bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessing the global risk of typhoid outbreaks caused by extensively drug resistant Salmonella Typhi
Authors: Walker Joseph , Chaguza Chrispin , Grubaugh Nathan D. , Carey Megan , Baker Stephen , Khan Kamran , Bogoch Isaac I. , Pitzer Virginia E. 
Published: October 2023

AbstractSince its emergence in 2016, extensively drug resistant (XDR) Salmonella enterica serovar Typhi (S. Typhi) has become the dominant cause of typhoid fever in Pakistan. The establishment of sustained XDR S. Typhi transmission in other countries represents a major public health threat. We show that the annual volume of air travel from Pakistan strongly discriminates between countries that have and have not imported XDR S. Typhi in the past, and identify a significant association between air travel volume and the rate of between-country movement of the H58 haplotype of S. Typhi from fitted phylogeographic models. Applying these insights, we analyze flight itinerary data cross-referenced with model-based estimates of typhoid fever incidence to identify the countries at highest risk of importation and sustained onward transmission of XDR S. Typhi. Future outbreaks of XDR typhoid are most likely to occur in countries that can support efficient local S. Typhi transmission and have strong travel links to regions with ongoing XDR typhoid outbreaks (currently Pakistan). Public health activities to track and mitigate the spread of XDR S. Typhi should be prioritized in these countries.
Primitive to conventional geometry projection for efficient phonon transport calculations
Authors: Li Xun , Thébaud Simon , Lindsay Lucas 
Published: October 2023

AbstractThe primitive Wigner-Seitz cell and corresponding first Brillouin zone (FBZ) are typically used in calculations of lattice vibrational and transport properties as they contain the smallest number of degrees of freedom and thus have the cheapest computational cost. However, in complex materials, the FBZ can take on irregular shapes where lattice symmetries are not apparent. Thus, conventional cells (with more atoms and regular shapes) are often used to describe materials, though dynamical and transport calculations are more expensive. Here we discuss an efficient anharmonic lattice dynamic method that maps conventional cell dynamics to primitive cell dynamics based on translational symmetries. Such symmetries have not been utilized in typical lattice dynamical calculations. This leads to phase interference conditions that act like conserved quantum numbers and a conservation rule for phonon scattering that is hidden in conventional dynamics which significantly reduces the computational cost. We demonstrate this method for phonon transport in a variety of materials with inputs from first-principles calculations and attribute its efficiency to reduced scattering phase space and fewer summations in scattering matrix element calculations.
A high-speed current source for magnetorheological applications
Authors: Xie Lei , Lu Chuan , Yin Jianfei , Wei Bo , Wang Yuhao , Wang Pengsai , Yang Zhipeng , Liao Changrong 
Published: October 2023

AbstractCurrent source is an indispensable component of magnetorheological (MR) systems. Though MR fluid has a phase change as fast as in 1 ms, the response of MR damper (MRD) to generate the damping force may be two orders of magnitude longer. Therefore, the rapid response of current source is a key to realize the real-time semi-active control of MR devices. This study proposes a programmable high-speed, low-cost current source exclusively for MR devices based on the synergy between supercapacitor and Buck converter (i.e., SSBC current source). SSBC current source features a strategy consisting of a lifting phase of supercapacitor and a following maintaining phase of Buck converter. Specifically, the high power density of supercapacitor contributes to rapidly lifting/raising the initial current, and then, like a “relay race”, the expected output is maintained through a Buck converter. Theoretical modeling and experiments are performed systematically. The response times (@ 95% of expected outputs) measured are 0.44, 0.84 and 1.88 ms for the outputs of 3, 6 and 9 A, respectively; these values are highlighted as the fastest level in this field. Besides, the response can be up to 24.6 and 43.7 times faster than the cases using supercapacitor and Buck converter to directly drive the MRD, respectively. SSBC current source is employed to generate a sequence of currents/magnetic inductions, only four variables of which need to be controlled programmatically: the order of lifting and maintaining phases, switching time of lifting phase, PWM duty cycle of Buck converter and duration of maintaining phase. The response time stability is verified by 100 cycles of on/off tests, showing a fluctuation of only 1.1%, which indicates a very reliable high-speed response. This study provides an exclusive power supply with a novel strategy for MR devices, which is believed to be an important promotion for MR technologies.
Computed tomographic imaging features to differentiate gastric schwannomas from gastrointestinal stromal tumours: a matched case–control study
Authors: Wang Lijia , Wang Qi , Yang Li , Ma Chongfei , Shi Gaofeng 
Published: October 2023

