bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dermatophytosis and its risk factors among children visiting dermatology clinic in Hawassa Sidama, Ethiopia
Authors: Haro Mengistu , Alemayehu Tsegaye , Mikiru Abraham 
Published: May 2023

AbstractDermatophytosis represents one of the common fungal diseases that attack the skin, hair and nail of human beings worldwide. It causes chronic morbidity in children and the condition is more common, in developing countries. The study aimed to determine dermatophytosis and its associated factors among children in Hawassa Sidama, Ethiopia April 2021–October 2021. A cross-sectional study was conducted on children suspected of cutaneous fungal infections. Data were surveyed based on a semi-structured questionnaire. Standard laboratory methods were used to identify the dermatophytes. The data entry and analysis were conducted with SPSS version 26. The Chi-square test was used to check the predictor and a p-value < 0.05 was taken as a significant value. A total of 83 study subjects included in the study in which all 83 (100%) patients were positive for fungal elements (hyphae/and spores) in microscopy, of this 81 (97.6%) yielded growth on culture. Hair scalps 75 (90.4%) were the dominant among the case. Trichophyton 52 (62.6%) was the dominant aetiology followed by Microsporum 22 (26.6%). Intervention measures to tackle dermatophytosis should emphasis on tinea capitis among 6–10 years old children with history of recent migration by raising awareness of communities through health extension programs.
Protein biomarkers of disease progression in patients with systemic sclerosis associated interstitial lung disease
Authors: Cerro-Chiang Giuliana , Ayres Matthew , Rivas Alejandro , Romero Tahmineh , Parker Sarah J. , Mastali Mitra , Elashoff David , Chen Peter , Van Eyk Jennifer E. , Wolters Paul J. , Boin Francesco , Zaman Tanzira 
Published: May 2023

AbstractSystemic sclerosis is a rare connective tissue disease; and interstitial lung disease (SSc–ILD) is associated with significant morbidity and mortality. There are no clinical, radiologic features, nor biomarkers that identify the specific time when patients are at risk for progression at which the benefits from treatment outweigh the risks. Our study aimed to identify blood protein biomarkers associated with progression of interstitial lung disease in patients with SSc–ILD using an unbiased, high-throughput approach. We classified SSc–ILD as progressive or stable based on change in forced vital capacity over 12 months or less. We profiled serum proteins by quantitative mass spectrometry and analyzed the association between protein levels and progression of SSc–ILD via logistic regression. The proteins associated with at a p value of < 0.1 were queried in the ingenuity pathway analysis (IPA) software to identify interaction networks, signaling, and metabolic pathways. Through principal component analysis, the relationship between the top 10 principal components and progression was evaluated. Unsupervised hierarchical clustering with heatmapping was done to define unique groups. The cohort consisted of 72 patients, 32 with progressive SSc–ILD and 40 with stable disease with similar baseline characteristics. Of a total of 794 proteins, 29 were associated with disease progression. After adjusting for multiple testing, these associations did not remain significant. IPA identified five upstream regulators that targeted proteins associated with progression, as well as a canonical pathway with a higher signal in the progression group. Principal component analysis showed that the ten components with the highest Eigenvalues represented 41% of the variability of the sample. Unsupervised clustering analysis revealed no significant heterogeneity between the subjects. We identified 29 proteins associated with progressive SSc–ILD. While these associations did not remain significant after accounting for multiple testing, some of these proteins are part of pathways relevant to autoimmunity and fibrogenesis. Limitations included a small sample size and a proportion of immunosuppressant use in the cohort, which could have altered the expression of inflammatory and immunologic proteins. Future directions include a targeted evaluation of these proteins in another SSc–ILD cohort or application of this study design to a treatment naïve population.
Effects of customized insoles with medial wedges on lower extremity kinematics and ultrasonographic findings in plantar fasciitis persons
Authors: Thong-On Suthasinee , Harutaichun Pavinee 
Published: May 2023

