Miracle-dz/UniFAR-FTUs huggingface dataset:
Hsl7 Localizes to a Septin Ring and Serves as an Adapter in a Regulatory Pathway That Relieves Tyrosine Phosphorylation of Cdc28 Protein Kinase inSaccharomyces cerevisiae

ABSTRACT Successful mitosis requires faithful DNA replication, spindle assembly, chromosome segregation, and cell division. In the budding yeast Saccharomyces cerevisiae, the G2-to-M transition requires activation of Clb-bound forms of the protein kinase, Cdc28. These complexes are held in an inactive state via phosphorylation of Tyr19 in the ATP-binding loop of Cdc28 by the Swe1 protein kinase. The HSL1 and HSL7 gene products act as negative regulators of Swe1. Hsl1 is a large (1,518-residue) protein kinase with an N-terminal catalytic domain and a very long C-terminal extension. Hsl1 localizes to the incipient site of cytokinesis in the bud neck in a septin-dependent manner; however, the function of Hsl7 was not previously known. Using both indirect immunofluorescence with anti-Hsl7 antibodies and a fusion of Hsl7 to green fluorescent protein, we found that Hsl7 also localizes to the bud neck, congruent with the septin ring that faces the daughter cell. Both Swe1 and a segment of the C terminus of Hsl1 (which has no sequence counterpart in two Hsl1-related protein kinases, Gin4 and Kcc4) were identified as gene products that interact with Hsl7 in a two-hybrid screen of a random S. cerevisiae cDNA library. Hsl7 plus Swe1 and Hsl7 plus Hsl1 can be coimmunoprecipitated from extracts of cells overexpressing these proteins, confirming that Hsl7 physically associates with both partners. Also consistent with the two-hybrid results, Hsl7 coimmunoprecipitates with full-length Hsl1 less efficiently than with a C-terminal fragment of Hsl1. Moreover, Hsl7 does not localize to the bud neck in an hsl1Δ mutant, whereas Hsl1 is localized normally in an hsl7Δ mutant. Phosphorylation and ubiquitinylation of Swe1, preludes to its destruction, are severely reduced in cells lacking either Hsl1 or Hsl7 (or both), as judged by an electrophoretic mobility shift assay. Collectively, these data suggest that formation of the septin rings provides sites for docking Hsl1, exposing its C terminus and thereby permitting recruitment of Hsl7. Hsl7, in turn, presents its cargo of bound Swe1, allowing phosphorylation by Hsl1. Thus, Hsl1 and Hsl7 promote proper timing of cell cycle progression by coupling septin ring assembly to alleviation of Swe1-dependent inhibition of Cdc28. Furthermore, like septins and Hsl1, homologs of Hsl7 are found in fission yeast, flies, worms, and humans, suggesting that its function in this control mechanism may be conserved in all eukaryotes.
A Conserved Sequence Extending Motif III of the Motor Domain in the Snf2-Family DNA Translocase Rad54 Is Critical for ATPase Activity

Rad54 is a dsDNA-dependent ATPase that translocates on duplex DNA. Its ATPase function is essential for homologous recombination, a pathway critical for meiotic chromosome segregation, repair of complex DNA damage, and recovery of stalled or broken replication forks. In recombination, Rad54 cooperates with Rad51 protein and is required to dissociate Rad51 from heteroduplex DNA to allow access by DNA polymerases for recombination-associated DNA synthesis. Sequence analysis revealed that Rad54 contains a perfect match to the consensus PIP box sequence, a widely spread PCNA interaction motif. Indeed, Rad54 interacts directly with PCNA, but this interaction is not mediated by the Rad54 PIP box-like sequence. This sequence is located as an extension of motif III of the Rad54 motor domain and is essential for full Rad54 ATPase activity. Mutations in this motif render Rad54 non-functional in vivo and severely compromise its activities in vitro. Further analysis demonstrated that such mutations affect dsDNA binding, consistent with the location of this sequence motif on the surface of the cleft formed by two RecA-like domains, which likely forms the dsDNA binding site of Rad54. Our study identified a novel sequence motif critical for Rad54 function and showed that even perfect matches to the PIP box consensus may not necessarily identify PCNA interaction sites.
Improvements in audio processing and language modeling in the CU communicator

