Assessment of Genomic Diversity and Selective Pressures in Crossbred Dairy Cattle of Pakistan.

Improving the low productivity levels of native cattle breeds in smallholder farming systems is a pressing concern in Pakistan. Crossbreeding high milk-yielding holstein friesian (HF) breed with the adaptability and heat tolerance of Sahiwal cattle has resulted in offspring that are well-suited to local conditions and exhibit improved milk yield. The exploration of how desirable traits in crossbred dairy cattle are selected has not yet been investigated. This study aims to provide the first overview of the selective pressures on the genome of crossbred dairy cattle in Pakistan. A total of eighty-one crossbred, thirty-two HF and twenty-four Sahiwal cattle were genotyped, and additional SNP genotype data for HF and Sahiwal were collected from a public database to equate the sample size in each group. Within-breed selection signatures in crossbreds were investigated using the integrated haplotype score. Crossbreds were also compared to each of their parental breeds to discover between-population signatures of selection using two approaches: cross-population extended haplotype homozygosity and fixation index. We identified several overlapping genes associated with production, immunity, and adaptation traits, including U6, TMEM41B, B4GALT7, 5S_rRNA, RBM27, POU4F3, NSD1, PRELID1, RGS14, SLC34A1, TMED9, B4GALT7, OR2AK3, OR2T16, OR2T60, OR2L3, and CTNNA1. Our results suggest that regions responsible for milk traits have generally experienced stronger selective pressure than others.

Archetype analysis and the PHATE algorithm as methods to describe and visualize pregnant women's levels of physical activity knowledge.

The knowledge of physical activity (PA) recommended for pregnant women and practical application of it has positive impact on the outcome. Nevertheless, it is estimated that in high-income countries over 40% of pregnant women are insufficiently physically active. One of the reasons is insufficient knowledge pregnant women have about allowed effort during pregnancy and both recommended and not recommended physical activities. Description of knowledge about physical activity the women have and distinguishing patterns of their knowledge is becoming an increasingly important issue. A common approach to handle survey data that reflect knowledge involves clustering methods or Principal Component Analysis (PCA). Nevertheless, new procedures of data analysis are still being sought. Using survey data collected by the Institute of Mother and Child Archetypal analysis has been applied to detect levels of knowledge reflected by answers given in a questionnaire and to derive patterns of knowledge contained in the data. Next, PHATE (Potential of Heat-diffusion for Affinity-based Trajectory Embedding) algorithm has been used to visualize the results and to get a deeper insight into the data structure. The results were compared with picture derived from PCA. Three archetypes representing three patterns of knowledge have been distinguished and described. The presentation of complex data in a low dimension was obtained with help of PHATE. The formations revealed by PHATE have been successfully described in terms of knowledge levels reflected by the survey. Finally, comparison of PHATE with PCA has been shown. Archetype analysis combined with PHATE provides novel opportunities in examining nonlinear structure of survey data and allows for visualization that captures complex relations in the data. PHATE has made it possible to distinguish sets of objects that have common features but were captured neither by Archetypal analysis nor PCA. Moreover, for our data, PHATE provides an image of data structure which is more detailed than interpretation of PCA.

Phytochemical composition of Lagenaria siceraria fruits from KwaZulu-Natal and Limpopo, South Africa.

Lagenaria siceraria (Molina) Standley is a food and medicinal source with anti-proliferative, anti-fertility, anti-HIV and anti-cancerous properties. The current study investigated the phytochemical constituents of L. siceraria fruits using gas chromatography/mass spectrometry (GC-MS). Five isoprenoids present in all investigated landraces were 1-Dodecene, 2,3-Dimethyldodecane, E-15-Heptadecenal, Eicosane, and Tridecane, 6-propyl. Lighter metabolites such as 1-Dodecene and 2,3-Dimethyldodecane were recorded at a shorter retention time range of 9.08-16.29 min over a lower relative peak area ranging from 1.09 to 6.97%. However, heavier compounds (E-15-Heptadecenal, Eicosane and Tridecane, 6-propyl) had a longer retention time range of 13.42-18.00 mins over a higher relative peak area range of 2.25-11.41%. Cluster analysis grouped landraces into 5 clusters (I -V) according to their fruit and seed attributes, and isoprenoid units significant to each cluster. Terpenoids were the prominent phytochemicals present in fruits. This is the most comprehensive study on the fruit phytochemical constituents of different L. siceraria landraces to date.

