bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design, fabrication, optimization and characterization of memantine-loaded biodegradable PLGA nanoscaffolds for treatment of Alzheimer’s disease
Authors: Rani Varsha , Chawla Ruchi 
Published: October 2022

Abstract
This study aimed to design and develop nanoscaffolds for the controlled release of memantine by non-solvent-induced phase separation (N-TIPS) method. The development and optimization of nanoscaffolds was performed by Box–Behnken Design in which two independent formulation variables and one independent process variable: poly(lactic-co-glycolic acid) (PLGA) (X
1), Pluronics F-127 (X
2), and rotation speed (X
3) were used. The design provided 15 formulation designs which were prepared to determine the response: percentage porosity (Y
1) and drug loading (Y
2). Polynomial equations were generated and analyzed statistically to establish a relationship between independent and dependent variables and develop an optimal formulation with maximized porosity (%) and drug loading (%). The optimized formulation batch was prepared using 19.18% w/v PLGA, 4.98% w/v Pluronics at 500 rpm rotation speed and exhibited drug loading of 11.66% and porosity of 82.62%. Further, correlation between the independent and dependent variables were established and statistically analyzed by using model generated mathematical regression equations, ANOVA, residual plots, interaction plot, main effect plot, contour plot and response surface designs. The analysis of model showed the significant individual effect of PLGA and significant interactive effect of Pluronics F-127 and rotation speed on drug loading and porosity. Further, its physicochemical characterization, and in-vitro (drug release kinetics, and PAMPA study), ex-vivo (enzyme inhibition assay and pro-inflammatory cytokines study) and in-vivo (neurobehavioral and histological study) studies were performed to evaluate the potential of memantine-loaded nanoscaffolds in the treatment of Alzheimer’s disease (AD).
Winter runoff events pose an unquantified continental-scale risk of high wintertime nutrient export
Authors: Seybold Erin C , Dwivedi Ravindra , Musselman Keith N , Kincaid Dustin W , Schroth Andrew W , Classen Aimee T , Perdrial Julia N , Adair E Carol 
Published: October 2022

Abstract
Winters in snow-covered regions have warmed, likely shifting the timing and magnitude of nutrient export, leading to unquantified changes in water quality. Intermittent, seasonal, and permanent snow covers more than half of the global land surface. Warming has reduced the cold conditions that limit winter runoff and nutrient transport, while cold season snowmelt, the amount of winter precipitation falling as rain, and rain-on-snow have increased. We used existing geospatial datasets (rain-on-snow frequency overlain on nitrogen and phosphorous inventories) to identify areas of the contiguous United States (US) where water quality could be threatened by this change. Next, to illustrate the potential export impacts of these events, we examined flow and turbidity data from a large regional rain-on-snow event in the United States’ largest river basin, the Mississippi River Basin. We show that rain-on-snow, a major flood-generating mechanism for large areas of the globe (Berghuijs et al 2019 Water Resour. Res.
55 4582–93; Berghuijs et al 2016 Geophys. Res. Lett.
43 4382–90), affects 53% of the contiguous US and puts 50% of US nitrogen and phosphorus pools (43% of the contiguous US) at risk of export to groundwater and surface water. Further, the 2019 rain-on-snow event in the Mississippi River Basin demonstrates that these events could have large, cascading impacts on winter nutrient transport. We suggest that the assumption of low wintertime discharge and nutrient transport in historically snow-covered regions no longer holds. Critically, however, we lack sufficient data to accurately measure and predict these episodic and potentially large wintertime nutrient export events at regional to continental scales.
Gaps in adoption and implementation limit the current and potential effectiveness of zero-deforestation supply chain policies for soy
Authors: Gollnow Florian , Cammelli Federico , Carlson Kimberly M , Garrett Rachael D 
Published: October 2022

Abstract
Tropical deforestation continues despite global efforts to curb forest loss. Corporate zero-deforestation supply chain commitments (ZDCs) have the potential to address this deforestation, especially if strong state-led forest governance is absent. Yet, because ZDC adoption is limited to particular locations and supply chains, these commitments may fall short at reducing regional deforestation and protecting biodiverse ecosystems. Here, we leverage timeseries of spatially explicit corporate commodity sourcing data and ZDCs to assess the current and potential effect of ZDCs within soybean supply chains on forest loss and biodiversity. We focus on the Brazilian Amazon, where the first ZDC (soy moratorium (SoyM)) was implemented, and the Cerrado, where companies have adopted but not implemented ZDCs. We found that in the Amazon, SoyM signatories that controlled the market caused a 57% reduction in direct deforestation for soy from 2006 to 2015. In the Cerrado, if companies had implemented their ZDCs with the same relative effectiveness as in the Amazon, deforestation for soy could have been reduced by 46%. Thus, ZDC implementation in the Cerrado via stringent monitoring and enforcement could contribute substantially to forest and habitat conservation. Yet, incomplete ZDC adoption leaves >50% of soy-suitable forests and the biodiversity that they harbor outside the reach of ZDCs. To protect these forests, it is vital to incentivize more companies—including smaller, less publicly exposed traders—to make and implement ZDCs, while also promoting forest governance through public policy.
Changes in surface water dynamics across northwestern Canada are influenced by wildfire and permafrost thaw
Authors: Travers-Smith H , Lantz T C , Fraser R H , Kokelj S V 
Published: October 2022

