bluuebunny/crossref_metadata_2025 huggingface dataset:
New and Developing Therapies in Spinal Muscular Atrophy: From Genotype to Phenotype to Treatment and Where Do We Stand?
Authors: Chen Tai-Heng
Published: May 2020

Spinal muscular atrophy (SMA) is a congenital neuromuscular disorder characterized by motor neuron loss, resulting in progressive weakness. SMA is notable in the health care community because it accounts for the most common cause of infant death resulting from a genetic defect. SMA is caused by low levels of the survival motor neuron protein (SMN) resulting from SMN1 gene mutations or deletions. However, patients always harbor various copies of SMN2, an almost identical but functionally deficient copy of the gene. A genotype–phenotype correlation suggests that SMN2 is a potent disease modifier for SMA, which also represents the primary target for potential therapies. Increasing comprehension of SMA pathophysiology, including the characterization of SMN1 and SMN2 genes and SMN protein functions, has led to the development of multiple therapeutic approaches. Until the end of 2016, no cure was available for SMA, and management consisted of supportive measures. Two breakthrough SMN-targeted treatments, either using antisense oligonucleotides (ASOs) or virus-mediated gene therapy, have recently been approved. These two novel therapeutics have a common objective: to increase the production of SMN protein in MNs and thereby improve motor function and survival. However, neither therapy currently provides a complete cure. Treating patients with SMA brings new responsibilities and unique dilemmas. As SMA is such a devastating disease, it is reasonable to assume that a unique therapeutic solution may not be sufficient. Current approaches under clinical investigation differ in administration routes, frequency of dosing, intrathecal versus systemic delivery, and mechanisms of action. Besides, emerging clinical trials evaluating the efficacy of either SMN-dependent or SMN-independent approaches are ongoing. This review aims to address the different knowledge gaps between genotype, phenotypes, and potential therapeutics.
Population Genetic Structure of Codling Moth, Cydia pomonella (L.) (Lepidoptera: Tortricidae), in Different Localities and Host Plants in Chile
Authors: Basoalto Alejandra , Ramírez Claudio C. , Lavandero Blas , Devotto Luis , Curkovic Tomislav , Franck Pierre , Fuentes-Contreras Eduardo
Published: May 2020

The codling moth, Cydia pomonella (L.) (Lepidoptera: Tortricidae), is a major pest introduced to almost all main pome fruit production regions worldwide. This species was detected in Chile during the last decade of the 19th century, and now has a widespread distribution in all major apple-growing regions. We performed an analysis of the genetic variability and structure of codling moth populations in Chile using five microsatellite markers. We sampled the codling moth along the main distribution area in Chile on all its main host-plant species. Low genetic differentiation among the population samples (FST = 0.03) was found, with only slight isolation by distance. According to a Bayesian assignment test (TESS), a group of localities in the coastal mountain range from the Bío-Bío Region formed a distinct genetic cluster. Our results also suggest that the codling moth that invaded the southernmost locality (Aysén Region) had two origins from central Chile and another unknown source. We did not find significant genetic differentiation between codling moth samples from different host-plant species. Our results indicate high genetic exchange among codling moth populations between the different Chilean regions and host plants.
Personal Autonomy as Quality of Life Predictor for Multiple Sclerosis Patients
Authors: Padureanu Rodica , Albu Carmen Valeria , Pirici Ionica , Mititelu Radu Razvan , Subtirelu Mihaela Simona , Turcu-Stiolica Razvan Aurelian , Sintonen Harri , Padureanu Vlad , Turcu-Stiolica Adina
Published: May 2020

Multiple sclerosis (MS) is a chronic, severe disease, characterized by a progressive alteration in neuronal transmission, which decreases personal independence and quality of life (QoL). This study aimed to investigate the relationship between QoL and personal autonomy in patients with MS, as well as its correlation with age, educational level, and diseases severity. Twenty-six MS patients were followed-up for six months. All patients completed the 15D questionnaire two times: at T0, when they started a new treatment, and at T1 after six months of treatment. At the end point, all patients completed the Personal Autonomy Questionnaire. The average patient age was 43 years (SD = 10), and 89% of them were female. The mean severity and duration of MS were 3.5 (SD = 1.75) and 9.5 (SD = 5.1), respectively. The average QoL of MS patients at T0 was 0.66 (SD = 0.18), and that at T1 was 0.71 (SD = 0.16). The scores of patients with different types of MS, i.e., relapsing–remitting MS (RRMS) or secondary progressive MS (SPMS), were compared. SPMS patients were older than RRMS patients (mean age 47.5 vs. 39.7 years; p = 0.032), and more RRMS patients were working (0.014). SPMS patients described the same QoL and personal autonomy as RRMS patients. Results from bivariate correlation analyses showed a significant relationship between QoL and age, education, and severity of MS. Also, the analysis showed no significant correlation between QoL and personal autonomy.
Intraosseous Bioplasty for a Subchondral Cyst in the Lateral Condyle of Femur
Authors: Potty Anish G.R. , Gupta Ashim , Rodriguez Hugo C. , Stone Ian W. , Maffulli Nicola
Published: May 2020

