Least-squares Analysis Research Articles

Metabolomics unveils the exacerbating role of arachidonic acid metabolism in atherosclerosis.

Atherosclerosis is a complex vascular disorder characterized by the deposition of lipids, inflammatory cascades, and plaque formation in arterial walls. A thorough understanding of its causes and progression is necessary to develop effective diagnostic and therapeutic strategies. Recent breakthroughs in metabolomics have provided valuable insights into the molecular mechanisms and genetic factors involved in atherosclerosis, leading to innovative approaches for preventing and treating the disease. In our study, we analyzed clinical serum samples from both atherosclerosis patients and animal models using laser desorption ionization mass spectrometry. By employing methods such as orthogonal partial least-squares discrimination analysis (OPLS-DA), heatmaps, and volcano plots, we can accurately classify atherosclerosis (AUC = 0.892) and identify key molecules associated with the disease. Specifically, we observed elevated levels of arachidonic acid and its metabolite, leukotriene B4, in atherosclerosis. By inhibiting arachidonic acid and monitoring its downstream metabolites, we discovered the crucial role of this metabolic pathway in regulating atherosclerosis. Metabolomic research provides detailed insights into the metabolic networks involved in atherosclerosis development and reveals the close connection between abnormal metabolism and the disease. These studies offer new possibilities for precise diagnosis, treatment, and monitoring of disease progression, as well as evaluating the effectiveness of therapeutic interventions.

Explorative study for the rapid detection of Fritillaria using gas chromatography-ion mobility spectrometry.

Fritillaria is a well-known health-promoting food, but it has many varieties and its market circulation is chaotic. In order to explore the differences in volatile organic compounds (VOCs) among different varieties of Fritillaria and quickly and accurately determine the variety of Fritillaria, this study selected six varieties of Fritillaria and identified and analyzed their volatile components using gas chromatography-ion mobility spectrometry (GC-IMS), establishing the characteristic fingerprints of VOCs in Fritillaria. In all samples, a total of 76 peaks were detected and 67 VOCs were identified. It was found that the composition of VOCs in different varieties of Fritillaria was similar, but the content was different. Combined with chemometric analysis, the differences between VOCs were clearly shown after principal component analysis, cluster analysis, and partial least-squares discriminant analysis. This may provide theoretical guidance for the identification and authenticity determination of different varieties of Fritillaria.

Ultratrace eNose Sensing of VOCs toward Breath Analysis Applications Utilizing an eNose-Based Analyzer.

This proof-of-principle study presents the ability of the recently developed iLovEnose to measure ultratrace levels of volatile organic compounds (VOCs) in simulated human breath based on the combination of multiple gas sensors. The iLovEnose was developed by our research team as a test bed for gas sensors that can be hosted in three serially connected compact low-volume and temperature-controlled compartments. Herein, the eNose system was equipped with conventional semiconducting metal oxide (MOX) gas sensors using a variety of base technologies providing 11 different sensor signals that were evaluated to determine six VOCs of interest at eight low to ultralow concentration levels (i.e., ranging from 3 to 0.075 ppm) at humid conditions (90% rh at 22 °C). The measurements were randomized and performed four times over a period of 2 weeks. Partial least-squares regression analysis was applied to estimate the concentration of these six analytes. It was shown that the iLovEnose system is able to discriminate between these VOCs and provide reliable quantitative information relevant for future applications in exhaled breath analysis as a diagnostic disease detection or monitoring device.

Hepatocellular Carcinoma Epigenetic Patterns Correspond to Differences in Ethnoracial Status and Treatment Response in a Single-Center Retrospective Study

