Combined Dataset Research Articles

Development of intensity – duration – frequency (IDF) curves using combined rain gauge (RG) and remote sense (TRMM) datasets for Weta Traditional Area in Ghana

This research developed IDF curves and derived empirical equations for the Weta Area to estimate design rainfall. Daily rainfall data of Rain Gauge and Tropical Rainfall Measuring Mission (TRMM) 3B42 over 22 years for Weta were obtained from the Ghana Meteorological Services and National Aeronautics and Space Administration, respectively. The Indian Meteorological Department formula was used to disaggregate the daily data into hourly synthetic series to develop the IDF curves. The combined dataset was subjected to frequency analysis to determine the distribution which best characterize the dataset. The results demonstrated that the IDF curves provided higher intensities for shorter durations (0.08-h, 0.17-h, 0.25 h, and 0.5 h) while lower intensities for longer durations (1 h, 2-h, 6-h, 12 h and 24 h). The rainfall intensities and return periods are parallel and high intensities correlated with brief durations for the same return period. TRMM overestimated rainfall compared to Rain Gauge data. The IDF curves and derived empirical equations are useful scientific tools for hydraulic infrastructure design and water resources planning and management.

Sample design in biodiversity studies matters: a fine-scale study of Lawrence's velvet worm, Peripatopsis lawrencei (Onychophora: Peripatopsidae), reveals hidden diversity.

A fine-scale phylogenetic and phylogeographic analysis of Peripatopsis lawrencei s.l. was conducted with both mitochondrial and nuclear DNA sequence data, using both external morphology and scanning electron microscopy of taxonomically important characters. A total of 119 sequences were used for the mitochondrial cytochrome c oxidase subunit I (COI ) whereas a single representative specimen from each locality was sequenced for the nuclear 18S rRNA locus. Phylogenetic analyses were conducted on the total COI data set and the combined COI + 18S rRNA data set using a Bayesian analysis and maximum likelihood analyses. For the combined DNA sequence data set, a divergence time estimation was further undertaken in BEAST and specimens placed in a phylogenetic framework including all the described Peripatopsis species from South Africa. In addition, a phylogeographic study was conducted exclusively on P. lawrencei s.s. (clade A) using an analysis of molecular variance and haplotype network. Phylogenetic results indicated that, at the Oubos sample locality, two highly distinct genetic lineages were present (clades A and B), whereas a divergence time estimation suggests a Miocene cladogenesis of the novel Oubos lineage. Marked phylogeographic structure was observed for P. lawrencei s.s. (restricted to clade A) across the distribution range with limited maternal dispersal. Morphologically, the two sympatric lineages at Oubos A and B differed in leg pair number, ventral colour and dorsal scale rank counts, as evident from scanning electron microscopy. Our results support the recognition of a distinct species that occurs in sympatry with P. lawrencei s.s. The new species, P. aereus sp. nov. (clade B) is described and the implication for fine-scale taxonomic studies on saproxylic taxa is discussed. ZooBank: urn:lsid:zoobank.org:pub:AB6E0BDA-7B5F-4FD3-A863-BA7C814E278C.

An Epileptic EEG Detection Method Based on Data Augmentation and Lightweight Neural Network.

Epilepsy, an enduring neurological disorder, afflicts approximately 65 million individuals globally, significantly impacting their physical and mental wellbeing. Traditional epilepsy detection methods are labor-intensive, leading to inefficiencies. Although deep learning techniques for brain signal detection have gained traction in recent years, their clinical application advancement is hindered by the significant requirement for high-quality data and computational resources during training. The neural network training initially involved merging two datasets of different data quality, namely Bonn University datasets and CHB-MIT datasets, to bolster its generalization capabilities. To tackle the issues of dataset size and class imbalance, we employed small window segmentation and Synthetic Minority Over-sampling Technique (SMOTE). algorithms to augment and equalize the data. A streamlined neural network architecture was then proposed, drastically reducing the model's training parameters. Notably, a model trained with a mere 9,371 parameters yielded impressive results. The three-classification task on the combined dataset delivered an accuracy of 98.52%, sensitivity of 97.99%, specificity of 99.35%, and precision of 98.44%. The experimental findings of this study underscore the superiority of the proposed method over existing approaches in both model size reduction and accuracy enhancement. As a result, it is more apt for deployment in low-cost, low computational hardware devices, including wearable technology, and various clinical applications. Clinical and Translational Impact Statement- This study is a Pre-Clinical Research. The lightweight neural network is easily deployed on hardware device for real-time epileptic EEG detection.

