bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fast VMAT planning for prostate radiotherapy: dosimetric validation of a deep learning-based initial segment generation method
Authors: Ni Yimin , Chen Shufei , Hibbard Lyndon , Voet Peter 
Published: July 2022

Abstract

Objective. To develop and evaluate a deep learning based fast volumetric modulated arc therapy (VMAT) plan generation method for prostate radiotherapy. Approach. A customized 3D U-Net was trained and validated to predict initial segments at 90 evenly distributed control points of an arc, linked to our research treatment planning system (TPS) for segment shape optimization (SSO) and segment weight optimization (SWO). For 27 test patients, the VMAT plans generated based on the deep learning prediction (VMATDL) were compared with VMAT plans generated with a previously validated automated treatment planning method (VMATref). For all test cases, the deep learning prediction accuracy, plan dosimetric quality, and the planning efficiency were quantified and analyzed. Main results. For all 27 test cases, the resulting plans were clinically acceptable. The V
95% for the PTV2 was greater than 99%, and the V
107% was below 0.2%. Statistically significant difference in target coverage was not observed between the VMATref and VMATDL plans (P = 0.3243 > 0.05). The dose sparing effect to the OARs between the two groups of plans was similar. Small differences were only observed for the Dmean of rectum and anus. Compared to the VMATref, the VMATDL reduced 29.3% of the optimization time on average. Significance. A fully automated VMAT plan generation method may result in significant improvement in prostate treatment planning efficiency. Due to the clinically acceptable dosimetric quality and high efficiency, it could potentially be used for clinical planning application and real-time adaptive therapy application after further validation.
3D printing methods for radiological anthropomorphic phantoms
Authors: Okkalidis Nikiforos 
Published: July 2022

Abstract
Three dimensional (3D) printing technology has been widely evaluated for the fabrication of various anthropomorphic phantoms during the last couple of decades. The demand for such high quality phantoms is constantly rising and gaining an ever-increasing interest. Although, in a short time 3D printing technology provided phantoms with more realistic features when compared to the previous conventional methods, there are still several aspects to be explored. One of these aspects is the further development of the current 3D printing methods and software devoted to radiological applications. The current 3D printing software and methods usually employ 3D models, while the direct association of medical images with the 3D printing process is needed in order to provide results of higher accuracy and closer to the actual tissues’ texture. Another aspect of high importance is the development of suitable printing materials. Ideally, those materials should be able to emulate the entire range of soft and bone tissues, while still matching the human’s anatomy. Five types of 3D printing methods have been mainly investigated so far: (a) solidification of photo-curing materials; (b) deposition of melted plastic materials; (c) printing paper-based phantoms with radiopaque ink; (d) melting or binding plastic powder; and (e) bio-printing. From the first and second category, polymer jetting technology and fused filament fabrication (FFF), also known as fused deposition modelling (FDM), are the most promising technologies for the fulfilment of the requirements of realistic and radiologically equivalent anthropomorphic phantoms. Another interesting approach is the fabrication of radiopaque paper-based phantoms using inkjet printers. Although, this may provide phantoms of high accuracy, the utilized materials during the fabrication process are restricted to inks doped with various contrast materials. A similar condition applies to the polymer jetting technology, which despite being quite fast and very accurate, the utilized materials are restricted to those capable of polymerization. The situation is better for FFF/FDM 3D printers, since various compositions of plastic filaments with external substances can be produced conveniently. Although, the speed and accuracy of this 3D printing method are lower compared to the others, the relatively low-cost, constantly improving resolution, sufficient printing volume and plethora of materials are quite promising for the creation of human size heterogeneous phantoms and their adaptation to the treatment procedures of patients in the current health systems.
Monitoring residual kidney function in haemodialysis patients using timed urine collections: validation of the use of estimated blood results to calculate GFR
Authors: Lindley Elizabeth , Keane David , Belcher John , Fernandes Da Silva Jeffcoat Nancy , Davies Simon 
Published: July 2022

