bluuebunny/crossref_metadata_2025 huggingface dataset:
Gene LY96 is an M2 macrophage‐related biomarker and is associated with immunosuppression in renal cell carcinoma
Authors: Li Weiquan , Meng Xiangui , Yu Tiexi , Shi Xin , Dong Wei , Ruan Hailong , Wang Tao , Xiao Wen
Published: September 2023

Lymphocyte Antigen 96 (LY96) was identified as an oncogene in several tumors. However, its role in renal cancer has not been explored. In the study, LY96 is identified abnormally expressed using several public kidney cancer sequencing data. The expression level of LY96 is validated using paired clinical samples. Survival analyses and ROC curves are used to examine its prognostic and diagnostic value. Gene sets enrichment analyses (GSEA) show LY96 might influence immune processes. Then immune infiltration analyses results suggest that LY96 is positively correlated with M2 macrophages infiltration in RCC. Single‐cell data are used to verify its association with macrophages. Moreover, LY96 is positively with various immune scores and might affect the efficacy of immunotherapy. Drug screening results show LY96 might be the target of vemurafenib and etoposide. Further experiments confirm the spatial co‐localization of LY96 and M2, and knocking down LY96 can inhibit M2 polarization. Cell viability experiments indicate that knocking down LY96 would result in a decrease in the resistance of M2 macrophages to etoposide. The study shows LY96 could act as an unfavorable biomarker for RCC and possibly contribute to cancer progression by promoting the infiltration of M2 macrophage. LY96 could be a novel therapeutic target for RCC.
Deep learning–based dose prediction to improve the plan quality of volumetric modulated arc therapy for gynecologic cancers
Authors: Gronberg Mary P. , Jhingran Anuja , Netherton Tucker J. , Gay Skylar S. , Cardenas Carlos E. , Chung Christine , Fuentes David , Fuller Clifton D. , Howell Rebecca M. , Khan Meena , Lim Tze Yee , Marquez Barbara , Olanrewaju Adenike M. , Peterson Christine B. , Vazquez Ivan , Whitaker Thomas J. , Wooten Zachary , Yang Ming , Court Laurence E.
Published: September 2023

Background

In recent years, deep‐learning models have been used to predict entire three‐dimensional dose distributions. However, the usability of dose predictions to improve plan quality should be further investigated.

Purpose

To develop a deep‐learning model to predict high‐quality dose distributions for volumetric modulated arc therapy (VMAT) plans for patients with gynecologic cancer and to evaluate their usability in driving plan quality improvements.

Methods

A total of 79 VMAT plans for the female pelvis were used to train (47 plans), validate (16 plans), and test (16 plans) 3D dense dilated U‐Net models to predict 3D dose distributions. The models received the normalized CT scan, dose prescription, and target and normal tissue contours as inputs. Three models were used to predict the dose distributions for plans in the test set. A radiation oncologist specializing in the treatment of gynecologic cancers scored the test set predictions using a 5‐point scale (5, acceptable as‐is; 4, prefer minor edits; 3, minor edits needed; 2, major edits needed; and 1, unacceptable). The clinical plans for which the dose predictions indicated that improvements could be made were reoptimized with constraints extracted from the predictions.

Results

The predicted dose distributions in the test set were of comparable quality to the clinical plans. The mean voxel‐wise dose difference was −0.14 ± 0.46 Gy. The percentage dose differences in the predicted target metrics of and were −1.05% ± 0.59% and 0.21% ± 0.28%, respectively. The dose differences in the predicted organ at risk mean and maximum doses were −0.30 ± 1.66 Gy and −0.42 ± 2.07 Gy, respectively. A radiation oncologist deemed all of the predicted dose distributions clinically acceptable; 12 received a score of 5, and four received a score of 4. Replanning of flagged plans (five plans) showed that the original plans could be further optimized to give dose distributions close to the predicted dose distributions.

Conclusions

Deep‐learning dose prediction can be used to predict high‐quality and clinically acceptable dose distributions for VMAT female pelvis plans, which can then be used to identify plans that can be improved with additional optimization.
Deformable lung 4DCT image registration via landmark‐driven cycle network
Authors: Matkovic Luke , Lei Yang , Fu Yabo , Wang Tonghe , Kesarwala Aparna H. , Axente Marian , Roper Justin , Higgins Kristin , Bradley Jeffrey D. , Liu Tian , Yang Xiaofeng
Published: September 2023

Background

An automated, accurate, and efficient lung four‐dimensional computed tomography (4DCT) image registration method is clinically important to quantify respiratory motion for optimal motion management.

Purpose

The purpose of this work is to develop a weakly supervised deep learning method for 4DCT lung deformable image registration (DIR).

Methods

The landmark‐driven cycle network is proposed as a deep learning platform that performs DIR of individual phase datasets in a simulation 4DCT. This proposed network comprises a generator and a discriminator. The generator accepts moving and target CTs as input and outputs the deformation vector fields (DVFs) to match the two CTs. It is optimized during both forward and backward paths to enhance the bi‐directionality of DVF generation. Further, the landmarks are used to weakly supervise the generator network. Landmark‐driven loss is used to guide the generator's training. The discriminator then judges the realism of the deformed CT to provide extra DVF regularization.

