bluuebunny/crossref_metadata_2025 huggingface dataset:
Phase I evaluation of CycloSam® (Sm‐153‐DOTMP) bone seeking radiopharmaceutical in dogs with spontaneous appendicular osteosarcoma
Authors: Selting Kim A. , Simon Jaime , Lattimer Jim C. , Ketring Alan , Axiak‐Bechtel Sandra , Frank Keith , Wendt Richard E. , Bryan Jeffrey N. , Tate Deborah , Maitz Charles , Lunceford Joni , Donnelly Lindsay , Keegan Kevin , Henry Carolyn J.
Published: July 2023

153Sm‐DOTMP (CycloSam®) is a newly‐patented radiopharmaceutical for bone tumor treatment. DOTMP (1,4,7,10‐tetraazacyclododecane‐1,4,7,10‐tetramethylene‐phosphonate) is a macrocyclic chelating agent with superior binding properties to 153Sm when compared with EDTMP (Quadramet™, used for palliative treatment of bone cancer). CycloSam® was administered at 1 mCi/kg (37 MBq/kg) in a prospective pilot study to seven dogs with bone cancer resulting in no myelosuppression. Then, 13 dogs were enrolled in a prospective clinical trial study using traditional 3+3 dose escalation and starting at 1.5 mCi/kg. Baseline evaluation included hematologic and biochemical testing, diagnosis confirmation, thoracic and limb radiographs, technetium‐99 m‐HDP bone scintigraphy, and 18F‐FDG PET scan (SUVmax). Toxicity (primary endpoint) was assessed through weekly blood counts and adverse events. Dogs received 1.5 mCi/kg (n = 4), 1.75 mCi/kg (n = 6), and 2 mCi/kg (n = 3) of 153Sm‐DOTMP. Dose‐limiting neutropenia and thrombocytopenia were seen at 2 mCi/kg. No dose‐limiting nonhematologic toxicities occurred. Efficacy (secondary endpoint) was assessed by objective lameness measurement (body‐mounted inertial sensors), owner quality‐of‐life (QoL) questionnaire, and repeat PET scan. Objective lameness measurement improved in four dogs (53%–60% decrease) was equivocal in three dogs, and worsened in four dogs (66%–115% increase); two dogs were not evaluable. Repeat 18F‐FDG PET scan results varied and change in lameness did not consistently correlate with SUVmax changes. QoL score worsened (n = 5) or was improved/stable (n = 7). Carboplatin chemotherapy (300 mg/m2 IV every 3 weeks ×4) started 4 weeks after 153Sm‐DOTMP injection. No dog died of chemotherapy‐related complications. All dogs completed study monitoring. The recommended dose for CycloSam® in dogs is 1.75 mCi/kg, which resulted in some pain control with minimal toxicity and was safely combined with chemotherapy.
Literature Review on Metadata Governance
Authors: Kaur Amarmeet , Usama Medzhakhed , Wong Hooi Ten Doris , Majid Nur Liana , Maarop Nurazean
Published: July 2023

The framework of metadata governance is a subset of the primary data governance framework implementation within an enterprise. Metadata management helps identify data provenance and destination systems, explain the categories of data in the system, and assist organisations in comprehending the elucidation of data in various structures. This study aims to construct a literature review on metadata governance including the overview, the definition and proposed work based on previous study in different industries related with metadata governance. According to DAMA-DMBOKv2, five activities are required to establish metadata management which are designing metadata strategy, understanding metadata needs, defining metadata architecture, producing and managing metadata, and querying, reporting, and analysing metadata. Three planning procedures for metadata management includes developing metadata strategy activities, identifying metadata needs, and designing metadata architecture. The metadata management industry would benefit from a comprehensive and effective data stewardship framework to maximise company economic value. An enterprise requires a metadata governance framework, which includes an assessment of metadata responsibilities, life cycles, and statistics, as well as how various business activities incorporate metadata. A metadata strategy guarantees that an organisation's whole data ecology is consistent.
Enhanced Recurrent Neural Network (RNN) For Heart Disease Risk Prediction Using Framingham Datasets
Authors: Krishnan Surenthiran , Magalingam Pritheega , Ibrahim Roslina
Published: July 2023

Heart disease is one of the leading causes of death globally, which takes 17.9 million lives each year. The existing heart disease prediction techniques have a gap that does not consider the smoking attributes from the heart disease data. So, the accuracy is based on the limited number of medical data and the deep learning model. The existing deep learning models which use the Recurrent Neural Network (RNN) for heart disease prediction consume more processing and analysing time, mainly due to the delay of data retrieval. This delay causes the prediction process to become slower and leads to a moderate prediction only. The backpropagation of the RNN with an update gate and internal memory to carry the updated data cause a minor data glitch that leads to lower accuracy. Therefore, an efficient heart disease prediction model is very crucial to provide early detection among patients. This research proposes a Heart Disease Risk Prediction Model (HDRPM) with an enhanced RNN to improve the prediction accuracy using Framingham heart disease datasets. The specificity and sensitivity are imposed to improve the quality of the predictions. Sensitivity measure is used for detecting patients with heart disease perfectly and specificity measure is used for detecting patients without the disease perfectly. Besides the accuracy and quality of the prediction problem, the imbalance of minority classes in the dataset occurred in most deep learning prediction fields. This research aims to improve the quality of imbalanced Framingham datasets using Synthetic Minority Over-sampling Technique (SMOTe), which will synthetic instances in a small class to be equalized. The existing RNN model faces vanishing gradients that impede the learning of long data sequences. These gradients that carry information in the RNN cells will become smaller gradually till it minimises the parameter updates and leads to poor learning. For this purpose, the presence of multiple Gated Recurrent Unit (GRU) is used to overcome the vanishing gradients and ensure the hidden layers. The neurons of RNN rapidly cater for the essential information during the training and validation phase of the HDRPM. The integration of multiple GRU with the RNN, operating on the Tensorflow as back-end and Keras as the core for the neural network library has increased the performance of the proposed model. The proposed model provides up to 98.78%, the highest accuracy achieved compared to related previous work, which is a quantum neural network model with 98.57. This HDRPM is expected to significantly contribute to early detection of heart disease patients.
Enterprise Architecture for Equipment Performance Analysis Based on Internet of Things (IoT) Technology in the Semiconductor Manufacturing Industry
Authors: Tengku Putri Nurain  , Hasan Al-Kubati Marwan Abdullah , Abu Bakar Nur Azaliah
Published: July 2023

