Browse Articles

Icaritin alleviates motor impairment and osteoporosis in Parkinson’s disease mice via the ER-PI3K/Akt pathway

A sensitive determination of lead by hydride generation integrated with micro-sampling gas–liquid separator-FAAS after preconcentration by NiFe2O4 nanoparticles

Empathy and helping: the role of affect in response to others’ suffering

An automatic cervical cell classification model based on improved DenseNet121

Predictors, patterns, and correlates of moderate-severe psychological distress among New York City College Students during Waves 2–4 of COVID-19

Abstract The COVID-19 pandemic may have exacerbated mental health conditions by introducing and/or modifying stressors, particularly in university populations. We examined longitudinal patterns, time-varying predictors, and contemporaneous correlates of moderate-severe psychological distress (MS-PD) among college students. During 2020–2021, participants completed self-administered questionnaires quarterly (T1 = 562, T2 = 334, T3 = 221, and T4 = 169). MS-PD reflected Kessler-6 scores ≥ 8. At T1 (baseline), most participants were cisgender women [96% vs. 4% transgender/gender non-conforming (TGNC)]. MS-PD prevalence was over 50% at all timepoints. MS-PD predictors included low self-rated health and perceptions of local pandemic control, verbal/physical violence experience, food insecurity, cohabitation dynamics, geographic location, and loneliness. Unique MS-PD correlates encompassed drug use and TGNC identity. Trajectories comprised Persistently (40%), Highly (24% MS-PD twice/thrice), Minimally (15% MS-PD once), and Never (21%) Distressed. Persistently Distressed students had low social support and self-rated health; high food insecurity, drug use, physical/verbal violence experience, need-based financial aid, and TGNC representation; and fluctuating self-rated health amid increasing COVID-19 symptomatology. In this sample, MS-PD prevalence was high, persistent, and associated with financial, behavioral, structural, experiential, and intra- and inter-personal factors. Given its complexity, improving and preserving college students’ mental health necessitates comprehensive, multi-component activities to change adjustable stressors while attenuating the adverse effects of immutable influences.

Multi-sequence magnetic resonance imaging radiomics combined with imaging features predicts the difficulty of HIFU treatment of uterine fibroids

Temperature dependence of liverwort diversification reveals a cool origin and hot hotspots

Abstract The evolutionary history underlying gradients in species richness is still subject to discussions and understanding the past niche evolution might be crucial in estimating the potential of taxa to adapt to changing environmental conditions. In this study we intend to contribute to elucidation of the evolutionary history of liverwort species richness distributions along elevational gradients at a global scale. For this purpose, we linked a comprehensive data set of genus occurrences on mountains worldwide with a time-calibrated phylogeny of liverworts and estimated mean diversification rates (DivElev) and mean ages (AgeElev) of the respective genera per elevational band. In addition, we reconstructed the ancestral temperature preferences of the genera. We found that diversification rates increase linearly with temperature, and hence decrease with elevation. This pattern is mainly driven by epiphytic genera. In contrast, overall genus age is highest at intermediate elevations where liverwort species richness peaks and decreases towards both ends of the elevational and thermal gradient. Our results further indicate that the ancestral lineages from which the extant liverwort genera descended had a preference for cool and humid habitats. We conclude that the extant liverwort species diversity accumulated over long time under these climatic conditions, which are today prevailing at mid-elevations of the world’s mountains. Subsequently, liverworts expanded their ranges from these temperate areas towards warm (with high diversification rates) and cold regions (with low diversification rates), located in contemporaneous (tropical) lowlands and high mountains, respectively. The conserved preference for temperate climates shared by the majority of liverwort lineages gives reason to the assumption that they will not be able to cope with the conditions induced by rapid climate warming, whereas the current low-elevation radiation may be less affected by climate change.

