bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Different Levels of Carbohydrates on Growth Performance, Hepatic and Intestinal Health, and Intestinal Microflora of Juvenile Pikeperch (Sander lucioperca)
Authors: Zhao Jie , Liu Yang , Sun Zhipeng , Wang Liansheng , Fan Ze , Pan Yadan , Gao Jiamin , Lu Cuiyun , Zheng Xianhu
Published: August 2024

Pikeperch (Sander lucioperca) is a species with great potential for aquaculture in Eurasian countries, while feed costs limit the scale of pikeperch farming. Adding carbohydrates to the feed as an energy source is a viable approach to reduce costs and to improve the culture status of pikeperch. In this study, in order to determine the optimal carbohydrate requirement of pikeperch, three tapioca starch (8%, 10%, and 12%) added feeds were produced with isonitrogenous (51%) and isolipidic (11%). For 8 weeks, body weight was 1.20 ± 0.01 g, pikeperch were manually fed the trio of experimental diets until they seemed fully satisfied. The finding revealed that pikeperch can utilize dietary carbohydrate, but excessive dietary carbohydrate will adversely affect the growth performance. The growth and survival rate were decreased in pikeperch in S12 (P < 0.05). The α‐amylase activity of S12 reduced in the intestine and lipid deposition was observed in the liver compared with the S8. In addition, proinflammatory cytokines, interleukin 1 beta (il1-β), interleukin 8 (il8), and tumor necrosis factor beta (tnf-β), in the liver and intestine elevate and anti‐inflammatory cytokines, interleukin 10 (il10) and transforming growth factor beta (tgf-β), decrease with increasing dietary carbohydrate levels. Hepatic and intestinal antioxidant capacity were also adversely affected, with S12 significantly increasing malondialdehyde (MDA) contents and decreasing glutathione (GSH) and total antioxidant capacity (T‐AOC) (P < 0.05). The intestinal barrier function is also damaged, the height and width of intestinal villi decreased, and the expression of occludin-a, occludin-b, and zonula occludens‐2 (zo-2) genes was decreased. Elevated levels of starch intake led to harm to gut microflora, reducing bacterial populations, simultaneously boosting the presence of detrimental bacteria (Proteobacteria, Actinobacteriota, Achromobacter, and Rhodococcus) and diminishing the beneficial bacteria (Firmicutes). In conclusion, moderate addition of starch as an energy source can reduce feed costs; however, over addition can bring about organism damage and is recommended to be added at less than 10%.
Presegmenter Cascaded Framework for Mammogram Mass Segmentation
Authors: Oza Urvi , Gohel Bakul , Kumar Pankaj , Oza Parita
Published: August 2024

Accurate segmentation of breast masses in mammogram images is essential for early cancer diagnosis and treatment planning. Several deep learning (DL) models have been proposed for whole mammogram segmentation and mass patch/crop segmentation. However, current DL models for breast mammogram mass segmentation face several limitations, including false positives (FPs), false negatives (FNs), and challenges with the end‐to‐end approach. This paper presents a novel two‐stage end‐to‐end cascaded breast mass segmentation framework that incorporates a saliency map of potential mass regions to guide the DL models for breast mass segmentation. The first‐stage segmentation model of the cascade framework is used to generate a saliency map to establish a coarse region of interest (ROI), effectively narrowing the focus to probable mass regions. The proposed presegmenter attention (PSA) blocks are introduced in the second‐stage segmentation model to enable dynamic adaptation to the most informative regions within the mammogram images based on the generated saliency map. Comparative analysis of the Attention U‐net model with and without the cascade framework is provided in terms of dice scores, precision, recall, FP rates (FPRs), and FN outcomes. Experimental results consistently demonstrate enhanced breast mass segmentation performance by the proposed cascade framework across all three datasets: INbreast, CSAW‐S, and DMID. The cascade framework shows superior segmentation performance by improving the dice score by about 6% for the INbreast dataset, 3% for the CSAW‐S dataset, and 2% for the DMID dataset. Similarly, the FN outcomes were reduced by 10% for the INbreast dataset, 19% for the CSAW‐S dataset, and 4% for the DMID dataset. Moreover, the proposed cascade framework’s performance is validated with varying state‐of‐the‐art segmentation models such as DeepLabV3+ and Swin transformer U‐net. The presegmenter cascade framework has the potential to improve segmentation performance and mitigate FNs when integrated with any medical image segmentation framework, irrespective of the choice of the model.
Clinical and Molecular Risk Factors in Extracranial Malignant Rhabdoid Tumors: Toward an Integrated Model of High-Risk Tumors
Authors: Fincke Victoria E. , Steinbügl Mona , Chun Hye-Jung E. , Nemes Karolina , Mucha Marlena , Loßner Maurice , Dorn Felix , Gastberger Katharina , Bühner Sebastian , Sill Martin , Kröncke Thomas , Siebert Reiner , Melchior Patrick , Furtwängler Rhoikos , Schlesner Matthias , Vokuhl Christian , Röcken Christoph , Johann Pascal D. , Frühwald Michael C.
Published: August 2024

Purpose:

Extracranial malignant rhabdoid tumors (eMRT) are a challenging entity. Despite the use of multimodal treatment approaches, therapy failure occurs in 55% to 67% of these. Molecular markers for identification of patients at increased risk for relapse or refractory (R/R) disease are not available. Clinical characteristics may only insufficiently predict the individual course of disease.

