bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Sourdough-Yeast Co-Fermentation on Physicochemical Properties of Corn Fagao Batter
Authors: Yang Qianhui , Lyu Yingguo , Wu Zhenhua , Li Xueqin , Liu Kunlun
Published: August 2024

Fagao is one of China's traditional gluten free staple foods made by rice or corn flour. Corn Fagao prepared by co-fermentation with sourdough and yeast exhibits better quality and less staling compared to traditional yeast-fermented Fagao. The physicochemical properties of corn Fagao batter during the sourdough-yeast co-fermentation were investigated. The results showed that compared with yeast fermentation, the gas production and viscosity of the batter increased by co-fermentation. The co-fermented batter shows higher hydrolysis of starch and less amylose content. The integrity of starch granules in co-fermented batter was damaged more seriously, the crystallinity and short-range ordered structure were less than yeast fermented batter, even though the crystal structure type of starch did not change obviously. The peak viscosity, minimum viscosity, final viscosity, decay value and recovery value of corn batter are reduced by co-fermentation, which can improve the thermal stability of the batter and slow down the aging. Co-fermentation also results in a more pronounced reduction in protein subunit contents than yeast fermentation. The changes in the physicochemical properties of corn Fagao batter help explain the quality improvement of corn Fagao made from co-fermentation method, and may provide theoretical references for co-fermentation with sourdough and yeast to other gluten free foods.
Comparative Analysis of Rapid and Less Invasive Methods for A2A2 Dairy Cattle Genotyping and A2 Milk Purity Detection
Authors: Albiero Leandra Oliveira Xavier , Ansiliero Rafaela , Kempka Aniela Pinto
Published: August 2024

This study analyzed methodologies for identifying β-casein genotypes in dairy cows and the purity of A2 fermented milk. The research was justified by the need for quick and accurate techniques for herd screening and A2 milk quality control. The methodology included the development of an ELISA test to detect β-casein A1 in fermented milk and the evaluation of a commercial Lateral Flow Immuno Assay (LFIA) to identify A2A2 genotypes and the purity of A2 milk. The results showed that the ELISA test had high sensitivity and specificity (100%) in detecting β-casein A1. The LFIA test was 100% effective in identifying A2A2 genotypes and verifying the purity of A2 milk with a minimum detectable contamination of 5% for raw milk and 10% for fermented milk. Compared to traditional genotyping, these methods proved to be highly reliable and more practical for large-scale screening. It was concluded that the ELISA and LFIA were valuable tools for ensuring the quality and authenticity of A2 milk, meeting the demands of producers and consumers for safe and healthy dairy products.
Fungal Methane Production Under High Hydrostatic Pressure in Deep Subseafloor Sediments
Authors: Zhao Mengshi , Li Dongxu , Liu Jie , Fang Jiasong , Liu Changhong
Published: August 2024

Fungi inhabiting deep subseafloor sediments have been shown to possess anaerobic methane (CH₄) production capabilities under atmospheric conditions. However, their ability to produce CH₄ under in situ conditions with high hydrostatic pressure (HHP) remains unclear. Here, Schizophyllum commune 20R-7-F01, isolated from ~ 2 km below the seafloor, was cultured in Seawater Medium (SM) in culture bottles fitted with sterile syringes for pressure equilibration. Subsequently, these culture bottles were transferred into 1-liter stainless steel pressure vessels at 30 °C for 5 days to simulate in situ HHP and anaerobic environments. Our comprehensive analysis of bioactivity, biomass, transcriptomics revealed that S. commune not only survived but significantly enhanced CH₄ production, reaching approximately 2.5 times higher levels under 35 MPa HHP compared to 0.1 MPa standard atmospheric pressure. Pathways associated with carbohydrate metabolism, methylation, hydrolase activity, cysteine and methionine metabolism, and oxidoreductase activity were notably activated under HHP. Specifically, key genes involved in fungal anaerobic CH₄ synthesis, including methyltransferase mct1 and dehalogenase dh3, were upregulated 7.9- and 12.5-fold, respectively, under HHP. Enhanced CH₄ production under HHP was primarily attributed to oxidative stress induced by pressure, supported by intracellular Reactive Oxygen Species (ROS) levels and comparative treatments with cadmium chloride and hydrogen peroxide. These findings suggest that fungi persisting in deep-sea sediments over millions of years have the potential to significantly contribute to global CH₄ flux.
Are Strong Baselines Enough? False News Detection with Machine Learning
Authors: Aslan Lara , Ptaszynski Michal , Jauhiainen Jukka
Published: August 2024

False news refers to false, fake, or misleading information presented as real news. In recent years, there has been a noticeable increase in false news on the Internet. The goal of this paper was to study the automatic detection of such false news using machine learning and natural language processing techniques and to determine which techniques work the most effectively. This article first studies what constitutes false news, and how it differs from other types of misleading information. We also study the results achieved by other researchers on the same topic. After building a foundation to understand false news, and the various ways of automatically detecting it, this article provides its own experiments. These experiments were done on four different datasets, one that was made just for this article, and using 10 different machine learning methods. The results of this article were satisfactory and provided answers to the original research questions set up at the beginning of this article. This article could determine from the experiments that passive-aggressive algorithms, support vector machines, and random forests are the most efficient methods for automatic false news detection. This article also concluded that more complex experiments, such as using multiple levels of identifying false news or detecting computer-generated false news, require more complex machine learning models.
Microfluidics and Nanofluidics in Strong Light–Matter Coupling Systems
Authors: Granizo Evelyn , Kriukova Irina , Escudero-Villa Pedro , Samokhvalov Pavel , Nabiev Igor R.
Published: August 2024

