A recent study published in the journal Cell has shed new light on the evolution of neurons. Researchers from the Centre for Genomic Regulation in Barcelona have focused on placozoans, which are millimeter-sized marine animals. They found that the specialized secretory cells in these unique and ancient creatures may have given rise to neurons in more complex animals.

Placozoans are small creatures that are about the size of a large grain of sand. They feed on algae and microbes found on rocks and other substrates in warm and shallow seas. Despite their simplicity, they have been around for almost 800 million years and are one of the five main lineages of animals, along with Ctenophora (comb jellies), Porifera (sponges), Cnidaria (corals, sea anemones, and jellyfish), and Bilateria (all other animals).

To coordinate their behavior, sea creatures use peptidergic cells, which release small peptides that guide their feeding and movement. In a recent study, the authors used molecular techniques and computational models to understand how placozoan cell types evolved. They were able to piece together how our ancient ancestors might have looked and functioned, shedding light on the origin of these special cells.

The researchers created a map of the different types of cells in placozoans, annotating their characteristics across four species. Each cell type performs a specialized function due to specific sets of genes. The researchers used these maps, called 'cell atlases', to identify clusters or 'modules' of genes that regulate these different cell types, giving them a clear understanding of how each cell works and how they work together. Finally, they compared the cell types across different species to reconstruct their evolution over time.

Through this research, the scientists discovered that the nine main cell types in placozoans are connected by many transitional cell types that change from one type to another. These cells grow and divide, helping to maintain the delicate balance of cell types needed for the animal to move and eat. Additionally, the researchers identified fourteen different types of peptidergic cells, which were unique from all other cells. Unlike other cell types, they showed no evidence of any transitional types or growth and division.

Interestingly, the peptidergic cells share many similarities with neurons - a cell type that didn't appear until millions of years later in more advanced animals such as Bilateria. Cross-species analysis showed that these similarities are unique to placozoans and are not found in other early-branching animals such as sponges or comb jellies (ctenophores).

The study found similarities between peptidergic cells and neurons in three ways. Firstly, the researchers discovered that these placozoan cells differentiate from a group of progenitor epithelial cells through developmental signals that resemble neurogenesis seen in Cnidaria and Bilateria, the process by which new neurons are formed. Secondly, peptidergic cells have many gene modules that build the pre-synaptic scaffold, which is a part of a neuron that can send out messages. However, these cells lack the components required for the receiving end of a neuronal message or the components required for conducting electrical signals, indicating that they are far from being true neurons. Finally, the authors used deep learning techniques to show that placozoan cell types communicate with each other using a system in cells where specific proteins, called GPCRs (G-protein coupled receptors), detect outside signals and start a series of reactions inside the cell. These outside signals are mediated by neuropeptides, chemical messengers used by neurons in many different physiological processes.

This study reveals that the building blocks of neurons formed 800 million years ago in ancestral animals that grazed inconspicuously in the shallow seas of ancient Earth. Early neurons might have started as something similar to the peptidergic secretory cells of today's placozoans. These cells communicated using neuropeptides but eventually gained new gene modules that enabled the creation of post-synaptic scaffolds, axons, and dendrites, as well as ion channels that generate fast electrical signals. These innovations were critical for the dawn of the neuron around 100 million years after the ancestors of placozoans first appeared on Earth.

However, the complete evolutionary story of nerve systems is yet to be told. The first modern neuron is thought to have originated in the common ancestor of cnidarians and bilaterians around 650 million years ago. Although ctenophores have neuronal-like cells, they have important structural differences and lack the expression of most genes found in modern neurons. The presence of some of these neuronal genes in the cells of placozoans and their absence in ctenophores raises new questions about the evolutionary trajectory of neurons.

The study was led by the Sebe-Pedrós Lab with the collaboration of Luis Serrano’s lab (CRG), the Schierwater lab (Hannover University), and the Gruber-Vodicka lab (Kiel University), and with the support of the Proteomics Unit and the Advanced Light Microscopy Unit at the Centre for Genomic Regulation.

“Placozoans lack neurons, but we’ve now found striking molecular similarities with our neural cells. Ctenophores have neural nets, with key differences and similarities with our own. Did neurons evolve once and then diverge, or more than once, in parallel? Are they a mosaic, where each piece has a different origin? These are open questions that remain to be addressed”, says Dr. Xavier Grau-Bové, co-first author of the study and postdoctoral researcher at the Centre for Genomic Regulation.

The origins of neurons and the evolution of other cell types will become clearer as researchers sequence high-quality genomes from diverse species. “Cells are the fundamental units of life, so understanding how they come into being or change over time is key to explaining the evolutionary story of life. Placozoans, ctenophores, sponges, and other non-traditional model animals harbor secrets that we are only just beginning to unlock,” concludes ICREA Research Professor Arnau Sebé-Pedros, corresponding author of the study and Junior Group Leader at the Centre for Genomic Regulation.

