bluuebunny/crossref_metadata_2025_split huggingface dataset:
Chaotic convection in a magnetic fluid in rotation subjected to a pseudo-vector type force
Authors: Hounvènou M L , Monwanou Vincent 
Published: August 2022

Abstract
This paper reports the chaos control on the convection in a magnetic fluid in rotation subjected to a pseudo-vector type force. We performed a truncated Galerkin expansion which allowed to describe the system by a Lorenz-type model. We characterized the dynamical system by plotting bifurcation diagrams, Lyapunov exponents and phase spaces. It is found that the system exhibits rich behaviors depending on the variation of the system parameters. Finally, we noticed that the chaotic domain is considerably reduced with the increase of the helical force whereas the Taylor number enlarges it.
Singularities and accumulation of singularities of πN scattering amplitudes
Authors: Li Qu-Zhi , Zheng Han-Qing 
Published: August 2022

Abstract
It is demonstrated that for the isospin I = 1/2 πN scattering amplitude, T

I=1/2(s, t),

s
=

m

N

2

−

m

π

2

2

/

m

N

2

and

s
=

m

N

2

+
2

m

π

2

are two accumulation points of poles on the second sheet of complex s plane, and are hence accumulation of singularities of T

I=1/2(s, t). For T

I=3/2(s, t),

s
=

m

N

2

−

m

π

2

2

/

m

N

2

is the accumulation point of poles on the second sheet of the complex s plane. The proof is valid up to all orders of chiral expansions.
Collapsing scenario for the k-essence emergent generalized Vaidya spacetime in the context of massive gravity's rainbow
Authors: Ray Saibal , Panda Arijit , Majumder Bivash , Islam Md. Rabiul , Manna Goutam 
Published: August 2022

Abstract
In this study, we investigate the collapsing scenario for the k-essence emergent Vaidya spacetime in the context of massive gravity's rainbow. For this study, we consider that the background metric is Vaidya spacetime in massive gravity's rainbow. We show that the k-essence emergent gravity metric closely resembles the new type of generalized Vaidya massive gravity metric with the rainbow deformations for null fluid collapse, where we consider the k-essence scalar field as a function solely of the advanced or the retarded time. The k-essence emergent Vaidya massive gravity rainbow mass function is also different. This new type k-essence emergent Vaidya massive gravity rainbow metric satisfies the required energy conditions. The existence of a locally naked central singularity and the strength and strongness of the singularities for the rainbow deformations of the k-essence emergent Vaidya massive gravity metric are the interesting outcomes of the present work.
Attributing observed increase in extreme precipitation in China to human influence
Authors: Dong Siyan , Sun Ying , Zhang Xuebin 
Published: August 2022

Abstract
This paper examines new evidence from observational and detection and attribution studies of changes in extreme precipitation in China since the early 1960s. We have also designed a series of sensitivity tests to explore the robustness of detection and attribution results to the differences in sample size, in extreme precipitation index, and in data processing procedure. Our analyses used the most recent update of observational records as well as simulations conducted with the climate models participated in the Coupled Model Intercomparison Project Phase 6. Based on the existing studies and our additional analyses, we found that human influence is detectable in extreme precipitation in China regardless of the period, extreme precipitation index, or data treatment considered, in both China as a whole and in northern and southern China separately. We also found, as is often encountered in detection and attribution studies, it is difficult to separate the contribution from anthropogenic forcing from that of natural external forcing, and it is also challenging to decompose the anthropogenic component into a greenhouse gas forcing component and a component that reflects other anthropogenic forcing agents (dominantly, aerosols).
Complex hypergeometric functions and integrable many-body problems
Authors: Sarkissian G A , Spiridonov V P 
Published: August 2022

Abstract
General reduction of the elliptic hypergeometric equation to the level of complex hypergeometric functions is described. The derived equation is generalized to the Hamiltonian eigenvalue problem for new rational integrable N-body systems emerging from particular degenerations of the elliptic Ruijsenaars and van Diejen models.
Incompatibility of observables, channels and instruments in information theories
Authors: D’Ariano Giacomo Mauro , Perinotti Paolo , Tosini Alessandro 
Published: August 2022

Abstract
Every theory of information, including classical and quantum, can be studied in the framework of operational probabilistic theories—where the notion of test generalizes that of quantum instrument, namely a collection of quantum operations summing to a channel, and simple rules are given for the composition of tests in parallel and in sequence. Here we study the notion of compatibility for tests of a causal operational probabilistic theory. Following the quantum literature, we first introduce the notion of strong compatibility, and then we illustrate its ultimate relaxation, that we deem weak compatibility. It is shown that the two notions coincide in the case of observation tests—which are the counterpart of quantum POVMs—while there exist weakly compatible channels that are not strongly compatible. We prove necessary and sufficient conditions for a theory to exhibit incompatible tests. We show that a theory admits of incompatible tests if and only if some information cannot be extracted without disturbance.
Modeling the dynamic of ecosystem service supply and demand in Chengdu-Chongqing twin-city economic circle, China
Authors: Tang Zhonglin , Wang Yuting , Fu Min 
Published: August 2022

