Veterinarian experts at Basepaws, a genetics testing company for pets in California, looked into the possibilities of how dog breeds of today will evolve 10,000 years down the line. The experts give their inputs to neural networks to generate some interesting visualizations.
Take a moment to see if you can recognize the breeds in the images below.
It is well known that modern-day dogs evolved from wolves that got friendly with humans. The exact timeline of when this friendship began is up for debate in the scientific community. But now that it has been established, it is unlikely that the bond will be shaken by anything in the future.
About 10–15% of pregnancies fail after conception has been recognized, amounting to 23 million losses a year. Chromosomal anomalies underlie many embryonic and fetal losses, but their exact frequency and localization to the embryo or placenta are still unclear. A new study published in Nature Medicine reports on a chromosomal analysis of over 1,700 spontaneous early miscarriages.
The most common period of pregnancy loss is before the ninth week, though many may occur earlier and pass unrecognized. While about 11% of women have at least one miscarriage, the proportion goes down with two or three, at 2% and 0.7. respectively.
Nervous systems are complex networks, comprised of billions of neurons connected by trillions of synapses. These connections are subject to specific wiring rules that are thought to result from competitive selection pressures to minimise wiring costs and promote complex, adaptive function. While most connections in the brain are short-range, a smaller subset of metabolically costly projections extend over long distances to connect disparate anatomical areas. These long-range connections support integrated brain function and are concentrated between the most highly connected network elements; the hubs of the brain. Hub connectivity thus plays a vital role in determining how a given nervous system negotiates the trade-off between cost and value, and natural. selection may favour connections that provide high functional benefit for low cost.
Consistent with this view, Professor Alex Fornito will present evidence. that hub connectivity is under strong genetic control. He will show that the strength of connectivity between hubs in the human brain is more heritable than connectivity between other nodes, and that the genetic variants influencing hub connectivity overlaps with those implicated in mental illness and intelligence. He will also discuss the progress and challenges of developing generative models that evaluate the role of different cost-value trade-offs in driving complex brain topology.
Professor Fornito completed his Clinical Masters (Neuropsychology) and PhD in 2007 at The University of Melbourne before undertaking postdoctoral training at the University of Cambridge, UK. In 2013, he assumed his current position at the Turner Institute of Brain and Mental Health, where he is Head of the Brain Mapping and Modelling Theme, Co-Director of the Brain, Mind, and Society Research Hub, and a Sylvia and Charles Viertel Senior Medical Research Fellow.
Alex’s research concentrates on developing new imaging techniques for mapping human brain connectivity and applying these methods to shed light on brain function in health and disease.
Weaving piezoelectric polymers into nanofibers reveals a surprising pathway to boost stem cell growth naturally, without external power.
Our bodies are a complex tapestry of cells, woven into tissues and organs, like bones, muscle, and skin. All these cells begin as blank slates called stem cells, which are directed to become all the unique cell types in the body by a myriad of genetic and environmental cues.
To harness the biomedical potential of stem cells, researchers have long sought ways to untangle these factors and find a recipe to efficiently grow any desired cell type. Now, expertise from textile research is helping create a new platform to achieve this goal.
Investigators from the laboratory of Ali Shilatifard, Ph.D., the Robert Francis Furchgott Professor and chair of Biochemistry and Molecular Genetics, have discovered a new repeat gene cluster sequence that is exclusively expressed in humans and non-human primates.
The discovery, detailed in a study published in Science Advances, is a breakthrough for human genome biology and has wide-ranging implications for future research in transcriptional regulation, human evolution, and the study of repetitive DNA sequences, according to the authors.
“This is an unbelievable discovery of the first elongation factor that is repeated within the human genome and is very primate-specific,” said Shilatifard, who is also director of the Simpson Querrey Institute for Epigenetics and a professor of Pediatrics.
To try everything Brilliant has to offer—free—for a full 30 days, visit http://brilliant.org/ArtemKirsanov/ The first 200 of you will get 20% off Brilliant’s annual premium subscription.
My name is Artem, I’m a computational neuroscience student and researcher. In this video we discuss engrams – fundamental units of memory in the brain. We explore what engrams are, how memory is allocated, where it is stored, and how different memories become linked with each other.
OUTLINE: 00:00 — Introduction. 00:39 — Historical background. 01:44 — Fear conditioning paradigm. 03:38 — Immediate-early genes as memory markers. 08:13 — Engrams are necessary and sufficient for recall. 10:16 — Excitabiliy and memory allocation. 16:19 — Brain-wide engrams. 18:12 — Linking memories together. 24:20 — Summary. 25:33 — Brilliant. 27:09 — Outro.