This study proposes a concept for establishing an Enterprise Architecture in the semiconductor manufacturing industry for equipment performance analysis using Internet of Things (IoT) technology.  The plan is to implement The Open Group Framework approach as Enterprise Architecture in the manufacturing analytics department.  The TOGAF approach improves data governance in the business process and analytics department.  Manufacturing focuses on providing excellent and high-quality products to customers.  As a result, the manufacturer must use a data analytics application to monitor the performance of the equipment.  The performance of the equipment is monitored around the clock to ensure that it meets the requirements and does not exceed the threshold.  The business process, data from the warehouse, and how it is processed will be discussed.  Implementing Enterprise Architecture in manufacturing will also be discussed, focusing on the three layers of the TOGAF Architecture Development Method (ADM).  The three layers are business, technology, and application.  The Enterprise Architecture framework is a blueprint for the architecture used to align the business and information technology.  Enterprise architecture optimises business processes and structures processes and functions to integrate information technology into the business.  The proposed Enterprise Architecture for equipment performance analysis in the semiconductor manufacturing industry can be used as a guideline for implementing a comprehensive framework for tool performance monitoring.
Counter-Narrative Cyber Security Model to Address the Issues of Cyber Terrorism
Authors: Ahmad Tamerin Ahmad Syafiq , Bakar Nur Azaliah Abu , Hassan Noor Hafizah , Maarop Nurazean
Published: July 2023

Terrorism is the imminent threat that Malaysia and the entire world are facing nowadays.  This has become a severe threat to Malaysia’s national security as extremist groups such as ISIS and DAESH are leveraging cyberspace to gain more supporters and sympathisers.  These extremists used social media to disseminate their narratives among the Malaysian Armed Force (MAF) personnel to recruit them for their excellent military skills.  Nevertheless, the current MAF counter-narrative is still using traditional media operations like roadshows, brochures dissemination, articles, and short videos in combating the terrorism agenda, which are less effective and time-consuming.  Therefore, this study proposes an enhanced counter-terrorism approach to fight this terrorism narrative by utilising the MAF IT infrastructure to focus on the social media platform.  The model was built based on ISO / IEC 27032:2012 standard, concerning three cyber-terrorism models.  Six military IT experts then verified this new enhanced model. They agreed that it is crucial to establish such a model and emphasise that counter-terrorism effort needs full cooperation with other services and collegiality.  This study’s final output is the Counter-Narrative Information Technology Model, which will later potentially be adopted to the MAF environment in line with the national inspiration in fighting terrorism.
The Role of Deep Learning in Manufacturing Applications: Challenges and Opportunities
Authors: Malhan Rishi , Gupta Satyandra K.
Published: July 2023

There is a growing interest in using deep learning technologies within the manufacturing industry to improve quality, productivity, safety, and efficiency, while also reducing costs and cycle time. This position paper discusses the applications of deep learning currently being employed in manufacturing, including identifying defects, optimizing processes, streamlining the supply chain, predicting maintenance needs, and recognizing human activity. This paper aims to provide a description of the challenges and opportunities in this area to beginning researchers. The paper offers a brief summary of the various components of deep learning technology and their roles. Additionally, the paper draws attention to the current challenges and limitations that need to be addressed to fully realize the potential of deep learning technology in manufacturing. Lastly, several future directions for research within the field are proposed to further improve the use of deep learning in manufacturing.
Uncertainty Reduction in Fatigue Life Validation Testing for Drilling Tools with a Universal Runout Compensator
Authors: Du Michael , Song Fei , Li Ke
Published: July 2023

Rotating bending fatigue (RBF) life is a critical performance index for oil drilling equipment. It can be measured via cantilever-type RBF testing, with a sideload applied on the specimen to generate the required bending moment at the point of interest. Multiple factors cause runout or eccentricity of the test specimen at the sideload point, leading to deviations in the effective sideload acting on the specimen and, consequently, deviations in the bending moment at the point of interest. Runout generates greater uncertainty in fatigue life test results.

A Universal Runout Compensator (URC) was developed to reduce this uncertainty by mitigating the test specimen runout. It consists of two eccentric bushings, one assembled inside the other. Depending on the relative orientation of the two bushings, the total eccentricity of the URC can be adjusted. With the properly set URC installed on the specimen, the final runout at the URC where the sideload is applied becomes negligible.

