bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine learning for localization of radioactive sources via a distributed sensor network
Authors: Abdelhakim Assem 
Published: May 2023

AbstractIn this paper, we focus on the detection and localization of radioactive sources by exploiting supervised machine learning. Machine learning is utilized in a wide variety of applications due to its effectiveness in prediction and autonomous decision-making. However, applying machine learning would only be effective when representative features for the application can be acquired, through which learning algorithms can be trained. Hence, first, we present a feature extraction technique for radioactive source localization, and then propose a parameter estimation method via machine learning. A distributed sensor network is employed to assist in estimating the radioactive source’s location and intensity. We propose a feature extraction method that evaluates a feature vector using the reading and position of each sensor located in a region where a radiation source is detected. The feature extraction is based on a data fusion process, where a single feature value is provided to represent both the reading value and position coordinates corresponding to a given sensor. After the feature extraction, we apply the decision tree machine learning method for regression to localize a radioactive source. To examine the effectiveness of the proposed work, a performance comparison is carried out with recent existing methods in terms of the estimation accuracy and the execution time. Experimental results show that the proposed algorithm provides accurate source intensity estimation and achieves a good compromise between localization accuracy and execution time.
Eosinophil-related diseases during treatment with glucagon-like peptide one receptor (GLP-1 RA): a case report and review of the literature
Authors: Posso-Osorio Iván , Vargas-Potes Carlos Julio , Mejía Mauricio , Cañas Carlos A. 
Published: May 2023

AbstractGlucagon-like peptide one-receptor agonists (GLP-1 RA) are drugs that differ in their pharmacological composition and homology to human GLP-1 and are used most frequently for the treatment of type 2 diabetes and weight loss. There are isolated reports of eosinophilic adverse reactions associated with GLP-1 RA. We present the case of a 42-year-old female patient who, after starting weekly subcutaneous semaglutide, developed eosinophilic fasciitis with favorable clinical evolution after the discontinuation of semaglutide and the initiation of immunosuppression. A review of the eosinophilic adverse events that have been previously reported with GLP-1 RA is provided.
Papillomaviruses and cancer: commonalities and differences in HPV carcinogenesis at different sites of the body
Authors: Egawa Nagayasu 
Published: May 2023

AbstractHuman papillomavirus (HPV) is associated with 5% of all cancers globally at a range of body sites, including cervix, anus, penis, vagina, vulva, and oropharynx. These cancers claim > 400,000 lives annually. The persistent infection of HPV and the function of viral oncogenes are the primary causes of HPV-related cancers. However, only some HPV-infected persons or infected lesions will progress to cancer, and the burden of HPV-associated cancer varies widely according to gender and the part of the body infected. The dissimilarity in infection rates at different sites can explain only a small part of the differences observed. Much responsibility likely sits with contributions of specific epithelial cells and the cellular microenvironment at infected sites to the process of malignant transformation, both of which affect the regulation of viral gene expression and the viral life cycle. By understanding the biology of these epithelial sites, better diagnosis/treatment/management of HPV-associated cancer and/or pre-cancer lesions will be provided.
Glendonite-bearing concretions from the upper Pliensbachian (Lower Jurassic) of South Germany: indicators for a massive cooling in the European epicontinental sea
Authors: Merkel Anna , Munnecke Axel 
Published: May 2023

