Noninvasive Risk Prediction Models for Heart Failure Using Proportional Jaccard Indices and Comorbidity Patterns.

Background: In the post-coronavirus disease 2019 (COVID-19) era, remote diagnosis and precision preventive medicine have emerged as pivotal clinical medicine applications. This study aims to develop a digital health-monitoring tool that utilizes electronic medical records (EMRs) as the foundation for performing a non-random correlation analysis among different comorbidity patterns for heart failure (HF). Methods: Novel similarity indices, including proportional Jaccard index (PJI), multiplication of the odds ratio proportional Jaccard index (OPJI), and alpha proportional Jaccard index (APJI), provide a fundamental framework for constructing machine learning models to predict the risk conditions associated with HF. Results: Our models were constructed for different age groups and sexes and yielded accurate predictions of high-risk HF across demographics. The results indicated that the optimal prediction model achieved a notable accuracy of 82.1% and an area under the curve (AUC) of 0.878. Conclusions: Our noninvasive HF risk prediction system is based on historical EMRs and provides a practical approach. The proposed indices provided simple and straightforward comparative indicators of comorbidity pattern matching within individual EMRs. All source codes developed for our noninvasive prediction models can be retrieved from GitHub.

What are the Andean Colombian anurans? Empirical regionalization proposals vs. observed patterns of compositional dissimilarity.

Background: Defining Andean anurans through their altitudinal limits has been a common practice in species lists, studies of responses to climate change among others, especially in the northern Andes. At least three proposals to differentiate Andean anurans from lowland anurans through elevation and at least one to differentiate Andean anurans from high mountain anurans have been formulated. However, the most frequently used altitudinal limits are not based on theoretical or numerical support, but on observations or practical definitions. Additionally, these proposals have been applied equally to different portions of the Andes, ignoring the fact that even between slopes of the same mountain, environmental conditions (and therefore the distribution of species) may differ. The objective of this work was to evaluate the concordance between the altitudinal distribution of anurans in the Colombian Andes and four different altitudinal delimitation proposals. Methods: We constructed our study area in a manner that allowed us to include species from the Andean region (as traditionally defined) and adjacent lowlands, because if the boundaries criteria were applied they would separate the species of the latter by themselves. We divided the study area into eight entities according to the watershed and the course of the most important rivers. We conducted a bibliographic search for all anurans in the cordilleras and inter-Andean valleys of Colombia and complemented the search with information on anurans for the region available in the GBIF. After curing the species distribution points, we generated elevation bands of 200 m amplitude for both the study area and for each Andean entity. Subsequently, we performed a cluster analysis to evaluate the grouping of the elevation bands according to their species composition. Results: In none of the cases (neither for the entire study area nor for any of the entities) we found a correspondence of any of the traditionally used boundaries and the altitudinal distribution of Anurans in the Andean region of Colombia. Instead, on average, the altitudinal delimitation proposals arbitrarily spanned the altitudinal distribution of about one third of the species distributed in the study area. Conclusions: We suggest that, although, based on our results, some Andean entities can be divided according to the altitudinal composition of the species that occur in them, we did not find any results that support the idea of a generalizable altitudinal limit for the Colombian Andes. Thus, to avoid biases in studies that may later be used by decision makers, the selection of anuran species in studies in the Colombian Andes should be based on biogeographic, phylogenetic or natural history criteria and not on altitudinal limits as they have been used.

A comparative study of the definitions of intradialytic hypotension correlated with increased mortality to identify universal predictors.

Intradialytic hypotension (IDH) is a common complication in patients undergoing hemodialysis therapy. No consensus on the definition of intradialytic hypotension has been established so far. As a result, coherent and consistent evaluation of its effects and causes is difficult. Some studies have highlighted existing correlations between certain definitions of IDH and the risk of mortality for the patients. This work is mainly focused on these definitions. Our aim is to understand if different IDH definitions, all correlated with increased mortality risk, catch the same onset mechanisms or dynamics. To check whether the dynamics captured by these definitions are similar, we performed analyses of the incidence, of the IDH event onset timing, and checked whether there were similarities between the definitions in those aspects. We evaluated how these definitions overlap with each other and we evaluated which common factors could allow identifying patients at risk of IDH at the beginning of a dialysis session. The definitions of IDH we analyzed through statistical and machine learning approaches, showed a variable incidence on the HD sessions and had different onset time. We found that the set of parameters relevant for the prediction of the IDH was not always the same for the definitions considered. However, it can be observed that some predictors, such as the presence of comorbidities such as diabetes or heart disease, and a low pre-dialysis diastolic blood pressure, have shown universal relevance in highlighting an increased risk of IDH during the treatment. Among those parameters, the one that showed a major importance is the diabetes status of the patients. Diabetes or heart disease presence are permanent risk factors pointing out an increased IDH risk during the treatments, while, pre-dialysis diastolic blood pressure is instead a parameter that can change at every session and should be used to evaluate the specific risk to develop IDH for each session. The identified parameters could be used in the future to train more complex prediction models.

