Sensory feedback restoration in leg amputees improves walking speed, metabolic cost and phantom pain


Conventional leg prostheses do not convey sensory information about motion or interaction with the ground to above-knee amputees, thereby reducing confidence and walking speed in the users that is associated with high mental and physical fatigue1,2,3,4. The lack of physiological feedback from the remaining extremity to the brain also contributes to the generation of phantom limb pain from the missing leg5,6. To determine whether neural sensory feedback restoration addresses these issues, we conducted a study with two transfemoral amputees, implanted with four intraneural stimulation electrodes7 in the remaining tibial nerve ( identifier NCT03350061). Participants were evaluated while using a neuroprosthetic device consisting of a prosthetic leg equipped with foot and knee sensors. These sensors drive neural stimulation, which elicits sensations of knee motion and the sole of the foot touching the ground. We found that walking speed and self-reported confidence increased while mental and physical fatigue decreased for both participants during neural sensory feedback compared to the no stimulation trials. Furthermore, participants exhibited reduced phantom limb pain with neural sensory feedback. The results from these proof-of-concept cases provide the rationale for larger population studies investigating the clinical utility of neuroprostheses that restore sensory feedback.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Neuroprosthesis.
Fig. 2: Walking speed, confidence and mental effort assessment.
Fig. 3: Metabolic consumption assessment.
Fig. 4: Pain treatments: NPSI measurement.

Data availability

Data that support the findings and software routines developed for the analysis are available from the corresponding author. Data can be made available to qualified individuals for collaboration provided that a written agreement is signed in advance between the included consortium and the requester’s affiliated institution.


  1. 1.

    Nolan, L. et al. Adjustments in gait symmetry with walking speed in trans-femoral and trans-tibial amputees. Gait Posture 17, 142–151 (2003).

    Article  Google Scholar 

  2. 2.

    Miller, W. C., Deathe, A. B., Speechley, M. & Koval, J. The influence of falling, fear of falling, and balance confidence on prosthetic mobility and social activity among individuals with a lower extremity amputation. Arch. Phys. Med. Rehabil. 82, 1238–1244 (2001).

    CAS  Article  Google Scholar 

  3. 3.

    Waters, R. L., Perry, J., Antonelli, D. & Hislop, H. Energy cost of walking of amputees: the influence of level of amputation. J. Bone Joint Surg. Am. 58, 42–46 (1976).

    CAS  Article  Google Scholar 

  4. 4.

    Williams, R. M. et al. Does having a computerized prosthetic knee influence cognitive performance during amputee walking? Arch. Phys. Med. Rehabil. 87, 989–994 (2006).

    Article  Google Scholar 

  5. 5.

    Flor, H., Nikolajsen, L. & Staehelin Jensen, T. Phantom limb pain: a case of maladaptive CNS plasticity? Nat. Rev. Neurosci. 7, 873–881 (2006).

    CAS  Article  Google Scholar 

  6. 6.

    Makin, T. R. et al. Phantom pain is associated with preserved structure and function in the former hand area. Nat. Commun. 4, 1570 (2013).

    Article  PubMed Central  Google Scholar 

  7. 7.

    Boretius, T. et al. A transverse intrafascicular multichannel electrode (TIME) to interface with the peripheral nerve. Biosens. Bioelectron. 26, 62–69 (2010).

    CAS  Article  Google Scholar 

  8. 8.

    Hargrove, L. J., Young, A. J. & Simon, A. M. Intuitive control of a powered prosthetic leg during ambulation: a randomized clinical trial. J. Vasc. Surg. 63, 1405–1406 (2016).

    Article  Google Scholar 

  9. 9.

    Clites, T. R. et al. Proprioception from a neurally controlled lower-extremity prosthesis. Sci. Transl. Med. 10, eaap8373 (2018).

    Article  Google Scholar 

  10. 10.

    Crea, S., Edin, B. B., Knaepen, K., Meeusen, R. & Vitiello, N. Time-discrete vibrotactile feedback contributes to improved gait symmetry in patients with lower limb amputations: case series. Phys. Ther. 97, 198–207 (2017).

    Article  PubMed Central  Google Scholar 

  11. 11.