AbstractTo investigate clinical data and computed tomographic (CT) imaging features in differentiating gastric schwannomas (GSs) from gastric stromal tumours (GISTs) in matched patients, 31 patients with GSs were matched with 62 patients with GISTs (1:2) in sex, age, and tumour site. The clinical and imaging data were analysed. A significant (P < 0.05) difference was found in the tumour margin, enhancement pattern, growth pattern, and LD values between the 31 patients with GSs and 62 matched patients with GISTs. The GS lesions were mostly (93.5%) well defined while only 61.3% GIST lesions were well defined.The GS lesions were significantly (P = 0.036) smaller than the GIST lesions, with the LD ranging 1.5–7.4 (mean 3.67 cm) cm for the GSs and 1.0–15.30 (mean 5.09) cm for GIST lesions. The GS lesions were more significantly (P = 0.001) homogeneously enhanced (83.9% vs. 41.9%) than the GIST lesions. The GS lesions were mainly of the mixed growth pattern both within and outside the gastric wall (74.2% vs. 22.6%, P < 0.05) compared with that of GISTs. No metastasis or invasion of adjacent organs was present in any of the GS lesions, however, 1.6% of GISTs experienced metastasis and 3.2% of GISTs presented with invasion of adjacent organs. Heterogeneous enhancement and mixed growth pattern were two significant (P < 0.05) independent factors for distinguishing GS from GIST lesions. In conclusion: GS and GIST lesions may have significantly different features for differentiation in lesion margin, heterogeneous enhancement, mixed growth pattern, and longest lesion diameter, especially heterogeneous enhancement and mixed growth pattern.
Periostin deficiency attenuates kidney fibrosis in diabetic nephropathy by improving pancreatic β-cell dysfunction and reducing kidney EMT
Authors: Cho Ara , Jin Wencheng , Lee Jeonghwan , Shin Nayeon , Lee Myoung Seok , Li Lilin , Yang Seung Hee , Park Kyong Soo , Yang Chul Woo , Kim Dong Ki , Oh Yun Kyu , Lim Chun Soo , Lee Jung Pyo 
Published: October 2023

AbstractDiabetic nephropathy (DN) is associated with kidney fibrosis. A previous study revealed that periostin (POSTN) contributes to kidney fibrosis. This study examined the role of POSTN in DN. The urinary concentrations of POSTN and TNC increased according to the severity of DN in human samples. Streptozotocin (STZ) was administered after unilateral nephrectomy (UNXSTZ) to induce DN in wild-type and Postn-null mice. Four experimental groups were generated: wild-typeham (WT Sham), wild-type UNXSTZ (WT STZ), Postn-null Sham (KO Sham), and Postn-null UNXSTZ (KO STZ). After 20 weeks, the KO STZ group had lower levels of urine albumin excretion, glomerular sclerosis, and interstitial fibrosis than those of the WT STZ group. Additionally, the KO STZ group had lower expression of fibrosis markers, including TNC. The KO STZ group showed better glucose regulation than the WT STZ model. Furthermore, the KO STZ group exhibited significantly preserved pancreatic islet integrity and insulin expression. HK-2 cells were used to observe the aggravation of fibrosis caused by POSTN under TGF-β conditions. We stimulated INS-1 cells with streptozotocin and evaluated the viability of these cells. The anti-POSTN antibody treatment of INS-1 cells with streptozotocin resulted in higher cell viability than that with treatment with streptozotocin alone. The absence of POSTN in DN contributes to renal fibrosis alleviation by improving pancreatic β-cell function. Additionally, there is an association between POSTN and TNC.
On-body non-invasive glucose monitoring sensor based on high figure of merit (FoM) surface plasmonic microwave resonator
Authors: Soltanian Farzad , Nosrati Mehdi , Mobayen Saleh , Li Chuan-Chun , Pan Telung , Ke Ming-Ta , Skruch Paweł 
Published: October 2023