PurposeTo correlate epigenetic patterns with ethnoracial status and locoregional therapy (LRT) response in patients with hepatocellular carcinoma (HCC). Materials and MethodsDNA and RNA were extracted from 47 distinct formalin-fixed paraffin-embedded tumor samples from 42 patients with HCC (n = 14 Black, n = 19 White, n = 9 Hispanic). LRT response was determined using computed tomography (CT) or magnetic resonance (MR) imaging 3 months posttreatment of 35 tumors (n = 22 complete response, n = 13 retreatment candidates). RNA expression and DNA methylation were used to stratify patients by ethnoracial status and treatment response using partial least-squares discriminant analysis (PLS-DA). Results were validated using hierarchical clustering. Ingenuity pathway analysis was performed to identify upstream regulators and pathways. ResultsPLS-DA identified 100 genes and 12 methylated regions that differentiated tumors from Black from White/Hispanic patients. Hierarchical clustering clustered samples with the top 16 genes or the top 5 methylation regions. Dysregulated pathways included adrenomedullin pathway (P = .030), EIF2 signaling (P = .007), and several metabolic pathways. AGTR1 (log2fold = 1.59) and GSTM3 (log2fold = 2.53) represented potential differentially expressed therapeutic targets. PLS-DA identified 100 genes and 150 methylation regions that differentiated between complete responders and retreatment candidates. Hierarchical clustering clustered samples with the top 30 genes or the top 13 methylation regions. Dysregulated pathways included metabolic and DNA repair–related pathways. ASAP2 (log2fold = 0.29) and RAD50 (log2fold = 0.22) represented potential differentially expressed therapeutic targets. ConclusionsVariation in gene expression and DNA methylation patterns in patients with HCC corresponded to ethnoracial status and LRT response. These initial results suggest tumor profiling has the potential to close ethnoracial disparities and improve treatment stratification.

Sustainability of the cassava processed agro industry business after covid-19 pandemic

Cassava is a food plant that has many advantages and can be processed into various kinds of processed foods. Salatiga City is one of the areas that has the potential to develop the sustainability of cassava processing businesses. At the start of the Covid-19 pandemic, sales of processed cassava experienced a decline. Business sustainability starts from the increase in income and sales volume after Covid-19. This research aims to find out (1) a portrait of the success of cassava processing businesses after the Covid-19 pandemic; (2) analyse the stage of managerial ability and entrepreneurial behaviour of processed cassava agro-industry entrepreneurs;(3) the impact of managerial ability and entrepreneurial behaviour on the achievement of cassava processing businesses in Salatiga City. Purposive sampling is the sample strategy employed in this study. This research uses analysis of partial least squares (PLS) with the help of the app SmartPLS4.0. The results show (1) Currently, the average cassava agro-industry business has experienced an increase in income and sales volume of 41% since the Covid-19 pandemic period; (2) Forming managerial skills are implementation, organization, and supervision. Entrepreneurial behaviour is risk-taking, and creative and innovative; (3) Managerial ability and entrepreneurial behaviour have a significant influence on business success.

Prediction of the Harvest Time of Cabernet Sauvignon Grapes Using Near-Infrared Spectroscopy

Harvest time assessment during the grape-ripening process can provide meaningful information for vineyard harvest scheduling. The purpose of this study was to investigate the identification of the harvest time of grape clusters using near-infrared (NIR) spectroscopy. During the harvest season from September to October 2019, bunches of Cabernet Sauvignon grapes were examined. Before establishing two classification models, namely partial least-squares discriminant analysis (PLS-DA) and support vector machine (SVM) models, raw spectra were processed by different pre-processing methods, including multiplicative signal correction (MSC), mean-centering, the standard normal variable (SNV), and the Savitzky-Golay method. Competitive adaptive weighted sampling (CARS) and the successive projections algorithm (SPA) were employed to select the optimal wavenumbers. The results indicate that NIR spectroscopy is a potentially promising approach for the rapid identification of different harvest times of Cabernet Sauvignon grapes, and the proposed technique is helpful for the prediction of ripened and over-ripened Cabernet Sauvignon grapes during the harvest time.

GC-MS, GC-IMS, and E-Nose Analysis of Volatile Aroma Compounds in Wet-Marinated Fermented Golden Pomfret Prepared Using Different Cooking Methods.

The cooking method is extremely important for the production of low-salt, wet-marinated, fermented golden pomfret because it strongly influences its flavor components and organoleptic quality. There are also significant differences in flavor preferences in different populations. The present study analyzed differences in the aroma characteristics of wet-marinated fermented golden pomfret after boiling, steaming, microwaving, air-frying, and baking using a combination of an electronic nose, GC-IMS, and SPME-GC-MS. Electronic nose PCA showed that the flavors of the boiled (A), steamed (B), and microwaved (C) treatment groups were similar, and the flavors of the baking (D) and air-frying (E) groups were similar. A total of 72 flavor compounds were detected in the GC-IMS analysis, and the comparative analysis of the cooked wet-marinated and fermented golden pomfret yielded a greater abundance of flavor compounds. SPME-GC-MS analysis detected 108 flavor compounds, and the results were similar for baking and air-frying. Twelve key flavor substances, including hexanal, isovaleraldehyde, and (E)-2-dodecenal, were identified by orthogonal partial least-squares discriminant analysis (OPLS-DA) and VIP analysis. These results showed that the cooking method could be a key factor in the flavor distribution of wet-marinated fermented golden pomfret, and consumers can choose the appropriate cooking method accordingly. The results can provide theoretical guidance for the more effective processing of fish products and the development of subsequent food products.