    Rusaw, D., Hagberg, K., Nolan, L. & Ramstrand, N. Can vibratory feedback be used to improve postural stability in persons with transtibial limb loss? J. Rehabil. Res. Dev. 49, 1239–1254 (2012).

    Article  PubMed Central  Google Scholar 

  12. 12.

    Dietrich, C. et al. Leg prosthesis with somatosensory feedback reduces phantom limb pain and increases functionality. Front. Neurol. 9, 270 (2018).

    Article  PubMed Central  Google Scholar 

  13. 13.

    Petrini, F. M. et al. Six‐month assessment of a hand prosthesis with intraneural tactile feedback. Ann. Neurol. 85, 137–154 (2019).

    Article  PubMed Central  Google Scholar 

  14. 14.

    Raspopovic, S. et al. Restoring natural sensory feedback in real-time bidirectional hand prostheses. Sci. Transl. Med. 6, 222ra19 (2014).

    Article  PubMed Central  Google Scholar 

  15. 15.

    Charkhkar, H. et al. High-density peripheral nerve cuffs restore natural sensation to individuals with lower-limb amputations. J. Neural Eng. 15, 056002 (2018).

    Article  PubMed Central  Google Scholar 

  16. 16.

    Clippinger, F. W., Seaber, A. V., McElhaney, J. H., Harrelson, J. M. & Maxwell, G. M. Afferent sensory feedback for lower extremity prosthesis. Clin. Orthop. Relat. Res. 169, 202–206 (1982).

    Google Scholar 

  17. 17.

    Tan, D. W. et al. A neural interface provides long-term stable natural touch perception. Sci. Transl. Med. 6, 257ra138 (2014).

    Article  PubMed Central  Google Scholar 

  18. 18.

    Rossini, P. M. et al. Double nerve intraneural interface implant on a human amputee for robotic hand control. Clin. Neurophysiol. 121, 777–783 (2010).

    Article  PubMed Central  Google Scholar 

  19. 19.

    Cruccu, G. et al. EFNS guidelines on neurostimulation therapy for neuropathic pain. Eur. J. Neurol. 14, 952–970 (2007).

    CAS  Article  PubMed Central  Google Scholar 

  20. 20.

    Wickens, C. D., Isreal, J. & Donchin, E. The event related cortical potential as an index of task workload. In Proc. of the Human Factors and Ergonomics Society Annual Meeting 282–286 (SAGE Publications, 1977).

  21. 21.

    Zink, R., Hunyadi, B., Van Huffel, S. & De Vos, M. Mobile EEG on the bike: disentangling attentional and physical contributions to auditory attention tasks. J. Neural Eng. 13, 046017 (2016).

    Article  PubMed Central  Google Scholar 

  22. 22.

    Isreal, J. B., Wickens, C. D., Chesney, G. L. & Donchin, E. The event-related brain potential as an index of display-monitoring workload. Hum. Factors 22, 211–224 (1980).

    CAS  Article  PubMed Central  Google Scholar 

  23. 23.

    Wickens, C., Kramer, A., Vanasse, L. & Donchin, E. Performance of concurrent tasks: a psychophysiological analysis of the reciprocity of information-processing resources. Science 221, 1080–1082 (1983).

    CAS  Article  PubMed Central  Google Scholar 

  24. 24.

    Burkitt, A. N. A review of the integrate-and-fire neuron model: I. homogeneous synaptic input. Biol. Cybern. 95, 1–19 (2006).

    CAS  Article  PubMed Central  Google Scholar 

  25. 25.

    Valle, G. et al. Biomimetic intraneural sensory feedback enhances sensation naturalness, tactile sensitivity, and manual dexterity in a bidirectional prosthesis. Neuron 100, 37–45.e7 (2018).

    CAS  Article  PubMed Central  Google Scholar 

  26. 26.

    Melzack, R. & Casey, K. L. Sensory, motivational, and central control determinants of pain: a new conceptual model. In Proc. of the First International Symposium on the Skin Senses (ed. Kenshalo, D. R.) 423–436, (Charles C Thomas Publisher, 1968).

  27. 27.

    Bouhassira, D. et al. Development and validation of the Neuropathic Pain Symptom Inventory. Pain 108, 248–257 (2004).

