Tag: nevin manimala

Proc Biol Sci. 2024 Apr 10;291(2020):20240250. doi: 10.1098/rspb.2024.0250. Epub 2024 Apr 3.

ABSTRACT

Communication needs to be complex enough to be functional while minimizing learning and production costs. Recent work suggests that the vocalizations and gestures of some songbirds, cetaceans and great apes may conform to linguistic laws that reflect this trade-off between efficiency and complexity. In studies of non-human communication, though, clustering signals into types cannot be done a priori, and decisions about the appropriate grain of analysis may affect statistical signals in the data. The aim of this study was to assess the evidence for language-like efficiency and structure in house finch (Haemorhous mexicanus) song across three levels of granularity in syllable clustering. The results show strong evidence for Zipf’s rank-frequency law, Zipf’s law of abbreviation and Menzerath’s law. Additional analyses show that house finch songs have small-world structure, thought to reflect systematic structure in syntax, and the mutual information decay of sequences is consistent with a combination of Markovian and hierarchical processes. These statistical patterns are robust across three levels of granularity in syllable clustering, pointing to a limited form of scale invariance. In sum, it appears that house finch song has been shaped by pressure for efficiency, possibly to offset the costs of female preferences for complexity.

PMID:38565151 | DOI:10.1098/rspb.2024.0250

J Neural Eng. 2024 Apr 2. doi: 10.1088/1741-2552/ad3987. Online ahead of print.

ABSTRACT

Objective.The study of emotion recognition through electroencephalography (EEG) has garnered significant attention recently. Integrating EEG with other peripheral physiological signals may greatly enhance performance in emotion recognition. Nonetheless, existing approaches still suffer from two predominant challenges: modality heterogeneity, stemming from the diverse mechanisms across modalities, and fusion credibility, which arises when one or multiple modalities fail to provide highly credible signals.Approach.In this paper, we introduce a novel multimodal physiological signal fusion model that incorporates both intra-inter modality reconstruction and sequential pattern consistency, thereby ensuring a computable and credible EEG-based multimodal emotion recognition. For the modality heterogeneity issue, we first implement a local self-attention transformer to obtain intra-modal features for each respective modality. Subsequently, we devise a pairwise cross-attention transformer to reveal the inter-modal correlations among different modalities, thereby rendering different modalities compatible and diminishing the heterogeneity concern. For the fusion credibility issue, we introduce the concept of sequential pattern consistency to measure whether different modalities evolve in a consistent way. Specifically, we propose to measure the varying trends of different modalities, and compute the inter-modality consistency scores to ascertain fusion credibility.Main results.We conduct extensive experiments on two benchmarked datasets (DEAP and MAHNOB-HCI) with the subject-dependent paradigm. For the DEAP dataset, our method improves the accuracy by 4.58%, and the F1 score by 0.63%, compared to the state-of-the-art baseline. Similarly, for the MAHNOB-HCI dataset, our method improves the accuracy by 3.97%, and the F1 score by 4.21%. In addition, we gain much insight into the proposed framework through significance test, ablation experiments, confusion matrices and hyperparameter analysis. Consequently, we demonstrate the effectiveness of the proposed credibility modelling through statistical analysis and carefully designed experiments.Significance.All experimental results demonstrate the effectiveness of our proposed architecture and indicate that credibility modelling is essential for multimodal emotion recognition.

PMID:38565099 | DOI:10.1088/1741-2552/ad3987

Kidney Blood Press Res. 2024 Apr 2. doi: 10.1159/000538506. Online ahead of print.