Scientific Appraisal and Therapeutic Properties of Plants Utilized for Veterinary Care in Poonch District of Jammu and Kashmir, India.

The importance of traditional and indigenous knowledge is acknowledged on a worldwide scale for its coexistence principles and sustainable use techniques. In view of this, the present study is an attempt to document the ethno-veterinary plants used by the tribal communities of Western Himalaya. This study also provides the scientific validation of herbal medicines used in ethno-veterinary practices through a reverse pharmacological approach. A total of 59 informants were selected through a non-probability sampling method. Detailed information on the medicinal plants used in ethno-veterinary practices along with their habits and habitats, part/s used, remedy preparation methods, additives/ingredients used during preparation and administration, dosages administered, and route of administration was collected. Data was analyzed for the Relative Frequency of Citations (RFC), Use Values (UV), Informant Consensus Factor (ICF), and Jaccard Index (JI). Further, a reverse pharmacological approach was used for scientific validations of the documented herbal knowledge of plant species. During the study, 56 plant species belonging to 54 genera and 39 families were documented. Asteraceae was the dominant family followed by Lamiaceae, Amaranthaceae and Fabaceae. Life forms were dominated by herbaceous species and leaves were the most common plant parts used. The highest Relative Frequency of Citations (RFC) and Use Values (UV) were recorded for Brassica rapa L. (Brassicaceae). The Pearson correlation coefficient between RFC and UV shows a strong positive correlation between the proportion of uses of a plant species within a sample of informants and the number of times that a particular use of a plant species was mentioned by the informant. Studies of the biological activity of ethno-veterinary plants can provide clues of promising leads for the isolation and identification of useful compounds that may be developed into pharmaceuticals for human welfare.

Comparison of swept-source OCTA and indocyanine green angiography in central serous chorioretinopathy.

BACKGROUND: To compare swept-source optical coherence tomography angiography (SS-OCTA) and indocyanine green angiography (ICGA) in patients with central serous chorioretinopathy (CSC). METHODS: SS-OCTA and ICGA images of 39 eyes with symptomatic CSC were collected and aligned. Spatial overlap of the annotations of the coarse granulated high reflective area on choriocapillary OCTA and the hyperfluorescence area on mid-phase ICGA was calculated according to the Jaccard index (JI). SS-OCTA findings of fellow eyes and changes in SS-OCTA abnormalities during the follow-up were also analyzed. RESULTS: Three main types of abnormalities in choriocapillaris SS-OCTA images were found: type A, coarse granulated high reflective area (39 eyes [100%]); type B, roundish dark halo around Type A (32 eyes [82.1%]); and type C, coarse granulated low reflective area (39 eyes [100%]). The mean JI of type A on SS-OCTA and the hyperfluorescence area on ICGA were 0.55 ± 0.15 for grader 1 and 0.49 ± 0.15 for grader 2. The mean area of type A abnormalities on SS-OCTA and hyperfluorescence on ICGA was 3.976 (IQR, 2.139-8.168) and 3.043 (IQR, 1.408-4.909) mm2 (P = 0.199). The areas of type A, B and C abnormalities on SS-OCTA after laser treatment or observation were 3.36mm2 (IQR, 2.399-9.312), 2.9mm2 (IQR, 2.15-3.7), and 0.19mm2 (IQR, 0.08-0.23), respectively, which was smaller than those in the baseline (7.311mm2 (IQR 3.788-11.209), P < 0.001; 4.3mm2 (IQR, 2.8-9.8), P = 0.002;0.33mm2 (IQR, 0.23-0.38), P < 0.001). The change in the type A, B or C area was not significantly different between the two groups (P = 0.679, 0.732, and 0.892). CONCLUSION: The coarse granulated high reflective area in SS-OCTA corresponded well with the hyperpermeability area in ICGA. SS-OCTA promotes noninvasive visualization and follow-up quantifications of the choroidal vasculature in CSC patients.