Experimental Design:

Using the EU-RHAB database, we analyzed a cohort of 121 patients with eMRT clinically. For 81 patients, molecular and clinical data were available, which were further complemented with publicly available DNA molecular data from 92 eMRTs. We aimed to delineate molecular risk factors by dissecting the DNA methylome of these tumors. Moreover, we establish clinical characteristics and treatment details of R/R disease in a subcohort of 80 patients.

Results:

Using consensus hierarchical clustering, we identified three distinct subgroups, one of which (eMRT standard risk) was associated with significantly improved survival, irrespective of germline status and/or localization. At the transcriptome level, this subgroup was characterized by an overexpression of genes involved in muscle development. A relevant proportion of patients developed distant relapses or progressions; the median time to the event was 4 months, underlining the need for early identification and risk stratification of R/R disease. The overall survival was significantly decreased in patients with progressive disease when compared with relapse cases, and reaching complete remission during salvage therapy provided a survival benefit.

Conclusions:

Our analysis of eMRT in this comprehensive cohort provides novel insights into the patterns of relapse and integrates molecular and clinical risk factors to guide clinical decision-making.
CD137 (4-1BB) and T-Lymphocyte Exhaustion
Authors: Molero-Glez Paula , Azpilikueta Arantza , Mosteo Laura , Glez-Vaz Javier , Palencia Belen , Melero Ignacio
Published: August 2024

CD137 (4-1BB) costimulation results in the potent activation of antitumor T lymphocytes and elicits antitumor efficacy that is synergistic with anti-PD(L)1 checkpoint inhibitors, especially when using bispecific constructs. Emerging experimental evidence indicates that 4-1BB ligation prevents and may revert T-cell exhaustion.

See related article by Jeon et al., p. 4155
Table 2 from p53R172H and p53R245W Hotspot Mutations Drive Distinct Transcriptomes in Mouse Mammary Tumors Through a Convergent Transcriptional Mediator
Authors: McDaniel Joy M. , Morrissey Rhiannon L. , Dibra Denada , Patel Lalit R. , Xiong Shunbin , Zhang Yun , Chau Gilda P. , Su Xiaoping , Qi Yuan , El-Naggar Adel K. , Lozano Guillermina
Published: August 2024

Transcription factor motifs enriched in MaPR172H/− and MaPR245W/− mammary tumors
Figure 6 from p53R172H and p53R245W Hotspot Mutations Drive Distinct Transcriptomes in Mouse Mammary Tumors Through a Convergent Transcriptional Mediator
Authors: McDaniel Joy M. , Morrissey Rhiannon L. , Dibra Denada , Patel Lalit R. , Xiong Shunbin , Zhang Yun , Chau Gilda P. , Su Xiaoping , Qi Yuan , El-Naggar Adel K. , Lozano Guillermina
Published: August 2024

p53R172H and p53R245W interact with Nr5a2 to alter transcription. A, Western blot analyses of p53R172H and p53R245W protein levels in Trp53-null breast tumor cells infected with lentiviral vectors expressing p53R172H or p53R245W, compared with empty vector control. B, qRT-PCR analysis of p53R172H downstream target genes Argef18, Fbxo2, Lime1, Msi1, Nrtn with Nr5a2 motifs after lentivirus infection of p53R172H (n = 6, P value <0.002, 0.01, 0.002, 0.002, 0.03, respectively, Mann–Whitney U test). C, qRT-PCR analysis of p53R245W downstream target genes Add2, Ankrd2, Myh4, and Prss35 with Nr5a2 motifs after lentivirus infection of p53R245W (n = 6, P value < 0.002, 0.4, 0.04, 0.6, 0.2, respectively, Mann–Whitney U test). C, control. D, qRT-PCR analysis of p53R245W downstream target genes Add2, Ankrd2, and Myh4 after siRNA-mediated knockdown of Nr5a2 in MaPR245W/− cell line, RM0014. C, control. E, Co-immunoprecipitation experiments of MaPR172H/− cell line YZ10 and MaPR245W/− cell line JM1564 using Nr5a2 pull-down and probing for mutant p53.
Figure 4 from Tumor Vessel Normalization via PFKFB3 Inhibition Alleviates Hypoxia and Increases Tumor Necrosis in Rectal Cancer upon Radiotherapy
Authors: Edelmann Marcus , Fan Shuang , De Oliveira Tiago , Goldhardt Tina , Sartorius Dorothée , Midelashvili Teona , Conrads Karly , Paul Niels B. , Beißbarth Tim , Fleischer Johannes R. , Blume Moritz L. , Bohnenberger Hanibal , Josipovic Natasa , Papantonis Argyris , Linnebacher Michael , Dröge Leif H. , Ghadimi Michael , Rieken Stefan , Conradi Lena-Christin
Published: August 2024