Several conditions can lead to the development of a subchondral cyst. The mechanism by which the cysts form, their location, and their severity depend on the underlying pathology, although the exact pathogenesis is not fully elucidated. Treatment options vary according to the location of the cyst, with less invasive procedures such as calcium phosphate cement injection to a joint arthroplasty when there is an extensive cyst in communication with the joint space. If the cyst is circumscribed, an intraosseous bioplasty (IOBP) can be performed. Described in this paper is an IOBP, a minimally invasive technique that preserves the joint and can be applied to most subchondral cysts. In our patient, both the appearance of the cyst at imaging and pain after IOBP greatly improved with the combined use of decompression and grafting. In those patients in whom conservative management fails to ameliorate symptoms, IOBP should be considered.
Diagnosing a 12-Item Dataset of Raven Matrices: With Dexter
Authors: Partchev Ivailo
Published: May 2020

We analyze a 12-item version of Raven’s Standard Progressive Matrices test, traditionally scored with the sum score. We discuss some important differences between assessment in practice and psychometric modelling. We demonstrate some advanced diagnostic tools in the freely available R package, dexter. We find that the first item in the test functions badly—at a guess, because the subjects were not given exercise items before the live test.
A Mokken Scale Analysis of the Last Series of the Standard Progressive Matrices (SPM-LS)
Authors: Myszkowski Nils
Published: May 2020

Raven’s Standard Progressive Matrices (Raven 1941) is a widely used 60-item long measure of general mental ability. It was recently suggested that, for situations where taking this test is too time consuming, a shorter version, comprised of only the last series of the Standard Progressive Matrices (Myszkowski and Storme 2018) could be used, while preserving satisfactory psychometric properties (Garcia-Garzon et al. 2019; Myszkowski and Storme 2018). In this study, I argue, however, that some psychometric properties have been left aside by previous investigations. As part of this special issue on the reinvestigation of Myszkowski and Storme’s dataset, I propose to use the non-parametric Item Response Theory framework of Mokken Scale Analysis (Mokken 1971, 1997) and its current developments (Sijtsma and van der Ark 2017) to shed new light on the SPM-LS. Extending previous findings, this investigation indicated that the SPM-LS had satisfactory scalability ( H = 0.469 ), local independence and reliability ( M S = 0.841 , L C R C = 0.874 ). Further, all item response functions were monotonically increasing, and there was overall evidence for invariant item ordering ( H T = 0.475 ), supporting the Double Monotonicity Model (Mokken 1997). Item 1, however, appeared problematic in most analyses. I discuss the implications of these results, notably regarding whether to discard item 1, whether the SPM-LS sum scores can confidently be used to order persons, and whether the invariant item ordering of the SPM-LS allows to use a stopping rule to further shorten test administration.
Response Surface Methodology and Artificial Neural Network-Based Models for Predicting Performance of Wire Electrical Discharge Machining of Inconel 718 Alloy
Authors: Lalwani Vishal , Sharma Priyaranjan , Pruncu Catalin Iulian , Unune Deepak Rajendra
Published: May 2020

This paper deals with the development and comparison of prediction models established using response surface methodology (RSM) and artificial neural network (ANN) for a wire electrical discharge machining (WEDM) process. The WEDM experiments were designed using central composite design (CCD) for machining of Inconel 718 superalloy. During experimentation, the pulse-on-time (TON), pulse-off-time (TOFF), servo-voltage (SV), peak current (IP), and wire tension (WT) were chosen as control factors, whereas, the kerf width (Kf), surface roughness (Ra), and materials removal rate (MRR) were selected as performance attributes. The analysis of variance tests was performed to identify the control factors that significantly affect the performance attributes. The double hidden layer ANN model was developed using a back-propagation ANN algorithm, trained by the experimental results. The prediction accuracy of the established ANN model was found to be superior to the RSM model. Finally, the Non-Dominated Sorting Genetic Algorithm-II (NSGA- II) was implemented to determine the optimum WEDM conditions from multiple objectives.
Artificial Structures Steer Morphological Development of Salt Marshes: A Model Study
Authors: Siemes Rutger W. A. , Borsje Bas W. , Daggenvoorde Roy J. , Hulscher Suzanne J. M. H.
Published: May 2020

Salt marshes are increasingly recognized as resilient and sustainable supplements to traditional engineering structures for protecting coasts against flooding. Nevertheless, many salt marshes face severe erosion. There is a consensus that providing structures that create sheltered conditions from high energetic conditions can improve the potential for salt marsh growth. However, little proof is provided on the explicit influence of structures to promote salt marsh growth. This paper investigates how artificial structures can be used to steer the morphological development of salt marshes. A morphological model (Delft3D Flexible Mesh) was applied, which enabled the analysis of various artificial structures with realistic representation. A salt marsh in the Wadden Sea which has seen heavy erosion (lateral retreat rate of 0.9 m/year) served as case study. We simulate both daily and storm conditions. Hereby, vegetation is represented by an increased bed roughness. The model is able to simulate the governing processes of salt marsh development. Results show that, without artificial structures, erosion of the salt marsh and tidal flat continues. With structures implemented, results indicate that there is potential for salt marsh growth in the study area. Moreover, traditional structures, which were widely implemented in the past, proved to be most effective to stimulate marsh growth. More broadly, the paper indicates how morphological development of a salt marsh can be steered by various configurations of artificial structures.
Airflow Field Around Hippophae rhamnoides in Alpine Semi-Arid Desert
Authors: Tian Lihui , Wu Wangyang , Zhang Dengshan , Yu Yang
Published: May 2020