Revisiting the interplay of trust and contracts: The roles of technological turbulence and dependence disadvantage

Drawing on information processing theory, this study revisits the interplay between trust and contracts by comprehensively examining the impact of the interaction of different types of trust (i.e., ability trust and goodwill trust) and contracts (i.e., control contract and coordination contract) under different uncertainties (i.e., technology turbulence and dependence disadvantage) on supplier opportunism. We build a moderated moderation model to test the proposed hypotheses using a combined dataset of a survey of 200 Chinese manufacturers and secondary data. The results show that coordination contract weakens the effectiveness of ability trust but strengthens the effectiveness of goodwill trust. When firms face a high level of technological turbulence, both types of contracts weaken the effectiveness of ability trust in curbing opportunism, while under a high level of dependence disadvantage, contracts weaken ability trust but reinforce goodwill trust in curbing opportunism. Our results are found to be robust after being verified by a variety of statistical specifications and estimations. The results provide nuanced insights for both the literature and practice.

Developing a Quality Improvement Implementation Taxonomy for Organizational Employee Wellness Initiatives.

Standardized taxonomies (STs) facilitate knowledge representation and semantic interoperability within health care provision and research. However, a gap exists in capturing knowledge representation to classify, quantify, qualify, and codify the intersection of evidence and quality improvement (QI) implementation. This interprofessional case report leverages a novel semantic and ontological approach to bridge this gap. This report had two objectives. First, it aimed to synthesize implementation barrier and facilitator data from employee wellness QI initiatives across Veteran Affairs health care systems through a semantic and ontological approach. Second, it introduced an original framework of this use-case-based taxonomy on implementation barriers and facilitators within a QI process. We synthesized terms from combined datasets of all-site implementation barriers and facilitators through QI cause-and-effect analysis and qualitative thematic analysis. We developed the Quality Improvement and Implementation Taxonomy (QIIT) classification scheme to categorize synthesized terms and structure. This framework employed a semantic and ontological approach. It was built upon existing terms and models from the QI Plan, Do, Study, Act phases, the Consolidated Framework for Implementation Research domains, and the fishbone cause-and-effect categories. The QIIT followed a hierarchical and relational classification scheme. Its taxonomy was linked to four QI Phases, five Implementing Domains, and six Conceptual Determinants modified by customizable Descriptors and Binary or Likert Attribute Scales. This case report introduces a novel approach to standardize the process and taxonomy to describe evidence translation to QI implementation barriers and facilitators. This classification scheme reduces redundancy and allows semantic agreements on concepts and ontological knowledge representation. Integrating existing taxonomies and models enhances the efficiency of reusing well-developed taxonomies and relationship modeling among constructs. Ultimately, employing STs helps generate comparable and sharable QI evaluations for forecast, leading to sustainable implementation with clinically informed innovative solutions.

USING RESNET18 IN A DEEP-LEARNING FRAMEWORK AND ASSESSING THE EFFECTS OF ADAPTIVE LEARNING RATES IN THE IDENTIFICATION OF MALIGNANT BREAST MASSES IN MAMMOGRAMS

Breast cancer is a prevalent disease that primarily affects women globally, but it can also affect men. Early detection is crucial for better treatment outcomes, and mammography is a common screening method. Recommendations for mammograms vary by age and country. Early breast cancer screening is vital for timely interventions. This paper aims to introduce AI methods through deep learning approaches utilizing pretrained CNN-based models for the diagnosis of masses depicted in breast images. These masses may be either malignant or benign, necessitating distinct management strategies for each scenario. The pretrained model ResNet18 applied to a combined dataset of three datasets (INbreast+MIAS+DDSM) yielded the best estimated result, with an accuracy of 95% (94.90% precision, 94.91% recall and 94.91% F1-score).

First report of Fusarium ussurianum causing leaf spot on Hemerocallis citrina in Sichuan Basin of China.

