bluuebunny/crossref_metadata_2025 huggingface dataset:
Eucommia ulmoides leaf extract attenuates cisplatin-induced kidney injury in mice through endoplasmic reticulum stress and biometabolic mechanism
Authors: Lin Kexin , Xiong Lijuan , Zhang Wen , Chen xuan , li Xiaofei , Zhang Jianyong
Published: February 2024

Cisplatin (CP) is a widely utilized anticancer drug but is associated with significant side effects, notably acute kidney injury (AKI). Eucommia ulmoides leaf (EUL), a valuable Chinese herbal remedy, is known for its renoprotective properties. However, the function and underlying pathways of EUL in AKI therapy have remained largely unexplored. This research aimed to elucidate the protective roles of EUL in an AKI mouse model through biochemical assays, and histopathological andexaminations while also investigating the underlying mechanisms via endoplasmic reticulum (ER) stress-related protein expression analysis and metabolomics. The findings demonstrated that pretreatment with orally administered EUL significantly reduced blood urea nitrogen (BUN) and serum creatinine (SCr) levels, ameliorated CP-induced kidney histopathological injuries, and attenuated CP-induced ER stress by reducing the protein expressions of PERK, IRE 1α, GRP78, ATF6, ATF4, and CHOP. Additionally, metabolomics analysis identified 31 significant differential metabolites affected by EUL treatment in AKI mice, impacting pathways related to taurine and hypotaurine metabolism, lysine degradation, and steroid hormone biosynthesis. These findings suggested that EUL could offer valuable insights for potential CP-induced AKI treatment strategies.
Optimization of placement of continuous air monitors in a radiological facility
Authors: Chakraborty Joy , K Sureshkumar M , S Anand , Joshi Manish , Kulkarni M S
Published: February 2024

Airborne radioactive materials are encountered in the atmosphere of any radiological facility. Their concentration is affected by the ventilation system and/or air purification system and by the processes of the facility. Continuous air monitors (CAMs) are installed in these workplaces during both routine and non-routine operations to monitor the ambient air quality in terms of concentration of airborne radioactive aerosols and trigger interventions. The location for these monitors is typically determined by rules of thumb like direction of maximum air flow. The aerodynamic behaviour of aerosols does not follow the behaviour of air streamlines and this poses a question on the appropriateness of selection of location of CAMs. Addressing this issue, we used a computational fluid dynamics model coupled to aerosol dynamics code to calculate a parameter termed as CAM placement parameter (CPP) that estimates the relative merit of positioning the CAMs in a radiological facility. Our method involved simulation of dispersing aerosol particles in a typical geometry of such a facility. The results imply that inclusion of aerosol microphysical processes (coagulation, gravitational settling, turbophoresis, thermophoresis etc) in the flow dynamics can help to determine the CPP in workplace environment and therefore minimize the occupational exposure.
Success stories: exploring the long-term impact of a global faculty development program for health professions educators
Authors: Yuan Shiyao , Rahim Asma , Kannappan Suvetha , Dongre Amol , Jain Animesh , Kar Sitanshu Sekhar , Mukherjee Snigdha , Vyas Rashmi
Published: February 2024

Background
Faculty development programs (FDPs) in health professions education (HPE) are instrumental in supporting, promoting, and improving HPE curricula and activities that target individual- and system-level capacity strengthening. FAIMER, a division of Intealth, has been offering FDPs for global health professions educators in HPE and leadership for over two decades through the International FAIMER Institute in USA and FAIMER Regional Institutes located globally. This paper examines the long-term impact of FAIMER's global FDPs by eliciting stories of success from program participants (Fellows).
Methods
The study utilizes a modified Success Case Method approach. Study participants recruited were 14 FAIMER Fellows nominated as exemplars of success cases by program directors and faculty members from seven global sites, using extreme case sampling technique. Of the 14 Fellows, two were from Africa, nine from Asia and three from Latin America. In-depth interviews were conducted with each Fellow to understand their self-perceived success and factors from both their local context and FAIMER’s FDPs that have contributed to their success. Theoretical thematic analysis method was used to analyze the data.
Results
The top three emergent areas of success perceived by Fellows were career advancement & recognition, professional development, and advancement of HPE. Fellows identified both institution-level factors and personal attributes as contributing to their success. They also reported that FAIMER’s FDPs have substantially contributed to their success by expanding professional networks, fostering academic achievement, and enhancing interpersonal development and leadership skills.
Conclusions
The findings show the long-term impact of FAIMER’s FDPs in advancing the field of HPE which is reflected by the multiple pathways for Fellows to succeed. The Fellow stories highlight the continuing value of FAIMER’s global FDP for HPE educators worldwide in professional development and leadership. The study also proposes recommendations for enhancing faculty development activities in HPE for long-term impact.
Restricted hybridisation in the secondary contact zone of closely related haplodiploid social spider mites
Authors: Sato Yukie , Konaka Shota , Hirota Shun , Suyama Yoshihisa , Tsumura Yoshihiko
Published: February 2024

