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1. Mitigating Interference in the Knowledge Continuum through Attention-Guided Incremental Learning

Author

Bhat, Prashant, Renjith, Bharath, Arani, Elahe, and Zonooz, Bahram

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Continual learning (CL) remains a significant challenge for deep neural networks, as it is prone to forgetting previously acquired knowledge. Several approaches have been proposed in the literature, such as experience rehearsal, regularization, and parameter isolation, to address this problem. Although almost zero forgetting can be achieved in task-incremental learning, class-incremental learning remains highly challenging due to the problem of inter-task class separation. Limited access to previous task data makes it difficult to discriminate between classes of current and previous tasks. To address this issue, we propose `Attention-Guided Incremental Learning' (AGILE), a novel rehearsal-based CL approach that incorporates compact task attention to effectively reduce interference between tasks. AGILE utilizes lightweight, learnable task projection vectors to transform the latent representations of a shared task attention module toward task distribution. Through extensive empirical evaluation, we show that AGILE significantly improves generalization performance by mitigating task interference and outperforming rehearsal-based approaches in several CL scenarios. Furthermore, AGILE can scale well to a large number of tasks with minimal overhead while remaining well-calibrated with reduced task-recency bias., Comment: Published at 3rd Conference on Lifelong Learning Agents (CoLLAs 2024)

Published
2024

2. IMEX-Reg: Implicit-Explicit Regularization in the Function Space for Continual Learning

Author

Bhat, Prashant, Renjith, Bharath, Arani, Elahe, and Zonooz, Bahram

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Continual learning (CL) remains one of the long-standing challenges for deep neural networks due to catastrophic forgetting of previously acquired knowledge. Although rehearsal-based approaches have been fairly successful in mitigating catastrophic forgetting, they suffer from overfitting on buffered samples and prior information loss, hindering generalization under low-buffer regimes. Inspired by how humans learn using strong inductive biases, we propose IMEX-Reg to improve the generalization performance of experience rehearsal in CL under low buffer regimes. Specifically, we employ a two-pronged implicit-explicit regularization approach using contrastive representation learning (CRL) and consistency regularization. To further leverage the global relationship between representations learned using CRL, we propose a regularization strategy to guide the classifier toward the activation correlations in the unit hypersphere of the CRL. Our results show that IMEX-Reg significantly improves generalization performance and outperforms rehearsal-based approaches in several CL scenarios. It is also robust to natural and adversarial corruptions with less task-recency bias. Additionally, we provide theoretical insights to support our design decisions further., Comment: Published in Transactions on Machine Learning Research

Published
2024

3. TriRE: A Multi-Mechanism Learning Paradigm for Continual Knowledge Retention and Promotion

Author

Vijayan, Preetha, Bhat, Prashant, Arani, Elahe, and Zonooz, Bahram

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Continual learning (CL) has remained a persistent challenge for deep neural networks due to catastrophic forgetting (CF) of previously learned tasks. Several techniques such as weight regularization, experience rehearsal, and parameter isolation have been proposed to alleviate CF. Despite their relative success, these research directions have predominantly remained orthogonal and suffer from several shortcomings, while missing out on the advantages of competing strategies. On the contrary, the brain continually learns, accommodates, and transfers knowledge across tasks by simultaneously leveraging several neurophysiological processes, including neurogenesis, active forgetting, neuromodulation, metaplasticity, experience rehearsal, and context-dependent gating, rarely resulting in CF. Inspired by how the brain exploits multiple mechanisms concurrently, we propose TriRE, a novel CL paradigm that encompasses retaining the most prominent neurons for each task, revising and solidifying the extracted knowledge of current and past tasks, and actively promoting less active neurons for subsequent tasks through rewinding and relearning. Across CL settings, TriRE significantly reduces task interference and surpasses different CL approaches considered in isolation., Comment: Accepted at 37th Conference on Neural Information Processing Systems (NeurIPS 2023)

Published
2023

6. BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning

Author

Jeeveswaran, Kishaan, Bhat, Prashant, Zonooz, Bahram, and Arani, Elahe

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing
Abstract

The ability of deep neural networks to continually learn and adapt to a sequence of tasks has remained challenging due to catastrophic forgetting of previously learned tasks. Humans, on the other hand, have a remarkable ability to acquire, assimilate, and transfer knowledge across tasks throughout their lifetime without catastrophic forgetting. The versatility of the brain can be attributed to the rehearsal of abstract experiences through a complementary learning system. However, representation rehearsal in vision transformers lacks diversity, resulting in overfitting and consequently, performance drops significantly compared to raw image rehearsal. Therefore, we propose BiRT, a novel representation rehearsal-based continual learning approach using vision transformers. Specifically, we introduce constructive noises at various stages of the vision transformer and enforce consistency in predictions with respect to an exponential moving average of the working model. Our method provides consistent performance gain over raw image and vanilla representation rehearsal on several challenging CL benchmarks, while being memory efficient and robust to natural and adversarial corruptions., Comment: Accepted at 40th International Conference on Machine Learning (ICML 2023)

