bluuebunny/crossref_metadata_2025 huggingface dataset:
Hyaluronic acid-ibuprofen conjugation: a novel ototherapeutic approach protecting inner ear cells from inflammation-mediated damage
Authors: Birru Bhaskar , Veit Joachim G. S. , Arrigali Elizabeth M. , Van Tine Jack , Barrett-Catton Emma , Tonnerre Zachary , Diaz Philippe , Serban Monica A.
Published: February 2024

There is a substantial need of effective drugs for the treatment of hearing loss, which affects nearly 500 million individuals globally. Hearing loss can be the result of intense or prolonged noise exposure, ototoxic drugs, infections, and trauma, which trigger inflammatory signaling cascades that lead to irreversible damage to cochlear structures. To address this, we developed and characterized a series of covalent conjugates of anti-inflammatory drugs to hyaluronic acid (HA), for potential use as topical ototherapeutics. These conjugates were tested in in vitro assays designed to mirror physiological processes typically observed with acoustic trauma. Intense noise exposure leads to macrophage recruitment to the cochlea and subsequent inflammatory damage to sensory cells. We therefore first tested our conjugates’ ability to reduce the release of inflammatory cytokines in macrophages. This anti-inflammatory effect on macrophages also translated to increased cochlear cell viability. In our initial screening, one conjugate, ibuprofen-HA, demonstrated significantly higher anti-inflammatory potential than its counterparts. Subsequent cytokine release profiling of ibuprofen-HA further confirmed its ability to reduce a wider range of inflammatory markers, to a greater extent than its equivalent unconjugated drug. The conjugate’s potential as a topical therapeutic was then assessed in previously developed tympanic and round window membrane tissue permeation models. As expected, our data indicate that the conjugate has limited tympanic membrane model permeability; however, it readily permeated the round window membrane model and to a greater extent than the unconjugated drug. Interestingly, our data also revealed that ibuprofen-HA was well tolerated in cellular and tissue cytocompatibility assays, whereas the unconjugated drug displayed significant cytotoxicity at equivalent concentrations. Moreover, our data highlighted the importance of chemical conjugation of ibuprofen to HA; the conjugate had improved anti-inflammatory effects, significantly reduced cytotoxicity, and is more suitable for therapeutic formulation. Overall, this work suggests that ibuprofen-HA could be a promising safe and effective topical ototherapeutic for inflammation-mediated cochlear damage.
A nomogram prognostic model for early hepatocellular carcinoma with diabetes mellitus after primary liver resection based on the admission characteristics
Authors: Zhang Menghan , Wang Qi , Zhang Gongming , Li Guangming , Jin Ronghua , Xing Huichun
Published: February 2024

Background: Patients diagnosed with early-stage hepatocellular carcinoma (HCC) and diabetes mellitus (DM) are at a higher risk of experiencing complications and facing increased mortality rates. Hence, it is crucial to develop personalized clinical strategies for this particular subgroup upon their admission. The objective of this study is to determine the key prognostic factors in early HCC patients who received liver resection combined with DM and develop a practical personalized model for precise prediction of overall survival in these individuals.

Method: A total of 1496 patients diagnosed hepatitis B virus (HBV) - related liver cancer from Beijing You’an Hospital were retrospectively enrolled, spanning from 1 January 2014, to 31 December 2019, and ultimately, 622 eligible patients of hepatocellular carcinoma (HCC) patients with diabetes were included in this present investigation. A multivariate COX regression analysis was conducted to identify prognostic factors that are independent of each other and develop a nomogram. The performance of the nomogram was evaluated using various statistical measures such as the C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) in both the training and validation groups. Survival rates were estimated using the Kaplan-Meier method.

Results: The study included a total of 622 early HCC patients who underwent liver resection combined with DM. Random Forrest model and Multivariate Cox regression analysis revealed that drinking, tumor number, monocyte-to-lymphocyte ratio, white blood cell count and international normalized ratio at admission were identified as independent prognostic factors for early HCC patients who underwent liver resection combined with DM. The nomogram demonstrated good predictive performance in the training and validation cohorts based on the C-index values of 0 .756 and 0 .739 respectively, as well as the area under the curve values for 3-, 5-, and 8-year overall survival (0.797, 0.807, 0.840, and 0.725, 0.791, 0.855). Calibration curves and decision curve analysis indicated high accuracy and net clinical benefit rates. Furthermore, the nomogram successfully stratified enrolled patients into low-risk and high-risk groups based on their risk of overall survival. The difference in overall survival between these two groups was statistically significant in both the training and validation cohorts (p < 0.0001 and p = 0.0064).