The combination of micro- or nanofluidics and strong light–matter coupling has gained much interest in the past decade, which has led to the development of advanced systems and devices with numerous potential applications in different fields, such as chemistry, biosensing, and material science. Strong light–matter coupling is achieved by placing a dipole (e.g. an atom or a molecule) into a confined electromagnetic field, with molecular transitions being in resonance with the field and the coupling strength exceeding the average dissipation rate. Despite intense research and encouraging results in this field, some challenges still need to be overcome, related to the fabrication of nano- and microscale optical cavities, stability, scaling up and production, sensitivity, signal-to-noise ratio, and real-time control and monitoring. The goal of this paper is to summarize recent developments of micro- and nanofluidic systems employing strong light–matter coupling. An overview of various methods and techniques used to achieve strong light–matter coupling in micro- or nanofluidic systems is presented, preceded by a brief outline of the fundamentals of strong light–matter coupling and optofluidics operating in the strong coupling regime. The potential applications of these integrated systems in sensing, optofluidics, and quantum technologies are explored. The challenges and prospects in this rapidly developing field are discussed.
Overview of High Dynamic Range Image Quality Assessment
Authors: Liu Yue , Tian Yu , Wang Shiqi , Zhang Xinfeng , Kwong Sam Tak Wu
Published: August 2024

In recent years, the High Dynamic Range (HDR) image has gained widespread popularity 1
across various domains such as security, multimedia, and biomedical fields, owing to its ability 2
to deliver an authentic visual experience. However, the extensive dynamic range and rich detail 3
in HDR images present challenges in assessing their quality. Therefore, current efforts involve 4
constructing subjective databases and proposing objective quality assessment metrics to achieve 5
efficient HDR Image Quality Assessment (IQA). Recognizing the absence of a systematic overview 6
of these approaches, this paper provides a comprehensive survey of existing HDR IQA methods. 7
Furthermore, this paper aims to serve as a valuable resource for researchers by discussing the 8
limitations of current methodologies and potential research directions in the future.
Challenges in Ensuring the Safety of Aluminum Adjuvanted Vaccine: A Case Study of Inactivated Rabies Vaccines by National Control Laboratory
Authors: Meena Jaipal , Shiekh Faraz , Bindra Gurminder , Yadav Shikha , Kasana Harit , Chander Harish , Anvikar Anupkumar R. , Chand Subhash
Published: August 2024

The vaccines have to undergo independent testing at National Control laboratory (NCL) for various quality control tests to ensure safety, purity, and potency before market authorization and surveillance testing. In this study, five different inactivated cell culture rabies vaccine preparations (Aluminum adjuvanted and non-adjuvanted) marketed in India were evaluated using compendial safety test of mouse inoculation test (MIT) for residual live virus. Investigations were done to see the interference of aluminum adjuvant in rabies vaccine preparation to ensure safety as per the in-vivo compendial test for virus inactivation. After intracerebral injections, mice received the aluminum phosphate adjuvanted inactivated rabies vaccine (ARV) had neurological complications that resulted in their deaths between days 4 and 10. Further investigations were carried out to find out the root cause for mice death including fluorescent antibody test (FAT), rabies virus amplification test. Additionally, alamar blue based cell cytoxicity assay was carried out using BHK-21 cells to estimate the cell toxicity of aluminum phosphate adjuvanted inactivated rabies vaccine. The present study highlighted the challenges in ensuring the safety of adjuvated inactivated rabies vaccine at level of NCL. Further, study demonstrated the toxicity of aluminum under both in vitro and in vivo settings, sparking controversy regarding the safety of aluminum adjuvants in vaccines at the suggested compendial limit of 1.25 mg/single human dosage.
Effects of Bacillus Subtilis on Growth Performance, Metabolic Profile, and Health Status in Dairy Calves at the First 90 Days of Age
Authors: Antanaitis Ramūnas , Džermeikaitė Karina , Krištolaitytė Justina , Armonavičiūtė Emilija , Arlauskaitė Samanta , Girdauskaitė Akvilė , Rutkauskas Arūnas , Baumgartner Walter
Published: August 2024