AbstractThe customized insole is widely recommended as an effective intervention for pain reduction and foot function improvement in plantar fasciitis persons. However, it is unclear whether the additional correction of medial wedges could change the kinematics from the only insole. The objectives of this study were thus to compare customized insoles with and without medial wedges on lower extremity kinematics during gait and to determine the short-term effects of the customized insole with medial wedges on pain intensity, foot function, and ultrasonographic findings in plantar fasciitis persons. A within-subject, randomized, crossover design within motion analysis research laboratory was conducted among 35 persons with plantar fasciitis. Main outcome measures included joint motions of the lower extremity and multi-segment foot, pain intensity, foot function, and ultrasonographic findings. The customized insole with medial wedges produced less knee motion in the transverse plane and hallux motion in all planes during the propulsive phase than that without medial wedges (all p < 0.05). After the 3-month follow-up, the insoles with medial wedges decreased pain intensity and increased foot function. Abnormal ultrasonographic findings also decreased significantly after the 3-month treatment of insoles with medial wedges. Customized insoles with medial wedges seem superior to those without medial wedges on both multi-segment foot motion and knee motion during propulsion. Positive outcomes from this study supported the use of customized insoles with medial wedges as an effective conservative treatment in patients with plantar fasciitis.Trial registration: TCTR20210928006 (28/09/2021).
Metabolomic analysis of Mycobacterium tuberculosis reveals metabolic profiles for identification of drug-resistant tuberculosis
Authors: Chaiyachat Pratchakan , Kaewseekhao Benjawan , Chaiprasert Angkana , Kamolwat Phalin , Nonghanphithak Ditthawat , Phetcharaburanin Jutarop , Sirichoat Auttawit , Ong Rick Twee-Hee , Faksri Kiatichai 
Published: May 2023

AbstractThe detection of pre-extensively (pre-XDR) and extensively drug-resistant tuberculosis (XDR-TB) is challenging. Drug-susceptibility tests for some anti-TB drugs, especially ethambutol (ETH) and ethionamide (ETO), are problematic due to overlapping thresholds to differentiate between susceptible and resistant phenotypes. We aimed to identify possible metabolomic markers to detect Mycobacterium tuberculosis (Mtb) strains causing pre-XDR and XDR-TB. The metabolic patterns of ETH- and ETO-resistant Mtb isolates were also investigated. Metabolomics of 150 Mtb isolates (54 pre-XDR, 63 XDR-TB and 33 pan-susceptible; pan-S) were investigated. Metabolomics of ETH and ETO phenotypically resistant subgroups were analyzed using UHPLC-ESI-QTOF-MS/MS. Orthogonal partial least-squares discriminant analysis revealed distinct separation in all pairwise comparisons among groups. Two metabolites (meso-hydroxyheme and itaconic anhydride) were able to differentiate the pre-XDR and XDR-TB groups from the pan-S group with 100% sensitivity and 100% specificity. In comparisons of the ETH and ETO phenotypically resistant subsets, sets of increased (ETH = 15, ETO = 7) and decreased (ETH = 1, ETO = 6) metabolites specific for the resistance phenotype of each drug were found. We demonstrated the potential for metabolomics of Mtb to differentiate among types of DR-TB as well as between isolates that were phenotypically resistant to ETO and ETH. Thus, metabolomics might be further applied for DR-TB diagnosis and patient management.
An object detection approach with residual feature fusion and second‐order term attention mechanism
Authors: Li Cuijin , Qu Zhong , Wang Shengye 
Published: May 2023

