bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of standard-dose and reduced-dose treatment of metastatic prostate cancer with enzalutamide, apalutamide or darolutamide: a rapid review
Authors: Bromley Hannah Louise , Varughese Mohini , Gilbert Duncan C , Hoskin Peter , Tannock Ian F , Reeves Kimberley , Choudhury Ananya
Published: February 2024

Objective

To review the efficacy and safety of low-dose versus standard-dose enzalutamide, apalutamide or darolutamide treatment for metastatic prostate cancer.

Methods and analysis

Keyword searches in MEDLINE and EMBASE up to 1 June 2023, with forward and backward citation searches of potentially relevant studies. Studies were included if primary outcome data were reported for patients with metastatic prostate cancer who had received reduced doses of enzalutamide, apalutamide or darolutamide. Searches were limited to original full-text and English-language studies. Key outcomes included overall survival (OS), progression-free survival (PFS), prostate-specific antigen response and treatment-related adverse events. The review was performed in accordance with Cochrane Rapid Reviews Methods Group guidelines.

Results

Ten studies were identified that met the eligibility criteria: five phase I studies, two post-hoc analyses of phase III trials and three retrospective analyses. No consistent association between OS, PFS and drug dose was identified. Fewer severe treatment-related adverse events were observed at lower drug doses.

Conclusion

This review provides evidence that enzalutamide, apalutamide or darolutamide could be given at a lower than the standard recommended dose without loss of antitumour activity. A prospective near-equivalence randomised trial should be undertaken to compare registered and lower doses of these agents.

PROSPERO registration number

CRD42023440371.
No evidence of fascicular injury following a low-volume intraneural injection of the median nerve: a cadaveric study
Authors: Mejia Jorge , Goffin Pierre , Reina Miguel A , Sala-Blanch Xavier
Published: February 2024

Background

The test dose or hydrolocation technique allows rapid detection of spread location. Though its primary aim is to enhance safety in peripheral nerve blocks, evidence on the potential risks of an intraneural test aliquot is lacking. We conducted a cadaveric study to evaluate the risk of fascicular injury following a low-volume (<1 mL) intraneural injection of the median nerve.

Methods

Ten upper limbs from fresh unembalmed human cadavers were studied. In-plane ultrasound-guided intraneural injections of the median nerve were performed at mid, proximal, and distal locations using 1 mL of methylene blue and heparinized blood solution. Nerves were extracted and samples immersed in 10% buffered formalin for 4 weeks. Perpendicular 3 mm slices were obtained for H&E staining and light microscopy analysis. Our main objective was to assess the number of injured fascicles. Secondarily, we evaluated the pattern of intraneural spread. Fascicular injury was defined as the presence perineurium or axonal disruption and/or the presence of erythrocytes inside a nerve fascicle.

Results

Thirty injections were performed in 10 median nerves. Sonographic swelling was confirmed in 100% of the cases. 352 histological sections were analyzed to assess study outcomes. The mean number of fascicles on each section of median nerve was 20±6 covering 49%±7% of the nerve area. No evidence of axonal disruption nor intra-fascicular erythrocytes was found in any of the analyzed sections.

Conclusions

Low-volume intraneural injections do not result in evident fascicular injury. Our findings support the use of a test dose in ultrasound-guided regional anesthesia.
Rising through the ranks: flavor hierarchies from a gauged $${\varvec{\mathrm {SU(2)}}}$$ symmetry
Authors: Greljo Admir , Thomsen Anders Eller
Published: February 2024

We propose an economical model to address the mass hierarchies of quarks and charged leptons. The light generations of the left-handed fermions form doublets under an $$ \textrm{SU}(2) $$

SU
(
2
)

flavor symmetry, which is gauged. The generational hierarchies emerge from three independent rank-one contributions to the Yukawa matrices: one is a renormalizable contribution, the second is suppressed by a mass ratio, and the last by an additional loop factor. The model is renormalizable, features only a handful of new fields, and is remarkably simple compared to typical completions of gauged flavor symmetries or Froggatt–Nielsen. The model has a rich phenomenology, and we highlight promising signatures, especially in the context of K and B meson physics. This includes an interpretation of the latest $$B^+ \rightarrow K^+ \nu \bar{\nu }$$

B
+

→

K
+

ν

ν

¯

measurement from Belle II.
On heavy-flavour jets with Soft Drop
Authors: Caletti Simone , Ghira Andrea , Marzani Simone
Published: February 2024

We study hadronic jets that are tagged as heavy-flavoured, i.e. they contain either beauty or charm. In particular, we consider heavy-flavour jets that have been groomed with the Soft Drop algorithm. In order to achieve a deeper understanding of these objects, we apply resummed perturbation theory to jets initiated by a massive quark and we perform analytic calculations for two variables that characterise Soft Drop jets, namely the opening angle and the momentum fraction of the splitting that passes Soft Drop. We compare our findings to Monte Carlo simulations. Furthermore, we investigate the correlation between the Soft Drop energy fraction and alternative observables that aim to probe heavy-quark fragmentation functions.
Representations of flat virtual braids which do not preserve the forbidden relations
Authors: Bardakov Valeriy , Chuzhinov Bogdan , Emel’yanenkov Ivan , Ivanov Maxim , Markhinina Elizaveta , Nasybullov Timur , Panov Sergey , Singh Nina , Vasyutkin Sergey , Yakhin Valeriy , Vesnin Andrei
Published: February 2024

In the paper, we construct a representation [Formula: see text] of the flat virtual braid group [Formula: see text] on [Formula: see text] strands by automorphisms of the free group [Formula: see text] with [Formula: see text] generators which does not preserve the forbidden relations in the flat virtual braid group. This representation gives a positive answer to the problem formulated by Bardakov in the list of unsolved problems in virtual knot theory and combinatorial knot theory by Fenn et al.

