Browse Articles

The clinical and genetic spectrum of twenty-six individuals with hearing loss affected by MYO15A variants

Long-term cardiovascular risk and mortality associated with uric acid to HDL-C ratio: a 20-year cohort study in adults over 40

Why politicians manipulate statistics — and what to do about it

Integrin linked kinase and threonine tyrosine kinase modulate TCR signaling

Abstract T cell activation is critical for adaptive immunity, helping to protect the body from infection and tumors. A key step in this activation is signal transduction downstream of the T cell antigen receptor. This signaling involves several steps, with early ones occurring at the plasma membrane and others that occur later, after TCR internalization. The late steps in TCR signaling remain poorly understood. Since the TCR can signal after its internalization, we postulated that kinases abundantly expressed in T cells may regulate TCR signaling. This study focuses on two such enzymes: integrin-linked kinase (ILKs) and threonine-tyrosine kinase (TTKs), whose involvement in TCR signaling has not been previously studied. Using specific depletion of TTK and ILK by lentiviral shRNA, we show that in the absence of ILK and TTK, the early steps of TCR signaling are strongly enhanced, while IL-2 production by activated T cells is strongly decreased. These findings are relevant because TTK and ILK are both important targets in oncology, and our results show that their inhibition affects the activation of T cells, which play an essential role in anti-tumor defense.

sST2 is a key outcome biomarker in COVID-19: insights from discovery randomized trial

Sowing solutions: my quest to save Kenya’s maize from a devastating invader

Erratic behavior of platinum-group elements along an olivine–Cr-spinel cotectic in sulfide-undersaturated basaltic magma: roles of Cr-spinel and alloys

Structural and electrochemical properties of ternary solid polymer electrolytes based on PVA:CS:FSG doped with sodium thiocyanate

Mnemonic factors associated with the tip-of-the-tongue phenomenon

Abstract The tip-of-the-tongue (ToT) phenomenon is a transient semantic memory retrieval failure. Here we examined to what extent different mnemonic factors (i.e., age of acquisition, frequency of retrieval, recency of last retrieval) impact ToTs during the retrieval of famous faces and places. Eighty adults completed a self-paced experiment for both stimuli. This required making judgements on whether they knew the name, were in a ToT state, the image was familiar or the name was unknown, as well as completing follow-up questions examining the mnemonic factors of interest. Results revealed that later acquired names, a lower frequency of retrieval, and less recently encountered names, all predicted an increase in ToT occurrences. These findings followed a similar pattern across faces and places, with places being stronger predictors for each mnemonic factor. By examining these factors simultaneously across these semantic categories, we provide further evidence regarding the variables determining transient retrieval failures.

Platelet-rich plasma and IL-1β antagonist receptor peptide attenuate the inflammatory process of muscle injury in wistar rats

Enhanced chemotaxis and degradation of nonylphenol in Pseudoxanthomonas mexicana via CRISPR-mediated receptor modification

An enhanced CNN with ResNet50 and LSTM deep learning forecasting model for climate change decision making

Abstract Climate change poses a significant challenge to wind energy production. It involves long-term, noticeable changes in key climatic factors such as wind power, temperature, wind speed, and wind patterns. Addressing climate change is essential to safeguarding our environment, societies, and economies. In this context, accurately forecasting temperature and wind power becomes crucial for ensuring the stable operation of wind energy systems and for effective power system planning and management. Numerous approaches to wind change forecasting have been proposed including both traditional forecasting models and deep learning models. Traditional forecasting models have limitations since they cannot describe the complex nonlinear relationship in climatic data, resulting in low forecasting accuracy. Deep learning techniques have promising non-linear processing capabilities in weather forecasting. To further advance the integration of deep learning in climate change forecasting, we have developed a hybrid model called CNN-ResNet50-LSTM, comprising a Convolutional Neural Network (CNN), a Deep Convolutional Network (ResNet50), and a Long Short-Term Memory (LSTM) model to predict two climate change factors: temperature and wind power. The experiment was conducted using three publicly available datasets: Wind Turbine Scada (Scada) Dataset, Saudi Arabia Weather history (SA) dataset, and Wind Power Generation Data for 4 locations (WPG) dataset. The forecasting accuracy is evaluated using several evaluation metrics, including the coefficient of determination ( $$\:{\text{R}}^{2}$$ ), Mean Squared Error (MSE), Mean Absolute Error (MAE), Median Absolute Error (MedAE) and Root Mean Squared Error (RMSE). The proposed CNN-ResNet50-LSTM model was also compared to five regression models: Dummy Regressor (DR), Kernel Ridge Regressor (KRR), Decision Tree Regressor (DTR), Extra Trees Regressor (ETR), and Stochastic Gradient Descent Regressor (SGDR). Findings revealed that CNN-ResNet50-LSTM model achieved the best performance, with $$\:{\text{R}}^{2}$$ scores of 98.84% for wind power forecasting in the Scada dataset, 99.01% for temperature forecasting in the SA dataset, 98.58% for temperature forecasting and 98.35% for wind power forecasting in the WPG dataset. The CNN-ResNet50-LSTM model demonstrated promising potential in forecasting both temperature and wind power. Additionally, we applied the CNN-ResNet50-LSTM model to predict climate changes up to 2030 using historical data, providing insights that highlight its potential for future forecasting and decision-making.

A robustly rooted tree of eukaryotes reveals their excavate ancestry

Photocatalytic degradation of reactive black 5 from synthetic and real wastewater under visible light with TiO2 coated PET photocatalysts

Analysis of the correlation between anthropometric indices and levels of selected hormones in relation to problematic internet use: blood parameters in problematic internet use

The role of vmPFC in accessing the temporality of life events for mental time travel

Print, melt, repeat: 3D-printing formula yields sturdy objects time after time

Liver cancer recurrence predicted by immune-cell location and gene expression

Correction: Developing an operational definition of housing instability and homelessness in Veterans Health Administration’s medical records

Sustainable approach for synthesis of new coumarin-linked Schiff bases in DABCO-based ionic liquid and their identification as aldose reductase inhibitors

Abstract Aldehyde reductase (ALR1) is the enzyme that speeds up the reduction of many types of aldehydes into sorbitol and D-glucose. The essential enzyme of the polyol pathway, aldose reductase (ALR2), is responsible for the development of chronic complications associated with diabetes when activated under hyperglycemic conditions. Since it is a crucial mediator for the oxidative and inflammatory signaling pathways, ALR is thought to be a target for various diseases. Many medicines are available for the treatment of ALR-associated issues but due to their long term side-effects they are not effectively used. Coumarin is a naturally occurring compound, and its derivatives are widely used in the treatment of many ailments. Therefore, in the pursuit to find potential alternate candidates as drug leads, we have prepared new coumarin-based Schiff base analogues using DABCO-C 7 -F ionic liquid and compared with conventional method. The sustainable approach making use of DABCO-C 7 -F ionic liquid, not only made the synthesis easier but also it is cost- and time-effective. The synthesized analogues were further examined for their potentials against ALR2 (IC50 = 1.61 to 11.20 µM) as well as checked selectivity via screening against ALR1 enzyme. Moreover, the molecular docking study was performed to elucidate the binding interactions of active compounds. The results showed that the synthesized compounds may have the potential to be further studied as new and selective anti-diabetic agents.