Functional brain abnormalities in major depressive disorder using a multiscale community detection approach.

Major depressive disorder (MDD) is a serious disease associated with abnormal brain regions, however, the interconnection between specific brain regions related to depression has not been fully explored. To solve this problem, the paper proposes a novel multiscale community detection method to compare the differences in brain regions between normal controls (NC) and MDD patients. This study adopted the Brainnetome Atlas to divide the brain into 246 regions and extract the time series of each region. The Pearson correlation was used to measure the similarity among different brain regions to conduct the brain functional network and to perform multiscale community detection. The optimal brain community structure of each group was further explored based on the modularized Qcut algorithm, normalized mutual information (NMI), and variation of information (VI). The Jaccard index was then applied to compare the abnormalities of each brain region from different community environments between the brain function networks of NC and MDD patients. The experiments revealed several abnormal brain regions between NC and MDD, including the superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, orbital gyrus, superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, posterior superior temporal sulcus, inferior parietal gyrus, precuneus, postcentral gyrus, insular gyrus, cingulate gyrus, hippocampus and basal ganglia. Finally, a new subnetwork related to cognitive function was discovered, which was composed of the island gyrus and inferior frontal gyrus. All experiments indicated that the proposed method is useful in detecting functional brain abnormalities in MDD, and it can provide valuable insights into the diagnosis and treatment of MDD.

Hierarchical cluster analysis of herbicide modes of action reveals distinct classes of multiple resistance in weeds.

BACKGROUND: The number of weed species resistant to multiple herbicide modes of action (MoAs) has increased over the last 30 years and may in the future render existing herbicide MoAs obsolete for many cropping systems. Yet few predictive tools exist to manage this risk. Using a worldwide dataset of weed species resistant to multiple herbicide MoAs, hierarchical clustering was used to classify MoAs into similar groups in relation to the suite of resistant weed species they have in common. Network analyses then were used to explore the relative importance of species prevalence and similarity in cluster patterns. RESULTS: Hierarchical clustering identified three similarly sized clusters of herbicide MoAs that were linked by the co-occurrence of resistant weeds: Herbicide Resistance Action Committee (HRAC) groups 2, 4, 5 and 9; HRAC groups 12, 14 and 15; and HRAC groups 1, 3 and 22. Cluster membership was consistent with similarities in the physiological or biochemical target of the herbicide MoAs. Network analyses revealed that the number of weed species resistant to two different MoAs was related to the number of weeds known to be resistant to each individual herbicide MoA. CONCLUSIONS: Hierarchical cluster analysis provided new insights into the risk of weeds becoming resistant to more than one herbicide MoA. By clustering herbicide MoAs into three distinct groups, the potential exists for farmers to manage resistance by rotating herbicides between rather than within clusters, as far as crop, weed and environmental conditions allow.

Large-scale Prediction of Drug-Protein Interactions Based on Network Information.

BACKGROUND: The prediction of drug-protein interaction (DPI) plays an important role in drug discovery and repositioning. Unfortunately, traditional experimental validation of DPIs is expensive and time-consuming. Therefore, it is necessary to develop in silico methods for the identification of potential DPIs. METHODS: In this work, the identification of DPIs was performed by the generated recommendation of the unexplored interaction of the drug-protein bipartite graph. Three kinds of recommenders were proposed to predict the potential DPIs. RESULTS: The simulation results showed that the proposed models obtained good performance in crossvalidation and independent test. CONCLUSION: Our recommendation strategy based on collaborative filtering can effectively improve the DPI identification performance, especially for certain DPIs lacking chemical structure similarity or genomic sequence similarity.

Network alignment and similarity reveal atlas-based topological differences in structural connectomes.

