Background: Malaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malarial infections (nMI) remains a challenge. Furthermore, the success of rapid diagnostic tests (RDT) is threatened by Pfhrp2/3 deletions and decreased sensitivity at low parasitemia. Analysis of haematological indices can be used to support identification of possible malaria cases for further diagnosis, especially in travelers returning from endemic areas. As a new application for precision medicine, we aimed to evaluate machine learning (ML) approaches that can accurately classify nMI, UM and severe malaria (SM) using haematological parameters.
Corn, coffee, chocolate, even wine are a few of the foods that stand to be massively disrupted by the effects of climate change, population growth and water scarcity -- if they haven't already. A recent study found the yields of the world's top ten crops have begun to decrease, a drop that is disproportionately affecting food-insecure countries. The situation stands to worsen. Researchers project that the global population will increase by 3 billion in 2050. To feed these additional global residents, agricultural production must increase by 50 percent, says Dr. Ranga Raju Vatsavai, an associate professor in computer science at North Carolina State University and the associate director of the Center for Geospatial Analytics.
The Global Artificial Intelligence and Robotics in Aerospace and Defense Market report studies the market comprehensively and provides an all-encompassing analysis of the key growth factors, Artificial Intelligence and Robotics in Aerospace and Defense market share, and the newest developments. Also, the Artificial Intelligence and Robotics in Aerospace and Defense Industry Market report provides growth rate, market demand and supply, and market potential for each geographical region. The Artificial Intelligence and Robotics in Aerospace and Defense report gives information about the Artificial Intelligence and Robotics in Aerospace and Defense market trend and share, market size analysis by region, and analysis of the global market size. The market study analysis presents an analysis of market share and segments by region and growth rate. Regional breakdown includes an in detail study of the key geological regions to gain a better accepting of the market and provide an accurate analysis.
A scientist from Russia has developed a new neural network architecture and tested its learning ability on the recognition of handwritten digits. The intelligence of the network was amplified by chaos, and the classification accuracy reached 96.3%. The network can be used in microcontrollers with a small amount of RAM and embedded in such household items as shoes or refrigerators, making them'smart.' The study was published in Electronics. Today, the search for new neural networks that can operate on microcontrollers with a small amount of random access memory (RAM) is of particular importance.
A new machine learning approach added to conventional magnetic resonance imaging can identify the regions of the brain causing dissociative symptoms in people with post-traumatic stress disorder, researchers found in a study published Friday by the American Journal of Psychiatry. Although MRI has long been used to document changes in the brain that occur as a result of a number of neurological conditions, bolstering the approach with machine learning enabled researchers to uncover and measure changes in functional connections between different regions of the brain in women with PTSD. These altered connections correlated with their dissociative symptoms, including memory loss or amnesia, the researchers said. "This new work may help us to establish a new standard of care for traumatized patients with PTSD who struggle with significant symptoms of dissociation," study co-author Dr. Milissa Kaufman, director of the Dissociative Disorders and Trauma Research Program at McLean Hospital, said in a statement. PTSD is a mental health disorder that occurs following trauma -- violent personal assaults, natural or human-caused disasters, accidents and military combat, for example -- according to the National Institute of Mental Health.
The typical American is recorded by security cameras 238 times a week, according to a new report from Safety.com. That figure includes surveillance video taken at work, on the road, in stores and in the home. The study found that Americans are filmed 160 times while driving, as there are about an average of 20 cameras on a span of 29 miles. And the average employee has been spotted by surveillance cameras at 40 times a week. However, for those who frequently travel or work in highly patrolled areas the number of times they are captured on film skyrockets to more than 1,000 times a week.
Of all the AI models in the world, OpenAI's GPT-3 has most captured the public's imagination. It can spew poems, short stories, and songs with little prompting, and has been demonstrated to fool people into thinking its outputs were written by a human. But its eloquence is more of a parlor trick, not to be confused with realintelligence. Nonetheless, researchers believe that the techniques used to create GPT-3 could contain the secret to more advanced AI. GPT-3 trained on an enormous amount of text data. What if the same methods were trained on both text and images?
If you've eaten vegan burgers that taste like meat or used synthetic collagen in your beauty routine--both products that are "grown" in the lab--then you've benefited from synthetic biology. It's a field rife with potential, as it allows scientists to design biological systems to specification, such as engineering a microbe to produce a cancer-fighting agent. Yet conventional methods of bioengineering are slow and laborious, with trial and error being the main approach. Now scientists at the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have developed a new tool that adapts machine learning algorithms to the needs of synthetic biology to guide development systematically. The innovation means scientists will not have to spend years developing a meticulous understanding of each part of a cell and what it does in order to manipulate it; instead, with a limited set of training data, the algorithms are able to predict how changes in a cell's DNA or biochemistry will affect its behavior, then make recommendations for the next engineering cycle along with probabilistic predictions for attaining the desired goal.
Recently computer scientists at USC Institute of Technologies (ICT) set out to assess under what conditions humans would employ deceptive negotiating tactics. Through a series of studies, they found that whether humans would embrace a range of deceptive and sneaky techniques was dependent both on the humans' prior negotiating experience in negotiating as well as whether virtual agents where employed to negotiate on their behalf. The findings stand in contrast to prior studies and show that when humans use intermediaries in the form of virtual agents, they feel more comfortable employing more deceptive techniques than they would normally use when negotiating for themselves. Lead author of the paper on these studies, Johnathan Mell, says, "We want to understand the conditions under which people act deceptively, in some cases purely by giving them an artificial intelligence agent that can do their dirty work for them." Nowadays, virtual agents are employed nearly everywhere, from automated bidders on sites like eBay to virtual assistants on smart phones.