: This study focused on assessing whether the inclusion of probiotics (B. subtilis) as feed additives during the preweaning stage can enhance the body weight and metabolic condition of neonatal calves. A total of 50 Holstein calves, all born on the same farm, were randomly divided into two homogeneous treatment groups after birth. The probiotic was administered 24 hours post-birth, signifying the start of the experimental period. The Control Group (CG), consisting of 25 calves, was fed with a milk replacer, while the Treatment Group (TG), also with 25 calves, received milk replacer supplemented with 7.5 mL/calf/day of B. subtilis probiotic, in accordance with the manufacturer's guidelines. It took one month to collect the animals. Body weight was measured at birth for all animals. A local veterinarian, working on the farm, conducted daily health checks of the calves, recording health parameters and any antibiotic treatments. Blood samples were collected from each calf at birth and again after 30 days by puncturing the jugular vein, using 10 mL evacuated serum tubes before morning feeding. Significant differences in body weight were observed at 30 days of age (p < 0.05). The CG's average body weight (BW) was 52.71 kg (±10.68), compared to 58.49 kg (±14.21) in the TG, a difference of 5.78 kg or an increase of 9.88%. Additionally, the TG exhibited significantly lower aspartate transaminase (AST) activity at 30 days of age (p < 0.001), with an average of 51.02 IU/L (±5.16) compared to 72.00 IU/L (±12.68) in the CG, a 41.12% decrease. The TG also had a significantly higher average phosphorus (P) concentration (p < 0.001) of 3.27 mmol/L (±0.22), a 9.36% increase over the CG's 2.99 mmol/L (±0.27). A significant increase in gamma-glutamyl transferase activity was observed in the TG compared to the CG (p < 0.001), with a 64.48% (or 26.29 IU/L) increase. The TG's average GGT activity was 40.64 IU/L (±22.43), while in the CG it was 14.35 IU/L (±3.45). Additionally, there was a significant 17.7% decrease (or 10.02 g/L) in total protein (TP) concentration in the TG compared to the CG, with the TG averaging 46.32 g/L (±17.43) and the CG 56.34 g/L (±9.81). Overall, dairy calves who were given conventional milk replacer along with a daily dose of 7.5 mL of B. subtilis probiotic experienced enhanced growth performance and a more favorable metabolic profile. Their body weight exhibited a 9.88% growth, but the activity of AST decreased by 41.12%, suggesting a decreased likelihood of liver damage. Additionally, their protein digestibility was 17.7% greater compared to the control group. In addition, B. subtilis had a positive impact on the overall concentration of phosphorus.
Collagen I Exacerbates Palmitate-Induced Cell Death in HepG2 Cells via Integrin-Mediated Death
Authors: Maseko Tumisang Edward , Peterová Eva , Elkalaf Moustafa , Koutová Darja , Melek Jan , Staňková Pavla , Špalková Veronika , Matar Reem , Lotková Halka , Červinková Zuzana , Kučera Otto
Published: August 2024

Various strategies have been employed to improve the reliability of 2D, 3D, and co-culture in vitro models of nonalcoholic fatty liver disease, including using extracellular matrix proteins, such as collagen I, to promote cell adhesion. While studies have demonstrated the significant benefits of culturing cells on collagen I, its effects on HepG2 and HepaRG cell lines after exposure to palmitate (PA) have not been investigated. Therefore, this study aimed to assess the effects of PA-induced lipotoxicity in HepG2 and HepaRG cultured in the absence or presence of collagen I. HepG2 and HepaRG cultured in the absence or presence of collagen I were exposed to PA, followed by analyses that assessed cell proliferation, viability, adhesion, cell death, mitochondrial respiration, reactive oxygen species production, gene and protein expressions, and triacylglycerol accumulation. Culturing HepG2 on collagen I was associated with increased cell proliferation, adhesion, expression of integrin receptors, and improved cellular spreading compared to culturing them in the absence of collagen I. PA-induced lipotoxicity was greater in collagen I-cultured HepG2 than those cultured in the absence of collagen I. In summary, the present study demonstrated for the first time that collagen I-cultured HepG2 exhibited exacerbated cell death following exposure to PA through integrin-mediated death. The findings from this study may serve as a caution to those using 2D models or 3D scaffold-based models of HepG2 in the presence of collagen I.
Big Epidemiology: the Birth, Life, Death, and Resurgence of Diseases on a Global Timescale
Authors: Bragazzi Nicola Luigi
Published: August 2024

Big Epidemiology is an innovative framework that extends the interdisciplinary approach of Big History to understand disease patterns, causes, and effects across human history on a global scale. This comprehensive methodology integrates epidemiology, genetics, environmental science, sociology, history, and data science to address contemporary and future public health challenges through a broad historical and societal lens. The foundational research agenda involves mapping the historical occurrence of diseases and their impacts on societies over time, utilizing archaeological findings, biological data, and historical records. By analyzing skeletal remains, ancient DNA, and artifacts, researchers can trace the origins and spread of diseases, such as Yersinia pestis in the Black Death. Historical documents, including chronicles and medical treatises, provide contextual narratives and quantitative data on past disease outbreaks, societal responses, and disruptions. Modern genetic studies reveal the evolution and migration patterns of pathogens and human adaptations to diseases, offering insights into co-evolutionary dynamics. This integrative approach allows for temporal and spatial mapping of disease patterns, linking them to social upheavals, population changes, and economic transformations. Big Epidemiology also examines the roles of environmental changes and socioeconomic factors in disease emergence and re-emergence, incorporating climate science, urban development, and economic history to inform public health strategies. The framework reviews historical and contemporary policy responses to pandemics, aiming to enhance future global health governance. By addressing ethical, legal, and social implications, Big Epidemiology seeks to ensure responsible and effective epidemiological research and interventions. This approach aims to profoundly impact how we understand, prevent, and respond to diseases, leveraging historical perspectives to enrich modern scientific inquiry and global public health strategies.
Remote Sensing of Chlorophyll-A in Clear vs. Turbid Waters in Lakes
Authors: Fendereski Forough , Creed Irena F. , Trick Charles G.
Published: August 2024

Chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, is one of the few biological water quality indices detectable using satellite observations. However, models for estimating Chl-a from satellite signals are currently unavailable for many lakes. The application of Chl-a prediction algorithms may be affected by the variance in optical complexity within lakes. Using Lake Winnipeg in Canada as a case study, we demonstrated that separating models by the lake’s basins [north basin (NB) and south basin (SB)] can improve Chl-a predictions. By calibrating more than 40 commonly used Chl-a estimation models using Landsat data for Lake Winnipeg, we achieved higher correlations between in situ and predicted Chl-a when building models with separate Landsat-to-in-situ matchups from NB and SB (R² = 0.85 and 0.76, respectively; p < 0.05), compared to using matchups from the entire lake (R² = 0.38, p < 0.05). In the deeper, clearer waters of the NB, a green-to-blue band ratio provided better Chl-a predictions, while in the shallower, highly turbid SB, a red-to-green band ratio was more effective. Our approach can be used for rapid Chl-a modeling in large lakes using cloud-based platforms like Google Earth Engine with any available satellite or time series length.
Agronomic Performance and Yield Stability of Elite White Guinea Yam (Dioscorea rotundata) Genotypes Grown in Multiple Environments in Nigeria
Authors: Olatunji Alice Adenike , Gana Andrew Saba , Tolorunse Kehinde D , Agre Paterne A. , Adebola Patrick , Asfaw Asrat
Published: August 2024

Abstract: This study aimed to assess the agronomic performance and yield stability of elite white yam (Dioscorea rotundata) genotypes across diverse Nigerian environments. Twenty-five genotypes were evaluated at three locations in two consecutive growing seasons, 2022 and 2023. The genotypes were assessed for their agronomic performance, tuber yield and related traits using various analytical tools such as AMMI (Additive main effects and multiplicative interaction) and MTSI (Multi-trait stability index). The AMMI analysis revealed significant differences among the genotypes and across the environ-ments for all traits (p
Enhancing the Interpretability of Malaria and Typhoid Diagnosis with Explainable AI and Large Language Models
Authors: Attai Kingsley , Ekpenyong Moses , Amannah Constance , Asuquo Daniel , Ajuga Peterben , Obot Okure , Johnson Ekemini , John Anietie , Maduka Omosivie , Akwaowo Christie , Uzoka Faith-Michael
Published: August 2024

Malaria and Typhoid fever are prevalent diseases in tropical regions, exacerbated by unclear protocols, drug resistance, and environmental factors. Prompt and accurate diagnosis is crucial to improve accessibility and reduce mortality rates. Traditional diagnosis methods cannot effectively capture the complexities of these diseases due to the presence of confusable symptoms. Although machine learning (ML) models offer accurate predictions, they operate as "black boxes" with non-interpretable decision-making processes, making it challenging for healthcare providers to comprehend how the conclusions are reached. This study employs explainable AI (XAI) models such as Local Interpretable Model-agnostic Explanations (LIME), and Large Language Models (LLMs) like ChatGPT, Gemini, and Perplexity, to clarify diagnostic results for healthcare workers, building trust and transparency in medical diagnostics. The models were implemented on Google Colab and Visual Studio Code because of their rich libraries and extensions. Results showed that the Random Forest model outperformed Extreme Gradient Boost and Support Vector Machines and important features for predicting Malaria and Typhoid were identified with the LIME plots. Among LLMs, ChatGPT 3.5 demonstrates a comparative advantage over Gemini and Perplexity, hence highlighting how crucial it is to use a hybrid strategy that combines ML, XAI, and LLMs to improve diagnostic performance and reliability in healthcare applications. The study recommends implementing the integrated ML, LIME, and LLM processes because it streamlines the development and maintenance process's workflow overall, makes use of resources more effectively, and improves explainability by allowing the LLM to consider the complete context that LIME provides.
Performance And Latency Efficiency Evaluation Of Kubernetes Container Network Interfaces For Built-In And Custom Tuned Profiles
Authors: Dakić Vedran , Redžepagić Jasmin , Bašić Matej , Žgrablić Luka
Published: August 2024

In the era of DevOps, developing new toolsets and frameworks that leverage DevOps principles is crucial. This paper demonstrates how Ansible’s powerful automation capabilities can be harnessed to manage the complexity of Kubernetes environments. This paper evaluates efficiency across various CNI (Container Network Interface) plugins by orchestrating performance analysis tools across multiple power profiles. Our methodology, which involves managing numerous components, dependencies, and configurations, requires high expertise and precision. Our performance evaluations across network interfaces with different theoretical bandwidths gave us a comprehensive understanding of CNI performance and overall efficiency. Our research confirms that certain CNIs are better suited for specific use cases, mainly when tuning our environment for smaller or larger network packages and workload types, but also that there are configuration changes we can make to mitigate that. This paper also provides research into how to use performance tuning to optimize the performance and efficiency of our CNI infrastructure, with practical implications for improving the performance of Kubernetes environments in real-world scenarios, particularly in more demanding scenarios such as High-Performance Computing (HPC) and Artificial Intelligence (AI).
The Mini-Pig as an Animal Model for Focal Cartilage Treatment of the Knee – A Comparison to the Domestic Pig
Authors: Kutaish Halah , Tieng Vannary , Tscholl Philippe Matthias
Published: August 2024