Finite element analysis (FEA) was used to confirm the URC structural integrity. Full-scale RBF tests were conducted to validate the URC design and FEA studies. With the URC used in the tests, bending moment variation decreased by up to 15%, reducing life cycle uncertainty by up to 44%.
Training and Interpretability of Deep-Neural Methods for Damage Calibration in Copper.
Authors: Hickmann Kyle , Callis Skylar , Andrews Stephen
Published: July 2023

We present an application of convolutional neural networks for calibration of a tensile plasticity (TePla) damage model simulating the spallation in copper under high-explosive shock loading. Using a high-fidelity, multi-physics simulation developed by the Advanced Simulation and Computing program at Los Alamos National Laboratory (LANL), we simulate hundreds of variations of a high-explosive shock experiment involving a copper coupon. From this synthetic data, we train neural networks to learn the inverse mapping between the coupon’s late-time density field, or an associated synthetic radiograph, and the simulation’s TePla damage parameters. It is demonstrated that, using a simple convolutional architecture, we can train networks to infer damage parameters from density fields accurately. Neural network inference directly from synthetic radiographs is significantly more challenging. Application of machine-learning methods must be accompanied by an analysis of how they are making inferences in order to build confidence in predictions and to identify likely shortcomings of the technique. To understand what the model is learning, individual layer outputs are extracted and examined. Each layer in the network identifies multiple features. However, each of these features are not necessarily of equal importance in the network’s final prediction of a given damage parameter. By examining the features overlaid on the input hydrodynamic fields, we assess the question of whether or not the model’s accuracy can be attributed to human-recognizable characteristics. In this work we give a detailed description of our data-generation methods and the learning problem we address. We then outline our neural architecture trained for damage calibration and discuss considerations made during training and evaluation of accuracy. Methods for human interpretation of the network’s inference process are then put forward, including extraction of learned features from the trained network and techniques to assess sensitivity of inferences to the learned features.
Acute and Long-Term Changes in Blood-Borne Biomarkers in Response to Dynamic Standing in Nonambulant Children With Cerebral Palsy
Authors: Varga Tibor V. , Andersson Åsa , Lauruschkus Katarina , Tornberg Åsa B.
Published: July 2023

Purpose: To investigate acute and long-term changes in hormonal and inflammatory biomarkers in nonambulant children with cerebral palsy in response to dynamic standing exercise. Methods: Fourteen children with severe cerebral palsy were recruited. Anthropometrics and body composition measures were obtained. Physical activity levels before the study were assessed using hip-worn accelerometry. All children underwent a 30-minute dynamic standing exercise using the Innowalk standing aid. Respiratory data during exercise were collected using indirect calorimetry. Blood samples were collected before and after exercise. Blood samples were also obtained after two 16-week exercise protocols, in a resting state. Hormonal and inflammatory metabolites were measured from blood serum/plasma, and acute and long-term changes in biomarker levels were assessed using Wilcoxon signed-rank tests. Results: Of the 14 children at baseline, all had slightly/moderately/severely elevated C-reactive protein and cortisol levels. C-reactive protein levels were decreased following a 30-minute bout of dynamic standing (before exercise: 53 mg/L [interquartile range: 40–201]; after exercise: 39 mg/L [interquartile range: 20–107]; P = .04). Conclusions: We show that several hormonal and inflammatory biomarkers are dysregulated in children with cerebral palsy. Our preliminary results from a small, but deep-phenotyped prospective cohort indicate acute and long-term alterations of several biomarkers in response to exercise.
Bias comes in layers
Authors: Foley John F.
Published: July 2023

GINIP promotes Gβγ signaling while inhibiting Gα
i
signaling to fine-tune GPCR-mediated neuromodulation.
Evaluation of a Quantitative Dual-Target EBV DNA Test on a Fully Automated Molecular Testing System
Authors: Lütgehetmann Marc , Albert Eliseo , Hamilton Aaron , Jarem Daniel , Pfefferle Susanne , Stucki Heinz , Navarro David
Published: July 2023

The measurement of Epstein–Barr virus (EBV) deoxyribonucleic acid (DNA) is key to diagnosing and managing EBV-associated complications in transplant recipients. The performance of the new Conformité Européenne (CE) and Food and Drug Administration (FDA)-cleared quantitative Roche cobas EBV real-time PCR assay was determined by using EDTA-plasma dilution panels and clinical samples that were spiked with either the World Health Organization’s EBV international standard or high-titer EBV lambda stock.
Single-strain behavior predicts responses to environmental pH and osmolality in the gut microbiota
Authors: Ng Katharine M. , Pannu Sagar , Liu Sijie , Burckhardt Juan C. , Hughes Thad , Van Treuren Will , Nguyen Jen , Naqvi Kisa , Nguyen Bachviet , Clayton Charlotte A. , Pepin Deanna M. , Collins Samuel R. , Tropini Carolina
Published: July 2023

Changes to gut environmental factors such as pH and osmolality due to disease or drugs correlate with major shifts in microbiome composition; however, we currently cannot predict which species can tolerate such changes or how the community will be affected. Here, we assessed the growth of 92 representative human gut bacterial strains spanning 28 families across multiple pH values and osmolalities
in vitro
. The ability to grow in extreme pH or osmolality conditions correlated with the availability of known stress response genes in many cases, but not all, indicating that novel pathways may participate in protecting against acid or osmotic stresses. Machine learning analysis uncovered genes or subsystems that are predictive of differential tolerance in either acid or osmotic stress. For osmotic stress, we corroborated the increased abundance of these genes
in vivo
during osmotic perturbation. The growth of specific taxa in limiting conditions in isolation
in vitro
correlated with survival in complex communities
in vitro
and in an
in vivo
mouse model of diet-induced intestinal acidification. Our data show that
in vitro
stress tolerance results are generalizable and that physical parameters may supersede interspecies interactions in determining the relative abundance of community members. This study provides insight into the ability of the microbiota to respond to common perturbations that may be encountered in the gut and provides a list of genes that correlate with increased ability to survive in these conditions.