AbstractHigh-figure of merit (FoM) plasmonic microwave resonator is researched as a non-invasive on-body sensor to monitor the human body's blood glucose variation rate in adults for biomedical applications, e.g., diabetic patients. The resonance frequencies of the proposed sensor are measured to be around$${f}_{01}=3.25$$f01=3.25GHz and$${f}_{01}=4.67$$f01=4.67GHz over the frequency band of DC to 6GHz which are suitable for monitoring interstitial fluid (ISF) changing rate. The$$40\times 40 {\mathrm{mm}}^{2}$$40×40mm2sensor is experimentally wrapped on the human body arm to monitor the blood glucose changing rate via amplitude and frequency variations of the sensor. Amplitude variation and frequency shift are measured to be around 7 dB and 30 MHz, respectively. The measured results demonstrate the high precision of the proposed approach to depict a valid diagram for glucose changing rate due to good impedance matching of the designed microwave sensor and human body. The sensor is shown to enhance the sensitivity by a factor of 5 compared to the conventional ones.
h-Analysis and data-parallel physics-informed neural networks
Authors: Escapil-Inchauspé Paul , Ruz Gonzalo A. 
Published: October 2023

AbstractWe explore the data-parallel acceleration of physics-informed machine learning (PIML) schemes, with a focus on physics-informed neural networks (PINNs) for multiple graphics processing units (GPUs) architectures. In order to develop scale-robust and high-throughput PIML models for sophisticated applications which may require a large number of training points (e.g., involving complex and high-dimensional domains, non-linear operators or multi-physics), we detail a novel protocol based on h-analysis and data-parallel acceleration through the Horovod training framework. The protocol is backed by new convergence bounds for the generalization error and the train-test gap. We show that the acceleration is straightforward to implement, does not compromise training, and proves to be highly efficient and controllable, paving the way towards generic scale-robust PIML. Extensive numerical experiments with increasing complexity illustrate its robustness and consistency, offering a wide range of possibilities for real-world simulations.
Quality control prediction of electrolytic copper using novel hybrid nonlinear analysis algorithm
Authors: Su Yuzhen , Ye Weichuan , Yang Kai , Li Meng , He Zhaohui , Xiao Qingtai 
Published: October 2023

AbstractTraditional linear regression and neural network models demonstrate suboptimal fit and lower predictive accuracy while the quality of electrolytic copper is estimated. A more dependable and accurate model is essential for these challenges. Notably, the maximum information coefficient was employed initially to discern the non-linear correlation between the nineteen factors influencing electrolytic copper quality and the five quality control indicators. Additionally, the random forest algorithm elucidated the primary factors governing electrolytic copper quality. A hybrid model, integrating particle swarm optimization with least square support vector machine, was devised to predict electrolytic copper quality based on the nineteen factors. Concurrently, a hybrid model combining random forest and relevance vector machine was developed, focusing on primary control factors. The outcomes indicate that the random forest algorithm identified five principal factors governing electrolytic copper quality, corroborated by the non-linear correlation analysis via the maximum information coefficient. The predictive accuracy of the relevance vector machine model, when accounting for all nineteen factors, was comparable to the particle swarm optimization—least square support vector machine model, and surpassed both the conventional linear regression and neural network models. The predictive error for the random forest-relevance vector machine hybrid model was notably less than the sole relevance vector machine model, with the error index being under 5%. The intricate non-linear variation pattern of electrolytic copper quality, influenced by numerous factors, was unveiled. The advanced random forest-relevance vector machine hybrid model circumvents the deficiencies seen in conventional models. The findings furnish valuable insights for electrolytic copper quality management.
Bifurcation analysis of a tuberculosis progression model for drug target identification
Authors: Flores-Garza Eliezer , Hernández-Pando Rogelio , García-Zárate Ibrahim , Aguirre Pablo , Domínguez-Hüttinger Elisa 
Published: October 2023

