Article
Spectroscopy
Veena Devi Singh, Vijay Kumar Singh
Summary: A combination of Darunavir ethanolate (DRV) and Cobicistat (CBS) has been developed for the treatment of HIV infections, with two UV-spectrophotometric methods successfully developed for simultaneous estimation. These methods exhibited good sensitivity and low error in laboratory mixtures and tablet dosage forms.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2021)
Article
Agronomy
Achiraya Tantinantrakun, Supawan Sukwanit, Anthony Keith Thompson, Sontisuk Teerachaichayut
Summary: This study tested two nondestructive techniques, SW-NIRS and NIR-HSI, for predicting the maturity index of intact pineapple fruit. Both techniques showed reliable performance in predicting the maturity index of individual fruit.
POSTHARVEST BIOLOGY AND TECHNOLOGY
(2023)
Article
Mathematics
Laura Vicente-Gonzalez, Jose Luis Vicente-Villardon
Summary: This paper proposes a generalization of Partial Least Squares Regression (PLS-R) for a matrix of several binary responses and a set of numerical predictors, referred to as Partial Least Squares Binary Logistic Regression (PLS-BLR). The paper also describes the use of Biplot and Triplot graphical representations for visualizing PLS-BLR models and provides an application to real data. The conclusion is that the proposed method and its visualization using Triplots are powerful tools for interpreting the relations between predictors and responses.
Article
Agriculture, Multidisciplinary
Bertolozzi-Caredio Daniele, Soriano Barbara, Bardaji Isabel, Garrido Alberto
Summary: This study aims to explore the resilience capacities that livestock farmers in extensive farms in the EU need to deal with various challenges, such as market liberalization, competition, changing consumer patterns, decreasing meat consumption, and increasing climate change risks. The study uses data from a survey of 120 cattle and sheep farmers in Spain and applies mixed statistical methods to quantify farmers' perception of resilience capacities and challenges. The findings suggest that adaptability and transformability are more effective in dealing with long-term challenges, while robustness performs poorly in both short and long-term challenges, and is more effective against economic and environmental challenges. Institutional challenges pose the main threats to resilience.
AGRICULTURAL SYSTEMS
(2022)
Article
Biochemical Research Methods
Jie Hao, Jiawei Zou, Jiaqiang Zhang, Ke Chen, Duojiao Wu, Wei Cao, Guoguo Shang, Jean Y. H. Yang, KongFatt Wong-Lin, Hourong Sun, Zhen Zhang, Xiangdong Wang, Wantao Chen, Xin Zou
Summary: Cell-state transition analysis using single-cell RNA-sequencing can reveal additional information in time-resolved biological phenomena. However, current methods are limited to short-term evolution of cell states based on gene expression derivative. This study presents scSTAR, a method that overcomes this limitation by constructing a paired-cell projection between different biological conditions with arbitrary time spans, leading to more accurate predictions and new discoveries in aging and cancer research.
BRIEFINGS IN BIOINFORMATICS
(2023)
Article
Environmental Sciences
Onuwa Okwuashi, Christopher E. Ndehedehe, Dupe Nihinlola Olayinka
Summary: This research explores the novel application of Tensor Partial Least Squares (TPLS) for hyperspectral image classification. The results show that TPLS performed better than unfolded PLS, but fell short of traditional classifiers.
GEOCARTO INTERNATIONAL
(2022)
Article
Computer Science, Hardware & Architecture
Hong Li, Qiang Wang, Huan Wang, WanKou Yang
Summary: Infrared small target detection is a challenging task due to low signal-to-noise ratio, small target size, and shape structure. The TLMS method proposed in this study effectively suppresses background noise and highlights small infrared targets. Experimental results demonstrate the effectiveness of TLMS in infrared small target detection.
COMPUTERS & ELECTRICAL ENGINEERING
(2021)
Article
Mathematics, Applied
Yuling Jiao, Dingwei Li, Min Liu, Xiliang Lu, Yuanyuan Yang
Summary: In this paper, the recovery of n-dimensional signals from m binary measurements corrupted by noises and sign flips is considered. It is assumed that the target signals have low generative intrinsic dimension. A least square decoder is proposed and its performance is proved. Through extensive numerical simulations and comparisons, it is demonstrated that the least square decoder is robust to noise and sign flips.
