Search Results (58)

The article investigates the accuracy of nonparametric univariate density estimation methods applied to various Gaussian mixture models. A comprehensive comparative analysis is performed for four popular estimation approaches: adaptive kernel density estimation, projection pursuit, log-spline estimation, and wavelet-based estimation. The study is extended with modified versions of these methods, where the sample is first clustered using the EM algorithm based on Gaussian mixture components prior to density estimation. Estimation accuracy is quantitatively evaluated using MAE and MAPE criteria, with simulation experiments conducted over 100,000 replications for various sample sizes. The results show that estimation accuracy strongly depends on the density structure, sample size, and degree of component overlap. Clustering before density estimation significantly improves accuracy for multimodal and asymmetric densities. Although no formal statistical tests are conducted, the performance improvement is validated through non-overlapping confidence intervals obtained from 100,000 simulation replications. In addition, several decision-making systems are compared for automatically selecting the most appropriate estimation method based on the sample’s statistical features. Among the tested systems, kernel discriminant analysis yielded the lowest error rates, while neural networks and hybrid methods showed competitive but more variable performance depending on the evaluation criterion. The findings highlight the importance of using structurally adaptive estimators and automation of method selection in nonparametric statistics. The article concludes with recommendations for method selection based on sample characteristics and outlines future research directions, including extensions to multivariate settings and real-time decision-making systems. Full article

Roof water inrush accidents in coal mine driving roadways occur frequently in China, accounting for a high proportion of major coal mine water hazard accidents and causing serious losses. Aiming at the lack of research on the mechanism of roof water inrush in driving roadways and the difficulty of predicting water inrush accidents, this paper constructs a local damage criterion for coal–rock mass and a seepage–fracture coupling model based on peridynamics (PD) bond theory. It identifies three zones of water-conducting channels in roadway surrounding rock, the water fracture zone, the driving fracture zone, and the water-resisting zone, revealing that the damage degree of the water-resisting zone dominates the transformation mechanism between delayed and instantaneous water inrush. A discriminant function for the effectiveness of water-conducting channels is established, and a single-index prediction and evaluation system based on damage critical values is proposed. A “geometry damage” dual-control water inrush prediction model within the PD framework is constructed, along with a non-local action mechanism model and quantitative prediction method for water inrush. Case studies verify the threshold for delayed water inrush and criteria for instantaneous water inrush. The research results provide theoretical tools for roadway water exploration design and water hazard prevention and control. Full article

Background: Prevention and clinical management of musculoskeletal injuries have historically focused on the assessment and training of modifiable physical factors, but perceptual decision-making has only recently been recognized as a potentially important capability. Immersive virtual reality (VR) systems can measure the speed, accuracy, and consistency of body movements corresponding to stimulus–response instructions for the completion of a forced-choice task. Methods: A cohort of 26 female college soccer players (age 19.5 ± 1.3 years) included 10 players who participated in a baseline assessment, 10 perceptual-response training (PRT) sessions, a post-training assessment that preceded the first soccer practice, and a post-season assessment. The remaining 16 players completed an assessment prior to the team’s first pre-season practice session, and a post-season assessment. The assessments and training sessions involved left- or right-directed neck rotation, arm reach, and step-lunge reactions to 40 presentations of different types of horizontally moving visual stimuli. The PRT program included 4 levels of difficulty created by changes in initial stimulus location, addition of distractor stimuli, and increased movement speed, with ≥90% response accuracy used as the criterion for training progression. Perceptual latency (PL) was defined as the time elapsed from stimulus appearance to initiation of neck rotation toward a peripheral virtual target. The speed–accuracy tradeoff was represented by Rate Correct per Second (RCS) of PL, and inconsistency across trials derived from their standard deviation for PL was represented by intra-individual variability (IIV). Perceptual Decision Efficiency (PDE) represented the ratio of RCS to IIV, which provided a single value representing speed, accuracy, and consistency. Statistical procedures included the bivariate correlation between RCS and IIV, dependent t-test comparisons of pre- and post-training metrics, repeated measures analysis of variance for group X session pre- to post-season comparisons, receiver operating characteristic analysis, and Kaplan–Meier time to injury event analysis. Results: Statistically significant (p < 0.05) results were found for pre- to post-training change, and pre-season to post-season group differences, for RCS, IIV, and PDE. An inverse logarithmic relationship was found between RCS and IIV (Spearman’s Rho = −0.795). The best discriminator between injured and non-injured statuses was PDE ≤ 21.6 (93% Sensitivity; 42% Specificity; OR = 9.29). Conclusions: The 10-session PRT program produced significant improvement in perceptual decision-making that appears to provide a transfer benefit, as the PDE metric provided good prospective prediction of musculoskeletal injury. Full article