Conclusion: Our results indicate that the admission characteristics demonstrate a highly effective ability to predict the overall survival of early HCC patients who have undergone liver resection in combination with DM. The developed model has the potential to support healthcare professionals in making more informed initial clinical judgments for this particular subgroup of patients.
Establishing the function relationship between time spectrum and proton range in proton therapy through Monte Carlo simulation
Authors: He Yibo , Tong Xin , Li Yuhan , Cheng Jingyi , Zhou Rong
Published: February 2024

To validate range shifts in proton therapy, we investigated the potential of using the temporal information of prompt gamma rays as an indicator. We simulated the proton transport process using Monte Carlo simulations and used a geometric scorer to obtain the location and timing of prompt gamma ray production. By using a homogeneous target material in the simulation model, we established a fitted relationship between the range of 90–210 MeV protons and the corresponding temporal spectral width. Additionally, by introducing air cavities of 2–20 mm in simulations of inhomogeneous target materials, we observed significant correlations between the range offsets and the temporal spectral widths. These correlations were fitted to derive a functional relationship between the two variables.
Differential responses of selectively bred mussels (Perna canaliculus) to heat stress—survival, immunology, gene expression and microbiome diversity
Authors: Ericson Jessica A. , Laroche Olivier , Biessy Laura , Delorme Natalí J. , Pochon Xavier , Thomson-Laing Jacob , Ragg Norman L. C. , Smith Kirsty F.
Published: February 2024

New Zealand’s green-lipped mussel (Perna canaliculus) is an ecologically and economically important species. Marine heatwaves are increasing in frequency around NZ’s coastline, and these events are correlated with increased stress and mortality of some aquaculture species. This study aimed to identify general biomarkers of heat stress in P. canaliculus and to assess whether responses differed between genetically distinct selectively bred mussels. We exposed three families of selectively bred mussels (families A, B and C) to three seawater temperature regimes in the laboratory: 1) a “control” treatment (ambient 12°C), 2) a 26°C heat challenge with a subsequent recovery period, and 3) a sustained 26°C heat challenge with no recovery. We investigated whether the survival, immune response (hemocyte concentration and viability, oxidative stress and total antioxidant capacity), hemocyte gene expression and gill microbiome differed between the families during the temperature challenges. In the sustained heat-stress treatment, family A had the highest survival rate (42% compared with 25% and 5% for families C and B, respectively). Gene expression levels significantly shifted during thermal stress and differed between families, with family A more dissimilar than families B and C. Family C had substantially more genes impacted by temperature treatment and timepoint than the other families, while family B had very little genes/pathways that responded to thermal stress. Genes related to heat shock proteins and immune responses (e.g., AIF1, CTSC, TOLL8, CASP9, FNTA, AHCY, CRYAB, PPIF) were upregulated in all families during heat stress. Microbiome species-richness differed between families before and during heat-stress, with family A having a distinctly different microbiome flora than the other families. Microbial diversity changed similarly in all families exposed to prolonged heat-stress, with species of Vibrio and Campylobacter increasing in these mussels. Our study highlights the use of non-lethal sampling of hemocytes as a diagnostic tool to explore the immune response and gene expression of selectively bred mussels, to predict their response to ocean warming. This approach can identify potential thermotolerant candidates for further selective breeding, which may increase the resilience of the mussel aquaculture industry in a warming ocean.
Assessment of intracranial aneurysm rupture risk using a point cloud-based deep learning model
Authors: Cao Heshan , Zeng Hui , Lv Lei , Wang Qi , Ouyang Hua , Gui Long , Hua Ping , Yang Songran
Published: February 2024

Background and Purpose: Precisely assessing the likelihood of an intracranial aneurysm rupturing is critical for guiding clinical decision-making. The objective of this study is to construct and validate a deep learning framework utilizing point clouds to forecast the likelihood of aneurysm rupturing.

Methods: The dataset included in this study consisted of a total of 623 aneurysms, with 211 of them classified as ruptured and 412 as unruptured, which were obtained from two separate projects within the AneuX morphology database. The HUG project, which included 124 ruptured aneurysms and 340 unruptured aneurysms, was used to train and internally validate the model. For external validation, another project named @neurIST was used, which included 87 ruptured and 72 unruptured aneurysms. A standardized method was employed to isolate aneurysms and a segment of their parent vessels from the original 3D vessel models. These models were then converted into a point cloud format using open3d package to facilitate training of the deep learning network. The PointNet++ architecture was utilized to process the models and generate risk scores through a softmax layer. Finally, two models, the dome and cut1 model, were established and then subjected to a comprehensive comparison of statistical indices with the LASSO regression model built by the dataset authors.

Results: The cut1 model outperformed the dome model in the 5-fold cross-validation, with the mean AUC values of 0.85 and 0.81, respectively. Furthermore, the cut1 model beat the morphology-based LASSO regression model with an AUC of 0.82. However, as the original dataset authors stated, we observed potential generalizability concerns when applying trained models to datasets with different selection biases. Nevertheless, our method outperformed the LASSO regression model in terms of generalizability, with an AUC of 0.71 versus 0.67.