Published
2023

7. Task-Aware Information Routing from Common Representation Space in Lifelong Learning

Author

Bhat, Prashant, Zonooz, Bahram, and Arani, Elahe

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Intelligent systems deployed in the real world suffer from catastrophic forgetting when exposed to a sequence of tasks. Humans, on the other hand, acquire, consolidate, and transfer knowledge between tasks that rarely interfere with the consolidated knowledge. Accompanied by self-regulated neurogenesis, continual learning in the brain is governed by a rich set of neurophysiological processes that harbor different types of knowledge, which are then integrated by conscious processing. Thus, inspired by the Global Workspace Theory of conscious information access in the brain, we propose TAMiL, a continual learning method that entails task-attention modules to capture task-specific information from the common representation space. We employ simple, undercomplete autoencoders to create a communication bottleneck between the common representation space and the global workspace, allowing only the task-relevant information to the global workspace, thus greatly reducing task interference. Experimental results show that our method outperforms state-of-the-art rehearsal-based and dynamic sparse approaches and bridges the gap between fixed capacity and parameter isolation approaches while being scalable. We also show that our method effectively mitigates catastrophic forgetting while being well-calibrated with reduced task-recency bias., Comment: Accepted as a conference paper at ICLR 2023

Published
2023

8. Task Agnostic Representation Consolidation: a Self-supervised based Continual Learning Approach

Author

Bhat, Prashant, Zonooz, Bahram, and Arani, Elahe

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Continual learning (CL) over non-stationary data streams remains one of the long-standing challenges in deep neural networks (DNNs) as they are prone to catastrophic forgetting. CL models can benefit from self-supervised pre-training as it enables learning more generalizable task-agnostic features. However, the effect of self-supervised pre-training diminishes as the length of task sequences increases. Furthermore, the domain shift between pre-training data distribution and the task distribution reduces the generalizability of the learned representations. To address these limitations, we propose Task Agnostic Representation Consolidation (TARC), a two-stage training paradigm for CL that intertwines task-agnostic and task-specific learning whereby self-supervised training is followed by supervised learning for each task. To further restrict the deviation from the learned representations in the self-supervised stage, we employ a task-agnostic auxiliary loss during the supervised stage. We show that our training paradigm can be easily added to memory- or regularization-based approaches and provides consistent performance gain across more challenging CL settings. We further show that it leads to more robust and well-calibrated models., Comment: Accepted at Conference on Lifelong Learning Agents (CoLLAs 2022)

Published
2022

9. Consistency is the key to further mitigating catastrophic forgetting in continual learning

Author

Bhat, Prashant, Zonooz, Bahram, and Arani, Elahe

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep neural networks struggle to continually learn multiple sequential tasks due to catastrophic forgetting of previously learned tasks. Rehearsal-based methods which explicitly store previous task samples in the buffer and interleave them with the current task samples have proven to be the most effective in mitigating forgetting. However, Experience Replay (ER) does not perform well under low-buffer regimes and longer task sequences as its performance is commensurate with the buffer size. Consistency in predictions of soft-targets can assist ER in preserving information pertaining to previous tasks better as soft-targets capture the rich similarity structure of the data. Therefore, we examine the role of consistency regularization in ER framework under various continual learning scenarios. We also propose to cast consistency regularization as a self-supervised pretext task thereby enabling the use of a wide variety of self-supervised learning methods as regularizers. While simultaneously enhancing model calibration and robustness to natural corruptions, regularizing consistency in predictions results in lesser forgetting across all continual learning scenarios. Among the different families of regularizers, we find that stricter consistency constraints preserve previous task information in ER better., Comment: Accepted at Conference on Lifelong Learning Agents (CoLLAs 2022)

Published
2022

10. Interplay Between Duration of Androgen Deprivation Therapy and External Beam Radiotherapy With or Without a Brachytherapy Boost for Optimal Treatment of High-risk Prostate Cancer

