We demonstrate that the subtasks of object detection and distance measurement are in synergy, resulting in the increase of the precision of the original bounding box functionality. We show that using the KITTI dataset, the proposed scheme yields a mean relative error of 11% considering all eight classes and the distance range within [0, 150] m, which makes the solution highly competitive with existing approaches. Finally, we show that the inference speed is identical to the unmodified YOLO, 45 frames per second.
Petroleum Hydrocarbon Composition of Oily Sludge and Contaminated Soils in a Decommissioned Oilfield Waste Pit under Desert Conditions
Authors: Al-Mebayedh Hamad , Niu Anyi , Lin Chuxia
Published: January 2022

Field and laboratory investigations were conducted to characterize the composition of petroleum hydrocarbons in oily sludge and the underlying contaminated soils in a decommissioned oilfield waste pit in Kuwait. The results show that the petroleum hydrocarbon composition in the oily sludge and contaminated soils was spatially variable. Highly toxic petroleum hydrocarbon species such as BTEX and PAHs were generally lacking, and both sludge- and soil-borne hydrocarbons were dominated by long-chain petroleum hydrocarbons. The soil contamination depth was generally very shallow although localized deep profiles (>0.5 m) were found. A loose relationship was established between TPH in the sludge and that in the underlying soil. On average, the soil had a greater percentage of shorter-chain hydrocarbon fractions (either aliphatics or aromatics), as compared to the sludge. The environmental risk from the oily sludge and contaminated soils is considered to be relatively low. For cost-effective management of the environmental risk of decommissioning an oilfield waste pit, containment of the sludge and contaminated soils using a soil-capping approach may be sufficient to minimize the possible adverse environmental impacts from the decommissioned waste pit, and this may represent an option that is superior to other costly remediation strategies.
Design, Simulation and Experimentation of a Polythene Film Debris Recovery Machine in Soil
Authors: Jin Wei , Liu Jingyi , Xu Chunbao , Zhang Xuejun , Bai Shenghe
Published: January 2022

With the rapid development of planting techniques using plastic film mulching, the content of residual plastic film in soil increases year by year, which pollutes the soil and water, endangers the growth of crops and reduces the quality and yield of agricultural products. Therefore, in order to solve the problem that plastic film with a thickness of 0.008 mm, commonly used in China, is difficult to recycle, this study designed the residue film recycling machine based on the existing research results. After harvesting cotton in Xinjiang, the working performance of the residue film recovery machine of plough layer was measured. Through theoretical optimization and field experiments, the effects of conveyor speed, distance between elastic teeth and type of elastic teeth on residual film recovery were studied. The relationship between the parameters of the residual film recovery machine and the recovery effect of the residual film was analyzed. When the rotational speed and inclination angle of the conveying and separating device were 74 rpm, 35° and 120 rpm, 45°, respectively, under the condition that the distance between C-shaped elastic teeth was 59 mm, the recovery and separation rate of residual plastic film were 88.12% and 83.27%, respectively. Based on the study results, it is recommended to accelerate the development of naturally degradable agricultural plastic film or to popularize and apply thickened film. Relevant local standards and policies have been formulated to address the problem of residual film pollution, and a governance system has been established for the benefit of the government, scientific research institutions, enterprises and farmers. This study provides a reference for mechanized recovery of residual plastic film in soil and treatment of soil pollution.
An Artificial Intelligence Approach to Fatigue Crack Length Estimation from Acoustic Emission Waves in Thin Metallic Plates
Authors: Garrett Joseph Chandler , Mei Hanfei , Giurgiutiu Victor
Published: January 2022

