Palynological analysis of genus Geranium (Geraniaceae) and its systematic implications using scanning electron microscopy

Pollen morphology of 23 species belonging to Geranium have been studied in details, which represent eight sections of two subgenera i.e., G. sect. Dissecta, Geranium, and Tuberosa of subgen. Geranium, Divaricata, Lucida, Ruberta and Trilopha of subgen. Robertium. These plant species were collected from different phytogeographical regions of Iran. The palynological investigation was done using scanning electron microscopy (SEM) techniques. Different palyno-morphological features have been observed, and the closely related species were distinguished. We used different multivariate statistical methods to reveal the species relationships. Ward clustering analyses have been done to check out the relationship among the species. The shapes of pollen grains were monad, radially symmetric, isopolar, apertures were tricolporate, and of spheroid, prolate-spheroid or sub-prolate classes. Three pollen types were recognized on the basis of differences in exine sculpturing pattern: reticulate-clavate, striate-rugulate, reticulum cristatum with clavae. Observed differences were not of diagnostic importance in subgenera and sections level. The main objective of this study is to find distinguish pollen characters in the species of the genus Geranium and to elucidate their systematics importance.

Essential oils of Eplingiella fruticosa populations: chemical, antioxidant, and cytotoxic analyses

This study investigates the variations in the chemical profiles and biological activities (antioxidant and cytotoxic) of Eplingiella fruticosa from the state of Sergipe, an endemic species from the Northeast region of Brazil. The essential oils were extracted from six populations by hydrodistillation and analyzed by GC/MS-FID. Cluster analysis was performed with the data of the constituents of the essential oils, and then a dissimilarity matrix, based on Euclidean distances, and a dendrogram, through the Ward clustering method, were constructed. The antioxidant activity of the essential oils was tested by different assays (DPPH, ABTS, β-carotene, and FRAP), and cytotoxic activity was tested by the SRB assay. The compounds found in greater amounts were α-pinene, β-pinene, 1,8-cineole, camphor, borneol, δ-elemene, α-cubebene, α-ylangene, (E)-caryophyllene, germacrene D, bicyclogermacrene, trans-calamenene, spathulenol, caryophyllene oxide, and viridiflorol. These compounds defined the formation of two groups. The first group was composed of the populations of São Cristóvão, Itaporanga, Japaratuba, and Malhada dos Bois municipalities and was characterized by the presence of the monoterpene camphor (8.39-11.27%) as the compound of greatest concentration in relation to the other municipal areas. The second group was composed of the populations of Moita Bonita and Pirambu municipalities and was characterized by the major presence of the sesquiterpene bicyclogermacrene (7.45% and 10.98%). The plants exhibited weak effects in terms of antioxidant activity; however, the essential oil showed significant toxicity for the lines A549 (51.00% cell viability) in the population of Japaratuba, and B16F10 (64.94% cell viability) in Malhada dos Bois. The observations of this study may open a way to optimize the use of the E. fruticosa populations in relation to their cytotoxic properties.

Analisis Perbandingan Pengelompokan Indeks Pembangunan Manusia Indonesia Tahun 2019 dengan Metode Partitioning dan Hierarchical Clustering

Indeks Pembangunan Manusia (IPM) merupakan indikator penting dalam pengukuran tingkat keberhasilan pembangunan kualitas hidup manusia. Pengelompokan Indeks Pembangunan Manusia (IPM) bertujuan untuk membagi wilayah-wilayah ke dalam kelompok berdasarkan Indeks Pembangunan Manusia wilayah tersebut tahun 2019. Pengelompokan Indeks Pembangunan Manusia Indonesia tahun 2019 membandingkan metode Partitioning Clustering dan Hierarchical Clustering. Algoritma Partitioning Clustering yang digunakan adalah algoritma K-Means Clustering, sedangkan algoritma Hierarchical Clustering adalah algoritma Agglomerative Ward Clustering. Hasil yang diperoleh adalah metode terbaik untuk pengelompokan provinsi di Indonesia berdasarkan Indeks Pembangunan Manusia untuk tahun 2019 adalah metode K-Means Clustering dengan jumlah kluster optimum adalah 6. Metode ini memberikan Silhoutte Score sebesar 0,6291, Calinski-Harabasz Index sebesar 241,8875, dan Davies-Bouldin Index sebesar 0,3038. Sedangkan metode terbaik untuk pengelompokan kabupaten/kota di Indonesia berdasarkan Indeks Pembangunan Manusia untuk tahun 2019 adalah metode K-Means Clustering dengan jumlah kluster optimum adalah 6. Metode ini memberikan Silhoutte Score sebesar 0,5511, Calinski-Harabasz Index sebesar 1525,4007, dan Davies-Bouldin Index sebesar 0,5234.