Author

Kishan, Amar U, Steigler, Alison, Denham, James W, Zapatero, Almudena, Guerrero, Araceli, Joseph, David, Maldonado, Xavier, Wong, Jessica K, Stish, Bradley J, Dess, Robert T, Pilar, Avinash, Reddy, Chandana, Wedde, Trude B, Lilleby, Wolfgang A, Fiano, Ryan, Merrick, Gregory S, Stock, Richard G, Demanes, D Jeffrey, Moran, Brian J, Tran, Phuoc T, Martin, Santiago, Martinez-Monge, Rafael, Krauss, Daniel J, Abu-Isa, Eyad I, Pisansky, Thomas M, Choo, C Richard, Song, Daniel Y, Greco, Stephen, Deville, Curtiland, McNutt, Todd, DeWeese, Theodore L, Ross, Ashley E, Ciezki, Jay P, Tilki, Derya, Karnes, R Jeffrey, Tosoian, Jeffrey J, Nickols, Nicholas G, Bhat, Prashant, Shabsovich, David, Juarez, Jesus E, Jiang, Tommy, Ma, T Martin, Xiang, Michael, Philipson, Rebecca, Chang, Albert, Kupelian, Patrick A, Rettig, Matthew B, Feng, Felix Y, Berlin, Alejandro, Tward, Jonathan D, Davis, Brian J, Reiter, Robert E, Steinberg, Michael L, Elashoff, David, Boutros, Paul C, Horwitz, Eric M, Tendulkar, Rahul D, Spratt, Daniel E, and Romero, Tahmineh

Subjects
Cancer, Clinical Research, Prostate Cancer, Urologic Diseases, Clinical Trials and Supportive Activities, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Androgen Antagonists, Androgens, Brachytherapy, Data Analysis, Humans, Male, Middle Aged, Prostatic Neoplasms, Retrospective Studies, Oncology and Carcinogenesis, Public Health and Health Services
Abstract

ImportanceRadiotherapy combined with androgen deprivation therapy (ADT) is a standard of care for high-risk prostate cancer. However, the interplay between radiotherapy dose and the required minimum duration of ADT is uncertain.ObjectiveTo determine the specific ADT duration threshold that provides a distant metastasis-free survival (DMFS) benefit in patients with high-risk prostate cancer receiving external beam radiotherapy (EBRT) or EBRT with a brachytherapy boost (EBRT+BT).Design, settings, and participantsThis was a cohort study of 3 cohorts assembled from a multicenter retrospective study (2000-2013); a post hoc analysis of the Randomized Androgen Deprivation and Radiotherapy 03/04 (RADAR; 2003-2007) randomized clinical trial (RCT); and a cross-trial comparison of the RADAR vs the Deprivación Androgénica y Radio Terapía (Androgen Deprivation and Radiation Therapy; DART) 01/05 RCT (2005-2010). In all, the study analyzed 1827 patients treated with EBRT and 1108 patients treated with EBRT+BT from the retrospective cohort; 181 treated with EBRT and 203 with EBRT+BT from RADAR; and 91 patients treated with EBRT from DART. The study was conducted from October 15, 2020, to July 1, 2021, and the data analyses, from January 5 to June 15, 2021.ExposuresHigh-dose EBRT or EBRT+BT for an ADT duration determined by patient-physician choice (retrospective) or by randomization (RCTs).Main outcomes and measuresThe primary outcome was DMFS; secondary outcome was overall survival (OS). Natural cubic spline analysis identified minimum thresholds (months).ResultsThis cohort study of 3 studies totaling 3410 men (mean age [SD], 68 [62-74] years; race and ethnicity not collected) with high-risk prostate cancer found a significant interaction between the treatment type (EBRT vs EBRT+BT) and ADT duration (binned to

Published
2022

11. Distill on the Go: Online knowledge distillation in self-supervised learning

Author

Bhat, Prashant, Arani, Elahe, and Zonooz, Bahram

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Self-supervised learning solves pretext prediction tasks that do not require annotations to learn feature representations. For vision tasks, pretext tasks such as predicting rotation, solving jigsaw are solely created from the input data. Yet, predicting this known information helps in learning representations useful for downstream tasks. However, recent works have shown that wider and deeper models benefit more from self-supervised learning than smaller models. To address the issue of self-supervised pre-training of smaller models, we propose Distill-on-the-Go (DoGo), a self-supervised learning paradigm using single-stage online knowledge distillation to improve the representation quality of the smaller models. We employ deep mutual learning strategy in which two models collaboratively learn from each other to improve one another. Specifically, each model is trained using self-supervised learning along with distillation that aligns each model's softmax probabilities of similarity scores with that of the peer model. We conduct extensive experiments on multiple benchmark datasets, learning objectives, and architectures to demonstrate the potential of our proposed method. Our results show significant performance gain in the presence of noisy and limited labels and generalization to out-of-distribution data., Comment: Spotlight @ Learning from Limited or Imperfect Data (L2ID) Workshop - CVPR 2021

Published
2021

12. Performance of a Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography–Derived Risk-Stratification Tool for High-risk and Very High-risk Prostate Cancer