Hemerocallis citrina is a popular vegetable crop in China, due to abundant nutrients in its edible flower buds. In March 2021, serious symptoms of leaf spot were observed on nearly 90% cultivated H. citrina seedlings in the fields of Dazhou city (31°17'56″ N, 107°31'59″ E), Sichuan, China. Symptomatic leaves were collected from 15 seedlings in five different sampling sites (3 seedlings per site). Small pieces (5 × 3 mm) of lesion margin were excised, surface disinfected in 70% ethanol for 20 s and 1% sodium hypochlorite (NaClO) for 40 s, washed, dried, placed on potato dextrose agar (PDA) amended with streptomycin sulfate (50 mg/L) and incubated in dark at 25 ℃ for two days. Finally, eight purified isolates, HHC-FL22, HHC-FL23, HHC-FL25, HHC-FL26, HHC-FL27, HHC-FL28, HHC-FL29 and HHC-FL30, showing similar morphology were obtained through transferring hyphal tips to fresh PDA plates. On PDA plates, mycelia were initially white but gradually became light yellow, and scarlet diffusible pigments were also produced with time. On carnation leaf agar, our isolates produced slightly curved macroconidia with 4 to 8 septa that measured 3.1 to 5.7 × 36.8 to 69.3 µm (n = 30). Microconidia and chlamydospores were not observed. Our isolates were initially identified as Fusarium species based on morphological features (Leslie and Summerell 2006). To further confirm accurate identity, primers EF1/EF2 (O'Donnell et al. 2010), TRI1015B/TRI1013E (Hao et al. 2017), RPB1-F5/RPB1-G2R (O'Donnell et al. 2010), and fRPB2-5F/fRPB2-11aR and RPB2-5f2/RPB2-7cr (O'Donnell et al. 2012) were used to amplify gene sequences of translation elongation factor-1 alpha (TEF1), 3-O-acetyltransferase (Tri101), and DNA-directed RNA polymerase II largest (RPB1) and second largest subunit (RPB2), respectively. Our sequences were deposited in GenBank under accession numbers OQ860946 to OQ860953 (TEF1), OR393245 to OR393252 (Tri101), OP131893 to OP131900 (RPB1), and OQ860954 to OQ860961 and OP131885 to OP131892 (RPB2), respectively. BLASTN searches of our sequences showed 99 ~ 100% identity with TEF1 (FJ240301.1), Tri101 (FJ240345.1), RPB1 (MW233297.1) and RPB2 (KM361666.1) of F. ussurianum NRRL 45681, and 99.05 ~ 100% identity with TEF1 (FJ240305.1) and Tri101 (FJ240349.1) of F. ussurianum NRRL 45833, respectively. Two independent maximum-likelihood phylogenetic trees based on different combined datasets of TEF1, Tri101, RPB1 and RPB2 of Fusarium species confirmed that our isolates were F. ussurianum. To test pathogenicity, conidial suspension from HHC-FL23 (106 conidia / mL) were sprayed to seedlings of cultivar "chuanhuanghua No.1" (n = 3) and incubated in a greenhouse (25°C under 90% relative humidity, 16/8 h light/dark cycle). Controls were treated with ddH2O. Ten days post-inoculation, natural symptoms appeared on leaves inoculated with HHC-FL23, but control group seedlings remained disease-free. This experiment was repeated three times. All re-isolated pathogens from diseased leaves were molecularly and morphologically identified using methods described above. Consequently, the re-isolated fungi were identical to these inoculated. The leaf spot disease could cause foliar damage and even drastic yield loss of flower buds under severe conditions. To our knowledge, this is the first report of F. ussurianum causing leaf spot in H. citrina worldwide. Our study will assist in monitoring causal agent diversity of leaf spot and breeding new resistant varieties in H. citrina.

