RIN1 Is an ABL Tyrosine Kinase Activator and a Regulator of Epithelial-Cell Adhesion and Migration

SummaryBackground: ABL tyrosine kinases control actin remodeling in development and in response to environmental stimuli. These changes affect cell adhesion, cell migration, and cell-cell contact. Little is known, however, about upstream mechanisms regulating ABL protein activation.Results: We report that the RAS effector RIN1 is an activator of ABL tyrosine kinases. RIN1 expression in fibroblasts promotes the formation of membrane spikes; similar effects have been reported for ABL overexpression. RIN1 binds to the ABL SH3 and SH2 domains, and these interactions stimulate ABL2 catalytic activity. This leads to increased phosphorylation of CRK and CRKL, inhibiting these cytoskeletal regulators by promoting intramolecular over intermolecular associations. Activated RAS participates in a stable RAS-RIN1-ABL2 complex and stimulates the tyrosine kinase-activation function of RIN1. Deletion of the RAS binding domain (RBD) strongly stimulated the ABL2 activation function of RIN1, suggesting that RAS activation results from the relief of RIN1 autoinhibition. The ABL binding domain of RIN1 (RIN1-ABD) increased the activity of ABL2 immune complexes and purified RIN1-ABD-stimulated ABL2 kinase activity toward CRK. Mammary epithelial cells (MECs) from Rin1−/− mice showed accelerated cell adhesion and increased motility in comparison to wild-type cells. Knockdown of RIN1 in epithelial-cell lines blocked the induction of CRKL phosphorylation, confirming that RIN1 normally functions as an inhibitor of cell motility.Conclusions: RIN1 is a directly binding ABL tyrosine kinase activator in cells as well as in a defined-component assay. In response to environmental changes, this novel signal pathway mediates actin remodeling associated with adhesion and migration of epithelial cells

ADBench: Anomaly Detection Benchmark

Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data? In this work, we answer these key questions by conducting (to our best knowledge) the most comprehensive anomaly detection benchmark with 30 algorithms on 57 benchmark datasets, named ADBench. Our extensive experiments (98,436 in total) identify meaningful insights into the role of supervision and anomaly types, and unlock future directions for researchers in algorithm selection and design. With ADBench, researchers can easily conduct comprehensive and fair evaluations for newly proposed methods on the datasets (including our contributed ones from natural language and computer vision domains) against the existing baselines. To foster accessibility and reproducibility, we fully open-source ADBench and the corresponding results.Comment: NeurIPS 2022. All authors contribute equally and are listed alphabetically. Code available at https://github.com/Minqi824/ADBenc

ADGym: Design Choices for Deep Anomaly Detection

Deep learning (DL) techniques have recently found success in anomaly detection (AD) across various fields such as finance, medical services, and cloud computing. However, most of the current research tends to view deep AD algorithms as a whole, without dissecting the contributions of individual design choices like loss functions and network architectures. This view tends to diminish the value of preliminary steps like data preprocessing, as more attention is given to newly designed loss functions, network architectures, and learning paradigms. In this paper, we aim to bridge this gap by asking two key questions: (i) Which design choices in deep AD methods are crucial for detecting anomalies? (ii) How can we automatically select the optimal design choices for a given AD dataset, instead of relying on generic, pre-existing solutions? To address these questions, we introduce ADGym, a platform specifically crafted for comprehensive evaluation and automatic selection of AD design elements in deep methods. Our extensive experiments reveal that relying solely on existing leading methods is not sufficient. In contrast, models developed using ADGym significantly surpass current state-of-the-art techniques.Comment: NeurIPS 2023. The first three authors contribute equally. Code available at https://github.com/Minqi824/ADGy

Design Space Exploration of Galois and Fibonacci Configuration based on Espresso Stream Cipher

Galois and Fibonacci are two different configurations of stream ciphers. Because the Fibonacci configuration is more convenient for cryptanalysis, most ciphers are designed as Fibonacci-configured. So far, although many transformations between Fibonacci and Galois configurations have been proposed, there is no sufficient analysis of their respective hardware performance. The 128-bit secret key stream cipher Espresso, its Fibonacciconfigured variant and linear Fibonacci variant have a similar security level. We take them as examples to design the optimization strategies in terms of both area and throughput, investigate which configuration is more efficient in a certain aspect. The Fibonacci-configured Espresso occupies 52 slices on Spartan-3 and 22 slices on Virtex-7, which are the minimum solutions among those three Espresso schemes or even smaller than 80-bit secret key ciphers. Based on our throughput improvement strategy, parallel Espresso design can perform 4.1 Gbps on Virtex-7 FPGA and 1.9 Gbps on Spartan-3 FPGA at most. In brief, the Fibonacci cipher is more suitable for extremely resource-constrained or extremely high-throughput applications, while the Galois cipher seems like a compromise between area and speed. Besides, the transformation from nonlinear feedback to linear feedback is not recommended for any hardware implementations