Author

Xiang, Michael, Martin, Ting, Savjani, Ricky, Pollom, Erqi L, Karnes, R Jeffrey, Grogan, Tristan, Wong, Jessica K, Motterle, Giovanni, Tosoian, Jeffrey J, Trock, Bruce J, Klein, Eric A, Stish, Bradley J, Dess, Robert T, Spratt, Daniel E, Pilar, Avinash, Reddy, Chandana, Levin-Epstein, Rebecca, Wedde, Trude B, Lilleby, Wolfgang A, Fiano, Ryan, Merrick, Gregory S, Stock, Richard G, Demanes, D Jeffrey, Moran, Brian J, Huland, Hartwig, Tran, Phuoc T, Martin, Santiago, Martinez-Monge, Rafael, Krauss, Daniel J, Abu-Isa, Eyad I, Alam, Ridwan, Schwen, Zeyad, Pisansky, Thomas M, Choo, C Richard, Song, Daniel Y, Greco, Stephen, Deville, Curtiland, McNutt, Todd, DeWeese, Theodore L, Ross, Ashley E, Ciezki, Jay P, Boutros, Paul C, Nickols, Nicholas G, Bhat, Prashant, Shabsovich, David, Juarez, Jesus E, Chong, Natalie, Kupelian, Patrick A, Rettig, Matthew B, Zaorsky, Nicholas G, Berlin, Alejandro, Tward, Jonathan D, Davis, Brian J, Reiter, Robert E, Steinberg, Michael L, Elashoff, David, Horwitz, Eric M, Tendulkar, Rahul D, Tilki, Derya, Czernin, Johannes, Gafita, Andrei, Romero, Tahmineh, Calais, Jeremie, and Kishan, Amar U

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Biomedical Imaging, Prostate Cancer, Cancer, Clinical Research, Urologic Diseases, Aging, Prevention, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Good Health and Well Being, Adult, Aged, Aged, 80 and over, Antigens, Surface, Biomarkers, Tumor, Clinical Decision Rules, Follow-Up Studies, Glutamate Carboxypeptidase II, Humans, Male, Middle Aged, Neoplasm Staging, Nomograms, Positron Emission Tomography Computed Tomography, Prognosis, Prostatic Neoplasms, Retrospective Studies, Risk Assessment, SEER Program, Survival Analysis, Biomedical and clinical sciences, Health sciences
Abstract

ImportanceProstate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) can detect low-volume, nonlocalized (ie, regional or metastatic) prostate cancer that was occult on conventional imaging. However, the long-term clinical implications of PSMA PET/CT upstaging remain unclear.ObjectivesTo evaluate the prognostic significance of a nomogram that models an individual's risk of nonlocalized upstaging on PSMA PET/CT and to compare its performance with existing risk-stratification tools.Design, setting, and participantsThis cohort study included patients diagnosed with high-risk or very high-risk prostate cancer (ie, prostate-specific antigen [PSA] level >20 ng/mL, Gleason score 8-10, and/or clinical stage T3-T4, without evidence of nodal or metastatic disease by conventional workup) from April 1995 to August 2018. This multinational study was conducted at 15 centers. Data were analyzed from December 2020 to March 2021.ExposuresCurative-intent radical prostatectomy (RP), external beam radiotherapy (EBRT), or EBRT plus brachytherapy (BT), with or without androgen deprivation therapy.Main outcomes and measuresPSMA upstage probability was calculated from a nomogram using the biopsy Gleason score, percentage positive systematic biopsy cores, clinical T category, and PSA level. Biochemical recurrence (BCR), distant metastasis (DM), prostate cancer-specific mortality (PCSM), and overall survival (OS) were analyzed using Fine-Gray and Cox regressions. Model performance was quantified with the concordance (C) index.ResultsOf 5275 patients, the median (IQR) age was 66 (60-72) years; 2883 (55%) were treated with RP, 1669 (32%) with EBRT, and 723 (14%) with EBRT plus BT; median (IQR) PSA level was 10.5 (5.9-23.2) ng/mL; 3987 (76%) had Gleason grade 8 to 10 disease; and 750 (14%) had stage T3 to T4 disease. Median (IQR) follow-up was 5.1 (3.1-7.9) years; 1221 (23%) were followed up for at least 8 years. Overall, 1895 (36%) had BCR, 851 (16%) developed DM, and 242 (5%) died of prostate cancer. PSMA upstage probability was significantly prognostic of all clinical end points, with 8-year C indices of 0.63 (95% CI, 0.61-0.65) for BCR, 0.69 (95% CI, 0.66-0.71) for DM, 0.71 (95% CI, 0.67-0.75) for PCSM, and 0.60 (95% CI, 0.57-0.62) for PCSM (P