Conclusion: The point cloud, as a 3D visualization technique for intracranial aneurysms, can effectively capture the spatial contour and morphological aspects of aneurysms. More structural features between the aneurysm and its parent vessels can be exposed by keeping a portion of the parent vessels, enhancing the model’s performance. The point cloud-based deep learning model exhibited good performance in predicting rupture risk while also facing challenges in generalizability.
An artificial intelligence-based bone age assessment model for Han and Tibetan children
Authors: Liu Qixing , Wang Huogen , Wangjiu Cidan , Awang Tudan , Yang Meijie , Qiongda Puqiong , Yang Xiao , Pan Hui , Wang Fengdan
Published: February 2024

Background: Manual bone age assessment (BAA) is associated with longer interpretation time and higher cost and variability, thus posing challenges in areas with restricted medical facilities, such as the high-altitude Tibetan Plateau. The application of artificial intelligence (AI) for automating BAA could facilitate resolving this issue. This study aimed to develop an AI-based BAA model for Han and Tibetan children.

Methods: A model named “EVG-BANet” was trained using three datasets, including the Radiology Society of North America (RSNA) dataset (training set n = 12611, validation set n = 1425, and test set n = 200), the Radiological Hand Pose Estimation (RHPE) dataset (training set n = 5491, validation set n = 713, and test set n = 79), and a self-established local dataset [training set n = 825 and test set n = 351 (Han n = 216 and Tibetan n = 135)]. An open-access state-of-the-art model BoNet was used for comparison. The accuracy and generalizability of the two models were evaluated using the abovementioned three test sets and an external test set (n = 256, all were Tibetan). Mean absolute difference (MAD) and accuracy within 1 year were used as indicators. Bias was evaluated by comparing the MAD between the demographic groups.

Results: EVG-BANet outperformed BoNet in the MAD on the RHPE test set (0.52 vs. 0.63 years, p < 0.001), the local test set (0.47 vs. 0.62 years, p < 0.001), and the external test set (0.53 vs. 0.66 years, p < 0.001) and exhibited a comparable MAD on the RSNA test set (0.34 vs. 0.35 years, p = 0.934). EVG-BANet achieved accuracy within 1 year of 97.7% on the local test set (BoNet 90%, p < 0.001) and 89.5% on the external test set (BoNet 85.5%, p = 0.066). EVG-BANet showed no bias in the local test set but exhibited a bias related to chronological age in the external test set.

Conclusion: EVG-BANet can accurately predict the bone age (BA) for both Han children and Tibetan children living in the Tibetan Plateau with limited healthcare facilities.
Development of KASP markers assisted with soybean drought tolerance in the germination stage based on GWAS
Authors: Jia Qianru , Zhou Miaomiao , Xiong Yawen , Wang Junyan , Xu Donghe , Zhang Hongmei , Liu Xiaoqing , Zhang Wei , Wang Qiong , Sun Xin , Chen Huatao
Published: February 2024

Soybean [Glycine max(L.)Merr.] is a leading oil-bearing crop and cultivated globally over a vast scale. The agricultural landscape in China faces a formidable challenge with drought significantly impacting soybean production. In this study, we treated a natural population of 264 Chinese soybean accessions using 15% PEG-6000 and used GR, GE, GI, RGR, RGE, RGI and ASFV as evaluation index. Using the ASFV, we screened 17 strong drought-tolerant soybean germplasm in the germination stage. Leveraging 2,597,425 high-density SNP markers, we conducted Genome-Wide Association Studies (GWAS) and identified 92 SNPs and 9 candidate genes significantly associated with drought tolerance. Furthermore, we developed two KASP markers for S14_5147797 and S18_53902767, which closely linked to drought tolerance. This research not only enriches the pool of soybean germplasm resources but also establishes a robust foundation for the molecular breeding of drought tolerance soybean varieties.
Language access differentially alters functional connectivity during emotion perception across cultures
Authors: Leshin Joseph , Carter Maleah J. , Doyle Cameron M. , Lindquist Kristen A.
Published: February 2024

Introduction

It is often assumed that the ability to recognize the emotions of others is reflexive and automatic, driven only by observable facial muscle configurations. However, research suggests that accumulated emotion concept knowledge shapes the way people perceive the emotional meaning of others’ facial muscle movements. Cultural upbringing can shape an individual’s concept knowledge, such as expectations about which facial muscle configurations convey anger, disgust, or sadness. Additionally, growing evidence suggests that access to emotion category words, such as “anger,” facilitates access to such emotion concept knowledge and in turn facilitates emotion perception.

Methods

To investigate the impact of cultural influence and emotion concept accessibility on emotion perception, participants from two cultural groups (Chinese and White Americans) completed a functional magnetic resonance imaging scanning session to assess functional connectivity between brain regions during emotion perception. Across four blocks, participants were primed with either English emotion category words (“anger,” “disgust”) or control text (XXXXXX) before viewing images of White American actors posing facial muscle configurations that are stereotypical of anger and disgust in the United States.

Results

We found that when primed with “disgust” versus control text prior to seeing disgusted facial expressions, Chinese participants showed a significant decrease in functional connectivity between a region associated with semantic retrieval (the inferior frontal gyrus) and regions associated with semantic processing, visual perception, and social cognition. Priming the word “anger” did not impact functional connectivity for Chinese participants relative to control text, and priming neither “disgust” nor “anger” impacted functional connectivity for White American participants.