3PO potentializes irradiation effects in colorectal cancer cells. A, Colony formation assay performed with HCT-116, HT-29, SW-1463, and SW-837 cancer cells upon 3PO treatment. Cells treated with DMSO or 2.5 µmol/L 3PO for 4 hours and exposed to indicated doses of RT. Six days (HCT-116), 7 days (HT-29), and 14 days (SW-1463 and SW-837) later, colonies were stained and counted. Data are displayed as means ± SD, n = 3. ∗, P < 0.05; one-way ANOVA. B, Colony formation assays performed with HCT-116, HT-29, SW-1463, and SW-837 cells treated with different concentrations of 3PO for 4 hours, exposed to either RT (6 Gy) or not irradiated and analyzed after 6 days (HCT-116), 7 days (HT-29), 14 days (SW-1463), and 14 days (SW-837) by counting colonies. C, Invasion assay performed with HCT-116, HT-29, SW-1463, and SW-837 cancer cells. Cells were plated in a Boyden multi-well chamber and treated with 10 µmol/L 3PO for 4 hours, subjected to RT (6Gy), and incubated for a period of 96 hours. Data are displayed as means SD, n = 3. ∗∗, P < 0.01; ∗∗∗∗, P < 0.0001; one-way ANOVA. D–F Cell migration assay quantification of HCT-116, HT-29, and SW-837 cancer cells upon combinatory treatment. Cells were treated with 3PO for 4 hours and subjected to RT (6 Gy). Data are displayed as means ± SD, n = 3. ∗, P < 0.05; ∗∗, P < 0.01; ∗∗∗, P < 0.001; ∗∗∗∗, P < 0.0001; one-way ANOVA.
Figure 5 from p53R172H and p53R245W Hotspot Mutations Drive Distinct Transcriptomes in Mouse Mammary Tumors Through a Convergent Transcriptional Mediator
Authors: McDaniel Joy M. , Morrissey Rhiannon L. , Dibra Denada , Patel Lalit R. , Xiong Shunbin , Zhang Yun , Chau Gilda P. , Su Xiaoping , Qi Yuan , El-Naggar Adel K. , Lozano Guillermina
Published: August 2024

Transcriptomic meta-analysis of human breast tumors with TP53R175H and TP53R248W/Q mutations recapitulates NR5A2 as a co-regulator. A, Supervised clustering of human breast tumors from METABRIC dataset harboring a TP53R175H mutation compared with human breast tumors with a homozygous deletion of TP53. B, Pie chart representing significantly DEGs for TP53R175H breast tumors compared with TP53-null breast tumors (limma), significance criteria were FDR <5% and fold change >1.5. C, Supervised clustering of human breast tumors from METABRIC dataset harboring a TP53R248Q or TP53R248W mutation compared with human breast tumors with a homozygous deletion of TP53. D, Pie chart representing DEGs for TP53R248Q/W breast tumors compared with TP53-null breast tumors (limma), significance criteria were FDR <5% and fold change >1.5. E, NR5A2 binding motif sequences identified by MEME from JASPAR database. F, Oncoprint analysis in cBioportal to assess co-occurrence of TP53 mutations with NR5A2 amplifications. G, Prevalence of co-occurrence of TP53 missense mutations with NR5A2 amplification across human molecular breast cancer subtypes: luminal A (38%), luminal B (26%), HER2-enriched (15%), and TNBC (21%).
Figure 3 from Tumor Vessel Normalization via PFKFB3 Inhibition Alleviates Hypoxia and Increases Tumor Necrosis in Rectal Cancer upon Radiotherapy
Authors: Edelmann Marcus , Fan Shuang , De Oliveira Tiago , Goldhardt Tina , Sartorius Dorothée , Midelashvili Teona , Conrads Karly , Paul Niels B. , Beißbarth Tim , Fleischer Johannes R. , Blume Moritz L. , Bohnenberger Hanibal , Josipovic Natasa , Papantonis Argyris , Linnebacher Michael , Dröge Leif H. , Ghadimi Michael , Rieken Stefan , Conradi Lena-Christin
Published: August 2024

Gene set enrichment analysis of 3PO-treated colon organoids. A, Representative pictures of patient-derived tumor organoids treated with 30 µmol/L 3PO for 24 hours. Scale bars, 200 μm. B, Patient-derived tumor organoid size quantification. Data are displayed as means ± SD. ∗, P < 0.05; one-way ANOVA. C, GSEA with gene sets derived from the GO Biological Process ontology. For the enrichment plot (score curves), refer to Supplementary Fig. S2. FDR, false discovery rate. D and E, Protein expression levels of NDUFB6, ACAD9, and LDHA in colorectal cancer organoids (PT-73, PT-70, and PT-79) and colorectal cancer cell lines treated with 3PO for 24 hours. ACTIN and HSC70 were used as loading control. Uncropped Western blot pictures at Supplementary Fig. S5. In all cases, densitometry analysis is performed using loading control as references. F, Seahorse analysis for extracellular acidification rate performed with patient-derived tumor organoids treated with 10 µmol/L 3PO for 24 hours. Data is displayed as SD. ∗∗∗, P < 0.001; ∗∗∗∗, P < 0.0001.
Figure 4 from p53R172H and p53R245W Hotspot Mutations Drive Distinct Transcriptomes in Mouse Mammary Tumors Through a Convergent Transcriptional Mediator
Authors: McDaniel Joy M. , Morrissey Rhiannon L. , Dibra Denada , Patel Lalit R. , Xiong Shunbin , Zhang Yun , Chau Gilda P. , Su Xiaoping , Qi Yuan , El-Naggar Adel K. , Lozano Guillermina
Published: August 2024