How frequently hybridisation and gene flow occur in the contact zones of diverging taxa is important for understanding the speciation process.Stigmaeopsis sabelisi and Stigmaeopsis miscanthi HG form are haplodiploid, social spider mites that infest the Chinese silver grass, Miscanthus sinensis. These two species are closely related and parapatrically distributed in Japan. In mountainous areas, S. sabelisi and S. miscanthi HG form are often found in the highlands and lowlands, respectively, suggesting that they are in contact at intermediate altitudes. It is estimated that they diverged from their common ancestors distributed in subtropical regions (south of Japan) during the last glacial period, expanded their distribution into the Japanese Archipelago, and came to have such a parapatric distribution (secondary contact). As their reproductive isolation is strong but incomplete, hybridisation and genetic introgression are expected at their distributional boundaries. In this study, we investigated their spatial distribution patterns along the elevation on Mt. Amagi using male morphological differences and investigated their hybridisation status using single-nucleotide polymorphisms by MIG-seq. We found their contact zone at altitudes of 150–430 m, suggesting that their contact zone is prevalent in the parapatric area, which is in line with a previous study. Interspecific mating was predicted based on the sex ratio in the contact zone. However, no obvious hybrids were found, and genetic introgression was estimated to be extremely low. Here, we discuss why gene flow is extremely restricted to the contact zone.
How is diagnostic uncertainty communicated and managed in real world primary care settings?
Authors: Russell Jessica , Boswell Laura , Ip Athena , Harris Jenny , Singh Hardeep , Meyer Ashley ND , Giardina Traber D. , Bhuiya Afsana , Whitaker Katriina L. , Black Georgia B.
Published: February 2024

Background
Managing diagnostic uncertainty is a major challenge in primary care due to factors such as the absence of definitive tests, variable symptom presentations and disease evolution. Maintaining patient trust during a period of investigative uncertainty, whilst minimising scope for diagnostic error is a challenge. Mismanagement can lead to diagnostic errors, treatment delays, and suboptimal patient outcomes.
Objective
Our aim was to explore how UK primary care physicians (GPs) address and communicate diagnostic uncertainty in practice.
Design
This qualitative study used video and audio-recordings. Verbatim transcripts were coded with a modified, validated tool to capture GPs’ actions and communication in primary care consultations that included diagnostic uncertainty. The tool includes items relating to advice regarding new symptoms or symptom deterioration (sometimes called ‘safety netting’). Video data was analysed to identify GP and patient body postures during and after the delivery of the management plan.
Participants
All patient participants had a consultation with a GP, were over the age of 50 and had (1) at least one new presenting problem or (2) one persistent problem that was undiagnosed.
Approach
Data collection occurred in GP-patient consultations during 2017-2018 across 7 practices in UK during 2017-2018.
Key Results
GPs used various management strategies to address diagnostic uncertainty, including (1) symptom monitoring without treatment, (2) prescribed treatment with symptom monitoring, and (3) addressing risks that could arise from administrative tasks. GPs did not make management plans for potential treatment side effects. Specificity of uncertainty management plans varied among GPs, with only some offering detailed actions and timescales. The transfer of responsibility for the management plan to patients was usually delivered rather than negotiated, with most patients confirming acceptance before concluding the discussion.
Conclusions
We offer guidance to healthcare professionals, improving awareness of using and communicating management plans for diagnostic uncertainty.
Nitrogen-containing bisphosphonate induces enhancement of OPG expression and inhibition of RANKL expression via inhibition of farnesyl pyrophosphate synthase to inhibit the osteogenic differentiation and calcification in vascular smooth muscle cells
Authors: Xu Wei , Gong Lifeng , Tang Weigang , Lu Guoyuan
Published: February 2024

Background
The osteogenic differentiation of vascular smooth muscle cells (VSMCs) plays a key role in the progression of Vascular calcification（VC）. The previous research showed that nitrogen-containing bisphosphonate（N-BP）had inhibitory effect on the osteogenic differentiation and calcification induced by high phosphorus in VSMCs. However, it is not clear how N-BP inhibits the osteogenic differentiation and calcification in VSMCs via specific regulatory mechanisms.
Methods
β-glycerophosphate (β-GP) was used to induce the osteogenic differentiation and calcification in VSMCs. In some experiments, VSMCs were cultured with β-GP , or β-GP plus different concentrations of N-BP. In some other experiments, the VSMCs were cultured with β-GP plus N-BP, β-GP plus N-BP and farnesol (FOH), or β-GP plus N-BP and geranylgeraniol (GGOH). Alizarin red S staining and determination of calcium content were used to detect calcium deposition.Western Blotting were used to detect expressions of proteins(OPG and RANKL ) and osteogenic marker proteins (Runx2 and OPN).
Results
β-GP induced the osteogenic differentiation and calcification in VSMCs , increased RANKL protein expression and had no significant effect on OPG protein expression. N-BP increased OPG protein expression and decreased RANKL protein expression in VSMCs of osteogenic differentiation and calcification induced by β-GP. In addition, N-BP reduced the osteogenic marker proteins (Runx2 and OPN) expression and calcium deposition in VSMCs of osteogenic differentiation and calcification induced by β-GP. These effects of N-BP on the osteogenic differentiation and calcification in VSMCs were concentration-dependent, which could be reversed by the downstream products of farnesyl pyrophosphate synthase(FPPS) in mevalonate pathway such as FOH or GGOH.
Conclusion
N-BP increases OPG expression and decreases RANKL expression via inhibition of FPPS to inhibit the osteogenic differentiation and calcification of VSMCs
A Comparison of Intraoperative Ultrasound and Post- Operative Mri in Paediatric Intra Axial Tumours
Authors: Fuad Nadiah Ahmad , Alias Azmi , Rosli Fadzlishah Johanabas , Idris Zamzuri , Ismail Rohazly , An Adi Afiq Md
Published: February 2024

