bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sustainable aviation fuels must control induced land use change: an integrated assessment modelling exercise for Brazil
Authors: Fiorini Ana Carolina Oliveira , Angelkorte Gerd , Maia Pedro Luiz , Bergman-Fonte Clarissa , Vicente Clarissa , Morais Taísa , Carvalho Lucas , Zanon-Zotin Marianne , Szklo Alexandre , Schaeffer Roberto , Portugal-Pereira Joana 
Published: December 2022

Abstract
Bio-sustainable aviation fuels (bio-SAFs) are an important pillar of the aviation sector decarbonisation strategy in the mid-term. Here we assess the induced Land-Use Change (LUC) implications of producing bio-SAFs in Brazil under different assumptions of forest conservation governance. We evaluate four bio-SAF routes via two main pathways: the Alcohol-to-Jet (ATJ) and the Hydroprocessed Esters and Fatty Acids (HEFA) syntheses. We chose the most promising agriculture-based feedstocks to produce bio-SAFs in all five macro-regions of Brazil, including sugarcane and maize ethanol to jet and palm and macaw HEFA routes. To this end, we calculated future projections of air transport demand in Brazil and used the Brazilian Land Use and Energy Systems integrated assessment model to estimate LUC greenhouse gas (GHG) emissions within five different levels of bio-SAF blends (10% to 50% of total aviation fuel demand) for each bio-SAFs evaluated. Estimated cumulated emissions vary widely, ranging from a carbon sequestration of −286.8 gCO2e.MJ−1 for a 10% blend of maize ATJ under a controlled deforestation scenario to a release of 15.0 gCO2e.MJ−1 for a 40% blend of high productivity macaw oil HEFA considering historical deforestation rates in the country. Results are highly sensitive to deforestation rate parameters, volume of bio-SAFs produced, the type of feedstock used, and methodological assumptions. Negative LUC GHG emissions were found under controlled deforestation assumptions and in low blends of bio-SAFs for maize and sugarcane ATJ routes. Under historical deforestation rates, the LUC GHG emissions are higher. Bio-SAF can be beneficial to reduce GHG emissions if effective land conservation policies are implemented. Therefore, large-scale bio-SAF production from sugar crops in Brazil may play an important role in the decarbonisation of the aviation sector if coupled with successful strategies to control deforestation. Additionally, when imposing bio-SAF demand, other biofuels demand reduces under the model optimal solution due to land restrictions.
Oxygen transport in the low–Pt catalyst layer of a PEM fuel cell: Impedance spectroscopy study
Authors: Reshetenko Tatyana , Kulikovsky Andrei 
Published: December 2022

Abstract
A model for PEM fuel cell impedance taking into account the pore size distribution (PSD) in the cathode catalyst layer is developed. Experimental PSD is approximated by pores of three sizes (small, medium and large) and in each kind of pores, the oxygen diffusion coefficient is allowed to have a separate value. The model is fitted to experimental impedance spectra of a low–Pt PEM fuel cell. The oxygen diffusivities of small and medium pores exhibit rapid growth with the cell current density, while in large pores, this parameter remains nearly constant. We show that oxygen reduction occurs mainly in the small and medium pores, leaving the large pores for mass transport only. This effect explains the discrepancy between small effective oxygen diffusivity of PEMFC catalyst layer measured in situ in operating cells by limiting current method, and much larger value of this parameter determined from ex situ experiments using Loschmidt cell.
Obstructions to reciprocity laws related to division fields of Drinfeld modules
Authors: Cojocaru Alina , Garai Sumita 
Published: December 2022

