bluuebunny/crossref_metadata_2025 huggingface dataset:
Deep learning for plant bioinformatics: an explainable gradient-based approach for disease detection
Authors: Shoaib Muhammad , Shah Babar , Sayed Nasir , Ali Farman , Ullah Rafi , Hussain Irfan
Published: October 2023

Emerging in the realm of bioinformatics, plant bioinformatics integrates computational and statistical methods to study plant genomes, transcriptomes, and proteomes. With the introduction of high-throughput sequencing technologies and other omics data, the demand for automated methods to analyze and interpret these data has increased. We propose a novel explainable gradient-based approach EG-CNN model for both omics data and hyperspectral images to predict the type of attack on plants in this study. We gathered gene expression, metabolite, and hyperspectral image data from plants afflicted with four prevalent diseases: powdery mildew, rust, leaf spot, and blight. Our proposed EG-CNN model employs a combination of these omics data to learn crucial plant disease detection characteristics. We trained our model with multiple hyperparameters, such as the learning rate, number of hidden layers, and dropout rate, and attained a test set accuracy of 95.5%. We also conducted a sensitivity analysis to determine the model’s resistance to hyperparameter variations. Our analysis revealed that our model exhibited a notable degree of resilience in the face of these variations, resulting in only marginal changes in performance. Furthermore, we conducted a comparative examination of the time efficiency of our EG-CNN model in relation to baseline models, including SVM, Random Forest, and Logistic Regression. Although our model necessitates additional time for training and validation due to its intricate architecture, it demonstrates a faster testing time per sample, offering potential advantages in real-world scenarios where speed is paramount. To gain insights into the internal representations of our EG-CNN model, we employed saliency maps for a qualitative analysis. This visualization approach allowed us to ascertain that our model effectively captures crucial aspects of plant disease, encompassing alterations in gene expression, metabolite levels, and spectral discrepancies within plant tissues. Leveraging omics data and hyperspectral images, this study underscores the potential of deep learning methods in the realm of plant disease detection. The proposed EG-CNN model exhibited impressive accuracy and displayed a remarkable degree of insensitivity to hyperparameter variations, which holds promise for future plant bioinformatics applications.
Exploring locally developed ELT materials in the context of curriculum-based value education in China: challenges and solutions
Authors: Deng Shiping , Wang Xuemei
Published: October 2023

The development of locally developed English language teaching (ELT) materials is believed to be conducive to meeting the requirements of local language education policies and practices, and to enhance learners’ agency. In China, in order to implement national educational policy of curriculum-based value education, many locally developed ELT materials have been recently developed. However, there is a paucity of empirical research concerning the actual effects of such materials in practice. This study presents an empirical study of a recently developed set of teaching materials in the context of curriculum-based value education by adopting an approach consisting of questionnaires and interviews to examine the attitudes and experiences of users of these materials. The findings revealed a range of challenges in the use of these materials, including issues related to language and content of the materials, the integration of ideological and value elements, and the development of multi-dimensional teaching materials. The article then proposes possible solutions from the perspectives of optimizing the writing community, carefully selecting teaching materials, innovating the integration of ideological and political elements, and improving the development of multi-dimensional teaching materials. A framework for the evaluation of locally developed ELT materials is also proposed, with the intention of offering a useful benchmark for enhancing the development of such materials.
Motor control and skill acquisition in pediatric physical therapy: an enactive proposal
Authors: Sørvoll Marit , Øberg Gunn Kristin , Girolami Gay L.
Published: October 2023

Theories of motor control and skill acquisition strongly influence and guide various fields of clinical practice. In last decades, changes in theoretical frameworks related to the conceptualization of brain plasticity, functional structures within the child, and environment have led to a revision of therapy approaches progressing from therapist-driven to child-initiated approaches. Even though theoretical frameworks and clinical practice are closely linked to the child’s body, the profession has paid less attention to theories concerning the body’s role and status in interpersonal relationships when fostering motor control and skill acquisition in children. In this theoretical paper we discuss the theoretical frameworks of motor control and skill acquisition that currently guide clinical practice. Through highlighting valuable contributions of these theories, we explore theoretical and practical benefits pediatric physical therapy can acquire by taking an enactive approach as a means to bring the child as a subject into focus. We rely on enactive concepts of embodiment, autonomy, and participatory sense-making in our exploration to provide an extended understanding of motor control and skill acquisition shaping our beliefs about what counts in therapeutic encounters in pediatric physical therapy.
Supporting teachers to actively respond to bullying and to build positive relationships with their students: effects of the T-SUPPORT training
Authors: Finet Chloë , Vandebosch Heidi , Lubon Anouck , Colpin Hilde
Published: October 2023

