Principal Component Analysis Research Articles

Spatiotemporal variations, source identification, and risk assessment of potentially toxic elements in the surface water of Felent Stream impacted by the silver mine.

The silver deposits located in the upper basin of the Felent Stream are currently the largest producing mine in the Türkiye. It is also significantly impacted by industrial, agricultural, and thermal spring-related waste in Kütahya Province. The main objectives of this study were to examine the spatiotemporal variations of 12 dissolved potentially toxic elements (PTEs) in the surface water of Felent Stream, to identify their possible sources, and to assess their probable risks. As a result of this study, among investigated PTEs, the highest mean concentrations of 3592-14,388µg/L for Mg and the lowest of 0.15-0.19µg/L for Cd were noted in Felent Stream water. The average concentrations of PTEs were found in the order of Mg > Ca > Na > As > Mn > B > Zn > Ni > Cu > Pb > Cr > Cd. Remarkably, during the dry season, there was a conspicuous escalation in the average PTEs contents of water, with an approximately multifold amplification. PTEs in stream water were evaluated for their potential ecotoxicological risks and possible sources. Based on ecological risk assessment indices, the stream exhibited low pollution levels during the wet season but displayed elevated pollution levels during the dry season, indicating a general shift towards heightened pollution conditions. The hazard index (HI) data for As exhibited significant potential noncarcinogenic risks across all monitoring stations. Conversely, the carcinogenic risk (CR) data underscored the imperative nature of addressing the health risks associated with As in the waters of the studied region. Mining activities were identified as the primary origin of PTEs based on principal component analysis (PCA). Moreover, upstream regions, proximal to the mining site, emerged as the most heavily contaminated areas according to cluster analysis (CA).

Inference of cosmological models with principal component analysis

Inference of cosmological models with principal component analysis

Inhibitory Kinetics and Mechanism of the Low Temperature Oxidation of Coal by Polyethylene Glycol-Citric Acid

ABSTRACT To address the issues of low inhibition rate, high cost, and insufficient research on physicochemical synergistic inhibition mechanisms, this study examined the inhibitory effect and mechanism of Polyethylene Glycol-Citric Acid (PEG-CA) on low-temperature oxidation of coal. Differential scanning calorimetry (DSC) was used to analyze the inhibition and thermal behavior at various inhibitor ratios. Fourier transform infrared spectroscopy was used to experimentally assess changes in the functional groups of the coal samples pre- and post-inhibition. Principal component analysis identified the key mechanism of PEG-CA in inhibiting the low-temperature oxidation of coal. It showed that PEG-CA increased the heat absorption of the coal and delayed the average thermal equilibrium temperature of the coal oxidation process by more than 70°C. The addition of PEG-CA increased the maximum heat absorption rate of the coal sample by 2.4 times and the activation energy of coal oxidation by 1.6–3.9 times. The primary inhibition mechanism is that PEG-CA-forming chelates with the metal ions of coal, preventing C-H bond activation and reducing the content of this functional group. Additionally, PEG-CA etherifies with the hydroxyl groups of the coal, creating relatively stable ether bonds. This study offers a cost-effective and eco-friendly solution for the coal mining industry and provides a new theoretical foundation for developing composite corrosion inhibitors and controlling spontaneous coal combustion.

Effect Analysis of Covid-19 on China’s Capital Market for Property-Rights Exchange Based on Functional Clustering

In August 2015, the State Council of the People’s Republic of China listed China’s capital market for property-rights exchange as the capital market along with the Chinese stock market. Consequently, China’s capital market for property-rights exchange is an integral part of the country’s multilayered capital market. The COVID-19 pandemic has had significant effects on the capital markets. Since China’s capital market for property-rights exchange mainly serves nonlisted companies, it is complicated to obtain its exchange data to perform COVID-19’s effect analysis. This study investigates COVID-19’s effect on China’s capital market for property-rights exchange. In this paper, we collected the online property rights-exchange data from 2017 to 2020 of an electric trading platform. The functional principal component analysis method is innovatively introduced to explore the online property rights-exchange fluctuation to get an insight into COVID-19’s effect on China’s capital market for property-rights exchange. Based on the principal component scores, COVID-19’s effect on different property rights-exchange institutions are divided into two categories using principal component clustering. A time-series model is used to quantify the effects in the two categories. The research results finally show that online property rights-exchange can reduce the negative effect of COVID-19 on China’s capital market for property-rights exchange, which encourages property rights-exchange institutions to accelerate the use of the online platform to overcome the challenges of the pandemic.

