bluuebunny/crossref_metadata_2025_split huggingface dataset:
Early effect of bivalent human papillomavirus vaccination on cytology outcomes in cervical samples among young women in the Netherlands
Authors: Schurink‐van ’t Klooster Tessa M. , Siebers Albert G. , Hoes Joske , van Kemenade Folkert J. , Berkhof Johannes , Bogaards Johannes A. , de Melker Hester E. 
Published: March 2023

AbstractBackgroundThe first HPV‐vaccine eligible cohorts in the Netherlands will enter the cervical screening program in 2023. However, a substantial number of young women already have had a cervical sample taken before entry into the regular screening program. This study was initiated to explore early effects of HPV vaccination on detection of cytological abnormalities in cervical samples of women younger than the screening age.MethodsResults of cervical samples were obtained from the Dutch National Pathology Databank (PALGA) and were linked to the women's HPV vaccination status from the national vaccination registry (Praeventis) (N = 42,171). Occurrence of low‐grade and high‐grade squamous intraepithelial lesions or worse (LSIL and HSIL+) and high‐risk HPV positive tests (hrHPV) in the first cervical sample were compared between vaccinated and unvaccinated women by multivariable logistic regression analysis, corrected for age at cervical sampling and age of vaccination (12/13 years, ≥ = 14 years).ResultsFor fully vaccinated women (three‐ or two‐dose schedule), statistically significant reductions were seen for all outcomes compared to unvaccinated women (hrHPV: adjusted OR, 0.70, 95% CI, 0.63–0.79; LSIL: 0.70, 0.61–0.80; HSIL+: 0.39, 0.30–0.51).ConclusionsBy linking nation‐wide registries on pathology and vaccination, we show significant beneficial early effects of HPV‐vaccination on LSIL, HSIL+, CIN3/AIS/carcinoma and hrHPV detection in young women upto 24 years of age who have a cervical sample taken before entry into the cervical cancer screening program.
Psychological distress is involved in CRCI in breast cancer survivors via mediating cytokine levels
Authors: Pang Lulian , Li Wen , Yao Senbang , Jing Yanyan , Yin Xiangxiang , Cheng Huaidong 
Published: March 2023

AbstractBackgroundCancer‐related cognitive impairment (CRCI) is a frequent consequence in breast cancer survivors after chemotherapy and lowers their quality of life (QOL). Psychological distress is frequently experienced by breast cancer survivors. There are currently few studies investigating the role of psychological distress in the genesis of CRCI.MethodsIn total, 122 breast cancer survivors after standard chemotherapy within a year were recruited and assessed using the Psychological Distress Thermometer (DT). Sixty breast cancer survivors had non‐psychological distress (NPD group) and sixty‐two breast cancer survivors with psychological distress (PD group). The scores of the Mini‐Mental State Examination (MMSE), prospective and retrospective memory (PM and RM) Questionnaire (PRMQ), and Functional Assessment of Cancer Therapy‐General (FACT‐G) and the levels of cytokines including interleukin‐1 beta (IL‐1β), tumor necrosis factor‐alpha (TNF‐α), and interleukin‐4 (IL‐4) were compared between the two groups. Using PROCESS, we investigated whether psychological distress predicted cognitive function based on MMSE through IL‐1β, TNF‐α, and IL‐4.ResultsThe PD group had higher scores on RM, PM, and FACT‐G and lower scores on MMSE than the NPD group (t = −11.357, t = −10.720, t = −15.419, t = 10.162, respectively; p < 0.05). Meanwhile, a higher level of IL‐1β, TNF‐α, and IL‐4 was observed in the PD group than in the NPD group (t = −3.961, t = −3.396, t = −3.269, respectively; p < 0.05). The link between psychological distress and cognitive function as measured by the MMSE was also mediated by IL‐1β, TNF‐α, and IL‐4 (effect size: 26%, 25%, and 24%).ConclusionBreast cancer patients with psychological distress displayed poor cognitive function, poor memory, and inferior quality of life, which was accompanied by higher cytokine levels of IL‐1β, TNF‐α, and IL‐4. This study demonstrated IL‐1β, TNF‐α, and IL‐4 as potential pathways to CRCI in response to ongoing psychological distress, which provided evidence for the involvement of psychological distress in CRCI in breast cancer survivors.
Chemoproteomic Profiling Reveals that Anticancer Natural Product Dankastatin B Covalently Targets Mitochondrial VDAC3**
Authors: Belcher Bridget P. , Machicao Paulo A. , Tong Binqi , Ho Emily , Friedli Julia , So Brian , Bui Helen , Isobe Yosuke , Maimone Thomas J. , Nomura Daniel K. 
Published: March 2023