Despite the central role that teachers can have in preventing and reducing bullying, they often feel insecure about how to deal with bullying. This study evaluated a short teacher training – called the Teachers SUPporting POsitive RelaTionships (T-SUPPORT) training – that aims to reduce bullying by supporting teachers in building positive teacher-student relationships and in actively dealing with bullying. The aim of the current study was to investigate whether the T-SUPPORT training resulted in higher quality teacher-student relationships, and more active and less passive responses to bullying incidents, and whether these improvements in turn resulted in lower levels of bullying victimization. In a Randomized Controlled Trial 10 Belgian primary schools were randomly assigned to an intervention or control condition. The Grades 4–6 teachers of the five schools in the intervention condition received the three-session school-based training; control teachers received no intervention. Grade 4–6 students (N = 964; 55 classrooms) in these schools completed questionnaires at pre- and post-test. In contrast to the hypotheses, results of the two-level linear mixed model analyses revealed no significant effect of the training on teacher-student relationship quality, teachers’ responses to bullying and bullying victimization. Yet, higher quality teacher-student relationships and more active teacher responses to bullying were significantly associated with less bullying victimization, whereas more passive responses were linked with more victimization. The latter findings are in line with theorizing and research on the role that teachers can play in reducing bullying.
Suicidal ideation, burnout, and their correlation among health care workers at the end of the fourth wave of the COVID-19 pandemic in Alborz Province, Iran
Authors: Badrfam Rahim , Zandifar Atefeh , Mohammadian Khonsari Nami , Qorbani Mostafa
Published: October 2023

Background

During the COVID-19 pandemic, Health Care Workers (HCWs) were more vulnerable than ever to Burnout and Suicidal thoughts due to stressful work conditions. This study, investigated the level of Burnout and Suicidal thoughts among HCWs during the fourth wave of the pandemic in Alborz Province in Iran and compared it with the conditions at the beginning of the pandemic.

Methods

A total of 305 HCWs from 3 referral hospitals for COVID-19, including 155 men and 150 women, participated in the study. A cross-sectional study was carried out with a sample of HCWs dealing with COVID-19 patients using the available sampling method. The results of this online survey, which was conducted from June 7 to July 5, 2021 (at the end of the Fourth Wave of the COVID-19 Pandemic in Iran), have been compared with the conditions of the First Wave of the Pandemic (from February 24 to April 27, 2020). The participants were evaluated by the Beck Scale for Suicidal Ideations (BSSI) and Maslach Burnout Inventory (MBI).

Results

The mean age of the participants was 36.34 ± 7.37. The means of Suicide Index (SI), Emotional Exhaustion (EE), Depersonalization (DP), and Personal Accomplishment (PA) scores were 0.76 ± 1.74, 19.94 ± 4.69, 4.92 ± 1.51, and 31.30 ± 5.88, respectively. SI and PA were significantly higher in workers other than nurses and physicians and EE was higher among workers with night shifts (p value<0.05 in all indices). Age had a significant and negative correlation with EE (p value<0.01) and DP (p value<0.05) and a significant and positive correlation with PA (p value<0.01).

Conclusion

This study showed a high level of SI and Burnout indices among HCWs in the fourth wave of the pandemic in Iran. Paying attention to the factors affecting the development of social capital and creating health policy changes may be effective in reducing Burnout indices and high Suicide index among HCWs.
Exploring the feasibility of a mental health application (JoyPopTM) for Indigenous youth
Authors: Au-Yeung Allison , Marfatia Daksha , Beers Kamryn , General Daogyehneh Amanda , McQueen Kahontiyoha Cynthia Denise , Martin-Hill Dawn , Wekerle Christine , Green Tehota'kerá:ton Jeremy ,   The Six Nations Youth Mental Wellness Committee
Published: October 2023

Objective

The purpose of the current study was to explore the acceptability and feasibility of a resilience-focused mobile application, JoyPop™, for use with Indigenous youth.

Methods

A Haudenosaunee community-based research advisory committee co-developed the research project, in accordance with OCAP™ principles. Adopting a mixed-method approach, five youths from an immersion school used the JoyPop™ app for four consecutive weeks, as well as completed pre-test questions and weekly usage surveys. Most participants also completed post-test questions and a semi-structured interview. Based on a semi-structured interview protocol, youth responded to questions, and the most common themes were categorized to capture the experience of using the app.

Results

All youth reported a positive impression, used the app daily, found it easy to navigate, and indicated that they would recommend it to a friend. All features were uniformly positively endorsed. There were features that youth used most often (Deep Breathing, “SquareMoves” game, and Art features) and moderately (Rate My Mood, Journaling, and SleepEase). The social connection feature, Circle of Trust, was least utilized, with youth reporting a preference for in-person problem-solving. The drop-down menu of crisis helplines was not used. Youth recommended more gaming options. In terms of cultural resonance, appreciation for the app's use of water sounds in the SleepEase feature was expressed, as was cultural consistency with the “Good Mind” perspective. Recommendations included additional nature sounds, Indigenous design elements, the inclusion of Native language words, and traditional stories.

Discussion

The JoyPop™ app was positively received by Six Nations youth, and ways to ensure its cultural appropriateness were identified. Moving forward, it is recommended that Indigenous designers create a new version with community design co-creation. Additional research with various groups of Indigenous youth is warranted as a pan-Indigenous approach is not recommended.
“It is unbearable to breathe here”: air quality, open incineration, and misinformation in Blantyre, Malawi
Authors: Tilley Elizabeth , Chilunga Hope , Kwangulero Jonathan , Schöbitz Lars , Vijay Saloni , Heilgendorff Heiko , Kalina Marc
Published: October 2023