AbstractAutomatically detecting and locating remote occlusion small objects from the images of complex traffic environments is a valuable and challenging research. Since the boundary box location is not sufficiently accurate and it is difficult to distinguish overlapping and occluded objects, the authors propose a network model with a second‐order term attention mechanism and occlusion loss. First, the backbone network is built on CSPDarkNet53. Then a method is designed for the feature extraction network based on an item‐wise attention mechanism, which uses the filtered weighted feature vector to replace the original residual fusion and adds a second‐order term to reduce the information loss in the process of fusion and accelerate the convergence of the model. Finally, an objected occlusion regression loss function is studied to reduce the problems of missed detections caused by dense objects. Sufficient experimental results demonstrate that the authors’ method achieved state‐of‐the‐art performance without reducing the detection speed. The mAP@.5 of the method is 85.8% on the Foggy_cityscapes dataset and the mAP@.5 of the method is 97.8% on the KITTI dataset.
SNENet: An adaptive stego noise extraction network using parallel dilated convolution for JPEG image steganalysis
Authors: Fan Wentong , Li Zhenyu , Li Hao , Zhang Yi , Luo Xiangyang 
Published: May 2023

AbstractThe steganalysis for JPEG image is an important research topic, as the enormous popularity of JPEG image on Internet. However, the stego noise feature extraction process of the existing deep learning‐based steganalytic methods are not adaptive enough to the content of the image, which may lead to suboptimal steganalysis performance. In order to solve this issue, an adaptive stego noise extraction network, named SNENet, for JPEG image steganalysis is proposed. The stego noise extraction module of the network is specifically designed for steganalysis, which consists of parallel dilated convolutional layer and inverted bottleneck layer. This specific design expands the receptive field of the network, which makes the extraction of the stego noise more global and adaptive to the content of the image. The experimental results indicate that proposed network outperforms the state‐of‐the‐art steganalytic method by as much as 6.25% for UED‐JC and 3.35% for J‐UNIWARD. The design of the network is also justified in the extensive ablation experiments.
Detection of Internet‐wide traffic redirection attacks using machine learning techniques
Authors: Subtil Ana , Oliveira M. Rosário , Valadas Rui , Salvador Paulo , Pacheco António 
Published: May 2023

AbstractInternet‐wide traffic redirection attacks have been reported for long, and are mainly caused by Border Gateway Protocol route hijacking. Such attacks can be quite harmful, impairing access to popular Internet sites for long periods. This work addresses the use of machine learning techniques (both unsupervised and supervised) leveraging from a distributed monitoring infrastructure of probes that measure the round trip time to Internet sites under surveillance. The detection process is separated into two stages: per‐probe classification and a combination of individual probe decisions. Our results show that the best strategy is to classify using an unsupervised technique based on Tukey's method and to combine using Hidden Markov Models, due to its performance and adaptability to different attack types.
Parameter identification of DC–DC converter based on dendrite net under fluctuating input voltages
Authors: Jiang Yueming , Liu Yanlong , Yang Zhiming 
Published: May 2023

AbstractThe traditional identification methods of direct current‐to‐direct current (DC–DC) converters usually consider the common situation where the training and testing sets have the same input voltage. In real‐world applications, the actual input voltage may fluctuate beyond its original value, thus negatively affecting the parameter identification accuracy. Therefore, this paper presents a parameter identification method under fluctuating input voltages. Firstly, to increase the identification accuracy, the paper proposes the coefficient of variation method to extract key features from all time‐ and frequency‐domain features as training data. Then, for fluctuating input voltages, the paper adds the input voltages into the training data to build the parameter identification network based on Dendrite Net. When using the established network to identify component parameters in practical applications, the actual input voltage may change to an unknown value because of irregular fluctuation. Hence, the actual input voltage needs to be identified before identifying the component parameters of a DC–DC converter. Finally, the paper builds the input voltage identification network. By combining the identification results of the actual input voltage, the parameter identification under fluctuating input voltages can be completed. The simulation and hardware experiments results validate the practicability and effectiveness of the proposed method.
Quantification of storage required for preserving frequency security in wind‐integrated systems
Authors: Bera Atri , Nguyen Nga , Chalamala Babu , Mitra Joydeep 
Published: May 2023