The research on wind regimes and the wind protection mechanism of sand-fixing plants has mainly relied on wind tunnel experiments; few observations have been made in the field. At the same time, airflow around individual standing vegetation elements and communities is relatively lacking in alpine semi-arid deserts. Therefore, this paper selected 10-year-old Hippophae rhamnoides (sea buckthorn) on sandy land on the eastern shore of Qinghai Lake as the study object. Based on spatial and temporal changes of wind regime in the afforestation forest, a structural simulation of airflow near the plant and at different layers above the ground, and the annual changes in wind protection, we studied the wind protection mechanisms of H. rhamnoides as single elements or communities. The results were as follows: the effective protection length of the sublayer of H. rhamnoides was 1.0 to 1.8 m. The higher the layer, the smaller the decrease in wind velocity behind elements, and the smaller the effective protection length. Wind velocity downwind of H. rhamnoides increased, with height increasing where the airflow decreases rate (R) decreased in the sublayer, and increasing in the middle layer as plant height increased. Meanwhile, the airflow decreases rate (R) was negative in the upper layer because it decreased as the plant height increased. The airflow movement between elements had various directions because the upper layer was prone to fluctuations due to the swinging of the crown and branches, and turbulence was seen at the sublayers owing to the mechanical resistance of the elements. When the wind speed at the standard point was 8.5 m/s and the wind direction was east (E), the increase of airflow velocity at the side and center in the upper layer was more significant, and there was a strong wind zone in the azimuth of NW‒N‒NE‒E‒SE, while the S‒SW‒W azimuth zone was weaker. The sand-fixing shrub H. rhamnoides had a significant windproof function, and the 1.5 m square interval density of H. rhamnoides was suitable for alpine desert control projects.
Elastohydrodynamic Lubrication
Authors: Greenwood James A.
Published: May 2020

The development of EHL theory from its tentative beginnings is outlined, with an account of how Ertel explained its relation to Hertz contact theory. The problems caused by the failure of the early numerical analysts to understand that the film thickness depends on only two variables are emphasised, and answers of the form H = F ( P , S ) given. Early methods of measuring the film thickness are described, but these became archaic with the development of optical EHL. The behaviour of surface roughness as it passes through the high pressure region and suffers elastic deformation is described, and the implication for the traditional Λ -ratio noted. In contrast, the understanding of traction is far from satisfactory. The oil in the high pressure region must become non-Newtonian: the early explanation that the viscosity reduction is the effect of temperature proved inadequate. There must be some form of shear thinning (perhaps according to the Eyring theory), but also a limiting shear stress under which the lubricant shears as an elastic solid. It seems that detailed, and difficult, measurements of the high pressure, high shear-rate behaviour of individual oils are needed before traction curves can be predicted.
New Oscillation Criteria for Advanced Differential Equations of Fourth Order
Authors: Bazighifan Omar , Ahmad Hijaz , Yao Shao-Wen
Published: May 2020

The main objective of this paper is to establish new oscillation results of solutions to a class of fourth-order advanced differential equations with delayed arguments. The key idea of our approach is to use the Riccati transformation and the theory of comparison with first and second-order delay equations. Four examples are provided to illustrate the main results.
Effective Method for Solving Different Types of Nonlinear Fractional Burgers’ Equations
Authors: Mukhtar Safyan , Abuasad Salah , Hashim Ishak , Abdul Karim Samsul Ariffin
Published: May 2020

In this study, a relatively new method to solve partial differential equations (PDEs) called the fractional reduced differential transform method (FRDTM) is used. The implementation of the method is based on an iterative scheme in series form. We test the proposed method to solve nonlinear fractional Burgers equations in one, two coupled, and three dimensions. To show the efficiency and accuracy of this method, we compare the results with the exact solutions, as well as some established methods. Approximate solutions for different values of fractional derivatives together with exact solutions and absolute errors are represented graphically in two and three dimensions. From all numerical results, we can conclude the efficiency of the proposed method for solving different types of nonlinear fractional partial differential equations over existing methods.
The Impact of the Disclosed R & D Expenditure on the Value Relevance of the Accounting Information: Evidence from Greek Listed Firms
Authors: Kalantonis Petros , Schoina Sotiria , Missiakoulis Spyros , Zopounidis Constantin
Published: May 2020

Although many empirical studies have focused on R & D performance models for markets globally, the available financial information for R & D expenditure is limited. In other words, can we assume that the reported accounting information for R & D investment is adequate and valuable? This study empirically investigates the effect of R & D reported information on the value relevance of the accounting information of firms’ financial statements. Specifically, using Ohlson’s equation, it is examined whether changes in stock prices are explained better when R & D factors are included in models, in conjunction with changes in book value and abnormal earnings. We focus on listed firms on the Athens Stock Exchange in order to explore whether R & D expenses are value relevant, in a market which has been affected for a long period by the global economic crisis of 2007. In our findings, we observe that the reported R & D expenses do not have any significant influence on the investors’ choices, in contrast to expectations based on the prior literature. Moreover, the panel data analysis employed in the paper overcomes common methodological problems (such as autocorrelation, multicollinearity, and heteroscedasticity) and allows the estimation of unbiased and efficient estimators.
Pricing European-Style Options in General Lévy Process with Stochastic Interest Rate
Authors: Tan Xiaoyu , Li Shenghong , Wang Shuyi
Published: May 2020