Aroma and taste analysis of pickled tea from spontaneous and yeast-enhanced fermentation by mass spectrometry and sensory evaluation

Anaerobically fermented pickled tea (PT) can be produced by spontaneous fermentation (SF) or yeast-enhanced fermentation (YF). Aroma and taste characteristics of PT during YF and SF were investigated using sensory evaluation, odour activity, aroma character impact values, HS-SPME-GC–MS, UPLC-QQQ-MS/MS, and spectrophotometry, annotating 198 volatile and 115 non-volatile components. The main contributing volatile components were β-ionone, and 1-octanol, promoted by YF and SF, and yielding floral and fruity aromas respectively. Additionally, compared with SF, YF promoted the formation of citronellol yielding a floral aroma, inhibited the stale aroma of methoxybenzenes, and reduced bitter, astringent, and sour tastes. Furthermore, partial least-squares regression analysis identified the main components related to the ‘acides aroma’ of PT as linalool oxide, n-decanoic acid, hexanoic acid, 3,7-dimethyl-2,6-octadienoic acid, 3-methyl-1-dodecyn-3-ol, and nerolidol. This application could be used as methodology for the comprehensive analysis of tea aroma and taste and these results can act as guidelines for PT production and quality control.

Comparison of the diagnostic effectiveness of ultrasound imaging coupled with three mathematical models for discriminating thyroid nodules.

The overlapping nature of thyroid lesions visualized on ultrasound (US) images could result in misdiagnosis and missed diagnoses in clinical practice. To compare the diagnostic effectiveness of US coupled with three mathematical models, namely logistic regression (Logistics), partial least-squares discriminant analysis (PLS-DA), and support vector machine (SVM), in discriminating between malignant and benign thyroid nodules. A total of 588 thyroid nodules (287 benign and 301 malignant) were collected, among which 80% were utilized for constructing the mathematical models and the remaining 20% were used for internal validation. In addition, an external validation cohort comprising 160 nodules (80 benign and 80 malignant) was employed to validate the accuracy of these mathematical models. Our study demonstrated that all three models exhibited effective predictive capabilities for distinguishing between benign and malignant nodules, whose diagnostic effectiveness surpassed that of the TI-RADS classification, particularly in terms of true negative diagnoses. SVM achieved a higher diagnostic rate for malignant thyroid nodules (93.8%) compared to Logistics (91.5%) and PLS-DA (91.6%). PLS-DA exhibited higher diagnostic rates for benign thyroid nodules (91.9%) compared to Logistics (86.7%) and SVM (88.7%). Both the area under the receiver operating characteristic curve (AUC) values of PLS-DA (0.917) and SVM (0.913) were higher than that of Logistics (0.891). Our findings indicate that SVM had significantly higher rates of true positive diagnoses and PLS-DA exhibited significantly higher rates of true negative diagnoses. All three models outperformed the TI-RADS classification in discriminating between malignant and benign thyroid nodules.

Nonlinear bond-slip model for fiber-reinforced polymer laminates externally bonded to thermally damaged concrete