AbstractThe penetration of wind power generation into the power grid has been accelerated in recent times due to the aggressive emission targets set by governments and other regulatory authorities. Although wind power has the advantage of being environment‐friendly, wind as a resource is intermittent in nature. In addition, wind power contributes little inertia to the system as most wind turbines are connected to the grid via power electronic converters. These negative aspects of wind power pose serious challenges to the frequency security of power systems as penetration increases. In this work, an approach is proposed where an energy storage system (ESS) is used to mitigate frequency security issues of wind‐integrated systems. ESSs are well equipped to supply virtual inertia to the grid due to their fast‐acting nature, thus replenishing some of the energy storage capability of displaced inertial generation. In this work, a probabilistic approach is proposed to estimate the amount of inertia required by a system to ensure frequency security. Reduction in total system inertia due to the displacement of conventional synchronous generation by wind power generation is considered in this approach, while also taking into account the loss of inertia due to forced outages of conventional units. Monte Carlo simulation is employed for implementing the probabilistic estimation of system inertia. An ESS is then sized appropriately, using the system swing equation, to compensate for the lost inertia. The uncertainty associated with wind energy is modeled into the framework using an autoregressive moving average technique. Effects of increasing the system peak load and changing the wind profile on the expected system inertia are studied to illustrate various factors that might affect system frequency security. The proposed method is validated using the IEEE 39‐bus test system.
Featureless transmission spectra of 12 giant exoplanets observed by GTC/OSIRIS
Authors: Jiang C. , Chen G. , Pallé E. , Murgas F. , Parviainen H. , Ma Y. 
Published: May 2023

Context. Exoplanet atmospheres are the key to understanding the nature of exoplanets. To this end, transit spectrophotometry provides us opportunities to investigate the physical properties and chemical compositions of exoplanet atmospheres.
Aims. We aim to detect potential atmospheric signatures in 12 gaseous giant exoplanets using transit spectrophotometry and we try to constrain their atmospheric properties.
Methods. The targets of interest were observed using transit spectrophotometry with the GTC OSIRIS instrument. We estimated the transit parameters and obtained the optical transmission spectra of the target planets using a Bayesian framework. We analyzed the spectral features in the transmission spectra based on atmospheric retrievals.
Results. Most of the observed transmission spectra were found to be featureless, with only the spectrum of CoRoT-1b showing strong evidence for atmospheric features. However, in combination with the previously published near-infrared transmission spectrum, we found multiple interpretations for the atmosphere of CoRoT-1b due to the lack of decisive evidence for alkali metals or optical absorbers.
Conclusions. Featureless spectra are not necessarily indicative of cloudy atmospheres if they poorly constrain the altitudes of cloud decks. Precise constraints on the models of hazes and clouds strongly depend on the significance of the observed spectral features. Further investigations on these exoplanets, especially CoRoT-1b, are required to confirm the properties of their atmospheres.
Methodological and Reporting Quality of Noninferiority Randomized Controlled Trials Comparing Antiretroviral Therapies: A Systematic Review
Authors: Lo Carson K L , Komorowski Adam S , Hall Clayton W , Sandstrom Teslin S , Alamer Amnah A M , Mourad Omar , Li Xena X , Al Ohaly Rand , Benoit Marie-Ève , Duncan D Brody , Fuller Charlotte A , Shaw Shazeema , Suresh Mallika , Smaill Fiona , Kapoor Andrew K , Smieja Marek , Mertz Dominik , Bai Anthony D ,   for the McMaster Infectious Diseases Fellow Research Group
Published: May 2023

Abstract

Background
It is unclear whether the reporting quality of antiretroviral (ARV) noninferiority (NI) randomized controlled trials (RCTs) has improved since the CONSORT guideline release in 2006. The primary objective of this systematic review was assessing the methodological and reporting quality of ARV NI-RCTs. We also assessed reporting quality by funding source and publication year.