AbstractTuberculosis (TB) is a major cause of morbidity and mortality worldwide. The emergence and rapid spread of drug-resistant M. tuberculosis strains urge us to develop novel treatments. Experimental trials are constrained by laboratory capacity, insufficient funds, low number of laboratory animals and obsolete technology. Systems-level approaches to quantitatively study TB can overcome these limitations. Previously, we proposed a mathematical model describing the key regulatory mechanisms underlying the pathological progression of TB. Here, we systematically explore the effect of parameter variations on disease outcome. We find five bifurcation parameters that steer the clinical outcome of TB: number of bacteria phagocytosed per macrophage, macrophages death, macrophage killing by bacteria, macrophage recruitment, and phagocytosis of bacteria. The corresponding bifurcation diagrams show all-or-nothing dose–response curves with parameter regions mapping onto bacterial clearance, persistent infection, or history-dependent clearance or infection. Importantly, the pathogenic stage strongly affects the sensitivity of the host to these parameter variations. We identify parameter values corresponding to a latent-infection model of TB, where disease progression occurs significantly slower than in progressive TB. Two-dimensional bifurcation analyses uncovered synergistic parameter pairs that could act as efficient compound therapeutic approaches. Through bifurcation analysis, we reveal how modulation of specific regulatory mechanisms could steer the clinical outcome of TB.
Few-shot learning for joint model in underwater acoustic target recognition
Authors: Tian Shengzhao , Bai Di , Zhou Junlin , Fu Yan , Chen Duanbing 
Published: October 2023

AbstractIn underwater acoustic target recognition, there is a lack of massive high-quality labeled samples to train robust deep neural networks, and it is difficult to collect and annotate a large amount of base class data in advance unlike the image recognition field. Therefore, conventional few-shot learning methods are difficult to apply in underwater acoustic target recognition. In this report, following advanced self-supervised learning frameworks, a learning framework for underwater acoustic target recognition model with few samples is proposed. Meanwhile, a semi-supervised fine-tuning method is proposed to improve the fine-tuning performance by mining and labeling partial unlabeled samples based on the similarity of deep features. A set of small sample datasets with different amounts of labeled data are constructed, and the performance baselines of four underwater acoustic target recognition models are established based on these datasets. Compared with the baselines, using the proposed framework effectively improves the recognition effect of four models. Especially for the joint model, the recognition accuracy has increased by 2.04% to 12.14% compared with the baselines. The model performance on only 10 percent of the labeled data can exceed that on the full dataset, effectively reducing the dependence of model on the number of labeled samples. The problem of lack of labeled samples in underwater acoustic target recognition is alleviated.
Optimising a urinary extraction method for non-targeted GC–MS metabolomics
Authors: Olivier Cara , Allen Bianca , Luies Laneke 
Published: October 2023

AbstractUrine is ideal for non-targeted metabolomics, providing valuable insights into normal and pathological cellular processes. Optimal extraction is critical since non-targeted metabolomics aims to analyse various compound classes. Here, we optimised a low-volume urine preparation procedure for non-targeted GC–MS. Five extraction methods (four organic acid [OA] extraction variations and a “direct analysis” [DA] approach) were assessed based on repeatability, metabolome coverage, and metabolite recovery. The DA method exhibited superior repeatability, and achieved the highest metabolome coverage, detecting 91 unique metabolites from multiple compound classes comparatively. Conversely, OA methods may not be suitable for all non-targeted metabolomics applications due to their bias toward a specific compound class. In accordance, the OA methods demonstrated limitations, with lower compound recovery and a higher percentage of undetected compounds. The DA method was further improved by incorporating an additional drying step between two-step derivatization but did not benefit from urease sample pre-treatment. Overall, this study establishes an improved low-volume urine preparation approach for future non-targeted urine metabolomics applications using GC–MS. Our findings contribute to advancing the field of metabolomics and enable efficient, comprehensive analysis of urinary metabolites, which could facilitate more accurate disease diagnosis or biomarker discovery.
Evaluation of sleep quality and pruritus severity in psoriatic patients and their impact on quality of life: a cross section correlational study
Authors: Zaky Mohamed S. , Elgamal Emad Eldin A. , Abd Al Maksoud Ayman A. , Mohamed Dina H. , Elsaie Mohamed L. 
Published: October 2023