The acoustic emission (AE) technique has become a well-established method of monitoring structural health over recent years. The sensing and analysis of elastic AE waves, which have involved piezoelectric wafer active sensors (PWAS) and time domain and frequency domain analysis, has proven to be effective in yielding fatigue crack-related information. However, not much research has been performed regarding (i) the correlation between the fatigue crack length and AE signal signatures and (ii) artificial intelligence (AI) methodologies to automate the AE waveform analysis. In this paper, this crack length correlation is investigated along with the development of a novel AE signal analysis technique via AI. A finite element model (FEM) study was first performed to understand the effects of fatigue crack length on the resulting AE waveforms and a fatigue experiment was performed to capture experimental AE waveforms. Finally, this database of experimental AE waveforms was used with a convolutional neural network to build a system capable of performing automated classification and prediction of the length of a fatigue crack that excited respective AE signals. AE signals captured during a fatigue crack growth experiment were found to match closely with the FEM simulations. This novel AI system proved to be effective at predicting the crack length of an AE signal at an accuracy of 98.4%. This novel AI-enabled AE signal analysis technique will provide a crucial step forward in the development of a comprehensive structural health monitoring (SHM) system.
A Collaborative Framework for Customized E-Learning Services by Analytic Hierarchy Processing
Authors: Tseng Frank S. C. , Yeh Chao-Tien , Chou Annie Y. H.
Published: January 2022

Thanks to the drastic proliferation of the Internet, e-learning has been recognized as an effective medium for various kinds of aggressive learners. However, due to the deficiencies of tutoring and guiding functionalities in current learning platforms, casual learners may deviate from the original course direction with frustration, when confronting inflexible course materials and fixed learning models. In the post-COVID-19 era, we believe that the most important functionality for a personal learning environment (PLE) to offer is a course recommendation process which adaptively provides a versatile course combination scheme for different learners from different perspectives. In this paper, we propose a flexible framework for users to customize their e-learning environment based on a two-stage Analytical Hierarchical Processing (AHP) structure for building adaptive course portfolios, which adaptively provides a versatile course scheme for different learners. The main objective of our framework is to transform a learner from a role of passively accepting the course content organized by instructors, into another role of proactively selecting the courses and contributing their knowledge to continuously improve the learning platform. We believe the approach proposed is a versatile way for supporting various challenges for the next generation of personal e-learning environment.
The Effect of Strengthening Methods on the Performance of Reinforced Concrete Columns against Vehicle Impact
Authors: Fuhaid Abdulrahman Al , Sohel Kazi Md Abu , Arifuzzaman Md
Published: January 2022

Columns at the ground floor and parking garages that could be hit by a car pose a significant risk to the structural stability of the building superstructures. Generally, these columns are not built to sustain the lateral impact force generated by car–column collision. In this study, the performance of axially loaded retrofitted reinforced concrete (RC) columns against car impact is evaluated using finite element (FE) simulation. The FE model of the RC column with axial load was validated with experimental results. For the car-crushing simulations, two SUV car models with a mass of about 2250 kg, which had been experimentally validated, were used to simulate the car–column collision. The results of the FE analysis revealed that once the impact speed exceeds 30 km/h, the horizontal impact force has a significant effect on the column joint at the foundation. The impact force increases linearly as the impact velocity of the car increases up to 20 km/h. When car impact velocities are more than 20 km/h, the generated impact force increases in power to the car-crashing velocity. Both types of cars have almost the same effect on the generation of impact force and the lateral displacement of the column. It is found that the generated impact forces are higher than the recommended design values of Eurocode 1. To protect the column from car impact damage, two types of column-strengthening systems were investigated. One form of strengthening system involves retrofitting the lower half of the column with an aramid fiber-reinforced polymer (AFRP) warp, while the other involves putting a reinforced concrete jacket of up to 1.3 m in the height of the column. Based on the comparative study, design recommendations are suggested to protect the RC column from accidental car-crashing damage.
GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System
Authors: Song Jiahui , Wu Haitao , Guo Xiaochen , Jiang Dehuai , Guo Xuqiang , Lv Tong , Luo Hanze
Published: January 2022