JOURNAL OF SCIENTIFIC COMPUTING
(2023)
Article
Biotechnology & Applied Microbiology
Victor Pozzobon, Wendie Levasseur, Cedric Guerin, Patrick Perre
Summary: The article introduces a machine learning workflow to construct spectrophotometric equations predicting nitrate and nitrite concentrations in microalgae culture samples. The workflow involves recording UV absorbance spectra of samples, constructing a machine learning model based on partial least square regression, and utilizing 3 wavelengths to quantify nitrate and nitrite concentrations. The proposed equations provide a faster and more accurate alternative to ion chromatography for determining sample concentrations.
JOURNAL OF APPLIED PHYCOLOGY
(2021)
Article
Construction & Building Technology
Eric Forcael, Carolina Puentes, Rodrigo Garcia-Alvarado, Alexander Opazo-Vega, Jaime Soto-Munoz, Ginnia Moroni
Summary: Nowadays, BIM has been introduced as a promising methodology for managing projects in the construction industry. However, there are few studies on how individual users are classified. This study developed a model that characterized BIM users based on several parameters and validated its validity and reliability. The main aspects that characterized users were their use and command of the software and methodology, experience and degree of technology adoption, and knowledge of process levels and standardization concerning BIM.
Article
Automation & Control Systems
Kristian Hovde Liland, Ulf Geir Indahl, Joakim Skogholt, Puneet Mishra
Summary: The article discusses using multilinear partial least squares for analyzing multiway datasets, which allows for building parsimonious models handling various continuous and categorical responses. An advantage in computational speed is achieved by deflating responses and orthogonalising scores.
JOURNAL OF CHEMOMETRICS
(2022)
Article
Agricultural Engineering
Chiara Cevoli, Luca Di Cecilia, Luca Ferrari, Angelo Fabbri, Giovanni Molari
Summary: High-quality hay or silage can be obtained by monitoring moisture levels and using mechanical conditioning and near-infrared spectroscopy. This study evaluated the potential of in-field Vis/NIR hyperspectral imaging combined with chemometric tools. The results showed good discrimination power and accurate estimation of moisture content.
BIOSYSTEMS ENGINEERING
(2022)
Article
Automation & Control Systems
Fabio Fornari, Fabio Montisci, Federica Bianchi, Marina Cocchi, Claudia Carraro, Francesca Cavaliere, Pietro Cozzini, Francesca Peccati, Paolo P. Mazzeo, Nicolo Riboni, Maria Careri, Alessia Bacchi
Summary: This study explores the use of chemometrics to aid the discovery of cocrystals of active ingredients suitable for various applications. Partial Least Squares-Discriminant Analysis is used to discern cocrystals from binary mixtures based on the molecular features of the coformers. The proposed methodology resulted in a successful prediction rate of 85% for the test set in the model validation phase and of 74% for the external validation set.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Automation & Control Systems
Yinran Xiong, Wuye Yang, Huiyun Liao, Zhenlin Gong, Zhenzhen Xu, Yiping Du, Wei Li
Summary: In this study, a new variable selection method called 'Attention-PLS' was proposed, which combines PLS with the attention mechanism in a neural network to build a linear model between chemical properties and multivariables. The results show that Attention-PLS has better prediction performances.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Agriculture, Multidisciplinary
K. R. Ball, H. Liu, C. Brien, B. Berger, S. A. Power, E. Pendall
Summary: Successful calibration of agronomic traits in mixed cultivations using hyperspectral imaging data is important for advancing precision agriculture. This study demonstrated the capability of hyperspectral imaging to predict plant biomass, foliar nitrogen concentration, and nitrogen yield in grass and legume monocultures and polycultures under differential nitrogen and phosphorus fertilization. The study also identified key wavelengths contributing to the predictive power of the models. This research contributes to the improvement of remote sensing technologies for broader application in polyculture field cropping.