Blantyre, Malawi’s Queen Elizabeth Central Hospital (QECH), or Queen’s, as it’s known locally, is the country’s largest public hospital. However, Queen’s is not served by regular municipal waste collection. Rather, most hospital waste (infectious and non-infectious) is gathered by grounds staff and openly burned, in several constantly smouldering piles, sending up clouds of smoke. Speaking directly to an identified knowledge gap on air quality impacts linked to trash burning and the paucity of African urban dwellers’ voices on air quality issues, this study employed a mixed-methods approach to both quantitatively measure the air quality around QECH, and to qualitatively investigate the perceived impacts amongst staff and caregivers. Low-cost sensors measuring particulate matter (PM) with particle sizes less than 10 μm (PM10) and less than 2.5 μm (PM2.5), expressed as the mass of PM per volume of air (μg PMx/m3 air) were recorded every 5 min at 8 locations across the QECH for 2 months. Qualitative data collection consisted of 56 interviews with patients, caregivers and hospital staff (including janitorial and maintenance staff, nurses, doctors, and administrators). Our results show that safe air quality thresholds are consistently exceeded across space and time and that the most problematic air quality surrounds the shelter for caregivers and those receiving treatment for HIV/AIDS. Moreover, staff and visitors are severely impacted by the poor air quality within the space, but feel powerless to make changes or address complaints. Waste management interventions are desperately needed lest the patients who arrive at Queen’s leave with more health issues than the ones with which they arrived.
Interpretable neural networks: principles and applications
Authors: Liu Zhuoyang , Xu Feng
Published: October 2023

In recent years, with the rapid development of deep learning technology, great progress has been made in computer vision, image recognition, pattern recognition, and speech signal processing. However, due to the black-box nature of deep neural networks (DNNs), one cannot explain the parameters in the deep network and why it can perfectly perform the assigned tasks. The interpretability of neural networks has now become a research hotspot in the field of deep learning. It covers a wide range of topics in speech and text signal processing, image processing, differential equation solving, and other fields. There are subtle differences in the definition of interpretability in different fields. This paper divides interpretable neural network (INN) methods into the following two directions: model decomposition neural networks, and semantic INNs. The former mainly constructs an INN by converting the analytical model of a conventional method into different layers of neural networks and combining the interpretability of the conventional model-based method with the powerful learning capability of the neural network. This type of INNs is further classified into different subtypes depending on which type of models they are derived from, i.e., mathematical models, physical models, and other models. The second type is the interpretable network with visual semantic information for user understanding. Its basic idea is to use the visualization of the whole or partial network structure to assign semantic information to the network structure, which further includes convolutional layer output visualization, decision tree extraction, semantic graph, etc. This type of method mainly uses human visual logic to explain the structure of a black-box neural network. So it is a post-network-design method that tries to assign interpretability to a black-box network structure afterward, as opposed to the pre-network-design method of model-based INNs, which designs interpretable network structure beforehand. This paper reviews recent progress in these areas as well as various application scenarios of INNs and discusses existing problems and future development directions.
Implementing smoking cessation in routine primary care—a qualitative study
Authors: Dannapfel Petra , Bendtsen Preben , Bendtsen Marcus , Thomas Kristin
Published: October 2023

Background

The use of digital tools has been proposed as a solution to some of the challenges of providing preventative services in primary care. Although there is a general acceptance among patients to use digital self-help tools to quit smoking, and healthcare organizations are increasingly urged to incorporate these tools in clinical practice, it is unclear how and for whom these innovations can be incorporated into clinical practice.

Objectives

To explore health care professionals' perceptions about smoking cessation practice in routine primary care and the use of digital tools in this work.

Methods

A qualitative study with nine in-depth telephone interviews with health care professionals working in primary care in Sweden. Convenience sampling and snowball technique was used as recruitment strategy. Informants included registered, district and auxiliary nurses as well as behavioral therapists. All informants were female, between 43 and 57 years old and experience of working with smoking cessation in primary care and possibility to recommend digital interventions to smokers.

Results

Informants described smoking cessation practice in primary care as (i) identifying smoking patients, (ii) pursuing standardized routines for smoking cessation practice and (iii) keeping smoking cessation practice on the agenda. Digital tools were described by informants to be used in different ways: (i) replicating practice, (ii) complementing practice and (iii) enabling access to health care practitioners. Finally, the analysis showed that patients' expectations and behaviors contributed to how and when smoking cessation practice was conducted, including the use of digital tools.

Conclusions

Implementing smoking cessation practice in primary care in Sweden encompass continuous work of reaching smoking patients, building buy-in among peers and keeping tobacco on the practice agenda. Digital interventions are used to replicate, complement and enabling access to care. The findings suggest that poor continuity of staff and negative attitudes towards preventative work may challenge smoking cessation practice. However, societal changes in the awareness of the health risks of tobacco use including shifting social norms regarding the acceptance of smoking may contribute to a normalization of speaking about smoking in primary care practice. Increased knowledge is needed on how, and for whom digital tools can be incorporated in clinical practice.
Assessment of Donor Derived Cell Free DNA (dd-cfDNA) at Surveillance and at Clinical Suspicion of Acute Rejection in Renal Transplantation
Authors: Mantios Evangelos , Filiopoulos Vassilis , Constantoulakis Pantelis , Liapis George , Vittoraki Angeliki , Casas Silvia , Marinaki Smaragdi , Boletis John N
Published: October 2023

