bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluating Drug Interactions between Ritonavir and Opioid Analgesics: Implications from Physiologically Based Pharmacokinetic Simulation
Authors: Ni Liang , Cao Zhihai , Jiang Jiakang , Zhang Wei , Hu Wei , Zhang Qian , Shen Chaozhuang , Chen Xijing , Zheng Liang
Published: May 2024

Several commonly used opioid analgesics, such as fentanyl, sufentanil, alfentanil, and hydrocodone, are by report primarily metabolized by the CYP3A4 enzyme. The concurrent use of ritonavir, a potent CYP3A4 inhibitor, can lead to significant drug interactions. Using physiologically based pharmacokinetic (PBPK) modeling and simulation, this study examines the effects of different dosing regimens of ritonavir on the pharmacokinetics of these opioids. The findings reveal that co-administration of ritonavir significantly increases the exposure of fentanyl analogs, with over a 10-fold increase in the exposure of alfentanil and sufentanil when given with ritonavir. Conversely, the effect of ritonavir on fentanyl exposure is modest, likely due to additional metabolism pathways. Additionally, the study demonstrates that the steady-state exposure of hydrocodone and its active metabolite hydromorphone can be increased by up to 87% and 95%, respectively, with concurrent use of ritonavir. The extended-release formulation of hydrocodone is particularly affected. These insights from PBPK modeling provide valuable guidance for optimizing opioid dosing and minimizing the risk of toxicity when used in combination with ritonavir-containing prescriptions.
Therapeutic Drug Monitoring in Psychiatry: Enhancing Treatment Precision and Patient Outcomes
Authors: Biso Letizia , Aringhieri Stefano , Carli Marco , Scarselli Marco , Longoni Biancamaria
Published: May 2024

Psychiatric disorders often require pharmacological interventions to alleviate symptoms and improve quality of life. However, achieving an optimal therapeutic outcome is challenging due to several factors, including variability in the individual response, inter-individual differences in drug metabolism, and drug interactions in polytherapy. Therapeutic drug monitoring (TDM), by measuring drug concentrations in biological samples, represents a valuable tool to address these challenges, by tailoring medication regimens to each individual. This review analyzes the current landscape of TDM in psychiatric practice, highlighting its significance in optimizing drug dosages, minimizing adverse effects, and improving therapeutic efficacy. The metabolism of psychiatric medications (i.e., mood stabilizers, antipsychotics, antidepressants) often exhibits significant inter-patient variability. TDM can help address this variability by enhancing treatment personalization, facilitating early suboptimal- or toxic-level detection, and allowing for timely interventions to prevent treatment failure or adverse effects. Furthermore, this review briefly discusses technological advancements and analytical methods supporting the implementation of TDM in psychiatric settings. These innovations enable quick and cost-effective drug concentration measurements, fostering the widespread adoption of TDM as a routine practice in psychiatric care. In conclusion, the integration of TDM in psychiatry can improve treatment outcomes by individualizing medication regimens within the so-called precision medicine.
Daidzein and Equol: Ex Vivo and In Silico Approaches Targeting COX-2, iNOS, and the Canonical Inflammasome Signaling Pathway
Authors: Márquez-Flores Yazmín K. , Martínez-Galero Elizdath , Correa-Basurto José , Sixto-López Yudibeth , Villegas Isabel , Rosillo María Á. , Cárdeno Ana , Alarcón-de-la-Lastra Catalina
Published: May 2024

Background: The inflammasome is a cytosolic multiprotein complex associated with multiple autoimmune diseases. Phytochemical compounds in soy (Glycine max) foods, such as isoflavones, have been reported for their anti-inflammatory properties. Aim: the anti-inflammatory activity of DZ (daidzein) and EQ (equol) were investigated in an ex vivo model of LPS-stimulated murine peritoneal macrophages and by molecular docking correlation. Methods: Cells were pre-treated with DZ (25, 50, and 100 µM) or EQ (5, 10, and 25 µM), followed by LPS stimulation. The levels of PGE2, NO, TNF-α, IL-6, and IL-1β were analyzed by ELISA, whereas the expressions of COX-2, iNOS, NLRP3, ASC, caspase 1, and IL-18 were measured by Western blotting. Also, the potential for transcriptional modulation by targeting NF-κB, COX-2, iNOS, NLRP3, ASC, and caspase 1 was investigated by molecular docking. Results: The anti-inflammatory responses observed may be due to the modulation of NF-κB due to the binding of DZ or EQ, which is translated into decreased TNF-α, COX-2, iNOS, NLRP3, and ASC levels. Conclusion: This study establishes that DZ and EQ inhibit LPS-induced inflammatory responses in peritoneal murine macrophages via down-regulation of NO and PGE2 generation, as well as the inhibition of the canonical inflammasome pathway, regulating NLRP3, and consequently decreasing IL-1β and IL-18 activation.
Antibiotic Loaded Phytosomes as a Way to Develop Innovative Lipid Formulations of Polyene Macrolides
Authors: Efimova Svetlana S. , Ostroumova Olga S.
Published: May 2024