AbstractThe Pliensbachian–Toarcian transition was characterised by a drastic turnover from a cool climate to a period of rapid global warming. While the warming associated with the Early Toarcian Oceanic Anoxic Event is rather well-studied, the cause, intensity and extent of the preceding cooling in the late Pliensbachian are still discussed. Occurrences of glendonite play an important role in this debate, since glendonite is a pseudomorph after the cryophilic carbonate mineral ikaite. This study describes the first glendonite-bearing carbonate concretions from South Germany (Buttenheim clay pit, northern Franconian Alb), which represent the southernmost glendonite occurrence in the late Pliensbachian documented so far. Based on petrographical and sedimentological investigations as well as stable isotope analyses it is concluded that a low temperature was the main factor for ikaite formation in the studied section, suggesting that the late Pliensbachian cooling had a more far-reaching impact on the temperature of the European epicontinental sea than previously assumed. To explain the low temperatures required for ikaite precipitation, a model for the sea-ice driven formation of cold bottom-water masses on the continental shelf is proposed. The occurrence of several layers containing reworked hiatus concretions in the studied outcrop is interpreted as the result of recurrent sea-level falls caused by multiple glacial pulses characterising the overall cool climate in the late Pliensbachian.
Forecasting ward-level bed requirements to aid pandemic resource planning: Lessons learned and future directions
Authors: Johnson Michael R. , Naik Hiten , Chan Wei Siang , Greiner Jesse , Michaleski Matt , Liu Dong , Silvestre Bruno , McCarthy Ian P. 
Published: May 2023

AbstractDuring the COVID-19 pandemic, there has been considerable research on how regional and country-level forecasting can be used to anticipate required hospital resources. We add to and build on this work by focusing on ward-level forecasting and planning tools for hospital staff during the pandemic. We present an assessment, validation, and deployment of a working prototype forecasting tool used within a modified Traffic Control Bundling (TCB) protocol for resource planning during the pandemic. We compare statistical and machine learning forecasting methods and their accuracy at one of the largest hospitals (Vancouver General Hospital) in Canada against a medium-sized hospital (St. Paul’s Hospital) in Vancouver, Canada through the first three waves of the COVID-19 pandemic in the province of British Columbia. Our results confirm that traditional statistical and machine learning (ML) forecasting methods can provide valuable ward-level forecasting to aid in decision-making for pandemic resource planning. Using point forecasts with upper 95% prediction intervals, such forecasting methods would have provided better accuracy in anticipating required beds on COVID-19 hospital units than ward-level capacity decisions made by hospital staff. We have integrated our methodology into a publicly available online tool that operationalizes ward-level forecasting to aid with capacity planning decisions. Importantly, hospital staff can use this tool to translate forecasts into better patient care, less burnout, and improved planning for all hospital resources during pandemics.
Do Autistic Traits Predict Outcome of Cognitive Behavioral Therapy in Pediatric Obsessive-Compulsive Disorder?
Authors: Højgaard Davíð R.M.A. , Arildskov Trine Wigh , Skarphedinsson Gudmundur , Hybel Katja A. , Ivarsson Tord , Weidle Bernhard , Melin Karin , Torp Nor Christian , Thomsen Per Hove 
Published: May 2023

AbstractThe first aim of this study was to explore whether children with obsessive compulsive disorder (OCD) and subclinical autistic traits can be differentiated from children with OCD without these traits based on clinical OCD-related characteristics, distinct OCD symptom patterns, and type of comorbidity. The second aim was to investigate whether autistic traits predict immediate and long-term outcome of exposure-based cognitive behavioral therapy (CBT) in pediatric OCD.The participants in this study were a total of 257 children and adolescents aged 7–17 years, recruited from Denmark, Norway, and Sweden as a part of the Nordic long-term OCD treatment study (NordLOTS). Inclusion criteria were an OCD diagnosis based on DSM-IV criteria and a Children’s Yale-Brown Obsessive-Compulsive Scale (CY-BOCS) total severity score of 16 or higher. No children with a diagnosis on the autism spectrum were included. An Autism Spectrum Screening Questionnaire (ASSQ) cut-off score of ≥ 17 was used to define the group of OCD patients with autistic traits and all participants were treated with 14 weekly sessions of manualized CBT.Comorbid attention-deficit/hyperactivity disorder and tic disorders, subclinical internalizing and externalizing symptoms, lower insight into OCD symptoms, more indecisiveness and pervasive slowness, and ordering/arranging OCD symptoms were found to be significantly associated with having OCD with autistic traits. No difference was found between the groups on treatment outcomes.Results suggest that children and adolescents with OCD and autistic traits portray a different clinical profile than those without these traits, but that CBT is equally effective for those with and without autistic traits.
Correlated responses to selection for different cell size in Chlamydomonas reinhardtii using divergent evolutionary pathways
Authors: Fae Neto Wladimir A. , Dosselli Ryan , Kennington W. Jason , Tomkins Joseph L. 
Published: May 2023