Identifying a common data dictionary across colorectal cancer outcome registries: A mapping exercise to identify opportunities for data dictionary harmonisation

ImportanceThe development of colorectal cancer outcome registries internationally has been organic, with differing datasets, data definitions and infrastructure across registries which has limited data pooling and international comparison. Currently there is no comprehensive data dictionary identified as a standard. This study is part of an international collaboration that aims to identify areas of data capture and usage which may be optimised to improve understanding of colorectal cancer outcomes. ObjectiveThis study aimed to compare and identify commonalities and areas of difference across major colorectal cancer registries. We sought to establish datasets comprising of mutually collected common fields, and a combined comprehensive dataset of all collected fields across major registries to aid in establishing a future colorectal cancer registry database standard. Design and MethodsThis mixed qualitative and quantitative study compared data dictionaries from three major colorectal cancer outcome registries: Bowel Cancer Outcomes Registry (BCOR) (Australia and New Zealand), National Bowel Cancer Audit (NBOCA) (United Kingdom) and Dutch ColoRectal Audit (DCRA) (Netherlands). Registries were compared and analysed thematically, and a common dataset and combined comprehensive dataset were developed. These generated datasets were compared to data dictionaries from Sweden (SCRCR), Denmark (DCCG), Argentina (BNCCR-A) and the USA (NAACCR and ACS NSQIP). Fields were assessed against prominent quality indicator metrics from the literature and current case-use. ResultsWe developed a combined comprehensive dataset of 225 fields under seven domains: demographic, pre-operative, operative, post-operative, pathology, neoadjuvant therapy, adjuvant therapy, and follow up/recurrence. A common dataset was developed comprising 38 overlapping fields, showing a low degree of mutually collected data, especially in preoperative, post operative and adjuvant therapy domains. The BNCCR-A, SCRCR and DCCG databases all contained a high percentage of common dataset fields. Fields were poorly comparable when viewed form current quality indicator metrics. ConclusionThis study mapped data dictionaries of prominent colorectal cancer registries and highlighted areas of commonality and difference The developed common field dataset provides a foundation for registries to benchmark themselves and work towards harmonisation of data dictionaries. This has the potential to enable meaningful large-scale international outcomes research.

Type 1 VWD classification revisited: novel insights from combined analysis of the LoVIC and WiN studies

AbstractThere is significant ongoing debate regarding type 1 von Willebrand disease (VWD) defintion. Previous guidelines recommended patients with von Willebrand factor (VWF) levels <30 IU/dL be diagnosed type 1 VWD, whereas patients with significant bleeding and VWF levels from 30 to 50 IU/dL be diagnosed with low VWF. To elucidate the relationship between type 1 VWD and low VWF in the context of age-induced increases in VWF levels, we combined data sets from 2 national cohort studies: 162 patients with low VWF from the Low VWF in Ireland Cohort (LoVIC) and 403 patients with type 1 VWD from the Willebrand in The Netherlands (WiN) studies. In 47% of type 1 VWD participants, VWF levels remained <30 IU/dL despite increasing age. Conversely, VWF levels increased to the low VWF range (30-50 IU/dL) in 30% and normalized (>50 IU/dL) in 23% of type 1 VWD cases. Crucially, absolute VWF antigen (VWF:Ag) levels and increase of VWF:Ag per year overlapped between low VWF and normalized type 1 VWD participants. Moreover, multiple regression analysis demonstrated that VWF:Ag levels in low VWF and normalized type 1 VWD patients would not have been different had they been diagnosed at the same age (β = 0.00; 95% confidence interval, −0.03 to 0.04). Consistently, no difference was found in the prevalence of VWF sequence variants; factor VIII activity/VWF:Ag or VWF propeptide/VWF:Ag ratios; or desmopressin responses between low VWF and normalized type 1 VWD patients. In conclusion, our findings demonstrate that low VWF does not constitute a discrete clinical or pathological entity. Rather, it is part of an age-dependent type 1 VWD evolving phenotype. Collectively, these data have important implications for future VWD classification criteria.

Simulation-driven training of vision transformers enables metal artifact reduction of highly truncated CBCT scans.