Background: The threat of antibiotic resistance of fungal pathogens and the high toxicity of the most effective drugs, polyene macrolides, force us to look for new ways to develop innovative antifungal formulations. Objective: The aim of this study was to determine how the sterol, phospholipid, and flavonoid composition of liposomal forms of polyene antibiotics, and in particular, amphotericin B (AmB), affects their ability to increase the permeability of lipid bilayers that mimic the membranes of mammalian and fungal cells. Methods: To monitor the membrane permeability induced by various polyene-based lipid formulations, a calcein leakage assay and the electrophysiological technique based on planar lipid bilayers were used. Key results: The replacement of cholesterol with its biosynthetic precursor, 7-dehydrocholesterol, led to a decrease in the ability of AmB-loaded liposomes to permeabilize lipid bilayers mimicking mammalian cell membranes. The inclusion of plant flavonoid phloretin in AmB-loaded liposomes increased the ability of the formulation to disengage a fluorescent marker from lipid vesicles mimicking the membranes of target fungi. I–V characteristics of the fungal-like lipid bilayers treated with the AmB phytosomes were symmetric, demonstrating the functioning of double-length AmB pores and assuming a decrease in the antibiotic threshold concentration. Conclusions and Perspectives: The therapeutic window of polyene lipid formulations might be expanded by varying their sterol composition. Polyene-loaded phytosomes might be considered as the prototypes for innovative lipid antibiotic formulations.
Swelling, Rupture and Endosomal Escape of Biological Nanoparticles Per Se and Those Fused with Liposomes in Acidic Environment
Authors: Ponomareva Natalia , Brezgin Sergey , Karandashov Ivan , Kostyusheva Anastasiya , Demina Polina , Slatinskaya Olga , Bayurova Ekaterina , Silachev Denis , Pokrovsky Vadim S. , Gegechkori Vladimir , Khaydukov Evgeny , Maksimov Georgy , Frolova Anastasia , Gordeychuk Ilya , Zamyatnin Jr. Andrey A. , Chulanov Vladimir , Parodi Alessandro , Kostyushev Dmitry
Published: May 2024

Biological nanoparticles (NPs), such as extracellular vesicles (EVs), exosome-mimetic nanovesicles (EMNVs) and nanoghosts (NGs), are perspective non-viral delivery vehicles for all types of therapeutic cargo. Biological NPs are renowned for their exceptional biocompatibility and safety, alongside their ease of functionalization, but a significant challenge arises when attempting to load therapeutic payloads, such as nucleic acids (NAs). One effective strategy involves fusing biological NPs with liposomes loaded with NAs, resulting in hybrid carriers that offer the benefits of both biological NPs and the capacity for high cargo loads. Despite their unique parameters, one of the major issues of virtually any nanoformulation is the ability to escape degradation in the compartment of endosomes and lysosomes which determines the overall efficiency of nanotherapeutics. In this study, we fabricated all major types of biological and hybrid NPs and studied their response to the acidic environment observed in the endolysosomal compartment. In this study, we show that EMNVs display increased protonation and swelling relative to EVs and NGs in an acidic environment. Furthermore, the hybrid NPs exhibit an even greater response compared to EMNVs. Short-term incubation of EMNVs in acidic pH corresponding to late endosomes and lysosomes again induces protonation and swelling, whereas hybrid NPs are ruptured, resulting in the decline in their quantities. Our findings demonstrate that in an acidic environment, there is enhanced rupture and release of vesicular cargo observed in hybrid EMNVs that are fused with liposomes compared to EMNVs alone. This was confirmed through PAGE electrophoresis analysis of mCherry protein loaded into nanoparticles. In vitro analysis of NPs colocalization with lysosomes in HepG2 cells demonstrated that EMNVs mostly avoid the endolysosomal compartment, whereas hybrid NPs escape it over time. To conclude, (1) hybrid biological NPs fused with liposomes appear more efficient in the endolysosomal escape via the mechanism of proton sponge-associated scavenging of protons by NPs, influx of counterions and water, and rupture of endo/lysosomes, but (2) EMNVs are much more efficient than hybrid NPs in actually avoiding the endolysosomal compartment in human cells. These results reveal biochemical differences across four major types of biological and hybrid NPs and indicate that EMNVs are more efficient in escaping or avoiding the endolysosomal compartment.
Self-Immolative Domino Dendrimers as Anticancer-Drug Delivery Systems: A Review
Authors: Kędra Karolina , Oledzka Ewa , Sobczak Marcin
Published: May 2024

Worldwide cancer statistics have indicated about 20 million new cancer cases and over 10 million deaths in 2022 (according to data from the International Agency for Research on Cancer). One of the leading cancer treatment strategies is chemotherapy, using innovative drug delivery systems (DDSs). Self-immolative domino dendrimers (SIDendr) for triggered anti-cancer drugs appear to be a promising type of DDSs. The present review provides an up-to-date survey on the contemporary advancements in the field of SIDendr-based anti-cancer drug delivery systems (SIDendr-ac-DDSs) through an exhaustive analysis of the discovery and application of these materials in improving the pharmacological effectiveness of both novel and old drugs. In addition, this article discusses the designing, chemical structure, and targeting techniques, as well as the properties, of several SIDendr-based DDSs. Approaches for this type of targeted DDSs for anti-cancer drug release under a range of stimuli are also explored.
Gold Nanoparticles: Tunable Characteristics and Potential for Nasal Drug Delivery
Authors: Maaz Aida , Blagbrough Ian S. , De Bank Paul A.
Published: May 2024