The research carried out by Spanish scientists has shed light on the ancient origins of neurons, and the use of deep learning has proven to be an invaluable tool in this endeavor. This study has the potential to pave the way for new avenues of exploration in the field of neuroscience and could lead to a better understanding of how the human brain functions. It is a testament to the fact that technology can be harnessed to uncover secrets of the past and make discoveries about our world. With further research, we may be able to unravel the mysteries of the brain and tap into the full potential of the human mind.

Discern Security has raised $3 million in funding to enhance cybersecurity tools, gain momentum with Fortune 500 companies, and establish partnerships with cybersecurity firms. 

Last year, global organizations invested over $150 billion in cybersecurity measures. Despite these efforts, the increasing number of cyberattacks has resulted in trillions of dollars in annual damages, making it clear that there is a critical need for cybersecurity innovation. Discern Security, a pioneering AI-driven policy management cybersecurity startup has emerged from stealth with $3 million in seed funding to address this existential threat.

The funding round was led by a diverse investor consortium including BoldCap, WestWave Capital, Cyber Mentor Fund, and Security Syndicate, along with influential Global Chief Information Security Officers (CISOs). This spread of investors underscores the opportunity Discern Security presents for the cybersecurity community. 

Discern Security was founded in 2023 by experienced entrepreneurs Sai Venkataraman, Santhosh Purathepparmbil, and Rohan Puri with a vision to revolutionize the cybersecurity landscape by utilizing the immense power of AI. The company operates as a "Policy Intelligence Hub," leveraging AI capabilities to monitor and optimize security controls across a diverse range of cybersecurity tools for its clients. This unique approach fosters the creation of a cohesive cybersecurity mesh architecture, enabling seamless integration among various security products.

Discern Security addresses three major challenges faced by the cybersecurity industry. Firstly, it helps organizations maximize the effectiveness of their security investments by improving the performance of existing products. Secondly, it helps to solve the industry-wide shortage of cybersecurity expertise by taking care of the operational heavy lifting, allowing teams to focus on more strategic initiatives. Lastly, it simplifies the complex configuration process of standalone security products, thus improving both security and productivity. With its Policy Intelligence Hub, Discern Security enables organizations to visualize and optimize their security configurations, ensuring continuous dynamic policy management throughout their cybersecurity tool arsenal. 

Discern Security's platform takes a different approach to cybersecurity compared to traditional security solutions. The platform uses AI to create a proactive and interconnected system for businesses. This approach allows companies to manage their security resources more efficiently and enhances their defenses against advanced cyber threats.

"The cybersecurity landscape demands perpetual innovation and fine-tuning, and our team at Discern Security is resolutely committed to ushering in a paradigm shift. With our platform, businesses gain the advantage of a comprehensive security strategy that not only mitigates risks but also optimizes their security investments. Our mission is to deliver world-class cybersecurity solutions transcending geographical boundaries, safeguarding organizations worldwide," said Sai Venkataraman, Co-founder and CEO at Discern Security.

Discern Security's solution has already gained considerable attention and success, forming partnerships with several Fortune 500 corporations and top-tier cybersecurity firms. 

The funds raised from this funding round will be utilized to expand the product line, integrate essential features, and continue recruiting a team of cybersecurity experts from around the globe. Discern Security is ready to extend these solutions to businesses worldwide, actively protecting their digital assets against ever-changing cyber threats.

"Security threats to organizations continually evolve, yet security policies and configurations at most organizations are infrequently updated, often once or twice a year. This leads to increased risk and vulnerability. Discern's AI-powered solution strategically addresses this need and is poised to create a new category and become a market leader in this space. We are delighted to lead their funding round," said Sathya Nellore Sampat, General Partner at BoldCap.

"Discern Security's innovative AI-driven approach to cybersecurity promises to make a significant impact in the industry. We believe that Sai, Santhosh, and Rohan's visionary leadership positions them as category creators, and we eagerly anticipate supporting their mission to fortify businesses against ever-evolving cyber threats," added Warren Weiss, Managing General Partner at WestWave Capital.

Discern Security has completed a funding round and launched the world's first AI-powered security policy management platform. This achievement highlights the company's commitment to offering advanced security solutions. With this innovative platform, organizations can now have more confidence in their security policies and focus on their core business objectives. Discern Security's customers can expect more cutting-edge security solutions as the company continues to innovate and develop new ways to protect their data.

A recent study by the National Institutes of Health found that Artificial intelligence (AI) and machine learning (ML) can be used to detect and diagnose Polycystic Ovary Syndrome (PCOS). PCOS is the most common hormone disorder among women between the ages of 15 and 45. The researchers reviewed published scientific studies that used AI/ML to analyze data to diagnose and classify PCOS. They concluded that AI/ML-based programs were successful in detecting PCOS.

