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1. Results of the Big ANN: NeurIPS'23 competition

Author

Simhadri, Harsha Vardhan, Aumüller, Martin, Ingber, Amir, Douze, Matthijs, Williams, George, Manohar, Magdalen Dobson, Baranchuk, Dmitry, Liberty, Edo, Liu, Frank, Landrum, Ben, Karjikar, Mazin, Dhulipala, Laxman, Chen, Meng, Chen, Yue, Ma, Rui, Zhang, Kai, Cai, Yuzheng, Shi, Jiayang, Chen, Yizhuo, Zheng, Weiguo, Wan, Zihao, Yin, Jie, and Huang, Ben

Subjects
Computer Science - Information Retrieval, Computer Science - Data Structures and Algorithms, Computer Science - Machine Learning, Computer Science - Performance, H.3.3
Abstract

The 2023 Big ANN Challenge, held at NeurIPS 2023, focused on advancing the state-of-the-art in indexing data structures and search algorithms for practical variants of Approximate Nearest Neighbor (ANN) search that reflect the growing complexity and diversity of workloads. Unlike prior challenges that emphasized scaling up classical ANN search ~\cite{DBLP:conf/nips/SimhadriWADBBCH21}, this competition addressed filtered search, out-of-distribution data, sparse and streaming variants of ANNS. Participants developed and submitted innovative solutions that were evaluated on new standard datasets with constrained computational resources. The results showcased significant improvements in search accuracy and efficiency over industry-standard baselines, with notable contributions from both academic and industrial teams. This paper summarizes the competition tracks, datasets, evaluation metrics, and the innovative approaches of the top-performing submissions, providing insights into the current advancements and future directions in the field of approximate nearest neighbor search., Comment: Code: https://github.com/harsha-simhadri/big-ann-benchmarks/releases/tag/v0.3.0

Published
2024

5. Complementary dual-virus strategy drives synthetic target and cognate T-cell engager expression for endogenous-antigen agnostic immunotherapy

Author

Taha, Zaid, Crupi, Mathieu Joseph François, Alluqmani, Nouf, MacKenzie, Duncan, Vallati, Sydney, Whelan, Jack Timothy, Fareez, Faiha, Alwithenani, Akram, Petryk, Julia, Chen, Andrew, Spinelli, Marcus Mathew, Ng, Kristy, Sobh, Judy, de Souza, Christiano Tanese, Bharadwa, Priya Rose, Lee, Timothy Kit Hin, Thomas, Dylan Anthony, Huang, Ben Zhen, Kassas, Omar, Poutou, Joanna, Gilchrist, Victoria Heather, Boulton, Stephen, Thomson, Max, Marius, Ricardo, Hooshyar, Mohsen, McComb, Scott, Arulanandam, Rozanne, Ilkow, Carolina Solange, Bell, John Cameron, and Diallo, Jean-Simon

Published
2024
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13. ABHD17 regulation of plasma membrane palmitoylation and N-Ras-dependent cancer growth

Author

Remsberg, Jarrett R, Suciu, Radu M, Zambetti, Noemi A, Hanigan, Thomas W, Firestone, Ari J, Inguva, Anagha, Long, Amanda, Ngo, Nhi, Lum, Kenneth M, Henry, Cassandra L, Richardson, Stewart K, Predovic, Marina, Huang, Ben, Dix, Melissa M, Howell, Amy R, Niphakis, Micah J, Shannon, Kevin, and Cravatt, Benjamin F

Subjects
Biochemistry and Cell Biology, Biological Sciences, Hematology, Rare Diseases, Cancer, Pediatric Cancer, Pediatric, Childhood Leukemia, Cell Membrane, Cell Proliferation, Cells, Cultured, Humans, Hydrolases, Leukemia, Myeloid, Acute, Leukemia, Promyelocytic, Acute, Lipoylation, Microsomes, Liver, Molecular Structure, ras Proteins, Medicinal and Biomolecular Chemistry, Biochemistry & Molecular Biology, Biochemistry and cell biology, Medicinal and biomolecular chemistry
Abstract

Multiple Ras proteins, including N-Ras, depend on a palmitoylation/depalmitoylation cycle to regulate their subcellular trafficking and oncogenicity. General lipase inhibitors such as Palmostatin M (Palm M) block N-Ras depalmitoylation, but lack specificity and target several enzymes displaying depalmitoylase activity. Here, we describe ABD957, a potent and selective covalent inhibitor of the ABHD17 family of depalmitoylases, and show that this compound impairs N-Ras depalmitoylation in human acute myeloid leukemia (AML) cells. ABD957 produced partial effects on N-Ras palmitoylation compared with Palm M, but was much more selective across the proteome, reflecting a plasma membrane-delineated action on dynamically palmitoylated proteins. Finally, ABD957 impaired N-Ras signaling and the growth of NRAS-mutant AML cells in a manner that synergizes with MAP kinase kinase (MEK) inhibition. Our findings uncover a surprisingly restricted role for ABHD17 enzymes as regulators of the N-Ras palmitoylation cycle and suggest that ABHD17 inhibitors may have value as targeted therapies for NRAS-mutant cancers.