Background: Locally acting gastrointestinal (GI) drugs present challenges for generic drug development because traditional bioequivalence measures, which rely on systemic drug levels, do not reflect local efficacy. This review examines regulatory guidelines for establishing therapeutic equivalence for such drugs, using rifaximin—a minimally absorbed, gut-localized antibiotic—as a case study. Methods: We reviewed bioequivalence guidelines from the United States Food and Drug Administration (FDA) and European Medicines Agency (EMA), along with the literature on rifaximin’s biopharmaceutical and clinical properties, to identify strategies and challenges for establishing equivalence for locally acting GI drugs. Results: Rifaximin exemplifies the limitations of standard bioequivalence methods: as a Biopharmaceutics Classification System (BCS) class IV drug with minimal absorption and low solubility, in vitro dissolution may not predict local drug availability. Clinical endpoint trials (e.g., traveler’s diarrhea, hepatic encephalopathy, IBS-D) are resource-intensive and insensitive to formulation differences. Pharmacokinetic (PK) studies in healthy volunteers show low, variable plasma levels, which may inaccurately discriminate between formulations. The EMA requires evidence of non-saturable absorption to accept PK data, a difficult-to-establish but potentially irrelevant criterion. Differences between FDA and EMA approaches highlight a lack of harmonization, complicating global generic development. Conclusions: A tailored, multifaceted approach is needed to demonstrate bioequivalence for GI-localized drugs like rifaximin. This case underscores the need for more sensitive surrogate methods (e.g. advanced in vitro or pharmacodynamic models) and flexible regulatory criteria. Harmonization across international guidelines and innovative bioequivalence study designs are key to facilitating the approval of safe and effective generic alternatives in this drug class. Full article

Background/Objectives: The aim of this study was to identify response prediction and prognostic biomarkers in muscle-invasive bladder cancer (MIBC) patients undergoing neoadjuvant chemotherapy (NAC). Methods: A retrospective multicentre study including 191 patients with MIBC who received NAC previous to radical cystectomy (RC) between 1996 and 2013. Gene expression patterns were analysed in 34 samples from transurethral resection of the bladder (TURB) using Illumina microarrays. The expression levels of 45 selected differentially expressed genes between responders and non-responders to NAC were validated by quantitative PCR in an independent cohort of 157 patients. Regression analysis was used to identify predictors of downstaging and relapse. A nomogram for predicting downstaging and relapse—including clinicopathological and gene expression variables—was developed. Results: The expression levels of 1352 transcripts differed between responders and non-responders to NAC. A nomogram based on the most predictive clinical variables (age, Tis (in situ), gender, history of NMIBC, and lymphadenopathy) and genes selected following the Akaike information criterion (AIC) (CBTB16, CHMP6, DDX54, CASP8, LOR, and PLEC) was then created. In addition, a three-gene expression prognostic model to predict tumour relapse was generated. This model was able to discriminate between two groups of patients with a significantly different probability of tumour relapse (HR: 2.11; CI: 1.16–3.83, p = 0.01). Conclusions: Our nomogram based on gene expression and clinical data is a useful tool to predict downstaging and tumour relapse after NAC in MIBC patients. Further validation is warranted. Full article

