Showing papers by "Worcester Polytechnic Institute published in 2022"

A photoredox-catalyzed approach for formal hydride abstraction to enable C –H functionalization with nucleophilic partners (F, C, O, N, and Br/Cl)

Abstract: A catalytic Csp3–H functionalization manifold for accessing reactive carbocation intermediates directly from a range of Csp3–H bonds is reported. The catalytic design relies on a strategy driven by visible-light photoredox catalysis that combines two mechanistic steps in one catalytic cycle: hydrogen-atom transfer (HAT) and radical-polar crossover (RPC), representing a net hydride abstraction. We herein demonstrate a mild and robust [HAT + RPC] platform that can efficiently enable a Csp3–H fluorination with nucleophilic fluoride—a classically poor nucleophile. Notably, the [HAT + RPC] protocol was successful in the late-stage fluorination of several commercial drugs. Difluorination of methylene groups is also demonstrated, and represents the first C–H difluorination with nucleophilic fluoride. Importantly, long-term potential of the design was demonstrated with the engagement of other nucleophile classes including halides, alcohols, water, N-heterocycle, carboxylic acids, azide, and arenes, thus showcasing its potential as a unified C–H functionalization method.

Improving legal judgment prediction through reinforced criminal element extraction

Abstract: Legal text mining is targeted at automatically analyzing the texts in the legal domain by employing various natural language processing techniques and has attracted enormous attention from the NLP community. As one of the most crucial tasks of legal text mining, Legal Judgment Prediction (LJP) aims to automatically predict judgment results (e.g., applicable law articles, charges, and terms of penalty) according to fact descriptions on law cases and becomes a promising application of artificial intelligence techniques. Unfortunately, ambiguous fact descriptions and law articles often appear due to a great number of shared words and legal concepts. Prior works are proposed to partially address these problems, focusing on introducing additional attributes to distinguish similar fact descriptions, or differentiating confusing law articles by grouping and distilling law articles. However, existing works still face two severe challenges: (1) indistinguishable fact descriptions with different criminals and targets and (2) misleading law articles with highly similar TF–IDF representations, both of which lead to serious misjudgments for the LJP task. In this paper, we present a novel reinforcement learning (RL) based framework, named Criminal Element Extraction Network (CEEN), to handle above challenges simultaneously. In CEEN, we propose four types of discriminative criminal elements, including the criminal, target, intentionality, and criminal behavior. To discriminate ambiguous fact descriptions, an reinforcement learning based extractor is designed to accurately locate elements for different cases. To enhance law article predictions, distinctive element representations are constructed for each type of criminal element. Finally, with the input of element representations, a multi-task predictor is adopted for the judgment predictions. Experimental results on real-world datasets show that extracting criminal elements is highly useful for predicting the judgment results.

Fabricating of N/O-codoping porous carbon interpenetrating networks for high energy aqueous supercapacitor

Abstract: Developing high-capacity carbon electrodes with an enlarged potential window is a significant strategy for high energy aqueous supercapacitor. However, engineering synthesis of hierarchical porous carbon networks with the broad electrochemical stability window remains a great challenge. In this work, N/O codoped hierarchical porous carbon networks (MPBx) are constructed based on Schiff-base reaction for the purpose of excellent charge storage and kinetics of electrolyte ions diffusion in “water-in-salt” (lithium bis(trifluoromethane sulfonyl)imide, LiTFSI) electrolyte. When fabricated as an electrode for symmetrical supercapacitor, the 2.25 V LiTFSI-based supercapacitor exploits a superior specific capacitance (180 F g−1, 0.5 A g−1) and an exceptional energy delivery of 31.7 W h kg−1 at the corresponding power output of 562 W kg−1, together with an impressive cycling stability with 93.8 % capacitance retention after 10,000 continuous cycles. The improvement in the superb electrochemical performance can be attributed to the excellent conductive networks, high ion-adsorption area (1902 m2 g−1), interpenetrating porous architecture (macro-, meso- and micropore) and high-level heteroatoms dopants (N: 2.56 at.%, O: 11.46 at.%). The excellent electrochemical performances demonstrate that the carbon networks hold great prospects for promoting the energy delivery of aqueous supercapacitors.