Background Malignant tumours of the Central Nervous System (CNS) is the second most common paediatric malignancy, following haematological malignancy. CNS tumours depend significantly on the extent of tumour resection. In the past decade, intraoperative ultrasound (IOUS) has evolved into a widespread neuroimaging tool that offers real-time surgical guidance, and is a widely accessible imaging modality with minimal identified risk or additional operative time, and also lower economic investment. Recent studies found a strong correlation between IOUS and post operative MRI findings when evaluating extent of tumour resection in adult population suggesting that IOUS might have significant clinical implications, however not many studies involved the paediatrics population. The objective of our study was to compare correlation and reliability of the ultrasound as compared to MRI for tumour resection in paediatric population. We also evaluated IOUS as a real-time navigation tool during surgical resection of intra axial tumours in paediatric patients.
Methods This was a prospective observational study done in a Hospital Tunku Azizah, Kuala Lumpur. Patients aged less than 12 years old, admitted between 1st December 2019 to 30th November 2021 with preoperative MRI diagnosis of intra-axial brain tumour were recruited in this study. IOUS was performed intraoperatively, and post-operative MRI performed within 24-72 hours post-op. IOUS utility assessment scoring was performed intraoperatively.
Results We analysed a total of 25 paediatric patients that met the inclusion criteria. Concordance analysis between IOUS evaluation and MRI showed good agreement (kappa = 0.527). A multivariate logistic regression analysis was performed to look for any significant factors of discordance, and none of the variables were found significant for discordance. Hosmer and Lemeshow goodness of fit test was performed, and showed that our model is a good fit, with a good prediction value of 0.497.
Conclusion The use of IOUS allowed reliable intra operative imaging modality to achieve more successful gross total resection of brain tumours in paediatrics.
0.68% of solar-to-hydrogen efficiency and super-high photostability: Panel H2O-to-H2 conversion based on a novel organic-inorganic membrane catalyst
Authors: Li Wei , Duan Wen , Liao Guocheng , Gao Fanfan , Wang Yusen , Cui Rongxia , Zhao Jincai , Wang Chuanyi
Published: February 2024

Solar-driven flat-panel H2O-to-H2 (HTH) conversion is an important technology for value-added solar fuel production. However, most frequently used particulate photocatalysts are hard to achieve stable photocatalysis in flat-panel reaction module due to the influence of mechanical shear force by fluid and generated bubbles. Constructing stable photocatalytic membrane can effectively address the critical issues of particulate photocatalysts. Herein, a highly active CdS@SiO2-Pt composite with rapid CdS-to-Pt electron-transfer kinetics and restrained photoexciton recombination kinetics was prepared to process into an organic-inorganic membrane by compounding with organic ferroelectric polyvinylidene fluoride (PVDF). This organic-inorganic synergistically interface networked membrane catalyst displays super-high photostability and excellent operability, dramatically overcoming the drawback of inorganic particulate catalysts, and achieving remarkable simulated sunlight (SSL)-driven alkaline (pH = 14.0) HTH activity (213.48 mmol∙m-2∙h-1) with 0.68% of solar-to-hydrogen (STH) efficiency. No obvious variation in its appearance and micromorphology was observed even being recycled for 50-times, which considerably outperforms the existing membrane photocatalysts. Subsequently, a homemade panel reaction system was fabricated to achieve alkaline water-splitting to obtain a 0.05% of STH efficiency under SSL-irradiation. This study opens up a new prospect for practical application of panel photocatalytic hydrogen production with organic-inorganic interface networked membrane technology.
Causal relationships between systolic blood pressure and ischemic stroke based on two-sample Mendelian randomization
Authors: Liu Xiao-Hu , Yang Ze-Hua , Zhou Yue , Luo Jia-zhuang , Yao Run-lan
Published: February 2024

Context: Stroke is a major public health issue globally. Therefore, ongoing research on the risk factors for ischemic stroke (IS) is essential for its prevention.
Objective: To assess the potential causal relationship between systolic blood pressure (SBP) and ischemic stroke (IS).
Design and Setting: A total of 810,865 SBP and 440,328 IS samples from publicly-available genome-wide association studies (2020 and 2018, respectively) were analyzed. A two-sample Mendelian randomization (MR) study was conducted to assess the causal relationships between SBP and the risk of IS. Inverse-variance weighting (IVW), and weighted median (WME), weighted mode (WM), and MR-Egger regression methods were also undertaken. Odds ratios (OR) and 95% confidence intervals (95%CI) were calculated. Heterogeneity and the presence of horizontal multicollinearity were tested using a Cochran’s Q test and MR-Egger intercept, respectively.
Main Outcome Measures: Correlation between SBP and IS was the main study outcome. Results: IVW showed a positive correlation between genetically-predicted SBP and IS (OR = 1.849, 95%CI = 1.628–2.099, P = 2.844×10-21). MR-Egger (OR = 2.192, 95%CI = 1.626–2.257, P = 2.610×10-7), WME (OR = 1.945, 95%CI = 1.676–2.257, P = 7.752×10-18), and WM methods (R = 2.246, 95%CI = 1.666–3.030, P = 4.008×10-8) supported the existence of a causal relationship between SBP and IS. MR-Egger intercept testing did not detect horizontal pleiotropy. The “leave-one-out” sensitivity analysis showed no bias in the results.
Conclusions:There is a positive causal relationship between genetically-predicted SBP and IS, suggesting that elevated SBP is a risk factor for IS.
Smart Home AI Model (SHAM): Optimizing Switches and Sockets space in Residential Building during Planning/Construction through Multilayer Perceptron Estimation
Authors: Singh Ashish Singh Ashish , Saxena Shubham Saxena Shubham , Singh Shailendra Kumar Singh Shailendra Kumar , Pandey Aatrey Pandey Aatrey
Published: February 2024