A general procedure to prepare gold nanourchins (GNUs) via a seed-mediated method was followed using dopamine hydrochloride as a reducing agent and silver nitrate salt (AgNO3) as a shape-directing agent. The novelty of this study comes from the successful incorporation of the prepared gold urchins as an aqueous suspension in a nasal pressurized metered dose inhaler (pMDI) formulation and the investigation of their potential for olfactory targeting for direct nose-to-brain drug delivery (NTBDD). The developed pMDI formulation was composed of 0.025% w/w GNUs, 2% w/w Milli-Q water, and 2% w/w EtOH, with the balance of the formulation being HFA134a propellant. Particle integrity and aerosolization performance were examined using an aerosol exposure system, whereas the nasal deposition profile was tested in a sectioned anatomical replica of human nasal airways. The compatibility of the gold dispersion with the nasal epithelial cell line RPMI 2650 was also investigated in this study. Colloidal gold was found to be stable following six-month storage at 4 °C and during the lyophilization process utilizing a pectin matrix for complete re-dispersibility in water. The GNUs were intact and discrete following atomization via a pMDI, and 13% of the delivered particles were detected beyond the nasal valve, the narrowest region in the nasal cavity, out of which 5.6% was recovered from the olfactory region. Moreover, the formulation was found to be compatible with the human nasal epithelium cell line RPMI 2650 and excellent cell viability was observed. The formulated GNU-HFA-based pMDI is a promising approach for intranasal drug delivery, including deposition in the olfactory region, which could be employed for NTBDD applications.
The Proteasome Inhibitor CEP-18770 Induces Cell Death in Medulloblastoma
Authors: Nath Varma Swastina , Ye Shany , Ferlin Sara , Comer Charley , Cotton Kian , Niklison-Chirou Maria Victoria
Published: May 2024

Medulloblastomas (MBs) represent the most prevalent malignant solid tumors in kids. The conventional treatment regimen for MBs includes surgical removal of the tumor, followed by radiation and chemotherapy. However, this approach is associated with significant morbidity and detrimental side effects. Consequently, there is a critical demand for more precise and less harmful treatments to enhance the quality of life for survivors. CEP-18770, a novel proteasome inhibitor that targets the 20S subunit, has emerged as a promising candidate, due to its anticancer activity in metastatic solid tumors and multiple myeloma, coupled with an acceptable safety profile. In this study, we aimed to assess the anticancer efficacy of CEP-18770 by employing a variety of MB patient-derived cells and cell lines. Our preclinical investigations revealed that CEP-18770 effectively inhibits proteasome activity and induces apoptosis in MBs cells. Furthermore, we discovered that CEP-18770 and cisplatin, a current component of MB therapy, exhibit a synergistic apoptotic effect. This paper shows that CEP-18770 holds potential as an adjunctive treatment for MB tumors, thereby paving the way for more targeted and less toxic therapeutic strategies.
Deep Learning Insights into the Dynamic Effects of Photodynamic Therapy on Cancer Cells
Authors: Rahman Md. Atiqur , Yan Feihong , Li Ruiyuan , Wang Yu , Huang Lu , Han Rongcheng , Jiang Yuqiang
Published: May 2024

Photodynamic therapy (PDT) shows promise in tumor treatment, particularly when combined with nanotechnology. This study examines the impact of deep learning, particularly the Cellpose algorithm, on the comprehension of cancer cell responses to PDT. The Cellpose algorithm enables robust morphological analysis of cancer cells, while logistic growth modelling predicts cellular behavior post-PDT. Rigorous model validation ensures the accuracy of the findings. Cellpose demonstrates significant morphological changes after PDT, affecting cellular proliferation and survival. The reliability of the findings is confirmed by model validation. This deep learning tool enhances our understanding of cancer cell dynamics after PDT. Advanced analytical techniques, such as morphological analysis and growth modeling, provide insights into the effects of PDT on hepatocellular carcinoma (HCC) cells, which could potentially improve cancer treatment efficacy. In summary, the research examines the role of deep learning in optimizing PDT parameters to personalize oncology treatment and improve efficacy.
High-Q Multiband Narrowband Absorbers Based on Two-Dimensional Graphene Metamaterials
Authors: Zhu Aijun , Bu Pengcheng , Cheng Lei , Hu Cong , Mahapatra Rabi
Published: May 2024

In this paper, an absorber with multi-band, tunable, high Q, and high sensitivity, based on terahertz periodic two-dimensional patterned graphene surface plasmon resonance (SPR), is proposed. The absorber consists of a bottom metal film separated by a periodically patterned graphene metamaterial structure and a SiO2 dielectric layer, where the patterned graphene layer is etched by “+” and “L” shapes and circles. It has simple structural features that can greatly simplify the fabrication process. We have analyzed the optical properties of a graphene surface plasmon perfect metamaterial absorber based on graphene in the terahertz region using the finite-difference method in time domain (FDTD). The results show that the absorber device exhibits three perfect absorption peaks in the terahertz bands of f1 = 1.55 THz, f2 = 4.19 THz, and f3 = 6.92 THz, with absorption rates as high as 98.70%, 99.63%, and 99.42%, respectively. By discussing the effects of parameters such as the geometrical dimensions of patterned graphene metamaterial structure “+” width W1, “L” width W2, circular width R, and the thickness of the dielectric layer on the absorption performance of absorber, as well as investigating the chemical potential and relaxation time of patterned-layer graphene material, it was found that the amplitude of the absorption peaks and the frequency of resonance of absorber devices can be dynamically adjusted. Finally, we simulated the spectra as the surrounding refractive index n varied to better evaluate the sensing performance of the structure, yielding structural sensitivities up to 382 GHz/RIU. Based on this study, we find that the results of our research will open new doors for the use of multi-band, tunable, polarization-independent metamaterial absorbers that are insensitive to large-angle oblique incidence.
Using Age-Stage Two-Sex Life Tables to Assess the Suitability of Three Solanaceous Host Plants for the Invasive Cotton Mealybug Phenacoccus solenopsis Tinsley
Authors: Abbes Khaled , Harbi Ahlem , Guerrieri Emilio , Chermiti Brahim
Published: May 2024