A New Series of Small Molecular Weight Compounds Induce Read Through of All Three Types of Nonsense Mutations in the ATM Gene

Chemical-induced read through of premature stop codons might be exploited as a potential treatment strategy for genetic disorders caused by nonsense mutations. Despite the promise of this approach, only a few read-through compounds (RTCs) have been discovered to date. These include aminoglycosides (e.g., gentamicin and G418) and nonaminoglycosides (e.g., PTC124 and RTC13). The therapeutic benefits of these RTCs remain to be determined. In an effort to find new RTCs, we screened an additional ~36,000 small molecular weight compounds using a high-throughput screening (HTS) assay that we had previously developed and identified two novel RTCs, GJ071, and GJ072. The activity of these two compounds was confirmed in cells derived from ataxia telangiectasia (A-T) patients with three different types of nonsense mutation in the ATM gene. Both compounds showed activity comparable to stop codons (TGA, TAG, and TAA) PTC124 and RTC13. Early structure-activity relationship studies generated eight active analogs of GJ072. Most of those analogs were effective on all three stop codons. GJ071 and GJ072, and some of the GJ072 analogs, appeared to be well tolerated by A-T cells. We also identified another two active RTCs in the primary screen, RTC204 and RTC219, which share a key structural feature with GJ072 and its analogs

A quantitative RT-PCR assay for rapid detection of Eurasianlineage H10 subtype influenza A virus

Influenza A viruses (IAVs) are single-stranded, negative sense RNA viruses. IAV subtype is determined on the basis of the viral surface glycoproteins, hemagglutinin (HA), and neuraminidase (NA). To date, 18 HA and 11 NA subtypes have been reported (Tong et al., 2012). IAVs can cause sporadic infections, local epidemics, and global pandemics among humans. In addition to humans, IAVs can naturally infect avian, swine, equines, canines, and sea mammals (Webster et al., 1992). Migratory waterfowl are the natural reservoir for IAVs, and the avianorigin IAVs play an important role in influenza ecology and have been involved in generation of the IAVs infection in humans. At least one or more genetic segments of all four known pandemic strains are of avian origin, and these avian-origin genes reassorted with those IAVs from domestic animals to generate pandemic viruses. For example, the HA genes (major antigenic determinants) of 1918, 1957, and 1968 pandemic viruses are all of avianorigin (Webster et al., 1997); the 2009 H1N1 pandemic virus has avian-origin PB2 and PA genes (Shinde et al., 2009). Besides pandemic viruses, in the past decades, there have also been a number of reported human infections with avian IAVs, including subtypes H5N1, H6N1, H7N2, H7N3, H7N7, H9N2, H10N7 and H7N9. Thus, monitoring the evolution of avian IAVs and rapidly detecting these viruses in human are important components of influenza surveillance and pandemic preparedness

A quantitative RT-PCR assay for rapid detection of Eurasianlineage H10 subtype influenza A virus

Influenza A viruses (IAVs) are single-stranded, negative sense RNA viruses. IAV subtype is determined on the basis of the viral surface glycoproteins, hemagglutinin (HA), and neuraminidase (NA). To date, 18 HA and 11 NA subtypes have been reported (Tong et al., 2012). IAVs can cause sporadic infections, local epidemics, and global pandemics among humans. In addition to humans, IAVs can naturally infect avian, swine, equines, canines, and sea mammals (Webster et al., 1992). Migratory waterfowl are the natural reservoir for IAVs, and the avianorigin IAVs play an important role in influenza ecology and have been involved in generation of the IAVs infection in humans. At least one or more genetic segments of all four known pandemic strains are of avian origin, and these avian-origin genes reassorted with those IAVs from domestic animals to generate pandemic viruses. For example, the HA genes (major antigenic determinants) of 1918, 1957, and 1968 pandemic viruses are all of avianorigin (Webster et al., 1997); the 2009 H1N1 pandemic virus has avian-origin PB2 and PA genes (Shinde et al., 2009). Besides pandemic viruses, in the past decades, there have also been a number of reported human infections with avian IAVs, including subtypes H5N1, H6N1, H7N2, H7N3, H7N7, H9N2, H10N7 and H7N9. Thus, monitoring the evolution of avian IAVs and rapidly detecting these viruses in human are important components of influenza surveillance and pandemic preparedness

A quantitative RT-PCR assay for rapid detection of Eurasianlineage H10 subtype influenza A virus