IMPORTANCE

To achieve greater predictability in microbiota studies, it is crucial to consider physical environmental factors such as pH and particle concentration, as they play a pivotal role in influencing bacterial function and survival. For example, pH is significantly altered in various diseases, including cancers, inflammatory bowel disease, as well in the case of over-the-counter drug use. Additionally, conditions like malabsorption can affect particle concentration. In our study, we investigate how changes in environmental pH and osmolality can serve as predictive indicators of bacterial growth and abundance. Our research provides a comprehensive resource for anticipating shifts in microbial composition and gene abundance during complex perturbations. Moreover, our findings underscore the significance of the physical environment as a major driver of bacterial composition. Finally, this work emphasizes the necessity of incorporating physical measurements into animal and clinical studies to better understand the factors influencing shifts in microbiota abundance.
Mrs4 loss of function in fungi during adaptation to the cystic fibrosis lung
Authors: Murante Daniel , Demers Elora G. , Kurbessoian Tania , Ruzic Marina , Ashare Alix , Stajich Jason E. , Hogan Deborah A.
Published: July 2023

The genetic disease cystic fibrosis (CF) frequently leads to chronic lung infections by bacteria and fungi. We identified three individuals with CF with persistent lung infections dominated by
Clavispora
(
Candida
)
lusitaniae
. Whole-genome sequencing analysis of multiple isolates from each infection found evidence for selection for mutants in the gene
MRS4
in all three distinct lung-associated populations. In each population, we found one or two unfixed, non-synonymous mutations in
MRS4
relative to the reference allele found in multiple environmental and clinical isolates including the type strain. Genetic and phenotypic analyses found that all evolved alleles led to loss of function (LOF) of Mrs4, a mitochondrial iron transporter. RNA-seq analyses found that Mrs4 variants with decreased activity led to increased expression of genes involved in iron acquisition mechanisms in both low iron and replete iron conditions. Furthermore, surface iron reductase activity and intracellular iron were much higher in strains with Mrs4 LOF variants. Parallel studies found that a subpopulation of a CF-associated
Exophiala dermatitidis
infection also had a non-synonymous LOF mutation in
MRS4
. Together, these data suggest that
MRS4
mutations may be beneficial during chronic CF lung infections in diverse fungi, perhaps, for the purposes of adaptation to an iron-restricted environment with chronic infections.

IMPORTANCE

The identification of
MRS4
mutations in
Clavispora
(
Candida
)
lusitaniae
and
Exophiala dermatitidis
in individuals with cystic fibrosis (CF) highlights a possible adaptive mechanism for fungi during chronic CF lung infections. The findings of this study suggest that loss of function of the mitochondrial iron transporter Mrs4 can lead to increased activity of iron acquisition mechanisms, which may be advantageous for fungi in iron-restricted environments during chronic infections. This study provides valuable information for researchers working toward a better understanding of the pathogenesis of chronic lung infections and more effective therapies to treat them.
Quantitative Proteomic Analysis of Cyanide and Mercury Detoxification by Pseudomonas pseudoalcaligenes CECT 5344
Authors: Biełło Karolina A. , Olaya-Abril Alfonso , Cabello Purificación , Rodríguez-Caballero Gema , Sáez Lara P. , Moreno-Vivián Conrado , Luque-Almagro Víctor Manuel , Roldán María Dolores
Published: July 2023

Cyanide, mercury, and arsenic are considered very toxic chemicals that are present in nature as cocontaminants in the liquid residues generated by different industrial activities like mining. Considering the huge amounts of toxic cyanide- and mercury-containing wastes generated at a large scale and the high biotechnological potential of
P. pseudoalcaligenes
CECT 5344 in the detoxification of cyanide present in these industrial wastes, in this work, proteomic, transcriptional, and bioinformatic approaches were used to characterize the molecular response of this bacterium to cyanide and mercury, highlighting the mechanisms involved in the simultaneous detoxification of both compounds.
Lipid A Modification and Metabolic Adaptation in Polymyxin-Resistant, New Delhi Metallo-β-Lactamase–Producing Klebsiella pneumoniae
Authors: Lu Jing , Han Meiling , Yu Heidi H. , Bergen Phillip J. , Liu Yiyun , Zhao Jinxin , Wickremasinghe Hasini , Jiang Xukai , Hu Yang , Du Haiyan , Zhu Yan , Velkov Tony
Published: July 2023

Antimicrobial resistance (AMR) is a major threat to global health. The emergence of resistance to the polymyxins that are the last line of defense in so-called Gram-negative “superbugs” has further increased the urgency to develop novel therapies.
Relation between duration of dual antiplatelet therapy and risk of ischemic stroke after stent-assisted treatment of cerebral aneurysm (DAPTS ACE-registry)
Authors: Ozaki Tomohiko , Yamagami Hiroshi , Morimoto Masafumi , Imamura Hirotoshi , Hatano Taketo , Oishi Hidenori , Haraguchi Koichi , Yoshimura Shinichi , Satow Tetsu , Sugiu Kenji , Iihara Koji , Matsumaru Yuji , Hayakawa Mikito , Matsumoto Yasushi , Sakai Chiaki , Miyamoto Susumu , Kitagawa Kazuo , Kagimura Tatsuo , Sakai Nobuyuki
Published: July 2023

Background

The optimal duration of dual antiplatelet therapy (DAPT) in patients with cerebral aneurysm who undergo stent-assisted coil embolization (SACE) has not been established. We aimed to clarify the association between duration of DAPT and incidence of ischemic stroke in patients with cerebral aneurysm.

