bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Adaptive Auxiliary Framework for Teleoperated Laparoscopic Surgery
Authors: Yiwei Wang , Haoyuan Cheng , Yubo Sheng , Huan Zhao 
Published: June 2024

Abstract

Novice surgeons perform poorly during robotic surgical teleoperation because of poor consistency, a laparoscopic view that lacks depth information, and low proficiency. To make the novice surgeon perform close to expert operation during teleoperation, this article proposes an auxiliary framework for robotic surgery. A Bayesian statistics-based target prediction algorithm is used to realize the prediction of the operator’s intention, and the information entropy is used to measure the credibility of the predicted target. An adaptive velocity-position hybrid control method based on dynamic bubbles is proposed to realize the efficient performance of surgical tasks by seamlessly switching between slow, precise movements and fast, less precise tasks with no need for frequent grasping and repositioning. Assistance for teleoperation is achieved using virtual fixture force assistance based on task guidance constraints and posture adaptive adjustment based on the predicted target. The proposed framework has been validated on a platform for simulated laparoscopic surgery based on a sigma7 teleoperated primary device and a redundant degree-of-freedom surgical robot secondary device to evaluate its usability and effectiveness. It has also shown its superiority in various metrics in a comprehensive performance comparison with the baseline solution.
Developing and Assessinginexact Multiplierarchitectures for Imageprocessing
Authors: Anguraj Parthibaraj , Krishnan Thiruvenkadam 
Published: June 2024

Abstract

In the realm of approximate computing, the inexact multiplier architecture stands out as a cornerstone, playing a pivotal role acrosserror-tolerated applications. This article delves into the intricacies ofthree distinct inexact multiplier architectures tailored specifically forimage processing tasks. The study revolves around efficiently partitioning the partial product stage into smaller modules and thenemploying decoder algorithms/truncation techniques, to obtain thesuggested multiplier’s final result. The resulting 8×8 imprecise multipliers are engineered with reduced design overhead and reasonableerror metrics. Through simulation with the Cadence RTL compilerusing TSMC 90 nm technology, the realization showcases substantialarea and power savings compared to conventional imprecise multipliers. Comparing one of the proposed approximate models to precisemultipliers reveals significant reductions in both the area and thepower requirement, amounting to 37.19% and 46.14%, respectively, allwhile ensuring acceptable error metrics. Furthermore, in comparison toalternative approximation multiplier designs, the suggested 8×8 multiplier showcases superior performance metrics. It achieves justifiablemean Structural Similarity Index (SSI) values, making it particularlyadvantageous for tasks such as picture multiplication and sharpening.
AET:Auxiliary and Embedded Teacher for Online Mutual Knowledge Distillation for Image Classification
Authors: Liu Wen , Liang Xingzhu , Bi Feilong , Lin Yu-e 
Published: June 2024

Abstract

Distillation (OKD) has emerged as a powerful technique for model compression, eliminating the need for pre-trained teachers in traditional methods. While recent advancements in feature fusion have further improved OKD's capabilities, existing approaches solely focus on final-layer fusion, potentially hindering the effectiveness of the fused classifier. In this work, we propose a novel Auxiliary and Embedded Teacher (AET) approach to tackle these challenges. AET addresses the critical issues of feature fusion position selection and potential performance degradation after fusion. We introduce embedded teachers, formed by combining multiple mid-level sub-networks, to promote mutual learning among student networks. Additionally, auxiliary teachers provide enriched information and guide the fusion classifier, ultimately enhancing overall performance. Extensive evaluations on four benchmark datasets (CIFAR-10/100, CINIC-10, and ImageNet2012) demonstrate the superiority of the proposed AET approach. Code is available: https://github.com/JSJ515-Group/AET
Extensive local geographic variation in locoweed toxin produced by a fungal endophyte
Authors: Davis Jeremy S , Scott Matthew , Cook Daniel , Morse Geoffrey , Grillo Michael 
Published: June 2024