A statistical shape analysis for the assessment of the main geometrical features of the distal femoral medullary canal.

Statistical Shape Models (SSMs) are widely used in orthopedics to extract the main shape features from bone regions (e.g., femur). This study aims to develop an SSM of the femoral medullary canal, investigate its anatomical variability, and assess variations depending on canal length. The canals were isolated from 72 CT femur scans, through a threshold-based segmentation. A region of interest (ROI) was selected; sixteen segments were extracted from the ROI, ranging from 25% of the full length down to the most distal segment. An SSM was developed to identify the main modes of variation for each segment. The number of Principal Components (PCs) needed to explain at least 90% of the shape variance were three/four based on the length of the canal segment. The study examined the relationship between the identified PCs and geometric parameters like length, radius of curvature, ellipticity, mean diameter, and conicity, reporting range and percentage variation of these parameters for each segment. The SSMs provide insights into the anatomical variability of the femoral canal, emphasizing the importance of considering different segments to capture shape variations at various canal length. These findings can contribute for the design of personalized orthopedic implants involving the distal femur.

Improving prediction of maternal health risks using PCA features and TreeNet model.

Maternal healthcare is a critical aspect of public health that focuses on the well-being of pregnant women before, during, and after childbirth. It encompasses a range of services aimed at ensuring the optimal health of both the mother and the developing fetus. During pregnancy and in the postpartum period, the mother's health is susceptible to several complications and risks, and timely detection of such risks can play a vital role in women's safety. This study proposes an approach to predict risks associated with maternal health. The first step of the approach involves utilizing principal component analysis (PCA) to extract significant features from the dataset. Following that, this study employs a stacked ensemble voting classifier which combines one machine learning and one deep learning model to achieve high performance. The performance of the proposed approach is compared to six machine learning algorithms and one deep learning algorithm. Two scenarios are considered for the experiments: one utilizing all features and the other using PCA features. By utilizing PCA-based features, the proposed model achieves an accuracy of 98.25%, precision of 99.17%, recall of 99.16%, and an F1 score of 99.16%. The effectiveness of the proposed model is further confirmed by comparing it to existing state of-the-art approaches.

Insights into population adaptation and biodiversity of lactic acid bacteria in challenged date palm leaves silaging, using MALDI-TOF MS.

The study focused on isolating indigenous Qatari lactic acid bacteria (LAB) from various challenged date palm tree leaf silages to construct a comprehensive strain collection, useful to study the diversity of these strains following their adaptation to the uncommon silage. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) was employed for strain identification and differentiation. The diversity of LAB populations and strains was assessed through principal component analysis (PCA) and dendrogram analyses. A total of 88 LAB isolates were obtained from silages of fresh palm leaves, silage of mixed leaves and dairy feed, along with fresh palm tree leaves, and dairy feed, adapted to local harsh environments. These isolates were categorized according to the new classification of 2020, belonging to genera of Pediococcus, Lactiplantibacillus plantarum, Lacticaseibacillus paracasei, Companilactobacillus farciminis, Limosilactobacillus oris, Limosilactobacillus vaginalis, Lactiplantibacillus pentosus and Lactobacillus johnsonii. Pediococcus was the most prevalent genus, falling mostly within the species Pediococcus lolii. MALDI-TOF MS protein profiles, PCA, and dendrogram analyses successfully grouped the LAB isolates into five distinctive clusters based on the protein's similarities. The high diversity of the indigenous LAB in spontaneous palm leaf silages demonstrated their adaptation and mutualistic interactions, forming robust consortia that ensure the quality of the silage. The straightforward, quick, and accurate identification of LAB in this silage using MALDI-TOF MS presents a valuable approach for formulating LAB consortia for silaging harsh agricultural by-products.