In our prospective, unicenter cohort study, we collected blood samples from 30 newly kidney transplanted patients, at month 1, 2, 3, and 5 for dd-cfDNA analysis, along with creatinine/eGFR and DSA monitoring, and from 32 patients who underwent an indication biopsy and whose dd-cfDNA levels were measured at the time of biopsy and 1 month afterwards. Fourteen of 32 (43.8%) patients in the biopsy group were diagnosed with TCMR and 5 of 32 (15.6%) with ABMR. Dd-cfDNA proved to be better than creatinine in diagnosing rejection from non-rejection in patients who were biopsied. When a dd-cfDNA threshold of 0.5% was chosen, sensitivity was 73.7% and specificity was 92.3% (AUC: 0.804, 0.646–0.961). In rejection patients, levels of dd-cfDNA prior to biopsy (0.94%, 0.3–2.0) decreased substantially after initiation of treatment with median returning to baseline already at 1 month (0.33%, 0.21–0.51, p = 0.0036). In the surveillance group, high levels of dd-cfDNA (>0.5%) from second month post-transplantation were correlated with non-increasing eGFR 1 year post-transplantation. The study used AlloSeq kit for kidney transplant surveillance for first time and confirmed dd-cfDNA’s ability to detect rejection and monitor treatment, as well as to predict worse long-term outcomes regarding eGFR.
Efficient BEM Modeling of the Heat Transfer in the Turbine Blades of Aero-Parts
Authors: Hsiao Yue-Fang , Shiah Yui-Chuin
Published: October 2023

The modeling of the turbine blades in aero-parts presents difficulties in conventional domain solution techniques, especially when internal cooling air passages and a thermal barrier coating (TBC) are applied. This paper presents a very efficient 3D modeling of the anisotropic heat conduction in turbine blades with the boundary element method (BEM), where both the TBC and cooling air passages are considered. The BEM is very ideal for this modeling, since only boundary meshes are required for it; however, a serious problem of nearly singular integration will arise in modeling with coarse meshes. In this article, an efficient modeling and computational algorithm using the BEM is applied for the simulation of heat conduction in the turbine blades of aero-parts. The present work proposes a simplified BEM model to replace multiple thin coating layers on the top of the blade. In the end, the veracity of the implemented BEM code as well as its computational efficiency are illustrated with a few examples, showing that the settled temperature on the substrate can be reduced by 20% by employing a TBC. As compared to the analyses with ANSYS, the percentages of difference were within 2%, while the CPU time spent by the BEM algorithm was about 1/8 of that of ANSYS, not to mention the meshing efforts saved by adopting by a treatment of equivalent convection.
Genome-Wide Association Study and Identification of Candidate Genes for Intramuscular Fat Fatty Acid Composition in Ningxiang Pigs
Authors: Zeng Qinghua , Gao Hu , Yin Shishu , Peng Yinglin , Yang Fang , Fu Yawei , Deng Xiaoxiao , Chen Yue , Hou Xiaohong , Wang Qian , Jin Zhao , Song Gang , He Jun , Yin Yulong , Xu Kang
Published: October 2023

Ningxiang pigs exhibit a diverse array of fatty acids, making them an intriguing model for exploring the genetic underpinnings of fatty acid metabolism. We conducted a genome-wide association study using a dataset comprising 50,697 single-nucleotide polymorphisms (SNPs) and samples from over 600 Ningxiang pigs. Our investigation yielded novel candidate genes linked to five saturated fatty acids (SFAs), four monounsaturated fatty acids (MUFAs), and five polyunsaturated fatty acids (PUFAs). Significant associations with SFAs, MUFAs, and PUFAs were found for 37, 21, and 16 SNPs, respectively. Notably, some SNPs have significant PVE, such as ALGA0047587, which can explain 89.85% variation in Arachidic acid (C20:0); H3GA0046208 and DRGA0016063 can explain a total of 76.76% variation in Elaidic Acid (C18:1n-9(t)), and the significant SNP ALGA0031262 of Arachidonic acid (C20:4n-6) can explain 31.76% of the variation. Several significant SNPs were positioned proximally to previously reported genes. In total, we identified 11 candidate genes (hnRNPU, CEPT1, ATP1B1, DPT, DKK1, PRKG1, EXT2, MEF2C, IL17RA, ITGA1 and ALOX5), six candidate genes (ALOX5AP, MEDAG, ISL1, RXRB, CRY1, and CDKAL1), and five candidate genes (NDUFA4L2, SLC16A7, OTUB1, EIF4E and ROBO2) associated with SFAs, MUFAs, and PUFAs, respectively. These findings hold great promise for advancing breeding strategies aimed at optimizing meat quality and enhancing lipid metabolism within the intramuscular fat (IMF) of Ningxiang pigs.
Intron Regions as Genetic Markers for Population Genetic Investigations of Opisthorchis viverrini sensu lato and Clonorchis sinensis
Authors: Tantrawatpan Chairat , Maleewong Wanchai , Thanchomnang Tongjit , Pilap Warayutt , Agatsuma Takeshi , Andrews Ross H. , Sithithaworn Paiboon , Saijuntha Weerachai
Published: October 2023