Methods
We searched Medline, Embase, and Cochrane Central from inception to 14 November 2022. We included NI-RCTs comparing ≥2 ARV regimens used for human immunodeficiency virus treatment or prophylaxis. We used the Cochrane Risk of Bias 2.0 tool to assess risk of bias. Screening and data extraction were performed blinded and in duplicate. Descriptive statistics were used to summarize data; statistical tests were 2 sided, with significance defined as P &lt; .05. The systematic review was prospectively registered (PROSPERO CRD42022328586), and not funded.

Results
We included 160 articles reporting 171 trials. Of these articles, 101 (63.1%) did not justify the NI margin used, and 28 (17.5%) did not provide sufficient information for sample size calculation. Eighty-nine of 160 (55.6%) reported both intention-to-treat and per-protocol analyses, while 118 (73.8%) described missing data handling. Ten of 171 trials (5.9%) reported potentially misleading results. Pharmaceutical industry–funded trials were more likely to be double-blinded (28.1% vs 10.3%; P = .03) and to describe missing data handling (78.5% vs 59.0%; P = .02). The overall risk of bias was low in 96 of 160 studies (60.0%).

Conclusions
ARV NI-RCTs should improve NI margin justification, reporting of intention-to-treat and per-protocol analyses, and missing data handling to increase CONSORT adherence.
Aristotelian themes in critical ethical naturalism
Authors: Ragkousis Antonis 
Published: May 2023

Abstract
Key features of critical ethical naturalism (CEN) can be more fully appreciated by considering them in relation to themes in Aristotle’s ethics and politics. Drawing on Aristotle’s writings, four central features of CEN are explored. The first aspect of CEN considered concerns its recognition that we are community beings that are mutually constituted and subject to co-development, Aristotle’s discussion of character friendship and our essentially political nature supports a fuller appreciation of what this involves and implies. A second aspect of CEN examined relates to the use of the term ‘eudaimonia’ to refer to a life of a human fulfilled (a life of flourishing)—Aristotle’s own use of the term is considered and shown to be valuable in clarifying its deployment in CEN. The third feature of CEN discussed concerns the extent to which all humans are critical ethical naturalists due to their inherent dispositions to care. Aristotle is seen as supporting the view that we have an inbuilt tendency to care but is shown to also maintain that humans, when rational, choose to endorse their caring (rather than other) tendencies not simply because they are natural but because they are good. These insights enable a better appreciation of CEN’s claims by clarifying the connections between our rational faculties and our innate tendencies to care for others. Finally, the recognition in CEN of the vulnerabilities of humans stemming from their communal nature and the analysis of ways in which certain configurations of social relations weaken our tendencies to care and form obstacles in the path to a good society are shown to echo important reservations Aristotle had about certain forms of association. Aristotle’s notion of organicity in the development of social arrangements is shown to provide useful clarifications regarding the notion of authenticity (and its connections with human flourishing) as the latter is deployed in CEN.
A machine-learning procedure to detect network attacks
Authors: Coppes Davide , Cermelli Paolo 
Published: May 2023

Abstract
The goal of this note is to assess whether simple machine-learning algorithms can be used to determine whether and how a given network has been attacked. The procedure is based on the k-Nearest Neighbour and the Random Forest classification schemes, using both intact and attacked Erdős–Rényi, Barabasi–Albert and Watts–Strogatz networks to train the algorithm. The types of attacks we consider here are random failures and maximum-degree or maximum-betweenness node deletion. Each network is characterized by a list of four metrics, namely the normalized reciprocal maximum degree, the global clustering coefficient, the normalized average path length and the degree assortativity: a statistical analysis shows that this list of graph metrics is indeed significantly different in intact or damaged networks. We test the procedure by choosing both artificial and real networks, performing the attacks and applying the classification algorithms to the resulting graphs: the procedure discussed here turns out to be able to distinguish between intact networks and those attacked by the maximum-degree of maximum-betweenness deletions, but cannot detect random failures. Our results suggest that this approach may provide a basis for the analysis and detection of network attacks.