“Given the large burden of under- and misdiagnosed PCOS in the community and its potentially serious outcomes, we wanted to identify the utility of AI/ML in the identification of patients that may be at risk for PCOS,” said Janet Hall, M.D., senior investigator and endocrinologist at the National Institute of Environmental Health Sciences (NIEHS), part of NIH, and a study co-author. “The effectiveness of AI and machine learning in detecting PCOS was even more impressive than we had thought.”

Polycystic ovary syndrome (PCOS) is a hormonal disorder that affects the proper functioning of the ovaries. In many cases, it is accompanied by higher levels of testosterone. This condition can lead to a range of symptoms, including irregular periods, acne, excessive facial hair growth, or baldness. Women with PCOS are more likely to develop type 2 diabetes, as well as sleep, psychological, cardiovascular, and other reproductive disorders such as uterine cancer and infertility.

“PCOS can be challenging to diagnose given its overlap with other conditions,” said Skand Shekhar, M.D., senior author of the study and assistant research physician and endocrinologist at the NIEHS.

“These data reflect the untapped potential of incorporating AI/ML in electronic health records and other clinical settings to improve the diagnosis and care of women with PCOS,” said Shekhar.

The diagnosis of polycystic ovary syndrome (PCOS) is based on standardized criteria that include clinical features, laboratory findings, and radiological evidence. However, PCOS is often difficult to diagnose because some of its symptoms can overlap with other conditions. To improve the accuracy of diagnosis, researchers suggest integrating large population-based studies with electronic health datasets and using machine learning (ML) to identify sensitive diagnostic biomarkers. ML is a type of artificial intelligence (AI) that can process large amounts of data, such as electronic health records. 

In a recent study, researchers conducted a systematic review of peer-reviewed studies published in the last 25 years that used AI/ML to diagnose PCOS. They screened 135 studies and included 31 in their analysis. The studies were all observational and assessed the use of AI/ML technologies for diagnosing PCOS. Ultrasound images were used in about half of the studies, and the average age of the participants was 29. 

The accuracy of PCOS diagnosis using AI/ML varied across the studies. Among the 10 studies that used standardized diagnostic criteria, the accuracy of detection ranged from 80-90%. The findings suggest that AI/ML can be a valuable tool for diagnosing difficult-to-diagnose disorders like PCOS.

“Across a range of diagnostic and classification modalities, there was an extremely high performance of AI/ML in detecting PCOS, which is the most important takeaway of our study,” said Shekhar. The use of AI/ML-based programs has the potential to significantly improve our ability to detect women with PCOS early, which can lead to cost savings and lessen the burden of PCOS on patients and the health system. Further studies with strong validation and testing practices will enable a smooth integration of AI/ML for chronic health conditions. NIEHS clinical studies are currently underway to understand and detect PCOS. To learn more, you can join an NIEHS study.

The findings of the NIH study are clear: AI/ML can be used to successfully diagnose Polycystic Ovary Syndrome. This is a major breakthrough in the medical field, as it demonstrates the potential of AI/ML to improve the accuracy and speed of diagnosis for this and other conditions. With further research and development, AI/ML could revolutionize the way medical professionals diagnose and treat patients.

Grants: This work was supported by the Intramural Research Program of the NIH/National Institute of Environmental Health Sciences (ZIDES102465 and ZIDES103323).

An international team of researchers, led by experts at the University of Adelaide in Australia has made progress in understanding the nature of dark matter.

Dark matter makes up 84% of the matter in the universe and its existence has been confirmed through its gravitational interactions, yet little is known about its precise nature.

The team believes that the key to understanding this mystery could lie with the dark photon, a hypothetical massive particle that could act as a portal between the dark sector of particles and regular matter.

Regular matter, which is what we and our physical world are made up of, is far less abundant than dark matter, with five times more dark matter existing than regular matter.

The team is pursuing different approaches, including testing existing theories about dark matter, to gain more clues into this elusive substance. In their latest study, they examined the potential effects that a dark photon could have on the complete set of experimental results from the deep inelastic scattering process. This process is used to probe the insides of hadrons, such as protons and neutrons, using electrons, muons, and neutrinos.

The team used the state-of-the-art Jefferson Lab Angular Momentum Parton distribution function global analysis framework and modified the underlying theory to allow for the possibility of a dark photon.

“Our work shows that the dark photon hypothesis is preferred over the standard model hypothesis at a significance of 6.5 sigma, which constitutes evidence for a particle discovery," said Professor Anthony Thomas, Elder Professor of Physics, University of Adelaide.

The discovery of new clues to the nature of elusive dark matter by Australian professor Thomas is a breakthrough in our understanding of the universe. This research has the potential to revolutionize our understanding of the universe and open up new possibilities for further exploration. Professor Thomas' work is an inspiring example of the power of scientific inquiry and the importance of never giving up in the pursuit of knowledge. We can only hope that this discovery will lead to further advances in our understanding of the universe and the mysteries of dark matter.