Abstract
The abundance and distribution of surface water at high latitudes is shifting rapidly in response to both climate change and permafrost thaw. In particular, the expansion and drainage of lakes and ponds is widespread but spatially variable, and more research is needed to understand factors driving these processes. In this study we used medium resolution (30 m) remote sensing data to analyse changes in lake area in permafrost-rich lowland regions across northwestern Canada. First, we used the Global Surface Water Dataset developed by the GLAD research group to map the absolute area of different land–water transitions across a 1.4 million km2 study domain. Next, we selected six regional study areas representing a range of climatic, geologic and hydrologic conditions. Within these regional study areas, we used the Landsat satellite archive to map annual trends in the area of 27 755 lakes between 1985 and 2020. We trained a random forests model to classify lakes exhibiting significant increasing or decreasing trends in area, and assessed the relative importance of climate, disturbance and environmental variables in determining the direction of change. Our analysis shows that significant increases in lake area were 5.6 times more frequent than decreases during the study period. Wildfire and ground ice abundance were the most important predictors of the direction of change. Greater ground ice content was associated with regions that experienced increases in lake area, while wildfire was associated with regions that experienced decreases in lake area. The effects of climate, including trends in mean annual temperature and total annual precipitation were smaller than disturbance and environmental factors, indicating that climate has likely had indirect effects on lake area changes over our period of study.
Elevation dependence of projected hydro-climatic change in eastern Siberia
Authors: Finnegan Joseph M , Miller James R 
Published: October 2022

Abstract
Over the last several decades, eastern Siberia has experienced some of the largest temperature increases worldwide. We use the RCP8.5 simulation of the Community Climate System Model version 4 to examine how projected monthly changes in temperature and hydro-climatic variables in eastern Siberia depend on latitude and elevation. Temperature increases are largest at the highest latitudes in winter and late fall and are smaller at higher elevations. For precipitation and snowfall, there is a latitudinal dependence in autumn and spring, with precipitation, snowfall, and snow depth mostly increasing between 60 and 70° N. Although snow cover extent (SCE) decreases almost everywhere, the largest changes occur during the transition seasons which we define as spring and autumn, and the timing of the changes depends on latitude, elevation, and the specific month within seasons. The decreases in SCE are larger at lower latitudes and lower elevations in April and November and larger at higher elevations and higher latitudes in June and September. For the highest latitudes, snow depth actually increases, and increases more at higher elevations. These projections are generally consistent with those of four other climate models. For precipitation, all models project increases in non-summer seasons, but they are not consistent with respect to the direction of the elevation dependence of precipitation. We discuss the complex interactions among the projected changes in all the variables.
A vectorial binary Darboux transformation for the first member of the negative part of the AKNS hierarchy
Authors: Müller-Hoissen Folkert 
Published: October 2022

Abstract
Using bidifferential calculus, we derive a vectorial binary Darboux transformation for the first member of the ‘negative’ part of the AKNS hierarchy. A reduction leads to the first ‘negative flow’ of the NLS hierarchy, which in turn is a reduction of a rather simple nonlinear complex PDE in two dimensions, with a leading mixed third derivative. This PDE may be regarded as describing geometric dynamics of a complex scalar field in one dimension, since it is invariant under coordinate transformations in one of the two independent variables. We exploit the correspondingly reduced vectorial binary Darboux transformation to generate multi-soliton solutions of the PDE, also with additional rational dependence on the independent variables, and on a plane wave background. This includes rogue waves.
RF-EMF Measurement for 5G over C-band and mm-Wave frequency band: Exposure assessment and Procedures
Authors: Qahtan Wali S 
Published: October 2022