REFERENCES (in no particular order): 1. Robins, S. The 21st century engram. WIRES Cognitive Science e1653 (2023) doi:10.1002/wcs.1653. 2. Roy, D. S. et al. Brain-wide mapping reveals that engrams for a single memory are distributed across multiple brain regions. Nat Commun 13, 1799 (2022). 3. Josselyn, S. A. & Tonegawa, S. Memory engrams: Recalling the past and imagining the future. Science 367, eaaw4325 (2020). 4. Chen, L. et al. The role of intrinsic excitability in the evolution of memory: Significance in memory allocation, consolidation, and updating. Neurobiology of Learning and Memory 173, 107266 (2020). 5. Rao-Ruiz, P., Yu, J., Yu, J. J., Kushner, S. A. & Josselyn, S. A. Neuronal competition: microcircuit mechanisms define the sparsity of the engram. Current Opinion in Neurobiology 54163–170 (2019). 6. Josselyn, S. A. & Frankland, P. W. Memory Allocation: Mechanisms and Function. Annu. Rev. Neurosci. 41389–413 (2018). 7. Choi, J.-H. et al. Interregional synaptic maps among engram cells underlie memory formation. Science 360430–435 (2018). 8. Abdou, K. et al. Synapse-specific representation of the identity of overlapping memory engrams. Science 360, 1227–1231 (2018). 9. Yokose, J. et al. Overlapping memory trace indispensable for linking, but not recalling, individual memories. Science 355398–403 (2017). 10. Rashid, A. J. et al. Competition between engrams influences fear memory formation and recall. Science 353383–387 (2016). 11. Poo, M. et al. What is memory? The present state of the engram. BMC Biol 14, 40 (2016). 12. Park, S. et al. Neuronal Allocation to a Hippocampal Engram. Neuropsychopharmacol 41, 2987–2993 (2016). 13. Morrison, D. J. et al. Parvalbumin interneurons constrain the size of the lateral amygdala engram. Neurobiology of Learning and Memory 135, 91–99 (2016). 14. Minatohara, K., Akiyoshi, M. & Okuno, H. Role of Immediate-Early Genes in Synaptic Plasticity and Neuronal Ensembles Underlying the Memory Trace. Front. Mol. Neurosci. 8, (2016). 15. Josselyn, S. A., Köhler, S. & Frankland, P. W. Finding the engram. Nat Rev Neurosci 16521–534 (2015). 16. Yiu, A. P. et al. Neurons Are Recruited to a Memory Trace Based on Relative Neuronal Excitability Immediately before Training. Neuron 83722–735 (2014). 17. Redondo, R. L. et al. Bidirectional switch of the valence associated with a hippocampal contextual memory engram. Nature 513426–430 (2014). 18. Ramirez, S. et al. Creating a False Memory in the Hippocampus. Science 341387–391 (2013). 19. Liu, X. et al. Optogenetic stimulation of a hippocampal engram activates fear memory recall. Nature 484381–385 (2012). 20. Silva, A. J., Zhou, Y., Rogerson, T., Shobe, J. & Balaji, J. Molecular and Cellular Approaches to Memory Allocation in Neural Circuits. Science 326391–395 (2009).
The groundbreaking gene-editing technology known as Crispr, which acts like a molecular pair of scissors that can be used to cut and modify a DNA sequence, has moved rather quickly from the pages of scientific journals to the medical setting. Earlier this month, about three years after Jennifer Doudna and Emmanuelle Charpentier won the Nobel Prize in Chemistry for describing how bacteria’s immune system could be used as a tool to edit genes, regulators in the U.K. approved the first Crispr-based treatment for sickle cell disease and beta-thalassemia patients. The treatment, from Vertex Pharmaceuticals and Crispr Therapeutics, could be approved by the U.S. Food and Drug Administration early next month for sickle cell patients.
While many obstacles lie ahead for the nascent field, such as how to pay for treatments that typically cost more than $1 million, these regulatory approvals are just the start as newer gene-editing technologies such as base and prime editing make their way through human studies. In an interview, Prof. Doudna says the approval is “a turning point in medicine because it really shows how genome editing can be used as a one-and-done cure for disease.”
Gene editing is part of a broader therapeutic revolution that encompasses genetic and cellular medicine. The pills and injections we are all familiar with generally target proteins or pathways in the body to treat disease. With gene and cell therapy, we can now target the root cause of disease, sometimes curing patients.
A research team from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences (CAS) has developed an analysis service platform called CRISPRimmunity, which was an interactive web server for identifying important molecular events related to CRISPR and regulators of genome editing systems. The study is published in Nucleic Acids Research.
The new CRISPRimmunity platform was designed for integrated analysis and prediction of CRISPR-Cas and anti-CRISPR systems. It includes customized databases with annotations for known anti-CRISPR proteins, anti-CRISPR-associated proteins, class II CRISPR-Cas systems, CRISPR array types, HTH structural domains and mobile genetic elements. These resources allow the study of molecular events in the co-evolution of CRISPR-Cas and anti-CRISPR systems.
To improve prediction accuracy, the researchers used strategies such as homology analysis, association analysis and self-targeting in prophage regions to predict anti-CRISPR proteins. When tested on data from 99 experimentally validated Acrs and 676 non-Acrs, CRISPRimmunity achieved an accuracy of 0.997 for anti-CRISPR protein prediction.