Phenacoccus solenopsis Tinsley (Hemiptera: Coccomorpha: Pseudococcidae), the cotton mealybug, is an invasive polyphagous species that has been extending its geographic range, posing a conspicuous threat to many Mediterranean crops of economic importance. These include three species of Solanaceae, namely Solanum lycopersicum L. (tomato), Solanum tuberosum L. (potato) and Solanum melongena L. (eggplant) all of which are economically important worldwide. In this study, we used age-stage two-sex life tables to investigate the suitability of these three plant species as hosts for P. solenopsis and to calculate pest fitness, life history parameters and population projection parameters. All tested host plants that were suitable for the pest and eggplant host plant induced a higher fecundity (276.50 ± 10.78 eggs/female), net reproductive rate (R0) (243.32 ± 15.83 offspring/female) and finite rate of increase (λ) (1.18 ± 0.0043 day−1) and more extended adult longevity (males: 6.50 ± 0.34 days and females: 24.15 ± 0.50 days). Population growth predictions over a period of 90 days of infestation, commencing with an initial population of 10 eggs showed that adult population size was 674,551 on tomato, 826,717 on potato and 355,139 on eggplant. Our data on plant host preference of P. solenopsis will aid the development of appropriate management strategies and achieve successful control of this invasive pest in key Mediterranean crop systems.
Bulk Segregant Analysis Sequencing and RNA-Seq Analyses Reveal Candidate Genes Associated with Sepal Color Phenotype of Eggplant (Solanum melongena L.)
Authors: Wang Benqi , Chen Xia , Huang Shuping , Tan Jie , Zhang Hongyuan , Wang Junliang , Chen Rong , Zhang Min
Published: May 2024

Eggplant is a highly significant vegetable crop and extensively cultivated worldwide. Sepal color is considered one of the major commercial traits of eggplant. Eggplant sepals develop from petals, and sepals have the ability to change color by accumulating anthocyanins, but whether the eggplants in sepal and their biosynthetic pathways are the same as those in petals is not known. To date, little is known about the underlying mechanisms of sepal color formation. In this study, we performed bulked segregant analysis and transcriptome sequencing using eggplant sepals and obtained 1,452,898 SNPs and 182,543 InDel markers, respectively, as well as 123.65 Gb of clean data using transcriptome sequencing. Through marker screening, the genes regulating eggplant sepals were localized to an interval of 2.6 cM on chromosome 10 by bulked segregant analysis sequencing and transcriptome sequencing and co-analysis, combined with screening of molecular markers by capillary electrophoresis. Eight possible candidate genes were then screened to further interpret the regulatory incentives for the eggplant sepal color.
Physiological and Agronomical Response of Coffee to Different Nitrogen Forms with and without Water Stress
Authors: Ramirez-Builes Victor Hugo , Küsters Jürgen , Thiele Ellen , Lopez-Ruiz Juan Carlos
Published: May 2024

Nitrogen (N) is the most important nutrient in coffee, with a direct impact on productivity, quality, and sustainability. N uptake by the roots is dominated by ammonium (NH4+) and nitrates (NO3−), along with some organic forms at a lower proportion. From the perspective of mineral fertilizer, the most common N sources are urea, ammonium (AM), ammonium nitrates (AN), and nitrates; an appropriate understanding of the right balance between N forms in coffee nutrition would contribute to more sustainable coffee production through the better N management of this important crop. The aim of this research was to evaluate the influences of different NH4-N/NO3-N ratios in coffee from a physiological and agronomical perspective, and their interaction with soil water levels. Over a period of 5 years, three trials were conducted under controlled conditions in a greenhouse with different growing media (quartz sand) and organic soil, with and without water stress, while one trial was conducted under field conditions. N forms and water levels directly influence physiological responses in coffee, including photosynthesis (Ps), chlorophyll content, dry biomass accumulation (DW), nutrient uptake, and productivity. In all of the trials, the plants group in soils with N ratios of 50% NH4-N/50% NO3-N, and 25% NH4-N/75% NO3-N showed better responses to water stress, as well as a higher Ps, a higher chlorophyll content, a higher N and cation uptake, higher DW accumulation, and higher productivity. The soil pH was significantly influenced by the N forms: the higher the NO3−-N share, the lower the acidification level. The results allow us to conclude that the combination of 50% NH4-N/50% NO3-N and 25% NH4-N/75% NO3-N N forms in coffee improves the resistance capacity of the coffee to water stress, improves productivity, reduces the soil acidification level, and improves ion balance and nutrient uptake.
Biodegradation of Polyurethane by Fungi Isolated from Industrial Wastewater—A Sustainable Approach to Plastic Waste Management
Authors: Rajan Aiswarya , Ameen Fuad , Jambulingam Ranjitha , Shankar Vijayalakshmi
Published: May 2024