Connectome caricatures: removing large-amplitude co-activation patterns in resting-state fMRI emphasizes individual differences.

High-amplitude co-activation patterns are sparsely present during resting-state fMRI but drive functional connectivity1-5. Further, they resemble task activation patterns and are well-studied3,5-10. However, little research has characterized the remaining majority of the resting-state signal. In this work, we introduced caricaturing-a method to project resting-state data to a subspace orthogonal to a manifold of co-activation patterns estimated from the task fMRI data. Projecting to this subspace removes linear combinations of these co-activation patterns from the resting-state data to create Caricatured connectomes. We used rich task data from the Human Connectome Project (HCP)11 and the UCLA Consortium for Neuropsychiatric Phenomics12 to construct a manifold of task co-activation patterns. Caricatured connectomes were created by projecting resting-state data from the HCP and the Yale Test-Retest13 datasets away from this manifold. Like caricatures, these connectomes emphasized individual differences by reducing between-individual similarity and increasing individual identification14. They also improved predictive modeling of brain-phenotype associations. As caricaturing removes group-relevant task variance, it is an initial attempt to remove task-like co-activations from rest. Therefore, our results suggest that there is a useful signal beyond the dominating co-activations that drive resting-state functional connectivity, which may better characterize the brain's intrinsic functional architecture.

Greater white matter degeneration and lower structural connectivity in non-amnestic vs. amnestic Alzheimer's disease.

Introduction: Multimodal evidence indicates Alzheimer's disease (AD) is characterized by early white matter (WM) changes that precede overt cognitive impairment. WM changes have overwhelmingly been investigated in typical, amnestic mild cognitive impairment and AD; fewer studies have addressed WM change in atypical, non-amnestic syndromes. We hypothesized each non-amnestic AD syndrome would exhibit WM differences from amnestic and other non-amnestic syndromes. Materials and methods: Participants included 45 cognitively normal (CN) individuals; 41 amnestic AD patients; and 67 patients with non-amnestic AD syndromes including logopenic-variant primary progressive aphasia (lvPPA, n = 32), posterior cortical atrophy (PCA, n = 17), behavioral variant AD (bvAD, n = 10), and corticobasal syndrome (CBS, n = 8). All had T1-weighted MRI and 30-direction diffusion-weighted imaging (DWI). We performed whole-brain deterministic tractography between 148 cortical and subcortical regions; connection strength was quantified by tractwise mean generalized fractional anisotropy. Regression models assessed effects of group and phenotype as well as associations with grey matter volume. Topological analyses assessed differences in persistent homology (numbers of graph components and cycles). Additionally, we tested associations of topological metrics with global cognition, disease duration, and DWI microstructural metrics. Results: Both amnestic and non-amnestic patients exhibited lower WM connection strength than CN participants in corpus callosum, cingulum, and inferior and superior longitudinal fasciculi. Overall, non-amnestic patients had more WM disease than amnestic patients. LvPPA patients had left-lateralized WM degeneration; PCA patients had reductions in connections to bilateral posterior parietal, occipital, and temporal areas. Topological analysis showed the non-amnestic but not the amnestic group had more connected components than controls, indicating persistently lower connectivity. Longer disease duration and cognitive impairment were associated with more connected components and fewer cycles in individuals' brain graphs. Discussion: We have previously reported syndromic differences in GM degeneration and tau accumulation between AD syndromes; here we find corresponding differences in WM tracts connecting syndrome-specific epicenters. Determining the reasons for selective WM degeneration in non-amnestic AD is a research priority that will require integration of knowledge from neuroimaging, biomarker, autopsy, and functional genetic studies. Furthermore, longitudinal studies to determine the chronology of WM vs. GM degeneration will be key to assessing evidence for WM-mediated tau spread.