Abstract

Legumes are notorious for coevolutionary arms races where chemical defenses are employed to ward off herbivores—particularly insect seed predators. Locoweeds are a group of plants containing the toxic alkaloid swainsonine which can poison livestock and causes millions in economic damage every year. Swainsonine is known to be produced by the fungal endophyte Alternaria section Undifilum, and the chemical composition of the toxin has been well characterized. Despite this knowledge, the ecological roles and evolutionary drivers of swainsonine toxins in locoweeds remain uncertain. Here, we quantitate swainsonine concentrations and herbivory levels in the hyper-diverse locoweed Astragalus lentiginosus to evaluate its role as an evolved chemical defense. We found that A. lentiginosus shows considerable variation in swainsonine concentrations according to variety, in particular showing presence/absence variation at both population and local geographic scales. Surprisingly, herbivory levels from presumed generalist insects emerging from fruits showed no correlation with swainsonine concentrations. Conversely, seed and fruit herbivory levels linked to specialist Acanthoscelides seed beetles actually increased with concentrations of swainsonine—suggesting a possible coevolutionary arms race. Our results highlight that variation in endophyte-produced toxin systems may not follow classical expectations for geographic variation and ecological roles of plant chemicals. We discuss the implications of these results on plant-endophytic toxin systems and coevolutionary dynamics more broadly, highlighting a considerable need for more research in these systems.
Harnessing Deep Learning for Sustainable E-Waste Management and Environmental Health Protection
Authors: Oise Godfrey Perfectson , Konyeha Susan 
Published: June 2024

Abstract

The AI-based e-waste management system presented in this study is a game-changing approach designed to solve the growing issues of e-waste collection, segregation, and its influence on environmental health. Rapid innovation and technological improvement have resulted in increased e-waste generation, necessitating an advanced, intelligent, and efficient strategy to e-waste segregation and disposal that takes into account environmental health. This system uses cutting-edge technology, particularly Artificial Intelligence (deep learning), to optimize e-waste sorting procedures while mitigating negative environmental health effects. The project aims to create a deep learning model (Sequential Neural Network) using TensorFlow, Keras, and Python programming tools, as well as Visual Studio Code for application development.
Assessment of Dietary Diversity and Sociodemographic Factors in Adolescent Girls and Boys in Urban Slums of Bangladesh
Authors: Mallick Trisha , Saida Umme , Hasan Md. Nazmul 
Published: June 2024

Abstract

Introduction: According to the World Health Organization (WHO), adolescents are individuals aged between 10 and 19 years. Currently, there are 1.2 billion adolescents on Earth. However, information on adolescent nutritional status and dietary diversity in the urban slums of Bangladesh is limited. Objectives: This study aimed to evaluate adolescents' dietary diversity and nutritional status, as well as the factors that are associated, in the urban slums of Bangladesh.
Methodology: A cross-sectional study was conducted among 772 adolescent girls and boys aged 11–19 years residing in the urban slums of Bangladesh from January 2022 to March 2022. A structured questionnaire was used to collect data about their diet, sociodemographic variables, and anthropometric characteristics. To determine the nutritional status of the study participants, BMI-for-age (z score) was measured by WHO AnthroPlus software. The individual dietary diversity score was calculated using the Food and Agricultural Organization's 2011 guidelines.
Results: The findings of the survey revealed that 11.1%, 66.2%, and 22.7% of the adolescents were thin, healthy, and overweight, respectively. The outcomes of the Individual Dietary Diversity Score indicated that 40.9% of the adolescents did not have an adequate variety of foods in their diets, while 59.1% of the adolescents did have adequate variety. At the secondary level, (adjusted odds ratio [AOR] = 0.038, 95% confidence interval CI = (1.031-2.828); education (AOR= 0.000, CI= 0.264-0.569); occupations such as day laborers, rickettsial pullers, and others; (AOR=0.000, CI=1.919-6.054); (AOR= 0.000, CI=1.584-5.070); and (AOR= 0.000, CI=1.789-5.087). Additionally, mothers' education (primary level, (AOR=0.029, CI=0.457-0.958), families' comparatively higher monthly income (AOR=0.000, CI=3.805-14.587) and nonsmoking history (AOR=0.000, CI=0.150-0.750) were significantly associated with adequate dietary diversity among study participants (p<0.05).
Conclusion: The results of this study indicate that the dietary diversity of adolescent girls and boys in the urban slum region of Bangladesh was adequate, and most of them were of normal weight according to their BMI-for-age (z score). However, it is preferable to design a nutritional intervention program that promotes nutritional education to encourage adolescents to eat a wide variety of meals.
Efficiently Updating Domain Knowledge in Large Language Models: Techniques for Knowledge Injection without Comprehensive Retraining
Authors: Czekalski Emily , Watson David 
Published: June 2024