Background: The LI-RADS classification is widely used for the hepatocellular carcinoma (HCC) risk stratification of liver nodules in cirrhotic patients. The evaluation of nodule enhancement, which is a major criterion, commonly relies on qualitative assessment. This study aims to investigate the potential role of material density (MD) parameters in the iodine maps of spectral computed tomography (SCT) to discriminate between LI-RADS (v2018 CORE) categories in cirrhotic patients. Methods: Dual-energy SCT scans of cirrhotic patients with suspected HCC, taken between March 1st, 2022 and September 30th, 2023, were retrospectively reviewed. All the images were reviewed by trained radiologists to classify nodules as LI-RADS 3, 4, or 5 by consensus. MD maps were generated in the hepatic arterial phase (HAP), portal venous phase (PVP), and equilibrium phase (EP). The iodine concentration density (ICD) values of nodules (ICDnodule) and the non-nodular liver parenchyma (ICDliver) were measured to calculate lesion-to-non-nodular liver ICD ratio (LNR), as well as their differences (ΔICD) and ratios (rLNR). Results were correlated with LI-RADS categories. Results: A total of 69 patients were included and 79 DECT exams were assessed. Overall, 197 nodules (size 24.67 ± 23.11 mm, mean ± SD) were categorized into different LI-RADS classes: 44 were classed as LI-RADS 3 (22.3%), 14 were classed as LI-RADS 4 (7.1%), and 139 were classed as LI-RADS 5 (70.6%). The arterial LNR, arterial ICDnodule, ΔICD, and rLNR between HAP and PVP discriminated between LI-RADS 3 and LI-RADS 4+5 nodules (p < 0.001). All the calculated MD parameters showed high diagnostic accuracy rates (all AUCs = 70–73%). Conclusions: MD parameters of liver nodules measured in SCT scans are viable diagnostic tools that may increase the radiologist’s confidence in LI-RADS class allocation in cirrhotic patients. This preliminary and speculative study can serve as a baseline for the potential quantification of iodine concentrations of focal liver lesions to reduce subjectivity in hepatic nodule assessment and reporting. Future perspectives include the quantification of iodine concentration for prognostic stratification before locoregional and systemic treatments in HCC patients. Full article

Non-negative Matrix Factorization (NMF) has gained popularity due to its effectiveness in clustering and feature selection tasks. It is particularly valuable for managing high-dimensional data by reducing dimensionality and providing meaningful semantic representations. However, traditional NMF methods may encounter challenges when dealing with noisy data, outliers, or when the underlying manifold structure of the data is overlooked. This paper introduces an innovative approach called SGRiT, which employs Stiefel manifold optimization to enhance the extraction of latent features. These learned features have been shown to be highly informative for clustering tasks. The method leverages a spectral decomposition criterion to obtain a low-dimensional embedding that captures the intrinsic geometric structure of the data. Additionally, this paper presents a solution for addressing the Stiefel manifold problem and utilizes a Riemannian-based trust region algorithm to optimize the loss function. The outcome of this optimization process is a new representation of the data in a transformed space, which can subsequently serve as input for the NMF algorithm. Furthermore, this paper incorporates a novel subspace graph regularization term that considers high-order geometric information and introduces a sparsity term for the factor matrices. These enhancements significantly improve the discrimination capabilities of the learning process. This paper conducts an impartial analysis of several essential NMF algorithms. To demonstrate that the proposed approach consistently outperforms other benchmark algorithms, four clustering evaluation indices are employed. Full article