This study presents the development and evaluation of a Multilayer Perceptron (MLP) model for estimating the optimal number of switches and sockets space in houses not designed by professional engineers. Utilizing data from 145 houses across diverse cities, the study employs the Back Propagation Levenberg-Marquardt (BPLM) technique for MLP model estimation The SHAM is AI based multilayer perceptron model which recommends optimum number of switches and sockets space by considering factors like topography, type of property, size of property, type of occupancy. This model predicts the most suitable combination of switches and sockets space for a newly planned house. Utilizing data from 145 houses across diverse cities, the MLP architecture selection was based on performance metrics such as Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) from testing and training data. The statistical analysis identified the 26-9-1 MLP architecture with a minimum MAPE value of 0.2572. Subsequently, this selected neural network model was applied to predict the optimal number of switches and sockets space. The study demonstrates the efficacy of the developed Smart Home AI Model (SHAM), proving valuable for both unprofessional and professional civil engineers during the planning stage. The implementation of the AI-based SHAM model is discussed, highlighting its adaptability to get optimum number of switches and sockets space without a detailed designing of electrical circuits. Results from real-world simulations demonstrate the effectiveness of the model in achieving energy savings, efficient construction and user satisfaction compared to traditional, non-optimized setups.
Drug-loaded MITO@HA nanodrugs for evaluating the efficacy of targeted therapy for pancreatic cancer
Authors: Liu Fengjun , Xu Zhiyang , Jia Xiaochao , Tang Yidan , Chen Mingsheng , Chen Chuan , Fang Fang , Shi Xiudong , Chen Tianyou , Shi Yuxin
Published: February 2024

The purpose of this study was to evaluate the efficiency of mitoxantrone hydrochloride (MITO@HA) as a targeted pancreatic cancer therapy. MITO@HA binds to sodium hyaluronate, which is highly expressed in pancreatic cancers. This study seeks to evaluate MITO@HA treatment efficacy, clarify its inhibitory effect on pancreatic cancer, and provide an experimental basis for the use of organic polymer nanoparticles loaded with antitumor drugs to treat pancreatic cancer. This treatment strategy was developed for pancreatic cancer based on the hydrophobic behavior of the nanopharmaceutical MITO@HA. The average particle size of MITO@HA was 51.4 ± 2.3 nm, and the particles had a spherical structure. CCK-8 assays revealed that both MITO and MITO@HA inhibited the proliferation of pancreatic cancer cells. that the most suitable experimental conditions were determined to be exposing pancreatic cancer cells to 0.5 uM/L MITO@HA for 2 days. PANC-1 pancreatic cancer cells and pancreatic cancer tissues were found to express high levels of CD44. In in vitro experiments, MITO@HA inhibited G0/G1 phase arrest, increased apoptosis, and decreased cell replication, cell migration and invasion in the pancreatic cancer cell cycle compared to MITO alone. Therefore, we believe that MITO@HA has a good tumor cell inhibitory effect. Furthermore, in vivo experiments revealed that the tumor volume in nude mice in the MITO@HA group decreased (P < 0.05), and both MITO and MITO@HA treatment decreased the tumor growth curves, with MITO@HA decreasing them more than MITO alone. Compared with those in the control group and the MITO group, the HE staining of tumors in the MITO@HA group showed massive liquefaction necrosis of the tumor tissues. Safety evaluation of the nude mice in the MITO@HA group revealed that the mice had a normal blood profile, normal liver and kidney function, and normal myocardial enzymes. The above results indicate that MITO@HA can effectively accumulate in pancreatic cancer tumor tissue through the EPR effect and CD44 receptor targeting, leading to liquefaction and necrosis of tumor tissue, thereby effectively reducing tumor growth. The above results showed that MITO@HA is highly safe and can enhance the antitumor effect on pancreatic cancer, providing an experimental basis for clinical application.
RETRACTED: Modeling Photochemical Pollution in Bizerte : Case of Tropospheric Ozone
Authors: SELLAMI Mounir
Published: February 2024

The authors have requested that this preprint be removed from Research Square.
Deciphering and Constructing the Quorum Sensing Language “Interpreter” Ecosystem for Microbial Community
Authors: Wu Shengbo , Zhang Hong , Zhou Yongsheng , Xu Chengyang , Yuan Boxin , Wang Haijiao , Chen Danlei , Liu Chunjiang , Caiyin Qinggele , Wu Hao , Yang Aidong , Zhao Kun , Qiao Jianjun
Published: February 2024