This paper presents a novel nonlinear local bond-slip model for fiber-reinforced polymer (FRP) laminates externally bonded to thermally damaged concrete substrates. The proposed model is an extension of an existing two-parameter bond-slip model and incorporates two key parameters including interfacial fracture energy ([Formula: see text]) and interfacial brittleness index ([Formula: see text]). To study the variations of [Formula: see text] and [Formula: see text] with different thermal damage levels of the concrete substrate, an extensive experimental database of shear tests on FRP-to-thermally damaged concrete bonded joints was collected from the existing literature. The [Formula: see text] values were calculated from the peak pull loads with proper consideration of the bond length and width effects, while the [Formula: see text] values were obtained by least-squares regression analysis using experimental load-displacement curves or measured strain distributions in the FRP laminates. The results have indicated that the [Formula: see text] values initially exhibit a slight increase accompanied by mild thermal damage of the concrete substrate after exposure to moderately high temperatures; however, these values significantly decrease when the exposure temperature exceeds 300°C. The [Formula: see text] values initially decrease with high-temperature exposure and stabilize at around 50% of the initial values when the temperatures reach around 400°C. Despite the inherent variability in the test database, the proposed temperature-dependent bond-slip model has demonstrated its accuracy, as demonstrated by the comparisons between the theoretical predictions generated by the model and the corresponding shear test results. This interfacial bond-slip model is expected to serve as a constitutive law to characterize the bond behavior between externally bonded FRP laminates and thermally damaged concrete substrate, thus facilitating the practical application of high-performance FRP composites in the repair and strengthening of thermally or fire-damaged RC members.

Profiles and transplacental transfer of per- and polyfluoroalkyl substances in maternal and umbilical cord blood: A birth cohort study in Zhoushan, Zhejiang Province, China

Per- and polyfluoroalkyl substances (PFAS) can pass through the placental barrier and pose health risks to fetuses. However, exposure and transplacental transfer patterns of emerging PFAS remain unclear. Here, 24 PFAS were measured in paired maternal whole blood (n = 228), umbilical cord whole blood (n = 119) and serum (n = 120). Orthogonal partial least-squares discriminant analysis (OPLS-DA) was used to differentiate PFAS between different matrices. The transplacental transfer (TPT) of PFAS was calculated using cord to maternal whole blood concentration ratios. PFOS and PFOA were still the dominant PFAS in maternal samples. The emerging PFAS had higher TPT than PFOS and PFOA. Moreover, PFAS with the same chain length but different functional groups and C-F bonds showed different TPT, such as PFOS and PFOSA (C8, median: 0.090 vs. 0.305, p < 0.05) and PFHxS and 4:2 FTS (C6, median: 0.220 vs. 1.190, p < 0.05). A significant sex difference in 4:2 FTS (median: boys 1.250, girls 1.010, p < 0.05) were found. Furthermore, we observed a significant U-shaped trend for the TPT of carboxylates with increasing carbon chain length. PFAS showed a compound-specific transfer through placental barrier and a compound-specific distribution between different matrices in this study.

Predicting m-health acceptance from the perspective of unified theory of acceptance and use of technology

Addressing the growing popularity of mobile health (m-Health) technology in the health industry, the current study examined consumers’ intention and behaviour related to the usage of digital applications based on the unified theory of acceptance and use of technology (UTAUT). In particular, this study quantitatively assessed the moderating role of perceived product value and mediating role of intention to use m-Health application among Indonesians. This study adopted a cross-sectional design and collected quantitative data from conveniently selected respondents through an online survey, which involved 2068 Telegram users in Indonesia. All data were subjected to the analysis of partial least square- structural equation modeling (PLS-SEM). The obtained results demonstrated the moderating effect of perceived product value on the relationship between intention to use m-Health application (m-health app) and actual usage of m-Health app and the mediating effects of intention to use m-Health app on the relationships of perceived critical mass, perceived usefulness, perceived convenience, perceived technology accuracy, and perceived privacy protection on actual usage of m-Health app. However, the intention to use m-Health app did not mediate the influence of health consciousness and health motivation on the actual usage of m-Health app. Overall, this study’s findings on the significance of intention to use m-Health app and perceived product value based on the UTAUT framework serve as insightful guideline to expand the usage of m-Health app among consumers.

Men, Masculinities, and Relational Leadership Attitudes in the Collegiate Fraternity Environment

Examinations of fraternity life have infrequently analyzed the interconnections between the focus on leadership attitudes and that of members’ adherence to masculinities. In this critical quantitative study, the authors sought to comprehend how conforming to gendered norms often associated with historically white masculinities informed relational leadership attitudes for fraternity members. Using data from 3,136 participants in a single national historically white men’s social fraternity, results from an ordinary least-squares regression analysis revealed that emotional control, self-reliance, and power over women had negative associations with the relational leadership orientations of fraternity men. Additionally, risk taking and primacy of work had positive associations with a member’s relational leadership orientation. Finally, those who identified as queer were less likely to adopt a relational leadership orientation, a result the article unpacks considering the gendered and heteronormative contexts of fraternities. It then provides recommendations for future research, as well as implications for practitioners.