Mammals can be very smart. They also have a brain with a cortex. It has thus often been assumed that the advanced cognitive skills of mammals are closely related to the evolution of the cerebral cortex. However, birds can also be very smart, and several bird species show amazing cognitive abilities. Although birds lack a cerebral cortex, they do have pallium, and this is considered to be analogous, if not homologous, to the cerebral cortex. An outstanding feature of the mammalian cortex is its layered architecture. In a detailed anatomical study of the bird pallium, Stacho et al. describe a similarly layered architecture. Despite the nuclear organization of the bird pallium, it has a cyto-architectonic organization that is reminiscent of the mammalian cortex. Science , this issue p. [eabc5534] ### INTRODUCTION For more than a century, the avian forebrain has been a riddle for neuroscientists. Birds demonstrate exceptional cognitive abilities comparable to those of mammals, but their forebrain organization is radically different. Whereas mammalian cognition emerges from the canonical circuits of the six-layered neocortex, the avian forebrain seems to display a simple nuclear organization. Only one of these nuclei, the Wulst, has been generally accepted to be homologous to the neocortex. Most of the remaining pallium is constituted by a multinuclear structure called the dorsal ventricular ridge (DVR), which has no direct counterpart in mammals. Nevertheless, one long-standing theory, along with recent scientific evidence, supports the idea that some parts of the sensory DVR could display connectivity patterns, physiological signatures, and cell type–specific markers that are reminiscent of the neocortex. However, it remains unknown if the entire Wulst and sensory DVR harbor a canonical circuit that structurally resembles mammalian cortical organization. ### RATIONALE The mammalian neocortex comprises a columnar and laminar organization with orthogonally organized fibers that run in radial and tangential directions. These fibers constitute repetitive canonical circuits as computational units that process information along the radial domain and associate it tangentially. In this study, we first analyzed the pallial fiber architecture with three-dimensional polarized light imaging (3D-PLI) in pigeons and subsequently reconstructed local sensory circuits of the Wulst and the sensory DVR in pigeons and barn owls by means of in vivo or in vitro applications of neuronal tracers. We focused on two distantly related bird species to prove the hypothesis that a canonical circuit comparable to the neocortex is a genuine feature of the avian sensory forebrain. ### RESULTS The 3D-PLI fiber analysis showed that both the Wulst and the sensory DVR display an orthogonal organization of radially and tangentially organized fibers along their entire extent. In contrast, nonsensory components of the DVR displayed a complex mosaic-like arrangement with patches of fibers with different orientations. Fiber tracing revealed an iterative circuit motif that was present across modalities (somatosensory, visual, and auditory), brain regions (sensory DVR and Wulst), and species (pigeon and barn owl). Although both species showed a comparable column- and lamina-like circuit organization, small species differences were discernible, particularly for the Wulst, which was more subdifferentiated in barn owls, which fits well with the processing of stereopsis, combined with high visual acuity in the Wulst of this species. The primary sensory zones of the DVR were tightly interconnected with the intercalated nidopallial layers and the overlying mesopallium. In addition, nidopallial and some hyperpallial lamina-like areas gave rise to long-range tangential projections connecting sensory, associative, and motor structures. ### CONCLUSION Our study reveals a hitherto unknown neuroarchitecture of the avian sensory forebrain that is composed of iteratively organized canonical circuits within tangentially organized lamina-like and orthogonally positioned column-like entities. Our findings suggest that it is likely that an ancient microcircuit that already existed in the last common stem amniote might have been evolutionarily conserved and partly modified in birds and mammals. The avian version of this connectivity blueprint could conceivably generate computational properties reminiscent of the neocortex and would thus provide a neurobiological explanation for the comparable and outstanding perceptual and cognitive feats that occur in both taxa. ![Figure] Fiber architectures of mammalian and avian forebrains. Schematic drawings of a rat brain (left) and a pigeon brain (right) depict their overall pallial organization. The mammalian dorsal pallium harbors the six-layered neocortex with a granular input layer IV (purple) and supra- and infragranular layers II/III and V/VI, respectively (blue). The avian pallium comprises the Wulst and the DVR, which both, at first glance, display a nuclear organization. Their primary sensory input zones are shown in purple, comparable to layer IV. According to this study, both mammals and birds show an orthogonal fiber architecture constituted by radially (dark blue) and tangentially (white) oriented fibers. Tangential fibers associate distant pallial territories. Whereas this pattern dominates the whole mammalian neocortex, in birds, only the sensory DVR and the Wulst (light green) display such an architecture, and the associative and motor areas (dark green), as in the caudal DVR, are devoid of this cortex-like fiber architecture. NC, caudal nidopallium. 3D RAT BRAIN (LEFT): SCALABLE BRAIN ATLAS, RESEARCH RESOURCE IDENTIFIER (RRID) SCR_006934 Although the avian pallium seems to lack an organization akin to that of the cerebral cortex, birds exhibit extraordinary cognitive skills that are comparable to those of mammals. We analyzed the fiber architecture of the avian pallium with three-dimensional polarized light imaging and subsequently reconstructed local and associative pallial circuits with tracing techniques. We discovered an iteratively repeated, column-like neuronal circuitry across the layer-like nuclear boundaries of the hyperpallium and the sensory dorsal ventricular ridge. These circuits are connected to neighboring columns and, via tangential layer-like connections, to higher associative and motor areas. Our findings indicate that this avian canonical circuitry is similar to its mammalian counterpart and might constitute the structural basis of neuronal computation. : /lookup/doi/10.1126/science.abc5534 : pending:yes