Polyurethane (PU) is a type of polymer, which exists in various forms in the environment. Very few studies are available concerning the structure or enzymatic mechanism of the microbial community, which can degrade PU. Degradation of PU remains a difficult problem with respect to the environmental and biological disciplines. This study mainly focused on identifying the micro-organisms able to degrade polyurethane and confirming the degradation by performing a plate assay, Sturm test and scanning electron microscopy. Optimal culture conditions for maximum PU degradation were also analyzed through classical methods. A soil burial test was conducted by placing polyurethane films in the soil for one month, and the microbe growing on the surface of polyurethane films—with a maximum degradation of 55%—was isolated and identified as Aspergillus versicolor (ARF5). The culture medium was also optimized with different physical and chemical parameters for maximum PU degradation. The presence of CO2 as a by-product of PU biodegradation was confirmed through the Sturm test.
Polydimethylsiloxane Surface Modification of Microfluidic Devices for Blood Plasma Separation
Authors: Gonçalves Margarida , Gonçalves Inês Maia , Borges Joel , Faustino Vera , Soares Delfim , Vaz Filipe , Minas Graça , Lima Rui , Pinho Diana
Published: May 2024

Over the last decade, researchers have developed a variety of new analytical and clinical diagnostic devices. These devices are predominantly based on microfluidic technologies, where biological samples can be processed and manipulated for the collection and detection of important biomolecules. Polydimethylsiloxane (PDMS) is the most commonly used material in the fabrication of these microfluidic devices. However, it has a hydrophobic nature (contact angle with water of 110°), leading to poor wetting behavior and issues related to the mixing of fluids, difficulties in obtaining uniform coatings, and reduced efficiency in processes such as plasma separation and molecule detection (protein adsorption). This work aimed to consider the fabrication aspects of PDMS microfluidic devices for biological applications, such as surface modification methods. Therefore, we studied and characterized two methods for obtaining hydrophilic PDMS surfaces: surface modification by bulk mixture and the surface immersion method. To modify the PDMS surface properties, three different surfactants were used in both methods (Pluronic® F127, polyethylene glycol (PEG), and polyethylene oxide (PEO)) at different percentages. Water contact angle (WCA) measurements were performed to evaluate the surface wettability. Additionally, capillary flow studies were performed with microchannel molds, which were produced using stereolithography combined with PDMS double casting and replica molding procedures. A PDMS microfluidic device for blood plasma separation was also fabricated by soft lithography with PDMS modified by PEO surfactant at 2.5% (v/v), which proved to be the best method for making the PDMS hydrophilic, as the WCA was lower than 50° for several days without compromising the PDMS’s optical properties. Thus, this study indicates that PDMS surface modification shows great potential for enhancing blood plasma separation efficiency in microfluidic devices, as it facilitates fluid flow, reduces cell aggregations and the trapping of air bubbles, and achieves higher levels of sample purity.
A Dyson Brownian Motion Model for Weak Measurements in Chaotic Quantum Systems
Authors: Gerbino Federico , Le Doussal Pierre , Giachetti Guido , De Luca Andrea
Published: May 2024

We consider a toy model for the study of monitored dynamics in many-body quantum systems. We study the stochastic Schrödinger equation resulting from continuous monitoring with a rate Γ of a random Hermitian operator, drawn from the Gaussian unitary ensemble (GUE) at every time t. Due to invariance by unitary transformations, the dynamics of the eigenvalues {λα}α=1n of the density matrix decouples from that of the eigenvectors, and is exactly described by stochastic equations that we derive. We consider two regimes: in the presence of an extra dephasing term, which can be generated by imperfect quantum measurements, the density matrix has a stationary distribution, and we show that in the limit of large size n→∞ it matches with the inverse-Marchenko–Pastur distribution. In the case of perfect measurements, instead, purification eventually occurs and we focus on finite-time dynamics. In this case, remarkably, we find an exact solution for the joint probability distribution of λ’s at each time t and for each size n. Two relevant regimes emerge: at short times tΓ=O(1), the spectrum is in a Coulomb gas regime, with a well-defined continuous spectral distribution in the n→∞ limit. In that case, all moments of the density matrix become self-averaging and it is possible to exactly characterize the entanglement spectrum. In the limit of large times tΓ=O(n), one enters instead a regime in which the eigenvalues are exponentially separated log(λα/λβ)=O(Γt/n), but fluctuations ∼O(Γt/n) play an essential role. We are still able to characterize the asymptotic behaviors of the entanglement entropy in this regime.
Schēma: A Semantic Puzzle—Some Hermeneutical and Translational Difficulties about Philippians 2:7d
Authors: Bartolomei Teresa
Published: May 2024

