bluuebunny/crossref_metadata_2025_split huggingface dataset:
A practical approach for adoption of a hub and spoke model for cell and gene therapies in low- and middle-income countries: framework and case studies
Authors: Saleh Shadi , Dabbous Omar , Sullivan Sean D. , Ankleshwaria Dipen , Trombini Daiane , Toumi Mondher , Diaa Mahmoud , Patel Anish , Kazazoglu Taylor Burcu , Tunis Sean 
Published: October 2023

AbstractIn the rapidly evolving landscape of biotechnologies, cell and gene therapies are being developed and adopted at an unprecedented pace. However, their access and adoption remain limited, particularly in low- and middle-income countries (LMICs). This study aims to address this critical gap by exploring the potential of applying a hub and spoke model for cell and gene therapy delivery in LMICs. We establish the identity and roles of relevant stakeholders, propose a hub and spoke model for cell and gene therapy delivery, and simulate its application in Brazil and the Middle East and North Africa. The development and simulation of this model were informed by a comprehensive review of academic articles, grey literature, relevant websites, and publicly available data sets. The proposed hub and spoke model is expected to expand availability of and access to cell and gene therapy in LMICs and presents a comprehensive framework for the roles of core stakeholders, laying the groundwork for more equitable access to these lifesaving therapies. More research is needed to explore the practical adoption and implications of this model.
Chemical proteomics reveals the target landscape of 1,000 kinase inhibitors
Authors: Reinecke Maria , Brear Paul , Vornholz Larsen , Berger Benedict-Tilmann , Seefried Florian , Wilhelm Stephanie , Samaras Patroklos , Gyenis Laszlo , Litchfield David William , Médard Guillaume , Müller Susanne , Ruland Jürgen , Hyvönen Marko , Wilhelm Mathias , Kuster Bernhard 
Published: October 2023

AbstractMedicinal chemistry has discovered thousands of potent protein and lipid kinase inhibitors. These may be developed into therapeutic drugs or chemical probes to study kinase biology. Because of polypharmacology, a large part of the human kinome currently lacks selective chemical probes. To discover such probes, we profiled 1,183 compounds from drug discovery projects in lysates of cancer cell lines using Kinobeads. The resulting 500,000 compound–target interactions are available in ProteomicsDB and we exemplify how this molecular resource may be used. For instance, the data revealed several hundred reasonably selective compounds for 72 kinases. Cellular assays validated GSK986310C as a candidate SYK (spleen tyrosine kinase) probe and X-ray crystallography uncovered the structural basis for the observed selectivity of the CK2 inhibitor GW869516X. Compounds targeting PKN3 were discovered and phosphoproteomics identified substrates that indicate target engagement in cells. We anticipate that this molecular resource will aid research in drug discovery and chemical biology.
Personalized relapse prediction in patients with major depressive disorder using digital biomarkers
Authors: Vairavan Srinivasan , Rashidisabet Homa , Li Qingqin S. , Ness Seth , Morrison Randall L. , Soares Claudio N. , Uher Rudolf , Frey Benicio N. , Lam Raymond W. , Kennedy Sidney H. , Trivedi Madhukar , Drevets Wayne C. , Narayan Vaibhav A. 
Published: October 2023

AbstractMajor depressive disorder (MDD) is a chronic illness wherein relapses contribute to significant patient morbidity and mortality. Near-term prediction of relapses in MDD patients has the potential to improve outcomes by helping implement a ‘predict and preempt’ paradigm in clinical care. In this study, we developed a novel personalized (N-of-1) encoder-decoder anomaly detection-based framework of combining anomalies in multivariate actigraphy features (passive) as triggers to utilize an active concurrent self-reported symptomatology questionnaire (core symptoms of depression and anxiety) to predict near-term relapse in MDD. The framework was evaluated on two independent longitudinal observational trials, characterized by regular bimonthly (every other month) in-person clinical assessments, weekly self-reported symptom assessments, and continuous activity monitoring data with two different wearable sensors for ≥ 1 year or until the first relapse episode. This combined passive-active relapse prediction framework achieved a balanced accuracy of ≥ 71%, false alarm rate of ≤ 2.3 alarm/patient/year with a median relapse detection time of 2–3 weeks in advance of clinical onset in both studies. The study results suggest that the proposed personalized N-of-1 prediction framework is generalizable and can help predict a majority of MDD relapses in an actionable time frame with relatively low patient and provider burden.
Utilization of synthesized silane-based silica Janus nanoparticles to improve foam stability applicable in oil production: static study
Authors: Saeedi Dehaghani Amir Hossein , Gharibshahi Reza , Mohammadi Mohammad 
Published: October 2023