Comparative analysis of efficacy and quality of life between totally extraperitoneal sublay and intraperitoneal onlay mesh repair for ventral hernia

Preoperative myosteatosis and perioperative serum chloride levels predict 180 day major complications after radical cystectomy

The diagnostic value of cytology in the mammary intraductal lesions of patients with pathological nipple discharge

Predicting land use and land cover changes for sustainable land management using CA-Markov modelling and GIS techniques

Research on attenuation rate correlation calibration method based on acoustic variable density logging

Leveraging two-dimensional pre-trained vision transformers for three-dimensional model generation via masked autoencoders

Potential value of novel multiparametric MRI radiomics for preoperative prediction of microsatellite instability and Ki-67 expression in endometrial cancer

Ultrasonic emulsification of Cananga odorata nanoemulsion formulation for enhancement of herbicidal potential

Abstract A nanoemulsion was fabricated from Cananga odorata essential oil (EO) and stabilized by incorporation of Tween 80 using ultrasonication. The major constituents of the EO were benzyl benzoate, linalool, and phenylmethyl ester. Differing sonication amplitude (20–60%) and time (2–10 min) were assessed for effects on nanoemulsion droplet size and polydispersity index (PI). The smallest droplet size of 43.98 nm (PI 0.222) was obtained using 40% amplitude for 8 min; this nanoemulsion was evaluated for its droplet characteristics and pre-emergence herbicidal activities on Amaranthus tricolor. FT-IR confirmed ultrasonic emulsification to not affect the EO components. Regarding stability, storage at 4 °C was determined appropriate, with droplet size changing slightly after five weeks. Assays of herbicidal potential showed the coarse emulsion and nanoemulsion to both reduce A. tricolor germination and growth, with the nanoemulsion being more effective at a given concentration and the difference in effectivity correlating to droplet size. Remarkably, treatment with 250 ppm nanoemulsion and coarse emulsion respectively resulted in 100% and 63.75% germination inhibition. Both emulsions decreased seed imbibition and alpha-amylase activity. The highest relative electrolyte leakage was achieved in seed treated with the nanoemulsion. Therefore, this ultrasonic-based nanoemulsion may have utility as bioherbicide alternative.

Improvement of flipped classroom teaching in colleges and universities based on virtual reality assisted by deep learning

Trace elements in fish species from the Punjnad headworks: Bioaccumulation and human health risk assessment

Aquatic toxicology, as a result of industrial and agrieqcultural effluences, has become a global concern impacting not only the well-being of aquatic organisms but human health as well. The current study evaluated the impact of four toxic trace elements (TTEs) Cadmium (Cd), copper (Cu), lead (Pb), and nickel (Ni) in three organs (liver, gills, and muscles) of five fish species viz, Rita rita, Sperata sarwari, Wallago attu, Mastacembelus armatus, and Cirrhinus mrigala collected from right and left banks of Punjnad headworks during winter, spring, and summer. We investigated the accumulation (mg/kg) of these TTEs in fish in addition to the human health risk assessment. The obtained results showed that W. attu accumulated significantly more TTEs (p < 0.00) as compared to other fish. Among seasons, summer had significantly more (p < 0.00) accumulation of TTEs than other seasons. Lead (Pb) accumulation was highest across TTEs in fish liver as compared to gills and muscles. The right bank showed higher accumulation (p < 0.00) of all TTEs in all fish species compared to the left bank. The human health risk assessment showed that Cd and Pb had higher exposure levels than Cu and Ni. Furthermore, the THQ was in the order of Cd > Pb > Ni > Cu. All fish species had THQ 1 for Cd and Pb and TTHQ > 1 for all fish. MPI index showed moderate to high levels of TTE contamination in all fish species. The study concluded that the right bank has higher metal accumulation than the left bank. However, fish consumption from both study sites was unsafe for human consumption. Further studies are required to evaluate the contamination of other trace elements in the aquatic ecosystem of the current site. This study will be useful for policymakers and the water department to take necessary counteractions to reduce the impact of TTEs at the study site.

A novel feature extraction method based on dynamic handwriting for Parkinson’s disease detection

Parkinson’s disease (PD) is a common disease of the elderly. Given the easy accessibility of handwriting samples, many researchers have proposed handwriting-based detection methods for Parkinson’s disease. Extracting more discriminative features from handwriting is an important step. Although many features have been proposed in previous researches, the insight analysis of the combination of handwriting’s kinematic, pressure, and angle dynamic features is lacking. Moreover, most existing feature is incompletely represented, with feature information lost. Therefore, to solve the above problems, a new feature extraction approach for PD detection is proposed using handwriting. First, built on the kinematic, pressure, and angle dynamic features, we propose a moment feature by composed these three types of features, an overall representation of these three types of features information. Then, we proposed a feature extraction method to extract time-frequency-based statistical (TF-ST) features from dynamic handwriting features in terms of their temporal and frequency characteristics. Finally, we proposed an escape Coati Optimization Algorithm (eCOA) for global optimization to enhance classification performance. Self-constructed and public datasets are used to verify the proposed method’s effectiveness respectively. The experimental results showed an accuracy of 97.95% and 98.67%, a sensitivity of 98.15% (average) and 97.78%, a specificity of 99.17% (average) and 100%, and an AUC of 98.66% (average) and 98.89%. The code is available at https://github.com/dreamhcy/MLforPD.

The impact of climate change on Korea’s agricultural sector under the national self-sufficiency policy

Evolving environmental conditions due to climate change have brought about changes in agriculture, which is required for human life as both a source of food and income. International trade can act as a buffer against potential negative impacts of climate change on crop yields, but recent years have seen breakdowns in global trade, including export bans to improve domestic food security. For countries that rely heavily on imported food, governments may institute policies to protect their agricultural industry from changes in climate-induced crop yield changes and other countries’ potential trade restrictions. This study assesses the individual and combined effects of climate impacts and food self-sufficiency policies in Korea, which is highly dependent on imports. We use the Global Change Analysis Model (GCAM), a global integrated assessment model, to explore (1) the direct impact of climate change on Korea’s agricultural yields, (2) the full impacts of global climate change on agricultural production, including trade-induced changes due to yield changes in other regions, (3) the impacts of food self-sufficiency policy, and (4) the interactive impact of climate change and self-sufficiency policies. We find that, in Korea, the direct impact of climate change on agricultural yields would be overshadowed by the impact of global climate change due to changing trade patterns. Second, global climate change leads to a rise (rice and wheat) or a decline (soybeans) in Korean producer revenues, while simultaneously raising consumer expenditures on both staples and non-staples. Third, implementing self-sufficiency policies for wheat and soybeans in Korea boosts the nation’s producer revenues, in conjunction with the effects of climate change, at the cost of additional increases in consumer expenditures for both staples and non-staples.

Time series segmentation for recognition of epileptiform patterns recorded via microelectrode arrays in vitro

Epilepsy is a prevalent neurological disorder that affects approximately 1% of the global population. Approximately 30-40% of patients respond poorly to antiepileptic medications, leading to a significant negative impact on their quality of life. Closed-loop deep brain stimulation (DBS) is a promising treatment for individuals who do not respond to medical therapy. To achieve effective seizure control, algorithms play an important role in identifying relevant electrographic biomarkers from local field potentials (LFPs) to determine the optimal stimulation timing. In this regard, the detection and classification of events from ongoing brain activity, while achieving low power consumption through computationally inexpensive implementations, represents a major challenge in the field. To address this challenge, we here present two algorithms, the ZdensityRODE and the AMPDE, for identifying relevant events from LFPs by utilizing time series segmentation (TSS), which involves extracting different levels of information from the LFP and relevant events from it. The algorithms were validated validated against epileptiform activity induced by 4-aminopyridine in mouse hippocampus-cortex (CTX) slices and recorded via microelectrode array, as a case study. The ZdensityRODE algorithm showcased a precision and recall of 93% for ictal event detection and 42% precision for interictal event detection, while the AMPDE algorithm attained a precision of 96% and recall of 90% for ictal event detection and 54% precision for interictal event detection. While initially trained specifically for detecting ictal activity, these algorithms can be fine-tuned for improved interictal detection, aiming at seizure prediction. Our results suggest that these algorithms can effectively capture epileptiform activity, supporting seizure detection and, possibly, seizure prediction and control. This opens the opportunity to design new algorithms based on this approach for closed-loop stimulation devices using more elaborate decisions and more accurate clinical guidelines.