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【Advanced Skin Disease Diagnosis and Treatment: Leveraging Convolutional Neural Networks for Image-Based Prediction and Comprehensive Health Assistance】 Full article: (Authored by Noshin Un Noor, et al., from World University of Bangladesh, Bangladesh.)

Skin_diseases are a major global health concern, encompassing a wide range of conditions with varying severity. Prompt and precise diagnosis is critical for effective treatment. However, traditional methods often rely on dermatologists, creating disparities in access to care. This study creates and assesses a highly accurate Convolutional Neural Network (CNN) model that can use digital photos of skin lesions to diagnose a variety of skin conditions, and looks into how well various CNN architectures and pre-trained models may increase the precision and effectiveness of diagnosing skin conditions.

Abstract

Skin conditions are a worldwide health issue that requires prompt and accurate diagnosis in order to be effectively treated. This study presents a Convolutional Neural Network (CNN)-based automated skin disease diagnostic method. The work uses preprocessing methods like scaling, normalization, and augmentation to improve model robustness using the DermNet dataset, which consists of 19,500 pictures from 23 disease categories. TensorFlow and Keras were used to create a unique CNN architecture, which produced an impressive accuracy of 94.65%. Metrics like precision, recall, and F1-score were used to validate the model’s performance, showing that it outperformed more conventional machine learning techniques like SVM and KNN. The system incorporates patient-reported symptoms in addition to diagnosis to provide a comprehensive approach to health support, allowing for remote accessibility and tailored therapy suggestions. This work recognizes issues like dataset variability and processing needs while showcasing the revolutionary potential of AI in dermatology. In order to improve model interpretability and clinical integration, future possibilities include dataset extension, real-world validation, and the use of explainable AI.

Skin Disease Diagnosis, Dermatological Image Analysis, Medical Image Classification, Convolutional Neural Networks (CNNs), Healthcare Accessibility, Deep Learning Applications, DermNet Dataset

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🌍 New research suggests more than half of global cropland areas could lose suitable crops under a warming scenario of 2C.

📚 The study mapped how climate change could reshape areas suited for 30 major crops across four warming scenarios — from 1.5C to 4C.

🔎 Even at 1.5C, over half of the crops studied could see a decline in suitable cropland, with tropical regions hit hardest. In contrast, areas far from the equator could gain crop diversity — opening doors for climate adaptation.

S impact on agriculture. + s findings here ⬇️ +.

More than half of global cropland areas could see a decline in the number of suitable crops under a warming scenario of 2C, new research finds.

The study, published in Nature Food, projects how climate change will modify the areas suited for growing 30 major crops under four scenarios, ranging from 1.5 to 4C of global warming.

Continue reading “Half of global croplands could see a drop in suitable crops at 2C of warming” | >

A small team of computational and evolutionary biologists from the University of Chinese Academy of Sciences, Zhongshan Hospital and the Max Planck Institute for Evolutionary Anthropology, reports that unique lactase genes carried by about 25% of East Asian people may have been inherited from Neanderthals.

In their study published in Proceedings of the National Academy of Sciences, the group compared the of thousands of people of African, East Asian and European descent against one another and then against Neanderthal genes.

Prior research has shown that many people of European descent carry genes that allow them to easily digest the sugars (lactose) present in milk, in sharp contrast to people of East Asian descent, who tend to have a high percentage of . However, in this new effort, the research team found unique versions of the lactase gene in some East Asian people along with evidence that they may have come from interbreeding between humans and Neanderthals thousands of years ago.

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In recent years, physicists have been trying to better understand the behavior of individual quantum particles as they move in space. Yet directly imaging these particles with high precision has so far proved challenging, due to the limitations of existing microscopy methods.

Researchers at CNRS and École Normale Supérieure in Paris, France, have now developed a new protocol to directly image the evolution of a single-atom wave packet, a delocalized quantum state that determines the probability that an associated atom will be found in a specific location. This imaging technique, introduced in Physical Review Letters, could open exciting possibilities for the precise study of complex quantum systems in continuous space.

