Global food security is under constant threat by fungal, bacterial, and viral plant pathogens. Although plants have evolved innate immune responses against pathogens, 10 to 23% of global crop yield is lost every year to fungal pathogens alone (1). Plantinteracting microorganisms, including pathogenic fungi, translocate effector proteins into plant cells. These effectors enhance pathogen virulence by directly interfering with the plant immune system or by manipulating other cellular functions. On page 955 of this issue, McCombe et al. (2) report that the pathogen Magnaporthe oryzae (which causes devastating rice blast) and two additional plant disease–causing fungi enhance their pathogenicity by manipulating the plant’s monitoring of phosphate, a phosphorus-containing compound essential for plant growth. The mechanism tricks plants into entering a phosphate starvation response, which alters metabolism but also disrupts immune signaling and responses to infection. These findings represent a mechanism by which fungal pathogens can manipulate plant defense indirectly.
{"title":"Outsmarted by fungi","authors":"Caroline Gutjahr","doi":"","DOIUrl":"","url":null,"abstract":"<div >Global food security is under constant threat by fungal, bacterial, and viral plant pathogens. Although plants have evolved innate immune responses against pathogens, 10 to 23% of global crop yield is lost every year to fungal pathogens alone (<i>1</i>). Plantinteracting microorganisms, including pathogenic fungi, translocate effector proteins into plant cells. These effectors enhance pathogen virulence by directly interfering with the plant immune system or by manipulating other cellular functions. On page 955 of this issue, McCombe <i>et al</i>. (<i>2</i>) report that the pathogen <i>Magnaporthe oryzae</i> (which causes devastating rice blast) and two additional plant disease–causing fungi enhance their pathogenicity by manipulating the plant’s monitoring of phosphate, a phosphorus-containing compound essential for plant growth. The mechanism tricks plants into entering a phosphate starvation response, which alters metabolism but also disrupts immune signaling and responses to infection. These findings represent a mechanism by which fungal pathogens can manipulate plant defense indirectly.</div>","PeriodicalId":21678,"journal":{"name":"Science","volume":"387 6737","pages":""},"PeriodicalIF":44.7,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143522042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-21Epub Date: 2025-02-20DOI: 10.1126/science.ads7942
Matthew R Devlin, Sangwoo Kim, Otger Campàs, Elliot W Hawkes
The vision of robotic materials-cohesive collectives of robotic units that can arrange into virtually any form with any physical properties-has long intrigued both science and fiction. Yet, this vision requires a fundamental physical challenge to be overcome: The collective must be strong, to support loads, yet flow, to take new forms. We achieve this in a material-like robotic collective by modulating the interunit tangential forces to control topological rearrangements of units within a tightly packed structure. This allows local control of rigidity transitions between solid and fluid-like states in the collective and enables spatiotemporal control of shape and strength. We demonstrate structure-forming and healing and show the collective supporting 700 newtons (500 times the weight of a robot) before "melting" under its own weight.
{"title":"Material-like robotic collectives with spatiotemporal control of strength and shape.","authors":"Matthew R Devlin, Sangwoo Kim, Otger Campàs, Elliot W Hawkes","doi":"10.1126/science.ads7942","DOIUrl":"https://doi.org/10.1126/science.ads7942","url":null,"abstract":"<p><p>The vision of robotic materials-cohesive collectives of robotic units that can arrange into virtually any form with any physical properties-has long intrigued both science and fiction. Yet, this vision requires a fundamental physical challenge to be overcome: The collective must be strong, to support loads, yet flow, to take new forms. We achieve this in a material-like robotic collective by modulating the interunit tangential forces to control topological rearrangements of units within a tightly packed structure. This allows local control of rigidity transitions between solid and fluid-like states in the collective and enables spatiotemporal control of shape and strength. We demonstrate structure-forming and healing and show the collective supporting 700 newtons (500 times the weight of a robot) before \"melting\" under its own weight.</p>","PeriodicalId":21678,"journal":{"name":"Science","volume":"387 6736","pages":"880-885"},"PeriodicalIF":44.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143469273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-21Epub Date: 2025-02-20DOI: 10.1126/science.adp4990
Kristin Andrews, Jonathan Birch, Jeff Sebo
An emerging field shows how animal feelings can be studied scientifically.