Opisthorchiasis and clonorchiasis are prevalent in Southeast and Far-East Asia, which are caused by the group 1 carcinogenic liver flukes Opisthorchis viverrini sensu lato and Clonorchis sinensis infection. There have been comprehensive investigations of systematics and genetic variation of these liver flukes. Previous studies have shown that O. viverrini is a species complex, called “O. viverrini sensu lato”. More comprehensive investigations of molecular systematics and population genetics of each of the species that make up the species complex are required. Thus, other polymorphic genetic markers need to be developed. Therefore, this study aimed to characterize the intron regions of taurocyamine kinase gene (TK) to examine the genetic variation and population genetics of O. viverrini and C. sinensis collected from different geographical isolates and from a range of animal hosts. We screened seven intron regions embedded in TK. Of these, we selected an intron 5 of domain 1 (TkD1Int5) region to investigate the genetic variation and population genetics of theses liver flukes. The high nucleotide and haplotype diversity of TkD1Int5 was detected in O. viverrine. Heterozygosity with several insertion/deletion (indel) regions were detected in TkD1Int5 of the O. viverrine samples, whereas only an indel nucleotide was detected in one C. sinensis sample. Several O. viverrine samples contained three different haplotypes within a particular heterozygous sample. There were no genetic differences between C. sinensis isolated from various animal host. Heterozygous patterns specifically detected in humans was observed in C. sinensis. Thus, TkD1Int5 is a high polymorphic genetic marker, which could be an alternative marker for further population genetic investigations of these carcinogenic liver flukes and other related species from a wide geographical distribution and variety of animal hosts.
YOLOv5-SA-FC: A Novel Pig Detection and Counting Method Based on Shuffle Attention and Focal Complete Intersection over Union
Authors: Hao Wangli , Zhang Li , Han Meng , Zhang Kai , Li Fuzhong , Yang Guoqiang , Liu Zhenyu
Published: October 2023

The efficient detection and counting of pig populations is critical for the promotion of intelligent breeding. Traditional methods for pig detection and counting mainly rely on manual labor, which is either time-consuming and inefficient or lacks sufficient detection accuracy. To address these issues, a novel model for pig detection and counting based on YOLOv5 enhanced with shuffle attention (SA) and Focal-CIoU (FC) is proposed in this paper, which we call YOLOv5-SA-FC. The SA attention module in this model enables multi-channel information fusion with almost no additional parameters, enhancing the richness and robustness of feature extraction. Furthermore, the Focal-CIoU localization loss helps to reduce the impact of sample imbalance on the detection results, improving the overall performance of the model. From the experimental results, the proposed YOLOv5-SA-FC model achieved a mean average precision (mAP) and count accuracy of 93.8% and 95.6%, outperforming other methods in terms of pig detection and counting by 10.2% and 15.8%, respectively. These findings verify the effectiveness of the proposed YOLOv5-SA-FC model for pig population detection and counting in the context of intelligent pig breeding.
Cardiogenic Pulmonary Edema in Emergency Medicine
Authors: Zanza Christian , Saglietti Francesco , Tesauro Manfredi , Longhitano Yaroslava , Savioli Gabriele , Balzanelli Mario Giosuè , Romenskaya Tatsiana , Cofone Luigi , Pindinello Ivano , Racca Giulia , Racca Fabrizio
Published: October 2023

Cardiogenic pulmonary edema (CPE) is characterized by the development of acute respiratory failure associated with the accumulation of fluid in the lung’s alveolar spaces due to an elevated cardiac filling pressure. All cardiac diseases, characterized by an increasing pressure in the left side of the heart, can cause CPE. High capillary pressure for an extended period can also cause barrier disruption, which implies increased permeability and fluid transfer into the alveoli, leading to edema and atelectasis. The breakdown of the alveolar-epithelial barrier is a consequence of multiple factors that include dysregulated inflammation, intense leukocyte infiltration, activation of procoagulant processes, cell death, and mechanical stretch. Reactive oxygen and nitrogen species (RONS) can modify or damage ion channels, such as epithelial sodium channels, which alters fluid balance. Some studies claim that these patients may have higher levels of surfactant protein B in the bloodstream. The correct approach to patients with CPE should include a detailed medical history and a physical examination to evaluate signs and symptoms of CPE as well as potential causes. Second-level diagnostic tests, such as pulmonary ultrasound, natriuretic peptide level, chest radiograph, and echocardiogram, should occur in the meantime. The identification of the specific CPE phenotype is essential to set the most appropriate therapy for these patients. Non-invasive ventilation (NIV) should be considered early in the treatment of this disease. Diuretics and vasodilators are used for pulmonary congestion. Hypoperfusion requires treatment with inotropes and occasionally vasopressors. Patients with persistent symptoms and diuretic resistance might benefit from additional approaches (i.e., beta-agonists and pentoxifylline). This paper reviews the pathophysiology, clinical presentation, and management of CPE.
Empirical Models of Respiration and Net Ecosystem Productivity and Their Applications in a Subtropical Coniferous Plantation in China
Authors: Bai Jianhui , Yang Fengting , Xu Mingjie , Wang Huimin
Published: October 2023