The interactions between different brain regions can be modeled as a graph, called connectome, whose nodes correspond to parcels from a predefined brain atlas. The edges of the graph encode the strength of the axonal connectivity between regions of the atlas that can be estimated via diffusion magnetic resonance imaging (MRI) tractography. Herein, we aim to provide a novel perspective on the problem of choosing a suitable atlas for structural connectivity studies by assessing how robustly an atlas captures the network topology across different subjects in a homogeneous cohort. We measure this robustness by assessing the alignability of the connectomes, namely the possibility to retrieve graph matchings that provide highly similar graphs. We introduce two novel concepts. First, the graph Jaccard index (GJI), a graph similarity measure based on the well-established Jaccard index between sets; the GJI exhibits natural mathematical properties that are not satisfied by previous approaches. Second, we devise WL-align, a new technique for aligning connectomes obtained by adapting the Weisfeiler-Leman (WL) graph-isomorphism test. We validated the GJI and WL-align on data from the Human Connectome Project database, inferring a strategy for choosing a suitable parcellation for structural connectivity studies. Code and data are publicly available.

Exploring reported genes of microglia RNA-sequencing data: Uses and considerations.

The advent of RNA-sequencing techniques has made it possible to generate large, unbiased gene expression datasets of tissues and cell types. Several studies describing gene expression data of microglia from Alzheimer's disease or multiple sclerosis have been published, aiming to generate more insight into the role of microglia in these neurological diseases. Though the raw sequencing data are often deposited in open access databases, the most accessible source of data for scientists is what is reported in published manuscripts. We observed a relatively limited overlap in reported differentially expressed genes between various microglia RNA-sequencing studies from multiple sclerosis or Alzheimer's diseases. It was clear that differences in experimental set up influenced the number of overlapping reported genes. However, even when the experimental set up was very similar, we observed that overlap in reported genes could be low. We identified that papers reporting large numbers of differentially expressed microglial genes generally showed higher overlap with other papers. In addition, though the pathology present within the tissue used for sequencing can greatly influence microglia gene expression, often the pathology present in samples used for sequencing was underreported, leaving it difficult to assess the data. Whereas reanalyzing every raw dataset could reduce the variation that contributes to the observed limited overlap in reported genes, this is not feasible for labs without (access to) bioinformatic expertise. In this study, we thus provide an overview of data present in manuscripts and their supplementary files and how these data can be interpreted.

Intra-session test-retest reliability of functional connectivity in infants.

Resting functional MRI studies of the infant brain are increasingly becoming an important tool in developmental neuroscience. Whereas the test-retest reliability of functional connectivity (FC) measures derived from resting fMRI data have been characterized in the adult and child brain, similar assessments have not been conducted in infants. In this study, we examined the intra-session test-retest reliability of FC measures from 119 infant brain MRI scans from four neurodevelopmental studies. We investigated edge-level and subject-level reliability within one MRI session (between and within runs) measured by the Intraclass correlation coefficient (ICC). First, using an atlas-based approach, we examined whole-brain connectivity as well as connectivity within two common resting fMRI networks - the default mode network (DMN) and the sensorimotor network (SMN). Second, we examined the influence of run duration, study site, and scanning manufacturer (e.g., Philips and General Electric) on ICCs. Lastly, we tested spatial similarity using the Jaccard Index from networks derived from independent component analysis (ICA). Consistent with resting fMRI studies from adults, our findings indicated poor edge-level reliability (ICC = 0.14-0.18), but moderate-to-good subject-level intra-session reliability for whole-brain, DMN, and SMN connectivity (ICC = 0.40-0.78). We also found significant effects of run duration, site, and scanning manufacturer on reliability estimates. Some ICA-derived networks showed strong spatial reproducibility (e.g., DMN, SMN, and Visual Network), and were labelled based on their spatial similarity to analogous networks measured in adults. These networks were reproducibly found across different study sites. However, other ICA-networks (e.g. Executive Control Network) did not show strong spatial reproducibility, suggesting that the reliability and/or maturational course of functional connectivity may vary by network. In sum, our findings suggest that developmental scientists may be on safe ground examining the functional organization of some major neural networks (e.g. DMN and SMN), but judicious interpretation of functional connectivity is essential to its ongoing success.

Termite Assemblage and Damage on Tree Trunks in Fast-Growing Teak Plantations of Different Age: A Case Study in West Java, Indonesia.