Association between Frailty and 90-Day Outcomes amongst the Chinese Population: A Hospital-Based Multicentre Cohort Study

<b><i>Introduction:</i></b> Frailty has gained increasing attention as it is by far the most prevalent geriatric condition amongst older patients which heavily impacts chronic health status. However, the relationship between frailty and adverse health outcomes in China is far from clear. This study explored the relation between frailty and a panel of adverse health outcomes. <b><i>Methods:</i></b> We performed a multicentre cohort study of older inpatients at 6 large hospitals in China, with two-stage cluster sampling, from October 2018 to April 2019. Frailty was measured according to the FRAIL scale and categorized into robust, pre-frail, and frail. A multivariable logistic regression model and multilevel multivariable negative binomial regression model were used to analyse the relationship between frailty and adverse outcomes. Outcomes were length of hospitalization, as well as falls, readmission, and mortality at 30 and 90 days after enrolment. All regression models were adjusted for age, sex, BMI, surgery, and hospital ward. <b><i>Results:</i></b> We included 9,996 inpatients (median age 72 years and 57.8% male). The overall mortality at 30 and 90 days was 1.23 and 1.88%, respectively. At 30 days, frailty was an independent predictor of falls (odds ratio [OR] 3.19; 95% CI 1.59–6.38), readmission (OR 1.45; 95% CI 1.25–1.67), and mortality (OR 3.54; 95% confidence interval [CI] 2.10–5.96), adjusted for age, sex, BMI, surgery, and hospital ward clustering effect. At 90 days, frailty had a strong predictive effect on falls (OR 2.10; 95% CI 1.09–4.01), readmission (OR 1.38; 95% CI 1.21–1.57), and mortality (OR 6.50; 95% CI 4.00–7.97), adjusted for age, sex, BMI, surgery, and hospital ward clustering effect. There seemed to be a dose-response association between frailty categories and fall or mortality, except for readmission. <b><i>Conclusions:</i></b> Frailty is closely related to falls, readmission, and mortality at 30 or 90 days. Early identification and intervention for frailty amongst older inpatients should be conducted to prevent adverse outcomes.

Determinants of Guest Experience in Airbnb: A Topic Modeling Approach Using LDA

This study inductively analyzes the topics of interest that drive customer experience and satisfaction within the sharing economy of the accommodation sector. Using a dataset of 1,086,800 Airbnb reviews across New York City, the text is preprocessed and latent Dirichlet allocation is utilized in order to extract 43 topics of interest from the user-generated content. The topics fall into one of several categories, including the general evaluation of guests, centralized or decentralized location attributes of the accommodation, tangible and intangible characteristics of the listed units, management of the listing or unit, and service quality of the host. The deeper complex relationships between topics are explored in detail using hierarchical Ward Clustering.

Extensive Hospital In-Ward Clustering Revealed By Molecular Characterization of Influenza A Virus Infection

Background Nosocomial transmission of influenza A virus (InfA) infection is not fully recognized. The aim of this study was to describe the characteristics of hospitalized patients with InfA infections during an entire season and to investigate in-ward transmission at a large, acute-care hospital. Methods During the 2016–17 season, all hospitalized patients ≥18 years old with laboratory-verified (real-time polymerase chain reaction) InfA were identified. Cases were characterized according to age; sex; comorbidity; antiviral therapy; viral load, expressed as cycle threshold values; length of hospital stay; 30-day mortality; and whether the InfA infection met criteria for a health care–associated influenza A infection (HCAI). Respiratory samples positive for InfA that were collected at the same wards within 7 days were chosen for whole-genome sequencing (WGS) and a phylogenetic analysis was performed to detect clustering. For reference, concurrent InfA strains from patients with community-acquired infection were included. Results We identified a total of 435 InfA cases, of which 114 (26%) met the HCAI criteria. The overall 30-day mortality rate was higher among patients with HCAI (9.6% vs 4.6% among non-HCAI patients), although the difference was not statistically significant in a multivariable analysis, where age was the only independent risk factor for death (P &lt; .05). We identified 8 closely related clusters (involving ≥3 cases) and another 10 pairs of strains, supporting in-ward transmission. Conclusions We found that the in-ward transmission of InfA occurs frequently and that HCAI may have severe outcomes. WGS may be used for outbreak investigations, as well as for evaluations of the effects of preventive measures.