Due to the high attenuation of metals, severe artifacts occur in cone beam computed tomography (CBCT). The metal segmentation in CBCT projections usually serves as a prerequisite for metal artifact reduction (MAR)algorithms. The occurrence of truncation caused by the limited detector size leads to the incomplete acquisition of metal masks from the threshold-based method in CBCT volume. Therefore, segmenting metal directly in CBCT projections is pursued in thiswork. Since the generation of high quality clinical training data is a constant challenge, this study proposes to generate simulated digital radiographs (dataI) based on real CT data combined with self-designed computer aided design (CAD) implants. In addition to the simulated projections generated from 3D volumes, 2D x-ray images combined with projections of implants serve as the complementary data set (dataII) to improve the network performance. In this work, SwinConvUNet consisting of shift window (Swin) vision transformers (ViTs) with patch merging as encoder is proposed for metalsegmentation. The model's performance is evaluated on accurately labeled test datasets obtained from cadaver scans as well as the unlabeled clinical projections. When trained on the dataI only, the convolutional neural network (CNN) encoder-based networks UNet and TransUNet achieve only limited performance on the cadaver test data, with an average dice score of 0.821 and 0.850. After using both dataII and dataI during training, the average dice scores for the two models increase to 0.906 and 0.919, respectively. By replacing the CNN encoder with Swin transformer, the proposed SwinConvUNet reaches an average dice score of 0.933 for cadaver projections when only trained on the dataI. Furthermore, SwinConvUNet has the largest average dice score of 0.953 for cadaver projections when trained on the combined dataset. Our experiments quantitatively demonstrate the effectiveness of the combination of the projections simulated under two pathways for network training. Besides, the proposed SwinConvUNet trained on the simulated projections performs state-of-the-art, robust metal segmentation as demonstrated on experiments on cadaver and clinical data sets. With the accurate segmentations from the proposed model, MAR can be conducted even for highly truncated CBCTscans.

First report of the genus Xerophorus from Asia with the description of Xerophorus pakistanicus (Callistosporiaceae), a new species from Southern Punjab, Pakistan

A novel species, Xerophorus pakistanicus, has been identified from Southern Punjab, Pakistan, based on morphological characters and molecular phylogenetic analyses. Xerophorus pakistanicus is characterized by a brown to dull orange, parabolic to campanulate pileus, a stipe turning light yellowish brown on handling, an absence of an annulus, amygdaliform to oblong basidiospores, utriform to pyriform caulocystidia, and the presence of clamp connections only in the stipitipellis. A phylogram, based on the combined nrITS-28S dataset, was constructed to assess the phylogenetic position of our strain, resulted Xerophorus pakistanicus sp. nov. within the genus Xerophorus. We present the new species with an illustrated morphological description and comparison with similar morphological or phylogenetically related species. This is the first record of the genus Xerophorus from Asia. A key to the known species of Xerophorus is also provided.

Genetic diversity and population differentiation in Earliella scabrosa, a pantropical species of Polyporales

Earliella scabrosa is a pantropical species of Polyporales (Basidiomycota) and well-studied concerning its morphology and taxonomy. However, its pantropical intraspecific genetic diversity and population differentiation is unknown. We initiated this study to better understand the genetic variation within E. scabrosa and to test if cryptic species are present. Sequences of three DNA regions, the nuclear ribosomal internal transcribed spacer (ITS), the large subunit ribosomal DNA (LSU), and the translation elongation factor (EF1α) were analysed for 66 samples from 15 geographical locations. We found a high level of genetic diversity (haplotype diversity, Hd = 0.88) and low nucleotide diversity (π = 0.006) across the known geographical range of E. scabrosa based on ITS sequences. The analysis of molecular variance (AMOVA) indicates that the genetic variability is mainly found among geographical populations. The results of Mantel tests confirmed that the genetic distance among populations of E. scabrosa is positively correlated with the geographical distance, which indicates that geographical isolation is an important factor for the observed genetic differentiation. Based on phylogenetic analyses of combined dataset ITS-LSU-EF1α, the low intraspecific divergences (0–0.3%), and the Automated Barcode Gap Discovery (ABGD) analysis, E. scabrosa can be considered as a single species with five different geographical populations. Each population might be in the process of allopatric divergence and in the long-term they may evolve and become distinct species.