Development of a multigenomic liquid biopsy (PROSTest) for prostate cancer in whole blood.

INTRODUCTION: We describe the development of a molecular assay from publicly available tumor tissue mRNA databases using machine learning and present preliminary evidence of functionality as a diagnostic and monitoring tool for prostate cancer (PCa) in whole blood. MATERIALS AND METHODS: We assessed 1055 PCas (public microarray data sets) to identify putative mRNA biomarkers. Specificity was confirmed against 32 different solid and hematological cancers from The Cancer Genome Atlas (n = 10,990). This defined a 27-gene panel which was validated by qPCR in 50 histologically confirmed PCa surgical specimens and matched blood. An ensemble classifier (Random Forest, Support Vector Machines, XGBoost) was trained in age-matched PCas (n = 294), and in 72 controls and 64 BPH. Classifier performance was validated in two independent sets (n = 263 PCas; n = 99 controls). We assessed the panel as a postoperative disease monitor in a radical prostatectomy cohort (RPC: n = 47). RESULTS: A PCa-specific 27-gene panel was identified. Matched blood and tumor gene expression levels were concordant (r = 0.72, p < 0.0001). The ensemble classifier ("PROSTest") was scaled 0%-100% and the industry-standard operating point of ≥50% used to define a PCa. Using this, the PROSTest exhibited an 85% sensitivity and 95% specificity for PCa versus controls. In two independent sets, the metrics were 92%-95% sensitivity and 100% specificity. In the RPCs (n = 47), PROSTest scores decreased from 72% ± 7% to 33% ± 16% (p < 0.0001, Mann-Whitney test). PROSTest was 26% ± 8% in 37 with normal postoperative PSA levels (<0.1 ng/mL). In 10 with elevated postoperative PSA, PROSTest was 60% ± 4%. CONCLUSION: A 27-gene whole blood signature for PCa is concordant with tissue mRNA levels. Measuring blood expression provides a minimally invasive genomic tool that may facilitate prostate cancer management.

Experimental study on evaluation of density, P-wave velocity, thermal conductivity, and thermal diffusion coefficient of granite after thermal treatments by using PCA.

Temperature significantly influences the physical parameters of granite, resulting in variations in the rock's thermal conductivity. In order to examine the impact of changes in multiple physical parameters of granite at different temperatures on the thermal conductivity of rocks, Principal Component Analysis (PCA) was employed to determine the correlation between granite at different temperatures and various physical parameters, including density (ρ), P-wave velocity (P), thermal conductivity (KT), and thermal diffusion coefficient (KD). Utilizing the linear contribution rate, a single indicator 'y' was derived to comprehensively represent the thermal conductivity of rocks. Research findings indicate that within the temperature range of 150-450 °C, the 'y'-value is relatively high, signifying favorable thermal conductivity of the rock. Notably, longitudinal wave velocity demonstrates higher sensitivity to temperature changes compared to other physical parameters.

Estimation of soil erodibility in Peninsular Malaysia: A case study using multiple linear regression and artificial neural networks.

Soil erodibility (K) is an essential component in estimating soil loss indicating the soil's susceptibility to detach and transport. Data Computing and processing methods, such as artificial neural networks (ANNs) and multiple linear regression (MLR), have proven to be helpful in the development of predictive models for natural hazards. The present case study aims to assess the efficiency of MLR and ANN models to forecast soil erodibility in Peninsular Malaysia. A total of 103 samples were collected from various sites and K values were calculated using the Tew equation developed for Malaysian soil. From several extracted parameters, the outcomes of correlation and principal component analysis (PCA) revealed the influencing factors to be used in the development of ANN and MLR models. Based on the correlation and PCA results, two sets of influencing factors were employed to develop predictive models. Two MLR (MLR-1 and MLR-2) models and four neural networks (NN-1, NN-2, NN-3, and NN-4) optimized using Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG) were developed and evaluated. The model performance validation was conducted using the coefficient of determination (R2), mean squared error (MSE), root mean squared error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE). The analysis showed that ANN models outperformed MLR models. The R2 values of 0.446 (MLR-1), 0.430 (MLR-2), 0.894 (NN-1), 0.855 (NN-2), 0.940 (NN-3), and 0.826 (NN-4); MSE values of 0.0000306 (MLR-1), 0.0000315 (MLR-2), 0.0000158 (NN-1), 0.0000261 (NN-2), 0.0000318 (NN-3), and 0.0000216 (NN-4) suggested the higher accuracy and lower modelling error of ANN models as compared with MLR. This study could provide an empirical basis and methodological support for K factor estimation in the region.