AbstractChlorinated gymnastatin and dankastatin alkaloids derived from the fungal strain Gymnascella dankaliensis have been reported to possess significant anticancer activity but their mode of action is unknown. These members possess electrophilic functional groups that can might undergo covalent bond formation with specific proteins to exert their biological activity. To better understand the mechanism of action of this class of natural products, we mapped the proteome‐wide cysteine reactivity of the most potent of these alkaloids, dankastatin B, by using activity‐based protein profiling chemoproteomic approaches. We identified a primary target of dankastatin B in breast cancer cells as cysteine C65 of the voltage‐dependent anion‐selective channel on the outer mitochondrial membrane VDAC3. We demonstrated direct and covalent interaction of dankastatin B with VDAC3. VDAC3 knockdown conferred hypersensitivity to dankastatin B‐mediated antiproliferative effects in breast cancer cells, thus indicating that VDAC3 was at least partially involved in the anticancer effects of this natural product. Our study reveals a potential mode of action of dankastatin B through covalent targeting of VDAC3 and highlights the utility of chemoproteomic approaches in gaining mechanistic understanding of electrophilic natural products.
Rare Earth Metal‐Containing Ionic Liquid Catalysts for Synthesis of Epoxide/Cyclic Anhydride Copolymers
Authors: Manjarrez Yvonne , Clark Allison M. , Fieser Megan E. 
Published: March 2023

AbstractThe perfectly alternating ring‐opening copolymerization of epoxides and cyclic anhydrides has been an emerging route to diverse polyesters. Simple catalysts have been targeted for this polymerization to decrease the cost and air‐sensitivity of the catalysis. This report improves upon the recently reported YCl3 ⋅ 6H2O/[PPN]Cl (bis(triphenylphosphine)iminium chloride) binary catalyst system, which shows fast rates of polymerization but suffers from low molecular weights due to the presence of many water chain transfer agents. In this study, use of a phosphonium chloride ionic liquid as both the cocatalyst and solubilizing agent for the metal salt to form a metal‐containing ionic liquid (MIL) leads to polymerization of high molecular weight polyesters at significantly faster rates than the original system. The identity of the ionic liquid, dryness of MIL, and synthetic prep of the MIL were all found to have a greater impact on the polymerization rate than the metal choice.
Influence of harvest aid on soybean seed quality affected by delayed harvest and environment in Louisiana
Authors: Campos Priscila , Miller Donnie , Copes Josh , Netterville Melanie , Brown Sebe , Price Trey , Moseley David , Gentimis Thanos , Egbedi Peters , Parvej Rasel 
Published: March 2023

AbstractHarvest aid application can expedite soybean (Glycine max) harvest and increase efficiency through both weed and crop desiccation and has become a common practice in Louisiana soybean production systems. Field studies in 2019–2020 evaluated the influence of harvest aid application (none, paraquat at 0.28 kg/ha, sodium chlorate at 6.72 kg/ha, or saflufenacil at 0.0498 kg/ha) on seed quality affected by delayed harvest (∼20, 30, or 44 days after optimum harvest timing, i.e., 13% seed moisture). Environment was investigated in both field (natural rainfall events) and environmentally controlled growth chambers (79 or 90°F with 30% or 100% relative humidity and exposed for 24, 48, 72, 96, or 144 h) for potential impacts of prolonged rainfall conditions on soybean seed quality at harvest. Seed quality was based on a rating scale of 1 to 10 where 1 means seed in good condition and 10 means seed in poor condition based on USDA reference images. Harvest aid application had no effect on soybean seed quality affected by delayed harvest and saturated (100% relative humidity) environment. Delaying harvest beyond approximately 20 days in the field past optimum harvest timing can result in reduced seed quality regardless of whether harvest aid application occurred (0.52–2 vs 4.18–5.91 rating). In addition, seedpod exposure to high relative humidity conditions (100%) for as little as 96 h after optimum harvest timing can result in severe seed quality issues (3.96 or greater rating) regardless of whether harvest aid was used.
Implementation and evaluation of a remote geriatric assessment and intervention program in Brazil
Authors: Bergerot Cristiane Decat , Bergerot Paulo Gustavo , Razavi Marianne , Philip Errol J. , Lakhdari Sabri , França Marcos Vinicius da Silva , Molina Lorena Nascimento Manrique , Freitas Alici Natalia de Sousa , Taveira Mariane Cunha , de Azeredo Andressa Cardoso , Fuzita William Hiromi , Fernandes Cristiano Menezes , Pio Raquel Batista , de Araujo Romildo , Couto Milena Macedo , de Vasconcellos Vitor Fiorin , Nonino Maria Fernanda , Lee David , de Matos Neto João Nunes , Buso Marco Murilo , Soto‐Perez‐de‐Celis Enrique , Dale William 
Published: March 2023