Discussion

These findings provide preliminary evidence that emotion concept accessibility differentially impacts perception based on participants’ cultural background.
Palliative and hegemonic dimensions of conservatism: the mitigating role of institutional trust in shaping attitudes toward migrants and migration policy preferences
Authors: Grežo Matúš , Adamus Magdalena , Tencerová Jana
Published: February 2024

The study explores the links between palliative and hegemonic dimensions of conservatism, attitudes toward migrants and restrictive migration policy preferences. Participants reported on their palliative dimension (social conservatism, traditionalism) and hegemonic dimension (social dominance orientation, collective narcissism) of conservatism, trust in government, attitudes toward migrants, and restrictive migration policy preferences. The results show that both dimensions of conservatism are indirectly linked to more restrictive migration policy preferences through negative attitudes toward migrants. Moreover, the present study indicates that increasing institutional trust may be an effective mechanism mitigating negative attitudes toward migrants for individuals high in the palliative dimension of conservatism.
Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia
Authors: Ellis Charles A. , Miller Robyn L. , Calhoun Vince D.
Published: February 2024

Introduction

Dynamic functional network connectivity (dFNC) analysis of resting state functional magnetic resonance imaging data has yielded insights into many neurological and neuropsychiatric disorders. A common dFNC analysis approach uses hard clustering methods like k-means clustering to assign samples to states that summarize network dynamics. However, hard clustering methods obscure network dynamics by assuming (1) that all samples within a cluster are equally like their assigned centroids and (2) that samples closer to one another in the data space than to their centroids are well-represented by their centroids. In addition, it can be hard to compare subjects, as in some cases an individual may not manifest a state strongly enough to enter a hard cluster. Approaches that allow a dimensional approach to connectivity patterns (e.g., fuzzy clustering) can mitigate these issues. In this study, we present an explainable fuzzy clustering framework by combining fuzzy c-means clustering with several explainability metrics and novel summary features.

Methods

We apply our framework for schizophrenia (SZ) default mode network analysis. Namely, we extract dFNC from individuals with SZ and controls, identify 5 dFNC states, and characterize the dFNC features most crucial to those states with a new perturbation-based clustering explainability approach. We then extract several features typically used in hard clustering and further present a variety of unique features specially designed for use with fuzzy clustering to quantify state dynamics. We examine differences in those features between individuals with SZ and controls and further search for relationships between those features and SZ symptom severity.

Results

Importantly, we find that individuals with SZ spend more time in states of moderate anticorrelation between the anterior and posterior cingulate cortices and strong anticorrelation between the precuneus and anterior cingulate cortex. We further find that individuals with SZ tend to transition more rapidly than controls between low-magnitude and high-magnitude dFNC states.

Conclusion

We present a novel dFNC analysis framework and use it to identify effects of SZ upon network dynamics. Given the ease of implementing our framework and its enhanced insight into network dynamics, it has great potential for use in future dFNC studies.
Prevalence and influencing factors of post-traumatic stress disorder among Chinese healthcare workers during the COVID-19 epidemic: a systematic review and meta-analysis
Authors: Zhang Min , Bo Mingyu , Wang Huixin , Fan Wenyi , Kong Lingling , Zhou Chunjie , Zhang Zhenxing
Published: February 2024

Background

Post-traumatic stress disorder is an important psychological problem affecting the physical mental health of Chinese healthcare workers during the COVID-19 pandemic.

Aims

To estimate the prevalence and influencing factors of post-traumatic stress disorder (PTSD) among Chinese healthcare workers during COVID-19.

Methods

Search of Chinese and English literature in PubMed, EMbase, Web of Science, Medline, Elsevier, SpringerLink, China Biomedical Literature Database, CNKI, Wan-fang, and CQVIP for the period from December 2019 to August 2023. Stata 14.0 software was used for data analysis. The methodological quality of each study was scored, and data were extracted from the published reports. Pooled prevalence was estimated using the Random-effects model. Publication bias was evaluated using Egger’s test and Begg’s test.

Results

Twenty-one studies included 11841 Chinese healthcare workers in this review. First, the overall prevalence of Post-traumatic stress disorder among Chinese healthcare workers during the COVID-19 epidemic was 29.2% (95% CI: 20.7% to 33.7%). Twelve factors included in the meta-analysis were found to be protective against PTSD among Chinese healthcare workers: female, nurse, married, front-line work, less work experience, family or friend diagnosed with COVID-19, history of chronic disease and fear of COVID-19. Conversely, outside Hubei, higher education, social support and psychological resilience are protective factors.