Methods

We registered patients with cerebral aneurysm who underwent SACE in 27 hospitals in Japan. Those treated with DAPT (aspirin and clopidogrel) were eligible for inclusion in a previously reported randomized control trial (RCT). Patients who were ineligible or refused to participate to the RCT were followed-up for 15 months after SACE as the non-RCT cohort. Our study examined both the RCT and non-RCT cohorts. The primary and secondary outcomes were ischemic stroke and hemorrhagic events.

Results

Among the 313 patients registered, 296 were included for analysis (of these, 136 were RCT patients and 160 were non-RCT patients). Patients who were treated with DAPT for more than 6 months (n=191) were classified as the long-term DAPT group. Those treated less than 6 months (n=105) were classified as the short-term group. The incidence of ischemic stroke did not significantly differ between the long-term group (2.5 per 100 person-years) and the short-term group (3.2 per 100 person-years); nor did incidence of hemorrhagic events (0.8 and 3.2 per 100 person-years, respectively). The period of DAPT was not significantly associated with incidence rates of ischemic stroke or hemorrhagic events.

Conclusions

Duration of DAPT was not associated with the incidence of ischemic stroke in the first 15 months after SACE.
Considerations in the planning and design of bridges in ice-affected rivers: a review
Authors: Burrell Brian C. , Comfort George , Beltaos Spyros
Published: July 2023

Bridge infrastructure assets should be planned, designed, constructed, and operated to account for the physical processes that may occur over their service lives. In ice-affected rivers, this includes the interaction of bridge infrastructure with river ice. Advancements in river-ice science and modelling of ice processes in ice-covered rivers have enhanced the abilities of civil/hydrotechnical engineers to plan, design, and construct bridges in and along rivers with seasonal ice covers. In this paper, an overview is presented of ice-related considerations with respect to bridges. The bottom-chord elevation of the bridge superstructure with respect to water and ice levels, pier design to withstand ice forces, the waterway opening for ice passage, ice-induced scour, and research needs are discussed. However, as outlined in the paper, research on ice loads and passage at bridge sites and on hydroclimatic and ice conditions that led to bridge damage and failure remains needed.
Fungal community dynamics in coarse woody debris across decay stage, tree species, and stand development stage in northern boreal forests
Authors: Hart Saskia C. , Porter Teresita M. , Basiliko Nathan , Venier Lisa , Hajibabaei Mehrdad , Morris Dave
Published: July 2023

Fungi are primary agents of coarse woody debris (CWD) decay in boreal forests, playing an essential role in nutrient cycling and carbon storage. We compared fungal community assemblages using alpha and beta diversity metrics, and physical and chemical properties of CWD across three tree species (trembling aspen [ Populus tremuloides], black spruce [ Picea mariana], and jack pine [ Pinus banksiana]), five decay classes, and two stand development stages, differing in time since stand replacing disturbance in Ontario’s boreal forest region. We sampled 180 individual CWD logs from 6 independent stands, with 3 replicates per each species × decay class combination at each site. Using high-throughput sequencing of marker DNA, we found that fungal community structure significantly differed across tree species, decay stage, and stand age. Fungal diversity was highest in decay class 4 CWD. We found that Mn and K concentrations, total carbon, C/N ratio, N/P ratio, and moisture content were important predictors of fungal composition across CWD species and/or decay stage. This study suggests that forest management guidelines that consider both deadwood quantity and quality will support a broader range of fungal species and communities through post-disturbance stand development.
Sleep deprivation disturbs uterine contractility and structure in pregnant rats: role of matrix metalloproteinase 9 and transforming growth factor-β
Authors: El-Malkey Nanees F. , Aref Mohammed , Goda Nehal I. A. , Hussien Marwa H. , Samy Walaa , Hadhod Shimaa
Published: July 2023

Sleep deprivation (SD) during pregnancy can impact the delivery procedure, with prolongation of the labor duration. Matrix metalloproteinase-9 (MMP9) and transforming growth factor-β (TGF-β) are regulators of uterine remodeling. Their dysregulation is vital for abnormal placentation and uterine enlargement in complicated pregnancies. Therefore, this study aims to explore the outcome of SD throughout pregnancy on ex vivo uterine contractility, MMP9 and TGF-β, and uterine microscopic structure. A total of 24 pregnant rats were divided into two groups. From the first day of pregnancy, animals were exposed to partial SD/6 h/day. Uterine in vitro contractile responses to oxytocin, acetylcholine, and nifedipine were assessed. Additionally, uterine levels of superoxide dismutase and malondialdehyde and uterine mRNA expression of MMP9, TGF-β, and apoptotic biomarkers were analyzed. The results showed that SD significantly reduced uterine contractile responses to oxytocin and acetylcholine, while it augmented the relaxing effect of nifedipine. In addition, it significantly increased oxidative stress status, MMP9, TGF-β, and apoptotic biomarkers' mRNA expression. All were accompanied by degeneration of endometrial glands, vacuolization with apoptotic nuclei, and increased area% of collagen fibers. Finally, increased uterine MMP9 and TGF-β mRNA expression during SD clarified their potential role in modulating uterine contractility and structure.
A viable relativistic charged model of super-dense star LMC X-4
Authors: Murguía Aurelio Tamez , Vázquez-Nambo Manuel , Yépez-García Víctor Manuel , Muñoz Nancy Cambrón , Vázquez José Oswald Téllez
Published: July 2023