AbstractBackgroundThis study sought to determine the feasibility and acceptability of a remote geriatric assessment (GA) and implementation (GAIN) program in Brazil. The authors also explored the effect of this program on health‐related quality of life (HR‐QOL) outcomes 3 months after initiating treatment.MethodsThis is a longitudinal study enrolling older adults (65+ years), diagnosed with any type of solid tumor, scheduled to initiate chemotherapy in a networked Brazilian cancer center. The GA was performed through telehealth. We assessed the feasibility of the remote GA, acceptability to patients, and changes in patient‐centered outcomes (HR‐QOL, mood, function) from baseline to month 3. Linear mixed model analysis was done, adjusting for age, gender, race, income, and disease stage.ResultsFifty‐six patients completed all intended assessments. Notably, the threshold of feasibility was 70% and there was 92% complete adherence. Average age was 76 years old (SD = 7.2). Most patients were female (57%), married (59%), and had a college degree (46%). The most common diagnoses were gastrointestinal (39%) and gynecological cancers (18%); most were diagnosed at an advance disease stage (77%). A total of 32 patients were referred to a remote appointment and 86% followed this recommendation(s). Significant improvement in Functional Assessment of Cancer Therapy ‐ General FACT‐G (mean difference, 6.04; p < .001), Geriatric Depression Scale (mean difference, −0.86; p = .008), and instrumental activities of daily living ratio (mean difference, 0.17; p < .001) were found.ConclusionRemote GAIN is feasible and acceptable to older adults with cancer receiving treatment in Brazil. The authors also found significant improvement in HR‐QOL outcomes over time. Notably, this GAIN program could guide early detection of chemotherapy toxicity and improving patient‐reported outcomes in low‐resource environments.
On the questionable utility of color space for understanding perception
Authors: Fairchild Mark D. 
Published: March 2023

AbstractThe concept of color space is ubiquitous in color science and technology and is applied to a wide variety of stimulus, signal, and perceptual dimensions. This Color Forum paper aims to discuss the concept of representing color in a multidimensional geometric space and whether such a concept is useful in helping us understand human color perception. The conclusion is that the concept of color space might not be of utility, but the hope is that this paper prompts further thoughts and review by others and motivates additional Color Forum papers that either provide examples of the utility of the color space concept or add further support to the idea that it might be time to look at color differently.
Shuffled shepherd political optimization‐based deep learning method for credit card fraud detection
Authors: Ganji Venkata Ratnam , Chaparala Aparna , Sajja Radhika 
Published: March 2023

SummaryDigital transactions based on credit cards are gradually increasing concept due to expediency. The amount of fraudulent transactions has intensely enlarged in modern days, because of the fast development of e‐services, namely e‐finance, mobile payments, and e‐commerce as well as the promotion of credit cards. Criminal fraud behaviors and user's payment behaviors are frequently varying, thus performance improvement of the fraud identification method and its stability are more challenging processes. The Shuffled Shepherd Political Optimization‐based Deep Residual network (SSPO‐based DRN) scheme is established for credit card fraud identification in this research. The SSPO is developed by merging the Political Optimization (PO) and Shuffled Shepherd Optimization Algorithm (SSOA). The quantile normalization model is an effective preprocessing technique, which normalizes the data for effective detection. Moreover, fisher score and class information gain effectively select the required features. Data augmentation is employed for increasing the data size, thereby the detection performance is improved. The Deep Residual Network (DRN) is employed for credit card fraud recognition, which is trained by devised SSPO algorithm. The SSPO‐based DRN approach achieved enhanced performance with testing sensitivity of 0.9279, specificity of 0.9023, and accuracy of 0.9120.
Alzheimer's disease classification using competitive swarm multi‐verse optimizer‐based deep neuro‐fuzzy network
Authors: Allada Apparna , Bhavani Rajaram , Chaduvula Kavitha , Priya Rajaram 
Published: March 2023

