bluuebunny/crossref_metadata_2025_split huggingface dataset:
Safety evaluation of fixed‐dose nivolumab in patients with gastric cancer
Authors: Iikura Yusuke , Aoyama Takeshi , Hiraide Makoto , Wakatsuki Takeru , Nakayama Izuma , Ogura Mariko , Ooki Akira , Takahari Daisuke , Chin Keisho , Yamaguchi Kensei , Hama Toshihiro 
Published: June 2022

AbstractBackground and AimsThis study aimed to examine the safety of fixed‐dose nivolumab.MethodsWe retrospectively reviewed the medical records of 113 Japanese patients with gastric cancer who were previously treated with cytotoxic chemotherapy and initiated nivolumab. The endpoints were the incidence of Grade 2 or higher immune‐related adverse events (irAEs) in the conventional dose (3 mg/kg) and fixed‐dose groups (240 mg).ResultsThe incidence rates of irAEs in the conventional‐dose and fixed‐dose groups were 29.9% and 19.4%, respectively, and the rates of Grade 2 or higher irAEs were 23.3% and 19.4%, respectively, with no significant difference between the two groups, suggesting that nivolumab at 240 mg is as safe as the 3 mg/kg dose.ConclusionThis is the first report on the safety of nivolumab at 240 mg in Japanese patients.
The oral microbiome and breast cancer and nonmalignant breast disease, and its relationship with the fecal microbiome in the Ghana Breast Health Study
Authors: Wu Zeni , Byrd Doratha A. , Wan Yunhu , Ansong Daniel , Clegg‐Lamptey Joe‐Nat , Wiafe‐Addai Beatrice , Edusei Lawrence , Adjei Ernest , Titiloye Nicholas , Dedey Florence , Aitpillah Francis , Oppong Joseph , Vanderpuye Verna , Osei‐Bonsu Ernest , Dagnall Casey L. , Jones Kristine , Hutchinson Amy , Hicks Belynda D. , Ahearn Thomas U. , Shi Jianxin , Knight Rob , Biritwum Richard , Yarney Joel , Wiafe Seth , Awuah Baffour , Nyarko Kofi , Figueroa Jonine D. , Sinha Rashmi , Garcia‐Closas Montserrat , Brinton Louise A. , Vogtmann Emily 
Published: June 2022

AbstractThe oral microbiome, like the fecal microbiome, may be related to breast cancer risk. Therefore, we investigated whether the oral microbiome was associated with breast cancer and nonmalignant breast disease, and its relationship with the fecal microbiome in a case‐control study in Ghana. A total of 881 women were included (369 breast cancers, 93 nonmalignant cases and 419 population‐based controls). The V4 region of the 16S rRNA gene was sequenced from oral and fecal samples. Alpha‐diversity (observed amplicon sequence variants [ASVs], Shannon index and Faith's Phylogenetic Diversity) and beta‐diversity (Bray‐Curtis, Jaccard and weighted and unweighted UniFrac) metrics were computed. MiRKAT and logistic regression models were used to investigate the case‐control associations. Oral sample alpha‐diversity was inversely associated with breast cancer and nonmalignant breast disease with odds ratios (95% CIs) per every 10 observed ASVs of 0.86 (0.83‐0.89) and 0.79 (0.73‐0.85), respectively, compared to controls. Beta‐diversity was also associated with breast cancer and nonmalignant breast disease compared to controls (P ≤ .001). The relative abundances of Porphyromonas and Fusobacterium were lower for breast cancer cases compared to controls. Alpha‐diversity and presence/relative abundance of specific genera from the oral and fecal microbiome were strongly correlated among breast cancer cases, but weakly correlated among controls. Particularly, the relative abundance of oral Porphyromonas was strongly, inversely correlated with fecal Bacteroides among breast cancer cases (r = −.37, P ≤ .001). Many oral microbial metrics were strongly associated with breast cancer and nonmalignant breast disease, and strongly correlated with fecal microbiome among breast cancer cases, but not controls.
Shared environment and colorectal cancer: A Nordic pedigree registry‐based cohort study
Authors: Elmahdi Rahma , Wennerström Elna C. M. , Andersson Mikael , Wohlfahrt Jan , Melbye Mads , Pukkala Eero , Hortlund Maria , Silander Kaisa , Sutinen Kyösti , Jess Tine , Dillner Joakim 
Published: June 2022