Net ecosystem exchange (NEE), solar radiation (including photosynthetically active radiation PAR), and meteorological parameters were measured in a subtropical coniferous plantation in China during 2013–2016. Applying the PAR balance principle at a canopy level and analyzing the observation data, an empirical model of respiration (Re, EMRe) considering 3-factor and 2-factor situations was developed and tested for all sky conditions. Generally, the respiration simulations were in reasonable agreement with the observations for the hourly, monthly, and annual sums of respiration. For example, using 3-factor and 2-factor models, the estimated annual sums of daytime and nighttime respiration in 2013–2016 overestimated that which was observed by about 31% and 26%, respectively. Further applications of EMRe and an empirical model of gross primary production (GPP, EMGPP) developed previously at this site, and an empirical model of net ecosystem productivity (NEP, EMNEP) using 3-factor and 2-factor models were obtained (NEP = GPP-Re) and evaluated for all sky conditions. Generally, the simulations of the hourly, monthly, and annual sums of NEP showed reasonable performances. The estimated NEP values overestimated the observations by 22% and 27% for the hourly sums in 2013–2016 when using the 3-factor and 2-factor models, respectively, and 7% and 12% for annual sums in 2013–2015 (2016 data were not used as the CO2 flux measurements had some problems in the 2016 summer). The NEP estimations were evidently improved when more factors (e.g., dark respiration) influencing Re were considered in the daytime respiration compared to those without considering these factors. To simplify the numerous and complicated CO2 processes in the simulations of Re and NEP, the PAR energy method was applied to capture and describe its main processes and energy interactions. The PAR energy method was suitable for studying the energy relationships associated with CO2 processes and developing empirical models for the simulations of GPP, Re, and NEP. These models were useful tools to investigate the multiple interactions and mechanisms between CO2, other atmospheric compositions, and PAR. Thus, the energy method is suggested to be applied to carbon balance.
Estimation of Daily Mean Land Surface Temperature over the Qinghai–Tibet Plateau Based on an RTM-DTC Model
Authors: Zhao Lei , Xue Dongjian , Zhang Xiaoxuan , Fu Yudi
Published: October 2023

Accurately estimating daily mean land surface temperature (LST) is crucial for studying the urban heat island effect, land–atmosphere energy exchange, and global climate change. However, limited research has been conducted on average surface temperature estimation, particularly in high-altitude regions like the Qinghai–Tibet Plateau with extensive cloud cover. In this study, we propose the Reanalysis Data and Thermal Infrared Remote Sensing Data Merging-Diurnal Temperature Cycle (RTM-DTC) model specifically for the Qinghai–Tibet Plateau, successfully estimating mean LST using the model. We apply the RTM method to reconstruct LST under cloud cover from the MODIS LST product and calculate the average temperature using the DTC model. Validation with in situ measurements from seven meteorological stations on the Tibetan Plateau yielded daily scale RMSEs ranging from 1.81 K to 2.021 K and monthly scale RMSEs ranging from 1.77 K to 2.0 K, with an average RMSE of 1.91 K. These results demonstrate the adaptability of the RTM-DTC model and its ability to depict the annual variation curve of the mean surface temperature, and provide further research on RTM-DTC as a valuable approach.
MM-EMOR: Multi-Modal Emotion Recognition of Social Media Using Concatenated Deep Learning Networks
Authors: Adel Omar , Fathalla Karma M. , Abo ElFarag Ahmed
Published: October 2023

Emotion recognition is crucial in artificial intelligence, particularly in the domain of human–computer interaction. The ability to accurately discern and interpret emotions plays a critical role in helping machines to effectively decipher users’ underlying intentions, allowing for a more streamlined interaction process that invariably translates into an elevated user experience. The recent increase in social media usage, as well as the availability of an immense amount of unstructured data, has resulted in a significant demand for the deployment of automated emotion recognition systems. Artificial intelligence (AI) techniques have emerged as a powerful solution to this pressing concern in this context. In particular, the incorporation of multimodal AI-driven approaches for emotion recognition has proven beneficial in capturing the intricate interplay of diverse human expression cues that manifest across multiple modalities. The current study aims to develop an effective multimodal emotion recognition system known as MM-EMOR in order to improve the efficacy of emotion recognition efforts focused on audio and text modalities. The use of Mel spectrogram features, Chromagram features, and the Mobilenet Convolutional Neural Network (CNN) for processing audio data are central to the operation of this system, while an attention-based Roberta model caters to the text modality. The methodology of this study is based on an exhaustive evaluation of this approach across three different datasets. Notably, the empirical findings show that MM-EMOR outperforms competing models across the same datasets. This performance boost is noticeable, with accuracy gains of an impressive 7% on one dataset and a substantial 8% on another. Most significantly, the observed increase in accuracy for the final dataset was an astounding 18%.
Prediction of the Proximal Humerus Morphology Based on a Statistical Shape Model with Two Parameters: Comparison to Contralateral Registration Method
Authors: van Schaardenburgh Florianne E. , Nguyen H. Chien , Magré Joëll , Willemsen Koen , van Rietbergen Bert , Nijs Stefaan
Published: October 2023

