bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pan-cancer efficacy of pralsetinib in patients with RET fusion–positive solid tumors from the phase 1/2 ARROW trial
Authors: Subbiah Vivek , Cassier Philippe A. , Siena Salvatore , Garralda Elena , Paz-Ares Luis , Garrido Pilar , Nadal Ernest , Vuky Jacqueline , Lopes Gilberto , Kalemkerian Gregory P. , Bowles Daniel W. , Seetharam Mahesh , Chang Jianhua , Zhang Hui , Green Jennifer , Zalutskaya Alena , Schuler Martin , Fan Yun , Curigliano Giuseppe 
Published: August 2022

AbstractOncogenic RET fusions occur in diverse cancers. Pralsetinib is a potent, selective inhibitor of RET receptor tyrosine kinase. ARROW (NCT03037385, ongoing) was designed to evaluate pralsetinib efficacy and safety in patients with advanced RET-altered solid tumors. Twenty-nine patients with 12 different RET fusion–positive solid tumor types, excluding non-small-cell lung cancer and thyroid cancer, who had previously received or were not candidates for standard therapies, were enrolled. The most common RET fusion partners in 23 efficacy-evaluable patients were CCDC6 (26%), KIF5B (26%) and NCOA4 (13%). Overall response rate, the primary endpoint, was 57% (95% confidence interval, 35–77) among these patients. Responses were observed regardless of tumor type or RET fusion partner. Median duration of response, progression-free survival and overall survival were 12 months, 7 months and 14 months, respectively. The most common grade ≥3 treatment-related adverse events were neutropenia (31%) and anemia (14%). These data validate RET as a tissue-agnostic target with sensitivity to RET inhibition, indicating pralsetinib’s potential as a well-tolerated treatment option with rapid, robust and durable anti-tumor activity in patients with diverse RET fusion–positive solid tumors.
A novel causative functional mutation in GATA6 gene is responsible for familial dilated cardiomyopathy as supported by in silico functional analysis
Authors: Khazamipour Afrouz , Gholampour-Faroji Nazanin , Zeraati Tina , Vakilian Farveh , Haddad-Mashadrizeh Aliakbar , Ghayour Mobarhan Majid , Pasdar Alireza 
Published: August 2022

AbstractDilated cardiomyopathy (DCM), one of the most common types of cardiomyopathies has a heterogeneous nature and can be seen in Mendelian forms. Next Generation Sequencing is a powerful tool for identifying novel variants in monogenic disorders. We used whole-exome sequencing (WES) and Sanger sequencing techniques to identify the causative mutation of DCM in an Iranian pedigree. We found a novel variant in the GATA6 gene, leading to substituting Histidine by Tyrosine at position 329, observed in all affected family members in the pedigree, whereas it was not established in any of the unaffected ones. We hypothesized that the H329Y mutation may be causative for the familial pattern of DCM in this family. The predicted models of GATA6 and H329Y showed the high quality according to PROCHECK and ERRAT. Nonetheless, simulation results revealed that the protein stability decreased after mutation, while the flexibility may have been increased. Hence, the mutation led to the increased compactness of GATA6. Overall, these data indicated that the mutation could affect the protein structure, which may be related to the functional impairment of GATA6 upon H329Y mutation, likewise their involvement in pathologies. Further functional investigations would help elucidating the exact mechanism.
Cortical waste clearance in normal and restricted sleep with potential runaway tau buildup in Alzheimer’s disease
Authors: Tekieh Tahereh , Robinson P. A. , Postnova Svetlana 
Published: August 2022

AbstractAccumulation of waste in cortical tissue and glymphatic waste clearance via extracellular voids partly drives the sleep-wake cycle and modeling has reproduced much of its dynamics. Here, new modeling incorporates higher void volume and clearance in sleep, multiple waste compounds, and clearance obstruction by waste. This model reproduces normal sleep-wake cycles, sleep deprivation effects, and performance decreases under chronic sleep restriction (CSR). Once fitted to calibration data, it successfully predicts dynamics in further experiments on sleep deprivation, intermittent CSR, and recovery after restricted sleep. The results imply a central role for waste products with lifetimes similar to tau protein. Strong tau buildup is predicted if pathologically enhanced production or impaired clearance occur, with runaway buildup above a critical threshold. Predicted tau accumulation has timescales consistent with the development of Alzheimer’s disease. The model unifies a wide sweep of phenomena, clarifying the role of glymphatic clearance and targets for interventions against waste buildup.
Climate change contributions to future atmospheric river flood damages in the western United States
Authors: Corringham Thomas W. , McCarthy James , Shulgina Tamara , Gershunov Alexander , Cayan Daniel R. , Ralph F. Martin 
Published: August 2022