Published
2021

13. RNA promotes the formation of spatial compartments in the nucleus

Author

Quinodoz, Sofia A, Jachowicz, Joanna W, Bhat, Prashant, Ollikainen, Noah, Banerjee, Abhik K, Goronzy, Isabel N, Blanco, Mario R, Chovanec, Peter, Chow, Amy, Markaki, Yolanda, Thai, Jasmine, Plath, Kathrin, and Guttman, Mitchell

Subjects
Genetics, Underpinning research, 1.1 Normal biological development and functioning, Generic health relevance, Animals, Cell Nucleus, Chromobox Protein Homolog 5, Chromosomes, DNA, DNA, Satellite, DNA-Binding Proteins, Dactinomycin, Female, Genome, HEK293 Cells, Heterochromatin, Humans, Mice, Models, Biological, Multigene Family, RNA, RNA Polymerase II, RNA Processing, Post-Transcriptional, RNA Splicing, RNA, Long Noncoding, RNA, Messenger, RNA, Ribosomal, RNA-Binding Proteins, Transcription, Genetic, RNA processing, cajal bodies, chromocenters, histone locus bodies, lncRNAs, ncRNAs, nuclear bodies, nuclear structure, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

RNA, DNA, and protein molecules are highly organized within three-dimensional (3D) structures in the nucleus. Although RNA has been proposed to play a role in nuclear organization, exploring this has been challenging because existing methods cannot measure higher-order RNA and DNA contacts within 3D structures. To address this, we developed RNA & DNA SPRITE (RD-SPRITE) to comprehensively map the spatial organization of RNA and DNA. These maps reveal higher-order RNA-chromatin structures associated with three major classes of nuclear function: RNA processing, heterochromatin assembly, and gene regulation. These data demonstrate that hundreds of ncRNAs form high-concentration territories throughout the nucleus, that specific RNAs are required to recruit various regulators into these territories, and that these RNAs can shape long-range DNA contacts, heterochromatin assembly, and gene expression. These results demonstrate a mechanism where RNAs form high-concentration territories, bind to diffusible regulators, and guide them into compartments to regulate essential nuclear functions.

Published
2021

14. Patterns of Clinical Progression in Radiorecurrent High-risk Prostate Cancer

Author

Philipson, Rebecca G, Romero, Tahmineh, Wong, Jessica K, Stish, Bradley J, Dess, Robert T, Spratt, Daniel E, Pilar, Avinash, Reddy, Chandana, Wedde, Trude B, Lilleby, Wolfgang A, Fiano, Ryan, Merrick, Gregory S, Stock, Richard G, Demanes, D Jeffrey, Moran, Brian J, Braccioforte, Michelle, Tran, Phuoc T, Martin, Santiago, Martinez-Monge, Rafael, Krauss, Daniel J, Abu-Isa, Eyad I, Valle, Luca, Chong, Natalie, Pisansky, Thomas M, Choo, C Richard, Song, Daniel Y, Greco, Stephen, Deville, Curtiland, McNutt, Todd, DeWeese, Theodore L, Ross, Ashley E, Ciezki, Jay P, Tilki, Derya, Karnes, R Jeffrey, Klein, Eric A, Tosoian, Jeffrey J, Boutros, Paul C, Nickols, Nicholas G, Bhat, Prashant, Shabsovich, David, Juarez, Jesus E, Kupelian, Patrick A, Rettig, Matthew B, Berlin, Alejandro, Tward, Jonathan D, Davis, Brian J, Reiter, Robert E, Steinberg, Michael L, Elashoff, David, Horwitz, Eric M, Tendulkar, Rahul D, and Kishan, Amar U

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Prevention, Urologic Diseases, Prostate Cancer, Cancer, Clinical Research, Aging, Brachytherapy, Humans, Male, Neoplasm Grading, Prostate, Prostatic Neoplasms, Salvage Therapy, Biochemical recurrence, Brachytherapy boost, EBRT, External beam radiation therapy, High-risk prostate cancer, Prostate cancer, Radiorecurrence, Recurrent prostate cancer, Urology & Nephrology, Clinical sciences
Abstract

The natural history of radiorecurrent high-risk prostate cancer (HRPCa) is not well-described. To better understand its clinical course, we evaluated rates of distant metastases (DM) and prostate cancer-specific mortality (PCSM) in a cohort of 978 men with radiorecurrent HRPCa who previously received either external beam radiation therapy (EBRT, n = 654, 67%) or EBRT + brachytherapy (EBRT + BT, n = 324, 33%) across 15 institutions from 1997 to 2015. In men who did not die, median follow-up after treatment was 8.9 yr and median follow-up after biochemical recurrence (BCR) was 3.7 yr. Local and systemic therapy salvage, respectively, were delivered to 21 and 390 men after EBRT, and eight and 103 men after EBRT + BT. Overall, 435 men developed DM, and 248 were detected within 1 yr of BCR. Measured from time of recurrence, 5-yr DM rates were 50% and 34% after EBRT and EBRT + BT, respectively. Measured from BCR, 5-yr PCSM rates were 27% and 29%, respectively. Interval to BCR was independently associated with DM (p