Also we find the set of normal generators of the groups [Formula: see text] in [Formula: see text], [Formula: see text] in [Formula: see text], [Formula: see text] in [Formula: see text], which play an important role in the study of the kernel of the representation [Formula: see text].
Relationship between COVID-19 waves and stock market: An event study analysis
Authors: Sharma Dhanraj , Verma Ruchita , Sam Shiney , Godara Prince
Published: February 2024

This study makes an attempt to evaluate the impact of first and second waves of COVID-19 on the major global stock markets. Further, the study also analyzed the effect of COVID-19 on the sectoral indices of India during the first and second waves. In order to achieve the objective, the event study methodology has been used and abnormal returns are computed around the event dates. The findings of the study reveal that major stock markets in the world adversely affected during the first wave of COVID-19 and the highest abnormal loss was observed in Toronto Stock Exchange (7.55%) on the event date. The impact of second wave of COVID-19 was not as severe as first wave and highest abnormal loss (2.37%) was observed in Moscow Exchange of Russia. The Indian sectoral indices also showed high volatility during both the waves of COVID-19.
A Dual-Particle Approach for Incompressible SPH Fluids
Authors: Liu Shusen , He Xiaowei , Guo Yuzhong , Chang Yue , Wang Wencheng
Published: February 2024

Tensile instability is one of the major obstacles to particle methods in fluid simulation, which would cause particles to clump in pairs under tension and prevent fluid simulation to generate small-scale thin features. To address this issue, previous particle methods either use a background pressure or a finite difference scheme to alleviate the particle clustering artifacts, yet still fail to produce small-scale thin features in free-surface flows. In this article, we propose a dual-particle approach for simulating incompressible fluids. Our approach involves incorporating supplementary virtual particles designed to capture and store particle pressures. These pressure samples undergo systematic redistribution at each time step, grounded in the initial positions of the fluid particles. By doing so, we effectively reduce tensile instability in standard SPH by narrowing down the unstable regions for particles experiencing tensile stress. As a result, we can accurately simulate free-surface flows with rich small-scale thin features, such as droplets, streamlines, and sheets, as demonstrated by experimental results.
HQ3DAvatar: High-quality Implicit 3D Head Avatar
Authors: Teotia Kartik , R Mallikarjun B , Pan Xingang , Kim Hyeongwoo , Garrido Pablo , Elgharib Mohamed , Theobalt Christian
Published: February 2024

Multi-view volumetric rendering techniques have recently shown great potential in modeling and synthesizing high-quality head avatars. A common approach to capture full head dynamic performances is to track the underlying geometry using a mesh-based template or 3D cube-based graphics primitives. While these model-based approaches achieve promising results, they often fail to learn complex geometric details such as the mouth interior, hair, and topological changes over time. This article presents a novel approach to building highly photorealistic digital head avatars. Our method learns a canonical space via an implicit function parameterized by a neural network. It leverages multiresolution hash encoding in the learned feature space, allowing for high quality, faster training, and high-resolution rendering. At test time, our method is driven by a monocular RGB video. Here, an image encoder extracts face-specific features that also condition the learnable canonical space. This encourages deformation-dependent texture variations during training. We also propose a novel optical flow-based loss that ensures correspondences in the learned canonical space, thus encouraging artifact-free and temporally consistent renderings. We show results on challenging facial expressions and show free-viewpoint renderings at interactive real-time rates for a resolution of 480
x
270. Our method outperforms related approaches both visually and numerically. We will release our multiple-identity dataset to encourage further research.
Joint Stroke Tracing and Correspondence for 2D Animation
Authors: Mo Haoran , Gao Chengying , Wang Ruomei
Published: February 2024

To alleviate human labor in redrawing keyframes with ordered vector strokes for automatic inbetweening, we for the first time propose a joint stroke tracing and correspondence approach. Given consecutive raster keyframes along with a single vector image of the starting frame as a guidance, the approach generates vector drawings for the remaining keyframes while ensuring one-to-one stroke correspondence. Our framework trained on clean line drawings generalizes to rough sketches, and the generated results can be imported into inbetweening systems to produce inbetween sequences. Hence, the method is compatible with standard 2D animation workflow. An adaptive spatial transformation module (ASTM) is introduced to handle non-rigid motions and stroke distortion. We collect a dataset for training with 10k+ pairs of raster frames and their vector drawings with stroke correspondence. Comprehensive validations on real clean and rough animated frames manifest the effectiveness of our method and superiority to existing methods.
A Multiscale Approach for Free‐Float Bike‐Sharing Electronic Fence Location Planning: A Case Study of Shenzhen City
Authors: Wei Zhonghua , Ma Houqiang , Li Yunxuan
Published: February 2024