This paper extends the traditional jump-diffusion model to a comprehensive general Lévy process model with the stochastic interest rate for European-style options pricing. By using the Girsanov theorem and Itô formula, we derive the uniform formalized pricing formulas under the equivalent martingale measure. This model contains not only the traditional jump-diffusion model, such as the compound Poisson model, the renewal model, the pure-birth jump-diffusion model, but also the infinite activities Lévy model.
Research on Advertising and Quality of Paid Apps, Considering the Effects of Reference Price and Goodwill
Authors: Liu Haoyu , Liu Shulin
Published: May 2020

Considering users’ perceptions can help managers develop more accurate business strategies. Utilizing optimal control theory, this paper takes the reference price and goodwill of paid App as the state variables to analyze the paid App’s optimal product quality and the platform’s optimal advertising effort with the revenue-sharing contract in the decentralized and integrated systems, respectively. Results show that: (1) The higher the users’ loyalty, the higher the reference price, while for the users who lack loyalty, the impact of reference price can be ignored. (2) When the revenue-sharing rate is equal to or more than 2/3, the paid App is unwilling to bear any advertising costs for the platform. (3) We propose a bilateral subsidy agreement to coordinate the decentralized system smoothly and implement the optimal objective of the integrated system. (4) Importantly, under some mild conditions, we prove that the platform and paid App can both benefit from the agreement.
Transfer of the Neurotoxin β-N-methylamino-l-alanine (BMAA) in the Agro–Aqua Cycle
Authors: Kim Sea-Yong , Rydberg Sara
Published: May 2020

The neurotoxic non-protein amino acid β-N-methylamino-l-alanine (BMAA) is connected to the development of neurodegenerative diseases. BMAA has been shown to accumulate in aquatic ecosystems, and filter-feeding molluscs seem particularly susceptible to BMAA accumulation. The blue mussels farmed along the Swedish coastline in the Baltic Sea are, due to their small size, exclusively used to produce feed for chicken and fish in the agro–aqua cycle. We have investigated the possible biotransfer of BMAA from mussels, via mussel-based feed, into chickens. Chickens were divided into two groups, the control and the treatment. BMAA was extracted from the muscle, liver, brain, and eye tissues in both chicken groups; a UPLC-MS/MS method was subsequently used to quantify BMAA. The results indicate detectable concentrations of BMAA in both chicken groups. However, the BMAA concentration in chicken was 5.65 times higher in the treatment group than the control group, with the highest concentration found in muscle tissue extracted from the treatment group chickens. These data suggest that there is a BMAA transfer route within the agro-aqua cycle, so further investigation is recommended before using mussel-based feed in the chicken industry.
The Key Role of Purine Metabolism in the Folate-Dependent Phenotype of Autism Spectrum Disorders: An In Silico Analysis
Authors: Geryk Jan , Krsička Daniel , Vlčková Markéta , Havlovicová Markéta , Macek Milan , Kremlíková Pourová Radka
Published: May 2020

Folate deficiency in the critical developmental period has been repeatedly associated with an increased risk of Autism spectrum disorders (ASD), but the key pathophysiological mechanism has not yet been identified. In this work, we focused on identifying genes whose defect has similar consequences to folate depletion in the metabolic network. Within the Flux Balance Analysis (FBA) framework, we developed a method of blocked metabolites that allowed us to define the metabolic consequences of various gene defects and folate depletion. We identified six genes (GART, PFAS, PPAT, PAICS, ATIC, and ADSL) whose blocking results in nearly the same effect in the metabolic network as folate depletion. All of these genes form the purine biosynthetic pathway. We found that, just like folate depletion, the blockade of any of the six genes mentioned above results in a blockage of purine metabolism. We hypothesize that this can lead to decreased adenosine triphosphate (ATP) and subsequently, an S-adenosyl methionine (SAM) pool in neurons in the case of rapid cell division. Based on our results, we consider the methylation defect to be a potential cause of ASD, due to the depletion of purine, and consequently S-adenosyl methionine (SAM), biosynthesis.
Staphylococcus sciuri Strain LCHXa is a Free-Living Lithium-Tolerant Bacterium Isolated from Salar de Atacama, Chile
Authors: Salazar-Ardiles Camila , Caimanque Tamara , Galetović Alexandra , Vilo Claudia , Araya Jorge E. , Flores Nataly , Gómez-Silva Benito
Published: May 2020

In addition to the industrial and biomedical applications of lithium, information on the tolerance of microorganisms to high Li concentrations in natural biological systems is limited. Strain LCHXa is a novel free-living Gram-positive, non-motile bacterium strain isolated from water samples taken at Laguna Chaxa, a non-industrial water body with the highest soluble Li content (33 mM LiCl) within the Salar de Atacama basin in northern Chile. Enrichment was conducted in Luria-Bertani (LB) medium supplemented with 1 M LiCl. Strain LCHXa was a Novobiocin-resistant and coagulase negative Staphylococcus. Phylogenetically, strain LCHXa belongs to the species Staphylococcus sciuri. Strain LCHXa grew optimally in LB medium at pH 6–8 and 37 °C, and it was able to sustain growth at molar Li concentrations at 2 M LiCl, with a decrease in the specific growth rate of 85%. Osmoregulation in strain LCHXa partially involves glycine betaine and glycerol as compatible solutes.
A Modular Metagenomics Pipeline Allowing for the Inclusion of Prior Knowledge Using the Example of Anaerobic Digestion
Authors: Becker Daniela , Popp Denny , Harms Hauke , Centler Florian
Published: May 2020