(1) Background: Complex proximal humerus fractures often result in complications following surgical treatment. A better understanding of the full 3D displacement would provide insight into the fracture morphology. Repositioning of fracture elements is often conducted by using the contralateral side as a reconstruction template. However, this requires healthy contralateral anatomy. The purpose of this study was to create a Statistical Shape Model (SSM) and compare its effectiveness to the contralateral registration method for the prediction of the humeral proximal segment; (2) Methods: An SSM was created from 137 healthy humeri. A prediction for the proximal segment of the left humeri from eight healthy patients was made by combining the SSM with parameters. The predicted proximal segment was compared to the left proximal segment of the patients. Their left humerus was also compared to the contralateral (right) humerus; (3) Results: Eight modes explained 95% of the variation. Most deviations of the SSM prediction and the contralateral registration method were below the clinically relevant 2 mm distance threshold.; (4) Conclusions: An SSM combined with parameters is a suitable method to predict the proximal humeral segment when the contralateral CT scan is unavailable or the contralateral humerus is unhealthy, provided that the fracture pattern allows measurements of these parameters.
Pediatric Brain Tissue Segmentation Using a Snapshot Hyperspectral Imaging (sHSI) Camera and Machine Learning Classifier
Authors: Kifle Naomi , Teti Saige , Ning Bo , Donoho Daniel A. , Katz Itai , Keating Robert , Cha Richard Jaepyeong
Published: October 2023

Pediatric brain tumors are the second most common type of cancer, accounting for one in four childhood cancer types. Brain tumor resection surgery remains the most common treatment option for brain cancer. While assessing tumor margins intraoperatively, surgeons must send tissue samples for biopsy, which can be time-consuming and not always accurate or helpful. Snapshot hyperspectral imaging (sHSI) cameras can capture scenes beyond the human visual spectrum and provide real-time guidance where we aim to segment healthy brain tissues from lesions on pediatric patients undergoing brain tumor resection. With the institutional research board approval, Pro00011028, 139 red-green-blue (RGB), 279 visible, and 85 infrared sHSI data were collected from four subjects with the system integrated into an operating microscope. A random forest classifier was used for data analysis. The RGB, infrared sHSI, and visible sHSI models achieved average intersection of unions (IoUs) of 0.76, 0.59, and 0.57, respectively, while the tumor segmentation achieved a specificity of 0.996, followed by the infrared HSI and visible HSI models at 0.93 and 0.91, respectively. Despite the small dataset considering pediatric cases, our research leveraged sHSI technology and successfully segmented healthy brain tissues from lesions with a high specificity during pediatric brain tumor resection procedures.
Correction of Arterial-Phase Motion Artifacts in Gadoxetic Acid-Enhanced Liver MRI Using an Innovative Unsupervised Network
Authors: Pan Feng , Fan Qianqian , Xie Han , Bai Chongxin , Zhang Zhi , Chen Hebing , Yang Lian , Zhou Xin , Bao Qingjia , Liu Chaoyang
Published: October 2023

This study aims to propose and evaluate DR-CycleGAN, a disentangled unsupervised network by introducing a novel content-consistency loss, for removing arterial-phase motion artifacts in gadoxetic acid-enhanced liver MRI examinations. From June 2020 to July 2021, gadoxetic acid-enhanced liver MRI data were retrospectively collected in this center to establish training and testing datasets. Motion artifacts were semi-quantitatively assessed using a five-point Likert scale (1 = no artifact, 2 = mild, 3 = moderate, 4 = severe, and 5 = non-diagnostic) and quantitatively evaluated using the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The datasets comprised a training dataset (308 examinations, including 58 examinations with artifact grade = 1 and 250 examinations with artifact grade ≥ 2), a paired test dataset (320 examinations, including 160 examinations with artifact grade = 1 and paired 160 examinations with simulated motion artifacts of grade ≥ 2), and an unpaired test dataset (474 examinations with artifact grade ranging from 1 to 5). The performance of DR-CycleGAN was evaluated and compared with a state-of-the-art network, Cycle-MedGAN V2.0. As a result, in the paired test dataset, DR-CycleGAN demonstrated significantly higher SSIM and PSNR values and lower motion artifact grades compared to Cycle-MedGAN V2.0 (0.89 ± 0.07 vs. 0.84 ± 0.09, 32.88 ± 2.11 vs. 30.81 ± 2.64, and 2.7 ± 0.7 vs. 3.0 ± 0.9, respectively; p < 0.001 each). In the unpaired test dataset, DR-CycleGAN also exhibited a superior motion artifact correction performance, resulting in a significant decrease in motion artifact grades from 2.9 ± 1.3 to 2.0 ± 0.6 compared to Cycle-MedGAN V2.0 (to 2.4 ± 0.9, p < 0.001). In conclusion, DR-CycleGAN effectively reduces motion artifacts in the arterial phase images of gadoxetic acid-enhanced liver MRI examinations, offering the potential to enhance image quality.
Identification of miR-128 Target mRNAs That Are Expressed in B Cells Using a Modified Dual Luciferase Vector
Authors: Schreiber Sandra , Daum Patrick , Danzer Heike , Hauke Manuela , Jäck Hans-Martin , Wittmann Jürgen
Published: October 2023