Abstract
The 5G network is intended to accommodate a significant quantity of mobile data traffic as well as a great number of wireless connections. It improves cost, power consumption, and offers ultra-low latency and ultra-high dependability to enable new services in a variety of sectors. However, the general public is concerned about the possible health dangers linked with 5G equipment's Radio Frequency (RF) radiation, and numerous localities are actively lobbying to prevent 5G implementation. This aims to demonstrate the cause of increasing the amount of RF-EMF exposure, and the international standards of RF-EMF accepted limits. In addition, it aims to illustrate some measurement procedures to conduct RF-EMF measurement for different researchers and two other procedures and the result of the maximum exposure emitted by a 5G base station operating on 3.5 GHz and the mm-Wave frequency band by the author of this paper.
Recent trends for 5G in vitro exposure systems dosimetry and setup
Authors: Arnaud-Cormos D , Orlacchio R , Leveque P 
Published: October 2022

Abstract
5G wireless communications networks are being massively studied and developed. This new technology introduces new operating frequencies and employs smart adaptative antennas that allow focusing electromagnetic waves in specific directions. Bioelectromagnetic investigations to assess exposure and interactions with living entities of these new 5G signals are carried out. This contribution aims to present current trends on exposure systems designs for bioelectromagnetic studies with associated computational and experimental dosimetry. The extension to 5G signals of classical exposure systems such as transverse electromagnetic cells (TEM cells) and new innovative designs are discussed.
Design, implementation and preliminary testing of an automated system for the evaluation of ionospheric channels
Authors: Șorecău E , Șorecău M , Craiu N , Sârbu A , Bechet P 
Published: October 2022

Abstract
Radio communications in the High Frequency (HF) range are used to transmit information over long distances between one or more correspondents. Techniques and methodsto improve the capacity and reliability of HF communications become key objectives as they are generally limited to narrow bandwidths and low transfer rates. This work aims to implement an automated system for real-time measurement of ionospheric radio channel parameters under Near Vertical Incidence Skywave (NVIS) propagation conditions using Software Define Radio (SDR) platforms. Preliminary testing of the system was carried out both under laboratory conditions using signal generators and high-performance spectral vector analyzers and under real propagation conditions by installation at the established sites. The results showed that the system is capable of performing real-time measurements of signal power, noise power and calculating signal-to-noise ratio for bandwidths up to 24 kHz.
Limitations of using electro-optical probes for the measurement of the electromagnetic field emitted by the new generations of wireless communication devices
Authors: Sârbu A , Bechet P , Miclăuş S 
Published: October 2022

Abstract
The rapid evolution of communication standards caused a paradigm shift in electromagnetic field (EMF) measurement campaigns, as exposure is more likely to take place in the proximity of the device. With the increased spatial variability of EMF exposure, electro-optical (EO) technology stands out as a promising alternative to conventional near field measurement instrumentation due to their miniature sizes and dielectric structure that does not interfere with the measured field. Motivated by the recent progress in the EO sensing technology, it is this article purpose to quantify the limitations of using a set of commercially available EO probes for measurement of EMF emitted by an IEEE 802.11ax router and a VoLTE mobile phone. To our knowledge, this is the first attempt to characterize the capabilities of this emerging sensing technology for EMF exposure assessment in real world communication situations. Present findings suggest that the sensitivity of the investigated EO probes is insufficient for measuring broadband signals at field levels that characterize state-of-the-art communication devices used by the public in common usage scenarios. This work also documents the EO probes suitability and response with respect to frequency, electric field strength and signal bandwidth.
Investigation of a radio-frequency temperature transducer with a thermosensitive resistive element based on a complex compound of heterometallic β-diketonate
Authors: Osadchuk O V , Osadchuk V S , Osadchuk I O , Semenov A O , Martyniuk V V , Prytula M O 
Published: October 2022

Abstract
The article considers a new electrical circuit of a microelectronicradio-frequency measuring temperature transducer with a thermosensitive resistive element based on a complex compound of heterometallicβ-diketonate.The main characteristics of the researched radio-frequency temperature transducer with a thermosensitive resistive element based on a complex compound of heterometallic β-diketonate are obtained: the dependences of the active and reactive components of the full impedance of the radio-frequency measuring temperature transducer, the conversion function and the sensitivity equation.
Expending the power of artificial intelligence in preclinical research: an overview
Authors: Diaconu A , Cojocaru F D , Gardikiotis I , Agrigoroaie L , Furcea D M , Pasat A , Suciu G , Rezuş C , Dodi G 
Published: October 2022

Abstract
Artificial intelligence (AI) is described as the joint set of data entry, able to receive inputs, interpret and learn from such feedbacks, and display related and flexible independent actions that help the entity reach a specific aim over a period of time. By extending its health-care applications continuously, the ultimate AI target is to use machine simulation of human intelligence processes such as learning, reasoning, and self-correction, to mimic human behaviour. AI is extensively used in diverse sectors of medicine, including clinical trials, drug discovery and development, understanding of target-disease associations, disease prediction, imaging, and precision medicine. In this review, we firstly describe the limitations and challenges of the AI tools and techniques utilized in medicine, followed by current uses and applications of AI in the translational field, highlighting the cardio-renal preclinical models with potential to contribute to future clinical research.
Proactive Health Support: a randomised controlled trial of telephone-based self-management support for persons at risk of hospital admission
Authors: Benthien Kirstine Skov , Rasmussen Knud , Nielsen Camilla Palmhøj , Kidholm Kristian , Grønkjær Mette , Toft Ulla 
Published: October 2022