CAM-QUS guided self-tuning modular CNNs with multi-loss functions for fully automated breast lesion classification in ultrasound images

Objective. Breast cancer is the major cause of cancer death among women worldwide. Deep learning-based computer-aided diagnosis (CAD) systems for classifying lesions in breast ultrasound images can help materialise the early detection of breast cancer and enhance survival chances. Approach. This paper presents a completely automated BUS diagnosis system with modular convolutional neural networks tuned with novel loss functions. The proposed network comprises a dynamic channel input enhancement network, an attention-guided InceptionV3-based feature extraction network, a classification network, and a parallel feature transformation network to map deep features into quantitative ultrasound (QUS) feature space. These networks function together to improve classification accuracy by increasing the separation of benign and malignant class-specific features and enriching them simultaneously. Unlike the categorical crossentropy (CCE) loss-based traditional approaches, our method uses two additional novel losses: class activation mapping (CAM)-based and QUS feature-based losses, to capacitate the overall network learn the extraction of clinically valued lesion shape and texture-related properties focusing primarily the lesion area for explainable AI (XAI). Main results. Experiments on four public, one private, and a combined breast ultrasound dataset are used to validate our strategy. The suggested technique obtains an accuracy of 97.28%, sensitivity of 93.87%, F1-score of 95.42% on dataset 1 (BUSI), and an accuracy of 91.50%, sensitivity of 89.38%, and F1-score of 89.31% on the combined dataset, consisting of 1494 images collected from hospitals in five demographic locations using four ultrasound systems of different manufacturers. These results outperform techniques reported in the literature by a considerable margin. Significance. The proposed CAD system provides diagnosis from the auto-focused lesion area of B-mode BUS images, avoiding the explicit requirement of any segmentation or region of interest extraction, and thus can be a handy tool for making accurate and reliable diagnoses even in unspecialized healthcare centers.

Forest mosaics, not savanna corridors, dominated in Southeast Asia during the Last Glacial Maximum.

The dominant paradigm is that large tracts of Southeast Asia's lowland rainforests were replaced with a "savanna corridor" during the cooler, more seasonal climates of the Last Glacial Maximum (LGM) (23,000 to 19,000 y ago). This interpretation has implications for understanding the resilience of Asia's tropical forests to projected climate change, implying a vulnerability to "savannization". A savanna corridor is also an important foundation for archaeological interpretations of how humans moved through and settled insular Southeast Asia and Australia. Yet an up-to-date, multiproxy, and empirical examination of the palaeoecological evidence for this corridor is lacking. We conducted qualitative and statistical analyses of 59 palaeoecological records across Southeast Asia to test the evidence for LGM savannization and clarify the relationships between methods, biogeography, and ecological change in the region from the start of Late Glacial Period (119,000 y ago) to the present. The pollen records typically show montane forest persistence during the LGM, while δ13C biomarker proxies indicate the expansion of C4-rich grasslands. We reconcile this discrepancy by hypothesizing the expansion of montane forest in the uplands and replacement of rainforest with seasonally dry tropical forest in the lowlands. We also find that smooth forest transitions between 34,000 and 2,000 y ago point to the capacity of Southeast Asia's ecosystems both to resist and recover from climate stressors, suggesting resilience to savannization. Finally, the timing of ecological change observed in our combined datasets indicates an 'early' onset of the LGM in Southeast Asia from ~30,000 y ago.

Gastrointestinal parasite community structure in horses after the introduction of selective anthelmintic treatment strategies

A relatively new method to study the species richness and diversity of nematode parasites in grazing animals is to perform deep sequencing on composite samples containing a mixture of parasites. In this work, we compared species composition of strongyles in two groups of horses as a function of egg count and age, based on a DNA barcoding approach. Faecal egg counts and larval cultures were obtained from nearly 300 horses, i.e., domestic horses (n = 167) and trotters (n = 130) sampled nationwide. The second internal transcribed spacer region (ITS2) of strongyle nematodes in the larval cultures was first amplified using barcoded universal primers and then sequenced on the PacBio platform. Subsequently, bioinformatic sequence analysis was performed using SCATA to assign operational taxonomic units (OTU). Finally, species occurrence and composition were assessed using R. ITS2 sequences were found in the majority (89%) of larval samples. Sequencing yielded an average of 140 (26 to 503) reads per sample. The OTUs were assigned to 28 different taxa, of which all but three could be identified as species. The average relative abundance of the seven most abundant species (all Cyathostominae) accounted for 87% of the combined data set. The three species with the highest prevalence in both horse groups were Cyathostomum catinatum, Cylicocyclus nassatus and Cylicostephanus calicatus, and they were frequently found in different combinations with other species regardless of horse group. Interestingly, this result is largely consistent with a previous Swedish study based on morphological analysis of adult worms. In addition, two migratory strongylids (Strongylus vulgaris and S. edentatus) occurred in few domestic horses and trotters. Except for C. minutus and C. nassatus, which decreased with age, and C. catinatum and S. vulgaris, which increased, no specific trends were observed with respect to horse age. Taken together, these results are broadly consistent with data obtained before the introduction of selective targeted treatment in Sweden in 2007. All in all, our results suggest that this treatment strategy has not led to a significant change in strongyle nematode community structure in Swedish horses. The study also confirms that nemabiome analysis in combination with diversity index analysis is an objective method to study strongyle communities in horses.