Published
2021

25. Novel Methodologies for Processing Structured Big Data Using Hadoop Framework

Author

Bhat, Prashant, Hegde, Prajna, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Saini, H. S., editor, Sayal, Rishi, editor, Govardhan, A., editor, and Buyya, Rajkumar, editor

Published
2021
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26. Effect of J48 and LMT Algorithms to Classify Movies in the Web—A Comparative Approach

Author

Bhat, Prashant, Malaganve, Pradnya, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Saini, H. S., editor, Sayal, Rishi, editor, Govardhan, A., editor, and Buyya, Rajkumar, editor

Published
2021
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31. On the Role of Three-Dimensional Genome Organization in Gene Regulation and mRNA Splicing

Author

Bhat, Prashant, Bhat, Prashant, Bhat, Prashant, and Bhat, Prashant

Abstract

The nucleus is spatially organized such that DNA, RNA, and protein molecules involved in shared functional and regulatory processes are compartmentalized in three-dimensional (3D) structures. These structures are emerging as a paradigm for gene regulation, a highly complex process that requires the dynamic coordination of hundreds of regulatory factors around precise targets in different cell states. We describe the discovery of hundreds of RNA-DNA hubs throughout the nucleus that are organized around essential nuclear functions such as RNA processing, centromeric heterochromatin organization, and gene regulation. Focusing on RNA processing, specifically co-transcriptional splicing, we find that genome-wide organization of active genes near nuclear speckles drives the efficiency of pre-mRNA splicing in a cell-type specific manner. The results of this thesis illustrate how spatial compartmentalization of biomolecules increases the local concentration of reactants and enzymes such that greater efficiency is achieved in scenarios where rapid responses are required for cell survival.

Published
2023

32. Surveillance and Processing of Foreign DNA by the Escherichia coli CRISPR-Cas System

Author

Redding, Sy, Sternberg, Samuel H, Marshall, Myles, Gibb, Bryan, Bhat, Prashant, Guegler, Chantal K, Wiedenheft, Blake, Doudna, Jennifer A, and Greene, Eric C

Subjects
Biochemistry and Cell Biology, Biological Sciences, Genetics, Emerging Infectious Diseases, Human Genome, Underpinning research, 1.1 Normal biological development and functioning, Bacteriophage lambda, CRISPR-Associated Proteins, CRISPR-Cas Systems, DNA Helicases, DNA, Viral, Endodeoxyribonucleases, Endonucleases, Escherichia coli, Escherichia coli Proteins, Models, Biological, Repetitive Sequences, Nucleic Acid, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

CRISPR-Cas adaptive immune systems protect bacteria and archaea against foreign genetic elements. In Escherichia coli, Cascade (CRISPR-associated complex for antiviral defense) is an RNA-guided surveillance complex that binds foreign DNA and recruits Cas3, a trans-acting nuclease helicase for target degradation. Here, we use single-molecule imaging to visualize Cascade and Cas3 binding to foreign DNA targets. Our analysis reveals two distinct pathways dictated by the presence or absence of a protospacer-adjacent motif (PAM). Binding to a protospacer flanked by a PAM recruits a nuclease-active Cas3 for degradation of short single-stranded regions of target DNA, whereas PAM mutations elicit an alternative pathway that recruits a nuclease-inactive Cas3 through a mechanism that is dependent on the Cas1 and Cas2 proteins. These findings explain how target recognition by Cascade can elicit distinct outcomes and support a model for acquisition of new spacer sequences through a mechanism involving processive, ATP-dependent Cas3 translocation along foreign DNA.

Published
2015

36. CasA mediates Cas3-catalyzed target degradation during CRISPR RNA-guided interference

Author

Hochstrasser, Megan L, Taylor, David W, Bhat, Prashant, Guegler, Chantal K, Sternberg, Samuel H, Nogales, Eva, and Doudna, Jennifer A

Subjects
Biochemistry and Cell Biology, Biological Sciences, Biotechnology, Genetics, Generic health relevance, Base Sequence, Binding Sites, CRISPR-Associated Proteins, Cryoelectron Microscopy, DNA Helicases, Escherichia coli K12, Escherichia coli Proteins, Imaging, Three-Dimensional, Macromolecular Substances, Models, Molecular, Molecular Sequence Data, Nucleic Acid Conformation, Protein Conformation, Protein Subunits, RNA Interference, RNA, Bacterial
Abstract