Results

We performed four‐fold cross‐validation on 10 4DCT datasets from the public DIR‐Lab dataset and a hold‐out test on our clinic dataset, which included 50 4DCT datasets. The DIR‐Lab dataset was used to evaluate the performance of the proposed method against other methods in the literature by calculating the DIR‐Lab Target Registration Error (TRE). The proposed method outperformed other deep learning‐based methods on the DIR‐Lab datasets in terms of TRE. Bi‐directional and landmark‐driven loss were shown to be effective for obtaining high registration accuracy. The mean and standard deviation of TRE for the DIR‐Lab datasets was 1.20 ± 0.72 mm and the mean absolute error (MAE) and structural similarity index (SSIM) for our datasets were 32.1 ± 11.6 HU and 0.979 ± 0.011, respectively.

Conclusion

The landmark‐driven cycle network has been validated and tested for automatic deformable image registration of patients’ lung 4DCTs with results comparable to or better than competing methods.
Rapid MR elastography of the liver for subsecond stiffness sampling
Authors: Anders Matthias , Meyer Tom , Warmuth Carsten , Pfeuffer Josef , Tzschaetzsch Heiko , Herthum Helge , Shahryari Mehrgan , Degenhardt Katja , Wieben Oliver , Schmitter Sebastian , Schulz‐Menger Jeanette , Schaeffter Tobias , Braun Juergen , Sack Ingolf
Published: September 2023

Purpose

Depicting the stiffness of biological soft tissues, MR elastography (MRE) has a wide range of diagnostic applications. The purpose of this study was to improve the temporal resolution of 2D hepatic MRE in order to provide more rapid feedback on the quality of the wavefield and ensure better temporal sampling of respiration‐induced stiffness changes.

Methods

We developed a rapid MRE sequence that uses 2D segmented gradient‐echo spiral readout to encode 40 Hz harmonic vibrations and generate stiffness maps within 625 ms. We demonstrate the use of this technique as a rapid test for shear wave amplitudes and overall MRE image quality and as a method for monitoring respiration‐induced stiffness changes in the liver in comparison to 3D MRE and ultrasound‐based time‐harmonic elastography.

Results

Subsecond MRE allowed monitoring of increasing shear wave amplitudes in the liver with increasing levels of external stimulation within a single breath‐hold. Furthermore, the technique detected respiration‐induced changes in liver stiffness with peak values (1.83 ± 0.22 m/s) at end‐inspiration, followed by softer values during forced abdominal pressure (1.60 ± 0.22 m/s) and end‐expiration (1.49 ± 0.22 m/s). The effects of inspiration and expiration were confirmed by time‐harmonic elastography.

Conclusion

Our results suggest that subsecond MRE of the liver is useful for checking MRE driver settings and monitoring breathing‐induced changes in liver stiffness in near real time.
Improved 1H body imaging at 10.5 T: Validation and VOP‐enabled imaging in vivo with a 16‐channel transceiver dipole array
Authors: Schmidt Simon , Ertürk M. Arcan , He Xiaoxuan , Haluptzok Tobey , Eryaman Yiğitcan , Metzger Gregory J.
Published: September 2023

Purpose

To increase the RF coil performance and RF management for body imaging at 10.5 T by validating and evaluating a high‐density 16‐channel transceiver array, implementing virtual observation points (VOPs), and demonstrating specific absorption rate (SAR) constrained imaging in vivo.

Methods

The inaccuracy of the electromagnetic model of the array was quantified based on B1+ and SAR data. Inter‐subject variability was estimated using a new approach based on the relative SAR deviation of different RF shims between human body models. The pTx performance of the 16‐channel array was assessed in simulation by comparison to a previously demonstrated 10‐channel array. In vivo imaging of the prostate was performed demonstrating SAR‐constrained static RF shimming and acquisition modes optimized for refocused echoes (AMORE).

Results

The model inaccuracy of 29% and the inter‐subject variability of 85% resulted in a total safety factor of 1.91 for pelvis studies. For renal and cardiac imaging, inter‐subject variabilities of 121% and 141% lead to total safety factors of 2.25 and 2.45, respectively. The shorter wavelength at 10.5 T supported the increased element density of the 16‐channel array which in turn outperformed the 10‐channel version for all investigated metrics. Peak 10 g local SAR reduction of more than 25% without a loss of image quality was achieved in vivo, allowing a theoretical improvement in measurement efficiency of up to 66%.

Conclusions

By validating and characterizing a 16‐channel dipole transceiver array, this work demonstrates, for the first time, a VOP‐enabled RF coil for human torso imaging enabling increased pTx performance at 10.5 T.
Physico‐chemical properties and in‐vitro biocompatibility of thermo‐sensitive hydrogel developed with enhanced antimicrobial activity for soft tissue engineering
Authors: Shahzadi Uzma , Zeeshan Rabia , Tabassum Sobia , Khadim Hina , Arshad Muhammad , Ansari Arsalan Ahmad , Safi Sher Zaman , ul Haq Rana Intisar , Asif Anila
Published: September 2023