Conclusion

These recent findings increase our understanding of the psychological status of Chinese healthcare workers and encourage that long-term monitoring and long-term interventions should be implemented to improve the mental health of Chinese healthcare workers in the aftermath of the COVID-19.
A public health approach to reducing violence within the CARICOM region
Authors: Maharaj Sandeep , Seepersad Randy , Sooknanan Joanna , Anderson Simon , Franco Darleen , Ramdass Amrica , Seemungal Terence
Published: February 2024

Widespread crime has become a worldwide problem so much so that violence is now ranked fourth globally in its contribution to disability-adjusted life years in the 10 to 24 age group. Homicides, a surrogate marker of violent crime, have shown an upward trend in almost all of the CARICOM countries, and homicide rates over the past 3 years have consistently increased, though the pattern of violence varies by country. This background has informed the need for greater emphasis on the need for a different approach to dealing with crime in the CARICOM region. The CARICOM governments recently hosted a symposium on crime and violence as a public health issue. The public health approach to crime has been used with measurable success in different parts of the world and, more recently in Trinidad, one of the CARICOM countries. The paper outlines the outcomes of the symposium and discusses its implications for the region.
Hybrid model: a promising type of public procurement in the healthcare sector of the European Union
Authors: Geropoulos Nikolaos , Voultsos Polychronis , Geropoulos Miltiadis , Tsolaki Fani , Tagarakis Georgios
Published: February 2024

The management of health supplies in public hospitals has been a major concern of national and European institutions over time, often being a field of reforms and regulatory interventions. Health procurement systems constitute complex decision-making and supply chain management mechanisms of public hospitals, involving suppliers, health providers, administrators and political bodies. Due to this complexity, the first important decision to be taken when designing a procurement system, concerns the degree of centralization, namely to what extent the decision-making power on the healthcare procurement (what, how and when) will be transferred either to a central public authority established for this purpose, or to the competent local authorities. In this perspective, we attempt to analyse the types of public procurement in the healthcare sector of the European Union, in terms of degree of centralization. Employing a narrative approach that summarizes recent interdisciplinary literature, this perspective finds that the healthcare procurement systems of the EU Member States, based on the degree of centralization, are categorized into three types of organizational structures: Centralized, Decentralized and Hybrid procurement. Each structure offers advantages and disadvantages for health systems. According to this perspective, a combination of centralized and decentralized purchases of medical supplies represents a promising hybrid model of healthcare procurement organization by bringing the benefits of two methods together.
Using implementation mapping to refine strategies to improve implementation of an evidence-based mobile market intervention: a study protocol
Authors: Kasprzak Christina M. , Canizares Andy , Lally Anne , Tirabassi Jill N. , Vermont Leah N. , Lev Samuel , Ammerman Alice S. , Leone Lucia A.
Published: February 2024

Objectives

The Veggie Van model is a mobile market model that is efficacious in increasing fruit and vegetable consumption for lower-income participants. The model is currently being evaluated for its effectiveness in a multi-state trial. Preliminary implementation data, collected through process measures surveys and implementation interviews, indicate that there are several barriers to implementation among partner organizations and implementation fidelity to the Veggie Van model was low. Consideration and planning for implementation ought to occur early and often throughout the research process order to ensure Veggie Van model effectiveness. This paper describes the step-by-step process for creating strategies to enhance implementation of Veggie Van model components.

Methods

Implementation mapping is a systematic process to develop implementation strategies through engagement with key stakeholders. We conducted a series of interviews (n = 31 representatives) with partner organizations (n = 8) to identify facilitators and barriers to Veggie Van model implementation. We then applied interview findings to an Implementation Mapping process to develop theory and practice-driven strategies to be integrated into existing implementation tools and technical assistance.

Results

We identified implementation outcomes (e.g., staff implement the Veggie Van model component of nutrition education with fidelity) and performance objectives (e.g., offer nutrition education, in the form of food lessons and/or food demonstrations, at least bi-weekly) to achieve them. We conducted a secondary qualitative analysis of the findings from implementation interviews with partner organizations to identify behavioral determinants (e.g., attitudinal beliefs, social support) which were combined with the performance objectives to generate change objectives (e.g., view the Veggie Van model as advantageous to an organization and communities served). To achieve the change objectives, we developed implementation strategies that would be integrated into existing Veggie Van training resources including an online toolkit, webinars and trainings, an annual mobile market conference, and technical assistance.

Conclusion

The development of theory and practice-driven implementation strategies will enable us to improve our implementation tools, thereby improving fidelity to the Veggie Van model among organizations and increasing the likelihood of its effectiveness. Detailing the design of a multifaceted implementation strategy using Implementation Mapping also provides a model to design similar strategies for other community-based interventions.
Meet Athena: A Realistic Computer Model of a Child
Authors: Ntolkeras Georgios , Jeong Hongbae , Zöllei Lilla , Dmytriw Adam A. , Lev Michael H. , Grant P. Ellen , Bonmassar Giorgio
Published: February 2024

Have you ever wondered how doctors and scientists test new medical devices or study how the body works? They often use computer models, which are like digital dolls that simulate the human body. But did you know that there are only few computer models of children, especially of girls? That is where Athena comes in. Athena is a brand-new computer model of a healthy 3.5-year-old girl’s body, made using real-life pictures of her brain and organs. We even had doctors check to make sure the model was accurate. Athena is so detailed that she has 267 parts, including 50 components of her brain alone. Scientists can use Athena to test the safety of new medical devices or to study how electricity affects children’s bodies, for example. And best of all, Athena will be free for everyone to use! So, meet Athena—and see what amazing things we can learn about the human body!
Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering
Authors: O’Leary Dave , Brogi Cosimo , Brown Colin , Tuohy Pat , Daly Eve
Published: February 2024

Introduction

The mapping of soil properties, such as soil texture, at the field scale is important Q6 in the context of national agricultural planning/policy and precision agriculture. Electromagnetic Induction (EMI) surveys are commonly used to measure soil apparent electrical conductivity and can provide valuable insights into such subsurface properties.