AbstractAtmospheric rivers (ARs) generate most of the economic losses associated with flooding in the western United States and are projected to increase in intensity with climate change. This is of concern as flood damages have been shown to increase exponentially with AR intensity. To assess how AR-related flood damages are likely to respond to climate change, we constructed county-level damage models for the western 11 conterminous states using 40 years of flood insurance data linked to characteristics of ARs at landfall. Damage functions were applied to 14 CMIP5 global climate models under the RCP4.5 “intermediate emissions” and RCP8.5 “high emissions” scenarios, under the assumption that spatial patterns of exposure, vulnerability, and flood protection remain constant at present day levels. The models predict that annual expected AR-related flood damages in the western United States could increase from $1 billion in the historical period to $2.3 billion in the 2090s under the RCP4.5 scenario or to $3.2 billion under the RCP8.5 scenario. County-level projections were developed to identify counties at greatest risk, allowing policymakers to target efforts to increase resilience to climate change.
Distance and similarity measures for normal wiggly dual hesitant fuzzy sets and their application in medical diagnosis
Authors: Ali Jawad , Naeem Muhammad 
Published: August 2022

AbstractThe normal wiggly dual hesitant fuzzy set (NWDHFS) is a modern mathematical tool that can be used to express the deep ideas of membership and non-membership information hidden in the thought-level of decision-makers (DMs). To enhance and expand the applicability of NWDHFSs, this study originates several types of distance and similarity measures between two NWDHFSs. The present paper first revises the basic operational laws of normal wiggly dual hesitant fuzzy elements (NWDHFEs) and then generalizes the rule of length extension for normal wiggly dual hesitant fuzzy setting. Meanwhile, we introduce a variety of distance and similarity measures under the background of NWDHFSs. After that, a family of weighted distance and similarity measures based on NWDHFS is presented and analyzed for discrete and continuous cases. The stated measures are the extension of several existing measures and have the capability to handle uncertain and vague information with a wider range of information. DMs can select the most suitable alternative based on these measures by determining the gap between each alternative and the ideal one. Finally, a practical example concerning disease detection is addressed to demonstrate the applicability and merits of the developed theory and depict the differences between the presented distance and similarity measures.
Development of deep learning chest X-ray model for cardiac dose prediction in left-sided breast cancer radiotherapy
Authors: Koide Yutaro , Aoyama Takahiro , Shimizu Hidetoshi , Kitagawa Tomoki , Miyauchi Risei , Tachibana Hiroyuki , Kodaira Takeshi 
Published: August 2022

AbstractDeep inspiration breath-hold (DIBH) is widely used to reduce the cardiac dose in left-sided breast cancer radiotherapy. This study aimed to develop a deep learning chest X-ray model for cardiac dose prediction to select patients with a potentially high risk of cardiac irradiation and need for DIBH radiotherapy. We used 103 pairs of anteroposterior and lateral chest X-ray data of left-sided breast cancer patients (training cohort: n = 59, validation cohort: n = 19, test cohort: n = 25). All patients underwent breast-conserving surgery followed by DIBH radiotherapy: the treatment plan consisted of three-dimensional, two opposing tangential radiation fields. The prescription dose of the planning target volume was 42.56 Gy in 16 fractions. A convolutional neural network-based regression model was developed to predict the mean heart dose (∆MHD) reduction between free-breathing (MHDFB) and DIBH. The model performance is evaluated as a binary classifier by setting the cutoff value of ∆MHD > 1 Gy. The patient characteristics were as follows: the median (IQR) age was 52 (47–61) years, MHDFB was 1.75 (1.14–2.47) Gy, and ∆MHD was 1.00 (0.52–1.64) Gy. The classification performance of the developed model showed a sensitivity of 85.7%, specificity of 90.9%, a positive predictive value of 92.3%, a negative predictive value of 83.3%, and a diagnostic accuracy of 88.0%. The AUC value of the ROC curve was 0.864. The proposed model could predict ∆MHD in breast radiotherapy, suggesting the potential of a classifier in which patients are more desirable for DIBH.
Coupled power generators require stability buffers in addition to inertia
Authors: Raman Gurupraanesh , Raman Gururaghav , Peng Jimmy Chih-Hsien 
Published: August 2022