Abstract
Research on ecosystem service supply and demand relations is necessary for effective ecosystem management and rational allocation of resources. Based on the Ecosystem Services Value Evaluation, calculation of Land Use Development Index(LDI) and Ecosystem Services Supply-Demand Ratio(ESSDR), the coupling pattern of ecosystem services supply and demand in the Chengdu-Chongqing Twin-city Economic Circle were described. The results showed that the ESSDR in the researched area basically maintained a pattern of gradual improvement from the dual core to the peripheral circle in terms of spatial structure. An obvious inter-annual difference in the supply and demand patterns was also observed, which showed that the change trend of ESSDR before and after 2000 was opposite. The accelerated urbanization and population migration brought about by the Western Development Strategy exacerbated the imbalance between supply and demand of ecosystem services, the relationship between supply and demand of ecosystem services show an overall negative correlation, but there were obvious regional difference. The coupling status was significantly greater in the southern Sichuan urban agglomeration than others in terms of the quantitative characteristics.
Evaluation of machine learning algorithms for grade estimation using GRNN & SVR
Authors: Goswami Agam Das , Mishra M K , Patra Dipti 
Published: August 2022

Abstract
Estimates of the ore grade need precise location prediction using the limited drill data available. It is a significant and important phase in the process of deciding how to invest in and develop various mining operations. This study analyses the effectiveness of using General Regression Neural Networks (GRNN) and Support Vector Regression (SVR) to improve grade estimate prediction in western India. The algorithm used to estimate grade took into account supplemental lithological information that differed throughout the study area. In this particular investigation, the factors considered as input variables were three-dimensional geographical coordinates and four underlying lithological units. On the other hand, the factors considered as output variables were CaO, Al2O3, Fe2O3, and SiO2. Ordinary kriging (OK), a geo-statistical technique, was utilized in order to verify the results of the comparative analysis performed on these models (OK). The GRNN model performed significantly better than the SVR model and the OK model when it came to generalization and the ability to predict grades.
A GPU-accelerated compute framework for pathogen genomic variant identification to aid genomic epidemiology of infectious disease: a malaria case study
Authors: Carpi Giovanna , Gorenstein Lev , Harkins Timothy T , Samadi Mehrzad , Vats Pankaj 
Published: August 2022

Abstract
As recently demonstrated by the COVID-19 pandemic, large-scale pathogen genomic data are crucial to characterize transmission patterns of human infectious diseases. Yet, current methods to process raw sequence data into analysis-ready variants remain slow to scale, hampering rapid surveillance efforts and epidemiological investigations for disease control. Here, we introduce an accelerated, scalable, reproducible, and cost-effective framework for pathogen genomic variant identification and present an evaluation of its performance and accuracy across benchmark datasets of Plasmodium falciparum malaria genomes. We demonstrate superior performance of the GPU framework relative to standard pipelines with mean execution time and computational costs reduced by 27× and 4.6×, respectively, while delivering 99.9% accuracy at enhanced reproducibility.
DLF-Sul: a multi-module deep learning framework for prediction of S-sulfinylation sites in proteins
Authors: Ning Qiao , Li Jinmou 
Published: August 2022

Abstract
Protein S-sulfinylation is an important posttranslational modification that regulates a variety of cell and protein functions. This modification has been linked to signal transduction, redox homeostasis and neuronal transmission in studies. Therefore, identification of S-sulfinylation sites is crucial to understanding its structure and function, which is critical in cell biology and human diseases. In this study, we propose a multi-module deep learning framework named DLF-Sul for identification of S-sulfinylation sites in proteins. First, three types of features are extracted including binary encoding, BLOSUM62 and amino acid index. Then, sequential features are further extracted based on these three types of features using bidirectional long short-term memory network. Next, multi-head self-attention mechanism is utilized to filter the effective attribute information, and residual connection helps to reduce information loss. Furthermore, convolutional neural network is employed to extract local deep features information. Finally, fully connected layers acts as classifier that map samples to corresponding label. Performance metrics on independent test set, including sensitivity, specificity, accuracy, Matthews correlation coefficient and area under curve, reach 91.80%, 92.36%, 92.08%, 0.8416 and 96.40%, respectively. The results show that DLF-Sul is an effective tool for predicting S-sulfinylation sites. The source code is available on the website https://github.com/ningq669/DLF-Sul.
Viral informatics: bioinformatics-based solution for managing viral infections
Authors: Kumar Sanjay , Kumar Geethu S , Maitra Subhrangsu Sundar , Malý Petr , Bharadwaj Shiv , Sharma Pradeep , Dwivedi Vivek Dhar 
Published: August 2022