Abstract

Objective. With growing recognition of the benefits of preserving residual kidney function (RKF) and use of incremental treatment regimes, the incentive to measure residual clearance in haemodialysis patients is increasing. Interdialytic urine collections used to monitor RKF in research studies are considered impractical in routine care, partly due to the requirement for blood samples before and after the collection. Plasma solute levels can be estimated if patients are in ‘steady state’, where urea and creatinine concentrations increase at a constant rate between dialysis sessions and are reduced by a constant ratio at each session. Validation of the steady state assumption would allow development of simplified protocols for urine collections in HD patients. Approach. Equations were derived for estimating plasma urea and creatinine at the start or end of the interdialytic interval for patients in steady state. Data collected during the BISTRO study was used to assess the agreement between measured and estimated plasma levels and the effect of using estimated levels on the calculated glomerular filtration rate (GFR). Main results. The mean difference between GFR calculated with estimated plasma levels for the HD session after the collection and a full set of measured levels was 2.0% (95% limits of agreement −10.7% to +14.7%, N = 316). Where plasma levels for the session before the collection were estimated, the mean difference was 1.2% (limits of agreement −10.3% to +7.9%, N = 275). Significance. Using estimated levels for one session led to a clinically significant difference in the calculated GFR for less than 3% of the collections studied. This indicates that the steady state assumption can be used to estimate solute levels when determining GFR from timed urine collections. A pragmatic approach to monitoring RKF in HD would be for patients to collect for approximately 24 h before routine bloods are taken.
An off-center fed patch antenna with overlapping sub-patch for simultaneous crack and temperature sensing
Authors: Li Xianzhi , Xue Songtao , Xie Liyu , Wan Guochun , Wan Chunfeng 
Published: July 2022

Abstract
This paper presents an off-center fed patch antenna for simultaneous crack and temperature sensing. The antenna sensor consists of an off-center fed underlying patch and an overlapping sub-patch. The bottom copper sheet of the sub-patch is tightly attached to the underlying radiation patch allowing the electric current to flow through the integrated patch. The off-center feeding can activate the resonant modes in both transverse and longitudinal directions. The transverse resonant frequencies of the combined patch are utilized for temperature sensing. Therefore, the crack width sensed by the longitudinal frequency shift can be adjusted, eliminating the temperature effect. In addition, this unstressed structure of the combined patch can avoid the issues of incomplete strain transfer ratio and the insufficient bonding strength of a monolithic antenna. The authors developed theoretical relationships between the antenna resonant frequencies, the temperature, and the crack width. They also developed simulations of the off-center fed patch antenna sensor as well as a series of experimental tests to demonstrate the feasibility of the proposed sensor for simultaneous crack and temperature sensing.
Bolt looseness monitoring based on damping measurement by using a quantitative electro-mechanical impedance method
Authors: Qiu Hao , Li Faxin 
Published: July 2022

Abstract

In-situ bolt looseness monitoring is very important for the stability and safety of engineering structures. The electro-mechanical impedance (EMI) method, which had been intensively studied in structure health monitoring, has offered a feasible solution. However, in traditional EMI method, the lack of modal pre-selection leads to dense resonant peaks which brings difficulty to signal analysis. The commonly used statistical damage indices typically do not have physics meaning and may fail to distinguish the defects from the environmental changes. In this work, a quantitative EMI (Q-EMI) method was proposed to measure the damping of structures for bolt looseness monitoring. The in-plane excitation method based on distributed thickness-shear-mode piezoelectric transducers was used for selective excitation of vibration modes. The modal-related sensitivity variation was theoretically analyzed and then validated by experiments. Three different specimens were tested and the results showed that during bolt loosening, the modal damping increased by 100%–300%, while the resonance frequency of the structure only decreased by several percent. Moreover, it was found that the loosened bolt may be located using the variation of modal-related sensitivity. Due to its good performance and easy mounting, the proposed Q-EMI method is very promising for bolt looseness monitoring and is expected to be widely used in near future.
Nonvolatile memory cell using a superconducting-ferromagnetic π Josephson junction
Authors: Zeng Junwen , Chen Lei , Zhong Xianghai , Wang Yue , Pan Yinping , Zhang Denghui , Yu Shujie , Wu Ling , Zhang Lu , Peng Wei , Wang Zhen 
Published: July 2022