As an emerging technological means for managing free‐float bike‐sharing parking, electronic fences have attracted increasing attention in major cities as a solution to the challenges posed by disorderly parking of free‐float bikes. Existing research has predominantly focused on employing clustering methods from the perspectives of free‐float bike‐sharing companies and users to plan and deploy electronic fences. However, the results often deviate significantly from the actual phenomenon. Therefore, scientific location selection is particularly important to fully harness the effectiveness of electronic fences. This paper proposes a multiscale clustering method based on free‐float bike‐sharing parking features to determine the optimal locations for electronic fences. A multiobjective mixed‐integer programming model is established to address the location planning problem of electronic fences, determining the planning positions, quantities, and areas of electronic fences. A case study is conducted using a local area free‐float bike‐sharing dataset from Shenzhen city to validate the effectiveness of the proposed method. Comparative results with traditional approaches solely relying on K‐means or DBSCAN methods demonstrate that the proposed approach achieves efficient location selection, through multiscale fusion site selection in the study area of 1.5∗1 km, and only 25 electronic fences need to be planned and deployed, covering a total area of 1691.88 square meters, which can provide rational placement solutions and better utilize the effectiveness of electronic fences. This method can thus offer decision‐making support for the planning and location selection of electronic fences in free‐float bike‐sharing systems.
Development of a Novel Multiplex PCR Method for the Rapid Detection of SARS-CoV-2, Influenza A Virus, and Influenza B Virus
Authors: Ma Liang , Zhu Haoyan , Jiang Yongwei , Kong Xiaomu , Gao Peng , Liu Yi , Zhao Meimei , Deng Guoxiong , Cao Yongtong
Published: February 2024

Objective. A sensitive and specific multiplex fluorescence rapid detection method was established for simultaneous detection of SARS-CoV-2, influenza A virus, and influenza B virus in a self-made device within 30 min, with a minimum detection limit of 200 copies/mL. Methods. Based on the genome sequences of SARS-CoV-2, influenza A virus (FluA), and influenza B virus (FluB) with reference to the Chinese Center for Disease Control and Prevention and related literature, specific primers were designed, and a multiplex fluorescent PCR system was established. The simultaneous and rapid detection of SARS-CoV-2, FluA, and FluB was achieved by optimizing the concentrations of Taq DNA polymerase as well as primers, probes, and Mg2+. The minimum detection limits of the nucleic acid rapid detection system for SARS-CoV-2, FluA, and FluB were evaluated. Results. By optimizing the amplification system, the N enzyme with the best amplification performance was selected, and the optimal concentration of Mg2+ in the multiamplification system was 3 mmol/L; the final concentrations of SARS-CoV-2 NP probe and primer were 0.15 μmol/L and 0.2 μmol/L, respectively; the final concentrations of SARS-CoV-2 ORF probe and primer were both 0.15 μmol/L; the final concentrations of FluA probe and primer were 0.2 μmol/L and 0.3 μmol/L, respectively; the final concentrations of FluB probe and primer were 0.15 μmol/L and 0.25 μmol/L, respectively. Conclusion. A multiplex real-time quantitative fluorescence RT-PCR system for three respiratory viruses of SARS-CoV-2, FluA, and FluB was established with a high amplification efficiency and sensitivity reaching 200 copies/mL for all samples. Combined with the automated microfluidic nucleic acid detection system, the system can achieve rapid detection in 30 minutes.
T-SPP: Improving GNSS Single-Point Positioning Performance Using Transformer-Based Correction
Authors: Wu Fan , Wei Liangrui , Luo Haiyong , Zhao Fang , Ma Xin , Ning Bokun
Published: February 2024

GNSS (global navigation satellite systems) technology enables high-precision single-point positioning (SPP) in open environments. However, the accuracy of GNSS positioning is significantly compromised in complex urban canyons due to signal obstructions and non-line-of-sight propagation errors. To address this challenge, we propose a GNSS displacement estimation algorithm. This method learns nonlinear dependencies between GNSS raw measurements and corresponding position changes, capturing dynamic and layered features in GNSS measurement data for displacement estimation. We introduce a denoising auto-encoder (DAE) to preprocess raw GNSS observations, reducing the impact of noise. The model simultaneously outputs estimated displacement and model confidence. The fusion process dynamically combines positioning results from the SPP algorithm and the D-Tran model, adaptively blending them to achieve accurate and optimal positioning estimation. This approach optimizes the accuracy of estimated positioning results while maintaining confidence in the estimation. Experimental results show a 61% reduction in root mean square error (RMSE) and 100% availability in urban canyon environments compared to traditional single-point positioning techniques.
Assessment of Effects of Turbidity Variation on Water Temperature and Evaporation of Gilgel Gibe I Reservoir, Omo‐Gibe River Basin, Ethiopia
Authors: Birmachu Aduna Bekele , Feyissa Tolera Abdissa , Tukura Nasir Gebi
Published: February 2024