{"title":"Evaluating animal consciousness.","authors":"Kristin Andrews, Jonathan Birch, Jeff Sebo","doi":"10.1126/science.adp4990","DOIUrl":"https://doi.org/10.1126/science.adp4990","url":null,"abstract":"<p><p>An emerging field shows how animal feelings can be studied scientifically.</p>","PeriodicalId":21678,"journal":{"name":"Science","volume":"387 6736","pages":"822-824"},"PeriodicalIF":44.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143469173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-21Epub Date: 2025-02-20DOI: 10.1126/science.adw8629
Dan Charles, Erik Stokstad
A court ruling may temporarily ease funding problems, but long-term outlook is uncertain.
{"title":"Crop research hit by U.S. foreign aid freezes.","authors":"Dan Charles, Erik Stokstad","doi":"10.1126/science.adw8629","DOIUrl":"https://doi.org/10.1126/science.adw8629","url":null,"abstract":"<p><p>A court ruling may temporarily ease funding problems, but long-term outlook is uncertain.</p>","PeriodicalId":21678,"journal":{"name":"Science","volume":"387 6736","pages":"816"},"PeriodicalIF":44.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143469151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-21Epub Date: 2025-02-20DOI: 10.1126/science.adw7713
Sandeep Ravindran
Many artificial intelligence models are power hungry and expensive. Researchers in the Global South are increasingly embracing low-cost, low-power alternatives.
{"title":"Cutting AI down to size.","authors":"Sandeep Ravindran","doi":"10.1126/science.adw7713","DOIUrl":"https://doi.org/10.1126/science.adw7713","url":null,"abstract":"<p><p>Many artificial intelligence models are power hungry and expensive. Researchers in the Global South are increasingly embracing low-cost, low-power alternatives.</p>","PeriodicalId":21678,"journal":{"name":"Science","volume":"387 6736","pages":"818-821"},"PeriodicalIF":44.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143469153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Maxim E Zaslavsky, Erin Craig, Jackson K Michuda, Nidhi Sehgal, Nikhil Ram-Mohan, Ji-Yeun Lee, Khoa D Nguyen, Ramona A Hoh, Tho D Pham, Katharina Röltgen, Brandon Lam, Ella S Parsons, Susan R Macwana, Wade DeJager, Elizabeth M Drapeau, Krishna M Roskin, Charlotte Cunningham-Rundles, M Anthony Moody, Barton F Haynes, Jason D Goldman, James R Heath, R Sharon Chinthrajah, Kari C Nadeau, Benjamin A Pinsky, Catherine A Blish, Scott E Hensley, Kent Jensen, Everett Meyer, Imelda Balboni, Paul J Utz, Joan T Merrill, Joel M Guthridge, Judith A James, Samuel Yang, Robert Tibshirani, Anshul Kundaje, Scott D Boyd
Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system's own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis, an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to severe acute respiratory syndrome coronavirus 2, influenza, and human immunodeficiency virus, highlight antigen-specific receptors, and reveal distinct characteristics of systemic lupus erythematosus and type-1 diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of immune responses.
{"title":"Disease diagnostics using machine learning of B cell and T cell receptor sequences.","authors":"Maxim E Zaslavsky, Erin Craig, Jackson K Michuda, Nidhi Sehgal, Nikhil Ram-Mohan, Ji-Yeun Lee, Khoa D Nguyen, Ramona A Hoh, Tho D Pham, Katharina Röltgen, Brandon Lam, Ella S Parsons, Susan R Macwana, Wade DeJager, Elizabeth M Drapeau, Krishna M Roskin, Charlotte Cunningham-Rundles, M Anthony Moody, Barton F Haynes, Jason D Goldman, James R Heath, R Sharon Chinthrajah, Kari C Nadeau, Benjamin A Pinsky, Catherine A Blish, Scott E Hensley, Kent Jensen, Everett Meyer, Imelda Balboni, Paul J Utz, Joan T Merrill, Joel M Guthridge, Judith A James, Samuel Yang, Robert Tibshirani, Anshul Kundaje, Scott D Boyd","doi":"10.1126/science.adp2407","DOIUrl":"10.1126/science.adp2407","url":null,"abstract":"<p><p>Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system's own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis, an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to severe acute respiratory syndrome <i>coronavirus</i> 2, influenza, and human immunodeficiency virus, highlight antigen-specific receptors, and reveal distinct characteristics of systemic lupus erythematosus and type-1 diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of immune responses.</p>","PeriodicalId":21678,"journal":{"name":"Science","volume":"387 6736","pages":"eadp2407"},"PeriodicalIF":44.7,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143469154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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