We conducted surveys of termite assemblages and tree trunks damaged by termites in teakwood (Tectona grandis L.f.) plantations. The surveys were conducted in five-, six-, and nine-year-old plantations. We used a standardized belt-transect to collect termites and build tree inventories. Data of collected termites at the genus and functional-group levels and termites' diversity between plantations were compared with their attack rate. The results showed that four genera of soil recycler termites belonging to groups IIf and III were present across the plantations. Distribution analysis suggested that termite communities might develop from a stochastic distribution to a nonrandom co-occurrence distribution over time. Diversity analysis showed an increased nestedness-resultant diversity contribution to the total dissimilarity over time. Observed attacks on tree trunks were superficial and limited to the outer bark, with group IIf as the main contributor. Furthermore, the level of damage done by termites to tree trunks was positively correlated with increases in the group IIf occupancy area and overgrown understory vegetation. Plantation management by maintaining an adequate understory might suppress termite attacks on fast-growing teakwood, although in the case of our study, termite attacks are inevitable when termites from group IIf were already present.

Automatic Lung Segmentation and Quantification of Aeration in Computed Tomography of the Chest Using 3D Transfer Learning.

BACKGROUND: Identification of lung parenchyma on computer tomographic (CT) scans in the research setting is done semi-automatically and requires cumbersome manual correction. This is especially true in pathological conditions, hindering the clinical application of aeration compartment (AC) analysis. Deep learning based algorithms have lately been shown to be reliable and time-efficient in segmenting pathologic lungs. In this contribution, we thus propose a novel 3D transfer learning based approach to quantify lung volumes, aeration compartments and lung recruitability. METHODS: Two convolutional neural networks developed for biomedical image segmentation (uNet), with different resolutions and fields of view, were implemented using Matlab. Training and evaluation was done on 180 scans of 18 pigs in experimental ARDS (u2Net Pig ) and on a clinical data set of 150 scans from 58 ICU patients with lung conditions varying from healthy, to COPD, to ARDS and COVID-19 (u2Net Human ). One manual segmentations (MS) was available for each scan, being a consensus by two experts. Transfer learning was then applied to train u2Net Pig on the clinical data set generating u2Net Transfer . General segmentation quality was quantified using the Jaccard index (JI) and the Boundary Function score (BF). The slope between JI or BF and relative volume of non-aerated compartment (S JI and S BF , respectively) was calculated over data sets to assess robustness toward non-aerated lung regions. Additionally, the relative volume of ACs and lung volumes (LV) were compared between automatic and MS. RESULTS: On the experimental data set, u2Net Pig resulted in JI = 0.892 [0.88 : 091] (median [inter-quartile range]), BF = 0.995 [0.98 : 1.0] and slopes S JI = -0.2 {95% conf. int. -0.23 : -0.16} and S BF = -0.1 {-0.5 : -0.06}. u2Net Human showed similar performance compared to u2Net Pig in JI, BF but with reduced robustness S JI = -0.29 {-0.36 : -0.22} and S BF = -0.43 {-0.54 : -0.31}. Transfer learning improved overall JI = 0.92 [0.88 : 0.94], P < 0.001, but reduced robustness S JI = -0.46 {-0.52 : -0.40}, and affected neither BF = 0.96 [0.91 : 0.98] nor S BF = -0.48 {-0.59 : -0.36}. u2Net Transfer improved JI compared to u2Net Human in segmenting healthy (P = 0.008), ARDS (P < 0.001) and COPD (P = 0.004) patients but not in COVID-19 patients (P = 0.298). ACs and LV determined using u2Net Transfer segmentations exhibited < 5% volume difference compared to MS. CONCLUSION: Compared to manual segmentations, automatic uNet based 3D lung segmentation provides acceptable quality for both clinical and scientific purposes in the quantification of lung volumes, aeration compartments, and recruitability.

Invertebrate turnover along gradients of anthropogenic salinisation in rivers of two German regions.

Rising salinity in freshwater ecosystems can affect community composition. Previous studies mainly focused on changes in freshwater communities along gradients of absolute levels of electrical conductivity (EC). However, both geogenic and anthropogenic drivers contribute to the EC level and taxa may regionally be adapted to geogenic EC levels. Therefore, we examined the turnover in freshwater invertebrates along gradients of anthropogenic EC change in two regions of Germany. The anthropogenic change of EC was estimated as the difference between the measured EC and the modeled background EC driven by geochemical and climate variables. Turnover in freshwater invertebrates (ß-diversity) was estimated using the Jaccard index (JI). We found that invertebrate turnover between EC gradient categories is generally greater than 47%, with a maximum of approximately 70% in sites with a more than 0.4 mS cm-1 change compared to the baseline (i.e. no difference between predicted and measured EC). The invertebrates Amphinemura sp., Anomalopterygella chauviniana and Leuctra sp. were reliable indicators of low EC change, whereas Potamopyrgus antipodarum indicated sites with the highest EC change. Variability within categories of EC change was slightly lower than within categories of absolute EC. Elevated nutrient concentrations that are often linked to land use may have contributed to the observed change of the invertebrate richness and can exacerbate effects of EC on communities in water. Overall, our study suggests that the change in EC, quantified as the difference between measured EC and modeled background EC, can be used to examine the response of invertebrate communities to increasing anthropogenic salinity concentrations in rivers. However, due to the strong correlation between EC change and observed EC in our study regions, the response to these two variables was very similar. Further studies in areas where EC change and observed EC are less correlated are required. In addition, such studies should consider the change in specific ions.