A Novel Classification and Identification Scheme of Emitter Signals Based on Ward’s Clustering and Probabilistic Neural Networks with Correlation Analysis

The rapid development of modern communication technology makes the identification of emitter signals more complicated. Based on Ward’s clustering and probabilistic neural networks method with correlation analysis, an ensemble identification algorithm for mixed emitter signals is proposed in this paper. The algorithm mainly consists of two parts, one is the classification of signals and the other is the identification of signals. First, self-adaptive filtering and Fourier transform are used to obtain the frequency spectrum of the signals. Then, the Ward clustering method and some clustering validity indexes are used to determine the range of the optimal number of clusters. In order to narrow this scope and find the optimal number of classifications, a sufficient number of samples are selected in the vicinity of each class center to train probabilistic neural networks, which correspond to different number of classifications. Then, the classifier of the optimal probabilistic neural network is obtained by calculating the maximum value of classification validity index. Finally, the identification accuracy of the classifier is improved effectively by using the method of Bivariable correlation analysis. Simulation results also illustrate that the proposed algorithms can accurately identify the pulse emitter signals.

Network analysis as a method of assessing terms in a dataset comprising eating disorders

Eating disorders are central reason of physical and psycho-social morbidity. Several factors have been identified as being associated with the prevalence and progression of eating disorders in humans. Scientific investigation was carried out to assess the usage of terms in manuscript titles of nearly 900 published articles followed by network analysis and network centralities using R programming. The tm package, term document matrix function was utilized to create a term document matrix (TDM) from the corpus. A binary word matrix comprising 17 terms was created based on higher probability of occurring a term in a column. An agglomerative hierarchical clustering technique using ward clustering algorithm was presented. A data frame from the TDM was created to store data and used to plot word cloud based on word frequencies. An undirected network graph was plotted based on terms that appeared in the term matrix. Centralization measures such as Degree centrality, Closeness, Eigenvector and betweenness Centrality were reported.

Epidemiology of Clostridium Difficile Infection in a Large Hospital in Northern Italy: Questioning the Ward-Based Transmission

Background:Clostridium Difficileinfection (CDI) is considered a ward-based nosocomial infection, due to contagion among patients. Molecular studies recently questioned ward-based contact for disease spread.Objective:To investigate whether it is plausible that CDI spread in San Martino Hospital of Genoa was due to a ward-based contact and patient-to-patient diffusion.Methods:We conducted a retrospective cohort study of CDI cases from April 2010 to March 2015. We referred to Hospital data set and Admission Service. Multilevel modelling approach and ecological analysis were used to assessC. difficileinfection risk according to wards and time of occurrence. Six representative CD strains were ribotyped to assess a possible equivalence.Results:The assessment of 514 CDI cases showed that the risk of disease and rate of incidence in wards were independent, while frequency of cases and number of wards involved exhibited a positive relationship, excluding the typical epidemic pattern of contagious diffusion,i.e., many cases in few wards. The extra-binomial variability due to ward clustering was not significant, indicating homogeneity in the probability of CDI occurrence across all wards. Three hundred sixty-eight patients changed ward, without showing connection between the frequency of cases in new wards and incidence among new subjects. Trigonometric components described a significant contribution of seasonality, with excess of CDI cases during the winter months. Molecular analysis showed different ribotypes of CD strains from the same ward.Conclusion:From our results it seems unlikely that in our institution CDI occurrence is due to ward-based contact and inter-human contagion of the organism.

Morphological Character Analysis and Signal Cryptic Speciation in Lasiodiplodia theobromae on Cashew (Anacardium occidentale L.)

Lasiodiplodia theobromae (Pat) Griffon & Maubl. is a pathogen causing inflorescence dieback disease of cashew in Nigeria and also a common pathogen with a wide host range in the tropics and sub-tropics. The character variations in this pathogen necessitate better understanding of it towards development of management strategies. Isolates identified as L. theobromae were cultured from inflorescence dieback disease of cashew across growing ecologies of Nigeria and studied base on morphological characters. Variability in mycelial texture and colour, conidia and septa sizes and pycnidia production were recorded in this study. The Principal Component Analysis (PCA) and WARD clustering analysis identified four well-supported traits within the isolate group. Isolates within each cluster was: 2, 2, 4 and 1 respectively and isolate CDA1416 (Obollo-Afor) and CDA2924 (Idi-Ayunre) in cluster III were the most similar. Members within clusters I and II united at the semi-partial R-Square distance of 0.0294 and 0.0278 respectively. Isolate CDA2308 (Oro) was distinguished among others and signal a potential cryptic specie, differences in these isolates were supported by conidial morphology and textural variations. This understanding will form the bases for development of diseases management strategy against the pathogen.