AbstractIncreasing the inertia is widely considered to be the solution to resolving unstable interactions between coupled oscillators. In power grids, Virtual Synchronous Generators (VSGs) are proposed to compensate for reducing inertia as rotating fossil-fuel-based generators are being phased out. Yet, modeling how VSGs and rotating generators simultaneously contribute energy and inertia, we surprisingly find that instabilities of a small-signal nature could arise despite fairly high system inertia if the generators’ controls are not coordinated at the system level. Importantly, we show there exist both an optimal and a maximum number of such VSGs that can be safely supported, a previously unknown result directly useful for power utilities in long-term planning and prosumer contracting. Meanwhile, to resolve instabilities in the short term until system-level coordination can be achieved, we argue that the new market should include another commodity that we call stability storage, whereby—analogous to energy storage buffering energy imbalances—VSGs act as decentralized stability buffers. While demonstrating the effectiveness of this concept for a wide range of energy futures, we provide policymakers and utilities with a roadmap towards achieving a 100% renewable grid.
Controlling the uncontrolled variation in the diet induced obese mouse by microbiomic characterization
Authors: Bondarenko Valeriia , Løkke Cecillie Reynolds , Dobrowolski Peter , Mentzel Caroline Junker , Castro-Mejía Josué L. , Hansen Camilla Hartmann Friis , Sørensen Dorte Bratbo , Nielsen Dennis Sandris , Krych Lukasz , Hansen Axel Kornerup 
Published: August 2022

AbstractGroup sizes in an animal study are calculated from estimates on variation, effect, power and significance level. Much of the variation in glucose related parameters of the diet-induced obese (DIO) mouse model is due to inter-individual variation in gut microbiota composition. In addition, standard tandem repeats (STRs) in the non-coding DNA shows that inbred mice are not always homogenic. C57BL/6NTac (B6NTac) mice from Taconic and C57BL/6NRj (B6NRj) mice from Janvier Labs were fed a high calorie diet and treated with liraglutide. The fecal microbiota was sequenced before high-calorie feeding (time 1) and after diet-induced obesity instantly before liraglutide treatment (time 2) and mice were divided into clusters on the basis of their microbiota. Although liraglutide in both sub-strains alleviated glucose intolerance and reduced body weight, in a one-way ANOVA a borderline reduction in glycosylated hemoglobin (HbA1c) could only be shown in B6NTac mice. However, if the microbiota clusters from time 1 or time 2 were incorporated in a two-way ANOVA, the HbA1c effect was significant in B6NTac mice in both analyses, while this did not change anything in B6NRj mice. In a one-way ANOVA the estimated group size needed for a significant HbA1c effect in B6NTac mice was 42, but in two-way ANOVAs based upon microbiota clusters of time 1 or time 2 it was reduced to 21 or 12, respectively. The lowering impact on glucose tolerance was also powered by incorporation of microbiota clusters of both times in both sub-strains. B6NRj had up to six, while B6NTac had maximum three alleles in some of their STRs. In B6NRj mice in 28.8% of the STRs the most prevalent allele had a gene frequency less than 90%, while this was only 6.6% in the B6NTac mice. However, incorporation of the STRs with the highest number of alleles or the most even distribution of frequencies in two-way ANOVAs only had little impact on the outcome of data evaluation. It is concluded that the inclusion of microbiota clusters in a two-way ANOVA in the evaluation of the glucose related effects of an intervention in the DIO mouse model might be an efficient tool for increasing power and reducing group sizes in mouse sub-strains, if these have a microbiota, which influences these parameters.
Global transcriptional and circadian regulation in a halotolerant cyanobacterium Halothece sp. PCC7418
Authors: Waditee-Sirisattha Rungaroon , Ito Hiroshi , Kageyama Hakuto 
Published: August 2022