The occurrence of the term σχήμα in Phil 2:7d is analyzed in comparison with two other crucial Pauline occurrences: 1 Cor 7:31 and Phil 3:21 (here as a semanteme included in the verb μετασχηματίσει). This comparative study aims to provide a revision of the current interpretation of the word as designating the outward, sensory, accidental appearance in which Christ’s human nature was manifested to those who dealt with him. This traditional reconstruction is unsatisfactory in two respects: (1) it is tributary to a substantialist ontology that identifies corporeality as a mere spatial extension, unrelated to historicity and (2) it is fraught with highly problematic theological, potentially docetic, implications. As an alternative, the term σχήμα is here interpreted within the framework of the great Pauline theology of history: as a temporal–eschatological marker designating the peculiar temporal state of transience and suffering corruptibility inherent in physicality and corporeal life. This change also clarifies the conceptual articulation of σχήμα with the parallel expression μορφὴν δούλου. According to this interpretation, contrary to the prevailing view, the locution “slave form” does not designate ‘the’ or ‘one’ ‘human form’ but the ‘creature form’, as cosmic submission to temporal finitude.
AMFNet: Attention-Guided Multi-Scale Fusion Network for Bi-Temporal Change Detection in Remote Sensing Images
Authors: Zhan Zisen , Ren Hongjin , Xia Min , Lin Haifeng , Wang Xiaoya , Li Xin
Published: May 2024

Change detection is crucial for evaluating land use, land cover changes, and sustainable development, constituting a significant component of Earth observation tasks. The difficulty in extracting features from high-resolution images, coupled with the complexity of image content, poses challenges for traditional change detection algorithms in terms of accuracy and applicability. The recent emergence of deep learning methods has led to substantial progress in the field of change detection. However, existing frameworks often involve the simplistic integration of bi-temporal features in specific areas, lacking the fusion of temporal information and semantic details in the images. In this paper, we propose an attention-guided multi-scale fusion network (AMFNet), which effectively integrates bi-temporal image features and diverse semantics at both the encoding and decoding stages. AMFNet utilizes a unique attention-guided mechanism to dynamically adjust feature fusion, enhancing adaptability and accuracy in change detection tasks. Our method intelligently incorporates temporal information into the deep learning model, considering the temporal dependency inherent in these tasks. We decode based on an interactive feature map, which improves the model’s understanding of evolving patterns over time. Additionally, we introduce multi-level supervised training to facilitate the learning of fused features across multiple scales. In comparison with different algorithms, our proposed method achieves F1 values of 0.9079, 0.8225, and 0.8809 in the LEVIR-CD, GZ-CD, and SYSU-CD datasets, respectively. Our model outperforms the SOTA model, SAGNet, by 0.69% in terms of F1 and 1.15% in terms of IoU on the LEVIR-CD dataset, by 2.8% in terms of F1 and 1.79% in terms of IoU on the GZ-CD dataset, and by 0.54% in terms of F1 and 0.38% in terms of IoU on the SYSU-CD dataset. The method proposed in this study can be applied to various complex scenarios, establishing a change detection method with strong model generalization capabilities.
Data Assimilation of Satellite-Derived Rain Rates Estimated by Neural Network in Convective Environments: A Study over Italy
Authors: Torcasio Rosa Claudia , Papa Mario , Del Frate Fabio , Mascitelli Alessandra , Dietrich Stefano , Panegrossi Giulia , Federico Stefano
Published: May 2024

The accurate prediction of heavy precipitation in convective environments is crucial because such events, often occurring in Italy during the summer and fall seasons, can be a threat for people and properties. In this paper, we analyse the impact of satellite-derived surface-rainfall-rate data assimilation on the Weather Research and Forecasting (WRF) model’s precipitation prediction, considering 15 days in summer 2022 and 17 days in fall 2022, where moderate to intense precipitation was observed over Italy. A 3DVar realised at CNR-ISAC (National Research Council of Italy, Institute of Atmospheric Sciences and Climate) is used to assimilate two different satellite-derived rain rate products, both exploiting geostationary (GEO), infrared (IR), and low-Earth-orbit (LEO) microwave (MW) measurements: One is based on an artificial neural network (NN), and the other one is the operational P-IN-SEVIRI-PMW product (H60), delivered in near-real time by the EUMETSAT HSAF (Satellite Application Facility in Support of Operational Hydrology and Water Management). The forecast is verified in two periods: the hours from 1 to 4 (1–4 h phase) and the hours from 3 to 6 (3–6 h phase) after the assimilation. The results show that the rain rate assimilation improves the precipitation forecast in both seasons and for both forecast phases, even if the improvement in the 3–6 h phase is found mainly in summer. The assimilation of H60 produces a high number of false alarms, which has a negative impact on the forecast, especially for intense events (30 mm/3 h). The assimilation of the NN rain rate gives more balanced predictions, improving the control forecast without significantly increasing false alarms.
Ammonia Detection by Electronic Noses for a Safer Work Environment
Authors: Reis Tiago , Moura Pedro Catalão , Gonçalves Débora , Ribeiro Paulo A. , Vassilenko Valentina , Fino Maria Helena , Raposo Maria
Published: May 2024

