242 innovations from Bar-Ilan University, available for licensing, co-investment, or spin-out through BIRAD.
Cohen Cyrille
This is an optimized BCMA-specific chimeric antigen receptor for the treatment of multiple myeloma and other hematological diseases that we have developed.
Aurbach Doron
An electrochemical reactor that works in asymmetric method using flow electrodes to produce low cost green hydrogen gas. The electrochemical cell produces hydrogen using faradaic reaction for hydrogen evolution and capacitance electrostatic adsorption instead of oxygen evolution. Based on our Nofar scholar research, we developed and improved the device that is based on the described patent named:"METHOD AND APPARATUS FOR HYDROGEN PRODUCTION BY ELECTROLYSIS". Instead of using a static electrodes for the capacitance behavior we use flow electrodes that allows the regeneration of the electrodes outside the reactor in a different cell; hence, we do not require to pause the evolution of hydrogen for electrode regeneration and we need less electrodes that lower the cost of the electrochemical reactor.
Fischer Bilha
The detection of subcellular domains in cells can be obtained by specific fluorescent markers. Here we report the use of styryl quinolinium dyes that selectively stain ribosomal RNA (rRNA) in nucleoli and in the cytoplasm of mammalian cells. Specifically, we synthesized a series of 1-methyl-4-(substituted) styryl-quinolinium derivatives, 12a–l. We developed highly efficient microwave-assisted synthesis which prevents the formation of side products, leading to the products in yields greater than 90%. Compounds 12c-f and 12i in various solvents exhibited maximum absorbance at 500–660 nm, molar extinction coefficient of 25400–49000 M
Cohen Cyrille
TGFβ is a major immunoinhibitory factor present in the microenvironment of solid tumors. Different cancer types acquire the ability to overexpress TGFβ to escape immune response. Indeed, TGFβ dampens cytotoxic T cell activity, and its presence has been shown to correlate with tumor invasion and poor prognosis. Herein, we developed two approaches to target the effects of TGFβ and provide a functional advantage to genetically engineered T cells in the immunoinhibitory tumor milieu. We designed a TGFβRI-based co-stimulatory switch receptor (CSRI) that includes the TGFβ receptor I extracellular binding domain and a 4-1BB co-stimulatory signaling moiety. Additionally, we tested the function of a TGFβ-binding scFv trap produced by T cells. We demonstrated that both approaches endowed tumor-specific T cells with superior cytokine secretion, upregulation of activation markers, and reduced expression of inhibition markers upon co-culture with melanoma targets. Moreover, we noted that CSRI and the anti-TGFβ trap showed an improved anti-tumor function in vivo. Overall, we show that it is possible to target the TGFβ pathway to improve cellular immunotherapy.
Teman Adam
The invention is the product of my Magneton project with CEVA. It includes a novel architecture for generating flip flop based register files with many ports (more than two read and write ports). The architecture uses clock gating based on word selection to reduce power consumption. It uses guided placement to reduce area.
Meital Gal Tanamy
Vaccines based on live attenuated viruses are the most effective strategy for controlling infections, since they elicit long-lasting natural and effective immune response, but entail challenges as safety and virulence. Hepatitis C Virus (HCV) is a major global health problem, causing liver diseases and liver cancer, with millions infected each year and hundreds of thousands of annual fatalities; but no vaccine is currently available for the virus. Here we present a novel computational approach for the accurate predication of virus attenuation. The approach is based on a rational design of weakened virus variants by insertion of high number of synonymous mutations to disrupt the viral RNA’s secondary structure and regulatory sequences important for the viral life cycle. By measuring RNA levels and virus spread in HCV infection model, we showed that these variants have lower viral fitness relative to the wild-type virus, with gradient of attenuation in concordance with the prediction model. Deep sequencing of replicating viruses demonstrated genomic stability of the attenuated variant. Differential expression analysis and evaluation of cancer-related phenotypes revealed that the variants have a lower pathogenic influence on the host cells, compared to the WT virus. These rationally designed variants may be further considered as a promising direction for a viable HCV vaccine. Importantly, the computational approach described here is based on the most fundamental viral regulatory motifs and therefore may be applied for almost all viruses as a new strategy for vaccine development.