Bioinformatics analysis of correlation between protein function and intrinsic disorder.

The correlation of molecular function and protein intrinsic disorder is an important aspect of understanding the relationship between function, sequence and structure. This research was inspired by statistical correlation evaluation method described by Xie et al. (J Proteome Res 6 (2007) 1882-1898, reference study), where the authors analyzed the relationship between structure and function of proteins from Swiss-Prot database and where these functions were described with Swiss-Prot function keywords. In this research, we investigated whether the conclusions from the reference study stand for another dataset with richer functional annotation. We used CAFA3 challenge training dataset where the function was described with terms from Gene Ontology (GO terms). In order to compare the results with the previous work, we associated the GO terms with the corresponding Swiss-Prot function keywords. The results were compared with the reference study by first repeating the analysis with Swiss-Prot function keywords and then by GO terms. We used PONDR VSL2b disorder predictor to label over 66,000 CAFA3 proteins as putatively disordered or ordered. Out of 186 Swiss-Prot keywords (belonging to molecular function type) with more than 20 annotated proteins, we found 47 to be highly order related and 44 highly disorder related. Using the same dataset and annotation constraints, out of 1781 GO term (belonging to molecular function type), we found 746 to be highly order related and 564 highly disorder related. GO term results are presented as interactive graphs displaying complex hierarchical structure of Gene Ontology. Comparison of two functional annotations, GO and Swiss-Prot keywords, showed consistent results in cases when it was possible to map a Swiss-Prot keyword to a corresponding GO term. Because of the small number of such cases, we propose a new method for deriving the missing mappings between Swiss-Prot keywords and GO terms with the highest likelihood by measuring similarity (Jaccard index) between sets of protein annotated by different functions. Comparison with results from the reference study revealed prevalence of binding related functions (disorder related) in the current dataset even though the same functions were not present in previous results.

Triple Alignment: Congruency of Perceived Preschool Classroom Social Networks Among Teachers, Children, and Researchers.

Classroom social networks are influential to young children's cognitive, social-emotional, and language development, but assessment and analyses of social networks are complex. Findings have been mixed regarding whether different informants (teachers, children, researchers) are congruent in perceiving classroom social networks. There is also a lack of discussion about the roles of network transformation (converting value networks into binary networks), a required data step for widely used statistical network analyses. This study addressed these issues based on network data of 16 preschool children containing 240 potential dyadic interactions collected from teacher ratings, child nominations, and researcher observations across 44 observation cycles over four school days. Results showed that the three informants were congruent in perceiving the classroom social network, whereas the level of congruency between the teacher-report and the researcher-report networks was the highest. Binary transformation of social networks tended to decrease the level of congruency across informants, although the level of congruency tended to be higher when more stringent binary transformation thresholds were selected.

Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network.

Clinical treatment of skin lesion is primarily dependent on timely detection and delimitation of lesion boundaries for accurate cancerous region localization. Prevalence of skin cancer is on the higher side, especially that of melanoma, which is aggressive in nature due to its high metastasis rate. Therefore, timely diagnosis is critical for its treatment before the onset of malignancy. To address this problem, medical imaging is used for the analysis and segmentation of lesion boundaries from dermoscopic images. Various methods have been used, ranging from visual inspection to the textural analysis of the images. However, accuracy of these methods is low for proper clinical treatment because of the sensitivity involved in surgical procedures or drug application. This presents an opportunity to develop an automated model with good accuracy so that it may be used in a clinical setting. This paper proposes an automated method for segmenting lesion boundaries that combines two architectures, the U-Net and the ResNet, collectively called Res-Unet. Moreover, we also used image inpainting for hair removal, which improved the segmentation results significantly. We trained our model on the ISIC 2017 dataset and validated it on the ISIC 2017 test set as well as the PH2 dataset. Our proposed model attained a Jaccard Index of 0.772 on the ISIC 2017 test set and 0.854 on the PH2 dataset, which are comparable results to the current available state-of-the-art techniques.