AbstractRisk of colorectal cancer (CRC) increases in relatives of patients with CRC. The extent to which this is attributable to genetic predisposition or shared environment is unclear. We explored this question using nationwide cohorts from Denmark, Finland and Sweden. From 1977 to 2013, we identified 359 879 individuals with a CRC diagnosis and 2 258 870 of their relatives who we followed for CRC incidence. We calculated standardized incidence ratios (SIR) and 95% confidence intervals (CI) for CRC in individuals with an affected relative. We used nationwide household and pedigree data along with national SIR estimates to calculate risk ratios (RR) for the contribution of shared household environment, childhood environment and genetic relationship to CRC risk in those with an affected relative. SIR of CRC was increased for individuals with an affected relative, across all countries and ages. For those with an affected parent, the SIR was 1.65 (95% CI: 1.61‐1.69), 1.98 (95% CI: 1.87‐2.09), for those with an affected sibling and 2.14 (95% CI: 1.84‐2.49) for those with an affected halfsibling. In those <65 years old, shared childhood (RR: 1.41, 95% CI: 1.26‐1.57) and household (RR: 2.08, 95% CI: 1.25‐3.46) environments were significantly greater contributors to familial risk of CRC than genetics (RR: 0.88, 95% CI: 0.53‐1.46). This large‐scale Nordic population‐based study of excess risk of CRC among relatives of those with CRC addresses the difficult disentangling of shared environment from genetic predisposition in the heritability of CRC. We found shared environment to be the most important contributor to CRC risk.
Encoder–decoder neural networks for predicting future FTIR spectra – application to enzymatic protein hydrolysis
Authors: Kuchta Miroslav , Wubshet Sileshi Gizachew , Afseth Nils Kristian , Mardal Kent‐André , Liland Kristian Hovde 
Published: June 2022

AbstractIn the process of converting food‐processing by‐products to value‐added ingredients, fine grained control of the raw materials, enzymes and process conditions ensures the best possible yield and economic return. However, when raw material batches lack good characterization and contain high batch variation, online or at‐line monitoring of the enzymatic reactions would be beneficial. We investigate the potential of deep neural networks in predicting the future state of enzymatic hydrolysis as described by Fourier‐transform infrared spectra of the hydrolysates. Combined with predictions of average molecular weight, this provides a flexible and transparent tool for process monitoring and control, enabling proactive adaption of process parameters.
Inside Cover
Authors: Luo Hongbo , Li Shuying , Zeng Yifeng , Cheema Hassam , Otegbeye Ebunoluwa , Ahmed Safee , Chapman William C. , Mutch Matthew , Zhou Chao , Zhu Quing 
Published: June 2022

We describe a miniaturized optical coherence tomography (OCT) catheter and a residual neural network (ResNet)‐based deep learning model to perform human colorectal tissue classification. An area under the receiver operating characteristic (ROC) curve (AUC) of 0.975 is achieved to distinguish between normal and cancerous colorectal tissue images.Further details can be found in the article by Hongbo Luo, Shuying Li, Yifeng Zeng, Hassam Cheema, Ebunoluwa Otegbeye, Safee Ahmed, William C. Chapman Jr, Matthew Mutch, Chao Zhou, and Quing Zhu (e202100349)image
Exploration of plant growth‐promoting endophytic bacteria from Pisum sativum and Cicer arietinum from South–West Haryana
Authors: Maheshwari Rajat , Kumar Pradeep , Bhutani Namita , Suneja Pooja 
Published: June 2022