MicroRNAs (miRNAs) are 21–25 nucleotide long non-coding ribonucleic acids that modulate gene expression by degrading transcripts or inhibiting translation. The miRNA miR-128, originally thought to be brain-specific, was later also found in immune cells. To identify a valuable immune cell model system to modulate endogenous miR-128 amounts and to validate predicted miR-128 target mRNAs in B cells, we first investigated miR-128 expression using Northern blot analysis in several cell lines representing different stages of B cell development. The results showed that only primary brain cells showed significant levels of mature miR-128. To study the function of miR-128 in immune cells, we modified dual luciferase vectors to allow easy transfer of 3′ UTR fragments with predicted miR-128 binding sites from widely used single to dual luciferase vectors. Comparison of in silico predicted miR-128-regulated mRNAs in single and dual luciferase constructs yielded similar results, validating the dual luciferase vector for miRNA target analysis. Furthermore, we confirmed miR-128-regulated mRNAs identified in silico and in vivo using the Ago HITS-CLIP technique and known to be expressed in B cells using the dual luciferase assay. In conclusion, this study provides new insights into the expression and function of miR-128 by validating novel target mRNAs expressed in B cells and identifying additional pathways likely controlled by this miRNA in the immune system.
Quantitative Phosphoproteomic Analysis Provides Insights into the Sodium Bicarbonate Responsiveness of Glycine max
Authors: Li Qiang , Li Minglong , Ma Huiying , Xue Man , Chen Tong , Ding Xiaodong , Zhang Shuzhen , Xiao Jialei
Published: October 2023

Sodium bicarbonate stress caused by NaHCO3 is one of the most severe abiotic stresses affecting agricultural production worldwide. However, little attention has been given to the molecular mechanisms underlying plant responses to sodium bicarbonate stress. To understand phosphorylation events in signaling pathways triggered by sodium bicarbonate stress, TMT-labeling-based quantitative phosphoproteomic analyses were performed on soybean leaf and root tissues under 50 mM NaHCO3 treatment. In the present study, a total of 7856 phosphopeptides were identified from cultivated soybeans (Glycine max L. Merr.), representing 3468 phosphoprotein groups, in which 2427 phosphoprotein groups were newly identified. These phosphoprotein groups contained 6326 unique high-probability phosphosites (UHPs), of which 77.2% were newly identified, increasing the current soybean phosphosite database size by 43.4%. Among the phosphopeptides found in this study, we determined 67 phosphopeptides (representing 63 phosphoprotein groups) from leaf tissue and 554 phosphopeptides (representing 487 phosphoprotein groups) from root tissue that showed significant changes in phosphorylation levels under sodium bicarbonate stress (fold change >1.2 or <0.83, respectively; p < 0.05). Localization prediction showed that most phosphoproteins localized in the nucleus for both leaf and root tissues. GO and KEGG enrichment analyses showed quite different enriched functional terms between leaf and root tissues, and more pathways were enriched in the root tissue than in the leaf tissue. Moreover, a total of 53 different protein kinases and 7 protein phosphatases were identified from the differentially expressed phosphoproteins (DEPs). A protein kinase/phosphatase interactor analysis showed that the interacting proteins were mainly involved in/with transporters/membrane trafficking, transcriptional level regulation, protein level regulation, signaling/stress response, and miscellaneous functions. The results presented in this study reveal insights into the function of post-translational modification in plant responses to sodium bicarbonate stress.
Identifying Suitable Reference Gene Candidates for Quantification of DNA Damage-Induced Cellular Responses in Human U2OS Cell Culture System
Authors: Barta Nikolett , Ördög Nóra , Pantazi Vasiliki , Berzsenyi Ivett , Borsos Barbara N. , Majoros Hajnalka , Páhi Zoltán G. , Ujfaludi Zsuzsanna , Pankotai Tibor
Published: October 2023

DNA repair pathways trigger robust downstream responses, making it challenging to select suitable reference genes for comparative studies. In this study, our goal was to identify the most suitable housekeeping genes to perform comparable molecular analyses for DNA damage-related studies. Choosing the most applicable reference genes is important in any kind of target gene expression-related quantitative study, since using the housekeeping genes improperly may result in false data interpretation and inaccurate conclusions. We evaluated the expressional changes of eight well-known housekeeping genes (i.e., 18S rRNA, B2M, eEF1α1, GAPDH, GUSB, HPRT1, PPIA, and TBP) following treatment with the DNA-damaging agents that are most frequently used: ultraviolet B (UVB) non-ionizing irradiation, neocarzinostatin (NCS), and actinomycin D (ActD). To reveal the significant changes in the expression of each gene and to determine which appear to be the most acceptable ones for normalization of real-time quantitative polymerase chain reaction (RT-qPCR) data, comparative and statistical algorithms (such as absolute quantification, Wilcoxon Rank Sum Test, and independent samples T-test) were conducted. Our findings clearly demonstrate that the genes commonly employed as reference candidates exhibit substantial expression variability, and therefore, careful consideration must be taken when designing the experimental setup for an accurate and reproducible normalization of RT-qPCR data. We used the U2OS cell line since it is generally accepted and used in the field of DNA repair to study DNA damage-induced cellular responses. Based on our current data in U2OS cells, we suggest using 18S rRNA, eEF1α1, GAPDH, GUSB, and HPRT1 genes for UVB-induced DNA damage-related studies. B2M, HPRT1, and TBP genes are recommended for NCS treatment, while 18S rRNA, B2M, and PPIA genes can be used as suitable internal controls in RT-qPCR experiments for ActD treatment. In summary, this is the first systematic study using a U2OS cell culture system that offers convincing evidence for housekeeping gene selection following treatment with various DNA-damaging agents. Here, we unravel an indispensable issue for performing and assessing trustworthy DNA damage-related differential gene expressional analyses, and we create a “zero set” of potential reference gene candidates.