Smart materials such as thermo‐sensitive in situ forming hydrogels can be effective agents in drug delivery and tissue regeneration with minimal invasion. Injection method would avoid complex surgical procedures facilitating rapid recovery process. In this research, we report the fabrication of an easy, reproducible thermo‐sensitive hydrogel constituting of chitosan (CHI), glycerol phosphate (GP) with variable quantity of ‐poly‐l‐lysine (PS). Fourier‐transform infrared spectra exhibited hydrogel formation where interactions between CHI and GP were seen. The gelation kinetics presented gelation time of 8 min at physiological temperature. The results indicated an increase in degradation rate with the passage of time. Contact angles measurements were employed to observe hydrophilic characteristics which were shown to be favorable. Mechanical strength was determined to be in the range of ~0.1–0.6 MPa for all the hydrogels. Due to intrinsic antibacterial features of CHI and PS, the hydrogels showed potent antibacterial activity against Escherichia coli, Staphylococcus aureus, and Methicillin‐resistant S. aureus (MR‐SA). Interestingly, PS's addition in the hydrogel resulted in potent antibacterial activity against clinically relevant MR‐SA. The hydrogels can hence be delivered to a specific target for localized treatments where the potential of inhibiting multidrug resistant strain is clinically relevant. Biocompatibility of the hydrogels was seen by an overall increase in cell viability of mouse fibroblast cells and scratch assay revealed favorable migration potential. Proangiogenic Vascular endothelial growth factor (VEGF)'s expression showed a gradual increase with increasing concentration of PS, whereas one composition demonstrated a slight increase in the expression of cytosolic prostaglandin E synthase (cPGES) as determined by RT‐PCR. Overall, an increase in PS content of the hydrogels resulted in simultaneously enhanced antibacterial efficiency and marked increase in fibroblast cell viability, hence, reiterating their potential as potent antibacterial agents that can be explored as wound healing agents. In conclusion, novel antibacterial thermo‐sensitive hydrogels were synthesized with a potential of regulating proangiogenic and tissue regeneration factors that highlight their role as wound healing agents.
Socioeconomic status significantly impacts childhood cancer survival in South Africa
Authors: Hendricks Marc , Cois Annibale , Geel Jennifer , van Heerden Jaques , Dandara Collet , Mohamed Kharnita , Donald Kirsten A. , Kruger Mariana
Published: September 2023

Background and aims

Significantly discrepant survival rates have been documented in single disease childhood cancer cohorts in South Africa; those from higher socioeconomic groups were shown to have a significantly lower risk of death than those from less affluent households. This study aimed to determine the impact of socioeconomic status (SES) on childhood cancer survival using pooled South African data.

Methods

Five databases spanning January 2000 to December 2021 were interrogated. SES status was assigned based on a public sector annual household income classification. H0 households (formally unemployed) received free healthcare. H1, H2 and H3 (annual income > United States Dollar [USD] 19,000) households paid for healthcare relative to their income. The Spearman test assessed correlations between SES and disease stage in patients with solid tumours. Hazard ratios were determined using Cox regression modelling. The Kaplan–Meier procedure estimated overall survival (OS).

Results

A total of 1598 children were eligible for analysis; 1269 had a solid tumour with a negative correlation between SES and stage (Spearman rho = −.178; p < .001). Patients with solid tumours and lower SES showed proportionately higher numbers of stage III and IV disease (p < .01). This proportion decreased with higher SES categories. In the multivariate analyses adjusted for sex, age, tumour type and stage, higher SES was associated with lower mortality risk (p < .001), indicating that the impact of SES on survival was in excess of any effect that could be explained by lower stage disease alone. There was a strong positive correlation between race and SES (Fisher's exact tests, p < .001) across all groups and all SES strata. Five‐year OS was 85.3% in children from H3 households versus 46.3% in children from H0 households (p < .001).

Conclusion

SES significantly impacts childhood cancer survival for children with solid tumours in South Africa. SES is a robust surrogate for race in South Africa as a prognostic metric of disease outcome in childhood cancer. Advocacy to increase social support for impoverished patients is essential to achieve equitable improvements in outcomes treated with standardised national treatment guidelines.
A meta‐analysis assessing the prevalence of drug–drug interactions among hospitalized patients
Authors: Aksoy Nilay , Ozturk Nur
Published: September 2023

Purpose

Drug–drug interactions (DDIs), particularly in hospitalized patients can result in adverse drug events and unfavorable health consequences. The aim of this meta‐analysis is to provide up‐to‐date evidence on the prevalence of clinically evident adverse drug events due to DDIs in hospitalized patients.

Methods

Data from Scopus, PubMed, Cochrane and Web of Science were extracted using these keywords (Drug interaction/drug–drug interactions, Hospital/ hospitals, Adverse drug event, Hospitalized patients, inpatient, Department, Hospital stay, Harm, Mortality, death). The studies that include Observational studies on hospitalized patients, reporting potential DDIs using an electronic database, and reporting the clinically observed adverse drug interactions (ADI) through symptoms, signs or Laboratory tests are included. Using Open meta‐Software (version 12.11.14), the incidence of clinically evident DDIs among hospitalized patients was determined and shown in a forest plot.

Results

Only 15 of the 8261 articles found through a literature search met the inclusion criteria and reported the desired outcome. The pooled prevalence of potential drug–drug interactions is 64.9% (CI 95% 0.618–0.736). While clinically evident DDIs have a pooled frequency of 17.17% (CI 95% 0.133–0.256).

Conclusion

The issue of DDIs remains a significant concern in hospitalized patients, with a notable rise in their prevalence. This meta‐analysis encompassed a greater quantity of studies and demonstrated a heightened proportion of drug–drug interaction prevalence in comparison to the percentages reported in the previously published meta‐analysis.
The impact of employment loss on mentally unhealthy days among LGBTQ+ cancer survivors during the COVID‐19 pandemic: Findings from the OUT National Survey
Authors: Waters Austin R. , Lyerly Reece , Scout N. F. N. , Kent Erin E.
Published: September 2023

Objective

Lesbian, Gay, Bisexual, Transgender, Queer, and all other sexual and gender minority (LGBTQ+) populations made up 7.1% of the US population in 2021. LGBTQ+ cancer survivors face a variety of economic and mental health disparities; however, the determinants of poor mental health among LGBTQ+ cancer survivors are understudied.