AbstractThis study investigated the effect of silane-based silica (SiO2) Janus nanoparticles (JNPs) on stabilizing the foam generated by different types of gases. Two types of SiO2 JNPs were synthesized through surface modification using HMDS and APTS silane compounds. Static analyses were conducted to examine the impact of different concentrations of the synthesized nanoparticles in various atmospheres (air, CO2, and CH4) on surface tension, foamability, and foam stability. The results indicated that the synthesized SiO2 JNPs and bare SiO2 nanoparticles exhibited nearly the same ability to reduce surface tension at ambient temperature and pressure. Both of these nanoparticles reduced the surface tension from 71 to 58–59 mN m−1 at 15,000 ppm and 25 °C. While bare SiO2 nanoparticles exhibited no foamability, the synthesis of SiO2 JNPs significantly enhanced their ability to generate and stabilize gas foam. The foamability of HMDS-SiO2 JNPs started at a higher concentration than APTS-SiO2 JNPs (6000 ppm compared to 4000 ppm, respectively). The type of gas atmosphere played a crucial role in the efficiency of the synthesized JNPs. In a CH4 medium, the foamability of synthesized JNPs was superior to that in air and CO2. At a concentration of 1500 ppm in a CH4 medium, HMDS-SiO2 and APTS-SiO2 JNPs could stabilize the generated foam for 36 and 12 min, respectively. Due to the very low dissolution of CO2 gas in water at ambient pressure, the potential of synthesized JNPs decreased in this medium. Finally, it was found that HMDS-SiO2 JNPs exhibited better foamability and foam stability in all gas mediums compared to APTS-SiO2 JNPs for use in oil reservoirs. Also, the optimal performance of these JNPs was observed at a concentration of 15,000 ppm in a methane gas medium.
Morphological investigation and 3D simulation of plasmonic nanostructures to improve the efficiency of perovskite solar cells
Authors: Mohammadi Mohammad Hosein , Eskandari Mehdi , Fathi Davood 
Published: October 2023

AbstractThe light absorption process is a key factor in improving the performance of perovskite solar cells (PSCs). Using arrays of metal nanostructures on semiconductors such as perovskite (CH3NH3PbI3), the amount of light absorption in these layers is significantly increased. Metal nanostructures have been considered for their ability to excite plasmons (collective oscillations of free electrons). Noble metal nanoparticles placed inside solar cells, by increasing the scattering of the incident light, effectively increase the optical absorption inside PSCs; this in turn increases the electric current generated in the photovoltaic device. In this work, by calculating the cross-sectional area of dispersion and absorption on gold (Au) nanoparticles, the effects of the position of nanoparticles in the active layer (AL) and their morphology on the increase of absorption within the PSC are investigated. The optimal position of the plasmonic nanoparticle was obtained in the middle of the AL using a three-dimensional simulation method. Then, three different morphologies of nano-sphere, nano-star and nano-cubes were investigated, where the short-circuit currents (Jsc) for these three nanostructures were obtained equal to 19.01, 18.66 and 20.03 mA/cm2, respectively. In our study, the best morphology of the nanostructure according to the Jsc value was related to the nano-cube, in which the device power conversion efficiency was equal to 16.20%, which is about 15% better than the PSC with the planar architecture.
Multifunctional solar water harvester with high transport selectivity and fouling rejection capacity
Authors: Hao Xuanzhang , Yao Houze , Zhang Panpan , Liao Qihua , Zhu Kaixuan , Chang Jian , Cheng Huhu , Yuan Jiayin , Qu Liangti 
Published: October 2023

