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Enhancing the prediction of TADF emitter properties using Δ-machine learning: A hybrid semi-empirical and deep tensor neural network approach
No full textThis study presents a machine learning (ML)-augmented framework for accurately predicting excited-state properties critical to thermally activated delayed fluorescence (TADF) emitters. By integrating the computational efficiency of semi-empirical PPP+CIS theory with a Δ-ML approach, the model overcomes the inherent limitations of PPP+CIS in predicting key properties, including singlet (S1) and triplet (T1) energies, singlet-triplet gaps (ΔEST), and oscillator strength (f). The model demonstrated exceptional accuracy across datasets of varying sizes and diverse molecular features, notably excelling in predicting oscillator strength and ΔEST values, including negative regions relevant to TADF molecules with inverted S1-T1 gaps. This work highlights the synergy between physics-inspired models and machine learning in accelerating the design of efficient TADF emitters, providing a foundation for future studies on complex systems and advanced functional materials
Identification of anatomical locations: its relevance for vibrotactile perception of individuals with Parkinson's disease
No full textBackground: Vibrotactile input is a useful sensory cue for individuals with Parkinson's Disease (PD) to overcome freezing of gait (FoG). For this input to serve as a cue, its accurate perception is required. This needs the input to be delivered at an anatomical location where it can be perceived. This is particularly true for individuals with PD whose tactile perception differs from that of healthy individuals. Literature indicates choice of various anatomical locations e.g., Finger, Wrist, Thigh, Shin, Calf, Ankle, Achilles Tendon, Heel and torso for the application of vibrotactile stimulation. Though studies have focused on the comparison of the vibrotactile perception (based on feedback) at various anatomical locations, yet these have involved only healthy individuals. However, such exploration remains as majorly untouched for individuals with PD. Methods: To bridge this gap, here we have conducted a study using our vibrotactile stimulation system while involving twenty-one individuals with PD to understand the choice of anatomical location with regard to vibrotactile perception. In addition, our study involved twenty-one age-matched healthy individuals to understand possible differences if any in vibrotactile perception between the two groups of participants. Results: Our results showed that for the healthy participants, both 'Wrist' and 'Thigh' were equally strong anatomical locations with regard to vibrotactile perception that were correctly identified 100% of the time closely followed by ‘Finger’ for which the correct identification was 98% of the time with correct identification for all these three locations being statistically (p < 0.05) higher than the other locations. In contrast, for individuals with PD, the 'Thigh' emerged as a strong candidate anatomical location with regard to vibrotactile perception even for those with severity of symptoms (based on clinical measure) that was correctly identified 96% of the time followed by ‘Wrist’ for which the correct identification was 92% of the time with the correct identification for only the ‘Thigh’ being statistically (p < 0.05) higher than all the other locations (except ‘Wrist’). Conclusion: This finding is clinically significant in deciding the right anatomical location to offer vibrotactile cues for it to be correctly perceived by one with PD, providing assistance to overcome FoG
Spatio-temporal variation of microclimate in mixed-use urban forms
No full textRapid urbanisation and climate change have intensified urban heat islands (UHI) and associated thermal discomfort, making accurate microclimate monitoring essential for sustainable urban development. Past research has classified urban areas into Local Climate Zones (LCZs), which represent a zone within an urban area with a distinctive climate. A typical LCZ is a few kilometres in size however, this approach may be insufficient for cities where land use and land cover (LULC) change abruptly over very short distances, as is the case in many developing countries. This study explores microclimatic variability due to varied LULC patterns within a university campus near Ahmedabad, India, where changes occur within just a few metres. The research utilises a custom-built, portable, sensor-equipped cart (WxCart) to perform mobile transect measurements of air temperature, relative humidity, and wind speed at the pedestrian level. Sky view factor (SVF) was also measured using a fisheye lens. Measurements were conducted over a year to capture seasonal variations in microclimatic conditions across four distinct areas. The data was also used to calculate thermal comfort indices viz., the heat index (HI) and normal effective temperature (NET). Results reveal that closely situated areas exhibit distinct microclimatic differences due to diverse LULC patterns, with these differences persisting across seasons. Comparison with historical climatological averages from Ahmedabad underscores the distinctiveness of these microclimates. Statistical analyses further validate that both rapidly changing LULC and seasonal changes have significant impacts on local microclimatic conditions. The study introduces the concept of Local Microclimate Zones (LMZs) as a refined approach for characterising microclimates at very short spatial scales, complementing the framework of LCZs. By highlighting the importance of detailed microclimate analysis in urban planning, this research supports the development of climate-responsive outdoor spaces and contributes valuable insights for urban heat adaptation strategies
Advancements in nanosensors for an early detection of cancer
No full textNanosensors are highly adaptable analytical tools with a vast potential for disease diagnosis, especially for cancer detection. Their effectiveness relies on multiple factors, including synthesis, material diversity, and transduction methods. This versatility opens doors to various applications, such as cancer biomarker detection, high-resolution bioimaging, and the development of compact, portable diagnostic devices. In addition, new avenues are emerging in nanosensors, notably through advanced domains, such as micro/nanorobotics and artificial intelligence (AI). Over the years, nanosensors have undergone transformative evolution, offering a wide array of applications, particularly in cancer detection. Due to their high specificity and sensitivity, these devices are extensively used for detecting various biomolecules and cancer-specific markers. One of the most remarkable features of nanosensors is their adaptability. Researchers can customize their composition, size, and surface properties, enabling the design of highly specific nanosensors to certain cancer biomarkers, thus reducing the risk of false results. Nanosensors are at the forefront of modern medical research. Their ability to seamlessly integrate traditional diagnostics with advanced technologies, such as AI, holds promise for reshaping healthcare. As researchers continue to enhance their capabilities and uncover new applications, nanosensors are poised to redefine disease diagnosis and treatment, paving the way for groundbreaking advances
The crosstalk between insulin resistance and tau pathology
No full textBackground
Diabetes is a modifiable risk factor for Alzheimer’s disease, and GLUT4, an insulin-dependent transporter, plays a crucial role in insulin-resistant conditions and, consequently, in diabetes development. The study aimed to investigate the relationship between tau pathology and insulin resistance by quantifying GLUT4 expression and glucose concentration.
Method
Initially, SH-SY5Y cells underwent transfection with either a wild-type tau plasmid or a mutant tau plasmid. Subsequently, insulin resistance was induced using high glucose or dexamethasone. The impact of these manipulations was assessed through a glucose uptake assay measuring cellular glucose concentration. Immunocytochemistry techniques were then applied to evaluate GLUT4 expression.
Result
A significant increase in glucose concentration was observed under the latter condition. Additionally, there was a marked decrease in GLUT4 expression in neuronal cells transfected with the mutant tau plasmid, simulating tau pathology.
Conclusion
The study provides evidence supporting insulin resistance as a contributing risk factor for tau pathology development, potentially leading to Alzheimer’s disease later in life. Tau aggregates may increase the likelihood of insulin resistance by impairing the insulin signaling pathway, ultimately resulting in Type 2 diabetes. The findings suggest that impaired insulin signaling in the brain could contribute to tau pathogenesis by decreasing GLUT4 expression, leading to hyperglycemia and cellular hypertrophy