AbstractCell size is an important trait that is often correlated with organisms’ survival under different thermal environments, and in the case of phytoplankton, it is also related to survival under different light irradiance. While smaller phytoplankton thrive under warm and high light (summer conditions), larger cells seem to be more efficient under cool and low light conditions (winter conditions). In this study, we used different evolutionary pathways, nitrogen depletion and size selection, to select for differences in average cell diameter in the single-celled alga Chlamydomonas reinhardtii. We tested whether there was a size-related fitness (growth rate) advantage across two light intensities and two temperatures; environmental effects that have dramatic effects on algal growth. Under these environmental conditions we then tested how the evolutionary pathway to reduced cell size affected fitness and photophysiology. The results suggest that the relationship between size and growth rate in C. reinhardtii is strongly correlated with changes in the photosynthetic apparatus in different sized cells. Large-selected cells evolved to have smaller light-harvesting antennae, making them less susceptible to photodamage. However, they accumulate larger amounts of reactive oxygen species, potentially due to their lack of antioxidant carotenoids. In addition, the maximum growth rate was reduced, potentially due to their reduced ability to repair PSII due to lower metabolic rates. Similar responses were observed in lines that evolved to have smaller cells under nitrogen depletion. The results demonstrate that different evolutionary pathways to reduced cell size result in different changes in metabolism, with N-depleted and Small-selected cell lineages having similar sizes, but thriving differently under winter/summer conditions. With large production systems in mind, it is possible to assume that the choice of strains selected using adaptative laboratory evolution or direct selection on size can affect the overall production yield and carbon capture according to the seasons and possibly other abiotic parameters.
Artificial Intelligence and Autonomy: On the Ethical Dimension of Recommender Systems
Authors: Bonicalzi Sofia , De Caro Mario , Giovanola Benedetta 
Published: May 2023

AbstractFeasting on a plethora of social media platforms, news aggregators, and online marketplaces, recommender systems (RSs) are spreading pervasively throughout our daily online activities. Over the years, a host of ethical issues have been associated with the diffusion of RSs and the tracking and monitoring of users’ data. Here, we focus on the impact RSs may have on personal autonomy as the most elusive among the often-cited sources of grievance and public outcry. On the grounds of a philosophically nuanced notion of autonomy, we illustrate three specific reasons why RSs may limit or compromise it: the threat of manipulation and deception associated with RSs; the RSs’ power to reshape users’ personal identity; the impact of RSs on knowledge and critical thinking. In our view, however, notwithstanding these legitimate concerns, RSs may effectively help users to navigate an otherwise overwhelming landscape. Our perspective, therefore, is not to be intended as a bulwark to protect thestatus quobut as an invitation to carefully weigh these aspects in the design of ethically oriented RSs.
Effects of Covid-19 on the autonomic nervous system in elite athletes assessed by heart rate variability
Authors: Zacher Jonas , Branahl Aike , Predel Hans-Georg , Laborde Sylvain 
Published: May 2023

Abstract
Introduction
Covid-19 is a viral airway and systemic infection which can negatively affect the function of the autonomic nervous system. Cardiovascular autonomic function is essential for peak athletic performance. The aim of this study was to assess the effects of a Covid-19 disease on the autonomic nervous system of German elite athletes using heart rate variability (HRV).

Methods
60 elite athletes (aged 22.88 ± 4.71 years) were recruited, 30 of whom had undergone a Covid-19 disease. Heart rate (HR), blood pressure (BP) and heart rate variability (HRV) were measured during rest and during an orthostatic challenge.