AbstractShortage of clean water continues to grow around the world, and the recent solar-powered interfacial system has emerged as a sustainable, efficient and CO2-neutral approach to produce clean water. However, complex contaminants in surface water accompanied with environment pollution set huge obstacles for harvesting clean water via previous strategies. Here we develop a solar-powered graphene/alginate hydrogel (GAH)-based clean water extractor of super resistance to the transport of complex contaminants and ultra-antifouling capacity. This GAH features a high selectivity in water transport by rejecting >99.5% of volatile organic compounds, >99.3% of ions (Na+, Mg2+, K+ and Ca2+) and 100% of non-volatile organic compounds and bacteria; meanwhile, GAH is capable of rejecting oil adhesion by forming a large contact angle >140° under water, deactivating nearly 100% bacteria on surface and preventing salt crystallization. Given such promising adaptability to a wide environment, this GAH can directly convert surface water of complex components into safe drinkable water.
Therapeutic activity of green synthesized selenium nanoparticles from turmeric against cisplatin-induced oxido-inflammatory stress and cell death in mice kidney
Authors: ALRashdi Barakat M. , Mohamed Roaya A. , Mohamed Amal H. , Samoul Feryal A. , Mohamed Mazen I. , Moussa Mohsen M. , Alrashidi Saad M. , Dawod Bassel , Habotta Ola A. , Abdel Moneim Ahmed E. , Ramadan Shimaa S. 
Published: October 2023

Abstract
Cisplatin (CDDP) is a commonly prescribed chemotherapeutic agent; however, its associated nephrotoxicity limits its clinical efficacy and sometimes requires discontinuation of its use. The existing study was designed to explore the reno-therapeutic efficacy of turmeric (Tur) alone or conjugated with selenium nanoparticles (Tur-SeNPs) against CDDP-mediated renal impairment in mice and the mechanisms underlying this effect. Mice were orally treated with Tur extract (200 mg/kg) or Tur-SeNPs (0.5 mg/kg) for 7 days after administration of a single dose of CDDP (5 mg/kg, i.p.). N-acetyl cysteine NAC (100 mg/kg) was used as a standard antioxidant compound. The results revealed that Tur-SeNPs counteracted CDDP-mediated serious renal effects in treated mice. Compared with the controls, Tur or Tur-SeNPs therapy remarkably decreased the kidney index along with the serum levels of urea, creatinine, Kim-1, and NGAL of the CDDP-injected mice. Furthermore, Tur-SeNPs ameliorated the renal oxidant status of CDDP group demonstrated by decreased MDA and NO levels along with elevated levels of SOD, CAT, GPx, GR, GSH, and gene expression levels of HO-1. Noteworthy, lessening of renal inflammation was exerted by Tur-SeNPs via lessening of IL-6 and TNF-α besides down-regulation of NF-κB gene expression in mouse kidneys. Tur-SeNPs treatment also restored the renal histological features attained by CDDP challenge and hindered renal apoptosis through decreasing the Bax levels and increasing Bcl-2 levels. Altogether, these outcomes suggest that the administration of Tur conjugated with SeNPs is effective neoadjuvant chemotherapy to guard against the renal adverse effects that are associated with CDDP therapy.
Quantifying Oropharyngeal Swallowing Impairment in Response to Bolus Viscosity
Authors: Garand Kendrea L. (Focht) , Armeson Kent , Hill Elizabeth G. , Blair Julie , Pearson William , Martin-Harris Bonnie 
Published: October 2023

Purpose:
The purpose of this study was to test the feasibility for quantifying changes in oropharyngeal swallowing impairment in response to alteration in bolus viscosity using a reliable and valid method of observational measurement—the Modified Barium Swallow Impairment Profile (MBSImP).

Method:
This retrospective analysis included a heterogeneous cohort of 119 patients with suspected dysphagia that underwent a videofluoroscopic swallowing study as part of clinical care. Using consensus scoring, two expert clinicians assigned MBSImP scores to components related to oropharyngeal swallowing function between two bolus viscosities (thin liquid and pudding): epiglottic movement, laryngeal elevation, anterior hyoid excursion, tongue base retraction, pharyngeal stripping wave, and pharyngoesophageal segment opening (PESO). Comparisons between the two bolus viscosities were investigated for each component.

Results:

Higher (worse) scores were observed in the thin-liquid trial compared with the pudding trial for the following MBSImP components: anterior hyoid excursion (
p
= .03), epiglottic movement (
p
< .001), pharyngeal stripping wave (
p
< .001), and PESO (
p
= .002). Lower (better) scores were observed in the liquid trial compared with the pudding trial for one component—tongue base retraction (Component 15) only (
p
< .001).