Subcritical water hydrolysis of poultry feathers for amino acids production

Abstract: This study assessed the subcritical water hydrolysis (SWH) of poultry feathers to recover amino acids. Experiments were conducted in a semi-continuous flow-through subcritical reactor (110 mL), which was operated over a range of temperatures (210–250 °C) and water flow rates (5–15 mL min–1), combined through a 22 central composite design, at constant feed (10 g) and pressure (15 MPa). The results demonstrated that non-essential and essential amino acids were obtained from SWH of poultry feathers. The highest hydrolysis temperature resulted in the highest concentrations of valine, methionine, tryptophan, phenylalanine, isoleucine, leucine, and lysine. Otherwise, threonine, histidine, and arginine were obtained more effectively at lower temperatures. The response surface methodology was adopted to identify the best conditions for amino acid production, and it was possible to identify the ranges of temperatures and water flow rates to be used to recover specific amino acids. This study allowed concluding that SWH is a promising eco-friendly technology to recover amino acids from protein-rich wastes.

Loss of RNase J leads to multi-drug tolerance and accumulation of highly structured mRNA fragments in Mycobacterium tuberculosis

Abstract: Despite the existence of well-characterized, canonical mutations that confer high-level drug resistance to Mycobacterium tuberculosis (Mtb), there is evidence that drug resistance mechanisms are more complex than simple acquisition of such mutations. Recent studies have shown that Mtb can acquire non-canonical resistance-associated mutations that confer survival advantages in the presence of certain drugs, likely acting as stepping-stones for acquisition of high-level resistance. Rv2752c/rnj, encoding RNase J, is disproportionately mutated in drug-resistant clinical Mtb isolates. Here we show that deletion of rnj confers increased tolerance to lethal concentrations of several drugs. RNAseq revealed that RNase J affects expression of a subset of genes enriched for PE/PPE genes and stable RNAs and is key for proper 23S rRNA maturation. Gene expression differences implicated two sRNAs and ppe50-ppe51 as important contributors to the drug tolerance phenotype. In addition, we found that in the absence of RNase J, many short RNA fragments accumulate because they are degraded at slower rates. We show that the accumulated transcript fragments are targets of RNase J and are characterized by strong secondary structure and high G+C content, indicating that RNase J has a rate-limiting role in degradation of highly structured RNAs. Taken together, our results demonstrate that RNase J indirectly affects drug tolerance, as well as reveal the endogenous roles of RNase J in mycobacterial RNA metabolism.

Operations research and analytics to combat human trafficking: A systematic review of academic literature

Abstract: Human trafficking is a widespread and compound social, economic, and human rights issue occurring in every region of the world. While there have been an increasing number of anti-human trafficking studies from the Operations Research and Analytics domains in recent years, no systematic review of this literature currently exists. We fill this gap by providing a systematic literature review that identifies and classifies the body of Operations Research and Analytics research related to the anti-human trafficking domain, thereby illustrating the collective impact of the field to date. We classify 142 studies to identify current trends in methodologies, theoretical approaches, data sources, trafficking contexts, target regions, victim-survivor demographics, and focus within the well-established 4Ps principles. Using these findings, we discuss the extent to which the current literature aligns with the global demographics of human trafficking and identify existing research gaps to propose an agenda for Operations Research and Analytics researchers.

Improving Variational Autoencoders for New Physics Detection at the LHC With Normalizing Flows

Abstract: We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider. As a working example, we consider the DarkMachines challenge dataset. We show how different design choices (e.g., event representations, anomaly score definitions, network architectures) affect the result on specific benchmark new physics models. Once a baseline is established, we discuss how to improve the anomaly detection accuracy by exploiting normalizing flow layers in the latent space of the variational autoencoder.

Mechanical and Thermal Properties of Sustainable Composite Building Materials Produced by the Reprocessing of Low-Density Polyethylene, Biochar, Calcium Phosphate, and Phosphogypsum Wastes

Abstract: This paper presents the results of the experimental and analytical studies of the mechanical and thermal properties of laterite composites mixed with reprocessed low-density polyethylene wa...

Porcine TRIM35 positively regulate TRAF3-mediated IFN-β production and inhibit Japanese encephalitis virus replication

Abstract: Tripartite motif 35 (TRIM35) protein is a ubiquitin E3 ligase that mediates interferon-beta (IFN-β) production via regulating ubiquitination of multiple adaptor proteins in innate immune signaling pathways. Here, we cloned the porcine TRIM35 (porTRIM35) gene and analyzed its involvement in IFN-β expression as well as the antiviral response against Japanese encephalitis virus (JEV). The full-length porTRIM35 gene encoded a 493-amino acid protein and exhibited 79.6%-89.5% sequence similarity with its orthologues in humans, mice, monkeys and rabbits. porTRIM35 possessed typical structural features of TRIMs, including a RING domain, a B-box domain, a coiled-coil domain and a PRY/SPYR domain. Exogenous overexpression of porTRIM35 significantly up-regulated the mRNA expression level of IFN-β in swine testicular (ST) cell in response to poly(I:C) stimulation, whereas knockdown endogenous expression of porTRIM35 lead to a decrease in the expression level of IFN-β. Mechanically, porTRIM35 directly interacted with porcine TNF-receptor associated factor 3 (TRAF3) and catalyzed its Lys63-linked polyubiquitination, thereby leading to the up-regulation of IFN-β production. Meanwhile, we demonstrated that the RING and PRY/SPRY domains were essential for the E3 ligase activity of porTRIM35. In response to JEV infection, the endogenous expression of porTRIM35 was markedly inhibited at the mRNA level, while exogenous expression of porTRIM35 significantly elevated the expression of IFN-β induced by JEV infection and reduced viral titers in ST cells, suggesting that porTRIM35 is a negative regulator for JEV replication. These data demonstrate the importance of porTRIM35 in IFN-β expression as well as the antiviral response against JEV replication.