In this work, we present an exact interior solution to a physically acceptable Einstein–Maxwell equation system, assuming a static and spherically symmetric spacetime with a distribution of matter from a perfect charged fluid to represent a generalization of a model for a perfect chargeless fluid. The charge parameter modifies the mass function, its compactness rate and the comportment of the speed of sound. The behavior analysis of the functions of density, pressure and charge shows that the solution is applicable for the description of relativistic compact stars. In particular, we analyze the behavior of these functions for the values of observed mass [Formula: see text] and the theoretical radius interval estimated previously [Formula: see text][Formula: see text]km from the star LMC X-4. Thus, the biggest charge value of maximum charge [Formula: see text]C occurs for the maximum compactness [Formula: see text].
Infrared Thermal Imaging Face Expression Recognition Based on Harris Algorithm
Authors: Wang Wentong , Wang Changyuan , Si Lipeng
Published: July 2023

In the field of human–computer interaction, there is a trend toward using emotional interaction and putting emotions at the center. However, traditional facial expression recognition is greatly affected by lighting, skin color and make-up, and is not well recognized. This paper addresses these shortcomings in the following areas: (1) The algorithm of infrared face feature localization is studied, using a combination of region growing algorithm to highlight the organ features of infrared faces, and the Harris corner point detection algorithm to detect the organ features of human beings and locate the location of the large organs related to expressions, such as eyes, nose and mouth. (2) The algorithm for feature extraction is investigated, and the local binarization LBP algorithm is proposed to extract the texture features of the infrared face. The LBP algorithm is synthesized to extract the overall LBP features by applying the innovation of chunking and layering of features. By analyzing and processing the infrared face image data from both visible and infrared face expression data, feature detection, feature extraction and effective classification of the four basic face expressions — happy, angry, disgusted and sad — are performed on the infrared images. The application of the algorithm is processed and improved accordingly to the characteristics of infrared face images, which improves the accuracy of recognition and classification compared to traditional methods.
Dynamics of a Submerged Compound Pendulum
Authors: Das Anupam , Konar Tanmoy , Ghosh Aparna , Maity Damodar
Published: July 2023

This paper focuses on the dynamics of a submerged compound pendulum (SCP). The considered pendulum consists of a horizontal cylindrical element at its lowermost portion and a plate between the cylindrical element and the pivot. An approximate analytical expression for the natural frequency of the submerged compound pendulum is proposed. Further, a two-dimensional (2D) numerical model of the pendulum is developed using ANSYS (FLUENT). The natural periods of the SCP are determined for different geometric configurations. The analytically estimated natural periods of the pendulum match well with those obtained from numerical simulation.
Free special Gelfand–Dorfman algebra
Authors: Gubarev V. , Sartayev B. K.
Published: July 2023

A Gelfand–Dorfman algebra is called special if it can be embedded into a differential Poisson algebra. We find a new basis of the free Novikov algebra. With its help, we construct the monomial basis of the free special Gelfand–Dorfman algebra.
Scalar fields described by Dirac quaternion wave equation
Authors: Mironov Victor L. , Mironov Sergey V.
Published: July 2023

In this paper, we discuss the fields described by Dirac wave equation written in Clifford algebra based on Macfalane quaternions. It is shown that the strengths of these fields are nonzero only in the area of sources and the interaction of such fields occurs by overlapping. We consider both the simple spherically symmetric models of sources, which demonstrate attractive and repulsive interaction, and more complicated core-shell systems, which provide the bound states formation.
Multi-View Graph Convolutional Networks with Differentiable Node Selection
Authors: Chen Zhaoliang , Fu Lele , Xiao Shunxin , Wang Shiping , Plant Claudia , Guo Wenzhong
Published: July 2023

Multi-view data containing complementary and consensus information can facilitate representation learning by exploiting the intact integration of multi-view features. Because most objects in the real world often have underlying connections, organizing multi-view data as heterogeneous graphs is beneficial to extracting latent information among different objects. Due to the powerful capability to gather information of neighborhood nodes, in this article, we apply Graph Convolutional Network (GCN) to cope with heterogeneous graph data originating from multi-view data, which is still under-explored in the field of GCN. In order to improve the quality of network topology and alleviate the interference of noises yielded by graph fusion, some methods undertake sorting operations before the graph convolution procedure. These GCN-based methods generally sort and select the most confident neighborhood nodes for each vertex, such as picking the top-
k
nodes according to pre-defined confidence values. Nonetheless, this is problematic due to the non-differentiable sorting operators and inflexible graph embedding learning, which may result in blocked gradient computations and undesired performance. To cope with these issues, we propose a joint framework dubbed Multi-view Graph Convolutional Network with Differentiable Node Selection (MGCN-DNS), which is constituted of an adaptive graph fusion layer, a graph learning module, and a differentiable node selection schema. MGCN-DNS accepts multi-channel graph-structural data as inputs and aims to learn more robust graph fusion through a differentiable neural network. The effectiveness of the proposed method is verified by rigorous comparisons with considerable state-of-the-art approaches in terms of multi-view semi-supervised classification tasks, and the experimental results indicate that MGCN-DNS achieves pleasurable performance on several benchmark multi-view datasets.
A New Approach to Selecting Optimal Parameters for the Sliding Mode Algorithm on an Automotive Suspension System
Authors: Nguyen Tuan Anh
Published: July 2023