Sharon Gannot
In this work, we propose using diffusion models for speech inpainting, i.e., restoring missing or severely corrupted speech segments obfuscated by severe noise. We leverage the ability of diffusion models to generate realistic speech conditioned on the available context. Our approach progressively refines the reconstructed speech by modeling the missing segments as a denoising process, ensuring smooth transitions and high-fidelity synthesis. As we emphasize semantically correct speech generation, we use automatic speech recognition with a language model (ASR +LM) to guide the speech generation process. Our findings demonstrate the proposed model’s ability to handle a wide range of scenarios, from short gaps to longer missing segments, making it suitable for reconstructing a speech signal corrupted by severe noise, e.g., explosive noise. Our solution has several distinct attributes: 1) It is independent of the speaker, i.e., the algorithm is not limited to specific known speakers; 2) it preserves the speaker’s natural voice style and prosody; and 3) it maintains the natural environment, e.g., reverberation level while eliminating the strong noise.
Singer Gonen
We developed Automatic Complementary Separation Pruning (ACSP), a novel and fully automated pruning method for convolutional neural networks. ACSP integrates the strengths of both structured pruning and activation-based pruning, enabling the efficient removal of entire components such as neurons and channels while leveraging activations to preserve the most relevant components. Our approach is designed specifically for supervised learning tasks, where we construct a graphic space that encodes the separation capabilities of each component with respect to all class pairs. By employing complementary selection principles and utilizing a clustering algorithm, ACSP ensures that the selected components maintain diverse and complementary separation capabilities, reducing redundancy and maintaining high network performance. The method automatically identifies the optimal subset of components for each layer, selecting the minimal subset that preserves performance. This methodology is applicable to any type of network, including large language models.
Shai Rahimipour
β-Sheet aggregation between amyloid proteins is a key trait of neurodegenerative conditions, such as Alzheimer’s (AD) and Parkinson’s disease (PD), having dire socioeconomic consequences. In the US alone, AD effects 5.7 million Americans and costs $277 billion/year, a burden due to increase over the next 10 years. Without a cure, FDA approved drugs for AD and PD treat only symptoms. The current invention describes novel Aza-cyclopeptides that are based on our previously discovered and patented cyclic D,L-
Klein Shmuel Tomi
Extending recently suggested methods, a new dynamic compression algorithm is proposed, which assigns larger weights to characters that have just been coded by means of an increasing weight function. Empirical results present its efficient compression performance, which, for input files with locally skewed distributions, can improve beyond the lower bound given by the entropy for static encoding, at the price of slower running times for compression, and comparable time for decompression.
Albeck Amnon
Development of new synthetic compounds that prevent the autistic symptoms and improve some brain biochemical factors that are associated with autism
Sharon Gannot
In this work, we propose a method to imitate the human ability to selectively attend to a single speaker, even in the presence of multiple simultaneous talkers. To achieve this, we propose a novel approach for binaural target speaker extraction that leverages the listener’s Head-Related Transfer Function (HRTF) to isolate the desired speaker. Notably, our method does not rely on speaker embeddings, making it speaker-independent and enabling strong generalization across multiple speech datasets in different languages. We employ a fully complex-valued neural network that operates directly on the complex-valued Short-Time Fourier transform (STFT) of the mixed audio signals. This approach deviates from conventional methods that utilize spectrograms or treat the real and imaginary components of the STFT as separate real-valued inputs. The method is first evaluated in an anechoic, noise-free scenario, where it demonstrates excellent extraction performance while effectively preserving the binaural cues of the target signal. Next, it is tested under mild reverberation conditions. The method remains robust to reverberant conditions, maintaining speech clarity, preserving source directionality, and simultaneously reducing reverberation. Demo-page: https://bi-ctse-hrtf.github.io