p53R172H and p53R245W show distinct transcriptomes via Nr5a2. A, Supervised clustering of MaP−/−, MaPR172H/− and MaPR245W/− tumors based on upregulated and downregulated genes. Green box, comparison of MaPR172H/− and MaP−/− downregulated genes; Pink box, comparison of MaPR172H/− and MaP−/− upregulated genes; Black boxes, comparison of MaPR245W/− and MaP−/− downregulated genes; Yellow boxes, comparison of MaPR245W/− and MaP−/− upregulated genes. B, Venn diagrams depicting genes significantly upregulated and (C) significantly downregulated in MaPR172H/− and MaPR245W/− mammary tumors. D, Pie charts representing the number of significantly upregulated genes in MaPR172H/− tumors and (E) MaPR245W/− tumors that have an Nr5a2 motif.
Data from Tumor Vessel Normalization via PFKFB3 Inhibition Alleviates Hypoxia and Increases Tumor Necrosis in Rectal Cancer upon Radiotherapy
Authors: Edelmann Marcus , Fan Shuang , De Oliveira Tiago , Goldhardt Tina , Sartorius Dorothée , Midelashvili Teona , Conrads Karly , Paul Niels B. , Beißbarth Tim , Fleischer Johannes R. , Blume Moritz L. , Bohnenberger Hanibal , Josipovic Natasa , Papantonis Argyris , Linnebacher Michael , Dröge Leif H. , Ghadimi Michael , Rieken Stefan , Conradi Lena-Christin
Published: August 2024

Abstract
Treatment of patients with locally advanced rectal cancer (RC) is based on neoadjuvant chemoradiotherapy followed by surgery. In order to reduce the development of therapy resistance, it is necessary to further improve previous treatment approaches. Recent in vivo experimental studies suggested that the reduction of tumor hypoxia by tumor vessel normalization (TVN), through the inhibition of the glycolytic activator PFKFB3, could significantly improve tumor response to therapy. We have evaluated in vitro and in vivo the effects of the PFKFB3 inhibitor 2E-3-(3-pyridinyl)-1-(4-pyridinyl)-2-propen-1-one (3PO) on cell survival, clonogenicity, migration, invasion, and metabolism using colorectal cancer cells, patient-derived tumor organoid (PDO), and xenograft (PDX). 3PO treatment of colorectal cancer cells increased radiation-induced cell death and reduced cancer cell invasion. Moreover, gene set enrichment analysis shows that 3PO is able to alter the metabolic status of PDOs toward oxidative phosphorylation. Additionally, in vivo neoadjuvant treatment with 3PO induced TVN, alleviated tumor hypoxia, and increased tumor necrosis. Our results support PFKFB3 inhibition as a possible future neoadjuvant addition for patients with RC.
Significance:
Novel therapies to better treat colorectal cancer are necessary to improve patient outcomes. Therefore, in this study, we evaluated the combination of a metabolic inhibitor (3PO) and standard radiotherapy in different experimental settings. We have observed that the addition of 3PO increased radiation effects, ultimately improving tumor cell response to therapy.
Comparative evaluation of centralized and decentralized solar street lighting systems
Authors: Joviancent Kenzie , Halim Levin , Naa Christian Fredy
Published: August 2024

Inadequate lighting can hinder outdoor activities such as traffic or pedestrian access. Solar street lighting system is planned to provide sufficient lighting for roads lacking proper illumination. DIALux software uses simulation to determine the lamp power and pole specifications, followed by applying formulas to establish component specifications. In this research, a performance comparison based on voltage drop and power losses will be conducted for solar street lighting systems with decentralized and centralized systems. With a road length of 130 meters and a width of 5 meters, simulations were performed for each variable of lamps (10, 12, 19, and 30 Watt). The calculations show that 4 streetlights are needed, and simulation results indicated that the most suitable lamp power is 12 Watt. The analysis showed that the centralized and decentralized designs do not have voltage drops exceeding the applicable limit. However, the centralized design has higher power losses amounting to 3.68 Watt. Another advantage of the decentralized design is its independence, with each load powered by a separate solar panel, while the centralized design is vulnerable to the overall system. In conclusion, the decentralized design is more suitable for implementation after comparing the centralized and decentralized designs based on the voltage drop and power losses.
Novel proposal for a smart electronic taximeter based on microcontroller systems
Authors: Hernandez Cesar , Farfán Ángel , Giral Diego
Published: August 2024

Public transport plays a significant role in the economic development of a country, so the state must guarantee its proper functioning, not only in terms of controlling vehicular traffic and generating adequate roads but also in terms of pricing and customer service. This article proposes a smart electronic taximeter that improves customer service quality and provides greater control for the taxi owner. To achieve this, the smart taximeter has a data entry module (keyboard), a location module (global position system), a time module (date and time), a storage module (memory), a display module (light emitting diode array), an auditory module (speech synthesizer), a communication module (Wi-Fi) and a microcontroller that controls the processes of setup, pricing, billing, and accounting. The results have shown a satisfactory response on the part of the client and the entrepreneur since it allows a higher level of inclusion from the auditory output in Spanish and English, as well as to carry out better financial accounting through the storage of information on the place, date and time, start and end, as well as the duration, distance, fare, surcharges, total cost, and number of each taxi service (ride). Finally, the smart electronic taximeter complies with all Colombian resolution No. 88918 relations of the Ministry of Commerce, Industry and Tourism.
Dynamic voltage restoration using neural networks for grid-connected wind turbine
Authors: Dahmane Kaoutar , Bouachrine Brahim , Imodane Belkasem , Idrissi Abdellah El , Benydir Mohamed , Ajaamoum Mohamed , Oubella M'hand
Published: August 2024