Letqqbe an odd prime power, denote byFq\mathbb {F}_qthe finite field withqqelements, and setA≔Fq[T]A ≔\mathbb {F}_q[T],F≔Fq(T)F ≔\mathbb {F}_q(T). Letψ:A→F{τ}\psi : A \to F\{\tau \}be a DrinfeldAA-module overFF, of rankr≥2r \geq 2, withEndF¯(ψ)=AEnd_{\overline {F}}(\psi ) = A. For a non-zero idealn\mathfrak {n}ofAA, denote its unique monic generator bynn, denote the degree ofnnas a polynomial inTTbydeg⁡n\deg n, and denote then\mathfrak {n}-division field ofψ\psibyF(ψ[n])F(\psi [\mathfrak {n}]). A reciprocity law forψ\psiasserts that, ifgcd(charF,r)=1\gcd (charF, r) = 1or ifn\mathfrak {n}is prime, then a non-zero prime idealp∤n\mathfrak {p} \nmid \mathfrak {n}ofAAsplits completely inF(ψ[n])F(\psi [\mathfrak {n}])if and only if the Frobenius tracea1,p(ψ)a_{1, \mathfrak {p}}(\psi )ofψ\psiatp\mathfrak {p}and the first componentb1,p(ψ)b_{1, \mathfrak {p}}(\psi )of the Frobenius index ofψ\psiatp\mathfrak {p}satisfy the congruencesa1,p(ψ)≡−r(modn)a_{1, \mathfrak {p}}(\psi ) \equiv -r \pmod nandb1,p(ψ)≡0(modn)b_{1, \mathfrak {p}}(\psi ) \equiv 0 \pmod n. We find the Dirichlet density of the set of non-zero prime idealsp\mathfrak {p}for which the latter congruence never holds, that is, for whichb1,p(ψ)=1b_{1, \mathfrak {p}}(\psi ) = 1. Using similar methods, we prove an asymptotic formula for the function ofxxdefined by the average1#{p: deg⁡p=x}∑p: deg⁡p=xτA(b1,p(ψ))\frac {1}{\#\{\mathfrak {p}: \ \deg p = x \}} \sum _{\mathfrak {p}: \ \deg p = x} \tau _A(b_{1, \mathfrak {p}}(\psi )), wherep=Ap\mathfrak {p} = A pdenotes an arbitrary non-zero prime ideal ofAAwhose monic generatorp∈Ap \in Ahas degreexxand whereτA(b1,p(ψ))\tau _A(b_{1, \mathfrak {p}}(\psi ))denotes the number of monic divisors ofb1,p(ψ)b_{1, \mathfrak {p}}(\psi ).
Asymptotic behavior of 𝐿^{𝑝} estimates for a class of multipliers with homogeneous unimodular symbols
Authors: Bulj Aleksandar , Kovač Vjekoslav 
Published: December 2022

We study Fourier multiplier operators associated with symbols

ξ
↦
exp
⁡
(

i

λ
ϕ
(
ξ

/

|

ξ

|

)
)

\xi \mapsto \exp (\mathbb {i}\lambda \phi (\xi /|\xi |))

, where

λ
\lambda

is a real number and

ϕ
\phi

is a real-valued

C

∞

\mathrm {C}^\infty

function on the standard unit sphere

S

n
−
1

⊂

R

n

\mathbb {S}^{n-1}\subset \mathbb {R}^n

. For

1
>
p
>
∞

1>p>\infty

we investigate asymptotic behavior of norms of these operators on

L

p

(

R

n

)

\mathrm {L}^p(\mathbb {R}^n)

as

|

λ

|

→
∞

|\lambda |\to \infty

. We show that these norms are always

O
(
(

p
∗

−
1
)

|

λ

|

n

|

1

/

p
−
1

/

2

|

)

O((p^\ast -1) |\lambda |^{n|1/p-1/2|})

, where

p
∗

p^\ast

is the larger number between

p
p

and its conjugate exponent. More substantially, we show that this bound is sharp in all even-dimensional Euclidean spaces

R

n

\mathbb {R}^n

. In particular, this gives a negative answer to a question posed by Maz’ya. Concrete operators that fall into the studied class are the multipliers forming the two-dimensional Riesz group, given by the symbols

r
exp
⁡
(

i

φ
)
↦
exp
⁡
(

i

λ
cos
⁡
φ
)

r\exp (\mathbb {i}\varphi ) \mapsto \exp (\mathbb {i}\lambda \cos \varphi )

. We show that their

L

p

\mathrm {L}^p

norms are comparable to

(

p
∗

−
1
)

|

λ

|

2

|

1

/

p
−
1

/

2

|

(p^\ast -1) |\lambda |^{2|1/p-1/2|}

for large

|

λ

|

|\lambda |

, solving affirmatively a problem suggested in the work of Dragičević, Petermichl, and Volberg.
Improving artificial intelligence pipeline for liver malignancy diagnosis using ultrasound images and video frames
Authors: Xu Yiming , Zheng Bowen , Liu Xiaohong , Wu Tao , Ju Jinxiu , Wang Shijie , Lian Yufan , Zhang Hongjun , Liang Tong , Sang Ye , Jiang Rui , Wang Guangyu , Ren Jie , Chen Ting 
Published: December 2022