Abstract

Recent advancements in natural language processing have highlighted the critical importance of efficiently updating pre-trained models with domain-specific knowledge. Traditional methods requiring comprehensive retraining are resource-intensive and impractical for many applications. The proposed techniques for knowledge injection, including the integration of adapter layers, retrieval-augmented generation (RAG), and knowledge distillation, offer a novel and significant solution to this challenge by enabling efficient updates without extensive retraining. Adapter layers allow for specialized fine-tuning, preserving the model's original capabilities while incorporating new information. RAG enhances the contextual relevance of generated responses by dynamically retrieving pertinent information from a domain-specific knowledge base. Knowledge distillation transfers specialized knowledge from smaller models to the larger pre-trained model, augmenting its performance in new domains. Experimental results demonstrated substantial improvements in accuracy, precision, recall, and F1-score, along with enhanced contextual relevance and coherence. The findings demonstrate the potential of the proposed methods to maintain the relevance and accuracy of language models in dynamic, information-rich environments, making them particularly useful in fields requiring timely and accurate information.
Validation of the FROM-16 in Family Members of Patients Receiving Advanced Therapy Medicinal Product (ATMP)
Authors: Brilliant Charles , Finlay Andrew , Salek Sam , Shah Rubina , Laing Hamish , Bacon Emily 
Published: June 2024

Abstract

Purpose: Outcome-based pricing models which consider domains of value not previously considered in healthcare, such as societal outcomes, are of increasing interest for healthcare systems. Societal outcomes can include family-reported outcome measures (FROMs), which measure the impact of disease upon the patient’s family members. The FROM-16 is a generic and easy-to-use family quality of life tool, but it has never been used in the context of patients undergoing advanced therapy medicinal product (ATMP) treatment. The use of potentially curative ATMPs is limited due their high cost and the low number of eligible patients. Using the FROM-16 to collect evidence of family impact in ATMP patients may demonstrate the potential value created by an ATMP intervention.
Methods: This feasibility study aimed to test the validity of the FROM-16 in family members of ATMP patients. Patients and family members (n = 24) were recruited from ATMP treatment centres in England and Wales. Family members completed the FROM-16 and were invited to a short debriefing interview.
Results: The FROM-16 showed high validity demonstrated by strong internal consistency (Cronbach’s alpha = 0.917) and intraclass correlation (0.803, 95%). Interviews identified that whilst the FROM-16 covered most areas of quality of life impact experienced by the participants, some explained that they also experienced other impacts upon their personal health and future outlook.
Conclusion: This feasibility study provides evidence that the FROM-16 could be used as part of a structured systematic approach to measure family quality of life impact in ATMP patients.
Practical procedures for sensor quality assessment
Authors: Alferes J. , Grievson O. , Heffernan C. , Lumley D. , Massiri T. , Menniti A. , Samuelsson O. , Villez K. , Zegers E. 
Published: June 2024

Sensors are increasingly deployed for process monitoring and control. These produce on-line measurements at a high frequency, in parallel with low-frequency laboratory measurements. Compared to laboratory practices, sensor data quality assessment and control practices are far less structured at most utilities. This leads to inaccurate sensor data with unknown uncertainty factors.
This chapter shows how to establish standard operating procedures (SOPs) to support sensor data quality assessment and control and subsequent maintenance actions by producing relevant sensor metadata. Furthermore, SOPs are provided for the most commonly used wastewater quality sensors, inspired by utility and academic best practices. This chapter builds on definitions provided in Chapter 3 and provides additional definitions specifically related to sensors maintenance. Chapter 6 complements the methods in this chapter, which are based on reference measurements, with data-analytical techniques.
The effect of using jug-type water filters on selected metals concentration in tap water – a case study
Authors: Kwiatkowski Hubert , Kreft Ksawery , Ronda Oskar , Cieślik Bartłomiej Michał 
Published: June 2024