In bacteria, the clustered regularly interspaced short palindromic repeats (CRISPR)-associated (Cas) DNA-targeting complex Cascade (CRISPR-associated complex for antiviral defense) uses CRISPR RNA (crRNA) guides to bind complementary DNA targets at sites adjacent to a trinucleotide signature sequence called the protospacer adjacent motif (PAM). The Cascade complex then recruits Cas3, a nuclease-helicase that catalyzes unwinding and cleavage of foreign double-stranded DNA (dsDNA) bearing a sequence matching that of the crRNA. Cascade comprises the CasA-E proteins and one crRNA, forming a structure that binds and unwinds dsDNA to form an R loop in which the target strand of the DNA base pairs with the 32-nt RNA guide sequence. Single-particle electron microscopy reconstructions of dsDNA-bound Cascade with and without Cas3 reveal that Cascade positions the PAM-proximal end of the DNA duplex at the CasA subunit and near the site of Cas3 association. The finding that the DNA target and Cas3 colocalize with CasA implicates this subunit in a key target-validation step during DNA interference. We show biochemically that base pairing of the PAM region is unnecessary for target binding but critical for Cas3-mediated degradation. In addition, the L1 loop of CasA, previously implicated in PAM recognition, is essential for Cas3 activation following target binding by Cascade. Together, these data show that the CasA subunit of Cascade functions as an essential partner of Cas3 by recognizing DNA target sites and positioning Cas3 adjacent to the PAM to ensure cleavage.

Published
2014

39. An Interview with Professor Roy Caldwell

Author

Chowdhary, Kuntal, Bhat, Prashant, Rosen, Jared, Naughton, Ida, and Wang, Jingyan

Subjects
Marine Biology, Behavioral Ecology, stomatopods, cephalopods, color vision
Abstract

Envision the underwater world: vibrant coral, swaying seaweed, and lively creatures abound. However, the postcards and National Geographic covers that try to capture the beauty of this picturesque habitat can only represent it at one moment in time -- in reality, the landscape is hardly static, since many of its animals are capable of body modifications to change their shape and color. To understand the coloration and color vision of sea creatures, UC Berkeley Professor Roy L. Caldwell researches various marine animals such as stomatopods and cephalopods. In an interview with Professor Caldwell on December 13, 2012 BSJ had an opportunity to discuss different aspects of color vision and color change in sea animals with respect to behavioral ecology.

Published
2013

42. Recommendations for COVID-19-related biomedical waste management in health care facilities

Author

Bhat, Vivek, Chavan, Preeti, and Bhat, Prashant

Subjects
Medical wastes, Refuse and refuse disposal, Disease transmission, Health facilities -- Waste management, Severe acute respiratory syndrome, Coronaviruses, Health
Abstract

Byline: Vivek. Bhat, Preeti. Chavan, Prashant. Bhat The advent of coronavirus disease 2019 (COVID-19) and the spread of the severe acute respiratory syndrome coronavirus 2 in health care facilities have [...]

Published
2022

43. Emotions and Consumer Behavior

Author

Andrade, Eduardo, Gururangan, Kapil, Iantorno, Stefano, Feng, Harvey, Cherone, Jennifer, Sawant, Manali, Neogi, Sushrita, Bhat, Prashant, and Lukus, Alan

Subjects
neuroscience, Business, Marketing, neuroscience, emotions, consumer decision making
Abstract

Professor Eduardo Andrade received his Ph.D. in Marketing from the University of Florida in 2004 before coming to theHaas School of Business. He studies the impact of emotions on consumer decision making. One of his studies publishedin 2009 shifted the concept of emotions from transient effects to long-term processes and his recent work is movinginto the burgeoning field of decision neuroscience, which uses neuroscience tools to study economic decision-making.When Berkeley Scientific Journal spoke with him in 2010, this latest project was only starting, but we were able to discusshis research in emotional judgment and marketing.

Published
2012

44. Interview with Professor Caroline Kane

Author

Gururangan, Kapil, Neogi, Sushrita, Bhat, Prashant, Rosen, Jared, and Wang, Jingyan

Subjects
Molecular Biology, Genetics, Education, Interview, RNA Polymerase, Transcription Factor, Advisor, BSJ
Abstract

The history of Berkeley Scientific Journal at Cal starts at 1996, when several undergraduates involved in research who wanted to create a new avenue to publish their research. The first faculty sponsor was Professor Caroline Kane, whose passionate support of undergraduate education through the Biology Fellows Program earned her the College of Letters and Science Award for Distinguished Research Mentoring of Undergraduates in 2002. She earned her PhD from UC Berkeley and has studied the mechanisms of genetic expression and regulation. Although she is retired, she is an active emerita who facilitatated BSJ's transition to a DeCal course in the MCB department by signing on as the faculty sponsor.