AbstractSubstantial evidence has been accumulated about the molecular basis underlying halotolerance; however, insights into the regulatory networks for relevant genes and mechanisms of their interplay remain elusive. Here, we present a comprehensive transcriptome investigation, using RNA sequencing, of specific metabolic pathways and networks in a halotolerant cyanobacterium, Halothece sp. PCC7418, including the circadian rhythm profile. Dissecting the transcriptome presented the intracellular regulation of gene expressions, which was linked with ion homeostasis, protein homeostasis, biosynthesis of compatible solutes, and signal transduction, for adaptations to high-salinity environments. The efficient production and distribution of energy were also implicated in this acclimation process. Furthermore, we found that high-salinity environments had a dramatic effect on the global transcriptional expression regulated by the circadian clock. Our findings can provide a comprehensive transcriptome for elucidating the molecular mechanisms underlying halotolerance in cyanobacteria.
On the randomness and correlation in the trajectories of alpha particle emitted from 241Am: statistical inference based on information entropy
Authors: El Ghazaly M. , Elmaghraby Elsayed K. , Al-Sayed A. , Mohamed Amal , Dawood Mahmoud S. 
Published: August 2022

AbstractMost particle detectors are based on the hypothesis that particles are emitted randomly upon nuclear decay. In the present work, we tested the hypothesis of the existence of correlation in the random trajectories of alpha particles emitted from$${}^{241}$$241Am source and the null hypothesis of random trajectories. The trajectories were clued through the registration of track in a solid-state nuclear track detector. The experimental parameters were optimized to identify the possible sources of correlation in the track registration and the detector conditions upon exposure and etching to avoid misleading results. The optimization included authentication of linearity in registration efficiency with exposure time to prevent coalescence of registered tracks. The statistical inference processes were based upon adaptive quadrates analysis of the spatial data, and entropy and divergence analysis of the quadrate data together with the null hypothesis of Poisson distribution of random trajectories. The clustering and dispersion analysis were performed with central deviation tendency, empirical K-function, radial distribution analysis, and proximity Analysis. Results showed a pattern of gained information within the registered tracks that may be attributed to the alteration in the alpha particles’ trajectories induced by the strong electric field due to atoms in the source compound and encapsulation film.
Edge artificial intelligence wireless video capsule endoscopy
Authors: Sahafi A. , Wang Y. , Rasmussen C. L. M. , Bollen P. , Baatrup G. , Blanes-Vidal V. , Herp J. , Nadimi E. S. 
Published: August 2022

AbstractGastrointestinal (GI) tract diseases are responsible for substantial morbidity and mortality worldwide, including colorectal cancer, which has shown a rising incidence among adults younger than 50. Although this could be alleviated by regular screening, only a small percentage of those at risk are screened comprehensively, due to shortcomings in accuracy and patient acceptance. To address these challenges, we designed an artificial intelligence (AI)-empowered wireless video endoscopic capsule that surpasses the performance of the existing solutions by featuring, among others: (1) real-time image processing using onboard deep neural networks (DNN), (2) enhanced visualization of the mucous layer by deploying both white-light and narrow-band imaging, (3) on-the-go task modification and DNN update using over-the-air-programming and (4) bi-directional communication with patient’s personal electronic devices to report important findings. We tested our solution in an in vivo setting, by administrating our endoscopic capsule to a pig under general anesthesia. All novel features, successfully implemented on a single platform, were validated. Our study lays the groundwork for clinically implementing a new generation of endoscopic capsules, which will significantly improve early diagnosis of upper and lower GI tract diseases.
Evaluating and mitigating clinical samples matrix effects on TX-TL cell-free performance
Authors: Voyvodic Peter L. , Conejero Ismael , Mesmoudi Khouloud , Renard Eric , Courtet Philippe , Cattoni Diego I. , Bonnet Jerome 
Published: August 2022

AbstractCell-free biosensors are promising tools for medical diagnostics, yet their performance can be affected by matrix effects arising from the sample itself or from external components. Here we systematically evaluate the performance and robustness of cell-free systems in serum, plasma, urine, and saliva using two reporter systems, sfGFP and luciferase. In all cases, clinical samples have a strong inhibitory effect. Of the different inhibitors, only RNase inhibitor mitigated matrix effects. However, we found that the recovery potential of RNase inhibitor was partially muted by interference from glycerol contained in the commercial buffer. We solved this issue by designing a strain producing an RNase inhibitor protein requiring no additional step in extract preparation. Furthermore, our new extract yielded higher reporter levels than previous conditions and tempered interpatient variability associated with matrix effects. This systematic evaluation and improvements of cell-free system robustness unified across many types of clinical samples is a significant step towards developing cell-free diagnostics for a wide range of conditions.
Association between long-term weight-change trajectory and cardiovascular disease risk by physical activity level
Authors: Lee Hye Ah , Park Hyesook 
Published: August 2022