The study objective is to evaluate the reliability of the Göttingen Mini-pig (G-MP) as a large animal model for preclinical studies addressing articular cartilage treatment procedures. Second this work compared the G-MP to the domestic pig (DP) in terms of surgical anatomy, postoperative management, and follow-up period possibilities and challenges. Six G-MP and four DP underwent a two-stage surgical procedure including cartilage harvesting using a superolateral approach followed by cartilage implantation using a medial parapatellar tendon approach. The superolateral approach exposed 11% (SD ± 5) of the entire trochlea in G-MP and 20% in the DP model. The medial parapatellar tendon approach in G-MP exposed 63% (SD ± 4) of the trochlear surface and exposed 34% (SD ± 13) of the medial femoral condyle, allowing 4 trochlear lesions of 6mm of diameter and one to the medial condyle in 4 out of 6 G-MP and in all DPs. Cartilage thickness was under 1mm in G-MP compared to more than 2mm in DP. Weight-progression was + 4 kg/week in DP and + 0.2 kg/week for G-MP. The G-MP is a feasible model for cartilage research, with adequate access to the joint via the dual approach. Thus enabling 4-5 lesions of 6mm, however a thinner cartilage in the G-MP needs to be considered.
Information Properties of Consecutive Systems using Fractional Generalized Cumulative Residual Entropy
Authors: Kayid Mohamed , Shrahili Mansour
Published: August 2024

We investigate some information properties of consecutive k-out-of-n:G systems in light
of fractional generalized cumulative residual entropy. We firstly derive a formula to compute
fractional generalized cumulative residual entropy related to the system's lifetime and explore
its preservation properties in terms of established stochastic orders. Additionally, we obtain
useful bounds. To aid practical applications, we propose two non parametric estimators for
the fractional generalized cumulative residual entropy in these systems. The efficiency and
performance of these estimators are illustrated using simulated and real datasets.
Repurposing cGMP Pathway Medications, from Vasodilation towards Changing the Face of Heart Failure
Authors: AbdelMassih Antoine , Ramadan Buthaina , ElDemerdash Jana , Zayed Monia , Soued M.Wasim , El-Husseiny Nadine , Husseiny Yousef , Saad Passant
Published: August 2024

Background:Advanced heart failure is characterized by repeated hospital admissions due to recurring need for intravenous inotropes. Among the most commonly used medications used in this context, is milrinone, which increases the myocardial relaxation velocity, what is known as lusitropy. This effect is mediated by increasing cyclic AMP, which in turn switches on phospholamban activity, an enzyme promoting calcium reuptake by sarcoplasmic reticulum. The aim of this review is to shed light on oral medications that can play a similar role by alternative pathways. Main Body:Phospholamban can be as well stimulated by cGMP, the latter is known to be increased by medications which inhibit phosphodiesterase 5. Two well-established drugs can perform this action and are currently exclusively used as vasodilators, namely sildenafil and tadalafil. Another emerging hope is vericiguat, a medication that can directly stimulates guanylyl cyclase, leading to increase in cGMP. Conclusion:We speculate that the possible introduction of these oral medications is thought to replace the need for repeated hospital and intensive care admissions. This also might mean, delaying the necessity for mechanical support or transplantations, which can change the face of advanced or end-stage heart failure.
MicroRNA in human umbilical cord blood
Authors: Yu Tengye , Yuan Yin
Published: August 2024

The blood that remains in the placenta and umbilical cord after the birth of the infant is referred to as umbilical cord blood (UCB), which has the advantages of convenient source and less ethical controversy. MicroRNA (miRNA) is a class of non-coding RNAs that can regulate gene expression after translation and transcription. Alterations in miRNA expression involve changes in a range of gene expression pathways. An increasing body of research indicates that miRNAs are implicated in many human disorders and are involved in almost all biological medium processes. Due to their high stability in UCB, circulating miRNAs are considered promising biomarkers for diagnosis and disease prognosis. Herein, we conduct a comprehensive review to summarize the roles of diverse UCB miRNAs from different perspectives, including UCB miRNA as biomarker for developmental projections and disease diagnosis, the relationship between maternal gestational status and neonatal UCB miRNA expression, the therapeutic function of exosomal UCB miRNA, miRNA expression profiles in UCB cell lineages. Taken together, miRNAs in UCB are very promising biomarkers for indicating physiological states, disease states, drug responses, development states, or health conditions, which can help in diagnosing, classifying, and predicting diseases, prognosis, or providing personalized treatment plans for patients.
Intelligent Vision System for Identifying Defects on African Plum Surfaces
Authors: Nguembang Fadja Arnaud , Che Sain Rigobert , Atemkemg Marcellin
Published: August 2024