Abstract

Background
Persons with frequent hospital admissions have a disease burden that may exceed their self-management skills. The evidence base of telephone-based interventions to support self-management is poor with mixed results. The aim of this study is to evaluate the effect of Proactive Health Support (PaHS): telephone-based self-management support for persons with risk of hospitalizations.

Methods
This study is a national randomised controlled trial of PaHS versus usual universal tax-funded healthcare. Participants were persons at risk of emergency hospital admissions. The intervention began with a physical start-up session followed by telephone sessions of self-management support. The two co-primary outcomes were Health-Related Quality of Life (HRQoL) (Mental Health Component Summary Score of SF36v2) analysed with mixed models and hospital admissions analysed with Poisson regression at 6 months. Secondary outcomes were at 3- and 12-month follow-up and disease subgroup analyses.

Results
During the study period, 6,402 persons were randomised (3,190 intervention, 3,212 control). HRQoL was significantly improved at 6 months (Est. 1.4992, P = &lt;0.0001) and at 3 and 12 months on all 10 scales. There was no overall effect on hospital admissions at 6 months with an adjusted estimate of 0.0074 (P = 0.8691). Persons with diabetes had significant improvement of HRQoL and reduced hospital admissions.

Conclusions
The PaHS intervention improved HRQoL for all participants and reduced hospital admissions for persons with diabetes only.
Line graph attention networks for predicting disease-associated Piwi-interacting RNAs
Authors: Zheng Kai , Zhang Xin-Lu , Wang Lei , You Zhu-Hong , Zhan Zhao-Hui , Li Hao-Yuan 
Published: October 2022

AbstractPIWI proteins and Piwi-Interacting RNAs (piRNAs) are commonly detected in human cancers, especially in germline and somatic tissues, and correlate with poorer clinical outcomes, suggesting that they play a functional role in cancer. As the problem of combinatorial explosions between ncRNA and disease exposes gradually, new bioinformatics methods for large-scale identification and prioritization of potential associations are therefore of interest. However, in the real world, the network of interactions between molecules is enormously intricate and noisy, which poses a problem for efficient graph mining. Line graphs can extend many heterogeneous networks to replace dichotomous networks. In this study, we present a new graph neural network framework, line graph attention networks (LGAT). And we apply it to predict PiRNA disease association (GAPDA). In the experiment, GAPDA performs excellently in 5-fold cross-validation with an AUC of 0.9038. Not only that, it still has superior performance compared with methods based on collaborative filtering and attribute features. The experimental results show that GAPDA ensures the prospect of the graph neural network on such problems and can be an excellent supplement for future biomedical research.
Deep learning radiomics under multimodality explore association between muscle/fat and metastasis and survival in breast cancer patients
Authors: Miao Shidi , Jia Haobo , Cheng Ke , Hu Xiaohui , Li Jing , Huang Wenjuan , Wang Ruitao 
Published: October 2022

AbstractSarcopenia is correlated with poor clinical outcomes in breast cancer (BC) patients. However, there is no precise quantitative study on the correlation between body composition changes and BC metastasis and survival. The present study proposed a deep learning radiomics (DLR) approach to investigate the effects of muscle and fat on distant metastasis and death outcomes in BC patients. Image feature extraction was performed on 4th thoracic vertebra (T4) and 11th thoracic vertebra (T11) on computed tomography (CT) image levels by DLR, and image features were combined with clinical information to predict distant metastasis in BC patients. Clinical information combined with DLR significantly predicted distant metastasis in BC patients. In the test cohort, the area under the curve of model performance on clinical information combined with DLR was 0.960 (95% CI: 0.942–0.979, P &lt; 0.001). The patients with distant metastases had a lower pectoral muscle index in T4 (PMI/T4) than in patients without metastases. PMI/T4 and visceral fat tissue area in T11 (VFA/T11) were independent prognostic factors for the overall survival in BC patients. The pectoralis muscle area in T4 (PMA/T4) and PMI/T4 is an independent prognostic factor for distant metastasis-free survival in BC patients. The current study further confirmed that muscle/fat of T4 and T11 levels have a significant effect on the distant metastasis of BC. Appending the network features of T4 and T11 to the model significantly enhances the prediction performance of distant metastasis of BC, providing a valuable biomarker for the early treatment of BC patients.