Chemical sensing of common microorganisms found in biopharmaceutical industries using MIR laser spectroscopy and multivariate analysis.

Mid-infrared laser spectroscopy was used to investigate common bacteria encountered in biopharmaceutical industries. The study involved the detection of bacteria using quantum cascade laser spectroscopy coupled to a grazing angle probe (QCL-GAP). Substrates similar to surfaces commonly used in biopharmaceutical industries were used as support media for the samples. Reflectance measurements were assisted by Multivariate Analysis (MVA) to assemble a powerful spectroscopic technique with classification and identification resources. The species analyzed, Staphylococcus aureus, Staphylococcus epidermidis, and Micrococcus luteus, were used to challenge the technique's capability to discriminate from microorganisms of the same family. Principal Components Analysis and Partial Least Squares-Discriminant Analysis differentiated between the bacterial species, using QCL-GAP-MVA as the reference. Spectral differences in the bacterial membrane were used to determine if these microorganisms were present in the samples analyzed. Results herein provided effective discrimination for the bacteria under study with high sensitivity and specificity.

Determination of the maximum bioaccumulation capacity of various metals in leaves of two Tillandsia species.

Tillandsia species are plants from the Bromeliaceae family which display biomonitoring capacities in both active and passive modes. The bioaccumulation potential of Tillandsia aeranthos (Loisiel.) Desf. and Tillandsia bergeri Mez acclimated to Southern/Mediterranean Europe has never been studied. More generally, few studies have detailed the maximum accumulation potential of Tillandsia leaves through controlled experiments. The aim of this study is to evaluate the maximum accumulation values of seven metals (Co, Cu, Mn, Ni, Pb, Pt, and Zn) in T. aeranthos and T. bergeri leaves. Plants were immersed in different mono elemental metallic solutions of Co (II), Cu (II), Mn (II), Ni (II), Pb (II), Pt (IV), and Zn (II) ions at different concentrations. In addition, cocktail solutions of these seven metals at different concentrations were prepared to study the main differences and the potential selectivity between metals. After exposure, the content of these metals in the leaves were measured by inductively coupled plasma-optical emission spectrometry. Data sets were evaluated by a fitted regression hyperbola model and principal component analysis, maximum metal loading capacity, and thermodynamic affinity constant were determined. The results showed important differences between the two species, with T. bergeri demonstrating higher capacity and affinity for metals than T. aeranthos. Furthermore, between the seven metals, Pb and Ni showed higher enrichment factors (EF). T. bergeri might be a better bioaccumulator than T. aeranthos with marked selectivity for Pb and Ni, metals of concern in air quality biomonitoring.

Quantitative integrative taxonomy informs species delimitation in Teloschistaceae (lichenized Ascomycota): the genus Wetmoreana as a case study.