Published
2012

45. Interview with BSJ Staff Advisor Leah Carroll: BSJ Throughout the Years

Author

Gururangan, Kapil, Neogi, Sushrita, Bhat, Prashant, Rosen, Jared, and Wang, Jingyan

Subjects
BSJ, democratization, research, education
Abstract

After graduating from Berkeley with a PhD in sociology, Leah Carroll returned to UC Berkeley in 2001 as the director of the Haas Scholars Program. Like Professor Kane, Leah became involved with Berkeley Scientific Journal through the undergraduate research programs in the Office of Undergraduate Research. She has guided BSJ since then and served as the staff advisor. Her experience in research and undergraduate education has been an invaluable asset, and her new position as the Director of the Office of Undergraduate Research is a testament to that. Leah’s new book, “Violent Democratization: Social Movements, Elites, and Politics in Columbia’s Rural War Zones,” shows her all-encompassing involvement in research, both in a mentoring and in pursuing her own fields of interest.

Published
2012

46. Interview with Robert Knight and Brian Pasley: Brain-Machine Interfaces: Neural Prosthetics and Patient Care

Author

Gururangan, Kapil, Wang, Jingyan, Bhat, Prashant, Neogi, Sushrita, and Rosen, Jared

Subjects
Electrical Engineering, brain-machine interface, neuroscience, prosthetics, human speech reconstruction
Abstract

Professor Robert Knight served as the Director of the Helen Wills Neuroscience Institute from 2001 to 2011 and has run a lab in human neuroscience within that institute since 1998. Using electrophysiological techniques, the Knight lab studies the role of the prefrontal cortex in human cognition. After graduating with a BS in Physics from the Illinois Institute of Technology, he earned his MD from Northwestern University, completed his neurology training at UC San Diego, and finished his post-doctoral work at the Salk Institute for Biological Studies. In 2010, Professor Knight helped found the Center for Neural Engineering and Prosthesis, a joint program between UC Berkeley and UCSF focused on brain-machine interfaces.Brian Pasley, a post-doctoral student in Dr. Knight’s lab since 2010, conceived the lab’s latest publication, which announced the possibility for scientists to reconstruct human speech from recorded brain activity. Both Pasley and Knight hope the research will lead to advances in neural prosthetics for patients with neurological disorders.

Published
2012

47. Interview with Professor Matt Francis

Author

Gururangan, Kapil, Dai, Joanne, Bhat, Prashant, Neogi, Sushrita, Wang, Jingyan, and Rosen, Jared

Subjects
Chemistry, Nutrition, Cooking, Nutrition, Souffle Recipe, Food, polymer tracers
Abstract

Professor Francis completed his doctoral thesis on the discovery and optimization of transition metal catalysts at Harvard University in 1999 and then moved to UC Berkeley as a postdoctoral scholar under organic chemistry Professor Jean Fréchet, working on DNA-facilitated assembly of polymers and the role of dendrimers in pharmaceuticals. In 2001, Francis began his own research career at Berkeley and has been studying organic reactions for protein modification. He has received numerous awards both for his research and his teaching, including the NSF Career Award and the University Distinguished Teaching Award. He is the faculty advisor for the Decal Chemistry 98, “The Chemistry of Cooking” which routinely receives a packed lecture room and rave reviews from the students. The Decal is accessible to students of all majors, regardless of chemistry background.

Published
2011

48. Interview with Sharon Fleming

Author

Gururangan, Kapil, Rosen, Jared, Dai, Joanne, Neogi, Sushrita, Sawant, Manali, Bhat, Prashant, and Wang, Jingyan

Subjects
Type II Diabetes, Inner-city Low-income Interventions, Obesity, Risk Factors, Cardiovascular Disease
Abstract

Dr. Sharon Fleming has been a professor in the Department of Nutritional Science & Toxicology at UC Berkeley since 1979. After getting her PhD in Food Science and Nutrition from the University of Saskatchewan, Saskatoon in 1975, her research has followed her interests from cellular and molecular pathways of macronutrients to public health in low-income inner-city communities. Professor Fleming was a co-founder of the Robert C and Veronica Atkins Center for Weight and Health and has also been very involved in assessing risk factors for type II diabetes. Her symposium in 2002 on type II diabetes in children comprehensively reviewed these various risk factors for use in public policy. BSJ interviewed Dr. Fleming just as she was clearing out her office in preparation for retiring from teaching and research at Berkeley to become a professor emeritus.

Published
2011

50. Interview with Jeffrey Long

Author

Gururangan, Kapil, Owen, Elaine, Rai, Manisha, and Bhat, Prashant

Subjects
Jeffrey Long
Published
2011

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