Wind energy is being integrated into the grid as a renewable energy source to meet the world's electricity needs. Grid-connected wind turbines are often disrupted by grid fault problems. Fault ride-through (FRT) ability has become the most important grid connection necessity for wind energy conversion systems (WECS). In the event of a voltage dip fault, the low voltage ride-through (LVRT) capacity is an imperative key to successful grid integration. This paper proposes a dynamic voltage restorer (DVR) controlled through an artificial neural network (ANN) to improve the LVRT capability of a grid-connected wind turbine (WT) based permanent magnet synchronous generator (PMSG). The DVR injects series voltage into the system through a series-connected transformer. The DVR can then restore the voltage to the pre-fault value. The injection transformer is connected to the line linking the PMSG-based wind turbine output to the utility grid. Design and simulation of the low voltage ride-through applied to symmetrical and asymmetrical fault conditions were performed in MATLAB/Simulink software. Simulation results approve that the performance of the technique fully demonstrates its effectiveness and practicality.
Model reference adaptive control of networked systems with state and input delays
Authors: Wafi Moh Kamalul , Indriawati Katherin , Widjiantoro Bambang L.
Published: August 2024

Adaptive control strategies have been developed in response to more advanced complex systems and to deal with uncertain systems while maintaining the desired conditions. This paper addresses the networked unknown and unstable heterogeneous systems following a stable reference (leader), which is related to network synchronization. We deliver two different scenarios; each agent both fully communicates to the leader and shares communication among neighborhood agents and the leader. The communication among agents and the leader are weighted using Laplacian-like matrix and the model weight matrix in turn. Also, the state and input delays are induced to the systems to capture the real limited communication while the prediction of the reference signals and the augmented systems are proposed to deal with them. Moreover, the rigorous mathematical foundations of two adaptive laws, the stability analysis, the threshold of network, and the communication network are thoroughly presented. Also, the numerical illustrations of the two scenarios are given to show the effectiveness of the proposed method in the networked system. The results show that for both scenarios working on the required setting, the perfect tracking to the leader is guaranteed. Beyond that, the future research would implement the distributed adaptive control-oriented learning of networked system under some faults.
Performance evaluation of single-mode fiber optic-based surface plasmon resonance sensor on material and geometrical parameters
Authors: Tazi Imam , Riana Dedi , Syahadi Mohamad , Muthmainnah Muthmainnah , Sasmitaninghidayah Wiwis , Aprilia Lia , Tresna Wildan Panji
Published: August 2024

Surface plasmon resonance (SPR) sensors are proficient at detecting minute changes in refractive index, making them ideal for biomolecule detection. Traditional prism-based SPR sensors encounter miniaturization challenges, encouraging exploration of alternatives like fiber optic-based SPR (FO-SPR) sensors. This study comprehensively investigates the effects of material and geometrical parameters on the performance of single-mode FO-SPR sensors using Maxwell's equation solver software based on the finite-difference time-domain (FDTD) method. The findings highlight the influence of plasmonic thin film materials and thickness on SPR spectrum profiles and sensitivity. Silver (Ag) demonstrates superior performance compared to copper (Cu) and gold (Au) in transmission type, achieving a sensitivity of up to 2×103 nm/RIU, while the sensitivities of Cu and Au are lower. Probe length and core diameter impact spectrum profiles, specifically resonance depth, without affecting sensitivity. Furthermore, variations in core refractive index influence both spectrum profiles and sensitivity. Probe types significantly affect both spectrum profiles and sensitivity, with the reflection type surpassing the transmission type. These results provide suggestions for optimizing FO-SPR sensors in biotechnological applications.
A review of object detection approaches for traffic surveillance systems
Authors: El-Alami Ayoub , Nadir Younes , Mansouri Khalifa
Published: August 2024

With the decreasing cost of traffic cameras and rapid advancement in computer vision and artificial intelligence, developing robust traffic surveillance systems has become more feasible and practical. These systems can easily outperform traditional human monitoring systems, as they can collect and analyze traffic data coming from multiple cameras efficiently. A good understanding of this data allows the detection easily road anomalies in real time and in an autonomous way. Therefore, an intelligent traffic system typically consists of three components: object detection, object tracking, and behavior analysis components. In this paper, we present a review of some of the well-known object detection techniques used in traffic video surveillance. The review begins with a brief introduction to the history of object detection and the evolution of its techniques. Then we review separately the two main approaches of detection, which are traditional and deep learning approaches of detection. Finally, an experimental analysis has been conducted to evaluate and compare the performance of some of the recent relevant detection methods in terms of speed and precision, in detecting vehicles in a traffic scenario.
Target loop antenna prototype with magnetic field reduction method
Authors: Assamoi Claude Daniel , Ouattara Yelakan Berenger , Youan Bi Tra Jean Claude , Gnamele N'tcho Assoukpou Jean , Doumbia Vafi
Published: August 2024

This paper proposes a new large loop antenna with a reduced magnetic field for a near field communication (NFC) target used in a metallic environment. It also defines a fairly clear method for controlling and, more specifically, reducing the magnetic field associated with loop antennas. This antenna consists of a circular winding, inside which we insert a square winding arranged in the shape of a diamond. The particular structure of this antenna shows that it is possible to dissociate the increase in the induced magnetic field, linked to its large size, from the increase in the number of windings. This is made possible by the application of the physical principle of overlapping magnetic fields, which results in partially destructive interference.
Methodology for the selection of an optimal optical sensor for a 6U CubeSat constellation
Authors: Chirán-Alpala William Efrén , Cárdenas-Espinosa Lorena Paola , Garces-Gomez Yeison Alberto
Published: August 2024

The payload, in defining the central objective of a satellite mission, plays a critical role in determining the overall efficiency of the satellite. Consequently, the satellite's effectiveness is strongly influenced by both the payload itself and its configuration. Given the essential importance of choosing an optimal payload and aware of the direct impact it has on the success of a space mission, this article presents a methodology for selecting an optical sensor intended for the 6U CubeSat constellation of the FACSAT-3 mission and future space missions of the Colombian Aerospace Force (FAC). The methodology includes the definition of mission objectives, definition of key parameters, performance modeling, risk and reliability assessment, and other critical aspects that influence mission efficiency and success.
Performance evaluation of a proposal for spectrum assignment based on combinative distance-based assessment multicriteria strategy
Authors: Hernandez Cesar , Giral Diego , Vaca Tania
Published: August 2024

Cognitive radio networks offer an alternative to low spectral availability in some frequency bands due to their high demand for frequency channels. This article proposes to improve the spectral assignment based on the combinative distance-based assessment multicriteria algorithm. The metrics obtained are compared with a simple additive weighting algorithm and a RANDOM selection. To establish the algorithm 's performance, five quality-of-service metrics are used: number of handoffs, number of failed handoffs, average bandwidth, average throughput, and cumulative average delay. From the analysis of the results obtained, combinative distance-based assessment (CODAS) presented the best result for the cost metrics with the lowest levels, and for the benefit metrics, the highest levels were obtained.
Detection and classification of breast cancer types using VGG16 and ResNet50 deep learning techniques
Authors: P. Ashwini , N. Suguna , N. Vadivelan
Published: August 2024

Breast cancer has become a major worldwide health issue, accounting for a large portion of the mortality rate among women. As a result, the need for early detection techniques to enhance prognosis is increasing. Many techniques are being used to detect breast cancer early, and treatment outcomes are frequently favorable when the disease is detected early. Mammography is a commonly used and very successful method for identifying breast cancer among these modalities. Through additional image processing operations like resizing and normalizing, this technology allows the detection of malignant spots from mammography pictures of the affected area. The goal of our research is to improve breast cancer detection and diagnosis speed and accuracy. In this study, we investigate the use of deep learning methods, particularly the visual geometry group (VGG16) and ResNet50 models, for mammography-based breast cancer detection. We assess the performance of the VGG16 and ResNet50 models by training and testing on a mammogram dataset that consists of 322 images from the mammographic image analysis society (MIAS) dataset. The suggested models aim to classify these images into normal, benign, and malignant groupings. Our results show better performance when compared to existing approaches. The proposed methods VGG16 and ResNet50 show promising results, achieving a classification accuracy of 91.23% and 99.01% respectively.
Conflict-driven learning scheme for multi-agent based intrusion detection in internet of things
Authors: Attluri Durga Bhavani , Prabhakara Srivani
Published: August 2024

This paper introduces an effective intrusion detection system (IDS) for the internet of things (IoT) that employs a conflict-driven learning model within a multi-agent architecture to enhance network security. A double deep Q-network (DDQN) reinforcement learning algorithm is implemented in the proposed IDS with two specialized agents, the defender and the challenger. These agents engaged in an antagonistic adaptation process that dynamically refined their strategies through continual interaction within a custom-made environment designed using OpenAI Gym. The defender agent aims to identify and mitigate threats by matching the actions of the challenger agent, which is designed to simulate potential attacks in the environment. The study introduces a binary reward mechanism to encourage both agents to explore and exploit different actions and discover new strategies as a response to adversarial actions. The results showcase the effectiveness of the proposed IDS in terms of higher detection rate the comparative analysis also validates the effectiveness of the proposed IDS scheme with an accuracy of approximately 96%, outperforming similar existing approaches.
Comparing hyperparameter optimized support vector machine, multi-layer perceptron and bagging classifiers for diabetes mellitus prediction
Authors: Yatoo Nuzhat Ahmad , Ali Ishok Sathik , Mirza Imran
Published: August 2024

Diabetes Mellitus (DM) is a chronic metabolic disorder that affects the way body processes blood glucose levels. Within the medical field, Machine Learning (ML) has significant potential for accurately forecasting and diagnosing a range of chronic conditions. If an accurate prognosis is achieved early, the risk to health and intensity of DM can be significantly mitigated. In this study, a robust methodology for DM prognosis was proposed, which included anomaly replacement, data normalization, feature extraction, and K-fold cross-validation. Three machine learning methods, Support Vector Machine, Multilayer Perceptron and Bagging, were employed to predict Diabetes Mellitus using the National Health and Nutritional Examination Survey (NHANES) 2011-2012 dataset. Accuracy, AUC and Recall were chosen as the evaluation metrics and subsequently optimized during hyperparameter tweaking. From all the comprehensive tests, Bagging outperformed the other two models with an Accuracy of 96.67, AUC score of 99.2 and Recall of 97.0. The proposed methodology surpasses other approaches for forecasting DM.
Pedestrian flow prediction in commercial avenue
Authors: Benhadou Marwane , Gonnouni Amina El , Lyhyaoui Abdelouahid
Published: August 2024

Mobility plans are one of the most important management tools for city development and an important factor for society and economic growth, where pedestrians are the end goal of any mobility plan. Human behavior is generally unpredictable, and many attempts have been interested at pedestrians' mobility in urban environments, both microscopic and macroscopic (flow, density, and speed) levels. The objective of pedestrian traffic flow prediction is to predict the number of pedestrians at the next moment. Assisting operators and city managers in making decisions in urban environments such as emergency support systems, and quality-of-service evaluation. This study aims to model and predict bi-directional pedestrian flow in a commercial avenue, based on two essential stages, data collection through video recording over two months (pedestrian flow) and data analysis using machine learning algorithms that provide a lower error and a higher accuracy rate. Two metrics were selected as basic measures to evaluate the model performances, root mean square error (RMSE) and coefficient of determination R2. Artificial neural network (ANN) gives a little better performance and fitness.
An approach toward improvement of ensemble method’s accuracy for biomedical data classification
Authors: Izonin Ivan , Muzyka Roman , Tkachenko Roman , Gregus Michal , Kustra Natalya , Mitoulis Stergios-Aristoteles
Published: August 2024

Amidst rapid technological and healthcare advancements, biomedical data classification using machine learning (ML) is pivotal for revolutionizing medical diagnosis, treatment, and research by organizing vast healthcare-related data. Despite efforts to apply single ML models on clean datasets, satisfactory classification accuracy can still be elusive. In such cases, ML-based ensembles offer a promising solution. This paper explores cascaded ensembles as highly accurate methods. Existing cascade classifiers often partition large datasets into equal unique parts, limiting accuracy due to insufficient amount of useful information processed by weak classifiers of all levels of the cascade ensemble. To address this, we propose an improved cascaded ensemble scheme using a different data sampling approach. Our method forms larger subsamples at each cascade level, enhancing accuracy, and generalization properties during biomedical data analysis. Experimental comparisons demonstrate substantial increases in classification accuracy and generalization properties of the improved cascade ensemble.
Enhanced accuracy estimation model energy import in on-grid rooftop solar photovoltaic
Authors: Sahrin Alfin , Abadi Imam , Musyafa Ali
Published: August 2024

Installing rooftop solar photovoltaic (PV) with an on-grid system benefits consumers because it can reduce imports of electrical energy from the grid. This study aims to model the estimation of energy imports generated from on-grid rooftop solar PV systems. This estimation model was carried out in 20 provincial capitals in Indonesia. The parameters used are weather conditions, orientation angle, and energy generated from the on-grid rooftop solar PV system. The value of imported energy is divided into three combinations based on the azimuth angle direction, which describes the type and shape of the roof of the building (one-direction, two-directions, and three-directions). Modeling was done using machine learning with neural network (NN), linear regression, and support vector machine. A comparison of the machine learning algorithm results is NN produces the smallest root mean square error (RMSE) value of the three. Model enhancement uses a grid search cross-validation (GSCV) to become the GSCV-NN model. The RMSE results were enhanced from 53.184 to 44.389 in the one-direction combination, 145.562 to 141.286 in the two-direction combination, and 81.442 to 76.313 in the three-direction combination. The imported energy estimation model on the on-grid rooftop solar PV system with GSCV-NN produces a more optimal and accurate model.
Estimation of kernel density function using Kapur entropy
Authors: Chawla Leena , Kumar Vijay , Saxena Arti
Published: August 2024

Information-theoretic measures play a vital role in training learning systems. Many researchers proposed non-parametric entropy estimators that have applications in adaptive systems. In this work, a kernel density estimator using Kapur entropy of order α and type β has been proposed and discussed with the help of theorems and properties. From the results, it has been observed that the proposed density measure is consistent, minimum, and smooth for the probability density function (PDF) underlying given conditions and validated with the help of theorems and properties. The objective of the paper is to understand the theoretical viewpoint behind the underlying concept.
Cloud based prediction of epileptic seizures using real-time electroencephalograms analysis
Authors: Thahniyath Gousia , Yadav Chelluboina Subbarayudu Gangaiah , Senkamalavalli Rajagopalan , Priya Shanmugam Sathiya , Aghalya Stalin , Reddy Kuppireddy Narsimha , Murugan Subbiah
Published: August 2024

This study aims to improve the accuracy of epileptic seizure prediction using cloud-based, real-time electroencephalogram analysis. The goal is to build a strong framework that can quickly process electroencephalogram (EEG) data, extract relevant features, and use advanced machine learning algorithms to predict seizures with high accuracy and low latency by taking advantage of cloud platforms' computing power and scalability. The main objective is to provide patients and their caregivers with timely notifications so that they may control epilepsy episodes proactively. The goal of this project is to improve the lives of people with epilepsy by reducing the impact of seizures and improving treatment results via real-time analysis of EEG data. Cloud computing also allows the suggested seizure prediction system to be more accessible and scalable, meaning more people worldwide could benefit from it. This section discusses the results from five separate datasets of patients with epileptic seizures who underwent EEG analysis with the following details as frontopolar (FP1, FP2), frontal (F3, F4), frontotemporal (F7, F8), central (C3, C4), temporal (T3, T4), parieto-temporal (T5, T6), parietal (P3, P4), occipital (O1, O2), time (HH:MM:SS).
Salud mental en jóvenes universitarios: reflexiones sobre familia, cuerpo y emociones
Authors: Zapata-Martínez Adriana , Rodríguez-Vargas María Alejandra , Salgado-Valencia Mónica Liliana , Naranjo-Londoño Claudia Marcela
Published: August 2024

(analytical)Derived from research carried out by the Manizales University System Alliance and the International Center of Education and Human Development, the results on the place of the family, the body, emotions and spirituality in the mental health of young people are presented. A qualitative methodology with a phenomenological-hermeneutic approach was used applying techniques such as corpography, family level plan and photo-voice. The participants were 137 young university students between 17 and 26 years of age. The leading place of the family as emotional support is evident from the narrative analysis, as well as the need to understand the body from a social perspective, which incorporates the emotional and spiritual dimension, to contribute to mental health.
A Conservação Preventiva e as reservas técnicas
Authors: Braga Gedley Belchior
Published: August 2024

This text explicits the importance of the planning of an area for the packing and storing of a collection, under the principIes of Preventive Conservation. Advices are presented based in the specialized Iiterature as well as on acquired experience with projects, either accomplished or in course, at the Storage Area of MAEIUSP, supported by FAPESP and VITAE Foundation.
Clinical Utility of Protein Language Models in Resolution of Variants of Uncertain Significance in
KCNQ1, KCNH2
, and
SCN5A
Compared With Patch-Clamp Functional Characterization
Authors: Ye Dan , Garmany Ramin , Martinez-Barrios Estefania , Gao Xiaozhi , Neves Raquel Almeida Lopes , Tester David J. , Bains Sahej , Zhou Wei , Giudicessi John R. , Ackerman Michael J.
Published: August 2024

BACKGROUND:

Genetic testing for cardiac channelopathies is the standard of care. However, many rare genetic variants remain classified as variants of uncertain significance (VUS) due to lack of epidemiological and functional data. Whether deep protein language models may aid in VUS resolution remains unknown. Here, we set out to compare how 2 deep protein language models perform at VUS resolution in the 3 most common long-QT syndrome–causative genes compared with the gold-standard patch clamp.

METHODS:

A total of 72 rare nonsynonymous VUS (9
KCNQ1,
19
KCNH2
, and 50
SCN5A
) were engineered by site-directed mutagenesis and expressed in either HEK293 cells or TSA201 cells. Whole-cell patch-clamp technique was used to functionally characterize these variants. The protein language models, evolutionary scale modeling, version 1b and AlphaMissense, were used to predict the variant effect of missense variants and compared with patch clamp.

RESULTS:

Considering variants in all 3 genes, the evolutionary scale modeling, version 1b model had a receiver operating characteristic curve–area under the curve of 0.75 (
P
=0.0003). It had a sensitivity of 88% and a specificity of 50%. AlphaMissense performed well compared with patch-clamp with an receiver operating characteristic curve-area under the curve of 0.85 (
P
<0.0001), sensitivity of 80%, and specificity of 76%.

CONCLUSIONS:

Deep protein language models aid in VUS resolution with high sensitivity but lower specificity. Thus, these tools cannot fully replace functional characterization but can aid in reducing the number of variants that may require functional analysis.
Proteome- and Transcriptome-Wide Genetic Analysis Identifies Biological Pathways and Candidate Drug Targets for Preeclampsia
Authors: Ardissino Maddalena , Truong Buu , Slob Eric A.W. , Schuermans Art , Yoshiji Satoshi , Morley Alec P. , Burgess Stephen , Ng Fu Siong , de Marvao Antonio , Natarajan Pradeep , Nicolaides Kypros , Gaziano Liam , Butterworth Adam , Honigberg Michael C.
Published: August 2024

BACKGROUND:

Preeclampsia is a leading cause of maternal and perinatal morbidity and mortality. However, the current understanding of its underlying biological pathways remains limited.

METHODS:

In this study, we performed a cross-platform proteome- and transcriptome-wide genetic analysis aimed at evaluating the causal relevance of >2000 circulating proteins with preeclampsia, supported by data on the expression of over 15 000 genes across 36 tissues leveraging large-scale preeclampsia genetic association data from women of European ancestry.

RESULTS:

We demonstrate genetic associations of 18 circulating proteins with preeclampsia (SULT1A1 [sulfotransferase 1A1], SH2B3 [SH2B adapter protein 3], SERPINE2 [serpin family E member 2], RGS18 [regulator of G-protein signaling 18], PZP [pregnancy zone protein], NOTUM [notum, palmitoleoyl-protein carboxylesterase], METAP1 [methionyl aminopeptidase 1], MANEA [mannosidase endo-alpha], jun-D [JunD proto-oncogene], GDF15 [growth differentiation factor 15], FGL1 [fibrinogen like 1], FGF5 [fibroblast growth factor 5], FES [FES proto-oncogene], APOBR [apolipoprotein B receptor], ANP [natriuretic peptide A], ALDH-E2 [aldehyde dehydrogenase 2 family member], ADAMTS13 [ADAM metallopeptidase with thrombospondin type 1 motif 13], and 3MG [N-methylpurine DNA glycosylase]), among which 11 were either directly or indirectly supported by gene expression data, 9 were supported by Bayesian colocalization analyses, and 5 (SERPINE2, PZP, FGF5, FES, and ANP) were supported by all lines of evidence examined. Protein interaction mapping identified potential shared biological pathways through natriuretic peptide signaling, blood pressure regulation, immune tolerance, and thrombin activity regulation.

CONCLUSIONS:

This investigation identified multiple targetable proteins linked to cardiovascular, inflammatory, and coagulation pathways, with SERPINE2, PZP, FGF5, FES, and ANP identified as pivotal proteins with likely causal roles in the development of preeclampsia. The identification of these potential targets may guide the development of targeted therapies for preeclampsia.