Published
2021

16. Inhibition of exchange proteins directly activated by cAMP as a strategy for broad-spectrum antiviral development

Author

Boulton, Stephen, Crupi, Mathieu J.F., Singh, Siddharth, Carter-Timofte, Madalina E., Azad, Taha, Organ, Bailey C., He, Xiaohong, Gill, Rida, Neault, Serge, Jamieson, Taylor, Dave, Jaahnavi, Kurmasheva, Naziia, Austin, Bradley, Petryk, Julia, Singaravelu, Ragunath, Huang, Ben Zhen, Franco, Noah, Babu, Kaaviya, Parks, Robin J., Ilkow, Carolina S., Olagnier, David, and Bell, John C.

Published
2023
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29. Recognition Location Method of Sound Source Based on Rotating Microphones

Author

Zheng, Xie, Wang, Xunnian, Zhang, Jun, Zhao, Kun, Chen, Zhengwu, Wang, Yong, Huang, Ben, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Braza, Marianna, editor, Hoarau, Yannick, editor, Zhou, Yu, editor, Lucey, Anthony D., editor, Huang, Lixi, editor, and Stavroulakis, Georgios E., editor

Published
2021
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30. Performance Optimization of Microphone Array Beamforming Based on Multi-circular Ring Microphone Arrays Combination

Author

Wang, Xunnian, Zheng, Xie, Zhang, Jun, Zhao, Kun, Chen, Zhengwu, Huang, Ben, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Braza, Marianna, editor, Hoarau, Yannick, editor, Zhou, Yu, editor, Lucey, Anthony D., editor, Huang, Lixi, editor, and Stavroulakis, Georgios E., editor

Published
2021
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39. New Transgenic Mouse Lines for Selectively Targeting Astrocytes and Studying Calcium Signals in Astrocyte Processes In Situ and In Vivo

Author

Srinivasan, Rahul, Lu, Tsai-Yi, Chai, Hua, Xu, Ji, Huang, Ben S, Golshani, Peyman, Coppola, Giovanni, and Khakh, Baljit S

Subjects
Neurosciences, Biotechnology, Genetics, 1.1 Normal biological development and functioning, Underpinning research, Neurological, Aldehyde Dehydrogenase, Animals, Astrocytes, Calcium, Calcium Signaling, Cytosol, Estrogen Receptor beta, GABA Plasma Membrane Transport Proteins, Gene Expression Profiling, Integrases, Mice, Mice, Transgenic, Models, Animal, RNA, Messenger, Selective Estrogen Receptor Modulators, Tamoxifen, Aldh1l1, Cre/ERT2, GCaMP, Lck-GCaMP, astrocyte, calcium, hippocampus, striatum, visual cortex, Psychology, Cognitive Sciences, Neurology & Neurosurgery
Abstract

Astrocytes exist throughout the nervous system and are proposed to affect neural circuits and behavior. However, studying astrocytes has proven difficult because of the lack of tools permitting astrocyte-selective genetic manipulations. Here, we report the generation of Aldh1l1-Cre/ERT2 transgenic mice to selectively target astrocytes in vivo. We characterized Aldh1l1-Cre/ERT2 mice using imaging, immunohistochemistry, AAV-FLEX-GFP microinjections, and crosses to RiboTag, Ai95, and new Cre-dependent membrane-tethered Lck-GCaMP6f knockin mice that we also generated. Two to three weeks after tamoxifen induction, Aldh1l1-Cre/ERT2 selectively targeted essentially all adult (P80) brain astrocytes with no detectable neuronal contamination, resulting in expression of cytosolic and Lck-GCaMP6f, and permitting subcellular astrocyte calcium imaging during startle responses in vivo. Crosses with RiboTag mice allowed sequencing of actively translated mRNAs and determination of the adult cortical astrocyte transcriptome. Thus, we provide well-characterized, easy-to-use resources with which to selectively study astrocytes in situ and in vivo in multiple experimental scenarios.

Published
2016

41. Construction and Validation of a Predictive Model for Coronary Artery Disease Using Extreme Gradient Boosting

Author

Zhang,Zheng, Shao,Binbin, Liu,Hongzhou, Huang,Ben, Gao,Xuechen, Qiu,Jun, Wang,Chen, Zhang,Zheng, Shao,Binbin, Liu,Hongzhou, Huang,Ben, Gao,Xuechen, Qiu,Jun, and Wang,Chen