Soil quality assessment and mapping in basaltic terrain of Central India for sustainable soil and crop management using integrated PCA and GIS

The cereal-based cropping system plays a vital role in ensuring food security in the Indian subcontinent. However, the productivity of these systems has seen a continuous decline due to the degradation of soil quality. This study aims to develop a Soil Quality Index (SQI) for such cropping systems. A detailed survey was conducted in the Bareli watershed of Seoni district, Madhya Pradesh, at a 1:10000 scale using high-resolution satellite data and Geographic Information System (GIS) technology. The survey identified and mapped 5 soil series: Diwartola, Diwara, Bareli-1, Bareli-2 and Bareli-3. Soil quality was evaluated based on morphological, physical and chemical properties as well as fertility parameters. Key indicators for soil quality assessment included sand, silt, clay content, bulk density, hydraulic conductivity, available water capacity and coefficient of linear extensibility (COLE). Additionally, pH, electrical conductivity, organic carbon, cation exchange capacity and nutrients like N, P, K, Fe, Mn, Cu and Zn were considered. The SQI was calculated using integrated principal component analysis, which involved selecting a minimum data set (MDS), assigning weights and scoring indicators. The results revealed that Diwartola soils had high quality (242.7 ha, 13.5 % TGA), Bareli-1 and Bareli-3 soils were of medium quality (462.8 ha, 25.7 % TGA), while Diwara and Bareli-2 soils were of low quality (966.1 ha, 53.8 % TGA). Agro-interventions such as agri-horticulture, agro-forestry, silvi-pasture, intensive cultivation and soil and water conservation measures were recommended based on the different mapping units.

Metabolite profiling highlights the effect of microbial intervention in the soaking step of tempe

SummarySoaking of soybeans is an essential step in tempe fermentation. Owing to the uncontrolled microflora, spontaneous soaking during tempe production leads to inconsistencies in tempe quality. Common methods to control it include the addition of acids or microbial starters. Despite knowing the benefits, their impact on tempe's composition was less understood. In this study, prior to tempe fungal fermentation, soybeans were soaked with Lactiplantibacillus plantarum NBRC 101978 and Pichia burtonii NBRC 0844. Tempe samples were subjected to comprehensive analysis using a widely targeted gas chromatography–mass spectrometry (GC–MS) metabolomics approach and evaluation of physical characteristics. A total of 100 metabolites of sugars, amino acids, fatty acids, and organic acids were annotated in all samples. Principal component analysis (PCA) explaining 47.6% of the variance showed that microbial interventions led to alterations in the metabolome of all samples, including the accumulation of amino acids in lactic acid bacteria (LAB)‐soaked soybean tempe and tyramine in yeast‐soaked soybean tempe. Unlike chemically added soaked soybean tempe, microbial intervention significantly reduced the relative intensity levels of several sugars by more than twofold. Furthermore, microbial interventions in the tempe‐soaking step significantly elevated the levels of bioactive metabolites more than twofold. The introduction of microbial interventions in the tempe‐soaking step also influences the physical characteristics of the end product. These findings merit further consideration for tempe development and the food industry.

Enhanced synthetic generation of channel state information for millimeter‐wave networks in 5G communication systems

AbstractIn 5G communication systems, millimeter‐wave networks are pivotal, relying heavily on Channel State Information (CSI) for effective user‐to‐base station (BS) transmission. However, the acquisition of genuine CSI data remains a hurdle, often due to the expenses associated with simulations or physical experiments. This paper introduces an innovative method for generating artificial CSI data from real datasets, aiming to closely replicate authentic CSI samples. The procedure begins with an initial clustering analysis, followed using Principal Component Analysis and Uniform Manifold Approximation and Projection to reduce dimensionality. Then, the data distributions are transformed into multivariate normal distributions using Probability Integral Transformations (PIT). For data synthesis, Multilayer Perceptron based regression models are utilized, followed by inverse PIT transformations to return the data to its original space. Our method is compared against KDE‐based algorithms, demonstrating superior fidelity in reproducing real CSI samples. Additionally, we stress the importance of capturing CSI correlations among different BSs to refine data synthesis. This research propels forward data synthesis techniques, offering potential solutions for mitigating interference challenges in dense MMW networks and advancing 5G communication systems.