Results
At rest and after orthostatic stress blood pressure and the root mean square of successive differences (RMSDD) were significantly lower in Covid-19 athletes (COV) than in control athletes (CON) (p = 0.002 and p = 0.004, respectively); heart rate was significantly higher (p = 0.001). COV showed a significantly greater reduction in blood pressure and elevation of heart rate than CON, but the change in RMSSD did not differ significantly during the orthostatic challenge.

Conclusion
These results show a change in cardiac parasympathetic activity and cardiovascular autonomic function in German elite athletes after Covid-19. These findings further the understanding of effects of the Covid-19 disease on the cardiovascular physiology in athletes. Heart rate variability may be a helpful tool in the return-to-play assessment of elite athletes.
Image-based recognition of surgical instruments by means of convolutional neural networks
Authors: Lehr Jan , Kelterborn Kathrin , Briese Clemens , Schlueter Marian , Kroeger Ole , Krueger Joerg 
Published: May 2023

Abstract
Purpose
This work presents a novel camera-based approach for the visual recognition of surgical instruments. In contrast to the state of the art, the presented approach works without any additional markers. The recognition is the first step for the implementation of tracking and tracing of instruments wherever they are visible and could be seen by camera systems. Recognition takes place at item number level. Surgical instruments that share the same article number also share the same functions. A distinction at this level of detail is sufficient for most clinical applications.

Methods
In this work, an image-based data set with over 6500 images is generated from 156 different surgical instruments. Forty-two images were acquired from each surgical instrument. The largest part is used to train convolutional neural networks (CNNs). The CNN is used as a classifier, where each class corresponds to an article number of the surgical instruments used. Only one surgical instrument exists per article number in the data set.

Results
With a suitable amount of validation and test data, different CNN approaches are evaluated. The results show a recognition accuracy of up to 99.9% for the test data. To achieve these accuracies, an EfficientNet-B7 was used. It was also pre-trained on the ImageNet data set and then fine-tuned on the given data. This means that no weights were frozen during the training, but all layers were trained.

Conclusion
With recognition accuracies of up to 99.9% on a highly meaningful test data set, recognition of surgical instruments is suitable for many track and trace applications in the hospital. But the system has limitations: A homogeneous background and controlled lighting conditions are required. The detection of multiple instruments in one image in front of various backgrounds is part of future work.
Depiction of neuroendocrine features associated with immunotherapy response using a novel one-class predictor in lung adenocarcinoma
Authors: Liu Hao , Han Yan , Liu Zhantao , Gao Liping , Yi Tienan , Yu Yuandong , Wang Yu , Qu Ping , Xiang Longchao , Li Yong 
Published: May 2023

Abstract
Background
Tumours with no evidence of neuroendocrine transformation histologically but harbouring neuroendocrine features are collectively referred to as non-small cell lung cancer (NSCLC) with neuroendocrine differentiation (NED). Investigating the mechanisms underlying NED is conducive to designing appropriate treatment options for NSCLC patients.

Methods
In the present study, we integrated multiple lung cancer datasets to identify neuroendocrine features using a one-class logistic regression (OCLR) machine learning algorithm trained on small cell lung cancer (SCLC) cells, a pulmonary neuroendocrine cell type, based on the transcriptome of NSCLC and named the NED index (NEDI). Single-sample gene set enrichment analysis, pathway enrichment analysis, ESTIMATE algorithm analysis, and unsupervised subclass mapping (SubMap) were performed to assess the altered pathways and immune characteristics of lung cancer samples with different NEDI values.

Results
We developed and validated a novel one-class predictor based on the expression values of 13,279 mRNAs to quantitatively evaluate neuroendocrine features in NSCLC. We observed that a higher NEDI correlated with better prognosis in patients with LUAD. In addition, we observed that a higher NEDI was significantly associated with reduced immune cell infiltration and immune effector molecule expression. Furthermore, we found that etoposide-based chemotherapy might be more effective in the treatment of LUAD with high NEDI values. Moreover, we noted that tumours with low NEDI values had better responses to immunotherapy than those with high NEDI values.