Porcine TRIM35 positively regulate TRAF3-mediated IFN-β production and inhibit Japanese encephalitis virus replication.

Abstract: Tripartite motif 35 (TRIM35) protein is a ubiquitin E3 ligase that mediates interferon-beta (IFN-β) production via regulating ubiquitination of multiple adaptor proteins in innate immune signaling pathways. Here, we cloned the porcine TRIM35 (porTRIM35) gene and analyzed its involvement in IFN-β expression as well as the antiviral response against Japanese encephalitis virus (JEV). The full-length porTRIM35 gene encoded a 493-amino acid protein and exhibited 79.6%–89.5% sequence similarity with its orthologues in humans, mice, monkeys and rabbits. porTRIM35 possessed typical structural features of TRIMs, including a RING domain, a B-box domain, a coiled-coil domain and a PRY/SPYR domain. Exogenous overexpression of porTRIM35 significantly up-regulated the mRNA expression level of IFN-β in swine testicular (ST) cell in response to poly(I:C) stimulation, whereas knockdown endogenous expression of porTRIM35 lead to a decrease in the expression level of IFN-β. Mechanically, porTRIM35 directly interacted with porcine TNF-receptor associated factor 3 (TRAF3) and catalyzed its Lys63-linked polyubiquitination, thereby leading to the up-regulation of IFN-β production. Meanwhile, we demonstrated that the RING and PRY/SPRY domains were essential for the E3 ligase activity of porTRIM35. In response to JEV infection, the endogenous expression of porTRIM35 was markedly inhibited at the mRNA level, while exogenous expression of porTRIM35 significantly elevated the expression of IFN-β induced by JEV infection and reduced viral titers in ST cells, suggesting that porTRIM35 is a negative regulator for JEV replication. These data demonstrate the importance of porTRIM35 in IFN-β expression as well as the antiviral response against JEV replication.

Influence of interactions between multiple point defects on wave scattering in granular media

Abstract: The wave dynamics of 2D finite granular crystals with point defects subjected to impact loading are investigated. The full-field kinematic and strain measurements are analyzed to decipher the influence of point defects on the wave dynamics of finite granular crystals. Impact experiments were performed for a defect-free polyurethane cubic granular crystal to establish a baseline for comparison. Subsequently, granular crystals with a single polycarbonate point defect were subjected to low-velocity impact. These experiments provided a quantification of the wave scattering effects around a single point defect in finite granular crystals. To gain further insight into the interaction of multiple point defects, a set of 8 impact experiments were conducted on granular crystals with three point defects. The location of the first point defect was the same as a single defect experiment while the other two defects in any experiments were placed at locations symmetric about the middle column. The changes in the strain energy distribution in the different regions of the granular crystals were used to assess the influence of the defects on the wave propagation process. These results revealed that a single defect acts as a scattering point for stress waves and alters the wave dynamics. The introduction of additional pair of defects has a significant influence on the wave propagation process. Based on the distance and the angle between the defects, these defects could act in tandem and have a total effect if the defects are close enough or could act independently if they are further away.

Activation of Gαq sequesters specific transcripts into Ago2 particles

Abstract: The Gαq/phospholipase Cβ1 (PLCβ1) signaling system mediates calcium responses from hormones and neurotransmitters. While PLCβ1 functions on the plasma membrane, there is an atypical cytosolic population that binds Argonaute 2 (Ago2) and other proteins associated with stress granules preventing their aggregation. Activation of Gαq relocalizes cytosolic PLCβ1 to the membrane, releasing bound proteins, promoting the formation of stress granules. Here, we have characterized Ago2 stress granules associated with Gαq activation in differentiated PC12 cells, which have a robust Gαq/PLCβ1 signaling system. Characterization of Ago2-associated stress granules shows shifts in protein composition when cells are stimulated with a Gαq agonist, or subjected to heat shock or osmotic stress, consistent with the idea that different stresses result in unique stress granules. Purified Ago2 stress granules from control cells do not contain RNA, while those from heat shock contain many different mRNAs and miRs. Surprisingly, Ago2 particles from cells where Gαq was stimulated show only two transcripts, chromogranin B, which is involved in secretory function, and ATP synthase 5f1b, which is required for ATP synthesis. RT-PCR, western blotting and other studies support the idea that Gαq-activation protects these transcripts. Taken together, these studies show a novel pathway where Gαq/PLCβ regulates the translation of specific proteins.