Agriculture stands as the cornerstone of Africa’s economy, supporting over 60% of the continent’s labor force. Despite its significance, the quality assessment of agricultural products remains a challenging task, particularly at large scale, consuming valuable time and resources. The African plum is an agricultural fruit that is widely consumed across West and Central Africa but remains underrepresented in AI research. In this paper, we collected a dataset of 2,892 African plum samples from fields in Cameroon representing the first dataset of its kind for training AI models. The dataset contains images of plums annotated with quality grades. We then trained and evaluated various state-of-the-art object detection and image classification models, including YOLOv5, YOLOv8, YOLOv9, Fast R-CNN, Mask R-CNN, VGG16, Detectron-121, MobileNet and ResNet, on this African plum dataset. Our experimentation resulted in mean average precision scores ranging from 88.2% to 89.9% and accuracies between 86% and 91% for the object detection models and the classification models respectively. We then performed model pruning to reduce model sizes while preserving performance, achieving up to 93.6% mean average precision and 99.09% accuracy after pruning YOLOv5, YOLOv8 and ResNet by 10-30%. We deployed the high-performing YOLOv8 system in a web application, offering an accessible AI-based quality assessment tool tailored for African plums. To the best of our knowledge, this represents the first such solution for assessing this underrepresented fruit, empowering farmers with efficient tools. Our approach integrates agriculture and AI to fill a key gap.
Insights from Clinical Trials: Evidence-Based Recommendations for Induction Treatment of Newly Diagnosed Transplant-Eligible Multiple Myeloma
Authors: Lytvynova Olga , Jwayyed Jenna , Pastel Daniel , Prasad Rohan , Khouri Jack , Williams Louis , Mazzoni Sandra , Raza Shahzad , Anwer Faiz
Published: August 2024

Multiple Myeloma (MM), is a hematological malignancy and poses significant therapeutic challenges. This review synthesizes evidence from pivotal clinical trials to guide induction treatment for transplant-eligible (TE), newly diagnosed MM (NDMM) patients. Emphasizing the evolution from three-drug to four-drug induction therapies, we highlight the integration of monoclonal antibodies, particularly CD38 recombinant monoclonal antibody agents, into treatment regimens. Our analysis includes a comprehensive literature review of research from major databases and conferences conducted between 2010 and 2024, culminating in the detailed evaluation of 47 studies.
The findings underscore the superiority of quadruple regimens in TE NDMM, notably those incorporating daratumumab, in achieving superior responses including progression-free survival (PFS), minimal residual disease (MRD) negativity, overall response rate (ORR), and overall survival (OS) when compared to triple-drug regimens. As treatment regimens evolve with additional agents, the improved outcomes with treatment-related adverse events should be carefully balanced. This review advocates for a paradigm shift towards quadruple induction therapies for TE NDMM, offers a detailed insight into the current landscape of MM treatment, and reinforces a new standard of care.
Optical Nanoscopy of Cytokine-Induced Structural Alteration of Endoplasmic Reticulum and Golgi Apparatus in Insulin Secreting Cells
Authors: Pugliese Licia Anna , De Lorenzi Valentina , Tesi Marta , Marchetti Piero , Cardarelli Francesco
Published: August 2024

Pro-inflammatory cytokines play a role in the failure of β-cells in type 1 and type 2 diabetes. While existing data from ‘omics’ experiments allow for some understanding of the molecular mechanisms behind cytokine-induced dysfunction in β-cells, no report thus far has provided information on the direct imaging of the β-cell landscape with nanoscale resolution following cytokine exposure. In this study, we use Airyscan-based optical super-resolution microscopy of Insulinoma 1E (INS-1E) cells to investigate the structural properties of two subcellular membranous compartments involved in the production, maturation and secretion of insulin-containing granules, the Endoplasmic Reticulum (ER) and the Golgi Apparatus (GA). Our findings reveal that exposure of INS-1E cells to IL-1β and IFN-γ for 24 hours leads to significant structural alterations of both compartments. In more detail, both ER and GA fragment and give rise to vesicle-like structures with markedly reduced characteristic area and perimeter and increased circularity with respect to the original structures. These findings complement the molecular data collected thus far on these compartments and their role in β-cell dysfunction using and lay the ground for future optical-microscopy-based ex-vivo and in-vivo investigations.
Tracking Phytoplankton Biomass amid Wildfire Interference Using Landsat 8 OLI
Authors: Mohammady Sassan , Erratt Kevin J. , Creed Irena F.
Published: August 2024

The increasing frequency and severity of phytoplankton blooms worldwide highlight the need for advancements in monitoring technologies. Optical remote sensors, such as Landsat, have proven to be a cost-effective method for large-scale, near-real-time assessments of phytoplankton biomass in lakes. However, its effectiveness is often compromised by atmospheric interferences like clouds, dust, and wildfire smoke, which can obscure the clear-sky conditions essential for accurate remote sensing. While partial atmospheric correction (removing only the Rayleigh effect) is commonly applied to address these interferences, it remains inadequate for mitigating the impact of wildfire smoke. This study investigates the potential of Landsat's coastal/aerosol band (B1) for assessing wildfire smoke interference effects on Chlorophyll-a (Chl-a) retrieval models, which serve as proxies for phytoplankton biomass. We employed cluster analysis of B1 values to create a screening system based on aerosol reflectance, categorizing smoke interference into low, moderate, and high levels. Subsequently, we applied both partial (Rayleigh-corrected reflectance) and full (Landsat 8 Level 2 surface reflectance) atmospheric corrections before developing the Chl-a retrieval models. Excluding high wildfire smoke interference (B1 > 0.07) from the Chl-a calibration dataset significantly enhanced model performance, increasing the r-squared value from approximately 0.55 to 0.80. Moderate smoke interference (0.05 < B1 < 0.07) yielded results comparable to low-interference conditions. Notably, Rayleigh-corrected reflectance, even without additional aerosol band filtering, achieved higher r-squared values for the Chl-a retrieval model than full atmospheric correction. B1 thus proves to be a valuable tool for identifying low smoke-impacted observations, offering an effective method to monitor phytoplankton biomass amid increasing wildfire activity and improving the capacity to monitor aquatic environments in a changing global landscape.
EfficientUNetViT: Efficient Breast Tumor Segmentation utilizing U-Net Architecture and Pretrained Vision Transformer
Authors: Anari Shokofeh , Oliveira Gabriel Gomes de , Ranjbarzadeh Ramin , Alves Angela Maria , Vaz Gabriel Caumo , Bendechache Malika
Published: August 2024