SummaryClassification of Alzheimer's disease (AD) from neuroimaging, like magnetic resonance imaging (MRI) through deep learning classifier has been increasing in research in recent decades. However, it is required for enhancing the accuracy of the AD classification for effective treatment. In this work, an efficient model termed competitive swarm multi‐verse optimizer + deep neuro‐fuzzy network (CSMVO + DNFN) is designed to accurately classify stages of AD. Preprocessing is done with a median filter. Then, the resulting image is segmented to find the interested regions by channel‐wise feature pyramid network module (CFPNet‐M). Some features obtained from the segmented image are haralick, convolutional neural network, and texture features. The devised method is more efficient in classifying different stages of AD with MRI modality. Furthermore, the developed model attained higher performance using metrics like the accuracy of 89.9%, sensitivity of 89.6%, and specificity of 87.0% based on the k‐fold value.
A generic resource augmentation architecture for efficient mobile communication
Authors: Anitha S. , Padma T. , Vallimayil V. 
Published: March 2023

SummaryMobile devices play a vital role in people's day‐to‐day activities and are essential for communication, accessing Internet resources, entertainment, and so forth. In most cases, the performance of mobile applications is restricted due to the inadequate resources of mobile specifications such as controlled battery power, predetermined storage, and limited processing competence. Thus to improve the processing efficiency of resource‐constrained mobile devices, this research proposes a model developed on offloading technique based on the recent literature observations which transfer the resource‐consuming tasks from mobile devices to proximate computing entities. This generic architecture aiming for resource augmentation is formulated with five fundamental components including a mobile device, offloading engine, resource augmentation engine, scheduler, and synchronizer. The offloading engine identifies the resource‐intensive tasks that need to be executed on external entities using soft computing techniques such as neural network training algorithms, fuzzy logic, and neuro‐fuzzy logic processing. The augmentation engine chooses feasible edge computing entities such as Arduino, Raspberry PI controller, and fog devices. The scheduler prioritizes the resource‐consuming tasks for offloading based on their importance. The synchronizer coordinates all the central components by updating the execution status of tasks at regular intervals of time. Further extension of this research work, based on the proposed architecture three resource augmentation frameworks are developed namely (a) an adaptive proximate computing framework (b) a web service‐based IoT framework, and (c) a neuro‐fuzzy hybrid framework. Moreover, these frameworks are realized through the development of resource‐efficient intelligent edge computing IoT mobile apps namely (a) an inventory monitoring app (b) a lecture recording app, and (c) an intelligent transport app. The performance of these three resource augmentation frameworks is analyzed through the simulation results and it illustrates that the neuro‐fuzzy hybrid framework outperforms the other two. The outcome of the proposed research contributions improves the effectiveness of mobile communication by providing seamless services to mobile users regardless of their resource‐limited devices. Besides the devised edge computing architecture supports mobile application developers in the design and deployment of resource‐rich mobile applications in low‐specification mobile devices. Also, there is a strong scope for implementing the proposed generic architecture as an industrial smart product with adequate hardware and software configurations.
Light interception and radiation use efficiency in quinoa (Chenopodium quinoa Willd.)
Authors: Hosseini Seyed Hosssein , Rahemi Karizaki Ali , Nakhzari Moghadam Ali , Biabani Abbas , Taliei Fakhtak 
Published: March 2023