Distance Preserving Model Order Reduction of Graph-Laplacians and Cluster Analysis

Abstract: Graph-Laplacians and their spectral embeddings play an important role in multiple areas of machine learning. This paper is focused on graph-Laplacian dimension reduction for the spectral clustering of data as a primary application, however, it can also be applied in data mining, data manifold learning, etc. Spectral embedding provides a low-dimensional parametrization of the data manifold which makes the subsequent task (e.g., clustering with k-means or any of its approximations) much easier. However, despite reducing the dimensionality of data, the overall computational cost may still be prohibitive for large data sets due to two factors. First, computing the partial eigendecomposition of the graph-Laplacian typically requires a large Krylov subspace. Second, after the spectral embedding is complete, one still has to operate with the same number of data points, which may ruin the efficiency of the approach. For example, clustering of the embedded data is typically performed with various relaxations of k-means which computational cost scales poorly with respect to the size of data set. Also, they become prone to getting stuck in local minima, so their robustness depends on the choice of initial guess. In this work, we switch the focus from the entire data set to a subset of graph vertices (target subset). We develop two novel algorithms for such low-dimensional representation of the original graph that preserves important global distances between the nodes of the target subset. In particular, it allows to ensure that target subset clustering is consistent with the spectral clustering of the full data set if one would perform such. That is achieved by a properly parametrized reduced-order model (ROM) of the graph-Laplacian that approximates accurately the diffusion transfer function of the original graph for inputs and outputs restricted to the target subset. Working with a small target subset reduces greatly the required dimension of Krylov subspace and allows to exploit the conventional algorithms (like approximations of k-means) in the regimes when they are most robust and efficient. This was verified in the numerical clustering experiments with both synthetic and real data. We also note that our ROM approach can be applied in a purely transfer-function-data-driven way, so it becomes the only feasible option for extremely large graphs that are not directly accessible. There are several uses for our algorithms. First, they can be employed on their own for representative subset clustering in cases when handling the full graph is either infeasible or simply not required. Second, they may be used for quality control. Third, as they drastically reduce the problem size, they enable the application of more sophisticated algorithms for the task under consideration (like more powerful approximations of k-means based on semi-definite programming (SDP) instead of the conventional Lloyd’s algorithm). Finally, they can be used as building blocks of a multi-level divide-and-conquer type algorithm to handle the full graph. The latter will be reported in a separate article.

Chemical Case Studies in KeYmaera X

Abstract: Safety-critical chemical processes are well-studied in the formal methods literature, including hybrid systems models which combine discrete and continuous dynamics. This paper is the first to use a theorem-prover to verify hybrid chemical models: the KeYmaera X prover for differential dynamic logic. KeYmaera X provides parametric results that hold for a whole range of parameter values, non-linear physical dynamics, and a small trusted computing base. We tell a general story about KeYmaera X: recent advances in automated reasoning about safety and liveness for differential equations have enabled elegant proofs about reaction dynamics.

Citation Oriented AuthorRank for Scientific Publication Ranking

Abstract: It is now generally accepted that an article written by influential authors often deserves a higher ranking in information retrieval. However, it is a challenging task to determine an author’s relative influence since information about the author is, much of the time, inaccessible. Actually, in scientific publications, the author is an important metadata item, which has been widely used in previous studies. In this paper, we bring an optimized AuthorRank, which is a topic-sensitive algorithm calculated by citation context, into citation analysis for testing whether and how topical AuthorRank can replace or enhance classical PageRank for publication ranking. For this purpose, we first propose a PageRank with Priors (PRP) algorithm to rank publications and authors. PRP is an optimized PageRank algorithm supervised by the Labeled Latent Dirichlet Allocation (Labeled-LDA) topic model with full-text information extraction. We then compared four methods of generating an AuthorRank score, looking, respectively, at the first author, the last author, the most famous author, and the “average” author (of a publication). Additionally, two combination methods (Linear and Cobb–Douglas) of AuthorRank and PRP were compared with several baselines. Finally, as shown in our evaluation results, the performance of AuthorRank combined with PRP is better (p < 0.001) than other baselines for publication ranking.