Background
Microbes in diverse natural communities communicate via quorum sensing (QS) signals that act as microbial languages. However, earlier work on the investigation on microbial interactions and systematic stability mostly based on the diverse modelling-assistant ecological pairwise interactions (such as cooperation and competition) or metabolite-based interactions (such as division of labor and auxotrophies). The investigation and synthesis of multiple QS signals transmission in the QS communication network (QSCN) are less explored. The aim of this work is to decipher and construct another type of microbial interactions, i.e., QSCN, in the complex environment by considering cell heterogeneity, environmental interference and other factors, so as to provide the basis and guidance for the more practical applications of gut microbiota composed of different strains.
Results
In this study, to deepen the understanding of QS-based complex interactions, we firstly constructed and analyzed the directed QSCN including nine QS systems for a validated stable gut microbiome. Then, QS language “interpreter” was proposed and constructed in five Escherichia coli strains to simulate the linear and circular QSCN among natural microbial communities. Specifically, by combining single-cell microscopic and bulk-level macroscopic measurements, we investigate the performances and dynamics of synthetic three-strain QS language “interpreter” ecosystems that are in response to dramatic environmental changes. Data analysis and experimental results showed that the existence of complex QS language "interpreter" ecosystems promote the stability maintenance of microbial community. Furthermore, a comprehensive kinetic computational model was developed for the optimization of tunable directed QSCN. Finally, the perspectives of the QSCN for the effective control of microbial communities were discussed and summarized.
Conclusion
Microbial QS language “interpreter” is an interesting and broad conceptual advance for the construction of the large-scale microbial communities. The dynamic characteristics and transmission law of QS "interpreter" in microbial communication studied in this paper is an important basis for understanding microbial social communications. The findings in this study will help to optimize QS-based consortia and benefit their potential applications in ecosystem-based engineering and microbiome-based therapeutics.
Knockdown of PTEN Disrupts PINK1/Parkin-mediated Mitophagy and Promotes Hippocampal Neuronal Cell Death Following Experimental Status Epilepticus
Authors: Lin Li-Han , Hsu Chung-Yao , Huang Chin-Wei , Lin Tsu-Kung , Chen Shang-Der , Pan Hsiu-Yung , Chuang Yao-Chung
Published: February 2024

Aberrant mitophagy due to mitochondrial defects via oxidative stress has a strong potential to cause epilepsy. However, PINK1 signaling regarding damaged mitochondria- and oxidative stress-mediated mitophagy during status epilepticus (SE) remains to be fully elucidated. Therefore, the present study aimed to clarify this molecular mechanism in SE animal model. We used pilocarpine-induced SE animal model to investigate the molecular mechanism of SE. Our data exhibited that the hippocampal tissue of rats during SE increased the expression and PTEN and mitophagy-related markers (PINK1, Parkin, p-Drp1 and LC3B-II), showed the mitochondria dysfunction (oxygen consumption and mitochondrial fission), increased the oxidative stress, displayed an mitophage feature (mitophagic vacuole formation), and induced apoptotic cell death. Of note, SE-increased oxidative stress from the hippocampal tissue induced PINK1/Parkin-mediated mitophagy effects via an PTEN-dependent manner. Inhibition of PTEN expression by siRNA transfection in the hippocampal tissue blocked PINK1/Parkin-mediated mitophagy effect caused by oxidative stress. Subsequently, inhibition of PTEN expression enhanced Drp1-mediated mitochondria fission and ensuing damaged mitochondrial-induced apoptotic cell death. In conclusions, these findings reveal that hippocampal neuronal cells during SE display features of oxidative stress, damaged mitochondria, apoptotic cell death and mitophagy, and PTEN contribute to activate these program in hippocampal neuronal cells.
Noradrenergic signaling controls Alzheimer's disease pathology via activation of microglial β2 adrenergic receptors
Authors: Majewska Ania , Le Linh , Feidler Alexis , Li Herman , Kara-Pabani Kallam , Lamantia Cassandra , O'Banion  M. Kerry
Published: February 2024

Norepinephrine (NE) is a potent anti-inflammatory agent in the brain. In Alzheimer’s disease (AD), the loss of NE signaling heightens neuroinflammation and exacerbates amyloid pathology. NE inhibits surveillance activity of microglia, the brain’s resident immune cells, via their β2 adrenergic receptors (β2ARs). Here, we investigate the role of microglial β2AR signaling in AD pathology in the 5xFAD mouse model of AD. We found that loss of cortical NE projections preceded the degeneration of NE-producing neurons and that microglia in 5xFAD mice, especially those microglia that were associated with plaques, significantly downregulated β2AR gene expression early in amyloid pathology. Importantly, dampening microglial β2AR signaling worsened plaque load and the associated neuritic damage, while stimulating microglial β2AR signaling attenuated amyloid pathology. Our results suggest that microglial β2AR could be explored as a potential therapeutic target to modify AD pathology.
Assessment of Public Breast Cancer Awareness among Females in Klang Valley, Malaysia
Authors: Hee Mei Qi , Fatokun Omotayo Oladuntoye , Abdullah Norlia , Farukkh Muhammad Junaid
Published: February 2024

