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On November 1st, NetEase Smart News reported that suicide is the second leading cause of death among young people aged 15 to 34 in the United States. Clinicians often face challenges in identifying individuals at risk of self-harm. However, a groundbreaking study published in *Nature Human Behavior* introduces a new machine learning approach that could help detect suicidal thoughts more effectively.
The research involved 34 young participants, half of whom had a history of suicidal ideation, while the other half served as a control group. Each participant underwent functional magnetic resonance imaging (fMRI) and was asked to complete three word lists containing 10 words each. These words were categorized into three themes: suicide-related (e.g., “death,†“pain,†“fatalâ€), positive (e.g., “carefree,†“kindness,†“naiveâ€), and negative (e.g., “boring,†“evil,†“internalâ€). The team also used previously mapped neural patterns associated with emotions like “shame†and “anger.â€
Researchers identified five key brain regions and six specific words that best distinguished between those with suicidal tendencies and the control group. Using this data, they trained a machine learning model that correctly classified 15 out of 17 suicidal participants and 16 out of 17 controls. In a follow-up analysis, the team split the suicidal group into two subgroups—those who had attempted suicide and those who had not—and developed a new classifier that accurately identified 16 out of 17 patients.
The findings revealed significant differences in how mental health participants and those with suicidal thoughts responded to certain words. For instance, when suicidal individuals saw the word “death,†their brain’s “shame†regions lit up much more intensely than in the control group. Similarly, the word “trouble†triggered stronger activity in the “sadness†region of the brain.
This study marks another step in integrating artificial intelligence into psychiatry. Researchers are exploring various AI applications, from analyzing brain scans to predicting depression and detecting PTSD through speech patterns. Earlier this year, *Wired* reported on systems that can identify at-risk individuals using health records with accuracy rates between 80% and 90%. Facebook also uses text mining to detect users who may be at risk of self-harm and connects them to mental health resources.
AI has already begun transforming healthcare, with some algorithms excelling at detecting tumors in CT scans. Geoffrey Hinton, a leading figure in deep learning, once suggested that radiologists might eventually become obsolete. However, in this case, the study is more likely to inspire new therapeutic approaches rather than replace human professionals entirely. By identifying distinct brain patterns and emotional responses, researchers hope to develop better treatments and support for those struggling with mental health issues.
Stay updated with the latest developments in AI by following the NetEase Smart Studio public account (smartman163). Discover the future of artificial intelligence and its impact on our lives.
How does TFT work?
TFT is the abbreviation of "Thin Film Transistor", generally refers to thin film liquid crystal displays, but actually refers to thin film transistors (matrix)-can "actively" control each independent pixel on the screen, which is The origin of the so-called active matrix TFT (active matrix TFT). So how exactly is the image produced? The basic principle is very simple: the Display Screen is composed of many pixels that can emit light of any color, and the purpose can be achieved by controlling each pixel to display the corresponding color. In TFT LCD, backlight technology is generally used. In order to accurately control the color and brightness of each pixel, it is necessary to install a shutter-like switch after each pixel. When the "blinds" are opened, light can pass through, and " When the shutters are closed, light cannot pass through. Of course, technically, it is not as simple as the one just mentioned. LCD (Liquid Crystal Display) utilizes the characteristics of liquid crystals (liquid when heated, and crystallized into solid when cooled). Generally, liquid crystals have three forms:
Smectic liquid crystal similar to clay
Nematic liquid crystal resembling a fine matchstick
Cholestic liquid crystal
The liquid crystal display uses filaments, and when the external environment changes, its molecular structure will also change, and thus have different physical properties-it can achieve the purpose of letting light through or blocking light-which is just like the blinds just now.
Everyone knows the three primary colors, so each pixel on the display screen needs three similar basic components described above to control the three colors of red, green, and blue respectively.
The most commonly used one is twisted nematic TFT LCD (Twisted Nematic TFT LCD). Existing technologies vary greatly, and we will cover them in detail in the second part of this article.
There are grooves on the upper and lower layers. The grooves on the upper layer are arranged longitudinally and the grooves on the lower layer are arranged horizontally. When no voltage is applied to the liquid crystal in its natural state, the light emitted from the light emitting layer of the twisted nematic TFT Display working principle diagram of Figure 2a will be twisted by 90 degrees after passing through the interlayer, so that it can pass through the lower layer smoothly.
When a voltage is applied between the two layers, an electric field is generated. At this time, the liquid crystals are aligned vertically, so the light will not be twisted-the result is that the light cannot pass through the lower layer.
(2) TFT pixel structure: The color filter is divided into red, green, and blue according to the color, which are arranged on the glass substrate to form a group (dot pitch) corresponding to a pixel. Each monochromatic filter is called It is a sub-pixel. In other words, if a TFT display supports a maximum resolution of 1280×1024, then at least 1280×3×1024 sub-pixels and transistors are required. For a 15-inch TFT display (1024×768), then a pixel is about 0.0188 inches (equivalent to 0.30mm), for an 18.1-inch TFT display (1280×1024), it is 0.011 inches (equivalent to 0.28mm) .
As we all know, pixels are decisive for the display. The smaller each pixel is, the larger the maximum resolution that the display can achieve. However, due to the limitation of the physical characteristics of the transistor, the size of each pixel of the TFT at this stage is basically 0.0117 inches (0.297mm), so for a 15-inch display, the maximum resolution is only 1280×1024.
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