A smoothing localization method for Global Navigation Satellite System (GNSS) and visual Simultaneous Localization and Mapping (SLAM) system is proposed to identify GNSS spoofing, optimize the cumulative error of the GNSS/visual SLAM system, and obtain smoothing localization results. The proposed method analyzes the joint error distribution of the GNSS/visual SLAM system, uses the visual frame to invert the relative error offset of the GNSS from the dimensions of time and localization, performs error analysis and mutual verification based on the verification threshold. According to the mutual verification results, the GNSS spoofing is identified, and the corresponding back-end optimization strategy is selected to obtain a smoothing localization result. Through simulation, the time verification threshold and localization verification threshold of the proposed method are obtained under the condition that the sensors frequency and accuracy are set. The KITTI datasets in rural and urban scenes are used for verification. The simulation results show that our method can identify GNSS spoofing and provide credible and smoothing localization results in the case of GNSS spoofing occurs.
Deterioration of Property of Aluminum Alloys (EN AW-1050A, EN AW-5754 and EN AW-6060) by Absorbed Hydrogen
Authors: Włodarczyk Paweł P. , Włodarczyk Barbara
Published: January 2022

The paper reports the results of research on the effect of hydrogen penetration on the variations in the mechanical properties of selected aluminum alloys. As a result of the study, it can be observed that such variations contribute to the deterioration of mechanical properties, which, in turn, contributes to shortening the reliability time associated with the operation of aluminum alloy structures. The analysis involved structural aluminum alloys: EN AW-1050A, EN AW-5754 and EN AW-6060. Tensile strength and impact strength were measured. It was demonstrated that the absorption of hydrogen by the analyzed alloys led to the deterioration of mechanical properties of aluminum alloys. The performed measurements were compared to the previous research results regarding the influence of hydrogen on the deterioration of corrosion properties. The analysis of the influence of hydrogen on both issues allowed us to notice the reasons for shortening the operational reliability time of aluminum alloy parts. The awareness of the influence of hydrogen on aluminum alloys may contribute to the development of hydrogen systems or installations in which aluminum alloys can be used as a construction material, e.g., fuel cells, hydrogen supply pipes, block of combustion engine powered by hydrogen, etc. In the era of the development of zero-emission vehicles, the use of hydrogen as fuel is gaining more and more importance, where the influence of hydrogen on the properties of materials is an important issue.
Comparison of Indoor Radon Reduction Effects Based on Apartment Housing Ventilation Methods
Authors: Choi Jiwon , Hong Hyongjin , Lee Jeongsub , Kim Seonhong , Kim Gahyun , Park Boram , Cho Eun-Min , Lee Cheolmin
Published: January 2022

A lack of ventilation in multi-unit dwellings can affect human health adversely owing to accumulated radon gases; therefore, developing immediate and effective methods to reduce radon gas in indoor living environments is urgently needed. Therefore, this study conducted a basic research study to develop ventilation guidelines for radon through assessments of various ventilation strategies on indoor radon concentration in multiple-unit dwellings, a typical residential type in Korea. Radon measurement was conducted in November 2020 using RAD 7 Radon Detector (Durridge Co., Billerica, MA, USA) for living rooms and rooms of an apartment located in Uijeongbu, Gyeonggi-do, Korea. The radon reduction effect according to the ventilation conditions per hour was compared and evaluated by performing seven natural ventilation scenarios through opening and closing windows leading to the outside and three types of mechanical ventilation with high, middle, and low operating intensity. The concentration ratio (Cr) was estimated to present the indoor radon increase/decrease ratio. Among the natural ventilation scenarios, Case 3 had the highest radon reduction rate in the living room and Case 1 in the room, and Case 2 using natural ventilation equipment showed the lowest reduction rate in both the room and the living room. When ventilated by mechanical ventilation conditions, the radon reduction rate (Pr) was 70% or less under all conditions. Under the natural ventilation measurement condition, when every doors and windows was opened, it showed an effective ventilation in a short time. With high mechanical ventilation, the radon reduction effect in a relatively large space was better. Additionally, with middle and low mechanical ventilation, the reduction effect seems to be good in a relatively narrow space.
Trend Analysis and Spatial Distribution of Meteorological Disaster Losses in China, 2004–2015
Authors: Qi Qian , Jiang Baofa , Ma Wei , Marley Gifty
Published: January 2022