Abstract
Recent developments of deep learning methods have demonstrated their feasibility in liver malignancy diagnosis using ultrasound (US) images. However, most of these methods require manual selection and annotation of US images by radiologists, which limit their practical application. On the other hand, US videos provide more comprehensive morphological information about liver masses and their relationships with surrounding structures than US images, potentially leading to a more accurate diagnosis. Here, we developed a fully automated artificial intelligence (AI) pipeline to imitate the workflow of radiologists for detecting liver masses and diagnosing liver malignancy. In this pipeline, we designed an automated mass-guided strategy that used segmentation information to direct diagnostic models to focus on liver masses, thus increasing diagnostic accuracy. The diagnostic models based on US videos utilized bi-directional convolutional long short-term memory modules with an attention-boosted module to learn and fuse spatiotemporal information from consecutive video frames. Using a large-scale dataset of 50 063 US images and video frames from 11 468 patients, we developed and tested the AI pipeline and investigated its applications. A dataset of annotated US images is available at https://doi.org/10.5281/zenodo.7272660.
Advances in the field of phage-based therapy with special emphasis on computational resources
Authors: Bajiya Nisha , Dhall Anjali , Aggarwal Suchet , Raghava Gajendra P S 
Published: December 2022

Abstract
In the current era, one of the major challenges is to manage the treatment of drug/antibiotic-resistant strains of bacteria. Phage therapy, a century-old technique, may serve as an alternative to antibiotics in treating bacterial infections caused by drug-resistant strains of bacteria. In this review, a systematic attempt has been made to summarize phage-based therapy in depth. This review has been divided into the following two sections: general information and computer-aided phage therapy (CAPT). In the case of general information, we cover the history of phage therapy, the mechanism of action, the status of phage-based products (approved and clinical trials) and the challenges. This review emphasizes CAPT, where we have covered primary phage-associated resources, phage prediction methods and pipelines. This review covers a wide range of databases and resources, including viral genomes and proteins, phage receptors, host genomes of phages, phage–host interactions and lytic proteins. In the post-genomic era, identifying the most suitable phage for lysing a drug-resistant strain of bacterium is crucial for developing alternate treatments for drug-resistant bacteria and this remains a challenging problem. Thus, we compile all phage-associated prediction methods that include the prediction of phages for a bacterial strain, the host for a phage and the identification of interacting phage–host pairs. Most of these methods have been developed using machine learning and deep learning techniques. This review also discussed recent advances in the field of CAPT, where we briefly describe computational tools available for predicting phage virions, the life cycle of phages and prophage identification. Finally, we describe phage-based therapy’s advantages, challenges and opportunities.
LncReader: identification of dual functional long noncoding RNAs using a multi-head self-attention mechanism
Authors: Liu Tianyuan , Zou Bohao , He Manman , Hu Yongfei , Dou Yiying , Cui Tianyu , Tan Puwen , Li Shaobin , Rao Shuan , Huang Yan , Liu Sixi , Cai Kaican , Wang Dong 
Published: December 2022

Abstract
Long noncoding ribonucleic acids (RNAs; LncRNAs) endowed with both protein-coding and noncoding functions are referred to as ‘dual functional lncRNAs’. Recently, dual functional lncRNAs have been intensively studied and identified as involved in various fundamental cellular processes. However, apart from time-consuming and cell-type-specific experiments, there is virtually no in silico method for predicting the identity of dual functional lncRNAs. Here, we developed a deep-learning model with a multi-head self-attention mechanism, LncReader, to identify dual functional lncRNAs. Our data demonstrated that LncReader showed multiple advantages compared to various classical machine learning methods using benchmark datasets from our previously reported cncRNAdb project. Moreover, to obtain independent in-house datasets for robust testing, mass spectrometry proteomics combined with RNA-seq and Ribo-seq were applied in four leukaemia cell lines, which further confirmed that LncReader achieved the best performance compared to other tools. Therefore, LncReader provides an accurate and practical tool that enables fast dual functional lncRNA identification.
Development and application of an efficient genomic mating method to maximize the production performances of three-way crossbred pigs
Authors: Tang Zhenshuang , Yin Lilin , Yin Dong , Zhang Haohao , Fu Yuhua , Zhou Guangliang , Zhao Yunxiang , Wang Zhiquan , Liu Xiaolei , Li Xinyun , Zhao Shuhong 
Published: December 2022