AbstractIn the present study, nonrhizobial endophytes were isolated from Pisum sativum and Cicer arietinum from Haryana, India. A total of 355 bacterial endophytes were screened for plant growth promoting traits. Out of all, 96 bacterial endophytes were selected based on morphological characters and multi‐PGP traits, and their diversity analyzed by amplified ribosomal DNA restriction analysis. Based on their ARDRA profile, the 25 representative isolates (12 from P. sativum and 13 from C. arietinum), were selected and identified by 16S ribosomal DNA sequencing. Genetic relatedness based on BLAST analysis revealed the similarity of these isolates with members of three prominent phyla, that is, Proteobacteria, Firmicutes, and Actinobacteria. The dominant cluster, Firmicutes, constituted 60% of the isolates, assigned to four different genera, Bacillus, Staphylococcus, Ornithinibacillus, and Lysinibacillus. Phylum α‐proteobacteria included two genera, namely Paenochrobactrum and Ochrobactrum and three genera in phylum γ‐proteobacteria, namely Pseudomonas, Pantoea and Proteus. The phylum Actinobacteria was constituted of two genera, Microbacterium and Arthrobacter. Bacillus zhangzhouensis, Bacillus safensis, Arthrobacter enclensis from P. sativum and Bacillus haynesii, Paenochrobactrum sp. from C. arietinum are documented as plant growth promoting endophytic bacteria for the first time in the present study. The in vitro and in vivo assessment based on bonitur score revealed that the endophytic isolates Bacillus mojavensis PRN2, Pseudomonas chlororaphis PHN9, B. safensis PRER2, Pseudomonas sp. RCP1, Pseudomonas lini PRN1 and B. haynensii RCP3 from P. sativum and C. arietinum significantly enhanced the plant growth parameters. Therefore, these potential isolates can be further harnessed for preparation of bioformulations to enhance sustainable agriculture.
Bacteriostatic effects of phage F23s1 and its endolysin on Vibrio parahaemolyticus
Authors: Xia Hai , Yang Houji , Yan Na , Hou Wenfu , Wang Huajuan , Wang Xiaohong , Wang Hongxun , Zhou Min 
Published: June 2022

AbstractVibrio parahaemolyticus is a common foodborne pathogenic bacterium and drug‐resistant strains are now widespread. Phages led by drug‐resistant V. parahaemolyticus strains are promising means to decrease the pressure on public health. We isolated a V. parahaemolyticus‐specific bacteriophage F23s1 that was active at wide ranges of temperature (30–60°C) and pH (4–10). Phage F23s1 exhibited a specific host range; in that, only 13 of the 23 V. parahaemolyticus strains were lysed. F23s1 effectively inhibited the growth of V. parahaemolyticus strain F23 in shrimp at 25°C within 12 h at a multiplicity of infection of 1000. We sequenced the genome of phage F23s1 which comprised a 76,648‐bp DNA with 105 open reading frames (ORFs) and identified an endolysin gene ORF52 that was then cloned and successfully expressed in Escherichia coli. The recombinant ORF52 protein significantly decreased OD600 nm of V. parahaemolyticus F23 from 0.978 to 0.249 when used at 20 µmol/L within 60 min. The endolysin also showed lytic activity against a panel of 23 drug‐resistant V. parahaemolyticus and 12 Salmonella strains with a higher lytic ability for V. parahaemolyticus. The phage F23s1 and its endolysin will be useful for preventing and controlling V. parahaemolyticus in food safety.
High‐speed identification system for fresh tea leaves based on phenotypic characteristics utilizing an improved genetic algorithm
Authors: Gan Ning , Sun Mufang , Lu Chengye , Li Menghui , Wang Yujie , Song Yan , Ning Jing‐Ming , Zhang Zheng‐Zhu 
Published: June 2022

AbstractBackgroundHigh‐quality tea requires leaves of similar size and tenderness. The grade of the fresh leaves determines the quality of the tea. The automated classification of fresh tea leaves improves resource utilization and reduces manual picking costs. The present study proposes a method based on an improved genetic algorithm for identifying fresh tea leaves in high‐speed parabolic motion using the phenotypic characteristics of the leaves. During parabolic flight, light is transmitted through the tea leaves, and six types of fresh tea leaves can be quickly identified by a camera.ResultsThe influence of combinations of morphology, color, and custom corner‐point morphological features on the classification results were investigated, and the necessary dimensionality of the model was tested. After feature selection and combination, the classification performance of the Naive Bayes, k‐nearest neighbor, and support vector machine algorithms were compared. The recognition time of Naive Bayes was the shortest, whereas the accuracy of support vector machine had the best classification accuracy at approximately 97%. The support vector machine algorithm with only three feature dimensions (equivalent diameter, circularity, and skeleton endpoints) can meet production requirements with an accuracy rate reaching 92.5%. The proposed algorithm was tested by using the Swedish leaf and Flavia data sets, on which it achieved accuracies of 99.57% and 99.44%, respectively, demonstrating the flexibility and efficiency of the recognition scheme detailed in the present study.ConclusionThis research provides an efficient tea leaves recognition system that can be applied to production lines to reduce manual picking costs. © 2022 Society of Chemical Industry.
Impact of agriculture intensification on forest degradation and tree carbon stock; promoting multi‐criteria optimization for restoration in Central India
Authors: Shukla Jayshree , Dhyani Shalini , Pujari Paras , Mishra Ashutosh , Verma Parikshit 
Published: June 2022