Meteorological disasters caused a lot of losses. We involved six categories (all disasters, floods, hail, typhoon, snow and heatwave) to observe their death and economic losses’ spatial-time distribution. The time trend of mortality was analyzed using a chi-square test for linear trends. Economic loss was described by direct economic loss and loss rate of GDP, whose trends were described by a trend line. Using annual percent change (APC) estimated by fitting weighted linear regression model, the change degree of mortality was assessed. On a national level, there was a statistically significant decreasing trend in mortality of all disasters (Z = −39.82, p < 0.05), floods (Z = −18.79, p < 0.05), hail (Z = −20.43, p < 0.05), typhoon (Z = −37.47, p < 0.05), snow (Z = −9.02, p < 0.05) and heatwave (Z = −8.76, p < 0.05) from 2004 to 2015 in China. The time trend of the loss rate of GDP was decreasing while the trend of direct economic losses was increasing. Western China was the most seriously hit area. APCs remained in downward trends (APCs < 0) in most of the provinces, while central provinces were with upward trends (APCs > 0). Areas with increasing mortality (APCs > 0) for different disasters included the southwest areas and Zhejiang (for floods), the northwest and south areas (for hail), Sichuan, Guangxi and Hainan (for typhoon), the west and northeast areas (for snow) and Hebei, Henan and Shanghai (for heatwave). As for economic losses, eastern areas were hit with a high amount of economic losses, but central areas were hit with a high GDP loss rate. Generally, nationwide death and economic losses caused by meteorological disasters have decreased. However, there were some relatively serious effects in the central and western areas for which urgent attention from policymakers is required.
Comparative Study in Software and Healthcare Industries between South Korea and US Based on Economic Input–Output Analysis
Authors: Mun Junhwan , Yun Eungyeong , Choi Hyoungyong , Kim Jonghyeon
Published: January 2022

In the modern era, software technology is being used not only as a core technology for manufacturing but also in various industries, such as telemedicine services, and the importance of the healthcare industry is being emphasized due to the demand for improved quality of life from the increase in the general level of earnings. However, if the industry emits a lot of carbon dioxide (CO2), it is questionable whether it is a sustainable industry. This study aimed to analyze the economic linkage effect of software and healthcare industries in South Korea and the United States by applying input–output analysis and examine whether these industries are sustainable in terms of CO2 emissions. The input–output tables and CO2 emissions from 2005 to 2015 were used for analysis from OECD. As a result of the analysis, CO2 emissions from the software and healthcare industries were less than 1% in both South Korea and the United States, suggesting that these industries are well-suited for low-carbon development in these countries. The forward and backward linkage effects of the software industry are different between South Korea and the United States. Specifically, the backward linkage effect of the software industry is large in South Korea, and the forward linkage effect is large in the United States. The forward linkage effect of the healthcare industry is different in the two countries, but the backward linkage effect is not. It means that there are differences in the industrial structure of the two countries. The software and healthcare industries need to devise strategies to drive production in other industries while maintaining current low carbon emission levels.
Logarithm of a Non-Singular Complex Matrix via the Dunford–Taylor Integral
Authors: Caratelli Diego , Ricci Paolo Emilio
Published: January 2022

Using the Dunford–Taylor integral and a representation formula for the resolvent of a non-singular complex matrix, we find the logarithm of a non-singular complex matrix applying the Cauchy’s residue theorem if the matrix eigenvalues are known or a circuit integral extended to a curve surrounding the spectrum. The logarithm function that can be found using this technique is essentially unique. To define a version of the logarithm with multiple values analogous to the one existing in the case of complex variables, we introduce a definition for the argument of a matrix, showing the possibility of finding equations similar to those of the scalar case. In the last section, numerical experiments performed by the first author, using the computer algebra program Mathematica©, confirm the effectiveness of this methodology. They include the logarithm of matrices of the fifth, sixth and seventh order.
Google Street View Images as Predictors of Patient Health Outcomes, 2017–2019
Authors: Nguyen Quynh C. , Belnap Tom , Dwivedi Pallavi , Deligani Amir Hossein Nazem , Kumar Abhinav , Li Dapeng , Whitaker Ross , Keralis Jessica , Mane Heran , Yue Xiaohe , Nguyen Thu T. , Tasdizen Tolga , Brunisholz Kim D.
Published: January 2022