Methods

This analysis utilized the OUT National Survey which consists of N = 2233 LGBTQ+ cancer survivors (complete cases). Multivariable negative binomial and logit regression models were used to generate predicted values, predicted probabilities, and average marginal effects (AME) to assess the association between COVID‐19 related employment loss and mentally unhealthy days (MUDs) and frequent mental distress among LGBTQ+ cancer survivors. Predicted values and marginal effects were generated with interaction terms (demographics interacted with employment loss) to explore the heterogeneity of the effect of employment loss among LGBTQ+ sub‐populations.

Results

In bivariate analyses employment loss was associated with a higher number of MUDs (10.3, SD = 9.9 vs. 8.4, SD = 9.6; p‐value<0.001) and frequent mental distress (34% vs. 26%; p‐value = 0.001). AME from a multivariable negative binomial model revealed that employment loss was associated with 1.42 more MUDs (95%CI: 0.33–2.86). Demographic factors such as some sexual orientations, cis‐female and non‐binary gender, younger age, and a current cancer diagnosis were also associated with significant expected increases in the number of MUDs. When assessing the heterogeneity of the effect of employment loss some sub‐populations experienced changes in the number of MUDs that pushed them over the threshold of frequent mental distress while others did not. Furthermore, identifying with multiple sexual orientations (AME: 0.19, 95%CI: 0.11–0.27), cis‐female and non‐binary genders (AME: 0.07, 95%CI: 0.2–0.12; AME: 0.18, 95%CI: 0.07–0.28), American Indian and Alaska Native race (AME: 0.17, 95%CI: 0.03–0.31), and a current cancer diagnosis (AME: 0.14, 95%CI: 0.09–0.19) were associated with an increase in the probability of experiencing frequent mental distress.

Conclusions

COVID‐19 related employment loss negatively impacted the mental health of LGBTQ+ cancer survivors. LGBTQ+ specific supportive services as well as equity‐based employment and income interventions are needed.
Model based assessment of food and acid reducing agent effects on oral absorption of mezigdomide (CC‐92480), a novel cereblon E3 ligase modulator
Authors: Wu Fan , Liu Liangang , Gaudy Allison , Wang Xiaomin , Carayannopoulos Leon , Pourdehnad Michael , Lamba Manisha
Published: September 2023

Mezigdomide is a novel cereblon E3 ligase modulator (CELMoD) agent with enhanced autonomous cell‐killing activity in multiple myeloma (MM) cells, and promising immunomodulatory and antitumor activity in patients with MM. We developed a population pharmacokinetics (PKs) model for mezigdomide in healthy subjects (HSs), and quantified effects of high‐fat meal and proton pump inhibitor (PPI) on human disposition parameters. Plasma concentrations from 64 HS in two phase I clinical studies (NCT03803644 and NCT04211545) were used to develop a population PK model. The HSs received single oral doses of 0.4–3.2 mg mezigdomide with full PK profiles collected. A two‐compartment linear PK model with first‐order absorption and lag time best described mezigdomide PK profiles in HSs. The population PK parameters of absorption rate constant, lag time, central volume of distribution, clearance, peripheral volume of distribution, and intercompartmental clearance were estimated to be 1.18 h−1 (interoccasion variability [IOV]: 65%), 0.423 h (IOV: 31%), 440 L (interindividual variability [IIV]: 63%), 35.1 L/h (IIV: 40%), 243 L (IIV: 26%), and 36.8 L/h (IIV: 26%), respectively. High‐fat meal increased oral bioavailability by ~30% and PPI co‐administration decreased oral bioavailability by ~64%. Mezigdomide demonstrated a linear dose‐exposure relationship in HSs. The PK model suggests a modest effect of high‐fat meal, and a substantial effect of PPIs on mezigdomide oral bioavailability. This population PK model enables data integration across studies to identify important covariate effects and is being used to guide dose selection in clinical study designs for mezigdomide in patients with MM.
A riming‐dependent parameterization of scattering by snowflakes using the self‐similar Rayleigh–Gans approximation
Authors: Maherndl Nina , Maahn Maximilian , Tridon Frederic , Leinonen Jussi , Ori Davide , Kneifel Stefan
Published: September 2023

Riming is a key process of precipitation formation in ice‐containing clouds, but quantifying riming from observations is challenging, limiting our ability to evaluate the riming process in numerical weather models. One challenge for radar observations is that riming changes both the physical properties (mass, area cross‐section) and scattering properties of ice particles. These changes need to be implemented consistently as a function of riming in radar forward operators, which are required for retrievals and model evaluation in observation space. In this study, mass–size, cross‐section area–size, and backscattering cross‐section relations are developed as a function of the normalized rime mass for aggregates composed of various monomer types (columns, dendrites, needles, plates, and rosettes). The proposed framework allows us to simulate scattering properties of aggregated ice particles consistently as a function of riming in retrievals and radar forward operators. The parameterizations are developed from a large data set of simulated rimed aggregates of different sizes and monomer crystal types. The backscattering cross‐section parameterization (the “riming‐dependent parameterization”) is evaluated for radar frequencies of 35.6 and 94.0 GHz and is based on the Self‐Similar Rayleigh–Gans approximation (SSRGA), which is increasingly used to calculate microwave scattering of ice crystals and snowflakes. Compared with parameterizations from the literature that do not consider riming, the riming‐dependent parameterization leads to significantly smaller biases in terms of backscattering cross‐section. When using the particle masses and scattering properties of the individual particles simulated by the aggregation and riming model as a reference, the bias of our parameterization is below 1 dB when integrating over an exponential particle size distribution with sizes from 0.1–10 mm.
Lactate dehydrogenase contribution to symptom persistence in long COVID: A pooled analysis
Authors: Udeh Raphael , Utrero‐Rico Alberto , Dolja‐Gore Xenia , Rahmati Masoud , McEVoy Mark , Kenna Tony
Published: September 2023