Methods

Multi-receiver or multi-frequency instruments provide a vertical distribution of apparent conductivity beneath the instrument, while the mobility of such instruments allows for spatial coverage. Clustering is the grouping together of similar multi-dimensional data, such as the processed EMI data over a field. A neural network clustering process, where the number of clusters can be objectively determined, results in a set of one-dimensional apparent electrical conductivity cluster centers, which are representative of the entire three-dimensional dataset. These cluster centers are used to guide inversions of apparent conductivity data to give an estimate of the true electrical conductivity distribution at a site.

Results and discussion

The method is applied to two sites and the results demonstrate a correlation between (true) electrical conductivity with soil texture (sampled prior to the EMI surveys) which is superior to correlations where no clustering is included. The method has the potential to be developed further, with the aim of improving the prediction of soil properties at cluster scale, such as texture, from EMI data. A particularly important conclusion from this initial study is that EMI data should be acquired prior to a focused soil sampling campaign to calibrate the electrical conductivity – soil property correlations.
Discriminant canonical analysis as a tool for genotype traceability testing based on turkey meat and carcass traits
Authors: Salgado Pardo José Ignacio , González Ariza Antonio , Navas González Francisco Javier , León Jurado José Manuel , Díaz Ruiz Esther , Delgado Bermejo Juan Vicente , Camacho Vallejo María Esperanza
Published: February 2024

The present study aims to develop a statistical tool for turkey breed traceability testing based on meat and carcass quality characteristics. To this end, a comprehensive meta-analysis was performed, collecting data from a total of 75 studies approaching meat and carcass attributes of 37 turkey strains and landraces since the late 1960s. A total of 22 meat and carcass traits were considered variables, grouped in the following clusters: carcass dressing traits, muscle fiber properties, pH, colorimetry, water-capacity traits, texture-related attributes, and nutritional composition of the meat. Once the multicollinearity analysis allowed the deletion of redundant variables, cold carcass weight, slaughter weight, muscle fiber diameter, sex-female, carcass/piece weight, meat redness, ashes, pH24, meat lightness, moisture, fat, and water-holding capacity showed explanatory properties in the discriminating analysis (p < 0.05). In addition, strong positive and negative correlations were found among those variables studied. Carcass traits were positively associated, particularly slaughter weight and cold carcass weight (+0.561). Among meat physical traits, pH showed positive correlations with drip loss (+0.490) and pH24 (+0.327), and water-holding capacity was positively associated with cholesterol (+0.434) and negatively associated with collagen (−0.398). According to nutritional traits, fat and ash showed a strong correlation (+0.595), and both were negatively associated with moisture (−0.375 and −0.498, respectively). Strong negative correlations were found as well between meat protein and fat (−0.460) and between collagen and cholesterol (−0.654). Finally, the Mahalanobis distance suggested a clustering pattern based on meat and carcass characteristics that report information about interbreeding and variety proximity. This study establishes a departure point in the development of a tool for breed traceability guaranteeing aimed at enhancing distinguished, local breed-based turkey meat.
Joint Exposure to Multiple Air Pollutants, Genetic Susceptibility, and Incident Dementia: A Prospective Analysis in the UK Biobank Cohort
Authors: Zhang Shuo , Cao Hongyan , Chen Keying , Gao Tongyu , Zhao Huashuo , Zheng Chu , Wang Ting , Zeng Ping , Wang Ke
Published: February 2024

Objectives: This study aimed to evaluate the joint effects of multiple air pollutants including PM2.5, PM10, NO2, and NOx with dementia and examined the modifying effects of genetic susceptibility.

Methods: This study included 220,963 UK Biobank participants without dementia at baseline. Weighted air pollution score reflecting the joint exposure to multiple air pollutants were constructed by cross-validation analyses, and inverse-variance weighted meta-analyses were performed to create a pooled effect. The modifying effect of genetic susceptibility on air pollution score was assessed by genetic risk score and APOE ε4 genotype.

Results: The HR (95% CI) of dementia for per interquartile range increase of air pollution score was 1.13 (1.07∼1.18). Compared with the lowest quartile (Q1) of air pollution score, the HR (95% CI) of Q4 was 1.26 (1.13∼1.40) (Ptrend = 2.17 × 10−5). Participants with high air pollution score and high genetic susceptibility had higher risk of dementia compared to those with low air pollution score and low genetic susceptibility.