Metagenomics analysis revealing the composition and functional repertoire of complex microbial communities typically relies on large amounts of sequence data. Numerous analysis strategies and computational tools are available for their analysis. Fully integrated automated analysis pipelines such as MG-RAST or MEGAN6 are user-friendly but not designed for integrating specific knowledge on the biological system under study. In order to facilitate the consideration of such knowledge, we introduce a modular, adaptable analysis pipeline combining existing tools. We applied the novel pipeline to simulated mock data sets focusing on anaerobic digestion microbiomes and compare results to those obtained with established automated analysis pipelines. We find that the analysis strategy and choice of tools and parameters have a strong effect on the inferred taxonomic community composition, but not on the inferred functional profile. By including prior knowledge, computational costs can be decreased while improving result accuracy. While automated off-the-shelf analysis pipelines are easy to apply and require no knowledge on the microbial system under study, custom-made pipelines require more preparation time and bioinformatics expertise. This extra effort is minimized by our modular, flexible, custom-made pipeline, which can be adapted to different scenarios and can take available knowledge on the microbial system under study into account.
The Role of Bacterial Membrane Vesicles in the Dissemination of Antibiotic Resistance and as Promising Carriers for Therapeutic Agent Delivery
Authors: Uddin Md Jalal , Dawan Jirapat , Jeon Gibeom , Yu Tao , He Xinlong , Ahn Juhee
Published: May 2020

The rapid emergence and spread of antibiotic-resistant bacteria continues to be an issue difficult to deal with, especially in the clinical, animal husbandry, and food fields. The occurrence of multidrug-resistant bacteria renders treatment with antibiotics ineffective. Therefore, the development of new therapeutic methods is a worthwhile research endeavor in treating infections caused by antibiotic-resistant bacteria. Recently, bacterial membrane vesicles (BMVs) have been investigated as a possible approach to drug delivery and vaccine development. The BMVs are released by both pathogenic and non-pathogenic Gram-positive and Gram-negative bacteria, containing various components originating from the cytoplasm and the cell envelope. The BMVs are able to transform bacteria with genes that encode enzymes such as proteases, glycosidases, and peptidases, resulting in the enhanced antibiotic resistance in bacteria. The BMVs can increase the resistance of bacteria to antibiotics. However, the biogenesis and functions of BMVs are not fully understood in association with the bacterial pathogenesis. Therefore, this review aims to discuss BMV-associated antibiotic resistance and BMV-based therapeutic interventions.
Unraveling the Microbiota of Natural Black cv. Kalamata Fermented Olives through 16S and ITS Metataxonomic Analysis
Authors: Kazou Maria , Tzamourani Aikaterini , Panagou Efstathios Z. , Tsakalidou Effie
Published: May 2020

Kalamata natural black olives are one of the most economically important Greek varieties. The microbial ecology of table olives is highly influenced by the co-existence of bacteria and yeasts/fungi, as well as the physicochemical parameters throughout the fermentation. Therefore, the aim of this study was the identification of bacterial and yeast/fungal microbiota of both olives and brines obtained from 29 cv. Kalamata olive samples industrially fermented in the two main producing geographical regions of Greece, namely Aitoloakarnania and Messinia/Lakonia. The potential microbial biogeography association between certain taxa and geographical area was also assessed. The dominant bacterial family identified in olive and brine samples from both regions was Lactobacillaceae, presenting, however, higher average abundances in the samples from Aitoloakarnania compared to Messinia/Lakonia. At the genus level, Lactobacillus, Celerinatantimonas, Propionibacterium and Pseudomonas were the most abundant. In addition, the yeasts/fungal communities were less diverse compared to those of bacteria, with Pichiaceae being the dominant family and Pichia, Ogataea, and Saccharomyces being the most abundant genera. To the best of our knowledge, this is the first report on the microbiota of both olives and brines of cv. Kalamata black olives fermented on an industrial scale between two geographical regions of Greece using metagenomics analysis.
Modeling Native EHEC Outer Membrane Vesicles by Creating Synthetic Surrogates
Authors: Kehl Alexander , Kuhn Ronja , Detzner Johanna , Steil Daniel , Müthing Johannes , Karch Helge , Mellmann Alexander
Published: May 2020