Collecting neighborhood data can both be time- and resource-intensive, especially across broad geographies. In this study, we leveraged 1.4 million publicly available Google Street View (GSV) images from Utah to construct indicators of the neighborhood built environment and evaluate their associations with 2017–2019 health outcomes of approximately one-third of the population living in Utah. The use of electronic medical records allows for the assessment of associations between neighborhood characteristics and individual-level health outcomes while controlling for predisposing factors, which distinguishes this study from previous GSV studies that were ecological in nature. Among 938,085 adult patients, we found that individuals living in communities in the highest tertiles of green streets and non-single-family homes have 10–27% lower diabetes, uncontrolled diabetes, hypertension, and obesity, but higher substance use disorders—controlling for age, White race, Hispanic ethnicity, religion, marital status, health insurance, and area deprivation index. Conversely, the presence of visible utility wires overhead was associated with 5–10% more diabetes, uncontrolled diabetes, hypertension, obesity, and substance use disorders. Our study found that non-single-family and green streets were related to a lower prevalence of chronic conditions, while visible utility wires and single-lane roads were connected with a higher burden of chronic conditions. These contextual characteristics can better help healthcare organizations understand the drivers of their patients’ health by further considering patients’ residential environments, which present both risks and resources.
Nuclear Lamins: Key Proteins for Embryonic Development
Authors: Paul Jasper Chrysolite , Fulka Helena
Published: January 2022

Lamins are essential components of the nuclear envelope and have been studied for decades due to their involvement in several devastating human diseases, the laminopathies. Despite intensive research, the molecular basis behind the disease state remains mostly unclear with a number of conflicting results regarding the different cellular functions of nuclear lamins being published. The field of developmental biology is no exception. Across model organisms, the types of lamins present in early mammalian development have been contradictory over the years. Due to the long half-life of the lamin proteins, which is a maternal factor that gets carried over to the zygote after fertilization, investigators are posed with challenges to dive into the functional aspects and significance of lamins in development. Due to these technical limitations, the role of lamins in early mammalian embryos is virtually unexplored. This review aims in converging results that were obtained so far in addition to the complex functions that ceases if lamins are mutated.
Leukemia Inhibitory Factor: An Important Cytokine in Pathologies and Cancer
Authors: Jorgensen Megan M. , de la Puente Pilar
Published: January 2022

Leukemia Inhibitory Factor (LIF) is a member of the IL-6 cytokine family and is expressed in almost every tissue type within the body. Although LIF was named for its ability to induce differentiation of myeloid leukemia cells, studies of LIF in additional diseases and solid tumor types have shown that it has the potential to contribute to many other pathologies. Exploring the roles of LIF in normal physiology and non-cancer pathologies can give important insights into how it may be dysregulated within cancers, and the possible effects of this dysregulation. Within various cancer types, LIF expression has been linked to hallmarks of cancer, such as proliferation, metastasis, and chemoresistance, as well as overall patient survival. The mechanisms behind these effects of LIF are not well understood and can differ between different tissue types. In fact, research has shown that while LIF may promote malignancy progression in some solid tumors, it can have anti-neoplastic effects in others. This review will summarize current knowledge of how LIF expression impacts cellular function and dysfunction to help reveal new adjuvant treatment options for cancer patients, while also revealing potential adverse effects of treatments targeting LIF signaling.
Neuroinflammation Is Associated with GFAP and sTREM2 Levels in Multiple Sclerosis
Authors: Azzolini Federica , Gilio Luana , Pavone Luigi , Iezzi Ennio , Dolcetti Ettore , Bruno Antonio , Buttari Fabio , Musella Alessandra , Mandolesi Georgia , Guadalupi Livia , Furlan Roberto , Finardi Annamaria , Micillo Teresa , Carbone Fortunata , Matarese Giuseppe , Centonze Diego , Stampanoni Bassi Mario
Published: January 2022