AbstractForest conservation entails both halting destruction and protecting the state of the forest's vegetation. Monitoring forest cover and restoring degraded forests are critical ecological aspects for India's forestry sector's long‐term growth. Cropland expansion and intensification are the primary approaches for increasing agricultural productivity in response to increased biomass demand, but they are also major drivers of biodiversity loss. We evaluated the ecosystem carbon (C) stock and sequestration potential for dry deciduous forests while assessing the drivers of forest degradation in Central India to advance scientific knowledge and minimize the anthropogenic C emissions from prevalent agriculture intensification in the region. A multi‐criteria optimization approach was used to identify the priority areas forrestoration and quantify the magnitude of drivers of biodiversity loss in the region by investigating the links between forest carbon stock, agriculture intensification, and altering forest structure at various degrees of forest disturbance. The total existing carbon stock of trees in study area during March 2020, was 199.12 Mg C ha−1. Overall carbon stock of these forests showed a significant correlation with disturbance levels (r = 0.50). Changes in vegetation especially to intensive agriculture practices have resulted in loss of carbon stocks and will cause large‐scale forest decline if not addressed immediately. Considering the rapid loss of forests in situation where India has to fulfill Nationally Determined Contributions to UNFCCC, Forest Landscape Restoration becomes priority and needs specialized efforts with enhanced funding to reduce and halt loss of forests.
Toward advancing theory on creativity in marketing and artificial intelligence
Authors: Ameen Nisreen , Sharma Gagan Deep , Tarba Shlomo , Rao Amar , Chopra Ritika 
Published: June 2022

AbstractCreativity has been identified as the future of marketing; at the same time, artificial intelligence (AI) is enabling more automation in this field. The theories and frameworks in the literature have not yet sufficiently explained the impact of AI on creativity in marketing. Hence, this research aims to advance theories on creativity in marketing and AI by conducting a comprehensive review of the literature. Our review covers 156 papers published between 1990 and 2021, compiled on the basis of the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR‐4‐SLR) protocol and the theory–context–characteristics–methodology framework. We advance theory in the field by proposing a new organizing framework to guide the integration of the identified themes and synthesize the antecedents, dimensions, and outcomes of creativity in marketing and AI. Furthermore, to assist academics and practitioners we propose a typology of the key skills required for creativity in marketing and the impact of specific AI capabilities on these skills. This article serves as a foundation for researchers by providing a holistic understanding of the integration of AI into creativity in marketing.
Variable diameter T‐shaped cell‐based photoacoustic sensor for trace gas detection
Authors: Zha Shenlong , Li Lingli , Zhang Qilei , Ma Hongliang , Zhan Shengbao , Li Yuanyuan , Zha Changli 
Published: June 2022

AbstractA compact photoacoustic gas sensor based on variable diameter T‐shaped cell for trace gas detection was developed. The acoustic pressure distribution in the variable diameter T‐shaped cell and conventional T‐shaped photoacoustic cell with the same size were simulated by COMSOL Multiphysics software, which indicated that at the first resonance frequency, the acoustic pressure at the end of the resonance tube of the designed photoacoustic cell is significantly stronger than that at the same position of the conventional T‐shaped cell. The performance of the sensor based on the variable diameter T‐shaped and conventional one were evaluated by detection of water vapor in atmospheric environment, the comparison shows that the signal‐to‐noise ratio of the variable diameter T‐shaped cell is about 1.6 times greater than that of the conventional one and a detection limit of 6.4 ppmv for water vapor detection was obtained.
A compact dual‐band coupled‐microstrip‐slotline coupler with arbitrary power division ratios
Authors: Zhao Xin , Zhu Fang 
Published: June 2022