This study introduces a sophisticated neural network structure for segmenting breast tumors. It achieves this by combining a pretrained Vision Transformer (ViT) model with a U-Net framework. The U-Net architecture, commonly employed for biomedical image segmentation, is further enhanced with Depthwise Separable Convolutional Blocks to decrease computational complexity and parameter count, resulting in better efficiency and less overfitting. The Vision Transformer, renowned for its robust feature extraction capabilities utilizing self-attention processes, efficiently captures the overall context within images, surpassing the performance of conventional convolutional networks. By using a pretrained ViT as the encoder in our U-Net model, we take advantage of its extensive feature representations acquired from extensive datasets, resulting in a major enhancement in the model’s ability to generalize and train efficiently. The suggested model has exceptional performance in segmenting breast cancers from medical images, highlighting the advantages of integrating transformer-based encoders with efficient U-Net topologies. This hybrid methodology emphasizes the capabilities of transformers in the field of medical image processing and establishes a new standard for accuracy and efficiency in activities related to tumor segmentation.
Chemical Recycling of Mixed Polyolefin Post-Consumer Plastic Waste Sorting Residues (MPO323)–Auto-Catalytic Reforming and Decontamination with Pyrolysis Char as an Active Material
Authors: Rieger Tobias , Nieberl Martin , Palchyk Volodymyr , Shah Pujan , Fehn Thomas , Hofmann Alexander , Franke Matthias
Published: August 2024

Mixed plastic packaging waste sorting residues (MPO323) were treated by thermal pyrolysis to utilize pyrolysis oil and char. The pyrolysis oil was found to contain aromatic and aliphatic hydrocarbons. Chlorine and bromine contents were up to 40,000 mg/kg and 200 mg/kg, respectively. Additionally, other elements like sulfur, phosphorous, iron, aluminum, and lead were detected, which can be interpreted as impurities regarding the utilization of the oils by means of chemical recycling. The pyrolysis char showed high contents of potentially active species like silicon, calcium, aluminum, iron, and others. To enhance the content of aromatic hydrocarbons and to reduce the level of contaminants, the pyrolysis oil was reformed with the corresponding pyrolysis char to act as an active material in a fixed bed. The temperature of the reactor and the flow rate of the pyrolysis oil feed were varied to gain insights on cracking and reforming reactions as well as on the performance with regard to decontamination.
Detection of Human Papillomavirus (HPV) and Bovine Leukemia Virus (BLV) in Breast Cancer Patients from Northeastern Brazil
Authors: da Mota Nunes Zhilbely , de Lucena Silva Douglas , Cordeiro Fernandes Igor , Conceição Gomes do Nascimento Kamylla , Luiz dos Santos Jesus André , Isis Pereira de Oliveira Niara , Felix dos Santos Anderson , Satomi Takeno Herrero Sylvia , Ricardo Gonçalves de Oliveira João , Rodrigues de Carvalho Filho Ivan , Carlos de Freitas Antonio , Tereza Cartaxo Muniz Maria , Ramos da Silva Maria Angélica , Venuti Aldo , Moura Lima Eleonidas , Almeida Diniz Gurgel Ana Pavla
Published: August 2024

This study evaluated the presence of Human Papillomavirus (HPV) and Bovine Leukemia Virus (BLV) DNA in breast cancer (BC) samples from women in Northeastern Brazil. We analyzed 186 formalin-fixed, paraffin-embedded tissue block samples using PCR and qPCR to detect HPV and BLV DNA. BLV DNA was not detected in any BC patients. However, HPV DNA was found in 46.2\% of the BC samples: 44.6\% were positive for HPV16, 3.5\% for HPV31, and 2\% were coinfected with HPV16 and HPV31. Additionally, we found an association between HPV-positive BC and Ki-67 expression (P=0.03, OR=1.85; CI=1.02-3.34), as well as between HPV31-positive BC and mitotic index (P=0.047). Although BLV was not detected in BC patients from Northeastern Brazil, the association between HPV and cell proliferation markers warrants further investigation, as HPV may contribute to the aggressiveness of BC in these patients.
Global Mean Based Nearest Feature Object Value Selection with Feature Creation Method for Clustering Accuracy Improvement
Authors: PRASAD M , Strikanth t
Published: August 2024

In the modern days the data is coming from various devices like mobiles, tabs, laptops, computers, IOT devices e.tc and is stored in the electronics form in the systems using the concept of files or data bases. Clustering Technique is used to extract data from these sources. Improving the accuracy of the model is improved using the feature selection or feature engineering or both. The feature engineering is used to add a new feature based on the global mean. Along with that new feature in addition to the feature selection increase the overall accuracy of the model.
Building A Portfolio Under Uncertainty
Authors: Nia Vera Mita , Siregar Hermanto , Sembel Roy , Zulbainarni Nimmi
Published: August 2024