Providing employees with proper work conditions should be one of the main concerns of any employer. Even so, in many cases, work shifts chronically expose the workers to a wide range of potentially harmful compounds, such as ammonia. Ammonia has been present in the composition of products commonly used in a wide range of industries, namely production in lines, and also laboratories, schools, hospitals, and others. Chronic exposure to ammonia can yield several diseases, such as irritation and pruritus, as well as inflammation of ocular, cutaneous, and respiratory tissues. In more extreme cases, exposure to ammonia is also related to dyspnea, progressive cyanosis, and pulmonary edema. As such, the use of ammonia needs to be properly regulated and monitored to ensure safer work environments. The Occupational Safety and Health Administration and the European Agency for Safety and Health at Work have already commissioned regulations on the acceptable limits of exposure to ammonia. Nevertheless, the monitoring of ammonia gas is still not normalized because appropriate sensors can be difficult to find as commercially available products. To help promote promising methods of developing ammonia sensors, this work will compile and compare the results published so far.
Deep Compressed Communication and Application in Multi-Robot 2D-Lidar SLAM: An Intelligent Huffman Algorithm
Authors: Zhang Liang , Deng Jinghui
Published: May 2024

Multi-robot Simultaneous Localization and Mapping (SLAM) systems employing 2D lidar scans are effective for exploration and navigation within GNSS-limited environments. However, scalability concerns arise with larger environments and increased robot numbers, as 2D mapping necessitates substantial processor memory and inter-robot communication bandwidth. Thus, data compression prior to transmission becomes imperative. This study investigates the problem of communication-efficient multi-robot SLAM based on 2D maps and introduces an architecture that enables compressed communication, facilitating the transmission of full maps with significantly reduced bandwidth. We propose a framework employing a lightweight feature extraction Convolutional Neural Network (CNN) for a full map, followed by an encoder combining Huffman and Run-Length Encoding (RLE) algorithms to further compress a full map. Subsequently, a lightweight recovery CNN was designed to restore map features. Experimental validation involves applying our compressed communication framework to a two-robot SLAM system. The results demonstrate that our approach reduces communication overhead by 99% while maintaining map quality. This compressed communication strategy effectively addresses bandwidth constraints in multi-robot SLAM scenarios, offering a practical solution for collaborative SLAM applications.
Improving Measurement Accuracy of Deep Hole Measurement Instruments through Perspective Transformation
Authors: Zhao Xiaowei , Du Huifu , Yu Daguo
Published: May 2024

Deep hole measurement is a crucial step in both deep hole machining and deep hole maintenance. Single-camera vision presents promising prospects in deep hole measurement due to its simple structure and low-cost advantages. However, the measurement error caused by the heating of the imaging sensor makes it difficult to achieve the ideal measurement accuracy. To compensate for measurement errors induced by imaging sensor heating, this study proposes an error compensation method for laser and vision-based deep hole measurement instruments. This method predicts the pixel displacement of the entire field of view using the pixel displacement of fixed targets within the camera’s field of view and compensates for measurement errors through a perspective transformation. Theoretical analysis indicates that the perspective projection matrix changes due to the heating of the imaging sensor, which causes the thermally induced measurement error of the camera. By analyzing the displacement of the fixed target point, it is possible to monitor changes in the perspective projection matrix and thus compensate for camera measurement errors. In compensation experiments, using target displacement effectively predicts pixel drift in the pixel coordinate system. After compensation, the pixel error was suppressed from 1.99 pixels to 0.393 pixels. Repetitive measurement tests of the deep hole measurement instrument validate the practicality and reliability of compensating for thermal-induced errors using perspective transformation.
The Potential of Fecal Volatile Organic Compound Analysis for the Early Diagnosis of Late-Onset Sepsis in Preterm Infants: A Narrative Review
Authors: de Kroon Rimke R. , Frerichs Nina M. , Struys Eduard A. , de Boer Nanne K. , de Meij Tim G. J. , Niemarkt Hendrik J.
Published: May 2024

Early diagnosis and treatment of late-onset sepsis (LOS) is crucial for survival, but challenging. Intestinal microbiota and metabolome alterations precede the clinical onset of LOS, and the preterm gut is considered an important source of bacterial pathogens. Fecal volatile organic compounds (VOCs), formed by physiologic and pathophysiologic metabolic processes in the preterm gut, reflect a complex interplay between the human host, the environment, and microbiota. Disease-associated fecal VOCs can be detected with an array of devices with various potential for the development of a point-of-care test (POCT) for preclinical LOS detection. While characteristic VOCs for common LOS pathogens have been described, their VOC profiles often overlap with other pathogens due to similarities in metabolic pathways, hampering the construction of species-specific profiles. Clinical studies have, however, successfully discriminated LOS patients from healthy individuals using fecal VOC analysis with the highest predictive value for Gram-negative pathogens. This review discusses the current advancements in the development of a non-invasive fecal VOC-based POCT for early diagnosis of LOS, which may potentially provide opportunities for early intervention and targeted treatment and could improve clinical neonatal outcomes. Identification of confounding variables impacting VOC synthesis, selection of an optimal detection device, and development of standardized sampling protocols will allow for the development of a novel POCT in the near future.
A Combined UWB/IMU Localization Method with Improved CKF
Authors: Ji Pengfei , Duan Zhongxing , Xu Weisheng
Published: May 2024