Conclusions
Our findings improve the understanding of NED and provide a useful strategy for applying NEDI-based risk stratification to guide decision-making in the treatment of LUAD.
Estimation of geomechanical rock characteristics from specific energy data using combination of wavelet transform with ANFIS-PSO algorithm
Authors: Mohammadi Behboud Mohammad , Ramezanzadeh Ahmad , Tokhmechi Behzad , Mehrad Mohammad , Davoodi Shadfar 
Published: May 2023

AbstractThe geomechanical characteristics of a drill formation are uncontrollable factors that are crucial to determining the optimal controllable parameters for a drilling operation. In the present study, data collected in wells drilled in the Marun oilfield of southwestern Iran were used to develop adaptive network-based fuzzy inference system (ANFIS) models of geomechanical parameters. The drilling specific energy (DSE) of the formation was calculated using drilling parameters such as weight-on-bit (WOB), rate of penetration (ROP), rotational speed of drilling string (RPM), torque, bit section area, bit hydraulic factor, and bit hydraulic power. A stationary wavelet transform was subsequently used to decompose the DSE signal to the fourth level. The approximation values and details of each level served as inputs for ANFIS models using particle swarm optimization (PSO) algorithm and genetic algorithm (GA). As model outputs, the Young’s Modulus, uniaxial compressive strength (UCS), cohesion coefficient, Poisson’s ratio, and internal friction angle were compared to the geomechanical parameters obtained from petrophysical logs using laboratory-developed empirical relationships. Both models predicted the Young’s modulus, UCS, and cohesion coefficient with high accuracy, but lacked accuracy in predicting the internal friction angle and Poisson’s ratio. The root mean square error (RMSE) and determination coefficient (R2) were lower for the ANFIS-PSO model than for the ANFIS-GA model, indicating that the ANFIS-PSO model presents higher accuracy and better generalization capability than the ANFIS-GA model. As drilling parameters are readily available, the proposed method can provide valuable information for strategizing a drilling operation in the absence of petrophysical logs.
Queuing models with Mittag-Leffler inter-event times
Authors: Butt Jacob , Georgiou Nicos , Scalas Enrico 
Published: May 2023

AbstractWe study three non-equivalent queueing models in continuous time that each generalise the classical M/M/1 queue in a different way. Inter-event times in all models are Mittag-Leffler distributed, which is a heavy tail distribution with no moments. For each of the models we answer the question of the queue being at zero infinitely often (the ‘recurrence’ regime) or not (the transient regime). Aside from this question, the different analytical properties of each models allow us to answer a number of questions such as existence and description of equilibrium distributions, mixing times, asymptotic behaviour of return probabilities and moments and functional limit theorems.
Knowledge and acceptance
Authors: Heil Roman 
Published: May 2023

AbstractIn a recent paper, Jie Gao (Synthese 194:1901–17, 2017) has argued that there are acceptance-based counterexamples to the knowledge norm for practical reasoning (KPR). KPR tells us that we may only rely on known propositions in practical reasoning, yet there are cases of practical reasoning in which we seem to permissibly rely on merely accepted propositions, which fail to constitute knowledge. In this paper, I will argue that such cases pose no threat to a more broadly conceived knowledge-based view of practical reasoning. I will first motivate the view that rational acceptance depends on a knowledge-based condition being met. I will then show how KPR can be amended—yielding what I call KPR+—to include this condition. I will argue that KPR+ not only avoids Gao’s counterexample, but harbours additional explanatory power by providing an account of the normative role of acceptance in practical reasoning. Finally, I will defend KPR+ against objections by employing theoretical tools that are readily available to those sympathetic to knowledge-based views.