Changes in the microbial community structure and diversity during the electricity generation process of a microbial fuel cell with algal-film cathode

Abstract A two-chamber microbial fuel cell (MFC) with algal-film cathode was constructed. It showed good electric-generating performance with three electric-generating stages: start-up, development, and stable. An average output voltage reached ~0.412 V during the stable period. A maximum power density during continuous operation was 19.76 mW/m2. Bacterial samples were collected from the anode in the three stages (A1, A2, and A3), and their community structure and diversity were analyzed using Illumina MiSeq high-throughput sequencing technology. A total of 4238 operational taxonomic units were identified based on the number of taxa. At the phylum level, Proteobacteria and Bacteroidetes played a dominant role in the three stages and increased significantly during electricity generation. Compared with A1, the relative abundances of Proteobacteria in A2 and A3 increased by 23.30% and 32.06%, respectively, whereas those of Bacteroidetes in A2 and A3 increased by 5.56% and 14.50%, respectively. At the genus level, there were differences in the composition of bacterial communities among the three stages. Acinetobacter and Chlorobium became the dominant genera in A2, replacing Nitrospira and norank_f__Saprospiraceae in A1, and Sphingobacterium and Ochrobactrum became the dominant genera in A3. According to the sample cluster and principal component analyses, A1 was clustered into one class, and A2 and A3 were clustered into a second class. This work revealed bacterial community succession at the anode of an algal-film cathode MFC during the electricity generation process, which provides a theoretical basis for the subsequent promotion of electricity generation by algal-film cathode MFCs.

Emotional and Work-Related Factors in the Self-Assessment of Work Ability among Italian Healthcare Workers

The Work Ability Index (WAI) is the most commonly used tool for evaluating work capacity. Self-assessments made by workers can be influenced by various occupational and emotional factors. We wanted to study the association of work-related factors, such as work annoyance, stress, overcommitment, job satisfaction, social support, and emotional factors, such as anxiety, depression, and happiness, with work ability, in a sample of 490 healthcare workers from an Italian public health company. A principal component analysis indicated the presence of two components of the WAI questionnaire; the first expresses “subjectively estimated work ability” (SEWA), and the second refers to “ill-health-related work ability” (IHRWA). Using stepwise multiple hierarchical linear regression, we identified the factors that best predicted the total score on the WAI and on the two components. The total score was negatively predicted by anxiety, depression, a lack of happiness, low job satisfaction, overcommitment, and work annoyance. Age, being female, anxiety, and occupational stress were associated with a reduction in the IHRWA component score, while overcommitment, work annoyance, a lack of social support, depression, and a lack of happiness were negatively associated with the SEWA component. These results can help interpret those of epidemiological studies and provide guidance on ways to improve work ability.

Proteomic profiles revealed enzymatic activities associated with the flavor formation of salted shrimp paste influenced by Bacillus subtilis K-C3 inoculation.

Enzymatic proteomic profiles were examined to comprehend the predominant enzymes involved in the flavor development of salted shrimp paste influenced by Bacillus subtilis K-C3 inoculation (Inoc), compared to those without inoculation (CON). Inoc showed greater proteolytic, lipolytic, and chitinolytic activities than CON (P < 0.05) throughout 30 days of fermentation, indicating B. subtilis's ability to accelerate the fermentation rate and render distinctive flavor profiles to shrimp paste. Among 50 differential abundance proteins (DAPs), 24 DAPs were identified as potential key regulating enzymes, with a P-value < 0.05 and |FC| > 0.50, indicating their significance and regulating capacity within specific metabolic pathways. Notably, 27 and 23 DAPs were up-regulated in Inoc and CON, respectively. Moreover, gene ontology (GO) enrichment analysis revealed that hydrolases, involved in carbohydrate metabolic processes and proteolysis, were the most differentiating pathways between Inoc and CON. Both samples exhibited different flavor profiles. A greater abundance of N-containing volatile compounds with a lower total abundance of aldehydes, ketones, alcohols, and acids could suggest a more favorable flavor in Inoc, compared to CON. Principal component analysis (PCA) revealed a positive correlation between L-ascorbate peroxidase, carboxypeptidase, and tripeptidyl peptidase sed2, with proteolytic and lipolytic activities in Inoc (P < 0.05). Meanwhile, acids and alcohols were positively correlated with CON. Therefore, B. subtilis inoculation could produce a distinctive flavor with a desirable sensory perception of shrimp paste regarding its ability to release extracellular enzymes/proteins. B. subtilis K-C3 inoculation could be suggested in the production of shrimp paste to improve its flavor characteristics.