Background: Astrocytes and microglia play an important role in the inflammatory process of multiple sclerosis (MS). We investigated the associations between the cerebrospinal fluid (CSF) levels of glial fibrillary acid protein (GFAP) and soluble triggering receptors expressed on myeloid cells-2 (sTREM-2), inflammatory molecules, and clinical characteristics in a group of patients with relapsing-remitting MS (RRMS). Methods: Fifty-one RRMS patients participated in the study. Clinical evaluation and CSF collection were performed at the time of diagnosis. The CSF levels of GFAP, sTREM-2, and of a large set of inflammatory and anti-inflammatory molecules were determined. MRI structural measures (cortical thickness, T2 lesion load, cerebellar volume) were examined. Results: The CSF levels of GFAP and sTREM-2 showed significant correlations with inflammatory cytokines IL-8, G-CSF, and IL-5. Both GFAP and sTREM-2 CSF levels positively correlated with age at diagnosis. GFAP was also higher in male MS patients, and was associated with an increased risk of MS progression, as evidenced by higher BREMS at the onset. Finally, a negative association was found between GFAP CSF levels and cerebellar volume in RRMS at diagnosis. Conclusions: GFAP and sTREM-2 represent suitable biomarkers of central inflammation in MS. Our results suggest that enhanced CSF expression of GFAP may characterize patients with a higher risk of progression.
Characterization of a Novel Regulator of Biofilm Formation in the Pathogen Legionella pneumophila
Authors: Marin Courtney , Kumova Ogan K. , Ninio Shira
Published: January 2022

Legionella pneumophila is a Gram-negative, facultative intracellular pathogen that causes severe pneumonia known as Legionnaires’ disease. The bacterium causes disease when contaminated water is aerosolized and subsequently inhaled by individuals, which allows the bacteria to gain access to the lungs, where they infect alveolar macrophages. L. pneumophila is ubiquitous in the environment, where it survives by growing in biofilms, intracellularly within protozoa, and planktonically. Biofilms are a major concern for public health because they provide a protective niche that allows for the continuous leaching of bacteria into the water supply. In addition, biofilms enhance the survival of the bacteria by increasing resistance to temperature fluctuations and antimicrobial agents. Currently, there is little known about biofilm formation and regulation by L. pneumophila. Here, we present evidence of a specific gene, bffA, which appears to be involved in the regulation of motility, biofilm formation, cellular replication, and virulence of L. pneumophila. A strain lacking bffA has an enhanced biofilm formation phenotype, forming biofilms that are both faster and thicker than wild type. Additionally, the knockout strain has significantly reduced motility, enhanced uptake into amoebae, and altered growth kinetics on solid media. Our data suggest a potential role for bffA in signaling pathways that govern changes in growth rate and motility in response to environmental conditions.
Effect of Dual-Task Motor-Cognitive Training in Preventing Falls in Vulnerable Elderly Cerebrovascular Patients: A Pilot Study
Authors: Spanò Barbara , Lombardi Maria , De Tollis Massimo , Szczepanska Maria , Ricci Claudia , Manzo Alice , Giuli Simone , Polidori Lorenzo , Griffini Ivo , Adriano Fulvia , Caltagirone Carlo , Annicchiarico Roberta
Published: January 2022

Falling is a frequent and major clinical problem among older adults, as well as in patients with chronic cerebrovascular diseases (CVD). At present, sequential (mixed) and simultaneously (dual-task) motor-cognitive trainings are the best approaches to affording patients more autonomy in their everyday motor independence while reducing fall risks and consequences. The objective of this study was to evaluate the efficacy of an advanced and innovative dual-task motor-cognitive rehabilitation program on fall risks in vulnerable older persons with chronic CVD. To this purpose, 26 consecutive older fallers with chronic CVD were recruited, and completed a mixed motor-cognitive or a dual-task motor-cognitive training program. Each patient also underwent two test evaluations to assess balance, gait, fear of falling, and walking performance at pre-and post-intervention. We found that our experimental motor-cognitive dual-task rehabilitation program could be an effective method to improve walking balance, gait, walking speed, and fear of falling, while reducing the risk of falls in older people with chronic CVD. Furthermore, results show that the simultaneous motor-cognitive training is more effective than the sequential motor-cognitive training. Therefore, our study brings innovative data, which can contribute positively to the management of this population.
Towards a Better Understanding on How Cognitive Impairment Affects Pain
Authors: Oosterman Joukje M. , Kunz Miriam
Published: January 2022