ABSTRACT

In this study, the effect of the filtration process on Na, K, Mg, Ca, Fe and Zn concentration in water, using filters from one of the leading European manufacturers, was investigated. The increase in Na (up to 300%) and K concentration (up to 320%) at the beginning of jug filter usage was reported. A decrease in Ca, Fe and Zn concentration was observed. Standard filters remove 80–90% of Mg from tap water at the beginning of the usage, while magnesium-enriching ones slightly increase its concentration – from around 8 to 12–25 mg/L. Significant changes in the filter's operating characteristics were also observed for other studied elements as they wore out. Using Mg-enriching filters can increase magnesium intake from 4–5 to 6–15% of the recommended daily dose from water consumption. The results were also discussed regarding the amounts of macroelements found in commercially available bottled water. The magnesium concentration in tap water after filtration through magnesium-enriching filters was lower compared to bottled mineral waters. The authors note a scope for the development of water filter production technology, in particular, the need to develop filters that more efficiently enrich water with magnesium and do not increase sodium concentration.
Vernodalin Alleviates Cardiotoxicity and Inflammation in Isoproterenol-Mediated Myocardial Infarction through NF-κB/AMPK Signaling Pathways in Rats
Authors: Tao Tao , Sun Xiaona , Zhang Tuowei , Vijayalakshmi Annamalai , Niu Fangqiao 
Published: June 2024

Background:
Myocardial infarction (MI) is the foremost cause of mortality in cardiovascular
diseases. MI ultimately exacerbates cardiotoxicity due to the release of toxicity biomarkers
and inflammatory infiltration.

Aim:
Vernodalin (VN) is a renowned cytotoxic sesquiterpene lactone that possesses antioxidant,
anticancer, and anti-inflammatory properties. The cardioprotective mechanism of VN remains
concealed. Hence, we explored the cardioprotective efficacy of VN on isoproterenol (ISO)-
mediated MI and analyzed its underlying mechanism.

Methods:
Wistar albino rats were injected ISO (85 mg/kg bw) subcutaneously to induce MI to
evaluate the cardioprotective potential of VN (10 mg/kg bw) by assessing heart weight/ body
weight index, hemodynamic, toxicity enzymes, histopathology, inflammatory mediators, and
signaling pathway. ISO enhanced heart weight/body weight index, cardiotoxicity enzymes, biomarkers,
inflammation, and histopathological changes while reducing hemodynamic parameters
and VEGF-B, AMPK, and eNOS signaling pathways.

Results:
Treatment with VN could significantly (p<0.05) mitigate the heart weight/body weight
index, cardiotoxicity enzymes, biomarkers, inflammatory cytokines, and histopathological
changes while enhancing hemodynamic parameters and VEGF-B, AMPK, and eNOS signaling
pathways. Collectively, our findings revealed that the VN ameliorated defensive action against
MI and averted myocardial injury by reducing the NF-κB-mediated inflammatory pathways in
rats.

Conclusion:
These findings established that VN expressively preserves the myocardium and
employs anti-inflammatory actions by regulating NF-κB, VEGF-B, AMPK, and eNOS signaling
pathways.
Artificial Intelligence in Eye Movements Analysis for Alzheimer’s Disease Early Diagnosis
Authors: Farabi Maleki Shadi , Yousefi Milad , Sobhi Navid , Jafarizadeh Ali , Alizadehsani Roohallah , Gorriz-Saez Juan Manuel 
Published: June 2024

:
As the world's population ages, Alzheimer's disease is currently the seventh most common
cause of death globally; the burden is anticipated to increase, especially among middle-class
and elderly persons. Artificial intelligence-based algorithms that work well in hospital environments
can be used to identify Alzheimer's disease.
A number of databases were searched for English-language articles published up until March 1,
2024, that examined the relationships between artificial intelligence techniques, eye movements,
and Alzheimer's disease.
A novel non-invasive method called eye movement analysis may be able to reflect cognitive processes
and identify anomalies in Alzheimer's disease. Artificial intelligence, particularly deep
learning, and machine learning, is required to enhance Alzheimer's disease detection using eye
movement data. One sort of deep learning technique that shows promise is convolutional neural
networks, which need further data for precise classification. Nonetheless, machine learning models
showed a high degree of accuracy in this context. Artificial intelligence-driven eye movement
analysis holds promise for enhancing clinical evaluations, enabling tailored treatment, and fostering
the development of early and precise Alzheimer's disease diagnosis.
A combination of artificial intelligence-based systems and eye movement analysis can provide a
window for early and non-invasive diagnosis of Alzheimer's disease. Despite ongoing difficulties
with early Alzheimer's disease detection, this presents a novel strategy that may have consequences
for clinical evaluations and customized medication to improve early and accurate diagnosis.
Alternatives to β-Lactams as Agents for the Management of Dentoalveolar Abscess
Authors: Kaushik Aditi , Rana Nidhika , Ashawat Mahendra Singh , Ankalgi Amardeep , Sharma Ankit 
Published: June 2024