Effect of weight loss interventions on metabolomic signatures in obese children with insulin resistance.

The obesity epidemic among children has become a major public health issue, and the presence of childhood insulin resistance (IR) has been demonstrated prior to the onset of type 2 diabetes mellitus. However, it is unclear whether the metabolomic signature is associated with weight loss interventions in obese children with IR. Thirty-six obese children with IR were selected from the weight loss camp (Shenzhen Sunshine Xing Yada health Technology Co., LTD). Clinical parameters were collected before and after weight loss intervention. Targeted metabolomics of plasma samples was performed by ultra-performance liquid chromatography coupled to the tandem mass spectrometry, and principal component analysis, variable importance in projection, and orthogonal partial least squares discriminant analysis were used to obtain the differentially expressed metabolites. Pathway analysis was conducted with the Homo sapiens (HSA) sets in the Kyoto Encyclopedia of Genes and Genomes. We used machine learning algorithms to obtain the potential biomarkers and Spearman correlation analysis to clarify the association between potential biomarkers and clinical parameters. We found that clinical parameters and metabolite clusters were significantly changed in obese children with IR before and after weight loss intervention. Mechanistically, weight loss intervention significantly changed 61 metabolites in obese children with IR. Furthermore, 12 pathways were significantly changed. Moreover, the machine learning algorithm found 6 important potential biomarkers. In addition, these potential biomarkers were strongly associated with major clinical parameters. These data indicate different metabolomic profiles in obese children with IR after weight loss intervention, providing insights into the clinical parameters and metabolite mechanisms involved in weight loss programs.

Distinct features of immune activation and exhaustion markers in people with perinatally-acquired HIV.

The aim of this study was to characterize T cell activation, exhaustion, maturation and Treg frequencies in individuals who acquire perinatal HIV (PHIV), in individuals who acquired HIV as adult (AHIV), and in healthy controls (HC). This cross-sectional study included people with HIV ≥ 14 and <40 years, HIV-RNA < 50 copies/mL on antiretroviral therapy for at least 6 months, and HC. We assessed the expression of PD-1, TIM-3, EOMES, CD38+ DR+, maturation status by CD4+ and CD8+T cells and the frequency of CD4+ and CD8+ Treg cells. Principal component analysis (PCA) and k-means cluster analysis investigated which combination of immunological parameters better associated with each group. 26 PHIV and 18 AHIV with median ages of 26 (8.0) and 28 (6.8) years were consecutively enrolled. PHIV showed significant higher frequency of naïve and lower frequency of terminal effector memory CD4+ and CD8+ T cells than AHIV. AHIV exhibited higher expression of exhaustion and activation markers. The statistical analysis returned two clusters with 94% of specificity and 88% of sensitivity identifying PHIV vs. AHIV. The 9 HC had a lower expression of exhaustion markers on both CD4+ and CD8+T lymphocytes than PHIV and AHIV. These data may exclude major alterations of lymphopoiesis in PHIV, with even lower state of immune-activation and exhaustion compared with AHIV. This suggests that recent lack of virological control, may affect immune activation and exhaustion of CD4+ and CD8+ T cells.

Construction and analysis of a lysosome-dependent cell death score-based prediction model for non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is the most common type of tumor globally and the leading cause of cancer-related deaths. Although treatment strategies such as immune checkpoint inhibitors and chemotherapy have advanced, the heterogeneity among NSCLC patients results in significant variability in treatment outcomes. Studies have shown that certain patients respond poorly to immune checkpoint inhibitors, indicating that treatment response is closely related to multiple factors. Therefore, it is necessary to develop predictive models to stratify patients based on gene expression and clinical characteristics, aiming for precision therapy. This study aims to construct a stratified prognostic model for NSCLC patients based on lysosome-dependent cell death (LDCD) scoring by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data. By analyzing the immune-related characteristics of high-risk and low-risk groups, we further explored the impact of cell death patterns on lung cancer and identified potential therapeutic targets. This study obtained single-cell RNA sequencing data and gene expression data of NSCLC patients and normal lung tissues from the GEO and TCGA databases. We used R packages such as Seurat and CellChat for data preprocessing and analysis, and performed dimensionality reduction and visualization through Principal Component Analysis (PCA) and UMAP algorithms. LASSO regression analysis was used to construct the predictive model, followed by cross-validation and ROC curve analysis. The model's effectiveness was validated through survival analysis and immune microenvironment analysis. The study showed a significant increase in the proportion of monocytes in NSCLC tissues, suggesting their important role in cancer progression. Cell communication analysis indicated that macrophages, smooth muscle cells, and myeloid cells exhibit strong intercellular communication during cancer progression. Using the constructed prognostic model based on 12 LDCD-related genes, we found significant differences in overall survival and immune microenvironment between the high-risk and low-risk groups.