Aiming at the problem that ultra-wide band (UWB) cannot be accurately localized in environments with large noise variations and unknown statistical properties, a combinatorial localization method based on improved cubature (CKF) is proposed. First, in order to overcome the problem of inaccurate local approximation or even the inability to converge due to the initial value not being set near the optimal solution in the process of solving the UWB position by the least-squares method, the Levenberg–Marquardt algorithm (L–M) is adopted to optimally solve the UWB position. Secondly, because UWB and IMU information are centrally fused, an adaptive factor is introduced to update the measurement noise covariance matrix in real time to update the observation noise, and the fading factor is added to suppress the filtering divergence to achieve an improvement for the traditional CKF algorithm. Finally, the performance of the proposed combined localization method is verified by field experiments in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios, respectively. The results show that the proposed method can maintain high localization accuracy in both LOS and NLOS scenarios. Compared with the Extended Kalman filter (EKF), unbiased Kalman filter (UKF), and CKF algorithms, the localization accuracies of the proposed method in NLOS scenarios are improved by 25.2%, 18.3%, and 11.3%, respectively.
Efficient IoT-Assisted Waste Collection for Urban Smart Cities
Authors: Khan Sangrez , Ali Bakhtiar , Alharbi Abeer A. K. , Alotaibi Salihah , Alkhathami Mohammed
Published: May 2024

Waste management is one of the many major challenges faced by all urban cities around the world. With the increase in population, the current mechanisms for waste collection and disposal are under strain. The waste management problem is a global challenge that requires a collaborative effort from different stakeholders. Moreover, there is a need to develop technology-based solutions besides engaging the communities and establishing novel policies. While there are several challenges in waste management, the collection of waste using the current infrastructure is among the top challenges. Waste management suffers from issues such as a limited number of collection trucks, different types of household and industrial waste, and a low number of dumping points. The focus of this paper is on utilizing the available waste collection transportation capacity to efficiently dispose of the waste in a time-efficient manner while maximizing toxic waste disposal. A novel knapsack-based technique is proposed that fills the collection trucks with waste bins from different geographic locations by taking into account the amount of waste and toxicity in the bins using IoT sensors. Using the Knapsack technique, the collection trucks are loaded with waste bins up to their carrying capacity while maximizing their toxicity. The proposed model was implemented in MATLAB, and detailed simulation results show that the proposed technique outperforms other waste collection approaches. In particular, the amount of high-priority toxic waste collection was improved up to 47% using the proposed technique. Furthermore, the number of waste collection visits is reduced in the proposed scheme as compared to the conventional method, resulting in the recovery of the equipment cost in less than a year.
Pattern Recognition of Partial Discharge Faults in Switchgear Using a Back Propagation Neural Network Optimized by an Improved Mantis Search Algorithm
Authors: Fei Zhangjun , Li Yiying , Yang Shiyou
Published: May 2024

The dependable functioning of switchgear is essential to maintain the stability of power supply systems. Partial discharge (PD) is a critical phenomenon affecting the insulation of switchgear, potentially leading to equipment failure and accidents. PDs are generally grouped into metal particle discharge, suspended discharge, and creeping discharge. Different types of PDs are closely related to the severity of a PD. Partial discharge pattern recognition (PDPR) plays a vital role in the early detection of insulation defects. In this regard, a Back Propagation Neural Network (BPNN) for PDPR in switchgear is proposed in this paper. To eliminate the sensitivity to initial values of BPNN parameters and to enhance the generalized ability of the proposed BPRN, an improved Mantis Search Algorithm (MSA) is proposed to optimize the BPNN. The improved MSA employs some boundary handling strategies and adaptive parameters to enhance the algorithm’s efficiency in optimizing the network parameters of BPNN. Principal Component Analysis (PCA) is introduced to reduce the dimensionality of the feature space to achieve significant time saving in comparable recognition accuracy. The initially extracted 14 feature values are reduced to 7, reducing the BPNN parameter count from 183 with 14 features to 113 with 7 features. Finally, numerical results are presented and compared with Decision Tree (DT), k-Nearest Neighbor classifiers (KNN), and Support Vector Machine (SVM). The proposed method in this paper exhibits the highest recognition accuracy in metal particle discharge and suspended discharge.
Smart Delivery Assignment through Machine Learning and the Hungarian Algorithm
Authors: Vásconez Juan Pablo , Schotborgh Elias , Vásconez Ingrid Nicole , Moya Viviana , Pilco Andrea , Menéndez Oswaldo , Guamán-Rivera Robert , Guevara Leonardo
Published: May 2024

Intelligent transportation and advanced mobility techniques focus on helping operators to efficiently manage navigation tasks in smart cities, enhancing cost efficiency, increasing security, and reducing costs. Although this field has seen significant advances in developing large-scale monitoring of smart cities, several challenges persist concerning the practical assignment of delivery personnel to customer orders. To address this issue, we propose an architecture to optimize the task assignment problem for delivery personnel. We propose the use of different cost functions obtained with deterministic and machine learning techniques. In particular, we compared the performance of linear and polynomial regression methods to construct different cost functions represented by matrices with orders and delivery people information. Then, we applied the Hungarian optimization algorithm to solve the assignment problem, which optimally assigns delivery personnel and orders. The results demonstrate that when used to estimate distance information, linear regression can reduce estimation errors by up to 568.52 km (1.51%) for our dataset compared to other methods. In contrast, polynomial regression proves effective in constructing a superior cost function based on time information, reducing estimation errors by up to 17,143.41 min (11.59%) compared to alternative methods. The proposed approach aims to enhance delivery personnel allocation within the delivery sector, thereby optimizing the efficiency of this process.