Background
Breast cancer has now replaced lung cancer as the most common form of cancer, and its burden has been rising over the past decades, including in Malaysia. The future burden of breast cancer is predicted to increase to over 3 million new cases and 1 million deaths by 2040. The implications of the migratory flows and population spike in Klang Valley piqued our interest in finding out more about female breast cancer awareness, particularly their breast cancer knowledge and knowledge, attitude, and practice (KAP) of breast self-examination (BSE).
Methods
A cross-sectional study was conducted between April to June 2021 on women who aged ≥ 20 years old and live in Klang Valley. A validated questionnaire of 61 questions about breast cancer awareness was distributed online (social media platform) and physical approaches (community pharmacy). The participant's overall KAP was categorized using Bloom's cut-off point. A KAP domain was considered sufficient if the score was ≥ 60% and insufficient if the score was < 60%. The Mann-Whiteny U test and Kruskal-Wallis H test were used to determine the differences in median. The relationships between KAP domains were measured using Spearman’s correlation test.
Results
A total of 491 responses were collected. We found that most participants had adequate breast cancer knowledge (67.8%) and BSE knowledge (80.5%) and positive attitude of BSE (97.1%). However, their BSE practice was extremely poor (72.9%), and forgetfulness was the main barrier. Young women had poorer KAP scores than older women. Significant correlations were found between breast cancer knowledge–BSE knowledge (r = 0.682, p < 0.005), breast cancer knowledge–BSE practice (r = 0.384, p < 0.005), BSE knowledge–BSE practice (r = 0.372, p < 0.005) and BSE attitude–BSE barrier (r = -0.380, p < 0.005).
Conclusion
BSE remains a viable and practical alternative screening method for Malaysian women. Overall, the participants have adequate knowledge and positive attitude, but implement transformational changes is needed address issues such as low understanding among young women and poor BSE practice to down-staging breast cancer in Malaysia.
CSCNN: Lightweight modulation recognition model for mobile multimedia intelligent information processing
Authors: Chen Jun , Huang Yiping , Zhang Ling , Si Guangzhen , Wang Juzhen
Published: February 2024

With the advancement of the Internet of Things, the importance of multimedia intelligent information processing technology is increasing. Faced with massive electromagnetic data and limited terminal equipment computing resources, previous technologies cannot meet the real-time decisionmaking requirements for processing short-term observations or burst signals in deployable systems. In this paper, our approach proposes the Deep Complex Separable Convolution (DCSC) operation by combining separable convolution operation and complex convolution operation. At the same time, to better preserve coupling information between channels and minimize the model size, we propose the Multilevel Separable Convolutional Residual Block (MSCRB). Based on the above two methods, we constructed the Complex Separable Convolutional Neural Network (CSCNN). This neural network significantly reduces the complexity of the deep learning model. The smallest network we constructed, CSCNN-Tiny, has a model size of 0.760M, which is only 6% of the size of MobileNet. With 0.815M Flops, it is 3.8% of MobileNet. However, it achieves a recognition accuracy of 50.97%, only 0.97% lower than MobileNet.
Impact of Yoga Training on Oxidative Stress: A Randomized Controlled Trial
Authors: Sagre Siddharth , Kumari Neetu , Bhardwaj Priya , Maruthi Mulaka , Jangra Ashok , Kumar Dinesh , Ahlawat Ravinder Pal
Published: February 2024

Background: Oxidative stress, a critical health parameter, occurs because of an imbalance of free radicals and antioxidants and resulting in the onset of chronic diseases. Yoga is being used widely as a complementary medicine, recognized for its preventive effects on age-related chronic diseases, but the underlying effects in relation to oxidative stress are not well known.
Aim: The current research aimed to find the impact of yoga training on oxidative stress by assessing the levels of Malondialdehyde (MDA), Ferric Reducing Ability of Plasma (FRAP), and Free radical scavenging capacity by 2,2-diphenyl-1-picrylhydrazyl(DPPH) assay.
Method: Thirty sedentary university students with an average age of 23.93±1.73 years were selected and randomly allocated into control (n=15) and experimental (n=15) group. The study employed a pre-test post-test random group design, with the 12-week yoga treatment given to the experimental group while the other group serves as control. The training program consisted of asana (body postures), pranayama (breathing exercises), and relaxation exercises. The blood samples were collected in fasting conditions at the beginning and end of the intervention to measure the level of oxidative stress biomarkers.
Results and Conclusion: At baseline, statistically insignificant differences were found in the level of oxidative stress level and demographic characteristics in both groups. After the training intervention, in only experimental group statistically significant changes were observed in the level of oxidative stress biomarkers (p<0.05). The results of the study suggest that integration of yoga practice may have beneficial effect in advancing health and wellness into the lives of college students.
Analyzing Multilingual Conversations During COVID-19: An Imbalanced Class-Ensemble Learning Approach with Reweighted AdaBoost-SVM for Code-Switched Text Classification
Authors: Jaballi Samawel , Zrigui Salah , Nicolas Henri , Zrigui Mounir
Published: February 2024

This study confronts the challenge of analyzing multilingual, code-switched conversations during the COVID-19 pandemic, a context where traditional classifiers often fall short. We developed a cost-sensitive ensemble learning approach that combines a reweighted AdaBoost-SVM model with an SVM as its base learner, specifically designed to effectively manage the imbalanced dataset common in code-switched communication scenarios. A key innovation of our approach is the novel rebalancing of AdaBoost weights. By incrementally adjusting the weights of misclassified samples from both minority and majority classes, we achieve a more balanced classification in each iteration. This strategy significantly improves the accuracy for minority class classification, a common issue with existing models. In the testing phase, we employed a comprehensive selection of both machine and deep learning classifiers, including Naive Bayes, Decision Trees, SMOTEBoost, CNN, Bi-LSTM, etc. These classifiers underwent comprehensive evaluation across two different multilingual datasets, assessed using six distinct metrics, including P-mean. The results from our experiments demonstrate that our proposed ensemble learning approach, fine-tuned with optimal hyperparameters and leveraging M-BERT for feature extraction, achieved remarkable accuracies of 78.84%, 86.56% and 83.96% on the test sets of the CTSA, TUNIZI and combined CTSA-TUNIZI datasets, respectively. This performance not only surpassed traditional classification methods but also outperformed advanced deep learning models, such as Bi-LSTM.
Analytical and Numerical Solution for Inverse Coefficient Non-Linear Hyperbolic Equation with Periodic Boundary Condition
Authors: Yernazar Akbala , Aslan Erman , Bağlan İrem
Published: February 2024