AbstractPsoriasis is a chronic inflammatory condition associated with genetic and immunological susceptibility. The objective of the study was to evaluate pruritus and sleep quality in correlation (r) to psoriasis severity and to detect their impact on quality of life. Two hundred (200) patients with psoriasis were included. Psoriasis severity was determined using the psoriasis area severity index (PASI), the quality of life (QoL) was assessed by the psoriasis disability index (PDI) questionnaire, and the sleep quality was evaluated by the Pittsburgh sleep quality index (PSQI). Finally, the severity of itching was evaluated using a 12-item pruritus severity scale (PSS). Poor sleep quality was found in 16.0% of patients in this study. Poor sleep was detected among 50.0% of cases with severe psoriasis. PASI scores correlated significantly with sleep quality, duration and sleep disturbances (p < 0.001). The global PSQI and PASI were also significantly correlated (p = 0.004). In conclusion patients complaining of psoriasis exacerbated by pruritus and sleep problems demonstrated lower quality of life in all domains. Sleep disturbances and depressive symptoms impairing quality of life should be taken into consideration when screening patients suffering from psoriasis.
Brownfield redevelopment evaluation based on structure-process-outcome theory and continuous ordered weighted averaging operator-topology method
Authors: Jian He , Hao Hu , Haidan Jiang , Haize Pan , Chuan Liu 
Published: October 2023

AbstractAs an important part of urban renewal, brownfield restoration and renovation are of great significance to the sustainable development of cities. The structure-process-outcome theory was introduced into this study to improve the rationality and scientific vigor of the redevelopment assessment process and to evaluate whether brownfield sites meet the conditions for redevelopment. Based on this theory, the relationship among structures, processes and outcomes can be well elucidated. Specifically, a good structure should contribute to an effective process, which will increase the possibility of a favorable outcome. The basic conditions, practice principles, and result orientation in the whole procedure of brownfield redevelopment were comprehensively analyzed. In addition, a more complete and reasonable three-level evaluation index system for brownfield redevelopment was established. In order to reduce the subjectivity in the evaluation process, an unbiased scientific brownfield redevelopment evaluation model was constructed using the continuous ordered weighted averaging operator-topology method. The evaluation decision system was applied to the renovation of a tract project in Chengdu, China. The results proved that the model could effectively and accurately evaluate the quality level of the brownfield redevelopment project, and the proposed recommendations can provide a basis for decision-making.
Computer-aided diagnosis of chest X-ray for COVID-19 diagnosis in external validation study by radiologists with and without deep learning system
Authors: Miyazaki Aki , Ikejima Kengo , Nishio Mizuho , Yabuta Minoru , Matsuo Hidetoshi , Onoue Koji , Matsunaga Takaaki , Nishioka Eiko , Kono Atsushi , Yamada Daisuke , Oba Ken , Ishikura Reiichi , Murakami Takamichi 
Published: October 2023

AbstractTo evaluate the diagnostic performance of our deep learning (DL) model of COVID-19 and investigate whether the diagnostic performance of radiologists was improved by referring to our model. Our datasets contained chest X-rays (CXRs) for the following three categories: normal (NORMAL), non-COVID-19 pneumonia (PNEUMONIA), and COVID-19 pneumonia (COVID). We used two public datasets and private dataset collected from eight hospitals for the development and external validation of our DL model (26,393 CXRs). Eight radiologists performed two reading sessions: one session was performed with reference to CXRs only, and the other was performed with reference to both CXRs and the results of the DL model. The evaluation metrics for the reading session were accuracy, sensitivity, specificity, and area under the curve (AUC). The accuracy of our DL model was 0.733, and that of the eight radiologists without DL was 0.696 ± 0.031. There was a significant difference in AUC between the radiologists with and without DL for COVID versus NORMAL or PNEUMONIA (p = 0.0038). Our DL model alone showed better diagnostic performance than that of most radiologists. In addition, our model significantly improved the diagnostic performance of radiologists for COVID versus NORMAL or PNEUMONIA.