Showing papers by "Carnegie Mellon University published in 2022"
TL;DR: The authors applied AlphaFold2 for the prediction of heterodimeric protein complexes and found that the predicted DockQ scores can identify 51% of all interacting pairs at 1% FPR.
Abstract: Abstract Predicting the structure of interacting protein chains is a fundamental step towards understanding protein function. Unfortunately, no computational method can produce accurate structures of protein complexes. AlphaFold2, has shown unprecedented levels of accuracy in modelling single chain protein structures. Here, we apply AlphaFold2 for the prediction of heterodimeric protein complexes. We find that the AlphaFold2 protocol together with optimised multiple sequence alignments, generate models with acceptable quality (DockQ ≥ 0.23) for 63% of the dimers. From the predicted interfaces we create a simple function to predict the DockQ score which distinguishes acceptable from incorrect models as well as interacting from non-interacting proteins with state-of-art accuracy. We find that, using the predicted DockQ scores, we can identify 51% of all interacting pairs at 1% FPR.
161 citations
TL;DR: Using a case study from Electric Reliability Council of Texas (ERCOT), it is shown that the proposed tailored Benders decomposition outperforms the nested Bender decomposition in solving GEP and TEP simultaneously.
45 citations
TL;DR: A detailed overview of the latest trends in research pertaining to visual and language modalities is presented, looking at its applications in their task formulations and how to solve various problems related to semantic perception and content generation.
44 citations
TL;DR: Tests that fail inconsistently, without changes to the code under test, are referred to as flaky tests as discussed by the authors, which do not give a clear indication of the presence of software bugs and thus limit the reliab...
Abstract: Tests that fail inconsistently, without changes to the code under test, are described as flaky. Flaky tests do not give a clear indication of the presence of software bugs and thus limit the reliab...
39 citations
United States Army Research Laboratory1, University of Maryland, College Park2, University of Texas at El Paso3, University of California, Los Angeles4, Cornell University5, University of North Carolina at Chapel Hill6, University of Michigan7, Microsoft8, University of Rochester9, Boise State University10, University of Maryland, Baltimore County11, University of Texas at Austin12, University of Sheffield13, University of Washington14, Mount Holyoke College15, Carnegie Mellon University16, Tufts University17, Adobe Systems18, Brown University19, Institute for Creative Technologies20, University of California, Davis21
TL;DR: This article identifies key scientific and engineering advances needed to enable effective spoken language interaction with robotics, and makes 25 recommendations, involving eight general themes: putting human needs first, better modeling the social and interactive aspects of language, improving robustness, creating new methods for rapid adaptation, and improving research infrastructure and resources.
38 citations
TL;DR: In this article, a cognitive study and a quantitative model together examine how changing positive and negative experiences affect these confidences and ultimate decisions and find that human self-confidence, not their confidence in AI, directs the decision to accept or reject AI suggestions.
32 citations
TL;DR: In this paper , a cognitive study and a quantitative model together examine how changing positive and negative experiences affect these confidences and ultimate decisions and find that human self-confidence, not their confidence in AI, directs the decision to accept or reject AI suggestions.
32 citations
TL;DR: In this paper , a mixed-integer linear programming (MILP) formulation for the GTEP problem is proposed, and three different formulations, i.e., a big-m formulation, a hull formulation, and an alternative big-M formulation, are reported for transmission expansion.
23 citations
13 Jan 2022
TL;DR: The US prison population stands at 1.43 million persons, with an additional 740,000 persons in local jails as discussed by the authors, with nearly all of them eventually returning to society.
Abstract: The US prison population stands at 1.43 million persons, with an additional 740,000 persons in local jails. Nearly all will eventually return to society. This review examines the available evidence...
22 citations
TL;DR: The engineered universal EVs harboring havPD-1 proteins can be used for cancer concurrent immunotherapy and chemotherapy and were shown to be comparable to clinical anti-PD1 monoclonal antibodies.
21 citations
TL;DR: In this paper , a comparison of the SF33 immersion cooling and forced air cooling (FAC) for the 18650 lithium-ion battery under 2C, 4C and dynamic load conditions is made.
TL;DR: This article established a genome-wide compendium of somatic mutation events in 3949 whole cancer genomes representing 19 tumor types and established a blueprint for capturing mutation events across the entire genome to guide advances in biological discovery, therapies, and diagnostics.