Abstract
Storage of a single magnetic flux quantum in a superconducting loop containing a Josephson junction represents a promising unit cell configuration for construction of a cryogenic memory of superconducting digital circuits. However, application of a DC bias current is required for operation of such a memory cell to maintain trapping of the flux quantum in the storage loop. In this work, we present a superconducting memory cell that uses a superconducting-magnetic π junction. The cell characteristics show flux quantum hysteresis centering at the zero-bias current. We develop a fabrication process that combines superconductor–ferromagnet–superconductor (SFS) junctions with superconductor–normal metal–superconductor (SNS) junctions. The critical current density of the SFS junctions shows a 0–π oscillation as a function of the ferromagnetic layer thickness. The formation of the π junction is confirmed further by the flux modulation curves of a superconducting quantum interference device made from SNS junctions with an additional SFS junction.
The fracture and merging of W nanofibers under low-energy He ion irradiations at an elevated temperature
Authors: Zhang Yang , Fan Hongyu , Liu Dongping , Li Xiaoping , Cui Yunqiu , Niu Chunjie , Liu Weifeng , Ni Weiyuan , Wang Wenchun 
Published: July 2022

Abstract
The microstructural evolution of tungsten (W) nanofibers has been observed during the W fuzz growth due to low-energy He+ irradiations at an elevated temperature. In this study, we find that the frequent fracture and merging of W nanofibers occur during the W fuzz growth. Our annealing experiments also show the fracture and instability of W nanofibers at an elevated temperature. Detailed analysis reveals the tensile-stress driven thermodynamic instability of W nanofibers containing plenty of helium (He) bubbles at an elevated temperature. The mergence of W nanofibers coming into contact with each other is due to the deposition of W adatoms diffusing over W nanofibers onto the mergence locations, which decreases the surface free energy of W nanofibers. The fracture of W nanofibers is attributed to the tensile stress induced cracking of W nanofibers containing He bubbles, and the subsequent W adatom surface diffusion over W nanofibers.
Evolution of charged particles in jet combustion chamber
Authors: Li Wang 
Published: July 2022

Abstract
The combustion process in the combustion chamber of the engine will change the electrostatic field characteristics of the jet aircraft, thereby affecting the accuracy of the electrostatic field information obtained by the electrostatic detector. Based on the quantitative analysis of the evolution of charged particles in the combustion chamber, this study further analyses the effect of different surface materials in the combustion chamber on the charging process of combustion products. The combustion chamber is divided into burning zone, intermediate zone and dilution zone, and the evolution model of charged particles in the combustion chamber is constructed under the time constraint. Finally, the variation curve of the charged particle concentration when the surface materials in the combustion chamber are metal materials and non-metal materials are calculated by simulation, and the influence of the conductivity of the surface materials in the combustion chamber on the combustion charging process is analyzed. This study provides the analytical basis for the application of electrostatic detection technology in jet aircraft.
Improvement of parameters of micro-pixel avalanche photodiodes
Authors: Sadigov A.Z. , Ahmadov F.I. , Sadygov Z.Y. , Ahmadov G.S. , Berikov D. , Holik M. , Mammadli A. , Akbarov R.A. , Nuruyev S.M. , Ajdarli K. , Garibli A. , Doganci E. , Mora Y. , Yilmaz E. 
Published: July 2022