This study investigates an inverse problem of unknown time-dependent coefficients in the one-dimensional nonlinear hyperbolic equation with periodic boundary conditions. The generalized Fourier method is employed to construct the Fourier coefficient for the solutions, and using iteration method convergence, the uniqueness and stability of the solution to the nonlinear problem are proved. Additionally, in order to solve the inverse problem numerically Finite Difference Method (FDM) with Gauss Seidel Iteration process is proposed. Two different implicit finite difference schemes are applied, namely, implicit and Crank-Nicolson. A numerical example is presented to illustrate the method's behavior. Both numerical predictions are close to experimental results, however, estimation of implicit scheme has lower true error and relative true error than Crank-Nicolson scheme.
Subsoiling combined with layered nitrogen application optimizes root distribution and increases summer maize grain yield
Authors: Li Xiang Ling , WANG Rui , Ji peng tao , Wang jian , Dong wei xin , Tao pei jun , Zhang yue chen
Published: February 2024

Background No-tillage of summer maize after the harvest of winter wheat is the primary agricultural practice on the North China Plain. However, prolonged no-tillage without deep tillage practices negatively impacts soil properties, which is detrimental to the growth and yield of summer maize.
Methods Xianyu 688 and Jifeng 2 were used as test materials, no-tillage and surface fertilizing with normal N (BC240), no-tillage and surface fertilizing with N reduction (BC180), subsoiling layered fertilization with normal N (FC240) were designed, and subsoiling layered fertilization with N reduction (FC180), assess root distribution, dry matter accumulation, N absorption and utilization, and grain yield of summer maize.
Results The FC240 and FC180 treatments significantly reduced soil bulk density in the 10-50 cm depth soil layer compared to the BC240 and BC180 treatments, and also increased total N content in the 20-50 cm depth soil layer. Root length in the 20-40 cm soil layer significantly increased with the FC240 treatment, along with a notable increase in root exudates, which improved root vitality during later growth stages. Additionally, the FC240 and FC180 treatments enhanced plant dry matter accumulation, N accumulation, and grain yield. Xianyu 688 exhibited increased N uptake efficiency, N fertilizer partial factor productivity, and N harvest index, while Jifeng 2 showed increased N uptake efficiency and N fertilizer partial factor productivity.
Conclusion It provided useful enlightenment for selecting suitable maize varieties, formulating effective management strategies and cultivating high N-efficient varieties under the condition of subsoiling combine with layered N application.
Progress in the Development of Broadly Reactive Norovirus Therapeutics
Authors: Hansman Grant S. , Kher Gargi , Svirina Anna D. , R.H.Tame Jeremy , Hartley-Tassell Lauren , Irie Hiro , von Itzstein Mark , Rudd Penny A. , Pancera Marie
Published: February 2024

Discovered over five decades ago, norovirus is frequently reported as the leading cause of outbreaks of acute gastroenteritis. No vaccines or antivirals are currently available. We further analyzed two of our leading norovirus inhibitors that either indirectly or directly obstructed norovirus from binding to histo-blood group antigen receptors. Using X-ray crystallography, we showed that both inhibitors engage highly conserved capsid residues amongst genetically diverse noroviruses. The indirect inhibitor, a modified Fc-linked Nanobody, showed improved binding affinity and neutralization capacity compared with the native Nanobody. More strikingly, we showed that the direct inhibitor, a chewable prebiotic tablet containing human milk oligosaccharide 2’-fucosyllactose, worked as an inhibitor for a leading norovirus. These new discoveries are expected to promote the development of effective norovirus treatments.
An Adaptive Margin Semi-supervisedLearning for Industrial Surface Inspection
Authors: Zheng Xiaoqing , Zhou Lixiang , Liu Yeqing , Xue Anke , Kong Yaguang , Wang Yaru
Published: February 2024

To meet the continuously increasing quality standards in industrial man-ufacturing processes, machine vision-based surface inspection systems arewidely used for checking surface quality and removing defective products.Automated Surface Inspection (ASI) algorithms are the core of the soft-ware for these systems, and their accuracy and effectiveness are crucial forthe success of these visual inspection systems. Compared with traditionalimage processing techniques-based ASI algorithms, deep learning-basedASI algorithms enable highly accurate automatic defect detection andare widely adopted by researchers in academia and industry. With alarge amount of labeled data, supervised deep learning can extract fea-tures from training samples and detect different types of surface defects.However, obtaining a large number of labeled samples is costly and oftenimpractical. Therefore, this paper proposes a semi-supervised learningapproach based on FixMatch, which requires only a limited numberof labeled samples but achieves performance comparable to supervisedlearning. This paper further introduces an uncertainty-adaptive marginmechanism to balance the learning weights between labeled and unla-beled samples, enhancing the model’s generalization ability under limitedlabeled data and preventing overfitting. Experiments on two public sur-face defect datasets (DAGM and NEU) and one industrial defect dataset(CCL) were conducted. The experimental results show that the proposedmethod, even with a small number of labeled samples, achieves high accu-racy rates of 99.83%, 99.81%, and 97.99% on the DAGM, NEU, and CCLdatasets, respectively, outperforming some supervised learning methodsand benchmark semi-supervised learning methods.
A novel measure of time series data pattern based on persistent homology that can detect the trend change of the daily all-cause mortality in Tokyo after the temperature rise
Authors: Oda Haruhisa , Ng Chris Fook Sheng , Hashizume Masahiro
Published: February 2024