Abstract

Zheng Zhang,1,2,* Binbin Shao,3,* Hongzhou Liu,2,4 Ben Huang,2,5 Xuechen Gao,1 Jun Qiu,1 Chen Wang1,2 1Center of Clinical Laboratory, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, People’s Republic of China; 2Center for Gene Diagnosis, Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province, People’s Republic of China; 3Department of Prenatal Diagnosis, Women’s Hospital of Nanjing Medical University, Nanjing Women and Children’s Healthcare Hospital, Nanjing, Jiangsu Province, People’s Republic of China; 4School of Clinical Medicine, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuang Province, People’s Republic of China; 5Department of Laboratory Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Chen Wang; Jun Qiu, Center of Clinical Laboratory, The First Affiliated Hospital of Soochow University, No. 899 Pinghai Road, Suzhou, Jiangsu Province, 215006, People’s Republic of China, Email 1617657224@qq.com; 13606136542@126.comPurpose: Early recognition of coronary artery disease (CAD) could delay its progress and significantly reduce mortality. Sensitive, specific, cost-efficient and non-invasive indicators for assessing individual CAD risk in community population screening are urgently needed.Patients and Methods: 3112 patients with CAD and 3182 controls were recruited from three clinical centers in China, and differences in baseline and clinical characteristics were compared. For the discovery cohort, the least absolute shrinkage and selection operator (LASSO) regression was used to identify significant features and four machine learning algorithms (logistic regression, support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost)) were applied to construct models for CAD

Published
2024

44. Global changes of miRNA expression indicates an increased reprogramming efficiency of induced mammary epithelial cells by repression of miR-222-3p in fibroblasts.

Author

Liu, Mingxing, Liu, Siyi, Qin, Liangshan, Lv, Danwei, Wang, Guodong, Liu, Quanhui, Huang, Ben, and Zhang, Dandan

Subjects
GENE expression, ADHERENS junctions, EPITHELIAL cells, GENETIC transcription regulation, FIBROBLASTS
Abstract

Background: Our previous studies have successfully reported the reprogramming of fibroblasts into induced mammary epithelial cells (iMECs). However, the regulatory relationships and functional roles of MicroRNAs (miRNAs) in the progression of fibroblasts achieving the cell fate of iMECs are insufficiently understood. Methods: First, we performed pre-and post-induction miRNAs sequencing analysis by using high-throughput sequencing. Following that, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment studies were used to determine the primary roles of the significantly distinct miRNAs and targeted genes. Finally, the effect of miR-222-3p on iMECs fate reprogramming in vitro by transfecting. Results: As a result goat ear fibroblasts (GEFs) reprogramming into iMECs activates a regulatory program, involving 79 differentially expressed miRNAs. Besides, the programming process involved changes in multiple signaling pathways such as adherens junction, TGF-β signaling pathway, GnRH secretion and the prolactin signaling pathway, etc. Furthermore, it was discovered that the expression of miR-222-3p downregulation by miR-222-3p inhibitor significantly increase the reprogramming efficiency and promoted lipid accumulation of iMECs. [ABSTRACT FROM AUTHOR]

Published
2024
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45. Construction and Validation of a Predictive Model for Coronary Artery Disease Using Extreme Gradient Boosting.

Author

Zhang, Zheng, Shao, Binbin, Liu, Hongzhou, Huang, Ben, Gao, Xuechen, Qiu, Jun, and Wang, Chen

Subjects
MACHINE learning, CORONARY artery disease, RECEIVER operating characteristic curves, SUPPORT vector machines, RANDOM forest algorithms
Abstract

Purpose: Early recognition of coronary artery disease (CAD) could delay its progress and significantly reduce mortality. Sensitive, specific, cost-efficient and non-invasive indicators for assessing individual CAD risk in community population screening are urgently needed. Patients and Methods: 3112 patients with CAD and 3182 controls were recruited from three clinical centers in China, and differences in baseline and clinical characteristics were compared. For the discovery cohort, the least absolute shrinkage and selection operator (LASSO) regression was used to identify significant features and four machine learning algorithms (logistic regression, support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost)) were applied to construct models for CAD risk assessment, the receiver operating characteristics (ROC) curve and precision-recall (PR) curve were conducted to evaluate their predictive accuracy. The optimal model was interpreted by Shapley additive explanations (SHAP) analysis and assessed by the ROC curve, calibration curve, and decision curve analysis (DCA) and validated by two external cohorts. Results: Using LASSO filtration, all included variables were considered to be statistically significant. Four machine learning models were constructed based on these features and the results of ROC and PR curve implied that the XGBoost model exhibited the highest predictive performance, which yielded a high area of ROC curve (AUC) of 0.988 (95% CI: 0.986– 0.991) to distinguish CAD patients from controls with a sensitivity of 94.6% and a specificity of 94.6%. The calibration curve showed that the predicted results were in good agreement with actual observations, and DCA exhibited a better net benefit across a wide range of threshold probabilities. External validation of the model also exhibited favorable discriminatory performance, with an AUC, sensitivity, and specificity of 0.953 (95% CI: 0.945– 0.960), 89.9%, and 87.1% in the validation cohort, and 0.935 (95% CI: 0.915– 0.955), 82.0%, and 90.3% in the replication cohort. Conclusion: Our model is highly informative for clinical practice and will be conducive to primary prevention and tailoring the precise management for CAD patients. [ABSTRACT FROM AUTHOR]

Published
2024
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