AbstractUsing data from the Korean Genome and Epidemiology Study, we identified weight-change patterns during midlife using a group-based trajectory model, and evaluated their associations with the incidence of cardiovascular disease (CVD). At baseline, there were 8774 CVD-free participants. Group-based modeling was used to analyze patterns of weight change over about 16 years. Using multiple model, we evaluated the association between weight-change patterns and CVD risk. During the follow-up period, 741 new CVD cases were identified. The weight-change patterns were characterized as ‘gradual weight gain’, ‘stable weight’, ‘slight weight loss’, and ‘gradual weight loss’. The association between weight-change patterns and CVD risk differed depending on the level of physical activity (PA) at baseline (pinteraction < 0.05). Compared with the stable-weight group, the risk of all CVD (HR 2.5, 95% CI 1.5–4.3) and non-fatal CVD (HR 2.8, 95% CI 1.6–4.9) among the gradual-weight-loss group was apparent in the lowest PA quartile. In addition, on average, a decrease in skeletal-muscle-mass (SMM) levels was observed during the follow-up period, but the decrease in SMM in the gradual-weight-loss group was greater than in the gradual-weight-gain group. Our findings show that gradual weight loss was associated with CVD risk, which was dependent on PA levels.
Tapped-inductor bi-directional Cuk converter with high step-up/down conversion ratio and its optimum design
Authors: Chen Hongxing , Lin Wei-ming , Liu Wen-ran , He Wei 
Published: August 2022

AbstractA bidirectional DC–DC converter is required for an energy storage system. High efficiency and a high step-up and step-down conversion ratio are the development trends. In this research, a series of bidirectional high-gain Cuk circuits was derived by combining tapped inductors and bidirectional Cuk. After analyzing and comparing the characteristics of each circuit, a bidirectional high-gain Cuk circuit with a tapped-inductor (reverse coupling) was proposed. The proposed converter has a simple structure and a high voltage gain in both the step-down (Buck) and step-up (Boost) operation modes. The voltage stress of S2 was low. The voltage stress of S1 was high, however, and this is a disadvantage of the proposed converter. The proposed circuit’s characteristics were thoroughly examined, including the voltage gain characteristics and the design of the main parameters. We established a power loss model of the new topology, and the tapped-inductor turn ratio was optimized for high efficiency. Finally, a 400 W experimental implementation of the converter was shown to achieve efficiencies of 93.5% and 92.4% in the step-up and step-down modes, respectively. These findings verified the validity of the proposed circuit’s theoretical analysis.
A neural network based approach to classify VLF signals as rock rupture precursors
Authors: Nardi Adriano , Pignatelli Alessandro , Spagnuolo Elena 
Published: August 2022

AbstractThe advent of novel technologies revealed that other geophysical signals than those directly related to fault motion could be used to probe the state of deformation of the Earth's crust. Electromagnetic signals belonging to this category have been increasingly investigated in the last decade in association to natural earthquakes and laboratory rock fractures. These studies are hampered by the lack of continuous recordings and a systematic mathematical processing of large data sets. Indeed, electromagnetic signals exhibit characteristic patterns on a specific frequency band (the very low frequency, VLF) that correlate uniquely with the paroxistic rupture of rocks specimens under uniaxial laboratory tests and were also detected in the atmosphere, in association to moderate magnitude earthquakes. The similarity of laboratory and atmospheric VLF offers an unique opportunity to study the relation between VLF and rock deformation on at least two different scales and to enlarge the dataset by combining laboratory and atmospheric data. In this paper we show that the enlarged VLF dataset can be successfully used, with a neural network approach based on LSTM neural networks to investigate the potential of the VLF spectrum in classifying rock rupture precursors both in nature and in the laboratory. The proposed approach lays foundation to the automatic detection of interesting VLF patterns for monitoring deformations in the seismically active Earth’s crust.