Abstract
The paper is concerned with the parameter study of a new
generation of micro-pixel avalanche photodiodes (MAPD) with deeply
buried pixel structure, also named silicon photomultipliers (SiPM)
or multi-pixel photon counter (MPPC). The new MAPD of type MAPD-3NM
was manufactured in the frame of collaboration with Zecotek
Company. Measurements were carried out and discussed in terms of the
important parameters such as the current-voltage and
capacitance-voltage characteristic, gain, the temperature
coefficient of breakdown voltage, breakdown voltage, and gamma-ray
detection performance using an LFS scintillator. The obtained
results showed that the newly developed MAPD-3NM photodiode
outperformed the previous generation in most parameters and can be
successfully applied in space application, medicine, high-energy
physics, and security. New proposals are also discussed, for further
improvement of the parameters of the MAPD photodiodes that will be
produced in the coming years.
Climate change impacts on water sustainability of South African crop production
Authors: Bonetti Sara , Sutanudjaja Edwin H , Mabhaudhi Tafadzwanashe , Slotow Rob , Dalin Carole 
Published: July 2022

Abstract
Agricultural production in arid and semi-arid regions is particularly vulnerable to climate change, which, combined with projected food requirements, makes the sustainable management of water resources critical to ensure national and global food security. Using South Africa as an example, we map the spatial distribution of water use by seventeen major crops under current and future climate scenarios, and assess their sustainability in terms of water resources, using the water debt repayment time indicator. We find high water debts, indicating unsustainable production, for potatoes, pulses, grapes, cotton, rice, and wheat due to irrigation in arid areas. Climate change scenarios suggest an intensification of such pressure on water resources, especially in regions already vulnerable, with a country-scale increase in irrigation demand of between 6.5% and 32% by 2090. Future land use planning and management should carefully consider the spatial distribution and local sustainability of crop water requirements to reduce water consumption in water risk hotspots and guarantee long-term food security.
Do Abdominal Binders Prevent Seroma Formation and Recurrent Diastasis Following Abdominoplasty?
Authors: Martins Maria Roberta Cardoso , Moraes Betina Zimmermann Fontes de , Fabri Daniel Capucci , Castro Hugo Alexandre Sócrates de , Rostom Lucas , Ferreira Lydia Masako , Nahas Fabio Xerfan 
Published: July 2022

Abstract

Background
For decades, the postoperative wearing of abdominal binders has been suggested to reduce dead space and prevent mobilization of the musculoaponeurotic layer in an attempt to decrease the risk of seroma formation and recurrent diastasis.

Objectives
This study sought to evaluate whether the postoperative wearing of an abdominal binder provides any additional contribution to the reduction of either seroma formation or recurrent diastasis recti when abdominoplasty is performed with quilting sutures.

Methods
Thirty-four women undergoing abdominoplasty were randomized into 2 groups: the binder group (n = 16) wore abdominal binders during the postoperative period, whereas the control group (n = 18) did not. Ultrasound examination was performed on postoperative days 7 and 14 to assess seroma formation and at 6 months postoperatively to assess recurrence of diastasis recti. A t test for independent samples was applied to compare means between 2 numeric variables. Generalized estimation equation models were used to evaluate seroma volume at different time points for the 2 groups.

Results
No significant differences in seroma volume were found between groups on postoperative days 7 (P = 0.830) and 14 (P = 0.882). Seven cases of subclinical recurrent diastasis were observed by ultrasound examination in the supraumbilical (4 cases) and infraumbilical regions (3 cases), but without significant differences (P = 1.000) between the 2 groups. Recurrent diastasis was not detected during physical examinations.

Conclusions
The postoperative wearing of abdominal binders was not effective in preventing either seroma formation or recurrent diastasis following abdominoplasty with quilting sutures.