There's critical need for risk predictors in long COVID. This meta‐analysis evaluates the evidence for an association between plasma lactate dehydrogenase (LDH) and long COVID and explores the contribution of LDH to symptoms persistent across the distinct post‐acute sequelae of COVID‐19 (PASC) domains. PubMed, EMBASE, Web of Science, and Google Scholar were searched for articles published up to 20 March 2023 for studies that reported data on LDH levels in COVID‐19 survivors with and without PASC. Random‐effect meta‐analysis was employed to estimate the standardized mean difference (SMD) with corresponding 95% confidence interval of each outcome. There were a total of 8289 study participants (3338 PASC vs. 4951 controls) from 46 studies. Our meta‐analysis compared to the controls showed a significant association between LDH elevation and Resp‐PASC [SMD = 1.07, 95%CI = 0.72, 1.41, p = 0.01] but not Cardio‐PASC [SMD = 1.79, 95%CI = −0.02, 3.61, p = 0.05], Neuro‐PASC [SMD = 0.19, 95%CI = −0.24, 0.61, p = 0.40], and Gastrointestinal‐PASC [SMD = 0.45, 95%CI = −1.08, 1.98, p = 0.56]. This meta‐analysis suggests elevated LDH can be used for predicting Resp‐PASC, but not Cardio‐PASC, Neuro‐PASC or gastrointestinal‐PASC. Thus, elevated plasma LDH following COVID infection may be considered as a disease biomarker.
Aligning global health policy and research with sustainable development: A strategic market approach
Authors: Haenssgen Marco J.
Published: September 2023

Attaining Sustainable Development Goal 3 (SDG 3: Health and Well‐Being) faces a dual challenge of diminishing returns of established intervention designs, and a threat to future gains from complex inter‐connected global health challenges like antimicrobial resistance and global biodiversity loss. The growing movement of context‐sensitive approaches could help realise yet untapped potential for intervention designs, but contemporary global health policy and research still remain dominated by a model of individual market style choices. This paper therefore aims to support the development of global health planning processes that are more grounded and integrative across the SDGs. Reiterating calls for disruptive policy change is unlikely to impact the modus operandi of global health policy and research. This paper therefore builds on a logic that already finds widespread and intuitive application in their underlying planning processes: ‘the market’. However, it challenges the dominant supply‐and‐demand approach to healthcare markets and redefines them from a strategic marketing perspective. Translated to the interface of populations and health systems, the strategic market is a site for solving problems that are defined by people with multidimensional health needs. This framework offers four guiding questions to define the strategic market and six premises as a simple intellectual starting point and checklist for more grounded and inter‐sectorial action across the SDGs. The analysis of data from one of the largest behavioural survey data sets, covering 6683 villagers across China, India, Lao PDR and Thailand, demonstrates the relevance of the premises empirically.
Bayesian predictive model averaging approach to joint longitudinal‐survival modeling: Application to an immuno‐oncology clinical trial
Authors: Yao Zixuan , Morita Satoshi , Nishida Sumiyuki , Sugiyama Haruo
Published: September 2023

In immuno‐oncology clinical trials, multiple immunological biomarkers are usually examined over time to comprehensively and appropriately evaluate the efficacy of treatments. Because predicting patients' future survival statuses on the basis of such recorded longitudinal information might be of great interest, joint modeling of longitudinal and time‐to‐event data has been intensively discussed as a toolkit to implement such a prediction. To achieve a desirable predictive performance, averaging over multiple candidate predictive models to account for the model uncertainty might be a more suitable statistical approach than selecting the single best model. Although Bayesian model averaging can be one of the approaches, several problems related to model weights with marginal likelihoods have been discussed. To address these problems, we here propose a Bayesian predictive model averaging (BPMA) method that uses Bayesian leave‐one‐out cross‐validation predictive densities to account for the subject‐specific and time‐dependent nature of the prediction. We examine the operating characteristics of the proposed BPMA method in terms of the predictive accuracy (ie, the calibration and discrimination abilities) in extensive simulation studies. In addition, we discuss the strengths and limitations of the proposed method by applying it to an immuno‐oncology clinical trial in patients with advanced ovarian cancer.
Fitting a stochastic model of intensive care occupancy to noisy hospitalization time series during the COVID‐19 pandemic
Authors: Awasthi Achal , Minin Volodymyr M. , Huang Jenny , Chow Daniel , Xu Jason
Published: September 2023