Abstract: We established a genome-wide compendium of somatic mutation events in 3949 whole cancer genomes representing 19 tumor types. Protein-coding events captured well-established drivers. Noncoding events near tissue-specific genes, such as ALB in the liver or KLK3 in the prostate, characterized localized passenger mutation patterns and may reflect tumor-cell-of-origin imprinting. Noncoding events in regulatory promoter and enhancer regions frequently involved cancer-relevant genes such as BCL6, FGFR2, RAD51B, SMC6, TERT, and XBP1 and represent possible drivers. Unlike most noncoding regulatory events, XBP1 mutations primarily accumulated outside the gene's promoter, and we validated their effect on gene expression using CRISPR-interference screening and luciferase reporter assays. Broadly, our study provides a blueprint for capturing mutation events across the entire genome to guide advances in biological discovery, therapies, and diagnostics.
TL;DR: The art of formulating and solving a class of stochastic resource-constrained scheduling problems for elective surgery scheduling and downstream capacity planning is described and areas of opportunity for developing tractable, implementable, and data-driven approaches that might be applicable within and outside healthcare operations.
TL;DR: In this article, the authors compared IV-injected lipid nanoparticles formulated with reporter mRNA incorporating five base modifications and four ionizable lipids (C12-200, cKK-E12, ZA3-Ep10, and 200Oi10) with different organs.
TL;DR: The overall results show that the AI manager agent introduced in this work is able to match the capabilities of humans, showing potential in automating the management of a complex design process.
Abstract:
Managing the design process of teams has been shown to considerably improve problem-solving behaviors and resulting final outcomes. Automating this activity presents significant opportunities in delivering interventions that dynamically adapt to the state of a team in order to reap the most impact. In this work, an artificial intelligence (AI) agent is created to manage the design process of engineering teams in real time, tracking features of teams’ actions and communications during a complex design and path-planning task in multidisciplinary teams. Teams are also placed under the guidance of human process managers for comparison. Regarding outcomes, teams perform equally as well under both types of management, with trends toward even superior performance from the AI-managed teams. The managers’ intervention strategies and team perceptions of those strategies are also explored, illuminating some intriguing similarities. Both the AI and human process managers focus largely on communication-based interventions, though differences start to emerge in the distribution of interventions across team roles. Furthermore, team members perceive the interventions from both the AI and human manager as equally relevant and helpful, and believe the AI agent to be just as sensitive to the needs of the team. Thus, the overall results show that the AI manager agent introduced in this work is able to match the capabilities of humans, showing potential in automating the management of a complex design process.
TL;DR: In this paper , the authors discuss the crucial factors of metal nanoclusters for their use as biomaterials, including the size, surface chemistry, photoluminescent properties, cytotoxicity, and metabolic activity.
Abstract: Noble metal nanoclusters (NCs) have emerged as a new class of nanomaterials which have great potential in bioapplications. Due to their ultrasmall size and versatile surface chemistry, they show excellent luminescence, high photostability, good biocompatibility, low toxicity, and a high renal clearance rate. In recent years, much work has been reported toward the bioapplications of metal NCs, especially the Au, Ag, and Cu NCs. In this Review, we first discuss the crucial factors of metal NCs for their use as biomaterials, including the size, surface chemistry, photoluminescent properties, cytotoxicity, and metabolic activity. Then, we highlight some recent advances of metal NCs in bioapplications, such as biosensing, bioimaging, biomedical diagnosis, and therapy. It is noteworthy that the metal NCs perform quite well in cancer treatment. Finally, we have briefly discussed the current challenges and our perspectives on metal NCs in bioapplication research and further transformation into clinical application.
TL;DR: In this paper, the authors investigated how organizational agent type (algorithmic versus human) influenced one's psychological contract depending on the organizational inducement type (transactional versus relational) and found that the human agent did not affect psychological contracts around transactional inducements but did so for relational inducements.
TL;DR: In this article , the authors provided insights into the deformation and failure process of the Bianjiazhai landslide near the Suofengying reservoir located at the Wu River in Guizhou province, China.