PRECISION AGRICULTURE
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Slimane Tounekti, Mahdi Alizadeh, Devon Middleton, James S. Harrop, Bassem Hiba, Laura Krisa, Choukri Mekkaoui, Feroze B. Mohamed
Summary: This study proposes and demonstrates a new method combining reduced field-of-view strategy with phase segmented EPI to address geometric distortion in post-operative DTI scans of patients with metal implants. The results show that the new method outperforms traditional techniques in reducing distortion.
MAGNETIC RESONANCE IMAGING
(2024)
Article
Radiology, Nuclear Medicine & Medical Imaging
Silvia Minosse, Eliseo Picchi, Valentina Ferrazzoli, Noemi Pucci, Valerio Da Ros, Raffaella Giocondo, Roberto Floris, Francesco Garaci, Francesca Di Giuliano
Summary: The aim of this study was to investigate the variation of DCE-MRI-derived kinetic parameters in brain tumors as a function of acquisition time. The results showed that K-ep and V-e were time-dependent and required longer scan times to obtain reliable parameter values, while K-trans was time-independent and remained the same in all acquisition times, making it a reliable parameter for short acquisition times.
MAGNETIC RESONANCE IMAGING
(2024)
Article
Radiology, Nuclear Medicine & Medical Imaging
Xingmin Guan, Xinheng Zhang, Hsin-Jung Yang, Rohan Dharmakumar
Summary: This study aims to investigate why DIR-prepared dark-blood T2* weighted images have lower SNR, CNR, and diagnostic accuracy for intramyocardial hemorrhage (IMH) detection compared to non-DIR-prepared bright-blood T2* images. Through phantom and animal studies, it was confirmed that the signal loss on DIR-prepared T2* images mainly originates from spin-relaxation during the DIR preparation. Therefore, when used for IMH detection, extra attention should be paid to the SNR of DIR-prepared dark-blood T2* imaging protocols.
MAGNETIC RESONANCE IMAGING
(2024)
Article
Radiology, Nuclear Medicine & Medical Imaging
Beatriz Laureano, Hassna Irzan, Helen OReilly, Sebastian Ourselin, Neil Marlow, Andrew Melbourne
Summary: Prematurity and preterm stressors have significant effects on the development of infants, especially at earlier gestations. While neonatal care advances have reduced preterm mortality rates, disability rates continue to grow in middle-income settings. Imaging the preterm brain using MR technology has improved our understanding of its development and the affected regions and networks. This research aims to support interventions, improve neurodevelopment, and provide accurate prognoses for preterm infants. This study focuses on the fully developed brain of extremely preterm subjects and examines myelin-related biomarkers to assess long-term effects. The findings suggest altered connectivity and cognitive outcomes in the adult preterm brain.
MAGNETIC RESONANCE IMAGING
(2024)
Article
Radiology, Nuclear Medicine & Medical Imaging
Julian Rauch, Frederik B. Laun, Peter Bachert, Mark E. Ladd, Tristan A. Kuder
Summary: This study presents a method for reducing concomitant field effects in double diffusion encoding (DDE) sequences by adding oscillating gradient pulses. The modified sequences successfully reduced accumulated concomitant phase without significant changes in the original sequence characteristics. The proposed method led to an increase in signal-to-noise ratio (SNR) for phantom and in vivo experiments, supported by simulations.
MAGNETIC RESONANCE IMAGING
(2024)
Article
Radiology, Nuclear Medicine & Medical Imaging
Marlon Bran Lorenzana, Shekhar S. Chandra, Feng Liu
Summary: Sparse reconstruction is important in MRI for reducing acquisition time and improving spatial-temporal resolution. This paper introduces two decoupling techniques for explicit 1D regularization and a combined 1D + 2D reconstruction technique that improves image quality.
MAGNETIC RESONANCE IMAGING
(2024)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yifan Gou, W. Christopher Golden, Zixuan Lin, Jennifer Shepard, Aylin Tekes, Zhiyi Hu, Xin Li, Kumiko Oishi, Marilyn Albert, Hanzhang Lu, Peiying Liu, Dengrong Jiang
Summary: ARTS algorithm improves the reliability of Y-v estimation in noncompliant subjects, enhancing the utility of Y-v as a biomarker for brain diseases.
MAGNETIC RESONANCE IMAGING
(2024)