    Article  PubMed Central  Google Scholar 

  28. 28.

    Wewers, M. E. & Lowe, N. K. A critical review of visual analogue scales in the measurement of clinical phenomena. Res. Nurs. Health 13, 227–236 (1990).

    CAS  Article  PubMed Central  Google Scholar 

  29. 29.

    Orendurff, M. S. et al. Gait efficiency using the C-Leg. J. Rehabil. Res. Dev. 43, 239–246 (2006).

    Article  PubMed Central  Google Scholar 

  30. 30.

    Peduzzi de Castro, M., Soares, D., Mendes, E. & Machado, L. Plantar pressures and ground reaction forces during walking of individuals with unilateral transfemoral amputation. PM R 6, 698–707.e1 (2014).

    Article  Google Scholar 

  31. 31.

    Gailey, R. et al. Unilateral lower-limb loss: prosthetic device use and functional outcomes in servicemembers from Vietnam war and OIF/OEF conflicts. J. Rehabil. Res. Dev. 47, 317–331 (2010).

    Article  Google Scholar 

  32. 32.

    Naschitz, J. E. & Lenger, R. Why traumatic leg amputees are at increased risk for cardiovascular diseases. QJM 101, 251–259 (2008).

    CAS  Article  Google Scholar 

  33. 33.

    Modan, M. et al. Increased cardiovascular disease mortality rates in traumatic lower limb amputees. Am. J. Cardiol. 82, 1242–1247 (1998).

    CAS  Article  Google Scholar 

  34. 34.

    Johansson, J. L., Sherrill, D. M., Riley, P. O., Bonato, P. & Herr, H. A clinical comparison of variable-damping and mechanically passive prosthetic knee devices. Am. J. Phys. Med. Rehabil. 84, 563–575 (2005).

    Article  Google Scholar 

  35. 35.

    Schmalz, T., Blumentritt, S. & Jarasch, R. Energy expenditure and biomechanical characteristics of lower limb amputee gait: the influence of prosthetic alignment and different prosthetic components. Gait Posture 16, 255–263 (2002).

    Article  Google Scholar 

  36. 36.

    Genin, J. J., Bastien, G. J., Franck, B., Detrembleur, C. & Willems, P. A. Effect of speed on the energy cost of walking in unilateral traumatic lower limb amputees. Eur. J. Appl. Physiol. 103, 655–663 (2008).

    Article  Google Scholar 

  37. 37.

    Detrembleur, C., Vanmarsenille, J.-M., De Cuyper, F. & Dierick, F. Relationship between energy cost, gait speed, vertical displacement of centre of body mass and efficiency of pendulum-like mechanism in unilateral amputee gait. Gait Posture 21, 333–340 (2005).

    Article  PubMed Central  Google Scholar 

  38. 38.

    Farrar, J. T., Young, J. P. Jr., LaMoreaux, L., Werth, J. L. & Poole, R. M. Clinical importance of changes in chronic pain intensity measured on an 11-point numerical pain rating scale. Pain 94, 149–158 (2001).

    CAS  Article  PubMed Central  Google Scholar 

  39. 39.

    Melzack, R. & Wall, P. D. Pain mechanisms: a new theory. Science 150, 971–979 (1965).

    CAS  Article  PubMed Central  Google Scholar 

  40. 40.

    Raspopovic, S., Petrini, F. M., Zelechowski, M. & Valle, G. Framework for the development of neuroprostheses: from basic understanding by sciatic and median nerves models to bionic legs and hands. Proc. IEEE Inst. Electr. Electron. Eng. 105, 34–49 (2017).

    Article  Google Scholar 

  41. 41.

    Kim, L. H., McLeod, R. S. & Kiss, Z. H. T. A new psychometric questionnaire for reporting of somatosensory percepts. J. Neural Eng. 15, 013002 (2018).

    CAS  Article  PubMed Central  Google Scholar 

  42. 42.

    Bellmann, M., Schmalz, T., Ludwigs, E. & Blumentritt, S. Immediate effects of a new microprocessor-controlled prosthetic knee joint: a comparative biomechanical evaluation. Arch. Phys. Med. Rehabil. 93, 541–549 (2012).