AbstractSeveral new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.
Deep learning-based pseudo-mass spectrometry imaging analysis for precision medicine
Authors: Shen Xiaotao , Shao Wei , Wang Chuchu , Liang Liang , Chen Songjie , Zhang Sai , Rusu Mirabela , Snyder Michael P 
Published: August 2022

Abstract
Liquid chromatography–mass spectrometry (LC–MS)-based untargeted metabolomics provides systematic profiling of metabolic. Yet, its applications in precision medicine (disease diagnosis) have been limited by several challenges, including metabolite identification, information loss and low reproducibility. Here, we present the deep-learning-based Pseudo-Mass Spectrometry Imaging (deepPseudoMSI) project (https://www.deeppseudomsi.org/), which converts LC–MS raw data to pseudo-MS images and then processes them by deep learning for precision medicine, such as disease diagnosis. Extensive tests based on real data demonstrated the superiority of deepPseudoMSI over traditional approaches and the capacity of our method to achieve an accurate individualized diagnosis. Our framework lays the foundation for future metabolic-based precision medicine.
Effects of controlled ovarian stimulation on thyroid function during pregnancy
Authors: Li Lingfei , Li Ling , Li Ping 
Published: August 2022

Abstract
Controlled ovarian stimulation (COS) is a major component of assisted reproductive technologies. Clinically, it has been observed that some women experience changes in thyrotropin levels following COS, which then bring about subclinical hypothyroidism and may adversely affect conception. Studies have also shown that the specific degree as well as the tendency of changes in thyroid function vary with differences in thyroid function before pregnancy, thyroid autoimmunity, the COS regimen, and the observation time point. However, the associated pathophysiological mechanism of the effects of COS on pregnancy has not yet been fully elucidated. This may be because increased estradiol levels, caused by COS, induce increased levels of thyroxine-binding globulin, resulting in a decrease in free thyroxine (FT4) level and an increase in TSH level. Conversely, it has also been observed that human chorionic gonadotropin (hCG) can act directly on thyroid cells, exerting opposite effects on FT4 and TSH levels. Additionally, the effects of COS on thyroid function may be more pronounced, especially in women with autoimmune thyroid disease or thyroid dysfunction before pregnancy, ultimately leading to subclinical hypothyroidism. Here, we review recent research progress regarding the effects of COS on thyroid function during pregnancy.
Radiopaque tissue transfer and X-ray system versus standard specimen radiography for intraoperative margin assessment in breast-conserving surgery: randomized clinical trial
Authors: Stachs Angrit , Bollmann Julia , Martin Annett , Stubert Johannes , Reimer Toralf , Gerber Bernd , Hartmann Steffi 
Published: August 2022

Abstract

Background
Reduction of positive margin rate (PMR) in breast-conserving surgery (BCS) of non-palpable breast cancer remains a challenge. The efficacy of intraoperative specimen radiography (SR) is unclear. This randomized trial evaluated whether the PMR was reduced by the use of devices that allow precise localization of the affected margins.

Methods
Patients with microcalcification-associated breast cancer undergoing planned BCS were enrolled. Study participants were randomized to receive either SR with radiopaque tissue transfer and X-ray system (KliniTrayTM) or the institutional standard procedure (ISO). In all patients with a radiological margin less than 5 mm, an immediate re-excision was conducted. The primary outcome was the PMR. Risk factors for positive margins and the effect of immediate re-excision on final surgery were secondary analyses.

Results
Among 122 randomized patients, 5 patients were excluded due to the extent of primary surgery and 117 were available for analysis. Final histopathology revealed a PMR of 31.7 per cent for the KliniTrayTM group and 26.3 per cent for the ISO group (P = 0.127). Independent factors for positive margins were histological tumour size more than 30 mm (adjusted OR (aOR) 10.73; 95 per cent c.i. 3.14 to 36.75; P &lt; 0.001) and specimen size more than 50 mm (aOR 6.65; 95 per cent c.i. 2.00 to 22.08; P = 0.002). Immediate re-excision due to positive SR led to an absolute risk reduction in positive margins of 13.6 per cent (from 42.7 to 29.1 per cent).

Conclusion
Specimen orientation with a radiopaque tissue transfer and X-ray system did not decrease the PMR in patients with microcalcification-associated breast cancer; however, SR and immediate re-excision proved to be helpful in the reduction of PMR.