Enterohemorrhagic Escherichia coli (EHEC) is a zoonotic pathogen responsible for life-threating diseases such as hemolytic uremic syndrome. While its major virulence factor, the Shiga toxin (Stx), is known to exert its cytotoxic effect on various endothelial and epithelial cells when in its free, soluble form, Stx was also recently found to be associated with EHEC outer membrane vesicles (OMVs). However, depending on the strain background, other toxins can also be associated with native OMVs (nOMVs), and nOMVs are also made up of immunomodulatory agents such as lipopolysaccharides and flagellin. Thus, it is difficult to determine to which extent a single virulence factor in nOMVs, such as Stx, contributes to the molecular pathogenesis of EHEC. To reduce this complexity, we successfully developed a protocol for the preparation of synthetic OMVs (sOMVs) with a defined lipid composition resembling the E. coli outer membrane and loaded with specific proteins, i.e., bovine serum albumin (BSA) as a proxy for functional Stx2a. Using BSA for parameter evaluation, we found that (1) functional sOMVs can be prepared at room temperature instead of potentially detrimental higher temperatures (e.g., 45 °C), (2) a 1:10 ratio of protein to lipid, i.e., 100 µg protein with 1 mg of lipid mixture, yields homogenously sized sOMVs, and (3) long-term storage for up to one year at 4 °C is possible without losing structural integrity. Accordingly, we reproducibly generated Stx2a-loaded sOMVs with an average diameter of 132.4 ± 9.6 nm that preserve Stx2a’s injuring activity, as determined by cytotoxicity assays with Vero cells. Overall, we successfully created sOMVs and loaded them with an EHEC toxin, which opens the door for future studies on the degree of virulence associated with individual toxins from EHEC and other bacterial pathogens.
Risk of Hepatitis E among Persons Who Inject Drugs in Hong Kong: A Qualitative and Quantitative Serological Analysis
Authors: Sridhar Siddharth , Chew Nicholas Foo-Siong , Situ Jianwen , Wu Shusheng , Chui Ernest Sing-Hong , Lam Athene Hoi-Ying , Cai Jian-Piao , Cheng Vincent Chi-Chung , Yuen Kwok-Yung
Published: May 2020

Hepatitis E virus (HEV) is an important cause of hepatitis, which can be transmitted via the bloodborne route. However, risk of hepatitis E among persons who inject drugs (PWIDs) is poorly understood. This study aimed to elucidate whether PWIDs are at risk for hepatitis E. We performed HEV IgM, IgG and nucleic acid detection on a cohort of 91 PWIDs and 91 age- and sex-matched organ donors. Blood HEV IgG was measured using the WHO HEV antibody standard. The effects of age, gender and addictive injection use on HEV serostatus and concentration were assessed. HEV IgG seroprevalence was 42/91 (46.2%) in the PWID group and 20/91 (22%) in the donor group (odds ratio = 3.04 (1.59–5.79), p = 0.0006). The median HEV IgG concentration was 5.8 U/mL (IQR: 2.5–7.9) in the PWID group and 2.1 U/mL (IQR: 1.2–5.3) in the donor group (p = 0.005). Increasing age and addictive injection use were significantly associated with HEV IgG serostatus, but only addictive injection use was associated with HEV IgG concentration (p = 0.024). We conclude that PWIDs are at increased risk for hepatitis E and are prone to repeated HEV exposure and reinfection as indicated by higher HEV IgG concentrations.
The Impact of Human Milk on Necrotizing Enterocolitis: A Systematic Review and Meta-Analysis
Authors: Altobelli Emma , Angeletti Paolo Matteo , Verrotti Alberto , Petrocelli Reimondo
Published: May 2020

Background. Premature infants receiving breastfeed have a lower incidence of NEC than those fed preterm formula. This study aimed: (1) to update a systematic review and meta-analyses to evaluate the relationship between feeding and necrotizing enterocolitis (NEC) in low weight premature infants; (2) to conduct meta-regression analyses by subgroups; (3) to describe geographical distribution of milk banks in the world. Methods. Papers included in the meta-analysis were updated as of June 2019. Relative risks were used as a measure of effect size. Random effect models were used to account for different sources of variation among studies. For milk banks, the data reviewed by the literature were integrated with the information collected from countries’ institutional sites and milk bank networks. Results. Thirty-two papers were included in meta-analysis: six randomized controlled trials (RCTs) and 26 observational studies (OS). The census has found 572 milk banks around in the world. Brazil has the most active milk banks. RCTs meta-analysis indicates a risk reduction of NEC using human milk respect to formula: Relative risk (RR) = 0.62 (0.42–0.93). Seven OS compared quantities lower than human milk or higher than the 50th quantile showing a risk reduction of NEC:RR = 0.51 (0.31–0.85); 3 OS that evaluated human milk versus mixed feeding showing that human milk has a protective role on the development of NEC:RR = 0.74 (0.63–0.91). Results of subgroups analysis show that the risk reduction is statistically significant only for studies in which premature infants are given both their own and donated breastmilk. Conclusions. The possibility of preserving human milk and promoting donations guarantees an improvement in the health of newborns.
Interventions Delivered in the Community Pharmacy to Manage Allergic Rhinitis- A Systematic Review of the Literature
Authors: José Jéssica , Cvetkovski Biljana , Kritikos Vicky , Tan Rachel , Bosnic-Anticevich Sinthia , Lourenço Olga
Published: May 2020

Pharmacists have a valuable role in the management of allergic rhinitis (AR) at the community pharmacy level. This role has been reported extensively in numerous papers. However, a systematic review of the available literature and a comprehensive analysis of the outcomes has not been published. This systematic review aimed to evaluate the impact of interventions developed by pharmacists on clinical AR outcomes. A thorough search was performed in three electronic databases, including studies published between January 2000 and June 2019. After the selection process, only three articles met the inclusion criteria and were further analysed. Despite the scarcity of the available studies, in all of them was clear that the pharmacist plays a pivotal role in the management of AR, significantly improving the patients’ quality of life and symptom control. This systematic review also stresses the utmost importance to investigate and report practices and interventions developed by pharmacists using measurable outcomes.
MCA1 and MCA2 Are Involved in the Response to Hypergravity in Arabidopsis Hypocotyls
Authors: Hattori Takayuki , Otomi Yasuhiro , Nakajima Yohei , Soga Kouichi , Wakabayashi Kazuyuki , Iida Hidetoshi , Hoson Takayuki
Published: May 2020