When judging whether someone is suffering from pain, the simplest and most reliable solution is to ask that person about it [...]
Prioritizing Post-Disaster Reconstruction Projects Using an Integrated Multi-Criteria Decision-Making Approach: A Case Study
Authors: Mohammadnazari Zahra , Mousapour Mamoudan Mobina , Alipour-Vaezi Mohammad , Aghsami Amir , Jolai Fariborz , Yazdani Maziar
Published: January 2022

As the destructive impacts of both human-made and natural disasters on societies and built environments are predicted to increase in the future, innovative disaster management strategies to cope with emergency conditions are becoming more crucial. After a disaster, selecting the most critical post-disaster reconstruction projects among available projects is a challenging decision due to resource constraints. There is strong evidence that the success of many post-disaster reconstruction projects is compromised by inappropriate decisions when choosing the most critical projects. Therefore, this study presents an integrated approach based on four multi-criteria decision-making (MCDM) techniques, namely, TOPSIS, ELECTRE III, VIKOR, and PROMETHEE, to aid decision makers in prioritizing post-disaster projects. Furthermore, an aggregation approach (linear assignment) is used to generate the final ranking vector since various methods may provide different outcomes. In the first stage, 21 criteria were determined based on sustainability. To validate the performance of the proposed approach, the obtained results were compared to the results of an artificial neural network (ANN) algorithm, which was applied to predict the projects’ success rates. A case study was used to assess the application of the proposed model. The obtained results show that in the selected case, the most critical criteria in post-disaster project selection are quality, robustness, and customer satisfaction. The findings of this study can contribute to the growing body of knowledge about disaster management strategies and have implications for key stakeholders involved in post-disaster reconstruction projects. Furthermore, this study provides valuable information for national decision makers in countries that have limited experience with disasters and where the destructive consequences of disasters on the built environment are increasing.
Compressive Strength Estimation and CO2 Reduction Design of Fly Ash Composite Concrete
Authors: Han Yi , Lin Run-Sheng , Wang Xiao-Yong
Published: January 2022

Fly ash is broadly utilized to produce concrete materials. This study presents a strength estimation model and a CO2 reduction design method for concrete with fly ash. First, a hydration-based strength (HBS) model is proposed for the evaluation of strength development at different ages of fly ash composite concrete with different mix proportions. Second, CO2 emissions for 1 MPa strength were evaluated. The analysis results show that, as the fly ash-to-binder ratio (FA/B) increased, the CO2 emissions for 1 MPa strength decreased. For concrete with a low water-to-binder ratio (W/B), the addition of high content of fly ash had an obvious dilution effect, which increased the reaction degree of cement and reduced CO2 emissions for 1 MPa strength. Moreover, the extension of the design age could reduce CO2 emissions for 1 MPa strength. Third, a genetic-algorithm-based optimal design model is proposed to find the individual mass of cement and fly ash of low-CO2 concrete. The analysis results show that, as the water contents increased from 160 to 170 kg/m3, to obtain the same strength, cement mass and fly ash mass increased, while the water/binder ratio and fly ash/binder ratio did not change. This means that the reduction in mixed water is one feasible way to lower CO2 emissions. In summary, the proposed strength–emission integrated analysis method is useful for designing sustainable fly ash composite concrete with the desired strength and low levels of CO2 emissions.
Asymmetrical Cross-Sectional Buckling in Arc-Prepared Multiwall Carbon Nanotubes Revealed by Iodine Filling
Authors: Torres-Dias Abraao Cefas , Impellizzeri Anthony , Picheau Emmanuel , Noé Laure , Pénicaud Alain , Ewels Christopher , Monthioux Marc
Published: January 2022