Registration number
DRKS00011527 (https://www.drks.de).
Variants in the zinc transporterTMEM163cause a hypomyelinating leukodystrophy
Authors: do Rosario Michelle C , Bey Guillermo Rodriguez , Nmezi Bruce , Liu Fang , Oranburg Talia , Cohen Ana S A , Coffman Keith A , Brown Maya R , Kiselyov Kirill , Waisfisz Quinten , Flohil Myrthe T , Siddiqui Shahyan , Rosenfeld Jill A , Iglesias Alejandro , Girisha Katta Mohan , Wolf Nicole I , Padiath Quasar Saleem , Shukla Anju 
Published: August 2022

AbstractHypomyelinating leukodystrophies comprise a subclass of genetic disorders with deficient myelination of the CNS white matter. Here we report four unrelated families with a hypomyelinating leukodystrophy phenotype harbouring variants in TMEM163 (NM_030923.5). The initial clinical presentation resembled Pelizaeus–Merzbacher disease with congenital nystagmus, hypotonia, delayed global development and neuroimaging findings suggestive of significant and diffuse hypomyelination. Genomic testing identified three distinct heterozygous missense variants in TMEM163 with two unrelated individuals sharing the same de novo variant. TMEM163 is highly expressed in the CNS particularly in newly myelinating oligodendrocytes and was recently revealed to function as a zinc efflux transporter. All the variants identified lie in highly conserved residues in the cytoplasmic domain of the protein, and functional in vitro analysis of the mutant protein demonstrated significant impairment in the ability to efflux zinc out of the cell. Expression of the mutant proteins in an oligodendroglial cell line resulted in substantially reduced mRNA expression of key myelin genes, reduced branching and increased cell death. Our findings indicate that variants in TMEM163 cause a hypomyelinating leukodystrophy and uncover a novel role for zinc homeostasis in oligodendrocyte development and myelin formation.
Mapping astrogliosis in the individual human brain using multidimensional MRI
Authors: Benjamini Dan , Priemer David S , Perl Daniel P , Brody David L , Basser Peter J 
Published: August 2022

AbstractThere are currently no non-invasive imaging methods available for astrogliosis assessment or mapping in the central nervous system despite its essential role in the response to many disease states, such as infarcts, neurodegenerative conditions, traumatic brain injury and infection. Multidimensional MRI is an increasingly employed imaging modality that maximizes the amount of encoded chemical and microstructural information by probing relaxation (T1 and T2) and diffusion mechanisms simultaneously. Here, we harness the exquisite sensitivity of this imagining modality to derive a signature of astrogliosis and disentangle it from normative brain at the individual level using machine learning. We investigated ex vivo cerebral cortical tissue specimens derived from seven subjects who sustained blast-induced injuries, which resulted in scar-border forming astrogliosis without being accompanied by other types of neuropathological abnormality, and from seven control brain donors. By performing a combined post-mortem radiology and histopathology correlation study we found that astrogliosis induces microstructural and chemical changes that are robustly detected with multidimensional MRI, and which can be attributed to astrogliosis because no axonal damage, demyelination or tauopathy were histologically observed in any of the cases in the study. Importantly, we showed that no one-dimensional T1, T2 or diffusion MRI measurement can disentangle the microscopic alterations caused by this neuropathology. Based on these findings, we developed a within-subject anomaly detection procedure that generates MRI-based astrogliosis biomarker maps ex vivo, which were significantly and strongly correlated with co-registered histological images of increased glial fibrillary acidic protein deposition (r = 0.856, P &lt; 0.0001; r = 0.789, P &lt; 0.0001; r = 0.793, P &lt; 0.0001, for diffusion-T2, diffusion-T1 and T1–T2 multidimensional data sets, respectively). Our findings elucidate the underpinning of MRI signal response from astrogliosis, and the demonstrated high spatial sensitivity and specificity in detecting reactive astrocytes at the individual level, and if reproduced in vivo, will significantly impact neuroimaging studies of injury, disease, repair and aging, in which astrogliosis has so far been an invisible process radiologically.
Impact of Seasonal Coronavirus Antibodies on Severe Acute Respiratory Syndrome Coronavirus 2 Vaccine Responses in Solid Organ Transplant Recipients
Authors: Karaba Andrew H , Zhou Weiqiang , Li Shuai , Aytenfisu Tihitina Y , Johnston Trevor S , Akinde Olivia , Eby Yolanda , Abedon Aura T , Alejo Jennifer L , Qin Caroline X , Thompson Elizabeth A , Garonzik-Wang Jacqueline M , Blankson Joel N , Cox Andrea L , Bailey Justin R , Klein Sabra L , Pekosz Andrew , Segev Dorry L , Tobian Aaron A R , Werbel William A 
Published: August 2022

Abstract
Antibody responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination are reduced in solid organ transplant recipients (SOTRs). We report that increased levels of preexisting antibodies to seasonal coronaviruses are associated with decreased antibody response to SARS-CoV-2 vaccination in SOTRs, supporting that antigenic imprinting modulates vaccine responses in SOTRs.