    Article  Google Scholar 

  43. 43.

    Andreu, D., Guiraud, D. & Souquet, G. A distributed architecture for activating the peripheral nervous system. J. Neural Eng. 6, 026001 (2009).

    Article  Google Scholar 

  44. 44.

    Hafner, B. J. & Smith, D. G. Differences in function and safety between Medicare Functional Classification Level-2 and -3 transfemoral amputees and influence of prosthetic knee joint control. J. Rehabil. Res. Dev. 46, 417–433 (2009).

    Article  Google Scholar 

  45. 45.

    Asano, M., Miller, W. C. & Eng, J. J. Development and psychometric properties of the ambulatory self-confidence questionnaire. Gerontology 53, 373–381 (2007).

    Article  Google Scholar 

  46. 46.

    Powell, L. E. & Myers, A. M. The Activities-specific Balance Confidence (ABC) Scale. J. Gerontol. A Biol. Sci. Med. Sci. 50A, M28–M34 (1995).

    CAS  Article  Google Scholar 

  47. 47.

    Polich, J. Updating P300: an integrative theory of P3a and P3b. Clin. Neurophysiol. 118, 2128–2148 (2007).

    Article  PubMed Central  Google Scholar 

  48. 48.

    Strayer, D. L. et al. Assessing cognitive distraction in the automobile. Hum. Factors 57, 1300–1324 (2015).

    Article  Google Scholar 

  49. 49.

    Giraudet, L., St-Louis, M.-E., Scannella, S. & Causse, M. P300 event-related potential as an indicator of inattentional deafness? PLoS One 10, e0118556 (2015).

    Article  PubMed Central  Google Scholar 

  50. 50.

    Deeny, S., Chicoine, C., Hargrove, L., Parrish, T. & Jayaraman, A. A simple ERP method for quantitative analysis of cognitive workload in myoelectric prosthesis control and human-machine interaction. PLoS One 9, e112091 (2014).

    Article  PubMed Central  Google Scholar 

  51. 51.

    Mullen, T. R. et al. Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE Trans. Biomed. Eng. 62, 2553–2567 (2015).

    Article  PubMed Central  Google Scholar 

  52. 52.

    Viola, F. C. et al. Semi-automatic identification of independent components representing EEG artifact. Clin. Neurophysiol. 120, 868–877 (2009).

    Article  PubMed Central  Google Scholar 

  53. 53.

    Luck, S. J. An Introduction to the Event-related Potential Technique (The MIT Press, 2014).

  54. 54.

    Kennedy, P. M. & Inglis, J. T. Distribution and behaviour of glabrous cutaneous receptors in the human foot sole. J. Physiol. 538, 995–1002 (2002).

    CAS  Article  PubMed Central  Google Scholar 

  55. 55.

    Steffen, T. M., Hacker, T. A. & Mollinger, L. Age- and gender-related test performance in community-dwelling elderly people: Six-Minute Walk Test, Berg Balance Scale, Timed Up & Go Test, and gait speeds. Phys. Ther. 82, 128–137 (2002).

    Article  PubMed Central  Google Scholar 

  56. 56.

    Traballesi, M., Porcacchia, P., Averna, T. & Brunelli, S. Energy cost of walking measurements in subjects with lower limb amputations: a comparison study between floor and treadmill test. Gait Posture 27, 70–75 (2008).

    Article  PubMed Central  Google Scholar 

  57. 57.

    Soin, A., Syed Shah, N. & Fang, Z.-P. High-Frequency electrical nerve block for postamputation pain: a pilot study. Neuromodulation 18, 197–206 (2015).

    Article  PubMed Central  Google Scholar 

Download references


The authors are deeply grateful to the study participants who freely donated months of their life for the advancement of knowledge and for a better future for traumatic leg amputees. Thanks are also due to T. Palibrk for helping during the surgical implantation/explantation of the TIMEs and M. Marazzi for helping during the data analysis. European Research Council grant no. 759998 (FeelAgain), European Commission grant no. 754497 (SensAgain) and Swiss National Science Foundation grant no. 176006 (SYMBIO-LEG) funded this research.