Improving human cardiac organoid design using transcriptomics.

Cardiovascular disease (CVD) is the leading cause of death worldwide. To this end, human cardiac organoids (hCOs) have been developed for improved organotypic CVD modeling over conventional in vivo animal models. Utilizing human cells, hCOs hold great promise to bridge key gaps in CVD research pertaining to human-specific conditions. hCOs are multicellular 3D models which resemble heart structure and function. Varying hCOs fabrication techniques leads to functional and phenotypic differences. To investigate heterogeneity across hCO platforms, we performed a transcriptomic analysis utilizing bulk RNA-sequencing from four previously published unique hCO studies. We further compared selected hCOs to 2D and 3D hiPSC-derived cardiomyocytes (hiPSC-CMs), as well as fetal and adult human myocardium bulk RNA-sequencing samples. Upon investigation utilizing Principal Component Analysis, K-means clustering analysis of key genes, and further downstream analyses such as Gene Set Enrichment (GSEA), Gene Set Variation (GSVA), and GO term enrichment, we found that hCO fabrication method influences maturity and cellular heterogeneity across models. Thus, we propose that adjustment of fabrication method will result in an hCO with a defined maturity and transcriptomic profile to facilitate its specified applications, in turn maximizing its modeling potential.

Recognition of Western Black-Crested Gibbon Call Signatures Based on SA_DenseNet-LSTM-Attention Network

As part of the ecosystem, the western black-crested gibbon (Nomascus concolor) is important for ecological sustainability. Calls are an important means of communication for gibbons, so accurately recognizing and categorizing gibbon calls is important for their population monitoring and conservation. Since a large amount of sound data will be generated in the process of acoustic monitoring, it will take a lot of time to recognize the gibbon calls manually, so this paper proposes a western black-crested gibbon call recognition network based on SA_DenseNet-LSTM-Attention. First, to address the lack of datasets, this paper explores 10 different data extension methods to process all the datasets, and then converts all the sound data into Mel spectrograms for model input. After the test, it is concluded that WaveGAN audio data augmentation method obtains the highest accuracy in improving the classification accuracy of all models in the paper. Then, the method of fusion of DenseNet-extracted features and LSTM-extracted temporal features using PCA principal component analysis is proposed to address the problem of the low accuracy of call recognition, and finally, the SA_DenseNet-LSTM-Attention western black-crested gibbon call recognition network proposed in this paper is used for recognition training. In order to verify the effectiveness of the feature fusion method proposed in this paper, we classified 13 different types of sounds and compared several different networks, and finally, the accuracy of the VGG16 model improved by 2.0%, the accuracy of the Xception model improved by 1.8%, the accuracy of the MobileNet model improved by 2.5%, and the accuracy of the DenseNet network model improved by 2.3%. Compared to other classical chirp recognition networks, our proposed network obtained the highest accuracy of 98.2%, and the convergence of our model is better than all the compared models. Our experiments have demonstrated that the deep learning-based call recognition method can provide better technical support for monitoring western black-crested gibbon populations.

A grey target performance evaluation model for aeroengine pressure test bench

PurposeThis paper aims to optimize the air pressure regulation scheme of the aeroengine pressure test bench.Design/methodology/approachBased on the requirements of pressure regulation process and the operating mechanism of aeroengine pressure test bench, a grey performance evaluation index system is constructed. The combination of principal component analysis and grey theory is employed to assign weights to grey indexes. The grey target evaluation model is introduced to evaluate the performance of historical regulation processes, and the evaluation results are analyzed to derive optimization mechanism for pressure regulating schemes.FindingsA case study based on monitoring data from nearly 300 regulation processes verifies the feasibility of the proposed method. On the one hand, the improved principal component analysis method can achieve rational weighting for grey indexes. On the other hand, the method comparison intuitively shows that the proposed method performs better.Originality/valueThe pressure test bench is a fundamental technical equipment in the aviation industry, serving the development and testing of aircraft engines. Due to the complex system composition, the pressure and flow adjustment of the test bench heavily rely on manual experience, leading to issues such as slow adjustment speed and insufficient accuracy. This paper proposes a performance evaluation method for the regulation process of pressure test bench, which can draw knowledge from historical regulation processes, provide guidance for the pressure regulation of test benches, and ultimately achieve the goal of reducing equipment operating costs.