Conclusion:
These findings provide further evidence for positive influences of viscosity on the swallow mechanism, including influences of sensory feedback on the sensorimotor swallow program.
Triple‐loss driven generative adversarial network for pansharpening
Authors: Huang Bo , Li Xiongfei , Zhang Xiaoli 
Published: October 2023

AbstractPansharpening aims at fusing a panchromatic (PAN) image and a low‐resolution multispectral (LRMS) image into a high‐resolution multispectral (HRMS) image. In recent years, GAN‐based pansharpening methods have achieved excellent results, but they suffer from inadequate feature preservation and unstable training. To address these issues, a novel GAN‐based model named TriLossGAN is proposed. This method constructs three loss components with the help of the generator and the dual‐discriminator, which are calculated in both the original spatial domain and the transform domain to better preserve high‐frequency and low‐frequency information in the fused image. Additionally, a new training strategy is designed to stabilize the training process. In extensive experiments, the proposed method achieved satisfactory results on three datasets with QNR values of 0.9584 on GaoFen‐2, 0.9601 on QuickBird, and 0.9138 on WorldView‐3. Qualitative and quantitative comparisons demonstrate that TriLossGAN outperforms other state‐of‐the‐art methods.
Optimising unplanned waste collection: An IoT‐enabled system for smart cities, a case study in Tangier, Morocco
Authors: Belhiah Meryam , El Aboudi Moaad , Ziti Soumia 
Published: October 2023

AbstractAn innovative approach to the collection of unplanned municipal waste through the integration of an Internet of Things (IoT) enabled system in urban settings is presented. Despite significant strides in waste management optimisation, traditional systems have largely overlooked the management of occasional or seasonal waste such as green waste, wild dump, and construction debris. The authors seek to address this gap by deploying an IoT‐enabled system to optimise resource utilisation and efficiency. Building on existing infrastructures for real‐time tracking of waste collection circuits, equipment, and bin filling levels, the system incorporates an additional module to manage unpredictable waste categories. The system collects field data leveraging existing resources with minimal investment. To manage the sporadic nature of these waste types, the system employs a flexible approach with the use of sensors and algorithms for dynamic route planning and waste collection. Using the city of Tangier, Morocco, as a case study, a comprehensive methodology for waste location capture, GIS mapping, priority‐based route identification, scenario testing, and operational cost estimation is implemented. A modified version of the Contraction Hierarchies algorithm is applied to compute optimal waste collection paths, ensuring timely and efficient waste removal while minimising environmental impact. The findings from this research promise significant implications for municipal waste collection, particularly in developing countries, opening new possibilities for sustainable waste management practices in smart cities.
Eigenbases for a force-constrained position control of adaptive shell mirrors
Authors: Vérinaud Christophe , Correia Carlos 
Published: October 2023

Context. The deployment of meter-scale (hitherto pre-focal) adaptive deformable mirrors finds some prominent examples in the leading ground-based visible to near-infrared facilities (e.g. the Very Large Telescope (VLT), the Large Binocular Telescope (LBT), or theMagellanTelescope) and is being adopted by several others (e.g. the Multiple Mirror Telescope (MMT) or Subaru). Furthermore, two out of the three giant segmented-mirror telescopes now under design will feature them. In all these cases, the proprietary technology is based on voice-coils and is limited in force, stroke, and velocity.Aims. Because of the nature of their purpose, that is, adaptive wave-front correction, any kind of optimality relies on the control of a subset of principal wave-front components or eigenmodes, for short, a basis of functions in a mathematical sense. Here we provide algorithmic procedures for generating such eigenbases, also called Karhunen–Loève (KL) modes, that integrate force limitations in their definitions whilst maintaining standard orthonormality, statistical independence, and deformable mirror span.Methods. The double-diagonalisation method was revisited to build KL modes ranked by the force applied on the actuators.Results. We analysed this new KL basis for von Kármán turbulence statistics and present the fitting error and the distribution of positions and forces. We further illustrate their use in the case of the quaternary mirror control for the European Extremely Large Telescope, and we include the outer actuator minioning and force policy constraints.
The Yarkovsky effect and bulk density of near-Earth asteroids from Gaia DR3
Authors: Dziadura Karolina , Oszkiewicz Dagmara , Spoto Federica , Carry Benoit , Tanga Paolo , Bartczak Przemysław 
Published: October 2023