Predicting effect of anti-PD-1/PD-L1 inhibitors therapy for hepatocellular carcinoma by detecting plasma metabolite based on UHPLC-MS.

Anti-PD-1/PD-L1 inhibitors therapy has become a promising treatment for hepatocellular carcinoma (HCC), while the therapeutic efficacy varies significantly among effects for individual patients are significant difference. Unfortunately, specific predictive biomarkers indicating the degree of benefit for patients and thus guiding the selection of suitable candidates for immune therapy remain elusive.no specific predictive biomarkers are available indicating the degree of benefit for patients and thus screening the preferred population suitable for the immune therapy. Ultra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS) considered is an important method for analyzing biological samples, since it has the advantages of high rapid, high sensitivity, and high specificity. Ultra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS) has emerged as a pivotal method for analyzing biological samples due to its inherent advantages of rapidity, sensitivity, and specificity. In this study, potential metabolite biomarkers that can predict the therapeutic effect of HCC patients receiving immune therapy were identified by UHPLC-MS. A partial least-squares discriminant analysis (PLS-DA) model was established using 14 glycerophospholipid metabolites mentioned above, and good prediction parameters (R2 = 0.823, Q2 = 0.615, prediction accuracy = 0.880 and p < 0.001) were obtained. The relative abundance of glycerophospholipid metabolite ions is closely related to the survival benefit of HCC patients who received immune therapy. This study reveals that glycerophospholipid metabolites play a crucial role in predicting the efficacy of immune therapy for HCC.

Metabolomics of 3D cell co-culture reveals alterations in energy metabolism at the cross-talk of colorectal cancer-adipocytes.

Colorectal cancer (CRC) is the third most incident and the second most lethal malignant tumor. Despite the recognized association between obesity and CRC, further clarification is necessary regarding the lipids that are overexpressed during the development of CRC. In this scenario, the combination of metabolomics and a three-dimensional (3D) co-culture model involving CRC tumor cells and lipids can enhance the knowledge of energy metabolism modifications at the cross-talk between colorectal cancer and adipocytes. This study aimed to screen potential metabolites in the three dimensional (3D) co-culture of CRC and adipocytes by investigating the metabolome composition of this co-culture released into the extracellular space, which is known as the secretome. Pre-adipocyte cells (3T3-L1), human colon carcinoma (HT-29), and the 3D co-culture (3T3-L1 + HT-29) were cultured for the secretome obtention. Then, ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS) was employed to analyze the metabolomics of each secretome. Overall, 3.731 molecules were detected independent of the cell culture. When comparing the three cultures, 105 molecules presented a statistically significant difference in abundance between groups. Among these molecules, 16 were identified, with a particular emphasis on six lipids (PG 20:0, octadecenal, 3-Hydroxytetracosanoyl-CoA, 9,10-dihydroxy-octadecenoic acid, palmitoleic acid, and PA 18:4) and one amino acid derivative (acetylglutamic acid), which presented significant scores during the partial least-squares discriminant analysis (PLS-DA). Although it is too early to determine the possible impact of such molecules in a CRC microenvironment, these results open new avenues for further studies on the energy metabolism at the cross-talk of colorectal cancer adipocytes.

Maturity Classification of Rapeseed Using Hyperspectral Image Combined with Machine Learning.

Oilseed rape is an important oilseed crop planted worldwide. Maturity classification plays a crucial role in enhancing yield and expediting breeding research. Conventional methods of maturity classification are laborious and destructive in nature. In this study, a nondestructive classification model was established on the basis of hyperspectral imaging combined with machine learning algorithms. Initially, hyperspectral images were captured for 3 distinct ripeness stages of rapeseed, and raw spectral data were extracted from the hyperspectral images. The raw spectral data underwent preprocessing using 5 pretreatment methods, namely, Savitzky-Golay, first derivative, second derivative (D2nd), standard normal variate, and detrend, as well as various combinations of these methods. Subsequently, the feature wavelengths were extracted from the processed spectra using competitive adaptive reweighted sampling, successive projection algorithm (SPA), iterative spatial shrinkage of interval variables (IVISSA), and their combination algorithms, respectively. The classification models were constructed using the following algorithms: extreme learning machine, k-nearest neighbor, random forest, partial least-squares discriminant analysis, and support vector machine (SVM) algorithms, applied separately to the full wavelength and the feature wavelengths. A comparative analysis was conducted to evaluate the performance of diverse preprocessing methods, feature wavelength selection algorithms, and classification models, and the results showed that the model based on preprocessing-feature wavelength selection-machine learning could effectively predict the maturity of rapeseed. The D2nd-IVISSA-SPA-SVM model exhibited the highest modeling performance, attaining an accuracy rate of 97.86%. The findings suggest that rapeseed maturity can be rapidly and nondestructively ascertained through hyperspectral imaging.