01 Jan 2022
TL;DR: Push-SAGA as mentioned in this paper combines node-level variance reduction to remove the uncertainty caused by stochastic gradients, network-level gradient tracking to address the distributed nature of the data, and push-sum consensus to tackle directed information exchange.
Abstract: In this letter, we propose Push-SAGA, a decentralized stochastic first-order method for finite-sum minimization over a directed network of nodes. Push-SAGA combines node-level variance reduction to remove the uncertainty caused by stochastic gradients, network-level gradient tracking to address the distributed nature of the data, and push-sum consensus to tackle directed information exchange. We show that Push-SAGA achieves linear convergence to the exact solution for smooth and strongly convex problems and is thus the first linearly-convergent stochastic algorithm over arbitrary strongly connected directed graphs. We also characterize the regime in which Push-SAGA achieves a linear speed-up compared to its centralized counterpart and achieves a network-independent convergence rate. We illustrate the behavior and convergence properties of Push-SAGA with the help of numerical experiments on strongly convex and non-convex problems.
TL;DR: In this paper , the authors investigate how these two driving forces play against each other until cholesterol's chemical potential is equilibrated, and they find that it is possible to counteract the phospholipid number bias, and the resultant stress in the membrane, via the control of cholesterol mixing in the leaflets.
TL;DR: In this paper , the authors describe a strategy that enables long-term in vivo spatial extracellular vesicle retention by chemoselective immobilization of metabolically incoporated azido ligand-bearing EVs (azido-EVs) within a dibenzocyclooctyne-modified collagen hydrogel.
TL;DR: A novel lightweight 3-D place recognition and fast sequence matching method, capable of recognizing places from a previous trajectory regardless of viewpoints and temporary observation differences, is proposed, which outperforms the relative state of the art.
Abstract: Recognizing the same place undervariant viewpoint differences is the fundamental capability for human beings and animals However, such a strong place recognition ability in robotics is still an unsolved problem Extracting local invariant descriptors from the same place under various viewpoint differences is difficult This article seeks to provide robots with a human-like place recognition ability using a new 3-D feature learning method This article proposes a novel lightweight 3-D place recognition and fast sequence matching to achieve robust 3-D place recognition, capable of recognizing places from a previous trajectory regardless of viewpoints and temporary observation differences Specifically, we extracted the viewpoint-invariant place feature from 2-D spherical perspectives by leveraging spherical harmonics’ orientation-equivalent property To improve sequence-matching efficiency, we designed a coarse-to-fine fast sequence-matching mechanism to balance the matching efficiency and accuracy Despite the apparent simplicity, our proposed approach outperforms the relative state of the art In both public and self-gathered datasets with orientation/translation differences or noise observations, our method can achieve above 95% average recall for the best match with only 18% inference time of PointNet-based place recognition methods
TL;DR: In this article , a peptide/DNA nanocomplex was developed for the targeted delivery of chemotherapeutics and photosensitizers to cancer cells for efficient combination therapy.
Abstract: A peptide/DNA nanocomplex was developed for the targeted delivery of chemotherapeutics and photosensitizers to cancer cells for efficient combination therapy. The chemotherapeutic drug doxorubicin (DOX) and the photosensitizer 5,10,15,20-tetra-(1-methylpyridine-4-yl)-porphyrin (TMPyP4) were physically incorporated by an aptamer (AS1411)-modified tetrahedral DNA nanostructure, where the tetrahedral DNA and aptamer-induced G-quadruplex provide binding sites of DOX and TMPyP4. The co-loaded 3A-TDN/DT displayed a targeted uptake by HeLa cancer cells through the high affinity and specificity between AS1411 and nucleolin, a protein overexpressed on many types of cancer cells. A polycationic polymer, mPEG-PAsp(TECH), was synthesized to complex with the DNA nanostructure to efficiently escape from lysosomes via the proton sponge effect upon the enhanced internalization by tumor cells. Under the irradiation of 660 nm laser light, TMPyP4 induced an upregulation of intracellular reactive oxygen species, which combined with DOX to fulfill the efficient inhibition of HeLa cells. Our study demonstrated a biocompatible peptide/DNA composite nanoplatform for combinational cancer therapy via the targeted delivery of therapeutic agents and efficient lysosomal escape.