Abstract
Creating synthetic lines is the standard mating mode for commercial pig production. Traditional mating performance was evaluated through a strictly designed cross-combination test at the ‘breed level’ to maximize the benefits of production. The Duroc–Landrace–Yorkshire (DLY) three-way crossbred production system became the most widely used breeding scheme for pigs. Here, we proposed an ‘individual level’ genomic mating procedure that can be applied to commercial pig production with efficient algorithms for estimating marker effects and for allocating the appropriate boar-sow pairs, which can be freely accessed to public in our developed HIBLUP software at https://www.hiblup.com/tutorials#genomic-mating. A total of 875 Duroc boars, 350 Landrace–Yorkshire sows and 3573 DLY pigs were used to carry out the genomic mating to assess the production benefits theoretically. The results showed that genomic mating significantly improved the performances of progeny across different traits compared with random mating, such as the feed conversion rate, days from 30 to 120 kg and eye muscle area could be improved by −0.12, −4.64 d and 2.65 cm2, respectively, which were consistent with the real experimental validations. Overall, our findings indicated that genomic mating is an effective strategy to improve the performances of progeny by maximizing their total genetic merit with consideration of both additive and dominant effects. Also, a herd of boars from a richer genetic source will increase the effectiveness of genomic mating further.
SimCH: simulation of single-cell RNA sequencing data by modeling cellular heterogeneity at gene expression level
Authors: Sun Lei , Wang Gongming , Zhang Zhihua 
Published: December 2022

AbstractSingle-cell ribonucleic acid (RNA) sequencing (scRNA-seq) has been a powerful technology for transcriptome analysis. However, the systematic validation of diverse computational tools used in scRNA-seq analysis remains challenging. Here, we propose a novel simulation tool, termed as Simulation of Cellular Heterogeneity (SimCH), for the flexible and comprehensive assessment of scRNA-seq computational methods. The Gaussian Copula framework is recruited to retain gene coexpression of experimental data shown to be associated with cellular heterogeneity. The synthetic count matrices generated by suitable SimCH modes closely match experimental data originating from either homogeneous or heterogeneous cell populations and either unique molecular identifier (UMI)-based or non-UMI-based techniques. We demonstrate how SimCH can benchmark several types of computational methods, including cell clustering, discovery of differentially expressed genes, trajectory inference, batch correction and imputation. Moreover, we show how SimCH can be used to conduct power evaluation of cell clustering methods. Given these merits, we believe that SimCH can accelerate single-cell research.
A systematic assessment of deep learning methods for drug response prediction: from in vitro to clinical applications
Authors: Shen Bihan , Feng Fangyoumin , Li Kunshi , Lin Ping , Ma Liangxiao , Li Hong 
Published: December 2022

Abstract
Drug response prediction is an important problem in personalized cancer therapy. Among various newly developed models, significant improvement in prediction performance has been reported using deep learning methods. However, systematic comparisons of deep learning methods, especially of the transferability from preclinical models to clinical cohorts, are currently lacking. To provide a more rigorous assessment, the performance of six representative deep learning methods for drug response prediction using nine evaluation metrics, including the overall prediction accuracy, predictability of each drug, potential associated factors and transferability to clinical cohorts, in multiple application scenarios was benchmarked. Most methods show promising prediction within cell line datasets, and TGSA, with its lower time cost and better performance, is recommended. Although the performance metrics decrease when applying models trained on cell lines to patients, a certain amount of power to distinguish clinical response on some drugs can be maintained using CRDNN and TGSA. With these assessments, we provide a guidance for researchers to choose appropriate methods, as well as insights into future directions for the development of more effective methods in clinical scenarios.
Magnetic sphincter augmentation: considerations for use in Barrett’s esophagus
Authors: Reiter Audra J , Farina Domenico A , Fronza Jeffrey S , Komanduri Srinadh 
Published: December 2022

Abstract
Barrett’s esophagus (BE) occurs in 5–15% of patients with gastroesophageal reflux disease (GERD). While acid suppressive therapy is a critical component of BE management to minimize the risk of progression to esophageal adenocarcinoma, surgical control of mechanical reflux is sometimes necessary. Magnetic sphincter augmentation (MSA) is an increasingly utilized anti-reflux surgical therapy for GERD. While the use of MSA is listed as a precaution by the United States Food and Drug Administration, there are limited data showing effective BE regression with MSA. MSA offers several advantages in BE including effective reflux control, anti-reflux barrier restoration and reduced hiatal hernia recurrence. However, careful patient selection for MSA is necessary.
Two-stage prediction model for postoperative delirium in patients in the intensive care unit after cardiac surgery
Authors: Cai Shining , Cui Hang , Pan Wenyan , Li Jingjing , Lin Xiaolei , Zhang Yuxia 
Published: December 2022

Abstract

OBJECTIVES
Postoperative delirium is a common severe complication in patients in the intensive care unit after cardiac surgery. We developed a two-stage prediction model and quantified the risk of developing postoperative delirium to assist in early prevention before and after surgery.