The genus Wetmoreana was studied using quantitative integrative taxonomy methods to resolve the genus delimitation and explore its taxonomy diversity at the species level. As a result, the genus Fulgogasparrea is synonymized with Wetmoreana, and the latter includes 15 formally described species, one subspecies, and three further, thus far undescribed species: W. appressa, W. awasthii comb. nov., W. bahiensis sp. nov., W. brachyloba comb. nov., W. brouardii, W. chapadensis comb. nov., W. circumlobata sp. nov., W. decipioides, W. intensa comb. nov., W. ochraceofulva comb. nov., W. rubra sp. nov., W. sliwae sp. nov., W. sliwae ssp. subparviloba subsp. nov., W. subnitida comb. nov., W. texana, and W. variegata sp. nov. Eleven of 19 examined taxa are newly placed within this genus or confirmed to belong to it. Two species, W. awasthii and W. intensa, are transferred to Wetmoreana without additional analysis but based on previous studies. The W. brouardii and W. ochraceofulva species complexes are discussed in detail. Additionally, Caloplaca muelleri and C. rubina var. evolutior are transferred to Squamulea, and the latter is elevated to the species rank.

ASPMN Position Statement: Authorized Agent Controlled Analgesia.

The American Society for Pain Management Nursing (ASPMN) has reviewed and updated its position statement on the use of authorized agent controlled analgesia (AACA) for patients who are unable to independently utilize a self-dosing analgesic infusion pump, commonly known as patient-controlled analgesia (PCA). ASPMN continues to support the use of AACA to provide timely and effective pain management while promoting equitable care for vulnerable patient populations who are unable to use PCA. ASPMN does not support the use of "PCA by Proxy" in which unauthorized individuals activate PCA for a patient. This position statement includes an updated review of the evidence related to AACA. Clinical practice recommendations for authorized agents, nurses, prescribers, and organizations are provided with an emphasis on the importance of appropriate authorized agent selection, education, diligent patient assessment and medication management.

New non-local mean methods for MRI denoising based on global self-similarity between values.

Magnetic resonance imaging (MRI) is a non-invasive medical imaging technique that provides high-resolution 3D images and valuable insights into human tissue conditions. Even at present, the refinement of denoising methods for MRI remains a crucial concern for improving the quality of the images. This study aims to improve the prefiltered rotationally invariant non-local principal component analysis (PRI-NL-PCA) algorithm. We relaxed the original restrictions using particle swarm optimization to determine optimal parameters for the PCA part of the original algorithm. In addition, we adjusted the prefiltered rotationally invariant non-local mean (PRI-NLM) part by traversing the signal intensities of voxels instead of their spatial positions to reduce duplicate calculations and expand the search volume to the whole image when estimating voxels' signal intensities. The new method demonstrated superior denoising performance compared to the original approach. Moreover, in most cases, the new algorithm ran faster. Furthermore, our proposed method can also be applied to process Gaussian noise in natural images and has the potential to enhance other NLM-based denoising algorithms.

Investigating the simultaneous effect of chitosan and arbuscular mycorrhizal fungi on growth, phenolic compounds, PAL enzyme activity and lipid peroxidation in Salvia nemorosa L.

Considering the importance of Salvia nemorosa L. in the pharmaceutical and food industries, and also beneficial approaches of arbuscular mycorrhizal fungi (AMF) symbiosis and the use of bioelicitors such as chitosan to improve secondary metabolites, the aim of this study was to evaluate the performance of chitosan on the symbiosis of AMF and the effect of both on the biochemical and phytochemical performance of this plant and finally introduced the best treatment. Two factors were considered for the factorial experiment: AMF with four levels (non-inoculated plants, Funneliformis mosseae, Rhizophagus intraradices and the combination of both), and chitosan with six levels (0, 50, 100, 200, 400 mg L-1 and 1% acetic acid). Four months after treatments, the aerial part and root length, the levels of lipid peroxidation, H2O2, phenylalanine ammonia lyase (PAL) activity, total phenol and flavonoid contents and the main secondary metabolites (rosmarinic acid and quercetin) in the leaves and roots were determined. The flowering stage was observed in R. intraradices treatments and the highest percentage of colonization (78.87%) was observed in the treatment of F. mosseae × 400 mg L-1 chitosan. Furthermore, simultaneous application of chitosan and AMF were more effective than their separate application to induce phenolic compounds accumulation, PAL activity and reduce oxidative compounds. The cluster and principal component analysis based on the measured variables indicated that the treatments could be classified into three clusters. It seems that different treatments in different tissues have different effects. However, in an overview, it can be concluded that 400 mg L-1 chitosan and F. mosseae × R. intraradices showed better results in single and simultaneous applications. The results of this research can be considered in the optimization of this medicinal plant under normal conditions and experiments related to abiotic stresses in the future.