TL;DR: In this paper, a skeletal similarity model was proposed to explain significant unique variance in the response profiles of V3 and LO, and the skeletal model remained predictive in these regions even when controlling for other models of visual similarity that approximate low-to high-level visual features such as Gabor-jet, GIST, HMAX, and AlexNet.
TL;DR: In this article , a skeletal similarity model was proposed to explain significant unique variance in the response profiles of V3 and LO, and the skeletal model remained predictive in these regions even when controlling for other models of visual similarity that approximate low-to high-level visual features such as Gabor-jet, GIST, HMAX, and AlexNet.
01 Jan 2022
TL;DR: HexDom as discussed by the authors uses a semi-automated polycube-based mesh generation method for hexahedral-dominant mesh generation, which includes hexahedra, tetrahedra and triangular prisms.
Abstract: In this paper, we extend our earlier polycube-based all-hexahedral mesh generation method to hexahedral-dominant mesh generation, and present the HexDom software package. Given the boundary representation of a solid model, HexDom creates a hex-dominant mesh by using a semi-automated polycube-based mesh generation method. The resulting hexahedral dominant mesh includes hexahedra, tetrahedra, and triangular prisms. By adding non-hexahedral elements, we are able to generate better quality hexahedral elements than in all-hexahedral meshes. We explain the underlying algorithms in four modules including segmentation, polycube construction, hex-dominant mesh generation and quality improvement, and use a rockerarm model to explain how to run the software. We also apply our software to a number of other complex models to test their robustness. The software package and all tested models are available in github ( https://github.com/CMU-CBML/HexDom ).
TL;DR: In this paper , the energy consumption of package-delivery drones has been investigated and the authors provided an open model based on 188 delivery-drone flights with a range of payloads, speeds, and altitudes.
TL;DR: In this article , the authors used atomic layer deposition of TiO2 and Pt coupled with thermal reductive annealing to prepare Pt3Ti electrocatalysts for the oxygen reduction reaction.
Abstract: Improved activity and stability Pt-based catalysts for the oxygen reduction reaction (ORR) are needed to perpetuate the deployment of polymer electrolyte fuel cells (PEFCs) in the transportation sector. Here, we use atomic layer deposition of TiO2 and Pt coupled with thermal reductive annealing to prepare Pt3Ti electrocatalysts. The atomic level synthetic control resulted in Pt3Ti nanoparticles with high ORR performance, including a mass activity of 1.84 A/mgPt and excellent electrochemical stability. The Pt3Ti nanoparticles show excellent specific activity — 5.3-fold higher than commercial Pt/C and 3-fold higher than polycrystalline Pt, exceeding the performance of any PtTi catalysts reported to date. Combined experimental and computational efforts indicate that Pt enrichment on the Pt3Ti enhances the activity, and the intrinsic stability of the Pt3Ti phase provides durability. This knowledge, along with the facile fabrication of alloys by atomic layer deposition, can be leveraged to designed improved performance catalysts.
TL;DR: In this article, the authors used atomic layer deposition of TiO2 and Pt coupled with thermal reductive annealing to prepare Pt3Ti electrocatalysts for the oxygen reduction reaction (ORR).
Abstract: Improved activity and stability Pt-based catalysts for the oxygen reduction reaction (ORR) are needed to perpetuate the deployment of polymer electrolyte fuel cells (PEFCs) in the transportation sector. Here, we use atomic layer deposition of TiO2 and Pt coupled with thermal reductive annealing to prepare Pt3Ti electrocatalysts. The atomic level synthetic control resulted in Pt3Ti nanoparticles with high ORR performance, including a mass activity of 1.84 A/mgPt and excellent electrochemical stability. The Pt3Ti nanoparticles show excellent specific activity — 5.3-fold higher than commercial Pt/C and 3-fold higher than polycrystalline Pt, exceeding the performance of any PtTi catalysts reported to date. Combined experimental and computational efforts indicate that Pt enrichment on the Pt3Ti enhances the activity, and the intrinsic stability of the Pt3Ti phase provides durability. This knowledge, along with the facile fabrication of alloys by atomic layer deposition, can be leveraged to designed improved performance catalysts.
TL;DR: In this paper, the authors investigated the emission, distribution, and removal of cooking-emitted particles in the simulated residential module of the Well Living Lab and found that the combination of Ventilation and Stove Hood was the best intervention in reducing the integrated PM2.5 concentration.