Characters evolution of Encyclia (Laeliinae-Orchidaceae) reveals a complex pattern not phylogenetically determined: insights from macro- and micromorphology

Encyclia is the second-largest genus in the neotropical subtribe Laeliinae (Orchidaceae) and has more than 150 species, which are characterized by fairly consistent flower morphology. Its taxonomy and species boundaries, however, seem to be still under debate. In the present study, we first examined the lip micromorphology of 61 species of Encyclia sensu stricto. We correlated our results with external flower morphology and phylogenetic analyses performed on a combined dataset that included both nuclear (ITS, Xdh, PhyC) and plastid markers (ycf1, rpl32, and trnL-trnF). Phylogenetic reconstruction showed that Encyclia sensu stricto species form a coherent, monophyletic group. However, it is difficult to determine the relationships between the different groups within one larger clade. The groups all form distinct lineages that evolved from a common ancestor. The UPGMA cluster analysis for the seven qualitative micromorphological features clearly divides the genus into two main groups, the larger of which is further subdivided into two subgroups. None of these, however, overlap with any of the phylogeographic units distinguished in previously published papers or in presented article. It is worth noting that the groups resulting from the UPGMA analysis cannot be defined by macromorphological features. The pattern of similarities between species, taking into account both macro- and micromorphological features, is eminently mosaic in nature, and only a multifaceted approach can explain this enigmatic group.

Exploring the Robustness of Causal Structures in Omics Data: A Sweet Cherry Proteogenomic Perspective

Causal discovery is a highly promising tool with a broad perspective in the field of biology. In this study, a causal structure robustness assessment algorithm is proposed and employed on the causal structures obtained, based on transcriptomic, proteomic, and the combined datasets, emerging from a quantitative proteogenomic atlas of 15 sweet cherry (Prunus avium L.) cv. ‘Tragana Edessis’ tissues. The algorithm assesses the impact of intervening in the datasets of the causal structures, using various criteria. The results showed that specific tissues exhibited an intense impact on the causal structures that were considered. In addition, the proteogenomic case demonstrated that biologically related tissues that referred to the same organ induced a similar impact on the causal structures considered, as was biologically expected. However, this result was subtler in both the transcriptomic and the proteomic cases. Furthermore, the causal structures based on a single omic analysis were found to be impacted to a larger extent, compared to the proteogenomic case, probably due to the distinctive biological features related to the proteome or the transcriptome. This study showcases the significance and perspective of assessing the causal structure robustness based on omic databases, in conjunction with causal discovery, and reveals advantages when employing a multiomics (proteogenomic) analysis compared to a single-omic (transcriptomic, proteomic) analysis.

Let's Move with Leon—A qualitative evaluation of a UK digital intervention to improve physical activity in people with a musculoskeletal condition

ObjectiveThis article presents a qualitative evaluation of a 13-week digital intervention, ‘Let's Move with Leon’, designed to improve physical activity in people with a musculoskeletal condition. Study designA qualitative evaluation embedded within a randomised controlled trial assessing the effectiveness of the intervention at improving self-reported physical activity. MethodsA total of 184 participants received the intervention and were asked each week for 13 weeks to provide comment on their use and the usefulness of the digital physical activity behaviour change intervention. In addition, after 13 weeks, 12 participants took part in a semistructured interview to understand their use and the usefulness of the intervention. A thematic analysis was conducted on the combined qualitative data set. ResultsA total of 128 of the 184 intervention participants (70.11%) provided at least one qualitative comment over the course of the evaluation (mean number of comments per participant = 5); in total, 674 comments were received. The thematic analysis identified three themes: (1) dipping in and out, (2) one size does not fit all and (3) monitoring and feedback. The qualitative data suggest that participants used the intervention sporadically, dipping in and out due to other commitments, and competing programmes, their changing physical ability, confidence and motivation. Not getting off to a good start was detrimental to use; many wanted to come back to the programme at a more appropriate time. A ‘one size fits’ approach catered for some but not all participants. Whilst not a predetermined intervention component the act of monitoring levels of activity as a data collection method seemed to encourage physical activity but may also result in negative social comparisons. ConclusionDigital physical activity behaviour change interventions are not one-size-fits-all; personalisation is key. Monitoring of activity by a named person can create commitment. Many dip in and out. Digital physical activity behaviour change interventions could complement physiotherapy exercises for people with musculoskeletal conditions. Signposting to local activities should be considered.