Level of Evidence: 2
Machine-designed biotherapeutics: opportunities, feasibility and advantages of deep learning in computational antibody discovery
Authors: Wilman Wiktoria , Wróbel Sonia , Bielska Weronika , Deszynski Piotr , Dudzic Paweł , Jaszczyszyn Igor , Kaniewski Jędrzej , Młokosiewicz Jakub , Rouyan Anahita , Satława Tadeusz , Kumar Sandeep , Greiff Victor , Krawczyk Konrad 
Published: July 2022

Abstract
Antibodies are versatile molecular binders with an established and growing role as therapeutics. Computational approaches to developing and designing these molecules are being increasingly used to complement traditional lab-based processes. Nowadays, in silico methods fill multiple elements of the discovery stage, such as characterizing antibody–antigen interactions and identifying developability liabilities. Recently, computational methods tackling such problems have begun to follow machine learning paradigms, in many cases deep learning specifically. This paradigm shift offers improvements in established areas such as structure or binding prediction and opens up new possibilities such as language-based modeling of antibody repertoires or machine-learning-based generation of novel sequences. In this review, we critically examine the recent developments in (deep) machine learning approaches to therapeutic antibody design with implications for fully computational antibody design.
Statistical challenges in longitudinal microbiome data analysis
Authors: Kodikara Saritha , Ellul Susan , Lê Cao Kim-Anh 
Published: July 2022

AbstractThe microbiome is a complex and dynamic community of microorganisms that co-exist interdependently within an ecosystem, and interact with its host or environment. Longitudinal studies can capture temporal variation within the microbiome to gain mechanistic insights into microbial systems; however, current statistical methods are limited due to the complex and inherent features of the data. We have identified three analytical objectives in longitudinal microbial studies: (1) differential abundance over time and between sample groups, demographic factors or clinical variables of interest; (2) clustering of microorganisms evolving concomitantly across time and (3) network modelling to identify temporal relationships between microorganisms. This review explores the strengths and limitations of current methods to fulfill these objectives, compares different methods in simulation and case studies for objectives (1) and (2), and highlights opportunities for further methodological developments. R tutorials are provided to reproduce the analyses conducted in this review.
De novo molecular design with deep molecular generative models for PPI inhibitors
Authors: Wang Jianmin , Chu Yanyi , Mao Jiashun , Jeon Hyeon-Nae , Jin Haiyan , Zeb Amir , Jang Yuil , Cho Kwang-Hwi , Song Tao , No Kyoung Tai 
Published: July 2022

Abstract
We construct a protein–protein interaction (PPI) targeted drug-likeness dataset and propose a deep molecular generative framework to generate novel drug-likeness molecules from the features of the seed compounds. This framework gains inspiration from published molecular generative models, uses the key features associated with PPI inhibitors as input and develops deep molecular generative models for de novo molecular design of PPI inhibitors. For the first time, quantitative estimation index for compounds targeting PPI was applied to the evaluation of the molecular generation model for de novo design of PPI-targeted compounds. Our results estimated that the generated molecules had better PPI-targeted drug-likeness and drug-likeness. Additionally, our model also exhibits comparable performance to other several state-of-the-art molecule generation models. The generated molecules share chemical space with iPPI-DB inhibitors as demonstrated by chemical space analysis. The peptide characterization-oriented design of PPI inhibitors and the ligand-based design of PPI inhibitors are explored. Finally, we recommend that this framework will be an important step forward for the de novo design of PPI-targeted therapeutics.
Prediction of allosteric druggable pockets of cyclin-dependent kinases
Authors: Ning Shangbo , Wang Huiwen , Zeng Chen , Zhao Yunjie 
Published: July 2022

Abstract
Cyclin-dependent kinase (Cdk) proteins play crucial roles in the cell cycle progression and are thus attractive drug targets for therapy against such aberrant cell cycle processes as cancer. Since most of the available Cdk inhibitors target the highly conserved catalytic ATP pocket and their lack of specificity often lead to side effects, it is imperative to identify and characterize less conserved non-catalytic pockets capable of interfering with the kinase activity allosterically. However, a systematic analysis of these allosteric druggable pockets is still in its infancy. Here, we summarize the existing Cdk pockets and their selectivity. Then, we outline a network-based pocket prediction approach (NetPocket) and illustrate its utility for systematically identifying the allosteric druggable pockets with case studies. Finally, we discuss potential future directions and their challenges.