Background: Evaluating the effect of temperature rise on mortality has been an important theme in environmental epidemiology. However, few studies examine the problem: ''Are there any differences in the mortality data pattern between recent years and former years that can be linked to climate change?" We might think we can investigate peak patterns. However, we do not have a complete list of peak types relevant to our problem. We can still try to take into account as many types of peaks as possible, but this microscopic method should not be the most straightforward way, given that our problem deals with macroscopic trends in data patterns. We will construct a new measure of data pattern based on persistent homology. With this measure, we will see the change in the mortality trends in Tokyo that took place after the climate change.
Methods: We circularly plot the time series data and apply persistent homology to this point cloud. The main output of our analysis is the hole structures represented by triangles connecting points in our data. We define our measure of data pattern to be whether multiple holes are detected. This single measure can behave like simplified versions of various peak analysis methods without the information on what peaks to focus on.
Results: With our measure, we succeed in detecting the change in the mortality trends preceded by the temperature rise in Tokyo.
Conclusions: We propose that we should regard the holes detected by our novel methodology as a fundamental characteristic of time series data.
Trial registration: Not applicable.
A Comprehensive Machine Learning Approach for COVID-19 Target Discovery in the Small-Molecule Metabolome
Authors: Sumon Md. Shaheenur Islam , Hossain Md. Sakib Abrar , Al-Sulaiti Haya , Yassine Hadi M. , Chowdhury Muhammad E. H.
Published: February 2024

Respiratory viruses, including influenza, RSV, and COVID-19, cause various respiratory infections. Distinguishing these viruses relies on diagnostic methods such as PCR testing. Challenges stem from overlapping symptoms and the emergence of new strains. Advanced diagnostics are crucial for accurate detection and effective management. This study leveraged nasopharyngeal metabolome data to predict respiratory virus scenarios including control vs RSV, control vs influenza A, control vs COVID-19, control vs all respiratory viruses, and COVID-19 vs influenza A/RSV. Our advanced machine learning models, including linear discriminant analysis, support vector machine, random forest, and logistic regression, exhibited superior accuracy, sensitivity, and specificity to previous supervised machine learning approaches. Key techniques such as feature ranking, standard scaling, and SMOTE were used to address class imbalances, thus enhancing model robustness. SHAP analysis identified crucial metabolites influencing positive predictions, thereby providing valuable insights into diagnostic markers. Our approach not only outperformed existing methods but also revealed top dominant features for predicting COVID-19, including Lysophosphatidylcholine acyl C18:2, Kynurenine, Phenylalanine, Valine, tyrosine, and aspartic Acid (Asp). These compounds play critical roles in metabolic pathways and have been identified as top contributors to predictive models in COVID-19 respiratory virus scenarios.
Long-lasting intense Cut-oﬀ Lows to become more frequent in the Northern Hemisphere
Authors: Mishra Aditya , Maraun Douglas , Schiemann Reinhard , Hodges Kevin , Zappa Giuseppe
Published: February 2024

Cut-off Lows (COLs) are mid-latitude storms that are detached from the main westerly flow. They tend to propagate slower than other mid-latitude storms and are often harbingers of heavy and persistent rainfall. The assessment of COLs in climate models is relatively limited, in fact, there are no studies conducted on the future changes of COLs within climate models. Given the importance of COLs in leading to severe hazards, here we study them in CMIP6's future (SSP5.85) simulations. The results show that COLs with high intensity and longer lifetimes are projected to become more frequent in spring over the land regions of the Northern Hemisphere. Such an increase in frequency indicates a substantial increase in related hazard as high-intensity long-lasting COLs are known to cause heavy rainfall. The effect may partly be compensated by the increasing COL propagation velocity with climate change. However, the overall increase in hazard is still high with 80% of all models indicating a surge in severe COL frequency. Lastly, the results are discussed in relation to the projected changes in COL drivers. The study forms a basis for future work on COL-associated rainfall and consequent impacts.
An AI Approach to Integrating Climate, Hydrology, and Agriculture Models
Authors: Berhanu Belete , Zeleke Ethiopia Bisrat , Gil Yolanda , Khider Deborah , Osorio Maximiliano , Ratnakar Varun , Vargas Hernán
Published: February 2024

Understanding the interactions between natural processes and human activities poses major challenges as it requires the integration of models and data across disparate disciplines. It typically takes many months and even years create valid end-to-end simulations as the different models need to be configured in consistent ways so their results can be meaningfully interpreted. MINT is a novel framework that uses AI for model integration. MINT captures extensive knowledge about models and data, including their requirements and constraints. MINT guides a user to pose a well-formed modeling question, select and configure models, find appropriate datasets, set up scenarios and parameters, run the simulations, and visualize the results. MINT currently includes climate, hydrology, and agriculture models for different areas of Ethiopia, Kenya, and South Sudan. Our goal is to understand droughts through integrating meteorological, hydrological, and agricultural analyses.