Author information




F.M.P. designed the study, developed the software and the overall system integration, performed and supervised the experiments, analyzed the data and wrote and reviewed the paper. M.B. performed the surgeries, was responsible for all the clinical aspects of the study and reviewed the manuscript. G.V. developed the software and the overall system integration, performed the experiments, analyzed the data and reviewed the manuscript. V.I. and S. Mazic collected and analyzed the metabolic measurements. P.M. and B.M. collected and analyzed the EEG measurements. P.C. and T.S. developed the TIME electrodes and delivered technical assistance during the implantation and explanation procedures. F.B. and D.B. developed the software and the overall system integration and performed the experiments. N.K. analyzed the data. D.G. and D.A. designed the hardware and embedded software (real-time control) for STIMEP. K.L. and A.A. participated in the experimental design, prosthesis fitting and system integration, discussed the results and reviewed the manuscript. A.L. assisted with the surgeries, selected the participants and managed the regulatory pathway and clinical aspects. S. Micera designed the study, discussed the results and wrote the manuscript. S.R. designed the study, performed and supervised the experiments, managed the regulatory pathway, coanalyzed the data and wrote the manuscript. All authors had access to the relevant data. All authors authorized submission of the manuscript; the final submission decision was taken by the corresponding author.

Corresponding author

Correspondence to Stanisa Raspopovic.

Ethics declarations

Competing interests

F.M.P., S.R. and S. Micera hold shares of SensArs Neuroprosthetics Sarl, a start-up company dealing with the commercialization of neurocontrolled artificial limbs. The other authors declare no competing interests.

Additional information

Peer review information: Brett Benedetti was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Surgical implantation of the neural interfaces.

After the nerve dissection from the surrounding tissues, a small window is opened on the nerve epineurium, exposing different fascicles, which can be visualized. The implants are placed by carefully pulling the guiding needle, which is connected to the electrodes. The implants are positioned to cross the majority of fascicles in a very close (longitudinal) space. Cables are fixed by flap preparation from the fascia tissue. The electrode cables are tunneled through the thigh and pulled out of the leg through small incisions (for each cable, a small skin incision is made) just a few centimeters below the iliac crest, to enable transcutaneous connection with the neurostimulator. Electrode positioning is shown in the X-ray pictures taken before explantation (bottom right).

Extended Data Fig. 2 Sensation characterization.

The sensation characterization process is implemented to determine the response of the individual to the stimulation. a, Distribution of tactile sensations over the foot elicited by the stimulation of the four electrodes (color-coded). The number of electrode sites evoking a sensation in the foot is reported. b, Distribution of sensations over the lower leg (A, gastrocnemius caput medialis; B, gastrocnemius caput lateralis; C, soleus; D, posterior ankle). The number of active sites eliciting sensations is also reported. c, The percentage of sensation types reported during the trial for each participant is shown. d, The evoked sensation extension according to the minimum and maximum perceived intensity is displayed. Data are reported for three different days and two active sites in both participants. e, The proportional relationship between the amplitude of the injected pulses and the normalized perceived sensation intensity for participant 2 is shown. Pulse width and stimulation frequency are displayed. The points indicate the mean ratings (n = 6 ramps of stimulation amplitudes); the error bars denote the s.d.; the faded line is the line of best fit. The coefficient of determination R2 and P = 5.7 × 10−7 obtained from the Pearson correlation coefficient calculation (to test if the corresponding correlation R is considered significant) are reported.

Extended Data Fig. 3 Pain treatments: cumulative VAS measurement.

