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San Diego based biotech Immunicom has developed an innovative therapeutic device that is intended to remove specific factors from blood that prevent our immune systems from fighting disease. Existing “therapeutic apheresis” treatments usually involve modification to cells harvested from patients or some form of plasma exchange. Immunicom’s technology uses a similar treatment procedure but says its approach of “capturing” targeted molecules is very different from other treatments.

Immunicom creates customised molecules that are designed to capture specific proteins or cytokines, but instead of turning those into drugs, it deploys them via its proprietary cartridges, which are essentially small liquid filters that can be fitted into any plasmapheresis machine. In a treatment similar to dialysis, the cartridges are used to treat a patient’s blood and plasma outside their body, reducing the levels of the target factors, with the goal of enabling the immune system to fight disease.

While Immunicom has focused its initial efforts on treatments for cancer, the company claims its technology can also potentially be applied to longevity and the treatment of a wide range of age-related diseases.

A study published Wednesday in the JAMA Psychiatry journal shows that four key genetic variations are more common in military veterans who have taken their own life or considered it.

Scientists from Duke University in Durham, North Carolina, found the pattern while analyzing blood samples from a database that included 633,778 U.S. veterans, cross-referenced with the International Suicide Genetics Consortium of more than 549,000 individuals.

The obtained samples were sequenced to create genetic profiles compared to participants’ medical records, showing that 121,211 recorded cases of attempted suicide or thoughts about killing themselves.

What are #dopamine, #serotonin, norepinephrine, glutamate, #GABA, acetylcholine? What does dopamine do?
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Objectives.

Human-like articulated neural avatars have several uses in telepresence, animation, and visual content production. These neural avatars must be simple to create, simple to animate in new stances and views, capable of rendering in photorealistic picture quality, and simple to relight in novel situations if they are to be widely adopted. Existing techniques frequently use monocular films to teach these neural avatars. While the method permits movement and photorealistic image quality, the synthesized images are constantly constrained by the training video’s lighting conditions. Other studies specifically address the relighting of human avatars. However, they do not provide the user control over the body stance. Additionally, these methods frequently need multiview photos captured in a Light Stage for training, which is only permitted in controlled environments.

Some contemporary techniques seek to relight dynamic human beings in RGB movies. However, they lack control over body posture. They need a brief monocular video clip of the person in their natural location, attire, and body stance to produce an avatar. Only the target novel’s body stance and illumination information are needed for inference. It is difficult to learn relightable neural avatars of active individuals from monocular RGB films captured in unfamiliar surroundings. Here, they introduce the Relightable Articulated Neural Avatar (RANA) technique, which enables photorealistic human animation in any new body posture, perspective, and lighting situation. It first needs to simulate the intricate articulations and geometry of the human body.

The texture, geometry, and illumination information must be separated to enable relighting in new contexts, which is a difficult challenge to tackle from RGB footage. To overcome these difficulties, they first use a statistical human shape model called SMPL+D to extract canonical, coarse geometry, and texture data from the training frames. Then, they suggest a unique convolutional neural network trained on artificial data to exclude the shading information from the coarse texture. They add learnable latent characteristics to the coarse geometry and texture and send them to their proposed neural avatar architecture, which uses two convolutional networks to produce fine normal and albedo maps of the person underneath the goal body posture.