Aims. The primary objective of this study is to utilize Gaia DR3 asteroid astrometry to detect the Yarkovsky effect, a non-gravitational acceleration that affects the orbits of small asteroids. We then computed the bulk densities for the sample of objects for which we obtained an estimation of the Yarkovsky effect.
Methods. We used the version of the OrbFit software that is currently developed at the Minor Planet Center (MPC). We utilized the complete astrometric dataset from the MPC, encompassing all radar data and Gaia DR3 observations. The orbital computation was performed for a total of 446 Near-Earth Asteroids (NEAs; including 93 Potentially Hazardous Asteroids (PHAs)), and 54094 Inner Main Belt Asteroids (IMBAs) as well as Mars Crossing asteroids. Furthermore, we used a new validation method which involved computing the A2 (the Yarkovsky effect) using different observational arcs to observe the stability of the result. We applied the Yarkovsky effect to determine the density of the studied asteroids.
Results. Thanks to Gaia DR3 we significantly constrained orbital uncertainties and determined reliable A2 values for 49 Near-Earth Asteroids, including 10 new detections and for all improvements in signal-to-noise ratio. Additionally, we successfully determined the density, along with their uncertainties, for all of these objects. However, regarding IMBAs, although we have made progress, we do not detect Yarkovsky drift for any asteroid in the main belt.
Conclusions. Adding a relatively small amount of ultra-precise astrometry from Gaia DR3 to the observations from the Minor Planet Center (MPC) not only significantly improves the orbit of the asteroid but also enhances the detectability of non-gravitational parameters. Utilizing this improved dataset, we were able to determine the densities, along with their uncertainties, for the studied asteroids. Looking ahead, with the upcoming release of Gaia DR4, we anticipate even more detections for NEAs and new detections for IMBA and Mars Crossing Asteroids.
Traitement orthodontico-chirurgical du patient en cours de croissance
Authors: Debize Clara , Le Gall Michel , Philip-Alliez Camille 
Published: October 2023

Les patients présentant un décalage squelettique sévère dépassant les limites d’un traitement orthodontique conventionnel, avec ou sans potentiel de croissance, sont redevables d’une prise en charge orthodontico-chirurgicale. Pour la grande majorité des praticiens, l’adolescent présentant un tel diagnostic devra attendre la fin de sa croissance avant de bénéficier d’une chirurgie orthognathique, la raison principale évoquée étant le risque de récidive lié à une croissance résiduelle.
Une bonne connaissance de la croissance faciale et de la chirurgie en période de croissance permet d’intervenir autour du pic pubertaire afin d’équilibrer le cadre squelettique et fonctionnel tout en améliorant l’esthétisme du visage qui revêt une grande importance relationnelle durant l’adolescence. Il est capital pour l’orthodontiste et le chirurgien maxillofacial d’évaluer le rapport bénéfice-risque d’une chirurgie précoce afin de poser la bonne indication.
‘What they’re not telling you about ChatGPT’: exploring the discourse of AI in UK news media headlines
Authors: Roe Jasper , Perkins Mike 
Published: October 2023

AbstractThis study analyses the discursive representation of Artificial Intelligence (AI) and ChatGPT in UK news media headlines from January to May 2023. A total of 671 headlines were collected and analysed using inductive thematic analysis, theoretically informed by Agenda-Setting theory and Framing theory. The results offer an initial picture of how recent technological advances in the fields of AI have been communicated to the public. The results show that there is a complex and at times paradoxical portrayal of AI in general and ChatGPT as well as other Large Language Models (LLMs), oscillating between promising potential for solving societal challenges while simultaneously warning of imminent and systemic dangers. Further to this, the analysis provides evidence for the claim that media representations of AI are often sensationalised and tend to focus more on warnings and caution to readers, as only a minority of headlines were related to helpful, useful, or otherwise positive applications of AI, ChatGPT, and other Large Language Models (LLMs). These findings underscore the pivotal role of media discourse in shaping public perceptions of AI. The study prompts reflections on news media practices in the United Kingdom and encourages future research to further examine the influence of social, cultural, and political contexts on AI representation during a period of technological change. This research provides relevant insights for policymakers, AI developers, and educators to support public engagement with AI technologies.