Intensive care occupancy is an important indicator of health care stress that has been used to guide policy decisions during the COVID‐19 pandemic. Toward reliable decision‐making as a pandemic progresses, estimating the rates at which patients are admitted to and discharged from hospitals and intensive care units (ICUs) is crucial. Since individual‐level hospital data are rarely available to modelers in each geographic locality of interest, it is important to develop tools for inferring these rates from publicly available daily numbers of hospital and ICU beds occupied. We develop such an estimation approach based on an immigration‐death process that models fluctuations of ICU occupancy. Our flexible framework allows for immigration and death rates to depend on covariates, such as hospital bed occupancy and daily SARS‐CoV‐2 test positivity rate, which may drive changes in hospital ICU operations. We demonstrate via simulation studies that the proposed method performs well on noisy time series data and apply our statistical framework to hospitalization data from the University of California, Irvine (UCI) Health and Orange County, California. By introducing a likelihood‐based framework where immigration and death rates can vary with covariates, we find, through rigorous model selection, that hospitalization and positivity rates are crucial covariates for modeling ICU stay dynamics and validate our per‐patient ICU stay estimates using anonymized patient‐level UCI hospital data.
Strong, Lightweight, and Shape‐Memory Bamboo‐Derived All‐Cellulose Aerogels for Versatile Scaffolds of Sustainable Multifunctional Materials
Authors: Dong Hongping , Li Xiazhen , Cai Zhiyong , Wei Song , Fan Shutong , Ge Yanglin , Li Xianjun , Wu Yiqiang
Published: September 2023

Strong, lightweight, and shape‐memory cellulose aerogels have great potential in multifunctional applications. However, achieving the integration of these features into a cellulose aerogel without harsh chemical modifications and the addition of mechanical enhancers remains challenging. In this study, a strong, lightweight, and water‐stimulated shape‐memory all‐cellulose aerogel (ACA) is created using a combination strategy of partial dissolution and unidirectional freezing from bamboo. Benefiting from the firm architecture of cellulose microfibers bridging cellulose nanofibers /regenerated cellulose aggregated layers and the bonding of different cellulose crystal components (cellulose Iβ and cellulose II), the ACA, with low density (60.74 mg cm−3), possesses high compressive modulus (radial section: 1.2 MPa, axial section: 0.96 MPa). Additionally, when stimulated with water, the ACA exhibits excellent shape‐memory features, including highly reversible compression‐resilience and instantaneous fold‐expansion behaviors. As a versatile scaffold, ACA can be integrated with hydroxyapatite, carboxyl carbon nanotubes, and LiCl, respectively, via a simple impregnation method to yield functionalized cellulose composites for applications in thermal insulation, electromagnetic interference shielding, and piezoresistive sensors. This study provides inspiration and a reliable strategy for the elaborately structural design of functional cellulose aerogels endows application prospects in various multifunction opportunities.
Searching High‐Potential Dihydroxynaphthalene Cathode for Rocking‐Chair All‐Organic Aqueous Proton Batteries
Authors: Zhao Guoqing , Yan Xiaorong , Dai Yujie , Xiong Jiakui , Zhao Qian , Wang Xinyu , Yu Haiping , Gao Jiefeng , Zhang Naibo , Hu Mingjun , Yang Jun
Published: September 2023

The lack of acid‐proof high‐potential cathode largely limits the development and competitiveness of proton batteries. Herein, the authors systematically investigated six dihydroxynaphthalenes (DHNs) and found that 2,6‐DHN delivered the best cathode performance in proton battery with the highest redox potential (0.84 V, vs SHE) and a specific capacity of 91.6 mAh g−1 at 1 A g−1. In situ solid‐state electropolymerization of DHNs is responsible for the voltage and capacity fading of DHNs, and 2,6‐DHN's excellent electrochemical performance is derived from its high polymerization energy barrier. By compounding with rGO, the 2,6‐DHN/rGO electrode can maintain a specific capacity of 89 mAh g−1 even after 12 000 cycles at 5 A g−1. When it is paired with the 2,6‐dihydroxyanthraquinone (DHAQ) anode, the assembled rocking‐chair all‐organic proton battery exhibited a high cell voltage of 0.85 V, and excellent energy/power densities (70.8 Wh kg−1/850 W kg−1). This study showcases a new‐type high‐potential proton‐containing organic cathode and paves the way for constructing a high‐voltage rocking‐chair proton battery. Also, in situ solid‐state electropolymerization will inspire the further study of phenol‐based small‐molecule electrodes.
MetaHate: AI‐based hate speech detection for secured online gaming in metaverse using blockchain
Authors: Sanghvi Harshil , Bhavsar Rushir , Hundlani Vini , Gohil Lata , Vyas Tarjni , Nair Anuja , Desai Shivani , Jadav Nilesh Kumar , Tanwar Sudeep , Sharma Ravi , Yamsani Nagendar
Published: September 2023

The emergence of Web 3.0, blockchain technology (BC), and artificial intelligence (AI) are transforming multiplayer online gaming in the metaverse. This development has its concerns about safety and inclusivity. Hate speech, in particular, poses a significant threat to the harmony of these online communities. Traditional moderation methods struggle to cope with the immense volume of user‐generated content, necessitating innovative solutions. This article proposes a novel framework,MetaHate, that employs AI and BC to detect and combat hate speech in online gaming environments within the metaverse. Various machine learning (ML) models are applied to analyze Hindi–English code mixed datasets, with gradient boosting proving the most effective, achieving 86.01% accuracy. AI algorithms are instrumental in identifying harmful language patterns, while BC technology ensures transparency and user accountability. Moreover, a BC‐based smart contract is proposed to support the moderation of hate speech in the game chat. Integrating AI and BC can significantly enhance the safety and inclusivity of the metaverse, underscoring the importance of these technologies in the ongoing battle against hate speech and in bolstering user engagement. This research emphasizes the potential of AI and BC synergy in creating a safer metaverse, highlighting the need for continuous refinement and deployment of these technologies.
Agglomeration Behavior and Atomistic Mechanism of Cerium Oxide Particles in the Fe–Ce–O–S Melts
Authors: Li Yutang , Wu Qiang , He Jianzhong , Zhang Daxian , Fu Jianxun , Wang Linzhu
Published: September 2023