METHODS
We conducted a prospective cohort study and consecutively recruited adult patients after cardiac surgery. The Confusion Assessment Method for patients in the intensive care unit was used to diagnose delirium 5 days postoperatively. The stage I model was constructed using patient demographics, health conditions and laboratory results obtained preoperatively, whereas the stage II model was built on both pre- and postoperative predictors. The model was validated internally using the bootstrap method and externally using data from an external cohort.

RESULTS
The two-stage model was developed with 654 patients and was externally validated with 214 patients undergoing cardiac surgery. The stage I model contained 6 predictors, whereas the stage II model included 10 predictors. The stage I model had an area under the receiver operating characteristic curve of 0.76 (95% confidence interval: 0.68–0.81), and the stage II model’s area under the receiver operating characteristic curve increased to 0.85 [95% confidence interval (CI): 0.81–0.89]. The external validation resulted in an area under the curve of 0.76 (95% CI: 0.67–0.86) for the stage I model and 0.78 (95% CI: 0.69–0.86) for the stage II model.

CONCLUSIONS
The two-stage model assisted medical staff in identifying patients at high risk for postoperative delirium before and 24 h after cardiac surgery. This model showed good discriminative power and predictive accuracy and can be easily accessed in clinical settings.

Trial registration
The study was registered with the US National Institutes of Health ClinicalTrials.gov (NCT03704324; registered 11 October 2018).
Near-chromosomal de novo assembly of Bengal tiger genome reveals genetic hallmarks of apex predation
Authors: Shukla Harsh , Suryamohan Kushal , Khan Anubhab , Mohan Krishna , Perumal Rajadurai C , Mathew Oommen K , Menon Ramesh , Dixon Mandumpala Davis , Muraleedharan Megha , Kuriakose Boney , Michael Saju , Krishnankutty Sajesh P , Zachariah Arun , Seshagiri Somasekar , Ramakrishnan Uma 
Published: December 2022

Abstract
The tiger, a poster child for conservation, remains an endangered apex predator. Continued survival and recovery will require a comprehensive understanding of genetic diversity and the use of such information for population management. A high-quality tiger genome assembly will be an important tool for conservation genetics, especially for the Indian tiger, the most abundant subspecies in the wild. Here, we present high-quality near-chromosomal genome assemblies of a female and a male wild Indian tiger (Panthera tigris tigris). Our assemblies had a scaffold N50 of &gt;140 Mb, with 19  scaffolds corresponding to the 19 numbered chromosomes, containing 95% of the genome. Our assemblies also enabled detection of longer stretches of runs of homozygosity compared to previous assemblies, which will help improve estimates of genomic inbreeding. Comprehensive genome annotation identified 26,068 protein-coding genes, including several gene families involved in key morphological features such as the teeth, claws, vision, olfaction, taste, and body stripes. We also identified 301 microRNAs, 365 small nucleolar RNAs, 632 transfer RNAs, and other noncoding RNA elements, several of which are predicted to regulate key biological pathways that likely contribute to the tiger's apex predatory traits. We identify signatures of positive selection in the tiger genome that are consistent with the Panthera lineage. Our high-quality genome will enable use of noninvasive samples for comprehensive assessment of genetic diversity, thus supporting effective conservation and management of wild tiger populations.
Principles, personalities, or trade? Explaining Taft’s 1911 prosecution of U.S. Steel
Authors: Brawley Mark R 
Published: December 2022

Abstract
In 1911, the Taft Administration undertook one of the most consequential antitrust prosecutions, charging U.S. Steel with violating the Sherman Act. Some accept Taft’s claim that he was bound to act by legal principles. Others view these prosecutions as a pre-emptive attack on Theodore Roosevelt, sparked by rivalries within the Republican Party. Neither account is persuasive. The Republicans did split, but the resulting cleavage does not align with either of these arguments. These prosecutions sprang from a struggle over trade policy. Taft tried to liberalize trade twice. With his second failure, Taft abandoned those efforts. He joined the protectionist Stand-Pat wing, turning to antitrust policy to defend the protectionists’ interests. The trade-based interpretation resolves several inconsistencies in the traditional narratives, and yields a clearer description of the resulting cleavage. Taft shifted antitrust policy for tactical reasons, not because of legal principles, nor a personal rivalry with Roosevelt.