The relationship between the returns and volatility of stocks, gold, bonds, and Bitcoin (BTC) to changes in inflation, interest rates (SBI), and exchange rates is a compelling topic, encompassing factors such as inter-asset interactions, and the roles of assets as hedge or safe-haven. We aims the inter-assetscorrelation using Granger Causality Test and the role of hedge or safe-haven assets against uncertainty using GARCH for normal condition and quantile regression in crisis. Last, we built an optimal portfolio using the Arbitrage Pricing Model (APT) consists of 28% stocks, 16% gold, 27% bonds, and 29% BTC, with returns 1.7796% over the Rf. Monthly trading records from 2018 until 2023 are used. The results demonstrate a correlation between the returns of gold and bonds with BTC, while stock volatility is found to correlate with BTC volatility. Furthermore we found a negative correlation between gold returns and exchange rates, though gold shows no correlation with other variables, indicating its effectiveness as a hedge in normal conditions. The bond serve primarily as a diversifier against exchange rate and inflation in normal, bearish, and bullish market conditions. These findings suggest that gold and bonds are more stable instruments and serve as risk mitigation strategy for BTC.
Does the Use of Digital Marketing Communication and Sales Tools Affect the Economic Results of Polish SMEs?
Authors: Rydzewska Alina , Szwajca Danuta
Published: August 2024

The purpose of this manuscript is to examine the extent to which Polish SMEs use digital marketing tools in the context of their impact on economic performance. The choice of the objective is due to the research gap related to the lack of studies on the impact of the use of digital marketing tools on economic performance by SME companies in Poland. In order to achieve the objective, a two-stage approach was used. The first stage identified the degree of use of digital marketing tools by Polish SMEs compared to EU countries based on secondary data. The second stage analyzed the impact of the use of selected digital marketing and sales communication tools on the economic performance of Polish SMEs. The analysis was based on primary data, which were the results of surveys conducted on a sample of 574 Polish SMEs in the industrial sector. The analysis used statistical methods such as difference significance tests (Mann-Whitney U test, ANOVA Kruskal-Wallis test and Dunn's post-hoc tests), Kendall's rank correlation coefficient and Multivariate Adaptive Regression Splines (MARSplines). The results obtained have theoretical and practical values. The theoretical implications are the enrichment of the marketing concept of customer value creation taking into account the digital customer, which is also the originality of the research. Practical implications refer to indicating to SME managers the need for, as well as ways of implementing and financing digital technologies within the framework of various EU programs for digital transformation of member states.
Chemokinergic and Dopaminergic Signalling Collaborates through the Heteromer Formed by CCR9 and Dopamine Receptor D5 Increasing the Migratory Speed of Effector CD4+ T-Cells to Infiltrate the Colonic Mucosa
Authors: Campos Javier , Osorio-Barrios Francisco , Villanelo Felipe , Gutierrez-Maldonado Sebastian E. , Vargas Pablo , Perez-Acle Tomas , Pacheco Rodrigo
Published: August 2024

Inflammatory bowel diseases (IBD) involve chronic inflammation of the gastrointestinal tract, where effector CD4+ T-cells play a central role. Thereby, the recruitment of T-cells into the colonic mucosa represents a key process in IBD. We recently found that CCR9 and DRD5 might form a heteromeric complex on the T-cell surface. The increase of CCL25 production and the reduction in dopamine levels associated with colonic inflammation represent a dual signal stimulating the CCR9:DRD5 heteromer, which promotes the recruitment of CD4+ T-cells into the colonic lamina propria. Here, we aimed to analysing the molecular requirements involved in the heteromer assembly as well as to determining the underlying cellular mechanisms involved in the colonic tropism given by the stimulation of the CCR9:DRD5 complex. The results show that dual stimulation of the CCR9:DRD5 heteromer potentiates the phosphorylation of the myosin light chain 2 (MLC2) and the migration speed in confined microchannels. Accordingly, disrupting the CCR9:DRD5 assembly induced a sharp reduction in the pMLC2 in vitro, decreased the migratory speed in confined microchannels and dampened the recruitment of CD4+ T-cells into the inflamed colonic mucosa. Furthermore, in silico analysis confirmed that the interface of interaction of CCR9:DRD5 is formed by the transmembrane segments 5 and 6 from each protomer. Our findings demonstrated that the CCR9:DRD5 heteromeric complex plays a fundamental role in the migration of CD4+ T-cells into the colonic mucosa upon inflammation. Thereby, disassembling the formation of the CCR9:DRD5 represents an important therapeutic opportunity to treat IBD.
Ambient Backscatter Based User Cooperation for mmWave Wireless Powered Communication Networks with Lens Antenna Arrays
Authors: Guo Rongbin , Yin Rui , Wang Guan , Xu Congyuan , Yuan JianTao
Published: August 2024

With the rapid consumer adoption of mobile devices such as tablets and smart phones, the tele-traffic has experienced a tremendous growth, low power technologies are highly desirable for future communication networks. In this paper, we consider an ambient backscatter (AB) based user cooperation (UC) scheme for mmWave wireless powered communication networks (WPCN) with lens antenna arrays. Firstly, we formulate an optimization problem to maximize the minimum rate of two users by jointly designing power and time allocation. Then we introduce auxiliary variables and transform the original problem into a convex form. Finally, we propose an efficient algorithm to solve the transformed problem. Simulation results demonstrate that the proposed AB based UC scheme outperforms the competing schemes, thus improve the fairness performance of throughput in WPCN.