This article aims to investigate the agglomeration behavior of cerium oxide particles at the interface of high‐temperature melts. The high‐temperature experiment with 0.15% cerium addition is carried out at 1600 °C and the type of cerium oxide is detected to be Ce–O–S particles according to thermodynamic calculation and energy‐dispersive analysis. The attractive force between particles is analyzed by high‐temperature confocal laser scanning microscopy and the Kralchevsky–Paunov model. The in situ observation indicates that the largest acting distance between Ce–O–S particle pairs is measured to be 80 μm. The attractive force becomes weak as the distance between Ce–O–S particle pairs (L) decreases, and a stronger attractive force exists between Ce–O–S particle pairs with a smaller radius ratio (R1/R2). The capillary force for Ce–O–S particles is weaker than that for Ce2O3 particles. The first‐principles results indicate that the wettability at the interface of the Fe/Ce2O3–S system is weaker than the Fe–S/Ce2O3 system, which is in agreement with the in situ observation and Kralchevsky–Paunov model results. Furthermore, the atomistic mechanism of agglomeration behavior is revealed.
Vector Control Technology of Five‐Phase Induction Motor without Speed Sensor Based on Speed‐Adaptive Full‐Order Observer
Authors: Luo Xiangyu , Zhao Jinghong , Xiong Yiyong , Zeng Rongtao
Published: September 2023

The five‐phase induction motor, being the most elementary form of multiphase induction motors, boasts low noise, low torque fluctuations, and high load‐bearing capacity, and is hence suitably employed in domains where high power and reliability are required. In light of the susceptibility of the speed encoder to interference from external environments, this paper proposes a sensorless vector control technique for the five‐phase induction motor based on a speed‐adaptive full‐order observer. The proposed approach first employs the nearest four‐vector space vector pulse width modulation (NFV‐SVPWM) as the modulation technique for the five‐phase inverter, in order to provide a constant switching frequency and low harmonic losses. Subsequently, the expression for the speed‐adaptive full‐order observer is constructed based on the mathematical model of the fundamental space of the five‐phase induction motor, and the expression for the adaptive speed law is deduced using the Lyapunov function. Additionally, the bilinear transformation method is utilized as the discretization technique for the full‐order observer during the process of digital implementation, in order to achieve higher discrete accuracy. Finally, an experimental study involving a 4.5 kW five‐phase induction motor was conducted to validate the accuracy and effectiveness of the proposed method. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
Drowsiness Estimation Based on Facial Near‐Infrared Images Using Sparse Coding
Authors: Okajima Tomoro , Yoshida Atsushi , Oiwa Kosuke , Nagumo Kent , Nozawa Akio
Published: September 2023

Level 3 automated driving mandates the installation of a driver monitoring system that monitors the driver's condition, such as drowsiness. Conventional drowsiness detection has been hampered by the mental and physical burden of wearing equipment and the time required for detection. In this study, we applied sparse coding to facial images captured with near‐infrared light to extract features of facial skin blood flow information related to drowsiness and to estimate drowsiness. As a result, skin blood flow features related to drowsiness were extracted in the nasal area and other areas, and drowsiness was estimated with 74.6% accuracy. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
Co‐occurrence of periodontal lesions and dental wear in incisor and masticatory teeth in two sheep flocks in Brazil
Authors: Agostinho Sabrina Donatoni , Borsanelli Ana Carolina , Campello Paula Letícia , Saraiva Júlia Rebecca , Silva Tamires Ataides , Schweitzer Christiane Marie , Gaetti‐Jardim Elerson , Dutra Iveraldo dos Santos
Published: September 2023

Background

Periodontitis and tooth wear are multifactorial diseases with distinct etiopathogenesis that affect the health, feed efficiency and welfare of sheep.

Methods

This study evaluated the co‐occurrence of tooth wear and periodontal lesions in 129 ewes from two Brazilian flocks, clinically classified the lesions and presence of dental calculus, and identified potential pathogens in the dental biofilm of 63 ewes by polymerase chain reaction.

Results

Of the 129 ewes included in the study, 75 presented periodontal lesions, while all animals presented tooth wear and dental calculus. Of the animals with periodontal lesions, 16.2% had lesions in incisor teeth and 52.7% in masticatory teeth. Regarding excessive tooth wear, 38.6% had severe wear on the incisor teeth and 89.1% on the masticatory teeth. Ewes older than 36 months had a higher frequency of periodontal lesions in incisor teeth (p < 0.001) and a greater amount of dental calculus (p < 0.001), but there was no association between tooth wear and animal age. Fusobacterium nucleatum, Tannerella forsythia and Fusobacterium necrophorum predominated in periodontal lesions.

Limitation

This study is limited by the small sample size and lack of diagnostic imaging to assess periodontal disease.