Presenting Symptoms and Diffusion-Weighted MRI Positivity by Time After Transient Neurologic Events: A Pooled Analysis of 3 Cohort Studies

The association between focal vs nonfocal presenting symptom and diffusion-weighted imaging (DWI) positivity in relation to onset-to-imaging time in patients with transient neurologic events remains unclear. We hypothesize that episodes consisting of focal symptoms would have proportionally higher DWI-positive imaging at later onset-to-imaging times. Patients with transient neurologic symptoms and a normal neurologic examination who had DWI in the combined data set of 3 cohort studies were included. We used logistic regression models to evaluate the association between each type of presenting symptom (motor weakness, speech impairment, sensory symptoms, vision loss, diplopia, gait instability, dizziness, headache, presyncope, and amnesia) and DWI positivity after adjusting for clinical variables (age, sex, history of stroke, dyslipidemia, coronary artery disease, atrial fibrillation, symptoms duration [<10, 10-59, ≥60 minutes, or unclear], and study source). We stratified the results by onset-to-imaging time categories (<6 hours, 6-23 hours, and ≥24 hours). Of the total 2,411 patients (1,345 male, median age 68 years), DWI-positive lesions were detected in 598 patients (24.8%). The prevalence of DWI positivity was highest in those with motor weakness (34.7%), followed by speech impairment (33.5%). In a multivariable analysis, the presence of motor weakness, speech impairment, and sensory symptoms was associated with DWI positivity, while vision loss and headache were associated with lower odds of DWI positivity, but nevertheless had 13.6% and 15.3% frequency of DWI positive. The odds of being DWI positive varied by onset-to-imaging time categories for motor weakness, with greater odds of being DWI positive at later imaging time (<6 hours: odds ratio [OR] 1.25, 95% confidence interval [CI] 0.84-1.87; 6-23 hours: OR 2.24, 95% CI 1.47-3.42; and ≥24 hours: OR 2.42, 95% CI 1.74-3.36; interaction p = 0.033). Associations of other symptoms with DWI positivity did not vary significantly by time categories. We found that onset-to-imaging time influences the relationship between motor weakness and DWI positivity in patients with transient neurologic events. Compared with motor, speech, and sensory symptoms, visual or nonfocal symptoms carry a lower but still a substantive association with DWI positivity.

Accuracy of TrUE-Net in comparison to established white matter hyperintensity segmentation methods: An independent validation study

White matter hyperintensities (WMH) are nearly ubiquitous in the aging brain, and their topography and overall burden are associated with cognitive decline. Given their numerosity, accurate methods to automatically segment WMH are needed. Recent developments, including the availability of challenge data sets and improved deep learning algorithms, have led to a new promising deep-learning based automated segmentation model called TrUE-Net, which has yet to undergo rigorous independent validation. Here, we compare TrUE-Net to six established automated WMH segmentation tools, including a semi-manual method. We evaluated the techniques at both global and regional level to compare their ability to detect the established relationship between WMH burden and age. We found that TrUE-Net was highly reliable at identifying WMH regions with low false positive rates, when compared to semi-manual segmentation as the reference standard. TrUE-Net performed similarly or favorably when compared to the other automated techniques. Moreover, TrUE-Net was able to detect relationships between WMH and age to a similar degree as the reference standard semi-manual segmentation at both the global and regional level. These results support the use of TrUE-Net for identifying WMH at the global or regional level, including in large, combined datasets.