Purpose: Interstitial cystitis/bladder pain syndrome (IC/BPS) is mysterious and difficult to diagnose without cystoscopic hydrodistention. This study aimed to explore non-invasive and highly reliable urine biomarkers to identify Hunner’s IC (HIC) and different non-Hunner’s IC (NHIC) subtypes. Methods: In total, 422 women with and without clinically diagnosed IC/BPS (n = 376 and 46, respectively) were retrospectively enrolled. Patients were diagnosed with HIC or NHIC by cystoscopic hydrodistention under anesthesia. Then, the maximal bladder capacity (MBC) and glomerulation grade were determined. Thirteen urine inflammatory cytokines, chemokines, and oxidative stress biomarkers based on the previously reported predictors of IC/BPS were assayed using commercial microsphere kits. The dataset was randomly divided into training (70%) and test (30%) sets for model construction and validation using logistic regression and stepwise variable selection techniques. To construct the predictive models, univariate analysis was performed to evaluate the discriminative power of each urinary biomarker, measured by the area under the curve (AUC). Biomarkers with AUC values < 0.6 were excluded from further modeling. Multivariate logistic regression was then employed, with variables selected through stepwise forward selection based on log-likelihood criteria. For dichotomization, cutoff values were determined using quartile ranges from the control group. The final model’s performance was assessed using AUC, accuracy, sensitivity, and specificity in both training and test sets. Results: By setting the screening criterion to AUC ≥ 0.60, the potential urinary biomarkers for identifying IC/BPS cases were eotaxin, monocyte chemoattractant protein-1, tumor necrosis factor-alpha (TNF-α), 8-hydroxy-2′-deoxyguanosine (8-OHdG), and 8-isoprostane. Those for identifying HIC from the IC/BPS cohort were interleukin (IL)-6, IL-8, interferon γ-inducible protein 10 (IP-10), and regulated on activation, normal T-cell expressed and secreted (RANTES). A diagnostic algorithm using a cluster of urinary biomarkers included TNF-α ≥ 0.95 pg/mL or 8-OHDG ≥ 22.34 pg/mL and 8-isoprastane ≥ 22.34 pg/mL for identifying IC/BPS from the overall cohort; for identifying HIC from the IC/BPS cohort, the urinary IP-10 ≥ 3.74 pg/mL or IP-10 ≥ 19.94 pg/mL was added. Conclusions: Using a cluster of urinary biomarkers such as TNF-α or 8-OHdG and 8-isoprostane can identify IC/BPS from a study cohort, and adding the urinary IP-10 can distinguish HIC from IC/BPS cases. Full article

Wuyi Rock tea (WRT), originating from the northern region of Fujian province, has a good reputation for its distinctive Yan flavor and floral–fruity aroma. The aroma quality, an essential element of the Yan flavor, undergoes various changes during the manufacturing process of WRT. To enhance the understanding of the formation patterns of WRT aroma and its influence on the flavor quality of WRT, we utilized both manufactured WRT (Rougui tea) and primary tea as materials. Utilizing a sensory evaluation, detection of volatile compounds, and multivariate statistical analysis, we identified and characterized the distinctive volatile components present in WRT. The sensory evaluation and radar chart analysis revealed that the primary tea exhibited a strong and lasting aroma, along with a mellow taste and a prominent Yan flavor. Through gas chromatography time-of-flight mass spectrometry (GC-TOF MS), a total of 251 volatile compounds were identified. The odor activity value (OAV) was calculated to identify key aroma-active compounds in the primary tea. The results indicated that a total of 14 compounds had an OAV greater than 1.0, including (2-nitroethyl) benzene, indole, and geranylacetone. These compounds exhibited floral and fruity aroma attributes. They primarily formed and accumulated during the latter stages of WRT. Using a partial least squares discrimination analysis (PLS-DA) combined with a variable importance in projection (VIP) score greater than 1.0 as a criterion, a total of 89 compounds were identified. Furthermore, out of the selected compounds, 15 types, including geraniol, 1-nonanol, and 1-butyl-2-ethyl-cyclopropene, were found to exclusively exist during the enzymatic manufacturing stages, particularly during the intermediate and later phases of the turn-over process (the last-three-times turn-over treatments), exhibiting predominantly floral and sweet fragrances. In contrast, during the non-enzymatic stages, only four compounds, such as pentanoic acid and phenylmethyl ester, were detected, exhibiting a fruity aroma profile. These volatile compounds significantly influenced the quality attributes of the final tea product, resulting in strong and lasting characteristics, particularly marked by a pronounced floral and fruity aroma. This study revealed how the aroma quality in WRT is developed and pinpointed possible volatile compounds that react to post-harvest treatments, thereby offering valuable insights relating to the intelligent production strategies of WRT. Full article

Recent advancements have been observed in the global legal framework, particularly in the recognition of Indigenous peoples’ rights, among which the right to self-determination stands out. This right encompasses the ability to define their political status and to pursue economic, social, and cultural development autonomously. The Inter-American Commission on Human Rights (IACHR) has identified six criteria to evaluate adherence to these rights. This study aims to assess the progress made by Chile, Colombia, and Mexico in meeting these criteria. Employing a non-experimental, cross-sectional, and exploratory research design, the study utilized a questionnaire based on a Likert scale to gather data from residents of Indigenous communities within the three countries under consideration to measure the perception that these individuals have regarding the compliance with the six standards of the IACHR, and specific questions have been designed based on the content of each standard. Once the degree of self-determination is understood, a multivariate analysis technique (Principal Component Analysis) will be used to compare the level of progress by country. The data collection instruments were applied in 2023 (September/November), with the results processed and the final report drafted in early 2024. Findings suggest that the criterion most adequately met is that of cultural identity and non-discrimination, whereas the criterion pertaining to the right to prior, free, and informed consultation and consent is the least fulfilled. Through principal component analysis, it was noted that Colombia exhibits a higher level of compliance with the right to self-determination, followed by Mexico, with Chile demonstrating a divergence and lagging in progress. The study concludes that a significant challenge for these countries lies in continuing efforts to effectively meet these standards and ensure the right to self-determination for Indigenous and tribal peoples. Full article