“Our group is interested in the study of ultracold atoms, the coldest systems in the universe, just a few billionths of degrees above absolute zero, where matter displays fascinating behaviors,” Tarik Yefsah, senior author of the paper, told Phys.org. “One of these behaviors is the so-called superfluidity, a remarkable state of matter, where particles flow without friction.

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Much of cell behavior is governed by the actions of biomolecular condensates: building block molecules that glom together and scatter apart as needed. Biomolecular condensates constantly shift their phase, sometimes becoming solid, sometimes like little droplets of oil in vinegar, and other phases in between.

Understanding the electrochemical properties of such slippery molecules has been a recent focus for researchers at Washington University in St. Louis.

In research published in Nature Chemistry, Yifan Dai, assistant professor of biomedical engineering at the McKelvey School of Engineering, shares the rules involving the intracellular electrochemical properties that affect movement and chemical activities inside the cell and how that might impact cell processes as a ages. The research can inform the development of treatments for diseases like amyotrophic lateral sclerosis (ALS) or cancer.

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Speech is a unique human ability that is known to be supported by various motor and cognitive processes. When humans start speaking, they can decide to cease at any point; for instance, if they are interrupted by something happening or by another person speaking to them.

The ability to voluntarily stop speaking plays a central role in social interactions, as it allows people to engage in conversations with others while adaptively responding to social cues, environmental stimuli or interruptions. While many past studies explored the neural and cognitive underpinnings of itself, the brain processes associated with speech inhibition remain poorly understood.

Researchers at the University of California San Francisco recently set out to better understand how the controls the ceasing of speech using tools to record neurophysiological signals. Their paper, published in Nature Human Behaviour, unveils a previously unknown premotor cortical network that could support voluntary speech inhibition.

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Recovered grasslands need more than 75 years of continuous management to regain their biodiversity because specialized pollinators are slow to return. Kobe University’s finding underscores the importance of preserving old grasslands as reservoirs of biodiversity, even if it is just as ski slopes.

Grasslands worldwide are rapidly disappearing due to land-use conversion and abandonment, leading to a well-documented loss of grassland biodiversity. Restoring abandoned grasslands by removing woody vegetation and resuming traditional land management practices has positive effects on biodiversity.

However, it is also known that this diversity lags behind that of old grasslands that have been under continued management for up to several millennia. The Kobe University ecologist Ushimaru Atushi says, “The reasons for this are not really clear and satisfying solutions have not been proposed.”

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Like engineers who design high-performance Formula One race cars, scientists want to create high-performance plasmas in twisty fusion systems known as stellarators. Achieving this performance means that the plasma must retain much of its heat and stay within its confining magnetic fields.

To ease the creation of these plasmas, physicists have created a new computer code that could speed up the design of the complicated magnets that shape the plasma, making stellarators simpler and more affordable to build.

Known as QUADCOIL, the code helps scientists rule out plasma shapes that are stable but require magnets with overly complicated shapes. With this information, scientists can instead devote their efforts to designing stellarators that can be built affordably.

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Our understanding of black holes, time and the mysterious dark energy that dominates the universe could be revolutionized, as new University of Sheffield research helps unravel the mysteries of the cosmos.

Black holes—areas of space where gravity is so strong that not even light can escape—have long been objects of fascination, with astrophysicists, and others dedicating their lives to revealing their secrets. This fascination with the unknown has inspired numerous writers and filmmakers, with novels and films such as “Interstellar” exploring these enigmatic objects’ hold on our collective imagination.

According to Einstein’s theory of , anyone trapped inside a black hole would fall toward its center and be destroyed by immense gravitational forces. This center, known as a singularity, is the point where the matter of a giant star, which is believed to have collapsed to form the black hole, is crushed down into an infinitesimally tiny point. At this singularity, our understanding of physics and time breaks down.

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