:
Dentoalveolar abscess are localized infections within the tooth or the surrounding alveolar bone, often resulting from untreated dental caries or dental trauma causing alveolar bone resorption or even loss. Serious consequences arising from the spread of a dental abscess can often lead to significant morbidity and mortality. The acute dentoalveolar abscess is a polymicro-bial infection comprising strict anaerobes, such as anaerobic cocci i.e., Prevotella fusobacterium species, and facultative anaerobes i.e., Streptococci viridians and Streptococcus anginosus. Moreover, inappropriately managed dental infections can progress to severe submandibular space infections with associated serious complications, such as sepsis and airway obstruction. An audit of the Hull Royal Infirmary between 1999 and 2004 showed an increase in the number of patients presenting to oral and maxillofacial surgery services with dental sepsis. Thus, the scientific com-munity is forced to focus on treatment strategies for the management of dentoalveolar abscess (DAA) and other related dental problems. The current treatment includes antibiotic therapy, including β-lactams and non-β- lactams drugs, but it leads to the development of resistant micro-organisms due to improper and wide usage. Furthermore, the currently used β-lactam therapeutics is non-specific and easily hydrolyzed by the β-lactamase enzymes. Thus, the research focused on the non-β-lactams that can be the potential pharmacophore and helpful in the management of DAA, as the appropriate use and choice of antibiotics in dentistry plays an important role in antibiotic stewardship. The newer target for the choice is NLRP inflammasome, which is the major chemical mediator involved in dental problems. This review focused on pathogenesis and current therapeutics for the treatment of dentoalveolar abscesses.
Gallocin-derived Engineered Peptides Targeting EGFR and VEGFR in
Colorectal Cancer: A Bioinformatic Approach
Authors: Kavyani Batoul , Saffari Fereshteh , Afgar Ali , Kavyani Sajjad , Rezaie Masoud , Sharifi Fatemeh , Ahmadrajabi Roya 
Published: June 2024

Background:
Colorectal cancer (CRC) treatment using time-saving and cost-effective
targeted therapies with high selectivity and low toxicity drugs, is a great challenge. In primary investigations
on Gallocin, as the most proposed factor in CRC pathogenesis caused by Streptococcus
gallolyticus, it was surprisingly found that this bacteriocin has four α-helix structures and
some anti-cancer sequences.

Objective:
The aim of this study was to determine the ability of Gallocin-based anticancer peptides
(ACPs) against epidermal growth factor receptor (EGFR) and vascular epidermal growth factor
receptor (VEGFR) and the evaluation of their pharmacokinetic properties using bioinformatic
approaches.

Methods:
Support vector machine algorithm web-based tools were used for predicting ACPs. The
physicochemical characteristics and the potential of anti-cancer activity of Gallocin-derived ACPs
were determined by in silico tools. The 3D structure of predicted ACPs was modeled using modeling
tools. The interactions between predicted ACPs and targets were investigated by molecular
docking exercises. Then, the stability of ligand-receptor interactions was determined by molecular
dynamic simulation. Finally, ADMET analysis was carried out to check the pharmacokinetic properties
and toxicity of ACPs

Results:
Four amino acid sequences with anti-cancer potential were selected. Through molecular
docking, Pep2, and Pep3 gained the best scores, more binding affinity, and strong attachments by
the formation of reasonable H-bonds with both EGFR and VEGFR. Molecular simulation confirmed
the stability of Pep3- EGFR. According to pharmacokinetic analysis, the ACPs were safe
and truthful.

Conclusion:
Designed peptides can be nominated as drugs for CRC treatment. However, different
in-vitro and in-vivo assessments are required to approve this claim.