Conclusion: Our study provides evidence that joint exposure to multiple air pollutants substantially increases the risk of dementia, especially among individuals with high genetic susceptibility.
A Comprehensive Survey of Isocontouring Methods: Applications, Limitations and Perspectives
Authors: Büscher Keno Jann , Degel Jan Philipp , Oellerich Jan
Published: February 2024

This paper provides a comprehensive overview of approaches to the determination of isocontours and isosurfaces from given data sets. Different algorithms are reported in the literature for this purpose, which originate from various application areas, such as computer graphics or medical imaging procedures. In all these applications, the challenge is to extract surfaces with a specific isovalue from a given characteristic, so called isosurfaces. These different application areas have given rise to solution approaches that all solve the problem of isocontouring in their own way. Based on the literature, the following four dominant methods can be identified: the marching cubes algorithms, the tessellation-based algorithms, the surface nets algorithms and the ray tracing algorithms. With regard to their application, it can be seen that the methods are mainly used in the fields of medical imaging, computer graphics and the visualization of simulation results. In our work, we provide a broad and compact overview of the common methods that are currently used in terms of isocontouring with respect to certain criteria and their individual limitations. In this context, we discuss the individual methods and identify possible future research directions in the field of isocontouring.
Ecological Risk Assessment and Source Analysis of Heavy Metals in Farmland Soil in Yangchun City Based on APCS-MLR and Geostatistics
Authors: Li Yingyuting , Zhang Yili , Chen Junyu , Yang Guangfei , Li Haihui , Wang Jinjin , Li Wenyan
Published: February 2024

Yangchun City, a typical polymetallic ore distribution area in Guangdong Province (China), was selected as the research region to study the content, distribution, source, and possible impacts of heavy metals (HMs) (Arsenic: As; Cadmium: Cd; Chromium: Cr; Copper: Cu; Mercury: Hg; Nickel: Ni; Lead: Pb; and Zinc: Zn) on the farmland soil of this City. According to our findings, the spatial distribution of HMs in Yangchun City shows higher concentrations in the north and southeast and lower in the west and other regions. Metal content in some sampled sites of the agricultural land exceeded the soil pollution risk screening values, particularly As (7.5%), Cd (12%), Cu (4%), Hg (14.5%), and Pb (3%). Additionally, the average content of As, Cu, Cd, Pb, Hg, and Zn from the studied areas surpassed the soil background value of Guangdong Province for all metals. The absolute principal component score-multiple linear regression (APCS-MLR) was used to identify potential sources of HMs in the soil samples. There are three potential sources identified by the model: traffic emissions, natural sources, and agricultural activities, accounting for 28.16%, 16.68%, and 14.42%, respectively. Based on the ecological risk assessment, the potential ecological risk (Eri = 310.77), Nemero pollution index (PN = 2.27), and multiple possible effect concentration quality (mPECQs = 0.23) indicated that the extent of heavy metal pollution in the soil samples was moderate. Three sources were identified: traffic emissions, natural sources, and agricultural activities. We suggest that by combining the above results, a monitoring and early warning system focused on Cd and Hg can be established. The system could utilize geographic information systems and remote sensing technologies to achieve dynamic monitoring and prediction of pollution. Regular testing of soils and sustainable management practices are also recommended to control and remediate contamination.
Technology for Production of Wheat Doubled Haploid via Maize Pollen Induction—Updated Review
Authors: Guan Xizhen , Peng Junhua , Fu Daolin
Published: February 2024

Chromosome elimination resulting in haploids is achieved by rapid loss of chromosomes from one parent during the zygote stage and is an important procedure to produce doubled haploid (DH) lines in plants. During crosses between an emasculated wheat (Triticum aestivum L.) and maize (Zea mays L.) as pollen donors, the complete loss of maize chromosomes results in wheat haploid embryos. Through embryo rescue and chromosome doubling processes, pure lines with stable traits can be quickly obtained. The technique is called the “Wheat × Maize System”. Although this technology is not new, it remains a practical approach to date. In order to optimize and improve this technology and to achieve its maximum potential in the winter wheat area of China, this paper reviews the previous and ongoing research and technical procedures for the production of wheat DH lines via the maize pollen induction and presents outlooks on DH research and its application in wheat breeding.
Understanding Communication Barriers: Demographic Variables and Language Needs in the Interaction between English-Speaking Animal Professionals and Spanish-Speaking Animal Caretakers
Authors: Martinez Aguiriano Allen Jimena , Salazar Leonor , Pietrosemoli Silvana , Schmidt Marcelo , Awosile Babafela , Garcia Arlene
Published: February 2024