In recent years, the non-invasive load monitoring (NILM) method based on sparse coding has shown promising research prospects. This type of method learns a sparse dictionary for each monitoring target device, and it expresses load decomposition as a problem of signal reconstruction using dictionaries and sparse vectors. The existing NILM methods based on sparse coding have problems such as inability to be applied to multi-state and time-varying devices, single-load characteristics, and poor recognition ability for similar devices in distributed manners. Using the analysis above, this paper focuses on devices with similar features in households and proposes a distributed non-invasive load monitoring method using Karhunen–Loeve (KL) feature extraction and an improved deep dictionary. Firstly, Karhunen–Loeve expansion (KLE) is used to perform subspace expansion on the power waveform of the target device, and a new load feature is extracted by combining singular value decomposition (SVD) dimensionality reduction. Afterwards, the states of all the target devices are modeled as super states, and an improved deep dictionary based on the distance separability measure function (DSM-DDL) is learned for each super state. Among them, the state transition probability matrix and observation probability matrix in the hidden Markov model (HMM) are introduced as the basis for selecting the dictionary order during load decomposition. The KL feature matrix of power observation values and improved depth dictionary are used to discriminate the current super state based on the minimum reconstruction error criterion. The test results based on the UK-DALE dataset show that the KL feature matrix can effectively reduce the load similarity of devices. Combined with DSM-DDL, KL has a certain information acquisition ability and acceptable computational complexity, which can effectively improve the load decomposition accuracy of similar devices, quickly and accurately estimating the working status and power demand of household appliances. Full article

Background: The side hop test (SHT) measures the number of jumps performed over 30 s. Although this measure has demonstrated its value in clinical practice, the temporal parameters of the SHT allow for a deeper analysis of the execution strategy. The aim of this study is to assess the reliability and construct validity of contact time parameters during the SHT recorded by a video analysis system in anterior cruciate ligament reconstructed (ACLR) patients. Methods: We investigated the reliability (intra-rater, standard error of measurement (SEM), and minimum detectable change (MDC)), discriminant validity (operated (OP) versus non-operated (NOP) side), and convergent validity (relationship with strength and psychological readiness) of SHT contact time parameters, number of valid hops and limb symmetry index (LSI) in 38 ACLR patients. Contact time parameters are presented as mean, standard deviation (SD), and coefficient of variation (CV) of contact time. Results: Intra-tester reliability was good to excellent for all contact time parameters. For discriminant validity, the mean and SD contact times of the OP leg were significantly longer than those of the NOP leg, although the difference was smaller than the SEM and MDC values. The number of valid jumps and CV contact time parameters were not significantly different. Isokinetic quadriceps strength (60°/s) was strongly correlated with mean contact time for both legs. However, psychological readiness was not correlated with any of the contact time parameters. Conclusions: Temporal parameters of the SHT measured on video analysis are valid and reliable parameters to assess the performance strategy of the SHT. The results should be interpreted with caution regarding the SEM and MDC values. Further studies are needed to measure criterion validity, inter-rater reliability, and responsiveness. Full article

The genus Betanucleorhabdovirus includes plant viruses with negative sense, non-segmented, single-stranded RNA genomes. Here, we characterized putative novel betanucleorhabdoviruses infecting a medically important plant, elderberry. Total RNA was purified from the leaves of several plants, ribodepleted and sequenced using the Illumina platform. Sequence data analysis led to the identification of thirteen contigs of approximately 13.5 kb, showing a genome structure (3′-N-P-P3-M-G-L-5′) typical of plant rhabdoviruses. The detected isolates showed 69.4 to 98.9% pairwise nucleotide identity and had the highest identity among known viruses (64.7–65.9%) with tomato betanucleorhabdovirus 2. A detailed similarity analysis and a phylogenetic analysis allowed us to discriminate the elderberry isolates into five groups, each meeting the sequence-based ICTV demarcation criterion in the Betanucleorhabdovirus genus (lower than 75% identity for the complete genome). Hence, the detected viruses appear to represent five novel, closely related betanucleorhabdoviruses, tentatively named Sambucus betanucleorhabdovirus 1 to 5. Full article

This study aims to investigate the relationship between the grades of Tuo tea and the quality of compounds. A combination of artificial sensory evaluation, intelligent sensory technologies (electronic nose and electronic tongue), gas chromatography–mass spectrometry (GC-MS), high-performance liquid chromatography (HPLC), chemical–physical analysis, and multivariate statistical analysis were employed to examine the differences among three grades of Tuo tea (SG, 1G, and 2G). The results of artificial sensory evaluation, electronic tongue, and electronic nose revealed that the aroma and taste of different grades of Tuo tea varied greatly. A total of 112 volatile compounds and 44 non-volatile compounds were identified. In order to elucidate the key components that cause differences in the quality of Tuo tea, 2 partial least squares discriminant analysis (PLS-DA) models with excellent parameters (volatile, R²Y = 0.999 and Q² = 0.996; non-volatile, R²Y = 0.992 and Q² = 0.972) were established. A total of 80 key differential volatile compounds were identified with the double selection criterion of variable importance in projection (VIP) greater than 1 and p < 0.05. Among these, 43 compounds with OAV > 1 were further identified as the odor-active compounds in all three grades of Tuo. Moreover, 22 key non-volatile compounds that contribute to the quality differences have been screened out. This investigation implied that the volatile and non-volatile compounds of Tuo tea could serve as indicators of its quality. The results provided a new approach to distinguish the grades of Tuo tea. Full article

Background: This study addresses the significant challenge of low survival rates in patients with cause-specific lung cancer accompanied by bone or brain metastases. Recognizing the critical need for an effective predictive model, the research aims to establish survival prediction models using both parametric and non-parametric approaches. Methods: Clinical data from lung cancer patients with at least one bone or brain metastasis between 2000 and 2020 from the SEER database were utilized. Four models were constructed: Cox proportional hazard, Weibull accelerated failure time (AFT), log-normal AFT, and Zografos–Balakrishnan log-normal (ZBLN). Independent prognostic factors for cause-specific survival were identified, and model fit was evaluated using Akaike’s and Bayesian information criteria. Internal validation assessed predictive accuracy and discriminability through the Harriel Concordance Index (C-index) and calibration plots. Results: A total of 20,412 patients were included, with 14,290 (70%) as the training cohort and 6122 (30%) validation. Independent prognostic factors selected for the study were age, race, sex, primary tumor site, disease grade, total malignant tumor in situ, metastases, treatment modality, and histology. Among the accelerated failure time (AFT) models considered, the ZBLN distribution exhibited the most robust model fit for the 3- and 5-year survival, as evidenced by the lowest values of Akaike’s information criterion of 6322 and 79,396, and the Bayesian information criterion of 63,495 and 79,396, respectively. This outperformed other AFT and Cox models (AIC = [156,891, 211,125]; BIC = [158,848, 211,287]). Regarding predictive accuracy, the ZBLN AFT model achieved the highest concordance C-index (0.682, 0.667), a better performance than the Cox model (0.669, 0.643). The calibration curves of the ZBLN AFT model demonstrated a high degree of concordance between actual and predicted values. All variables considered in this study demonstrated significance at the 0.05 level for the ZBLN AFT model. However, differences emerged in the significant variations in survival times between subgroups. The study revealed that patients with only bone metastases have a higher chance of survival compared to only brain and those with bone and brain metastases. Conclusions: The study highlights the underutilized but accurate nature of the accelerated failure time model in predicting lung cancer survival and identifying prognostic factors. These findings have implications for individualized clinical decisions, indicating the potential for screening and professional care of lung cancer patients with at least one bone or brain metastasis in the future. Full article