AbstractQuinoa (Chenopodium quinoa Willd.) is an annual plant belonging to the Amaranthaceae family. The total accumulated incident photosynthetic active radiation (PAR) in a plant is a function of the site, year, sowing date, and phenology, whereas the amount of PAR intercepted by the plant is dependent on its leaf area index (LAI) and light extinction coefficient (KPAR). This study investigated changes in KPAR and radiation use efficiency (RUE) levels in quinoa before and after inflorescence (fruit set) color change under various temperature and light conditions through the sowing date. This study was conducted in the Jihad Keshavarzi allocated field of Khalilabad city in Khorasan Razavi province, Iran, using a randomized complete block design with three replications over two seasons, 2017–2018 and 2018–2019. Treatments included eight and seven sowing dates in the first and second season years, respectively. The results showed that the mean of KPAR was 0.66 and 0.31 in the first and second years, respectively. Also RUE in vegetative stages of development was higher than during the grain‐filling period. The mean of RUE was 2.60 ± 0.06 and 0.78 ± 0.17g/MJ in the first year before and after changing the color of the inflorescence (fruit set), respectively; these values were 2.12 ± 0.04 and 1.01 ± 0.61 g/MJ in the second year. The results indicated the base (TBRUE), lower optimum (TP1RUE), upper optimum, and ceiling temperatures of quinoa for RUE were determined to be 3, 18.8, 25.5, and 35°C, respectively.
Bringing intelligence to Edge/Fog in Internet of Things‐based healthcare applications: Machine learning/deep learning‐based use cases
Authors: Makina Hela , Ben Letaifa Asma 
Published: March 2023

SummaryInternet of things and smart medical applications are deeply changing the way healthcare is delivered worldwide. A typical Internet of Things (IoT)‐based eHealth system includes medical sensors for data collection, access network to transmit data, and Cloud servers for data processing and storage. Machine learning/deep learning (ML/DL) have proven to be a powerful tool for data classification, disease prognosis, and diagnosis and even medication prescription. ML/DL models need a large amount of data and significant computational and storage capacities especially for the training phase. Deploying ML/DL algorithms in the Cloud can be effectively done due to the processing and storage power of Cloud data centers. However, it raises many issues related to the availability, latency, energy consumption, bandwidth, security, and privacy. Recently, there is a growing interest to run as much processing as possible nearer the data sources, in the Edge, to compensate the Cloud‐based solutions limitations. In this paper, we propose to investigate the benefits of using IoT, ML/DL, and Edge Computing to enhance healthcare applications. Then, we are going to review the main approaches and trends for executing ML/DL in the Edge, to give their benefits and limitations, and draw finally conclusions about existing research issues.
Association of thyroid stimulating hormone and time in range with risk of diabetic retinopathy in euthyroid type 2 diabetes
Authors: Wang Yaxin , Lu Jingyi , Ni Jiaying , Wang Ming , Shen Yun , Lu Wei , Zhu Wei , Bao Yuqian , Zhou Jian 
Published: March 2023

AbstractAimsDiabetic retinopathy (DR) can occur even in well‐controlled type 2 diabetes, suggesting residual risks of DR in this population. In particular, we investigated the combined effect of thyroid function and glycaemic control assessed by an emerging metric, time in range (TIR) with DR.Materials and MethodsIn this cross‐sectional study, a total of 2740 euthyroid patients with type 2 diabetes were included. Thyroid indicators, including thyroid‐stimulating hormone (TSH), free triiodothyronine, free thyroxine, thyroid peroxidase antibody and thyroglobulin antibody, were measured. TIR was measured using continuous glucose monitoring data.ResultsOverall, the multivariable‐adjusted odds ratios (ORs) for DR across ascending tertiles of TSH were 1.00 (reference), 1.06 (95% confidence interval [CI] 0.85–1.32), and 1.48 (95% CI 1.19–1.85). Even in well‐controlled participants who achieved a TIR target of >70% (n = 1449), the prevalence of DR was 23.8%, which was significantly related to TSH (OR = 1.54, 95% CI 1.12–2.12, highest vs. lowest TSH tertile). Participants were then classified into 6 groups by the joint categories of TIR (>70%, ≤70%) and TSH (tertiles), and the multivariable‐adjusted ORs for DR were highest in TIR ≤70% and the highest TSH tertile group (OR = 1.96, 95% CI 1.41–2.71) when compared with the TIR >70% and the lowest TSH tertile group.ConclusionsIn type 2 diabetic patients with well‐controlled glycaemic status, higher TSH within the normal range was associated with an increased risk of DR. The combination of suboptimal TSH and TIR further increased the risk of DR.