This study focused on assessing the language needs of English-speaking animal professionals in their interactions with Hispanic/Spanish-speaking animal caretakers. A survey was administered to a target audience of non-Spanish speaking and bilingual animal professionals to identify communication gaps while interacting with Hispanic/Spanish-speaking animal caretakers. The data was analyzed with descriptive and inferential statistics, including ordinal regression analyses to examine the impact of demographic variables on respondents’ answer choices. The results showed that English-speaking professionals struggled with written and oral communication, which differed compared to bilingual professionals (p < 0.05). Additionally, responses of female professionals varied regarding the aspects of Spanish necessary for interacting with Hispanic/Spanish-speaking animal caretakers, as well as the topics likely to be addressed when agriculture professionals communicate with animal caretakers (p < 0.05). Communication difficulties in the oral medium for both oral receptive skills (listening) and oral productive skills (speaking) were reported as the major barriers that animal professionals need to overcome in their attempt to communicate with the Hispanic/Spanish-speaking workforce in farm settings. This emphasizes the need to address oral communication barriers, and to a lesser degree, the development of reading and writing skills. The topics: typical clinical signs of illness, euthanasia, treatment—drugs, and identification of sick or injured animals were identified as the most likely to be addressed during on-farm interactions. These findings indicate that there are gaps in communication that need to be overcome to improve communication with on-farm Hispanic/Spanish-speaking animal caretakers and consequently contribute to enhancing animal health, welfare, and production.
A Novel Approach to the Production of Printed Patch Antennas
Authors: Popela Miroslav , Olivová Jana , Plíva Zdeněk , Petržílka Leoš , Krchová Michaela , Joska Zdeněk , Janů Přemysl
Published: February 2024

This paper presents the manufacturing of a patch antenna using an advanced 3D printing technology called lights-out digital additive manufacturing (LDM). This 3D LDM printing technology is mainly used for printing circuit boards (PCBs); however, it has also been used to print a patch antenna from conductive (CI) and dielectric ink (DI). This 3D LDM-printed antenna was compared with antennas on different dielectric substrates (Arlon 25N and FR4). The obtained results are compared and analyzed in this paper.
Scheme Design and Energy-Saving Optimization of Cold and Heat Energy Supply System for Substation Main Control Building in Cold Area
Authors: Wang Ying , Jin Xu , Zhang Jiapeng , Zeng Cong , Gao Xiuyun , Zhao Lei , Sha Shuai
Published: February 2024

In the context of global climate change, the implementation of building energy conservation and carbon reduction, as well as the realization of zero-energy buildings, is a key measure to cope with climate change and resource depletion. A substation is an indispensable building in the process of urbanization construction. However, in existing cold areas, the heating form of substations generally adopts electric heating, which consumes a large amount of energy. This paper optimizes the existing HVAC form of substations through the rational utilization of surrounding environmental resources and puts forward reasonable building energy-saving and carbon-reduction methods. It demonstrates the feasibility of combining solar photovoltaic power generation systems, air source heat pumps, and natural ventilation to optimize energy savings and carbon reduction in the main control building of a substation in a cold area. The computational fluid dynamics (CFD) method is used to demonstrate the feasibility of natural ventilation during the summer and transition seasons. The data indicate that the installation of a solar photovoltaic power generation system results in an average annual power generation of 18.75 MWh. Additionally, using an air source heat pump can save 44.5% of electricity compared to electric heating. When both a solar photovoltaic power generation system and an air source heat pump are used to provide a building with cold and heat sources, the annual emissions of CO2 can be reduced by 4.90 tons compared to a traditional electric heating system.
Integrating Machine Learning in Urban Pedagogy: Addressing Homelessness in Skid Row
Authors: Meshkani Taraneh
Published: February 2024

This paper investigates the application of machine learning in urban and architectural education, with a focus on addressing homelessness in Skid Row, Los Angeles. It presents a case study of an urban design studio utilizing data-driven methods to propose transitional housing solutions, emphasizing the importance of design in the context of social justice. The study explores the use of machine learning and digital cartography for a detailed analysis of Skid Row’s dense homeless population, offering students a thorough insight into urban challenges. The research also identifies the complexities involved in integrating these technologies into educational frameworks, including issues with data accuracy, technical hurdles, and ethical considerations. The paper concludes by advocating for an interdisciplinary, data-informed, and socially conscious approach in architectural and urban design education, highlighting its necessity in preparing students to effectively tackle contemporary urban problems.
Enhancing Knee MR Image Clarity through Image Domain Super-Resolution Reconstruction
Authors: Patel Vishal , Wang Alan , Monk Andrew Paul , Schneider Marco Tien-Yueh
Published: February 2024

This study introduces a hybrid analytical super-resolution (SR) pipeline aimed at enhancing the resolution of medical magnetic resonance imaging (MRI) scans. The primary objective is to overcome the limitations of clinical MRI resolution without the need for additional expensive hardware. The proposed pipeline involves three key steps: pre-processing to re-slice and register the image stacks; SR reconstruction to combine information from three orthogonal image stacks to generate a high-resolution image stack; and post-processing using an artefact reduction convolutional neural network (ARCNN) to reduce the block artefacts introduced during SR reconstruction. The workflow was validated on a dataset of six knee MRIs obtained at high resolution using various sequences. Quantitative analysis of the method revealed promising results, showing an average mean error of 1.40 ± 2.22% in voxel intensities between the SR denoised images and the original high-resolution images. Qualitatively, the method improved out-of-plane resolution while preserving in-plane image quality. The hybrid SR pipeline also displayed robustness across different MRI sequences, demonstrating potential for clinical application in orthopaedics and beyond. Although computationally intensive, this method offers a viable alternative to costly hardware upgrades and holds promise for improving diagnostic accuracy and generating more anatomically accurate models of the human body.