Principal Component Analysis and Genetic Divergence Studies for Yield and Quality Related Attributes of Hybrid Rice

Principal Component Analysis and Genetic Divergence Studies for Yield and Quality Related Attributes of Hybrid Rice

HIV Symptom Clusters Among Sexual Minority Men in Ghana, West Africa: A Cross-sectional Study.

The aims of this study were to determine if HIV symptoms among sexual minority men formed clusters and to examine the sociodemographic and clinical characteristics that are associated with these clusters. We analyzed cross-sectional data from Ghanaian sexual minority men (N = 225) living with HIV. We used both principal component analysis and multivariable linear regression. Our findings indicate that sadness (64.0%) and headache (62.7%) were the most prevalent symptoms among our sample. Seven symptom clusters were identified: neurological symptoms, psychological symptoms, gastrointestinal symptoms, dermatological symptoms, self-concept/self-esteem, weight/diet-related symptoms, and sleepquality and potential disturbances. Late HIV diagnosis was significantly associated with higher distress scores for all symptom clusters except for the self-concept/self-esteem and gastrointestinal symptoms clusters. The findings emphasize the importance of early HIV symptom identification.

Effects of Milling Methods on Cutting Performance of Wood–Plastic Composites Based on Principal Component Analysis

Effects of Milling Methods on Cutting Performance of Wood–Plastic Composites Based on Principal Component Analysis

Size matters: lower body weight pigs have a different response to immune challenge and amino acids supplementation above the estimated requirement compared to heavy pigs.

The immune response varies between pigs, as not all pigs have the same response to a stressor. This variation may exist between individuals due to body weight (BW) or body composition, which may impact the capacity for coping with an immune challenge (IC). Tryptophan (Trp), threonine (Thr), and methionine (Met) requirements might also play a considerable part in supporting immune system activation while reducing variation between pigs; however, the latter has yet to be reported. This exploratory study investigated the effect of initial BW (light vs. heavy-weight) and supplementation of Trp, Thr, and Met above NRC requirements on feeding behavior and the coping capacity of growing pigs under an IC. Eighty gilts were categorized into two groups according to BW: light-weight (LW, 22.5kg) and heavy-weight pigs (HW, 28.5kg). Both BW groups were group-housed for a 28-d trial in a good or poor sanitary condition (SC). Pigs within a poor SC were orally inoculated with 2×109 colony units of Salmonella Typhimurium, and fresh manure from a pig farm was spread on the floor. Pigs within good SC were not inoculated, nor was manure spread. Two diets were provided within each SC: control (CN) or supplemented (AA+) with Trp, Thr, and Met at 120% of NRC recommended levels. A principal component analysis was performed in R, and a feeding behavior index was calculated in SAS. Results showed that LW and HW pigs were clustered separately on d 0, where LW pigs had a positive correlation with body lipid percentage (r = 0.83), and HW pigs had a positive correlation with body protein percentage (r = 0.75). After the IC, the cluster configuration changed, with diets influencing LW more than HW pigs within poor SC. On d 14, LW fed AA+ diet in poor SC were clustered separately from LW pigs fed CN diet, whereas LW fed AA+ and CN diets in good SC were clustered together. For feeding behavior, in both analyzed periods (period 1: d 7 to 14; period 2: d 21 to 28), LW had lower total feed intake and shorter meals than HW pigs (P < 0.10), independent of the SC. Furthermore, LW pigs fed AA+ diet had a more regular feed intake pattern than those fed CN diet, while a more irregular pattern was observed for HW pigs fed AA+ diet than CN diet at period 2. These findings suggest that supplementing Trp, Thr, and Met above requirements may be a nutritional strategy for LW pigs under IC by improving feed intake regularity and reducing the probability of being susceptible to IC.