Plants respond to and resist gravitational acceleration, but the mechanism of signal perception in the response is unknown. We studied the role of MCA (mid1-complementing activity) proteins in gravity perception by analyzing the expression of the MCA1 and MCA2 genes, and the growth of hypocotyls of mca mutants, under hypergravity conditions in the dark. An MCA1 promoter::GUS fusion reporter gene construct (MCA1p::GUS) and MCA2p::GUS were expressed almost universally in etiolated seedlings. Under hypergravity conditions, the expression levels of both genes increased compared with that under the 1 g condition, and remained higher, especially in the basal supporting region. On the other hand, mca-null and MCA-overexpressing seedlings showed normal growth under the 1 g condition. Hypergravity suppressed elongation growth of hypocotyls, but this effect was reduced in hypocotyls of mca-null mutants compared with the wild type. In contrast, MCA-overexpressing seedlings were hypersensitive to increased gravity; suppression of elongation growth was detected at a lower gravity level than that in the wild type. These results suggest that MCAs are involved in the perception of gravity signals in plants, and may be responsible for resistance to hypergravity.
Characterization of the Bulk Flow Properties of Industrial Powders from Shear Tests
Authors: Macri Domenico , Chirone Roberto , Salehi Hamid , Sofia Daniele , Materazzi Massimiliano , Barletta Diego , Lettieri Paola , Poletto Massimo
Published: May 2020

Bulk flow properties from shear analysis of compacted powders can be evaluated following different approaches. Experimental values of shear stresses obtained by conventional shear cells are traditionally used to build yield loci, from which the most relevant flow properties could be found. Such flow properties play an important role in determining their performance under fluidization conditions. In this work, a useful app, named cYield, was developed by using the new Matlab’s App Developer environment. This tool enables users to calculate both linear (Coulomb) and non-linear (Warren–Spring) yield loci as the best fitting of the σ-τ experimental shear points. It also provides a wide range of statistical information related to the quality of the outcomes obtained. The different features of the tool are presented, and the crucial steps for the execution of its calculations are illustrated. Moreover, it has been applied for the yield loci analysis of four different materials traditionally used in manufacturing processes. The results confirm that the flow behavior of many industrial powders, especially if cohesive, is better described by a non-linear yield locus.
Combining Unmanned Aircraft Systems and Image Processing for Wastewater Treatment Plant Asset Inspection
Authors: Sancho Martínez Jorge , Fernández Yadira Bajón , Leinster Paul , Casado Mónica Rivas
Published: May 2020

Wastewater treatment plants are essential for preserving the water quality of freshwater and marine ecosystems. It is estimated that, in the UK, as much as 11 billion liters of wastewater are treated on a daily basis. Effective and efficient treatment of wastewater requires treatment plants to be maintained in good condition. Recent studies have highlighted the potential of unmanned aircraft systems (UASs) and image processing to be used in autonomous and automated monitoring systems. However, the combined use of UASs and image processing for wastewater treatment plant inspections has not yet been tested. This paper presents a novel image processing-UAS framework for the identification of failures in trickling filters and activated sludge facilities. The results show that the proposed framework has an accuracy of 95% in the detection of failures in activated sludge assets, with the accuracy ranging between 55% and 81% for trickling filters. These results are promising and they highlight the potential use of the technology for the inspection of wastewater treatment plants.
Using Satellite Interferometry to Infer Landslide Sliding Surface Depth and Geometry
Authors: Intrieri Emanuele , Frodella William , Raspini Federico , Bardi Federica , Tofani Veronica
Published: May 2020

Information regarding the shape and depth of a landslide sliding surface (LSS) is fundamental for the estimation of the volume of the unstable masses, which in turn is of primary importance for the assessment of landslide magnitude and risk scenarios as well as in refining stability analyses. To assess an LSS is not an easy task and is generally time-consuming and expensive. In this work, a method existing in the literature, based on the inclination of movement vectors along a cross-section to estimate the depth and geometry LSSs, is used for the first time while exploiting satellite interferometric data. Given the advent of satellite interferometric data and the related increasing availability of spatially dense and accurate measurements, we test the effectiveness of this method—here named the vector inclination method (VIM)—to four case landslides located in Italy characterized by different types of movement, kinematics and volume. Geotechnical and geophysical information of the LSS is used to validate the method. Our results show that each of the presented cases provides useful insight into the validity of VIM using satellite interferometric data. The main advantages of VIM applied to satellite interferometry are that it enables estimation of the LSS with a theoretical worldwide coverage, as well as with no need for onsite instrumentation or even direct access; however, a good density of measurement points in both ascending and descending geometry is necessary. The combined use of VIM and traditional investigations can provide a more accurate LSS model.
A Comparison of Estimating Crop Residue Cover from Sentinel-2 Data Using Empirical Regressions and Machine Learning Methods
Authors: Ding Yanling , Zhang Hongyan , Wang Zhongqiang , Xie Qiaoyun , Wang Yeqiao , Liu Lin , Hall Christopher C.
Published: May 2020