2D-QSAR and molecular docking study on nitrofuran analogues as antitubercular agents

&lt;abstract&gt;&lt;sec&gt; &lt;title&gt;Background&lt;/title&gt; &lt;p&gt;Resistance to most of the antitubercular drugs has been on rising trends due to the misuse of existing drugs. This has encouraged us to explore a novel scaffold that has the potential for quick antimicrobial action with minimum side effects. Nitrofurans have attracted us due to their extensive biological activities, such as antibacterial and antifungal activities.&lt;/p&gt; &lt;/sec&gt;&lt;sec&gt; &lt;title&gt;Objective&lt;/title&gt; &lt;p&gt;The antitubercular activities of 126 nitrofuran derivatives have been investigated by using indicator parameters and topological and structural fragment descriptors.&lt;/p&gt; &lt;/sec&gt;&lt;sec&gt; &lt;title&gt;Methods&lt;/title&gt; &lt;p&gt;The different quantitative structure activity relationship (QSAR) models have been created and validated by using two different methodologies: combinatorial protocol in multiple linear regression (CP-MLR) and partial least-squares (PLS) analysis.&lt;/p&gt; &lt;/sec&gt;&lt;sec&gt; &lt;title&gt;Results&lt;/title&gt; &lt;p&gt;The 16 descriptors identified in CP-MLR are from six different classes: Constitutional, Functional, Atom Centered Fragments, Topological, Galvez, and 2D autocorrelation. Indicator parameters and Dragon descriptors suggested that the presence of a furan ring substituted by nitro group is essential for antitubercular activity. Further descriptors from constitutional, and functional classes suggest that the number of double bonds, number of sulphur atoms and number of fragments like thiazole, morpholine and thiophene should be minimum, along with the positive influence of Kier-Hall electrotopological states (Ss) for improved activity. The ACF class descriptors, GALVEZ class descriptors, and 2D-AUTO descriptor GATS4p have also shown positive influence on the antitubercular activity. The TOPO class descriptor T(O…S) suggests that the minimum gap between sulphur and oxygen is favorable for activity.&lt;/p&gt; &lt;/sec&gt;&lt;sec&gt; &lt;title&gt;Conclusions&lt;/title&gt; &lt;p&gt;The models acknowledged in the study have explained the variance between 72 to 76% in the training set and in the prediction of the test set compounds. Also, compounds &lt;bold&gt;122&lt;/bold&gt;, &lt;bold&gt;123&lt;/bold&gt; and &lt;bold&gt;82&lt;/bold&gt; were found to possess good binding affinity towards nitroreductase.&lt;/p&gt; &lt;/sec&gt;&lt;/abstract&gt;

Ofloxacin weakened treatment performance of rural domestic sewage in an aerobic biofilm system by affecting biofilm resistance, bacterial community, and functional genes

Ofloxacin (OFL) is a typical fluoroquinolone antibiotic widely detected in rural domestic sewage, however, its effects on the performance of aerobic biofilm systems during sewage treatment process remain poorly understood. We carried out an aerobic biofilm experiment to explore how the OFL with different concentrations affects the pollutant removal efficiency of rural domestic sewage. Results demonstrated that the OFL negatively affected pollutant removal in aerobic biofilm systems. High OFL levels resulted in a decrease in removal efficiency: 9.33% for chemical oxygen demand (COD), 18.57% for ammonium (NH4+-N), and 8.49% for total phosphorus (TP) after 35 days. The findings related to the chemical and biological properties of the biofilm revealed that the OFL exposure triggered oxidative stress and SOS responses, decreased the live cell number and extracellular polymeric substance content of biofilm, and altered bacterial community composition. More specifically, the relative abundance of key genera linked to COD (e.g., Rhodobacter), NH4+-N (e.g., Nitrosomonas), and TP (e.g., Dechlorimonas) removal was decreased. Such the OFL-induced decrease of these genera might result in the down-regulation of carbon degradation (amyA), ammonia oxidation (hao), and phosphorus adsorption (ppx) functional genes. The conventional pollutants (COD, NH4+-N, and TP) removal was directly affected by biofilm resistance, functional genes, and bacterial community under OFL exposure, and the bacterial community played a more dominant role based on partial least-squares path model analysis. These findings will provide valuable insights into understanding how antibiotics impact the performance of aerobic biofilm systems during rural domestic sewage treatment.