Stimuli from the road surface cause car vibrations. The suspension system ensures the vehicle’s stability and smoothness when moving. In this research, the author introduces the use of active suspension with a hydraulic actuator to improve the comfort of cars. Car oscillations are described based on a quarter-dynamics model with five state variables. Besides, the nonlinear system’s SM (sliding mode) control algorithm is used to control the system. The SM algorithm’s parameters are optimally selected using the multiple loop algorithm. The multiple loop algorithm allows the values to vary within a predefined limit and provides an optimal parameter based on the vehicle’s vibration conditions. This is a new point in the paper compared to previous studies. The output of the simulation problem includes displacements and accelerations of both the sprung and unsprung masses. In addition, the change of dynamics force at the wheel is also considered when evaluating the interaction between the wheel and the road surface. According to the paper’s results, the vehicle body’s acceleration and displacement values are strongly reduced when using the SM algorithm, compared with cars with only traditional suspension. In addition, the change in dynamics force at the wheel is not significant when using this algorithm. As a result, the car’s smoothness has been improved while the vehicle’s stability has not been lost. The phenomenon of “chattering” still occurs in some cases; however, its effect is negligible.
Supplementary Data 2 from Clinical Activity of Combined Telomerase Vaccination and Pembrolizumab in Advanced Melanoma: Results from a Phase I Trial
Authors: Ellingsen Espen B. , O'Day Steven , Mezheyeuski Artur , Gromadka Agnieszka , Clancy Trevor , Kristedja Timothy S. , Milhem Mohammed , Zakharia Yousef
Published: July 2023

Table S2
Supplementary Fig. 4 from Discovery and Targeting of a Noncanonical Mechanism of Sarcoma Resistance to ADI-PEG20 Mediated by the Microenvironment
Authors: Rogers Leonard C. , Kremer Jeff C. , Brashears Caitlyn B. , Lin Zongtao , Hu Zhixian , Bastos Alliny C.S. , Baker Adriana , Fettig Nicole , Zhou Dong , Shoghi Kooresh I. , Dehner Carina A. , Chrisinger John S.A. , Bomalaski John S. , Garcia Benjamin A. , Oyama Toshinao , White Eileen P. , Van Tine Brian A.
Published: July 2023

Supplemental Figure 4. Validation of gene knockouts and ASS1 expression in MEFs.
Data from Identification and Validation of T-cell Receptors Targeting RAS Hotspot Mutations in Human Cancers for Use in Cell-based Immunotherapy
Authors: Levin Noam , Paria Biman C. , Vale Nolan R. , Yossef Rami , Lowery Frank J. , Parkhurst Maria R. , Yu Zhiya , Florentin Maria , Cafri Gal , Gartner Jared J. , Shindorf Mackenzie L. , Ngo Lien T. , Ray Satyajit , Kim Sanghyun P. , Copeland Amy R. , Robbins Paul F. , Rosenberg Steven A.
Published: July 2023

AbstractPurpose:
Immunotherapies mediate the regression of human tumors through recognition of tumor antigens by immune cells that trigger an immune response. Mutations in the RAS oncogenes occur in about 30% of all patients with cancer. These mutations play an important role in both tumor establishment and survival and are commonly found in hotspots. Discovering T-cell receptors (TCR) that recognize shared mutated RAS antigens presented on MHC class I and class II molecules are thus promising reagents for “off-the-shelf” adoptive cell therapies (ACT) following insertion of the TCRs into lymphocytes.
Experimental Design:
In this ongoing work, we screened for RAS antigen recognition in tumor-infiltrating lymphocytes (TIL) or by in vitro stimulation of peripheral blood lymphocytes (PBL). TCRs recognizing mutated RAS were identified from the reactive T cells. The TCRs were then reconstructed and virally transduced into PBLs and tested.
Results:
Here, we detect and report multiple novel TCR sequences that recognize nonsynonymous mutant RAS hotspot mutations with high avidity and specificity and identify the specific class-I and -II MHC restriction elements involved in the recognition of mutant RAS.
Conclusions:
The TCR library directed against RAS hotspot mutations described here recognize RAS mutations found in about 45% of the Caucasian population and about 60% of the Asian population and represent promising reagents for “off-the-shelf” ACTs.
Supplementary Table 4 from Integrative Prognostic Machine-Learning Models in Mantle Cell Lymphoma
Authors: Hill Holly Ann , Jain Preetesh , Ok Chi Young , Sasaki Koji , Chen Han , Wang Michael L. , Chen Ken
Published: July 2023

Hyperparameters for XGBoost Models
Supplementary Table 3 from Integrative Prognostic Machine-Learning Models in Mantle Cell Lymphoma
Authors: Hill Holly Ann , Jain Preetesh , Ok Chi Young , Sasaki Koji , Chen Han , Wang Michael L. , Chen Ken
Published: July 2023

Missingness among Full Dataset Features
Supplementary Figure 4 from Integrative Prognostic Machine-Learning Models in Mantle Cell Lymphoma
Authors: Hill Holly Ann , Jain Preetesh , Ok Chi Young , Sasaki Koji , Chen Han , Wang Michael L. , Chen Ken
Published: July 2023

S4. Correlation Plot of Numeric Features
Supplementary Figure 3 from Integrative Prognostic Machine-Learning Models in Mantle Cell Lymphoma
Authors: Hill Holly Ann , Jain Preetesh , Ok Chi Young , Sasaki Koji , Chen Han , Wang Michael L. , Chen Ken
Published: July 2023

S3. Feature Importance from other XGBoost models
Supplementary Figure 2 from Integrative Prognostic Machine-Learning Models in Mantle Cell Lymphoma
Authors: Hill Holly Ann , Jain Preetesh , Ok Chi Young , Sasaki Koji , Chen Han , Wang Michael L. , Chen Ken
Published: July 2023