ABSTRACT

Objective To investigate whether immunosuppressive therapy is beneficial in IgA nephropathy (IgAN) patients with eGFR < 45ml/min/1.73m2. Methods This retrospective study involved 110 IgAN patients for whom clinical data was available; of these, 90 had complete follow-up data. Patients were grouped based on whether they received immunotherapy during follow-up, their renal function, proteinuria levels, and the percentage of crescentic glomeruli observed at the time of renal biopsy. Results The mean eGFR for the participants was 32.0 ± 10.2 ml/min/1.73 m². The average follow-up duration was 46.1 ± 37.9 months. The mean rate of decline in eGFR was 3.6 ml/min/1.73 m² per year. There were 43 (47.8%) composite kidney endpoint occurred in these patients. In the group that received immunotherapy, the incidence of kidney endpoint events was lower than in the untreated group (45.1% vs. 57.9%), but the difference was not statistically significant (P = 0.320). Among patients with stage CKD 3b, the incidence of endpoint events was lower than in those with stages CKD 4 and 5 (36.8% vs. 66.7%, P = 0.006). Conversely, the high proteinuria group saw a higher incidence of endpoint events compared to the low proteinuria group (51.9% vs. 23.1%), although this difference was not statistically significant (P = 0.054). Meanwhile, there was no significant difference in the incidence of endpoint events between the two crescent glomerular ratio groups (48.7% vs. 41.7%, P = 0.649). Kaplan-Meier survival analysis indicated that renal function level (P＜0.001) and proteinuria (P = 0.023) were associated with renal survival in IgAN patients. In contrast, the administration of immunosuppressive therapy (P = 0.288) and the prevalence of C lesions (P = 0.982) did not show a significant association with renal survival. Further, Cox regression analysis identified systolic blood pressure, fibrinogen, and CKD stage as risk factors for eGFR decline in IgAN patients (all P < 0.05). Conclusions IgAN patients with stage 3b-5 CKD exhibited a poor prognosis. It appears that in this specific cohort of IgAN patients, immunosuppressive therapy may not provide significant advantages over supportive care therapeutic regimens in terms of disease management.

PMID:38565098 | DOI:10.1159/000538506

Neonatology. 2024 Apr 2:1-8. doi: 10.1159/000538134. Online ahead of print.

ABSTRACT

INTRODUCTION: Increased fetal heart rate variability (IFHRV), defined as fetal heart rate (FHR) baseline amplitude changes of &gt;25 beats per minute with a duration of ≥1 min, is an early sign of intrapartum fetal hypoxia. This study evaluated the level of agreement of machine learning (ML) algorithms-based recognition of IFHRV patterns with expert analysis.

METHODS: Cardiotocographic recordings and cardiotocograms from 4,988 singleton term childbirths were evaluated independently by two expert obstetricians blinded to the outcomes. Continuous FHR monitoring with computer vision analysis was compared with visual analysis by the expert obstetricians. FHR signals were graphically processed and measured by the computer vision model labeled SALKA.

RESULTS: In visual analysis, IFHRV pattern occurred in 582 cardiotocograms (11.7%). Compared with visual analysis, SALKA recognized IFHRV patterns with an average Cohen’s kappa coefficient of 0.981 (95% CI: 0.972-0.993). The sensitivity of SALKA was 0.981, the positive predictive rate was 0.822 (95% CI: 0.774-0.903), and the false-negative rate was 0.01 (95% CI: 0.00-0.02). The agreement between visual analysis and SALKA in identification of IFHRV was almost perfect (0.993) in cases (N = 146) with neonatal acidemia (i.e., umbilical artery pH &lt;7.10).

CONCLUSIONS: Computer vision analysis by SALKA is a novel ML technique that, with high sensitivity and specificity, identifies IFHRV features in intrapartum cardiotocograms. SALKA recognizes potential early signs of fetal distress close to those of expert obstetricians, particularly in cases of neonatal acidemia.

PMID:38565092 | DOI:10.1159/000538134

Gynecol Obstet Invest. 2024 Apr 2. doi: 10.1159/000538374. Online ahead of print.

ABSTRACT

OBJECTIVES: To evaluate the effect of maternal age and body mass index (BMI) on oral misoprostol induction of labor for late-term pregnancies.

DESIGN: Retrospective cross-sectional study (ClinicalTrial iD: NCT06184139), including only late-term pregnancies in healthy nulliparous women and single cephalic fetus with normal birthweight. Specify the type of study (randomized, prospective cohort, case-control, other) and include the number of study subjects (cases/controls), treatment type and duration, sampling procedures if applicable.

PARTICIPANTS/MATERIALS, SETTING, METHODS: One-hundred-and-four pregnant women underwent induction of labor with oral misoprostol for late-term pregnancy on the 290th day of gestation. Study population was divided in two groups based on age (<35 and ≥35 years) and obesity (BMI <30 and ≥30). Statistical analysis was performed using SPSS V.21.0 (IBM Corporation, Armonk, NY). The inclusion of 51 women from each of the two arms achieved 80% power with an alpha error of 0.05. Continuous variables were expressed as the mean and standard deviation (SD). Categorical variables are expressed as frequencies and percentages. Results No statistically significant differences were recorded between younger and older women. Obese women reported a longer time between the last dose of misoprostol and cervical dilation of 6 cm (p=0.01), a longer time between the last dose of misoprostol and delivery (p=0.04), and a higher rate of grade II vaginal lacerations (p=0.02). Limitations While this study contributes novel insights into cervical ripening and labor induction using oral misoprostol for late-term pregnancies, its scope is limited by the retrospective study design, inherently carrying biases compared to prospective approaches, and the limited sample size within the study cohort. Conclusions Maternal BMI is a factor negatively influencing the efficacy of oral misoprostol for induction of labor in late-term pregnancy.

PMID:38565086 | DOI:10.1159/000538374

Spectrochim Acta A Mol Biomol Spectrosc. 2024 Mar 26;314:124185. doi: 10.1016/j.saa.2024.124185. Online ahead of print.

ABSTRACT

Lung cancer is the most common cancer and the leading cause of death in China. The current gold standard for clinical lung cancer diagnosis is based on histopathological examination of tumors, but it has the limitation for easy operation and convenient applications. Therefore, researchers are still striving to develop other tools and methods for non-invasive and rapid assessment of the health conditions of lung cancer patients. Hair, as a reflection of the metabolism of the body, is closely related to human health conditions. In principle, Fourier-transform infrared (FTIR) spectroscopy can probe the major chemical compositions in the hair. However, as indicated by previous studies, there is still the challenge to make good use of FTIR spectroscopy for achieving reliable analysis of hair from cancer patients. In this study, hair samples from 82 lung cancer patients were collected and subjected to FTIR measurements and analysis, which showed the protein content in the hair is closely related to the protein content in the blood serum of patients, and the contents of protein and lipid are statistically lower in the lung cancer patients. Furthermore, we demonstrated that FTIR spectroscopy could be employed to monitor the hair of lung cancer patients undergoing chemotherapy, and confirmed that the FTIR spectra of the hair may reflect the resultant effect of the chemotherapy. As such, this work validates the way of using FTIR spectroscopy in hair analysis for the assistance of medical diagnosis of lung cancer as well as monitoring the conditions of the patients under the medical treatment.

PMID:38565049 | DOI:10.1016/j.saa.2024.124185

Knee. 2024 Apr 1;48:94-104. doi: 10.1016/j.knee.2024.03.005. Online ahead of print.

ABSTRACT

BACKGROUND: The aims were to assess whether a specific subgroup(s) of patients had a clinically significant benefit in their knee specific outcome or health-related quality of life (HRQoL) when undergoing robotic total knee arthroplasty (rTKA) when compared to manually performed TKA (mTKA).

METHODS: One hundred patients were randomised to either rTKA or mTKA, 50 to each group, of which 46 and 41 were available for functional review at 6-months, respectively. Subgroup analysis was undertaken for sex, age (<67-years versus ≥ 67-years), preoperative WOMAC score (<40 versus ≥ 40) and EQ-5D utility (<0.604 versus ≥ 0.604).

RESULTS: Male patients undergoing rTKA had a clinically and statistically significant greater improvement in WOMAC pain (mean difference (MD) 16.3, p = 0.011) at 2-months, function (MD 12.6, p = 0.032) and total score (MD 12.7, p = 0.030), and OKS (MD 6.0, p = 0.030) at 6-months. Patients < 67-years old undergoing rTKA had a clinically and statistically significant greater improvement in WOMAC pain (MD 10.3, p = 0.039) at 2-months, and function (MD 12.9, p = 0.040) and total (MD 13.1, p = 0.038) scores at 6-months. Patients with a preoperative WOMAC total score of < 40 points undergoing rTKA had a clinically and statistically significant greater improvement in WOMAC pain (MD 14.6, p = 0.044) at 6-months. Patients with a preoperative EQ-5D utility of <0.604 undergoing rTKA had a clinically and statistically significant greater improvement in WOMAC pain (MD 15.5, p = 0.011) at 2-months.

CONCLUSION: Patients of male sex, younger age, worse preoperative knee specific function and HRQoL had a clinically significantly better early functional outcome with rTKA when compared to mTKA.

PMID:38565038 | DOI:10.1016/j.knee.2024.03.005

J Environ Manage. 2024 Apr 1;357:120721. doi: 10.1016/j.jenvman.2024.120721. Online ahead of print.

ABSTRACT

Accurate and frequent nitrate estimates can provide valuable information on the nitrate transport dynamics. The study aimed to develop a data-driven modeling framework to estimate daily nitrate concentrations at low-frequency nitrate monitoring sites using the daily nitrate concentration and stream discharge information of a neighboring high-frequency nitrate monitoring site. A Long Short-Term Memory (LSTM) based deep learning (DL) modeling framework was developed to predict daily nitrate concentrations. The DL modeling framework performance was compared with two well-established statistical models, including LOADEST and WRTDS-Kalman, in three selected basins in Iowa, USA: Des Moines, Iowa, and Cedar River. The developed DL model performed well with NSE >0.70 and KGE >0.70 for 67% and 79% nitrate monitoring sites, respectively. DL and WRTDS-Kalman models performed better than the LOADEST in nitrate concentration and load estimation for all low-frequency sites. The average NSE performance of the DL model in daily nitrate estimation is 20% higher than that of the WRTDS-Kalman model at 18 out of 24 sites (75%). The WRTDS-Kalman model showed unrealistic fluctuations in the estimated daily nitrate time series when the model received limited observed nitrate data (less than 50) for simulation. The DL model indicated superior performance in winter months’ nitrate prediction (60% of cases) compared to WRTDS-Kalman models (33% of cases). The DL model also better represented the exceedance days from the USEPA maximum contamination level (MCL). Both the DL and WRTDS-Kalman models demonstrated similar performance in annual stream nitrate load estimation, and estimated values are close to actual nitrate loads.

PMID:38565027 | DOI:10.1016/j.jenvman.2024.120721

J Hazard Mater. 2024 Mar 29;470:134159. doi: 10.1016/j.jhazmat.2024.134159. Online ahead of print.

ABSTRACT

Household air pollution prevails in rural residences across China, yet a comprehensive nationwide comprehending of pollution levels and the attributable disease burdens remains lacking. This study conducted a systematic review focusing on elucidating the indoor concentrations of prevalent household air pollutants-specifically, PM2.5, PAHs, CO, SO₂, and formaldehyde-in rural Chinese households. Subsequently, the premature deaths and economic losses attributable to household air pollution among the rural population of China were quantified through dose-response relationships and the value of statistical life. The findings reveal that rural indoor air pollution levels frequently exceed China’s national standards, exhibiting notable spatial disparities. The estimated annual premature mortality attributable to household air pollution in rural China amounts to 966 thousand (95% CI: 714-1226) deaths between 2000 and 2022, representing approximately 22.2% (95% CI: 16.4%-28.1%) of total mortality among rural Chinese residents. Furthermore, the economic toll associated with these premature deaths is estimated at 486 billion CNY (95% CI: 358-616) per annum, constituting 0.92% (95% CI: 0.68%-1.16%) of China’s GDP. The findings quantitatively demonstrate the substantial disease burden attributable to household air pollution in rural China, which highlights the pressing imperative for targeted, region-specific interventions to ameliorate this pressing public health concern.

PMID:38565018 | DOI:10.1016/j.jhazmat.2024.134159

J Hazard Mater. 2024 Mar 21;470:134076. doi: 10.1016/j.jhazmat.2024.134076. Online ahead of print.

ABSTRACT

Recently, the rampant administration of antibiotics and their synthetic organic constitutes have exacerbated adverse effects on ecosystems, affecting the health of animals, plants, and humans by promoting the emergence of extreme multidrug-resistant bacteria (XDR), antibiotic resistance bacterial variants (ARB), and genes (ARGs). The constraints, such as high costs, by-product formation, etc., associated with the physico-chemical treatment process limit their efficacy in achieving efficient wastewater remediation. Biodegradation is a cost-effective, energy-saving, sustainable alternative for removing emerging organic pollutants from environmental matrices. In view of the same, the current study aims to explore the biodegradation of ciprofloxacin using microbial consortia via metabolic pathways. The optimal parameters for biodegradation were assessed by employing machine learning tools, viz. Artificial Neural Network (ANN) and statistical optimization tool (Response Surface Methodology, RSM) using the Box-Behnken design (BBD). Under optimal culture conditions, the designed bacterial consortia degraded ciprofloxacin with 95.5% efficiency, aligning with model prediction results, i.e., 95.20% (RSM) and 94.53% (ANN), respectively. Thus, befitting amendments to the biodegradation process can augment efficiency and lead to a greener solution for antibiotic degradation from aqueous media.

PMID:38565014 | DOI:10.1016/j.jhazmat.2024.134076