Quantifying crop residue cover (CRC) on field surfaces is important for monitoring the tillage intensity and promoting sustainable management. Remote-sensing-based techniques have proven practical for determining CRC, however, the methods used are primarily limited to empirical regression based on crop residue indices (CRIs). This study provides a systematic evaluation of empirical regressions and machine learning (ML) algorithms based on their ability to estimate CRC using Sentinel-2 Multispectral Instrument (MSI) data. Unmanned aerial vehicle orthomosaics were used to extracted ground CRC for training Sentinel-2 data-based CRC models. For empirical regression, nine MSI bands, 10 published CRIs, three proposed CRIs, and four mean textural features were evaluated using univariate linear regression. The best performance was obtained by a three-band index calculated using (B2 − B4)/(B2 − B12), with an R2cv of 0.63 and RMSEcv of 6.509%, using a 10-fold cross-validation. The methodologies of partial least squares regression (PLSR), artificial neural network (ANN), Gaussian process regression (GPR), support vector regression (SVR), and random forest (RF) were compared with four groups of predictors, including nine MSI bands, 13 CRIs, a combination of MSI bands and mean textural features, and a combination of CRIs and textural features. In general, ML approaches achieved high accuracy. A PLSR model with 13 CRIs and textural features resulted in an accuracy of R2cv = 0.66 and RMSEcv = 6.427%. An RF model with predictors of MSI bands and textural features estimated CRC with an R2cv = 0.61 and RMSEcv = 6.415%. The estimation was improved by an SVR model with the same input predictors (R2cv = 0.67, RMSEcv = 6.343%), followed by a GPR model based on CRIs and textural features. The performance of GPR models was further improved by optimal input variables. A GPR model with six input variables, three MSI bands and three textural features, performed the best, with R2cv = 0.69 and RMSEcv = 6.149%. This study provides a reference for estimating CRC from Sentinel-2 imagery using ML approaches. The GPR approach is recommended. A combination of spectral information and textural features leads to an improvement in the retrieval of CRC.
Predictive Analytics for Identifying Land Cover Change Hotspots in the Mekong Region
Authors: Poortinga Ate , Aekakkararungroj Aekkapol , Kityuttachai Kritsana , Nguyen Quyen , Bhandari Biplov , Soe Thwal Nyein , Priestley Hannah , Kim Jiwon , Tenneson Karis , Chishtie Farrukh , Towashiraporn Peeranan , Saah David
Published: May 2020

Understanding land cover change dynamics and potential pathways of change is of critical importance for sustainable resource management, to promote food security and resilience on a range of spatial scales. Data scarcity is a key concern, however, with the availability of free Earth Observation (EO) data, such challenges can be suitably addressed. In this research we have developed a robust machine learning (random forest) approach utilizing EO and Geographic Information System (GIS) data, which enables an innovative means for our simulations to be driven only by historical drivers of change and hotspot prediction based on probability to change. We used the Mekong region as a case study to generate a training and validation sample from historical land cover patterns of change and used this information to train a random forest machine learning model. Data samples were created from the SERVIR-Mekong land cover data series. Data sets were created for 2 categories both containing 8 classes. The 2 categories included—any generic class to change into a specific one and vice versa. Classes included the following: Aquaculture; Barren; Cropland; Flooded Forest; Mangroves; Forest; Plantations; Wetlands; and Urban. The training points were used to sample a series of satellite-derived surface reflectance products and other data layers such as information on slope, distance to road and census data, which represent the drivers of change. The classifier was trained in binary mode and showed a clear separation between change and no change. An independent validation dataset of historical change pixels show that all median change probabilities are greater than 80% and all lower quantiles, except one, are greater than 70%. The 2018 probability change maps show high probabilities for the Plantations and Forest classes in the ‘Generic to Specific’ and ’Specific to generic’ category, respectively. A time-series analysis of change probability shows that forests have become more likely to convert into other classes during the last two decades, across all countries. We successfully demonstrated that historical change patters combined with big data and machine learning technologies are powerful tools for predictive change analytics on a planetary scale.
Habitat Suitability Estimation Using a Two-Stage Ensemble Approach
Authors: Rew Jehyeok , Cho Yongjang , Moon Jihoon , Hwang Eenjun
Published: May 2020

Biodiversity conservation is important for the protection of ecosystems. One key task for sustainable biodiversity conservation is to effectively preserve species’ habitats. However, for various reasons, many of these habitats have been reduced or destroyed in recent decades. To deal with this problem, it is necessary to effectively identify potential habitats based on habitat suitability analysis and preserve them. Various techniques for habitat suitability estimation have been proposed to date, but they have had limited success due to limitations in the data and models used. In this paper, we propose a novel scheme for assessing habitat suitability based on a two-stage ensemble approach. In the first stage, we construct a deep neural network (DNN) model to predict habitat suitability based on observations and environmental data. In the second stage, we develop an ensemble model using various habitat suitability estimation methods based on observations, environmental data, and the results of the DNN from the first stage. For reliable estimation of habitat suitability, we utilize various crowdsourced databases. Using observational and environmental data for four amphibian species and seven bird species in South Korea, we demonstrate that our scheme provides a more accurate estimation of habitat suitability compared to previous other approaches. For instance, our scheme achieves a true skill statistic (TSS) score of 0.886, which is higher than other approaches (TSS = 0.725 ± 0.010).