ah, A pain treatment session consisted of 10 min of stimulation. Before and after the session, participants completed the cumulative VAS questionnaire. The cumulative VAS was also recorded over time before and after the implant/explant. The VAS score during the sessions with frequency-invariant and frequency-variant stimulation treatments, and the control are shown for participants 1 (a) and 2 (e). A comparison between the cumulative VAS score before and after the different treatments is shown for participants 1 (b) and 2 (f). The cumulative VAS evolution over the weeks is shown in participants 1 (c), and 2 (g). A comparison of pain treatments for participants 1 (d) and 2 (h) is shown. In each box plot, the thick horizontal line denotes the median, the lower and upper hinges correspond to the first and third quartiles, the whiskers extend from the hinge to the most extreme value no further than 1.5 × interquartile range from the hinge and the dots beyond the whiskers are outliers. Statistical evaluations were performed using the Kruskal–Wallis test with Tukey–Kramer correction for multigroup comparison. *P < 0.05. For participant 1, the average reduction of VAS from before to after the treatments was significant for frequency-invariant stimulation (VAS: n = 7 stimulation sessions, d.f. = 1, P = 0.03, χ2 = 4.52) and frequency-variant stimulation (n = 7 stimulation sessions, d.f. = 1, P = 0.04, χ2 = 4.22) as was the case for participant 2 (for frequency-invariant stimulation, n = 10, d.f. = 1, P = 0.0002, χ2 = 13.82; for frequency-variant stimulation, n = 10, d.f. = 1, P = 0.009, χ2 = 6.7). In d, Pfrequency-invariant-frequency-variant = 0.89, Pfrequency-invariant-control = 0.0014, Pfrequency-variant-control = 0.0067, d.f. = 2, χ2frequency-invariant-frequency-variant= 8.9, χ2frequency-invariant-control = 18.76, χ2frequency-variant-control = 17.33; in h, Pfrequency-invariant-frequency-variant = 0.41, Pfrequency-invariant-control = 0.000085, Pfrequency-variant-control = 0.0098, d.f. = 2, χ2frequency-invariant-frequency-variant = 13.7, χ2frequency-invariant-control = 24.81, χ2frequency-variant-control = 20.01.

Extended Data Fig. 4 Gait analysis during the outdoor sand task.

a, Vertical ground reaction force (vGRF) mean value (n = 43 steps) for the healthy leg for participant 1 (left), and vGRF mean value (n = 47 steps) for participant 2 (right). The integrals of vGRF (as function of time; figure insets) are statistically different (ANOVA, P < 0.05), showing that higher work is applied on the ground when the feedback (sensory feedback) is provided with regard to the no feedback condition. b, vGRF mean value for the prosthetic leg for participants 1 (left) (n = 62 steps) and 2 (right) (n = 42 steps).The integrals of vGRF are not statistically different. n.u., normalized units with respect to the maximum force applied by both feet. In each box plot, the thick horizontal line denotes the median, the lower and upper hinges correspond to the first and third quartiles, the whiskers extend from the hinge to the most extreme value no further than 1.5× interquartile range from the hinge and the dots beyond the whiskers are outliers. Statistical evaluations were performed using ANOVA. *P < 0.0001. Healthy leg, participant 1, d.f. = 1, P = 2.89 × 10−8, F = 37.44. Prosthetic leg, participant 1: d.f. = 1, P = 0.98, F = 0. Healthy leg, participant 2, d.f. = 1, P = 2.87 × 10−37, F = 451.93. Prosthetic leg, participant 2, d.f. = 1, P = 0.07, F = 3.34. c, Limb Symmetry Index1 between healthy leg and prosthesis calculated using the mean values of the integrals of vGRF (a,b). When artificial sensory feedback is provided, the Limb Symmetry Index is closer to 0 than during the no feedback condition. That means that participants are walking more symmetrically, that is, more similarly to how healthy individuals walk.

Extended Data Fig. 5 Pain treatment: frequency-invariant and frequency-variant stimulation.

The stimulation strategies used to treat phantom limb pain are reported. Frequency-invariant stimulation consists of 10-min neural stimulation characterized by constant pulse width, amplitude and frequency (50 Hz). Frequency-variant stimulation is generated using a Poisson noise added at the carrier frequency (50 Hz). The effect is a 10-min pulse train where the inter-pulse interval varies.

Supplementary information

Supplementary information

Additional results, Supplementary Tables 1–4, Clinical protocol (original protocol, original statistical analysis plan and final statistical analysis plan).

Reporting Summary

Supplementary Video 1

Neural sensory feedback is intuitively integrated during walking.

Supplementary Video 2

Neuroprosthesis increases walking speed.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Petrini, F.M., Bumbasirevic, M., Valle, G. et al. Sensory feedback restoration in leg amputees improves walking speed, metabolic cost and phantom pain. Nat Med 25, 1356–1363 (2019).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing