The Leo Foundation has awarded a $600,000 grant to Prof. Yossi Buganim’s lab at The Hebrew University of Jerusalem to support groundbreaking research on fibroblast dysfunction in aging skin. This project aims to develop innovative technology for rejuvenating aged fibroblasts to improve wound healing.
The Hebrew University of Jerusalem proudly congratulates three of its esteemed researchers for receiving prestigious European Research Council (ERC) Proof of Concept Grants. These grants, each valued at €150,000, are designed to bridge the gap between groundbreaking research and its practical application, including early phases of commercialization.
The recipients from Hebrew University are:
Researchers have developed a new method using ArchCUT3-D software to study rock engravings, integrating technological and visual analysis to reveal intricate details of ancient techniques. This new approach bridges the gap between production processes and visual outcomes, offering comprehensive insights into the cultural significance of engravings in Timna Park, southern Israel.
Historically, rock engravings have been examined primarily through their visual characteristics using comparative and interpretative methodologies. While recent works have focused on identifying production processes, these studies often neglected the visual outcomes. Dubinsky and Prof. Grosman’s research bridges this gap by using computational analysis to integrate both technological and visual aspects, offering a comprehensive understanding of ancient engraving practices.
"We employed ArchCUT3-D software to conduct a detailed analysis of 3-D data from various rock engravings. This method allowed us to extract micro-morphological evidence from engraved lines, decoding technical trends and variabilities in the execution of these ancient artworks. By examining a specific group of engraved figures, we established a link between the techniques used and the visual considerations guiding them," explains Lena Dubinsky.
Based on their findings, the researchers propose the term "Techné" to describe the choice of technique that goes beyond mere practicality, encompassing the intentional design and cultural concepts embedded in the engravings. This integrative approach challenges the traditional dichotomy between visual and technological research, presenting a unified framework for understanding ancient production acts.
The study highlights how social structures and individual actions influence production methods, suggesting that the decisions related to technique selection are reflective of broader sociocultural contexts. This perspective offers a richer narrative of ancient engravers' cognitive and material interactions, providing deeper insights into their cultural and technological environment.
The research underscores the potential of digital tools in archaeological studies. Their methodology not only advances the study of rock engravings but also sets a precedent for exploring other archaeological artifacts. By identifying "techno-visual codes" and the “fingerprints” of engraved complexes, this approach enhances our ability to understand the cultural and technological nuances of ancient societies.
"This study marks a significant step forward in archaeological research, combining advanced computational analysis with a nuanced understanding of ancient techniques and visual styles. It opens new avenues for exploring the interplay between technology and visuality in historical contexts, promising to deepen our knowledge of the past," says Prof. Grosman.
The research paper titled “Techné of Rock Engravings—the Timna Case Study” is now available in Journal of Archaeological Method and Theory and can be accessed at https://doi.org/10.1007/s10816-024-09658-5.
Researchers:
Lena Dubinsky1,2,3, Leore Grosman1
Institution:
Pictures:
Stela engraving scanning process. (Credit: Liron Narunsky)
Stela engraving: annotated 3-D model (a); photograph (b). Annotation based on the analytical study of the micromorphology. (Credit: Liron Narunsky)
Researchers have developed a new method to significantly enhance quantum technology performance by using the cross-correlation of two noise sources to extend coherence time, improve control fidelity, and increase sensitivity for high-frequency sensing. This innovative strategy addresses key challenges in quantum systems, offering a tenfold increase in stability and paving the way for more reliable and versatile quantum devices.
Quantum technologies, including quantum computers and sensors, hold immense potential for revolutionizing various fields such as computing, cryptography, and medical imaging. However, their development has been hampered by the detrimental effects of noise, which can disrupt quantum states and lead to errors.
Many traditional approaches to mitigating noise in quantum systems primarily focus on temporal autocorrelation, which examines how noise behaves over time. While effective to some extent, these methods fall short when other types of noise correlations are present.
The research was conducted by experts in quantum physics, PhD. student Alon Salhov under the guidance of Prof. Alex Retzker from Hebrew University, PhD. student Qingyun Cao under the guidance of Prof. Fedor Jelezko and Dr. Genko Genov from Ulm University and Prof. Jianming Cai from Huazhong University of Science and Technology. They have introduced an innovative strategy that leverages the cross-correlation between two noise sources. By exploiting the destructive interference of cross-correlated noise, the team has managed to significantly extend the coherence time of quantum states, improve control fidelity, and enhance sensitivity for high-frequency quantum sensing.
Key achievements of this new strategy include:
Tenfold Increase in Coherence Time: The duration for which quantum information remains intact is extended ten times longer compared to previous methods.
Improved Control Fidelity: Enhanced precision in manipulating quantum systems leads to more accurate and reliable operations.
Superior Sensitivity: The ability to detect high-frequency signals surpasses the current state-of-the-art, enabling new applications in quantum sensing.
Alon Salhov said, "Our innovative approach extends our toolbox for protecting quantum systems from noise. By focusing on the interplay between multiple noise sources, we've unlocked unprecedented levels of performance, bringing us closer to the practical implementation of quantum technologies."
This advancement not only marks a significant leap in the field of quantum research but also holds promise for a wide range of applications. Industries that rely on highly sensitive measurements, such as healthcare, stand to benefit enormously from these improvements.
The study titled “Protecting Quantum Information via Destructive Interference of Correlated Noise” is now available in Physical Review Letters and can be openly accessed at https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.132.223601
Researchers:
Alon Salhov1, Qingyun Cao2,3, Jianming Cai3, Alex Retzker1,4, Fedor Jelezko2, and Genko Genov2
Institutions:
1. Racah Institute of Physics, The Hebrew University of Jerusalem
2. Institute for Quantum Optics, Ulm University, Germany
3. School of Physics, International Joint Laboratory on Quantum Sensing and Quantum Metrology, Huazhong University of Science and Technology, China
4. AWS Center for Quantum Computing, USA
Funding
Clore Israel Foundation Scholars Programme, the Israeli Council for Higher Education, and the Milner Foundation. This work was funded by the German Federal Ministry of Research (BMBF) by future cluster QSENS and projects DE-Brill (No. 13N16207), SPINNING, DIAQNOS (No. 13N16463), quNV2.0 (No. 13N16707), QR. X and Quamapolis (No. 13N15375), DLR via project QUASIMODO (No. 50WM2170), Deutsche Forschungsgemeinschaft (DFG) via Projects No. 386028944, No. 445243414, and No. 387073854, and Excellence Cluster POLiS European Union’s HORIZON Europe program via projects QuMicro (No. 101046911), SPINUS (No. 101135699), CQuENS (No. 101135359), QCIRCLE (No. 101059999) and FLORIN (No. 101086142), European Research Council (ERC) via Synergy grant HyperQ (No. 856432) and Carl-Zeiss-Stiftung via the Center of Integrated Quantum Science and technology (IQST) and project Utrasens-Vir. A. R. acknowledges the support of European Research Council grant QRES, Project No. 770929, Quantera grant MfQDS, Israel Science Foundation and the Schwartzmann university chair. J. M. acknowledges the National Natural Science Foundation of China (Grants No. 12161141011).
Pictures credit - the authors of the paper.
Image 1)
Title: Enhanced Quantum Memory and Sensitivity by Interfering Noise
Description:
Schematic representation of destructive interference of cross-correlated noise, control sequences and experimental setup.
Detailed description (from the paper):
(a) The qubit is subjected to environmental noise δ(t). Applying a resonant drive with Rabi frequency Ω1 creates a protected dressed qubit which decoheres mainly due to ε1(t) - the amplitude noise in Ω1. Applying a second drive with modulation frequency eΩ1, Rabi frequency Ω2 and amplitude fluctuations ε2(t), reduces decoherence due to ε1(t).
(b) If the cross-correlation, c, of ε1(t) and ε2(t) is nonzero, a detuning eΩ1 = Ω1 + c Ω2 2/Ω1 tilts the effective-drive axis and induces a destructive interference of the cross-correlated noise, resulting in a doubly-dressed qubit with a longer coherence time.
(c) Measurement sequences for standard and correlated double drive (DD) protocols. (d) Experimental setup and level
scheme of the NV center
Image 2)
Title: Enhanced Quantum Memory and Sensitivity by Interfering Noise
Description: A Bloch-sphere of a qubit subjected to cross-correlated noise (blue and red). The method destructively interferes this noise, resulting in superior performance.
The Hebrew University of Jerusalem is Israel’s premier academic and research institution. With over 25,000 students from 90 countries, it is a hub for advancing scientific knowledge and holds a significant role in Israel’s civilian scientific research output, accounting for nearly 40% of it and has registered over 11,000 patents. The university’s faculty and alumni have earned eight Nobel Prizes and a Fields Medal, underscoring their contributions to ground-breaking discoveries. In the global arena, the Hebrew University ranks 86th according to the Shanghai Ranking. To learn more about the university’s academic programs, research initiatives, and achievements, visit the official website at http://new.huji.ac.il/en
Researchers have developed a machine learning approach to identify potential subtypes in diseases, significantly enhancing disease classification and treatment strategies. The model, which achieved an 89.4% ROC AUC, uncovered 515 previously unannotated disease subtypes, demonstrating the potential for more precise and personalized medical treatments.
Distinguishing diseases into distinct subtypes is pivotal for accurate study and effective treatment strategies. The Open Targets Platform integrates biomedical, genetic, and biochemical datasets to support disease ontologies, classifications, and potential gene targets. However, many disease annotations remain incomplete, often necessitating extensive expert medical input. This challenge is especially significant for rare and orphan diseases, where resources are limited.
The research introduces a novel machine learning approach to identify diseases with potential subtypes. Utilizing the extensive database of approximately 23,000 diseases documented in the Open Targets Platform, they derived new features to predict diseases with subtypes using direct evidence. Machine learning models were then applied to analyze feature importance and evaluate predictive performance, uncovering both known and novel disease subtypes.
The model achieved an impressive 89.4% ROC Area Under the Receiver Operating Characteristic Curve in identifying known disease subtypes. The integration of pre-trained deep-learning language models further enhanced the model's performance. Notably, the research identified 515 disease candidates predicted to possess previously unannotated subtypes, paving the way for new insights into disease classification.
"This project demonstrates the incredible potential of machine learning in expanding our understanding of complex diseases," said Dan Ofer. "By leveraging advanced models, we can uncover patterns and subtypes that were previously hidden, ultimately contributing to more precise and personalized treatments."
This innovative methodology enables a robust and scalable approach for improving knowledge-based annotations and provides a comprehensive assessment of disease ontology tiers. "We are excited about the potential of our machine learning approach to revolutionize disease classification," said Prof. Michal Linial. "Our findings can significantly contribute to personalized medicine, offering new avenues for therapeutic development."
The research paper titled “Automated annotation of disease subtypes” is now available in Journal of Biomedical Informatics and can be accessed at https://doi.org/10.1016/j.jbi.2024.104650.
Researchers:
Dan Ofer, Michal Linial
Institution:
Department of Biological Chemistry, The Life Science Institute, The Hebrew University of Jerusalem, Israel
The Hebrew University of Jerusalem is Israel's premier academic and research institution. Serving over 23,000 students from 80 countries, the University produces nearly 40% of Israel’s civilian scientific research and has received over 11,000 patents. Faculty and alumni of the Hebrew University have won eight Nobel Prizes and a Fields Medal. For more information about the Hebrew University, please visit http://new.huji.ac.il/en.
Hebrew University is proud to announce that Prof. Inbal Goshen has been elected as a member of the prestigious European Molecular Biology Organization (EMBO). This achievement recognizes Prof. Goshen's exceptional contributions to neuroscience research.
A new study reveals how parents naturally adjust their speech patterns to match their children's language proficiency. It shows that parents use less redundant language with older children, highlighting the impact of perceived language proficiency on communication. The findings offer valuable insights for our understanding of language development.
A new study presents an evolutionary-biophysical model that sheds new light on the evolution of the collaborative non-self recognition self-incompatibility, a genetic mechanism in plants that prevents self-fertilization and promotes cross-fertilization. Their innovative model introduces promiscuous molecular interactions as a key ingredient, enhancing our understanding of genetic diversity and evolution in hermaphroditic plants. This research enriches our understanding of plant biology and has broader implications for deciphering the evolution of biological networks and managing biodiversity.
Self-incompatibility (SI) is a widespread biological mechanism in plants having both male and female reproductive organs, that prevents self-fertilization and promotes genetic diversity. Under this mechanism, fertilization relies on the specific recognition between highly diverse proteins: the RNase (female determinant) and the SLF (male determinant). The interaction between these proteins ensures that plants are only compatible with non-self mates, thus maintaining a diverse gene pool.
The new model proposed by Dr. Friedlander and her team represents a significant advancement in understanding the evolutionary dynamics of self-incompatibility proteins. By allowing for promiscuous interactions—where interactions with unfamiliar partners are likely – and for multiple distinct partners per protein, the model aligns more closely with empirical findings than previous models that assumed only one-to-one interactions. This promiscuity enables a flexible interaction pattern between male and female proteins, offering new insights into how these proteins evolve and interact over generations.
"Our research shows that the ability of proteins to engage in promiscuous interactions is crucial for the long-term evolutionary maintenance of self-incompatibility systems," explained Dr. Friedlander. "We propose that the default state of this system is that recognition is likely and an evolutionary pressure is needed to avoid it, in contrast to what was previously thought. This flexibility not only helps in maintaining genetic diversity but also suggests that similar mechanisms could be operating in other biological systems."
The study also reveals how populations of these plants spontaneously organize into distinct compatibility classes, ensuring full compatibility across different classes while maintaining incompatibility within the same class. The model predicts various evolutionary paths that could lead to the formation or elimination of these compatibility classes based solely on point mutations. The dynamic balance between the emergence and decay of these classes, which provides a sustainable model of evolution, was analyzed by the researchers using a mixture of empirical and theoretical tools borrowed from the field of statistical mechanics in physics.
"These insights from our study have profound implications not only for plant biology but also for understanding the fundamental principles of molecular recognition and its impact on the evolution of biological networks," Dr. Friedlander added. "Our findings could also help in the conservation of plant biodiversity."
This research, which highlights the role of promiscuous and multi-partner molecular interactions, is likely to inspire seeking these two elements in additional biological systems, and help in explaining the evolution of various complex molecular networks.
The research paper titled “The role of promiscuous molecular recognition in the evolution of RNase-based self-incompatibility in plants” is now available in Nature Communications and can be accessed at https://doi.org/10.1038/s41467-024-49163-7.
Pictures
Title: A shift of paradigm in the molecular recognition model: from one-to –one (left) into many-to-many (right).
Description: Previous models of self-incompatibility accounted for only one-to-one interactions between male and female-determinant proteins. The new model allows for a more general network of interactions, where each protein can interact with any number of partners.
Credit: Tamar Friedlander and Amit Jangid.
Title: Tamar Friedlander holding two petunias
Credit: Nathan Mengisto, Faculty of Agriculture, HUJI.
Researchers:
Keren Erez1, Amit Jangid1, Ohad Noy Feldheim2 & Tamar Friedlander1
Institutions:
1) The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Faculty of Agriculture, The Hebrew University of Jerusalem
2) The Einstein Institute of Mathematics, Faculty of Natural Sciences, The Hebrew University of Jerusalem
The Hebrew University of Jerusalem is Israel's premier academic and research institution. Serving over 23,000 students from 80 countries, the University produces nearly 40% of Israel’s civilian scientific research and has received over 11,000 patents. Faculty and alumni of the Hebrew University have won eight Nobel Prizes and a Fields Medal. For more information about the Hebrew University, please visit http://new.huji.ac.il/en.
A new study has developed a predictive model for post-traumatic stress disorder (PTSD) following the mass terror attack on October 7th, 2023, and the subsequent Israel-Hamas war. The research determined that approximately 5.3% of the population, or about 519,923 individuals, may develop PTSD due to these events. This model serves as a vital tool for preparing mental health interventions and can be adapted for future mass trauma situations globally.
The terror attack by Hamas militants on October 7th, 2023, marked the beginning of a profound national crisis in Israel, leading to widespread trauma and significant mental health challenges across the country. The primary objective of this research was to create a model that can predict the prevalence of PTSD in the aftermath of trauma exposure across different segments of the Israeli population based on their exposure levels to the trauma.
The research team categorized the Israeli population into six distinct groups depending on their exposure to the conflict: direct exposure to terror, close-proximity to terror, involvement of soldiers in combat and support units, intense and moderate exposure to rocket attacks, and communities indirectly affected. Utilizing national databases, the team estimated the size of each group, conducted a literature review to derive PTSD prevalence rates, and performed a random-effects meta-analysis for the prevalence of PTSD in each group.
The findings suggest that approximately 5.3% of the Israeli population, or about 519,923 individuals, may develop PTSD as a result of the terror attack and the conflict, with a prediction interval ranging from 160,346 to 879,502. The study emphasizes the substantial mental health impact of such mass trauma and provides a crucial tool for policymakers, clinicians, and researchers. This model not only facilitates the planning and implementation of necessary mental health interventions but also has the potential to serve as a framework for addressing future mass trauma incidents worldwide.
This predictive model for post-traumatic stress disorder (PTSD) presents a pivotal opportunity for policy action. Following the mass terror attack and subsequent war, proactive measures are imperative. Policy recommendations should prioritize resource allocation for mental health services, including increased funding for counseling, therapy programs, and psychiatric care. As the need across the population is expected to be substantial, there is a pressing need for the adoption of comprehensive, system-wide models facilitating large-scale interventions. Such models should incorporate evidence-based group therapies, short-term individual protocols, initiatives for prevention and early intervention, and the utilization of digital technologies for monitoring and management of mental health symptoms. Governments should invest in training programs for mental health professionals to enhance their ability to identify and treat PTSD effectively. Integrating predictive models into disaster preparedness plans can assist in implementation of mental health interventions following mass trauma events, while global collaboration facilitates knowledge sharing and best practices for addressing mental health needs on a broader scale.
The research paper titled “A Prediction Model of PTSD in the Israeli Population in the Aftermath of October 7th, 2023, Terrorist Attack and the Israel-Hamas War” is now available in medRxiv and can be accessed at https://doi.org/10.1101/2024.02.25.24303235.
Researchers:
Dana Katsoty1, Michal Greidinger2, Yuval Neria3,4, Aviv Segev5,6,7, Ido Lurie5,6
Institutions:
The Hebrew University of Jerusalem is Israel's premier academic and research institution. Serving over 23,000 students from 80 countries, the University produces nearly 40% of Israel’s civilian scientific research and has received over 11,000 patents. Faculty and alumni of the Hebrew University have won eight Nobel Prizes and a Fields Medal. For more information about the Hebrew University, please visit http://new.huji.ac.il/en.
(photo credit: DALL-E, AI)