Turbidity has a significant impact on reservoir water by raising the temperature and evaporation rates. This study provided clear and concise information about the effects of turbidity alteration on reservoir water. The main objective of this study was to assess the effects of turbidity variation on reservoir water temperature and evaporation. To determine these effects, the samples were taken from the reservoir by stratifying it randomly along the reservoir course. To evaluate the relationship between turbidity and water temperature and also to measure the vertical alteration of water temperature, ten pools were burrowed, and they were filled with turbid water. Two class A pans were installed in the field to determine the effect of turbidity on reservoir evaporation. The data were analyzed using SPSS software and MS Excel. The results depicted that turbidity has a direct, solid positive relationship with water temperature at 9:00 and 13:00 and a vigorous negative relationship at 17:00, and water temperature decreased vertically from the top to the bottom layer. There was a greater extinction of sunlight in most turbid water. The differences in water temperature between the top and bottom layers were 9.78°C and 1.53°C for most and least turbid water at 13:00 observation hour, respectively. Turbidity has a direct and strong positive relationship with reservoir evaporation. The relation was analyzed using Spearman’s ranked correlation coefficient, and the vertical alteration of water temperatures was analyzed using a box and whisker plot. The tested results were statistically significant. The study concluded that an increment in reservoir turbidity immensely heightens both reservoir water temperature and evaporation.
Molecular Detection and Isolation of Lumpy Skin Disease Virus during an Outbreak in West Hararghe Zone, Eastern Ethiopia
Authors: Geletu Umer Seid , Musa Ahmedin Abdurehman , Usmael Munera Ahmednur , Keno Melaku Sombo
Published: February 2024

Lumpy skin disease (LSD) is a highly contagious viral disease that causes significant economic losses in cattle populations globally. This study aimed to isolate and detect the LSD virus responsible for an outbreak in selected areas (Daaroo Labuu, Hawwii Guddina, and Gumbi Bordede district) of the West Hararghe Zone in Ethiopia between January 2020 and December 2021. Out of the 625 animals examined for the presence of LSD, only 73 animals showed clinical signs, and skin scrapes were collected from these animals for further analysis. Among those, 12 animals (1.9%) succumbed to the disease. Skin biopsy samples from 45 animals displaying clinical signs of LSD were inoculated in Vero cell lines because of limited equipment. After three blind passages, all samples developed cytopathic effects (CPEs). The presence of the LSD virus was confirmed using real‐time PCR. Conventional PCR detected LSDV in 47 (64.4%) of the skin scrap samples, while high‐resolution melt qPCR detected it in 49 (67.1%) samples. The study revealed a morbidity rate of 11.68%, a mortality rate of 1.92%, and a case fatality rate of 16.44% based on clinical data. The findings suggest that LSD causes significant economic losses, even in vaccinated animals prior to an outbreak. To effectively control and eradicate LSD, the government should develop new strategic policies. Community awareness campaigns are necessary to improve vector control measures and drainage systems. In addition, the present vaccination policy and strategy should be re‐evaluated for effectiveness. This study focused on a specific region and timeframe, limiting generalizability. Factors such as environmental conditions and management practices were not extensively explored. Similar studies should be conducted in different regions to assess the prevalence and genetic diversity of LSDV. The effectiveness of control measures and vaccination strategies should be investigated. The impact of environmental factors and management practices on LSD transmission and disease severity warrants further exploration. This study provides insights into the detection and isolation of the LSD virus during an outbreak in the West Hararghe Zone of Ethiopia. The results highlight the need for continued surveillance and monitoring of emerging infectious diseases in the region. Furthermore, the importance of using molecular methods for detecting and characterizing viral outbreaks in livestock populations is emphasized.
Spatial Mapping of Hematopoietic Clones in Human Bone Marrow
Authors: Young Andrew L. , Davis Hannah C. , Cox Maggie J. , Parsons Tyler M. , Burkart Samantha C. , Bender Diane E. , Sun Lulu , Oh Stephen T. , Challen Grant A.
Published: February 2024

Clonal hematopoiesis (CH) is the expansion of somatically mutated cells in the hematopoietic compartment of individuals without hematopoietic dysfunction. Large CH clones (i.e., >2% variant allele fraction) predispose to hematologic malignancy, but CH is detected at lower levels in nearly all middle-aged individuals. Prior work has extensively characterized CH in peripheral blood, but the spatial distribution of hematopoietic clones in human bone marrow is largely undescribed. To understand CH at this level, we developed a method for spatially aware somatic mutation profiling and characterized the bone marrow of a patient with polycythemia vera. We identified the complex clonal distribution of somatic mutations in the hematopoietic compartment, the restriction of somatic mutations to specific subpopulations of hematopoietic cells, and spatial constraints of these clones in the bone marrow. This proof of principle paves the way to answering fundamental questions regarding CH spatial organization and factors driving CH expansion and malignant transformation in the bone marrow.

Significance:

CH occurs commonly in humans and can predispose to hematologic malignancy. Although well characterized in blood, it is poorly understood how clones are spatially distributed in the bone marrow. To answer this, we developed methods for spatially aware somatic mutation profiling to describe clonal heterogeneity in human bone marrow.