Conclusion

The co‐occurrence of periodontal lesions and excessive dental wear involving both the incisor and masticatory teeth suggests that although the two diseases have different aetiologies, they likely have common risk factors.
Exploring the potential of long‐term agreements for achieving landscape‐scale environmental recovery
Authors: Barkley Lucy V. , Short Christopher J. , Chivers Charlotte‐Anne
Published: September 2023

This systematic review examines existing literature on environmental long‐term agreements (LTAs), such as conservation covenants, to identify the enablers and barriers to adoption and thus their potential for use in new contexts such as across land boundaries. To achieve environmental and societal targets, there is a clear and urgent need for more ambitious, longer‐term agri‐environment funding mechanisms which operate at a large scale. This is, in part, due to the recognition by national governments that shifts in policy for long‐term sustainability are needed since many existing agri‐environment schemes have not had the desired outcomes. The recent inclusion of conservation covenants in the UK's Environment Act demonstrates one such attempt to address this issue. Through critically analyzing the use of LTAs across the world using a systematic rapid evidence assessment approach, this article assesses the potential of these agreements to deliver landscape‐scale environmental benefits and public goods that are increasingly being sought by national governments as they strive for transformative policies to prevent and mitigate environmental and climate catastrophes. Exploring the enablers and barriers to adoption, alongside examples of successful LTAs, we provide an overview from which interested parties—policymakers, land managers, and private enterprises, among others—can begin to understand the opportunities and barriers which surround these agreements. Overall, this literature review identifies several factors likely to affect the uptake of LTAs, including concerns around land managers' and stakeholders' real/perceived capacity to engage, a need for flexibility, finance structures, and how monitoring and evaluation are managed.

This article is categorized under:

Climate and Environment > Circular Economy

Climate and Environment > Ecosystem Services

Climate and Environment > Net Zero Planning and Decarbonization

Policy and Economics > Regional and International Strategies
Estimation of Temporal Variations in the Earth’s Gravity Field Using Novel Optical Clocks Onboard of Low Earth Orbiters
Authors: Shabanloui Akbar , Wu Hu , Müller Jürgen
Published: September 2023

The current generation of optical atomic clocks has reached a fractional frequency uncertainty of 1
$$\times $$

×
10
$$^{-18}$$

−18

(and beyond) which corresponds to a geopotential difference of 0.1 m
$$^{2}$$

2

/s
$$^{2}$$

2

. Those gravitational potential differences can be observed as gravitational redshift when comparing the frequencies of optical clocks. Even temporal potential variations might be determined with precise novel optical atomic clocks onboard of low-orbiting satellites such as SLR-like (e.g. LAGEOS-1/2) and GRACE-like missions.

In this simulation study, the potential of precise space-borne optical clocks for the determination of temporal variations of low-degree Earth’s gravity field coefficients are investigated. Different configurations of satellite orbits, i.e. at different altitudes (between 400 and 6000 km) and inclinations, are selected as well as certain assumptions on the clock performance are made. A particular focus is put on how well degree-2 coefficients can be estimated from those optical clock measurements and how it compares to results from SLR.
Plagiarism Detection and Intertextuality Software
Authors: Anson Chris M. , Kruse Otto
Published: September 2023

Software for plagiarism detection was developed in the early 2000s when powerful search engines offered writers opportunities for unattributed copy-and-pasting from other sources. Many algorithms were developed to reveal overlaps between original and source text. Although the software was imperfect, its use has spread across higher education, precipitating intense debates about its application to the teaching of writing. Because of instructors’ fear of false accusation and the effects on students’ anxiety, many educators have eschewed plagiarism detection systems. Others, however, have adopted plagiarism detection for formative and developmental reasons, such as helping students to understand intertextuality and making referencing a manageable skill. This chapter will briefly historicize the effects of the internet on the practice of plagiarism; describe the technology behind digital programs for plagiarism detection and its functional specifications; summarize some of the research on plagiarism detection programs; describe a few of the more popular programs; and conclude with implications.
Automated Text Generation and Summarization for Academic Writing
Authors: Benites Fernando , Delorme Benites Alice , Anson Chris M.
Published: September 2023

In this chapter, we discuss the implications of automatic text generation for academic writing. We first review the current state of the technology and how it is being used. We then discuss the implications of using automatic text generators for academic writing, including the need for users to be aware of the limitations of the technology and how to use it effectively. We also discuss how the use of automatic text generation can change the traditional stages of writing, and how the content generated by these systems is not justified by semantic or extra-linguistic criteria. We finally argue that notions that have been useful for explaining, analyzing, and teaching academic writing will need to be re-examined in the light of human–machine-interaction. (This abstract has been automatically generated using OpenAI and slightly post-edited; see this article’s Appendix for an explanation.)
Automated Scoring of Writing
Authors: Link Stephanie , Koltovskaia Svetlana
Published: September 2023

For decades, automated essay scoring (AES) has operated behind the scenes of major standardized writing assessments to provide summative scores of students’ writing proficiency (Dikli in J Technol Learn Assess 5(1), 2006). Today, AES systems are increasingly used in low-stakes assessment contexts and as a component of instructional tools in writing classrooms. Despite substantial debate regarding their use, including concerns about writing construct representation (Condon in Assess Writ 18:100–108, 2013; Deane in Assess Writ 18:7–24, 2013), AES has attracted the attention of school administrators, educators, testing companies, and researchers and is now commonly used in an attempt to reduce human efforts and improve consistency issues in assessing writing (Ramesh and Sanampudi in Artif Intell Rev 55:2495–2527, 2021). This chapter introduces the affordances and constraints of AES for writing assessment, surveys research on AES effectiveness in classroom practice, and emphasizes implications for writing theory and practice.