Replication of fMRI group activations in the neuroimaging battery for the Mainz Resilience Project (MARP).

Motivated by the recent replicability crisis we tested replicability of functional magnetic resonance imaging (fMRI) group activations in two independent samples. An identical behavioral and fMRI test battery for the longitudinal investigation of stress resilience mechanisms was developed for the Mainz Resilience Project (MARP) and conducted in a discovery (N = 54) and a replication sample (N = 103). The test battery consisted of a stress reactivity task, a reward sensitivity task, a fear conditioning and extinction paradigm, two volitional reappraisal tasks and an emotional interference inhibition task. Replicability of group activations was tested with the Jaccard index and the Intra Class Correlation (ICC). Overall, we observed good to excellent replicability of activations at the whole brain level. Only a minority of contrasts showed unsatisfactory replicability. Replicability at the level of individual regions of interest (ROIs) was generally lower. Tasks with stronger activation in the discovery sample showed better replicability.

Quantitative ethnopharmacological profiling of medicinal shrubs used by indigenous communities of Rawalakot, District Poonch, Azad Jammu and Kashmir, Pakistan

Abstract This paper presents the first comprehensive report on traditional uses of medicinal shrubs of Rawalakot city, district Poonch, Azad Jammu and Kashmir, Pakistan. Ethobotanical data obtained from 120 informants were analyzed by relative frequency citation, use value, family use value, informant consensus factor, fidelity level and Jaccard index. In total, 41 shrubs belonging to 24 families and 34 genera were documented. Rosaceae was reported the most dominant family in the area (six species) and Berberidaceae showed maximum family use value (0.68). Leaves (35%) and fruits (33%) were the most commonly used plant parts and most of the medicines were prepared in the form of decoction. The high informant consensus factor value (0.94) was recorded for diabetic disease category. Medicinal plants with high fidelity level values (100% each) were Berberis lycium, Cydonia oblanga, Ricinus communis, Ziziphus jujuba and Nerium oleander. Berberis lycium was the most significant shrub in the area with highest use value (0.68). Relative frequency citation value was maximum for Rubus ellipticus (0.30), Nerium oleander and Indigofera heterantha (0.10 each). Percentage of similar plant uses ranged from 21.05 to 0.62% and dissimilarity percentage ranged from 32.50 to 0.66%. Out of the 41 shrub species, six were reported with new therapeutic uses and may represent new bioresources. These were Debregeasia salicifolia (diabetes), Desmodium elegans (anti-cancerous), Hibiscus rosa-sinensis (jaundice), Hypericum oblongifolium (arthritis), Sarcococca saligna (tuberculosis), Rubus niveus (chronic cough) and Otostegia limbata (renal disorders). We suggest that species reported with high use value should be involved in cultivation and agricultural practices for their sustainable use and those reported with new therapeutic uses should be employed in further biotechnological, pharmacological and clinical studies in order to validate their traditional uses.

Segmentation and Boundary Detection of Fetal Kidney Images in Second and Third Trimesters Using Kernel-Based Fuzzy Clustering.

Organ segmentation is an important step in Ultrasound fetal images for early prediction of congenital abnormalities and to estimate delivery date. In many applications of 2D medical imaging, they face problems with speckle noise and object contours. Frequent scanning of fetal leads to clinical disturbances to the fetal growth and the quantitative interpretation of Ultrasonic images also a difficult task compared to other image modalities. In the present work a three-stage hybrid algorithm has been developed to segment the US fetal kidney images for the detection of shape and contour. At the first stage the hybrid Mean Median (Hybrid MM) filter is applied to reduce the speckle noise. Then a kernel based Fuzzy C - means clustering is used to detect the shape and contour. Finally, the texture features are obtained from the segmented images. Based on the obtained texture features, the abnormalities are detected. The Gaussian Radial basis function provides an accuracy of 80% at the second and third trimesters with weighted constant ranging from 4 to 8, compared to other global kernel functions. Similarly the proposed method has an accuracy of 86% with compared to other FCM techniques.