See related commentary by Austin and Aifantis, p. 139.
Supplementary Table 3 from Cumulative Incidence, Risk Factors, and Overall Survival of Disease Recurrence after Curative Resection of Stage II–III Colorectal Cancer: A Population-based Study
Authors: Boute Tara C. , Swartjes Hidde , Greuter Marjolein J.E. , Elferink Marloes A.G. , van Eekelen Rik , Vink Geraldine R. , de Wilt Johannes H.W. , Coupé Veerle M.H.
Published: February 2024

Univariable and multivariable competing risk regression output for the risk of recurrent disease in colon cancer patients
TABLE 3 from Characterizing Neutrophil Subtypes in Cancer Using scRNA Sequencing Demonstrates the Importance of IL1β/CXCR2 Axis in Generation of Metastasis-specific Neutrophils
Authors: Fetit Rana , McLaren Alistair S. , White Mark , Mills Megan L. , Falconer John , Cortes-Lavaud Xabier , Gilroy Kathryn , Lannagan Tamsin R.M. , Ridgway Rachel A. , Nixon Colin , Naiker Varushka , Njunge Renee , Clarke Cassie J. , Whyte Declan , Kirschner Kristina , Jackstadt Rene , Norman Jim , Carlin Leo M. , Campbell Andrew D. , Sansom Owen J. , Steele Colin W.
Published: February 2024

Description of colorectal cancer mouse models used
FIGURE 3 from Characterizing Neutrophil Subtypes in Cancer Using scRNA Sequencing Demonstrates the Importance of IL1β/CXCR2 Axis in Generation of Metastasis-specific Neutrophils
Authors: Fetit Rana , McLaren Alistair S. , White Mark , Mills Megan L. , Falconer John , Cortes-Lavaud Xabier , Gilroy Kathryn , Lannagan Tamsin R.M. , Ridgway Rachel A. , Nixon Colin , Naiker Varushka , Njunge Renee , Clarke Cassie J. , Whyte Declan , Kirschner Kristina , Jackstadt Rene , Norman Jim , Carlin Leo M. , Campbell Andrew D. , Sansom Owen J. , Steele Colin W.
Published: February 2024

CD4+ T cells are transcriptomically altered in metastasis. A and B, UMAP plots of CD4+ and CD8+ T cells in PT and LM grouped by cell type and tumor type, respectively. C, Differential gene expression of CD4+ T cells in LM compared with PT. D and E, GO and KEGG analyses of metastatic CD4+ T cells. F and G, Global cell-cell communication network and the interaction strengths between neutrophils, CD4+, CD8+, and Tregs. H–J, The contribution of each L-R pair to the overall signaling pathway for CXCL, IL1, and TNF pathways. K–M, Visualization of the cell-cell communication patterns mediated by each significant L-R pair for the three pathways.
Data from Characterizing Neutrophil Subtypes in Cancer Using scRNA Sequencing Demonstrates the Importance of IL1β/CXCR2 Axis in Generation of Metastasis-specific Neutrophils
Authors: Fetit Rana , McLaren Alistair S. , White Mark , Mills Megan L. , Falconer John , Cortes-Lavaud Xabier , Gilroy Kathryn , Lannagan Tamsin R.M. , Ridgway Rachel A. , Nixon Colin , Naiker Varushka , Njunge Renee , Clarke Cassie J. , Whyte Declan , Kirschner Kristina , Jackstadt Rene , Norman Jim , Carlin Leo M. , Campbell Andrew D. , Sansom Owen J. , Steele Colin W.
Published: February 2024

Abstract
Neutrophils are a highly heterogeneous cellular population. However, a thorough examination of the different transcriptional neutrophil states between health and malignancy has not been performed. We utilized single-cell RNA sequencing of human and murine datasets, both publicly available and independently generated, to identify neutrophil transcriptomic subtypes and developmental lineages in health and malignancy. Datasets of lung, breast, and colorectal cancer were integrated to establish and validate neutrophil gene signatures. Pseudotime analysis was used to identify genes driving neutrophil development from health to cancer. Finally, ligand–receptor interactions and signaling pathways between neutrophils and other immune cell populations in primary colorectal cancer and metastatic colorectal cancer were investigated. We define two main neutrophil subtypes in primary tumors: an activated subtype sharing the transcriptomic signatures of healthy neutrophils; and a tumor-specific subtype. This signature is conserved in murine and human cancer, across different tumor types. In colorectal cancer metastases, neutrophils are more heterogeneous, exhibiting additional transcriptomic subtypes. Pseudotime analysis implicates IL1β/CXCL8/CXCR2 axis in the progression of neutrophils from health to cancer and metastasis, with effects on T-cell effector function. Functional analysis of neutrophil-tumoroid cocultures and T-cell proliferation assays using orthotopic metastatic mouse models lacking Cxcr2 in neutrophils support our transcriptional analysis. We propose that the emergence of metastatic-specific neutrophil subtypes is driven by the IL1β/CXCL8/CXCR2 axis, with the evolution of different transcriptomic signals that impair T-cell function at the metastatic site. Thus, a better understanding of neutrophil transcriptomic programming could optimize immunotherapeutic interventions into early and late interventions, targeting different neutrophil states.
Significance:
We identify two recurring neutrophil populations and demonstrate their staged evolution from health to malignancy through the IL1β/CXCL8/CXCR2 axis, allowing for immunotherapeutic neutrophil-targeting approaches to counteract immunosuppressive subtypes that emerge in metastasis.
Nanoparticle-mediated Photodynamic Therapy as a Method to Ablate Oral Cavity Squamous Cell Carcinoma in Preclinical Models
Authors: Sahovaler Axel , Valic Michael S. , Townson Jason L. , Chan Harley H.L. , Zheng Mark , Tzelnick Sharon , Mondello Tiziana , Pener-Tessler Alon , Eu Donovan , El-Sayes Abdullah , Ding Lili , Chen Juan , Douglas Catriona M. , Weersink Robert , Muhanna Nidal , Zheng Gang , Irish Jonathan C.
Published: February 2024

Photodynamic therapy (PDT) is a tissue ablation technique able to selectively target tumor cells by activating the cytotoxicity of photosensitizer dyes with light. PDT is nonsurgical and tissue sparing, two advantages for treatments in anatomically complex disease sites such as the oral cavity. We have previously developed PORPHYSOME (PS) nanoparticles assembled from chlorin photosensitizer–containing building blocks (∼94,000 photosensitizers per particle) and capable of potent PDT. In this study, we demonstrate the selective uptake and curative tumor ablation of PS-enabled PDT in three preclinical models of oral cavity squamous cell carcinoma (OCSCC): biologically relevant subcutaneous Cal-33 (cell line) and MOC22 (syngeneic) mouse models, and an anatomically relevant orthotopic VX-2 rabbit model. Tumors selectively uptake PS (10 mg/kg, i.v.) with 6-to 40-fold greater concentration versus muscle 24 hours post-injection. Single PS nanoparticle–mediated PDT (PS-PDT) treatment (100 J/cm2, 100 mW/cm2) of Cal-33 tumors yielded significant apoptosis in 65.7% of tumor cells. Survival studies following PS-PDT treatments demonstrated 90% (36/40) overall response rate across all three tumor models. Complete tumor response was achieved in 65% of Cal-33 and 91% of MOC22 tumor mouse models 14 days after PS-PDT, and partial responses obtained in 25% and 9% of Cal-33 and MOC22 tumors, respectively. In buccal VX-2 rabbit tumors, combined surface and interstitial PS-PDT (200 J total) yielded complete responses in only 60% of rabbits 6 weeks after a single treatment whereas three repeated weekly treatments with PS-PDT (200 J/week) achieved complete ablation in 100% of tumors. PS-PDT treatments were well tolerated by animals with no treatment-associated toxicities and excellent cosmetic outcomes.

Significance:

PS-PDT is a safe and repeatable treatment modality for OCSCC ablation. PS demonstrated tumor selective uptake and PS-PDT treatments achieved reproducible efficacy and effectiveness in multiple tumor models superior to other clinically tested photosensitizer drugs. Cosmetic and functional outcomes were excellent, and no clinically significant treatment-associated toxicities were detected. These results are enabling of window of opportunity trials for fluorescence-guided PS-PDT in patients with early-stage OCSCC scheduled for surgery.
An optimal model for detection of lung cancer using convolutional neural network
Authors: Chandraiah Kavitha Belegere , Bhoganna Naveen Kalenahalli
Published: February 2024

In terms of frequency and mortality, lung cancer ranks second among all cancers worldwide for both men and women. It is suggested that pattern classification and machine learning be applied to the identification and categorization of lung cancer. Convolution neural network (CNN) techniques divide the input data into groups according to the distinctive characteristics of the input. Using a standard approach to analyze a large number of computed tomography images, early detection of lung cancer can save lives. The suggested effort is centered on identifying the precise type of cancer and making predictions about whether it is benign or aggressive. The deployment of proposed model is an attempt to improve the accuracy of the system. The proposed work showed an overall accuracy of 98.4% during the detection of lung cancer and 98.8% accuracy towards the prediction of specific type in the lung cancer. Mean average precision score of 97.17% and 98.75% test and validation respectively. 0.96, 0.93, and 0.95 for malignant test data.
Adaptive fuzzy weighted median filter for microcalcifications detection in digital breast tomosynthesis images
Authors: Saifudin Syafiqah Aqilah , Sulaiman Siti Noraini , Osman Muhammad Khusairi , Isa Iza Sazanita , A Karim Noor Khairiah , Harron Nur Athiqah
Published: February 2024

Breast cancer is a global leading cause of female mortality. Digital breast tomosynthesis (DBT) is pivotal for early breast cancer detection, with microcalcifications serving as crucial indicators. However, the movement of the DBT machine introduces blurry artefacts, potentially impacting accurate diagnosis. This study addresses this challenge by proposing an adaptive fuzzy weighted median filter (AFWMF) to enhance DBT images and aid microcalcification diagnosis. AFWMF automatically determines optimal parameters based on input images, outperforming conventional methods with a threshold range (C) from peak to end of switching. Quantitative assessment reveals peak signal to noise ratio (PSNR), and mean absolute error (MAE) values of 96.2267 and 0.0000636, respectively, demonstrating a significant improvement in microcalcification detection. This study contributes an effective and adaptive enhancement technique for DBT images, promising better breast cancer diagnosis, particularly in microcalcification scenarios.
Clustering method for criminal crime acts using K-means and principal component analysis
Authors: Maharrani Ratih Hafsarah , Abda’u Prih Diantono , Faiz Muhammad Nur
Published: February 2024

Criminality is an act of violating the values and norms of society that causes a lot of harm. Much of the criminal data is often just a collection of data that has no information. Analysis of crime data is key in efforts to reduce crime rates that provide an overview of the incidence of crime, patterns, levels of vulnerability, and the level of security of an area. This research proposes data analysis that provides an understanding of crime using data mining techniques, especially the K-means cluster method, both traditional and with principal component analysis (PCA) dimension reduction. Before the PCA process, the values are transformed first with Z score normalization. From the processing through the davies bouldin index (DBI) performance test with 3 clusters, it is concluded that traditional K-means produces a DBI Index value of 0.019 and K-means PCA of 0.299. Meanwhile, to see the optimal cluster, several iterations were performed and resulted in the most optimal DBI index of 4 clusters in K-means of 0.014 and K-means PCA of 0.172. From the performance test value, it means that in the context of clustering the traditional criminal K-means data is declared more optimal than K-means PCA.
Available medical imaging modalities for melanoma screening
Authors: Owida Hamza Abu , Al-Ayyad Muhammad Saleh , Al-Nabulsi Jamal , Turab Nidal
Published: February 2024

The prevalence of melanoma of the skin has seen a significant rise in recent decades, constituting approximately one-third of all diagnosed cancer cases. Melanoma, the most fatal variant among cutaneous malignancies, exhibits a 4% probability of occurrence over an individual’s lifetime. The increasing incidence and mortality rates of skin cancer impose a substantial burden on healthcare resources and the economy. In recent years, several optical modalities, including dermoscopy, reflectance confocal microscopy (RCM), optical coherence tomography, multiphoton excited fluorescence imaging, and dermatofluorescence, have been extensively studied and utilized to improve the non-invasive diagnosis of skin cancer. This review article provides an analysis of the approach employed in the recently developed optical non-invasive diagnostic technologies. It explores the clinical uses of these techniques, while also examining their respective advantages and disadvantages. Furthermore, the paper explores the possibility for additional advancements in these technologies in the future.
Identification of chronic obstructive pulmonary disease using graph convolutional network in electronic nose
Authors: Aulia Dava , Sarno Riyanarto , Hidayati Shintami Chusnul , Rosyid Alfian Nur , Rivai Muhammad
Published: February 2024

Chronic obstructive pulmonary disease (COPD) is a progressive lung dysfunction that can be triggered by exposure to chemicals. This disease can be identified with spirometry, but the patient feels uncomfortable, affecting the diagnosis results. Other disease markers are being investigated, including exhaled breath. This method can be applied easily, is non-invasive, has minimal side effects, and provides accurate results. This study applies the electronic nose method to distinguish healthy people and COPD suspects using exhaled breath samples. Twenty semiconductor gas sensors combined with machine learning algorithms were employed as an electronic nose system. Experimental results show that the frequency feature of the sensor responses used by the principal component analysis (PCA) method combined with graph convolutional network (GCN) can provide the highest accuracy value of 97.5% in distinguishing between healthy and COPD subjects. This method can improve the detection performance of electronic nose systems, which can help diagnose COPD.
MDVC corpus: empowering Moroccan Darija speech recognition
Authors: Ahmed Boumehdi , Abdellah Yousfi
Published: February 2024

Automatic speech recognition (ASR) technology has significantly transformed human-machine interactions, but it remains limited in its representation of diverse languages and dialects. Moroccan Darija, the lively Moroccan dialect, has long been underrepresented in the realm of language technology. To address this gap, we present a novel corpus of audio files accompanied by meticulously transcribed Moroccan Darija speech. The corpus comprises 1,000 hours of diverse content, featuring multiple Moroccan accents, extracted from 80 YouTube channels. To standardize the representation of Moroccan Darija in our corpus, we made efforts to establish consistent writing norms and conventions. In addition to the dataset creation, we applied fine-tuning using the Wav2Vec2 model on the Moroccan Darija voice corpus (MDVC) dataset achieving a remarkable word error rate (WER) of 9%. This article discusses the current state of Moroccan Darija research, highlighting the scarcity of resources and the need for robust ASR systems. Our contribution offers a valuable resource for researchers and developers, and by standardizing the Darija language, we strive to improve ASR system for this low resource language.
Investigating power scaling factor for pattern division multiple access
Authors: Meylani Linda , Widhi Prabowo Vinsensius Sigit , Hidayat Iswahyudi , Alwiyah Nisa
Published: February 2024

Pattern division multiple access (PDMA) is a one type of multi-domain non-orthogonal multiple access (NOMA) that support massive connectivity and can improve spectral efficiency. The unique pattern is used by each user to map its transmitting data into a group of resource, which consist of frequency, code and spatial domain or combination of these resources. Power scaling and phase shifting are used to resolve ambiguity as consequence non uniform distribution of the received combined constellation. In this paper, we propose investigation on power scaling factor for each user in PDMA matrix to increase sum rate transmission and propose combine successive interference cancellation (SIC) based on diversity order and power scaling factor for each user. The simulation results confirm that the proper implement power scaling factor in pattern type 2 show best performance in Rician fading channels.
Revolutionizing healthcare image analysis in pandemic-based fog-cloud computing architectures
Authors: Elsayed Alzahraa , Mohamed Khalil , Harb Hany
Published: February 2024

Healthcare data analysis has become essential after epidemic outbreaks. The manual examination of medical images such as X-rays and computed tomography (CT) scans became one of these challenges. This paper introduces a healthcare architecture that tackles the analysis efficiency and accuracy challenges by harnessing artificial intelligence (AI) capabilities. This architecture utilizes fog computing and presents a modified convolutional neural network (CNN) designed specifically for image analysis. Different architectures of CNN layers are thoroughly explored and evaluated to optimize overall performance. To demonstrate the effectiveness of the proposed approach, a dataset of X-ray images is utilized for analysis and evaluation. Comparative assessments are conducted against recent models such as VGG16, VGG19, MobileNet, and related research papers. Notably, the proposed approach achieves an exceptional accuracy rate of 99.88% in classifying normal cases, accompanied by a validation rate of 96.5%, precision and recall rates of 100%, and an F1 score of 100%. These results highlight the immense potential of fog computing and modified CNNs in revolutionizing healthcare image analysis and diagnosis, not only during pandemics but also in the future. By leveraging these technologies, healthcare professionals can improve the efficacy and accuracy of medical image analysis, leading to improved patient care and outcomes.
A secure framework for effective workload resource management
Authors: Nayagi Dharuman Salangai , Mohan Hosaagrahara Savalegowda
Published: February 2024

An efficient and dynamic role-based access-control (RBAC) model is presented in this work which utilizes access-control for internet of things (IoT) nodes while minimizing storage and computational overhead. Also, for the identification of the malicious packets at the gateway server, a machine learning method has been presented. In addition, a framework for data management techniques in the IoT environment is designed to ensure efficient and secure storage, management, and processing of IoT data. The results have been evaluated by using the Montage and Cybershake workload in terms of energy consumption, processing time, detection accuracy and misclassification rate. The results show that the proposed secure framework for effective workload resource management (SFE-WRM) attains better performance in comparison to the reliable and energy‐efficient route selection (REERS) and FTA-WRM method. Also, by using the security method, the proposed method provides better security to the IoT nodes during the data aggregation and processing of the workload. The ultimate aim of this work is to provide a solution for the development of a secure and efficient IoT environment that can address critical security challenges and enable the widespread adoption of IoT devices and services.
Sentiment analysis and classification of Ghanaian football tweets from the 2022 FIFA World Cup
Authors: Michael Eshun , Rose-Mary Owusuaa Gyening Mensah , Kate Takyi , Peter Appiahene , Kwame Peasah Ofosuhene , Linda Banning Amoako
Published: February 2024

Football as an attractive sport generates huge volumes of tweets concerning fans' opinions, feelings, and judgments during prime events. Such data can be leveraged in sentiment analysis, an algorithmic approach to analyzing text in tweets by extracting emotional tones. This paper presents a novel benchmark dataset of 132,115 tweets collected during the 2022 world cup on 𝕏 (formerly Twitter) for football-related sentiment classification. We also performed sentiment analysis on the dataset using lexicon-based tools, traditional machine learning algorithms, and pre-trained models, robustly optimized bidirectional encoder representations from transformers (BERT)- pretraining approach RoBERTa and distilled version of BERT (DistilBERT) to understand the emotions and reactions of football fans during different phases of the football matches. Results from the study indicate that most tweets had neutral sentiments in both context-aware and context-free analysis. We also describe our novel GhaFootBERT, a sentiment classification model based on transfer learning on BERT, which provides an effective approach to sentiment classification of football-related tweets. Our model performs robustly, outperforming the traditional models with 92% accuracy.
Comparing machine learning techniques for software requirements risk prediction
Authors: Vera Yasiel Pérez , Del Carpio Álvaro Fernández
Published: February 2024

Software requirements are the most critical phase focused on documenting, eliciting, and maintaining the stakeholders’ requirements. Risk identification and analysis are preemptive actions designed to anticipate and prepare for potential issues. Usually, this classification of risks is done manually, a practice that the personal judgment of the risk analyst or the project manager might influence. Machine learning (ML) techniques were proposed to predict the risk level in software requirements. The techniques used were logistic regression (LR), multilayer perceptron (MLP) neural network, support vector machine (SVM), decision tree (DT), naive bayes, and random forest (RF). Each model was trained and tested using cross-validation with k-folds, each with its respective parameters, to provide optimal results. Finally, they were compared based on precision, accuracy, and recall metrics. Statistical tests were performed to determine if there were significant differences between the different ML techniques used to classify risks. The results concluded that the DT and RF are the techniques that best predict the risk level in software requirements.
DNA based phenotype optimization of oryza sativa using machine learning and MolCNN
Authors: Soren Nikita , Rajendran Paramasivan Selvi
Published: February 2024

The prediction of phenotype from the genotype of oryza sativa (rice) is very crucial for optimizing the crop management. utilizing molecular convolutional neural networks (MolCNNs) and machine learning for crop management in oryza sativa provides a data-driven method for phenotype prediction based on DNA data, improving farming techniques. Data gathering, preparation, and integration of phenotypic and DNA data are all part of this process. Meaningful DNA features are extracted by MolCNN, and phenotypic qualities are predicted by a machine learning algorithm. Making educated decisions is ensured by assessing the model’s effectiveness, applying it to crop management, and updating it frequently. Choosing crop varieties, planting schedules, and management techniques are guided by molecular insights, which support sustainable agriculture and increase yields and quality. In the proposed research we are calculating pearson correlation coefficients between anticipated and actual trait values and the model’s performance on a test set. Additionally, it determines the (PCC) for every characteristic in the model and we have received a binary accuracy of 0.9998 in 139 seconds.