S2. Variable Importance from Full XGBoost Model (training cross-validation sets)
Figure S2 from The African-centric P47S Variant of TP53 Confers Immune Dysregulation and Impaired Response to Immune Checkpoint Inhibition
Authors: Stieg David C. , Parris Joshua L. D. , Yang Tyler Hong Loong , Mirji Gauri , Reiser Sarah Kim , Murali Nivitha , Werts Madison , Barnoud Thibaut , Lu David Y. , Shinde Rahul , Murphy Maureen E. , Claiborne Daniel T.
Published: July 2023

Figure S2 shows that the tumor microenvironment in both P47 and S47 mice is comprised of similar proportions of subpopulations of immune cells with similar phenotypes.
FIGURE 7 from The African-centric P47S Variant of TP53 Confers Immune Dysregulation and Impaired Response to Immune Checkpoint Inhibition
Authors: Stieg David C. , Parris Joshua L. D. , Yang Tyler Hong Loong , Mirji Gauri , Reiser Sarah Kim , Murali Nivitha , Werts Madison , Barnoud Thibaut , Lu David Y. , Shinde Rahul , Murphy Maureen E. , Claiborne Daniel T.
Published: July 2023

S47 mice are less responsive to PD-L1 blockade therapy. A, Tumor growth curves for P47 and S47 mice treated with rat IgG2b isotype control antibody or anti-PD-L1 antibody following MC38 cell injections. n = 7 P47 IgG2b isotype control; n = 5 S47 IgG2b isotype control; n = 9 P47 anti-PD-L1; n = 10 S47 anti-PD-L1. B, Graph showing the frequency of palpable tumors in S47 or P47 mice receiving anti-PD-L1 antibody. C and D, Individual tumor growth curves for each P47 and S47 mouse treated with rat IgG2b isotype control antibody or anti-PD-L1 antibody following MC38 cell injections. Statistics were derived from Wilcoxon rank-sum test. *, P < 0.05.
FIGURE 6 from The African-centric P47S Variant of TP53 Confers Immune Dysregulation and Impaired Response to Immune Checkpoint Inhibition
Authors: Stieg David C. , Parris Joshua L. D. , Yang Tyler Hong Loong , Mirji Gauri , Reiser Sarah Kim , Murali Nivitha , Werts Madison , Barnoud Thibaut , Lu David Y. , Shinde Rahul , Murphy Maureen E. , Claiborne Daniel T.
Published: July 2023

TILs in S47 mice exhibit elevated coexpression of multiple IRs. Tumors and spleens were harvested 21 days after injection of MC38 cells and T cells were analyzed for surface expression of multiple IRs by flow cytometry. Comparison of frequency of CD4+ TILs (A) and CD8+ TILs (B) expressing the LAG3, PD1, TIGIT, or Tim3 IRs between S47 and P47 mice. Comparison of the frequency of IR coexpression profiles in CD4+ TILs (C) and CD8+ TILs (D) between S47 and P47 mice. Bar graphs represent frequencies of specific IR coexpression profiles in the total CD4+ or CD8+ T cell populations derived from tumors, whereas pie charts represent relative frequencies of specific IR profiles in the total CD4+ or CD8+ TIL populations expressing any IR. Pie chart slices represent the average relative frequencies of 5 mice.
World Stroke Organization Brain & hEart globAl iniTiative Program
Authors: Sposato Luciano A. , Martins Sheila , Scheitz Jan F. , Aspberg Sara , Gurol M Edip , Abdalla Mohamed , Arauz Antonio , Cano-Nigenda Vanessa , Fiorilli Paul , Israel Carsten , Kusano Kengo , Mansour Ossama , Messé Steven R , Pille Arthur , Secchi Thaís , Polanczyk Carisi A. , Biolo Andreia , Ramadan Ismail , Sallam Ashraf , Schäbitz Wolf , Toyoda Kazunori , Valencia Sharon , Wang Shang , Xiong Yunyun , Zaki Amr , Saposnik Gustavo , Fisher Marc , Bahit M. Cecilia
Published: July 2023

Introduction: The World Stroke Organization (WSO) Brain & Heart Task Force developed the Brain & hEart globAl iniTiative (BEAT), a pilot feasibility implementation program to establish clinical collaborations between cardiologists and stroke physicians who work at large healthcare facilities. Methods: The WSO BEAT pilot project focused on atrial fibrillation (AF) and patent foramen ovale (PFO) detection and management, and poststroke cardiovascular complications known as the stroke-heart syndrome. The program included 10 sites from 8 countries: Brazil, China, Egypt, Germany, Japan, Mexico, Romania, and the USA The primary composite feasibility outcome was the achievement of the following 3 implementation metrics (1) developing site-specific clinical pathways for the diagnosis and management of AF, PFO, and the stroke-heart syndrome; (2) establishing regular Neurocardiology rounds (e.g., monthly); and (3) incorporating a cardiologist to the stroke team. The secondary objectives were (1) to identify implementation challenges to guide a larger program and (2) to describe qualitative improvements. Results: The WSO BEAT pilot feasibility program achieved the prespecified primary composite outcome in 9 of 10 (90%) sites. The most common challenges were the limited access to specific medications (e.g., direct oral anticoagulants) and diagnostic (e.g., prolonged cardiac monitoring) or therapeutic (e.g., PFO closure devices) technologies. The most relevant qualitative improvement was the achievement of a more homogeneous diagnostic and therapeutic approach. Conclusion: The WSO BEAT pilot program suggests that developing neurocardiology collaborations is feasible. The long-term sustainability of the WSO BEAT program and its impact on quality of stroke care and clinical outcomes needs to be tested in a larger and longer duration program.