Influenza A viruses (IAVs) are single-stranded, negative sense RNA viruses. IAV subtype is determined on the basis of the viral surface glycoproteins, hemagglutinin (HA), and neuraminidase (NA). To date, 18 HA and 11 NA subtypes have been reported (Tong et al., 2012). IAVs can cause sporadic infections, local epidemics, and global pandemics among humans. In addition to humans, IAVs can naturally infect avian, swine, equines, canines, and sea mammals (Webster et al., 1992). Migratory waterfowl are the natural reservoir for IAVs, and the avianorigin IAVs play an important role in influenza ecology and have been involved in generation of the IAVs infection in humans. At least one or more genetic segments of all four known pandemic strains are of avian origin, and these avian-origin genes reassorted with those IAVs from domestic animals to generate pandemic viruses. For example, the HA genes (major antigenic determinants) of 1918, 1957, and 1968 pandemic viruses are all of avianorigin (Webster et al., 1997); the 2009 H1N1 pandemic virus has avian-origin PB2 and PA genes (Shinde et al., 2009). Besides pandemic viruses, in the past decades, there have also been a number of reported human infections with avian IAVs, including subtypes H5N1, H6N1, H7N2, H7N3, H7N7, H9N2, H10N7 and H7N9. Thus, monitoring the evolution of avian IAVs and rapidly detecting these viruses in human are important components of influenza surveillance and pandemic preparedness

An Overview on IEEE 802.11bf: WLAN Sensing

With recent advancements, the wireless local area network (WLAN) or wireless fidelity (Wi-Fi) technology has been successfully utilized to realize sensing functionalities such as detection, localization, and recognition. However, the WLANs standards are developed mainly for the purpose of communication, and thus may not be able to meet the stringent requirements for emerging sensing applications. To resolve this issue, a new Task Group (TG), namely IEEE 802.11bf, has been established by the IEEE 802.11 working group, with the objective of creating a new amendment to the WLAN standard to meet advanced sensing requirements while minimizing the effect on communications. This paper provides a comprehensive overview on the up-to-date efforts in the IEEE 802.11bf TG. First, we introduce the definition of the 802.11bf amendment and its formation and standardization timeline. Next, we discuss the WLAN sensing use cases with the corresponding key performance indicator (KPI) requirements. After reviewing previous WLAN sensing research based on communication-oriented WLAN standards, we identify their limitations and underscore the practical need for the new sensing-oriented amendment in 802.11bf. Furthermore, we discuss the WLAN sensing framework and procedure used for measurement acquisition, by considering both sensing at sub-7GHz and directional multi-gigabit (DMG) sensing at 60 GHz, respectively, and address their shared features, similarities, and differences. In addition, we present various candidate technical features for IEEE 802.11bf, including waveform/sequence design, feedback types, as well as quantization and compression techniques. We also describe the methodologies and the channel modeling used by the IEEE 802.11bf TG for evaluation. Finally, we discuss the challenges and future research directions to motivate more research endeavors towards this field in details.Comment: 31 pages, 25 figures, this is a significant updated version of arXiv:2207.0485

Transcriptome and Metabolome Analyses Reveal Differences in Terpenoid and Flavonoid Biosynthesis in Cryptomeria fortunei Needles Across Different Seasons

Cryptomeria fortunei (Chinese cedar) has outstanding medicinal value due to its abundant flavonoid and terpenoid contents. The metabolite contents of C. fortunei needles differ across different seasons. However, the biosynthetic mechanism of these differentially synthesized metabolites (DSMs) is poorly understood. To improve our understanding of this process, we performed integrated non-targeted metabolomic liquid chromatography and gas chromatography mass spectrometry (LC-MS and GC-MS), and transcriptomic analyses of summer and winter needles. In winter, the C. fortunei needle ultrastructure was damaged, and the chlorophyll content and Fv/Fm were significantly (p < 0.05) reduced. Based on GC-MS and LC-MS, we obtained 106 and 413 DSMs, respectively; based on transcriptome analysis, we obtained a total of 41.17 Gb of clean data and assembled 33,063 unigenes, including 14,057 differentially expressed unigenes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that these DSMs/DEGs were significantly (p < 0.05) enriched in many biosynthesis pathways, such as terpenoids, photosynthates, and flavonoids. Integrated transcriptomic and metabonomic analyses showed that seasonal changes have the greatest impact on photosynthesis pathways, followed by terpenoid and flavonoid biosynthesis pathways. In summer Chinese cedar (SCC) needles, DXS, DXR, and ispH in the 2-methyl-pentaerythritol 4-phosphate (MEP) pathway and GGPS were highly expressed and promoted the accumulation of terpenoids, especially diterpenoids. In winter Chinese cedar (WCC) needles, 9 genes (HCT, CHS, CHI, F3H, F3'H, F3'5'H, FLS, DFR, and LAR) involved in flavonoid biosynthesis were highly expressed and promoted flavonoid accumulation. This study broadens our understanding of the metabolic and transcriptomic changes in C. fortunei needles caused by seasonal changes and provides a reference regarding the adaptive mechanisms of C. fortunei and the extraction of its metabolites