Paeonia mascula subsp. russoi (biv.) Cullen & Heywood: the chemical composition of the aerial parts essential oils of two different populations collected in Sicily (Italy).

In the present study, the chemical composition of the essential oil from aerial parts of two populations of Paeonia mascula subsp. russoi, collected in Sicily, was evaluated by GC-MS. No previously phytochemical investigation has been reported for this subspecies. The main components of the essential oil of the population with pink flowers were salicylaldehyde (34.31%), nonanal (16.95%) and 2-hexenal (10.17%), whereas essential oil of the population with white flowers, was shown to be rich of myrtanal (14.14%), eugenol (14.02%) and salicylaldehyde (12.21%). Furthermore, a complete literature review, not present in literature, on the composition of the essential oils of all the other taxa of Paeonia, studied so far, was performed. PCA and HCA analyses of the composition of essential oils obtained from the aerial parts were also carried out.

Discrimination of male-sterility and male-fertility in Japanese cedar (Cryptomeria japonica) using near-infrared diffuse transmission spectroscopy.

The increasing demand for pollen-free seedlings of Japanese cedar (Cryptomeria japonica) has created a need for a simple method to discriminate between male-sterile and male-fertile strobili. The objective of this study was to establish a classification model to quickly and easily distinguish male-sterile and male-fertile strobili in C. japonica using near-infrared (NIR) diffuse transmission spectroscopy. The absorbance spectra of C. japonica were obtained for three different months from December 2022 to February 2023 and preprocessed using three methods: untreated, smoothing, and second derivative. Principal component analysis was applied to the NIR spectra and classification models were built using a support vector machine. The sample collected in January 2023 showed the highest discrimination accuracy of 89.38% with the smoothing preprocessing, which was improved to 89.97% by limiting the wavelengths to the NIR region. Furthermore, discrimination accuracy for independent test data was evaluated by splitting the data into training and testing sets using January 2023 data with smoothing preprocessing. The discrimination accuracy for test data sets was more than 85%, and the misclassification ratio was less than 20% for each sample group. These results indicate the potential of using NIR diffuse transmission spectroscopy to discriminate between male-sterility and fertility in C. japonica.

Integration of diffusion tensor imaging parameters with mesh morphing for in-depth analysis of brain white matter fibre tracts.

Averaging is commonly used for data reduction/aggregation to analyse high-dimensional MRI data, but this often leads to information loss. To address this issue, we developed a novel technique that integrates diffusion tensor metrics along the whole volume of the fibre bundle using a 3D mesh-morphing technique coupled with principal component analysis for delineating case and control groups. Brain diffusion tensor MRI scans of high school rugby union players (n = 30, age 16-18) were acquired on a 3 T MRI before and after the sports season. A non-contact sport athlete cohort with matching demographics (n = 12) was also scanned. The utility of the new method in detecting differences in diffusion tensor metrics of the right corticospinal tract between contact and non-contact sport athletes was explored. The first step was to run automated tractography on each subject's native space. A template model of the right corticospinal tract was generated and morphed into each subject's native shape and space, matching individual geometry and diffusion metric distributions with minimal information loss. The common dimension of the 20 480 diffusion metrics allowed further data aggregation using principal component analysis to cluster the case and control groups as well as visualization of diffusion metric statistics (mean, ±2 SD). Our approach of analysing the whole volume of white matter tracts led to a clear delineation between the rugby and control cohort, which was not possible with the traditional averaging method. Moreover, our approach accounts for the individual subject's variations in diffusion tensor metrics to visualize group differences in quantitative MR data. This approach may benefit future prediction models based on other quantitative MRI methods.