We report the intercalation of iodine chains in highly crystalline arc-discharge multiwalled carbon nanotubes (MWCNTs), not in the central cavity but instead between the concentric graphene shells. High-resolution transmission electron microscopy demonstrated that the intercalation was asymmetric with respect to the longitudinal axis of the nanotubes. This filling is explained through the existence of asymmetric intershell channels which formed as the tubes shrank upon cooling after growth. Shrinkage occurred because the geometrically constrained equilibrium intershell spacing was higher at growth than room temperature, due to the highly anisotropic coefficient of thermal expansion of graphite (or graphene stacks). Computational modelling supported the formation of such cavities and explained why they all formed on the same side of the tubes. The graphene shells were forced to bend outward, thereby opening aligned intergraphene nanocavities, and subsequently allowing the intercalation with iodine once the tube ends were opened by oxidative treatment. These observations are specific to catalyst-free processes because catalytic processes use too low temperatures, but they are generally applicable in geometrically closed carbon structures grown at high temperatures and so should be present in all arc-grown MWCNTs. They are likely to explain multiple observations in the literature of asymmetric interlayer spacings in multiple-shell graphenic carbon structures.
Overview of Immune Checkpoint Inhibitors in Gynecological Cancer Treatment
Authors: Pirš Boštjan , Škof Erik , Smrkolj Vladimir , Smrkolj Špela
Published: January 2022

In the last ten years, clinical oncology has been revolutionized by the introduction of oncological immunotherapy, mainly in the form of immune checkpoint inhibitors (ICIs) that transformed the standard of care of several advanced solid malignancies. Using ICIs for advanced gynecological cancers has yielded good results, especially for endometrial cancer. In ovarian or cervical cancer, combining ICIs with other established agents has shown some promise. Concurrently with the clinical development of ICIs, biomarkers that predict responses to such therapy have been discovered and used in clinical trials. The translation of these biomarkers to clinical practice was somewhat hampered by lacking assay standardization and non-comprehensive reporting of biomarker status in trials often performed on a small number of gynecological cancer patients. We can expect increased use of ICIs combined with other agents in gynecological cancer in the near future. This will create a need for reliable response prediction tools, which we believe will be based on biomarker, clinical, and tumor characteristics. In this article, we review the basic biology of ICIs and response prediction biomarkers, as well as the latest clinical trials that focus on subgroup effectiveness based on biomarker status in gynecological cancer patients.
A Stochastic Binary Model for the Regulation of Gene Expression to Investigate Responses to Gene Therapy
Authors: Giovanini Guilherme , Barros Luciana R. C. , Gama Leonardo R. , Tortelli Tharcisio C. , Ramos Alexandre F.
Published: January 2022

In this manuscript, we use an exactly solvable stochastic binary model for the regulation of gene expression to analyze the dynamics of response to a treatment aiming to modulate the number of transcripts of a master regulatory switching gene. The challenge is to combine multiple processes with different time scales to control the treatment response by a switching gene in an unavoidable noisy environment. To establish biologically relevant timescales for the parameters of the model, we select the RKIP gene and two non-specific drugs already known for changing RKIP levels in cancer cells. We demonstrate the usefulness of our method simulating three treatment scenarios aiming to reestablish RKIP gene expression dynamics toward a pre-cancerous state: (1) to increase the promoter’s ON state duration; (2) to increase the mRNAs’ synthesis rate; and (3) to increase both rates. We show that the pre-treatment kinetic rates of ON and OFF promoter switching speeds and mRNA synthesis and degradation will affect the heterogeneity and time for treatment response. Hence, we present a strategy for reaching increased average mRNA levels with diminished heterogeneity while reducing drug dosage by simultaneously targeting multiple kinetic rates that effectively represent the chemical processes underlying the regulation of gene expression. The decrease in heterogeneity of treatment response by a target gene helps to lower the chances of emergence of resistance. Our approach may be useful for inferring kinetic constants related to the expression of antimetastatic genes or oncogenes and for the design of multi-drug therapeutic strategies targeting the processes underpinning the expression of master regulatory genes.