AbstractIn this paper, a novel dual‐band coupler with arbitrary power division ratios using coupled‐microstrip‐slotlines (CMSLs) is proposed. It consists of only four CMSL sections and occupies a small circuit area due to the centric arrangement of the excitation ports. Different from the microstrip implemented edge‐coupled‐line, the odd and even modes of the CMSL are physically separated, this feature enables the compensation of their phase velocities, facilitates the control of their characteristic impedances, and reduces the design complexity of the dual‐band coupler. Explicit design formula for the proposed coupler is developed. To verify the proposed concept, a dual‐band CMSL coupler with an unequal power division ratio is designed and characterized.
Improved signal‐to‐noise performance of MultiNet GRAPPA 1H FID MRSI reconstruction with semi‐synthetic calibration data
Authors: Chan Kimberly L. , Ziegs Theresia , Henning Anke 
Published: June 2022

PurposeTo further develop MultiNet GRAPPA, a neural‐network‐based reconstruction, for lower SNR proton MRSI (1H MRSI) data using adapted undersampling schemes and improved training sets.Methods1H FID‐MRSI data and an anatomical image for GRAPPA reconstruction were acquired in two slices in the human brain (n = 6) at 7T. MRSI data were retrospectively undersampled for a 4×, 6×, and 7× acceleration rate. Signal‐to‐noise, relative error (RE) between accelerated and fully sampled metabolic maps, RMS of the lipid artifacts, and fitting reliability were compared across acceleration rates, to the fully sampled data, and with different kinds and amounts of training images.ResultsTraining with semi‐synthetic images resulted in higher SNR and lower lipid RMS relative to training with acquired images from one or several subjects. SNR increased with the number of semi‐synthetic training images and the 4× accelerated data retains ∼30% more SNR than other accelerated data. Spectra reconstructed with 20 semi‐synthetic averages retained ∼100% more SNR and had ∼5% lower lipid RMS than those reconstructed with the center k‐space points of one image as was originally proposed for very high SNR MRSI data and had higher fitting reliability. The metabolite RE was lowest when training with 20‐semi‐synthetic training images and highest when training with the center k‐space points of one image.ConclusionMultiNet GRAPPA is feasible with lower SNR 1H MRSI data if 20‐semi‐synthetic training images are used at a 4× acceleration rate. This acceleration rate provided the best trade‐off between scan time and spectral SNR.
Metabolic activity diffusion imaging (MADI): I. Metabolic, cytometric modeling and simulations
Authors: Springer Charles S. , Baker Eric M. , Li Xin , Moloney Brendan , Wilson Gregory J. , Pike Martin M. , Barbara Thomas M. , Rooney William D. , Maki Jeffrey H. 
Published: June 2022

Evidence mounts that the steady‐state cellular water efflux (unidirectional) first‐order rate constant (kio [s−1]) magnitude reflects the ongoing, cellular metabolic rate of the cytolemmal Na+, K+‐ATPase (NKA), cMRNKA (pmol [ATP consumed by NKA]/s/cell), perhaps biology's most vital enzyme. Optimal 1H2O MR kio determinations require paramagnetic contrast agents (CAs) in model systems. However, results suggest that the homeostatic metabolic kio biomarker magnitude in vivo is often too large to be reached with allowable or possible CA living tissue distributions. Thus, we seek a noninvasive (CA‐free) method to determine kio in vivo. Because membrane water permeability has long been considered important in tissue water diffusion, we turn to the well‐known diffusion‐weighted MRI (DWI) modality. To analyze the diffusion tensor magnitude, we use a parsimoniously primitive model featuring Monte Carlo simulations of water diffusion in virtual ensembles comprising water‐filled and ‐immersed randomly sized/shaped contracted Voronoi cells. We find this requires two additional, cytometric properties: the mean cell volume (V [pL]) and the cell number density (ρ [cells/μL]), important biomarkers in their own right. We call this approach metabolic activity diffusion imaging (MADI).We simulate water molecule displacements and transverse MR signal decays covering the entirety of b‐space from pure water (ρ = V = 0; kio undefined; diffusion coefficient, D0) to zero diffusion. The MADI model confirms that, in compartmented spaces with semipermeable boundaries, diffusion cannot be described as Gaussian: the nanoscopic D (Dn) is diffusion time‐dependent, a manifestation of the “diffusion dispersion”. When the “well‐mixed” (steady‐state) condition is reached, diffusion becomes limited, mainly by the probabilities of (1) encountering (ρ, V), and (2) permeating (kio) cytoplasmic membranes, and less so by Dn magnitudes. Importantly, for spaces with large area/volume (A/V; claustrophobia) ratios, this can happen in less than a millisecond. The model matches literature experimental data well, with implications for DWI interpretations.