Effects of Dietary Protein Levels on Sheep Gut Metabolite Profiles during the Lactating Stage.

Diet-associated characteristics such as dietary protein levels can modulate the gut's primary or secondary metabolites, leading to effects on the productive performance and overall health of animals. Whereas fecal metabolite changes are closely associated with gut metabolome, this study aimed to see changes in the rumen metabolite profile of lactating ewes fed different dietary protein levels. For this, eighteen lactating ewes (approximately 2 years old, averaging 38.52 ± 1.57 kg in their initial body weight) were divided into three groups (n = 6 ewes/group) by following the complete randomized design, and each group was assigned to one of three low-protein (D_I), medium-protein (D_m), and high-protein (D_h) diets containing 8.58%, 10.34%, and 13.93% crude protein contents on a dry basis, respectively. The fecal samples were subjected to untargeted metabolomics using ultra-performance liquid chromatography (UPLC). The metabolomes of the sheep fed to the high-protein-diet group were distinguished as per principal-component analysis from the medium- and low-protein diets. Fecal metabolite concentrations as well as their patterns were changed by feeding different dietary protein levels. The discriminating metabolites between groups of nursing sheep fed different protein levels were identified using partial least-squares discriminant analysis. The pathway enrichment revealed that dietary protein levels mainly influenced the metabolism-associated pathways (n = 63 and 39 in positive as well as negative ionic modes, respectively) followed by protein (n = 15 and 8 in positive as well as negative ionic modes, respectively) and amino-acid (n = 14 and 7 in positive as well as negative ionic modes, respectively) synthesis. Multivariate and univariate analyses showed comparative changes in the fecal concentrations of metabolites in both positive and negative ionic modes. Major changes were observed in protein metabolism, organic-acid biosynthesis, and fatty-acid oxidation. Pairwise analysis and PCA reveal a higher degree of aggregation within the D-h group than all other pairs. In both the PCA and PLS-DA plots, the comparative separation among the D_h/D_m, D_h/D_I, and D_m/D_I groups was superior in positive as well as negative ionic modes, which indicated that sheep fed higher protein levels had alterations in the levels of the metabolites. These metabolic findings provide insights into potentiated biomarker changes in the metabolism influenced by dietary protein levels. The target identification may further increase our knowledge of sheep gut metabolome, particularly regarding how dietary protein levels influence the molecular mechanisms of nutritional metabolism, growth performance, and milk synthesis of sheep.

Characterization of a HPGe detector response for activation cross section measurements: regression method versus Monte Carlo method

ABSTRACT The full energy peak efficiencies and covariances of a high purity germanium (HPGe) detector studied by the regression method and Monte Carlo method were compared. The impact of the obtained results on the neutron activation cross-sections measured relative to monitor cross-sections was studied. In the regression method, the efficiencies measured for a set of calibration point sources were analysed by the least-squares analysis. In the Monte Carlo method, the efficiencies for the calibration point sources were calculated by MCNP. The covariances of the efficiencies determined by the regression method were calculated analytically. Perturbation analysis was performed to estimate the covariances of the efficiencies calculated by the Monte Carlo method. Positive correlations higher than 0.8 were found in the uncertainties of the MCNP data for point-like sources. In the case of the regression method, the correlation coefficient contains both positive and negative terms. The efficiencies and their covariances for finite sample geometry were also estimated by both methods to take into account sample effects such as geometrical effect, and gamma-ray self-absorption, and found considerable differences in the cross-sections and their uncertainties for reaction products quantified with low energy gammas. The efficiencies and covariances were clearly affected by the properties of the sample.