This paper presents some up-to-date audio processing techniques which have been developed and integrated into the University of Colorado (CU) communicator system. The CU Communicator is an interactive human-machine dialogue system for airline, hotel and rental car information. The baseline system was fully functional in June 1999. Since then, many improvements have been made. The paper will concentrate on acoustic echo cancellation, voice activity detection (VAD) and language modeling techniques and provide a paradigm for speech and audio processing in a dialog system with barge-in capabilities. Specifically, a real-time block least-mean-square (LMS) algorithm is discussed. A robust voice activity detector using energy threshold is applied to detect user voice. Experimental results are presented and some real-time implementation issues are addressed.
Interpreting Scope of Predictive Analytics in Advanced Driving Assistant System

Distracted driving, caused by various factors such as human emotions or reading distracting messages on the roadside, has become a leading cause of traffic accidents today. Ensuring the safety of both individuals and vehicles while minimizing maintenance costs poses a significant challenge for the automotive industry. Fortunately, recent advancements in machine learning offer a potential solution. One promising method is the further development of Advanced Driver Assistance Systems (ADAS), for which machine learning serves as an ideal solution. The proposed model develops an advanced predictive learning enabled driving assistance system with prediction capabilities like traffic light behavior and parking availability detection. The model gave an optimum accuracy of 98.2% with 50 epochs count and the validation loss retains a constant value of 0.3 over epochs.
Comparisons of Deep Learning Approaches to Detect Lung Cancer through Efficient Computer Based CT-based Screening

The avoidance of mortality in lung cancer is highly dependent on finding defects in the lungs early, to initiate effective treatments in time. Most often, lung disorders are diagnosed and treated using chest radiographs and CT scans. Methods based on machine learning can complement human observations and increase precisions of accuracy by mapping an CT image against a trained artificial neural network. The efficiency and accuracy of training such a network, however, depends on the availability of the performance of an underlying computer system, and the quality and size of images. The use of neural network structures with high intrinsic performance is therefore significant. This contribution focuses on comparisons between different Convolutional Neural Networks and formats on datasets to contribute to a good basis for decision-making in the context of possible lung cancer.
Circulating Current Mitigation of Paralleled AC/DC Rectifiers with Zero-Sequence Compensation and Carrier Synchronization

With the rapid development of power electronics, the demand for high-power rectifiers is increasing. The use of parallel-connected modular rectifiers helps improve system performance and reduce costs. It not only increases the power capacity but also enhances the resilience of the power supply system. However, when there are mismatched parameters between rectifiers, circulating currents would be generated due to different PWM control strategies or switching timing among the rectifiers. This leads to current distortion and a decrease in system performance. In this paper, the 0-axis ab feedback control method with carrier synchronization technique is proposed to mitigate circulating currents. Some simulation and experimental results are offered to verify the validity of the proposed circulating current control method.
Convolutional AutoEncoders for Anomaly Detection in Semiconductor Manufacturing

Semiconductor manufacturing, characterised by its complex processes, demands efficient anomaly detection (AD) systems for quality assurance. This study extends from previous work utilising unsupervised Convolutional AutoEncoders for AD in Semiconductor batch manufacturing by applying the technique to a novel dataset supplied by a local Semiconductor Manufacturer. Our method uses an approach that employs 1-dimensional Convolutional Autoencoders (1d-CAE) to improve AD performance and interpretability through the numerical decomposition of reconstruction errors. Identifying anomalies this way allows engineering resources to explain anomalies more effectively than traditional methods. We validate our approach with experiments, demonstrating its performance in accurately detecting anomalies while providing insights into the nature of these irregularities. The experiments also demonstrate the impact of training setup on detection capability, outlining an efficient framework for determining an optimal hyperparameter setup in an industrial dataset. The proposed unsupervised learning approach with AE reconstruction error improves model explainability, which is expected to be beneficial for deployment in semiconductor manufacturing, where interpretable and trustworthy results are critical for solution adoption by process engineering teams.
FlexHash - Hybrid Locality Sensitive Hashing for IoT Device Identification

Recent growth in the utilization of IoT has offered convenience and utility, but has also increased security risk. Many devices lack the capacity to support adequate encryption or other common means of protection, and are often designed for easy connection out-of-the-box exposing vulnerabilities related to default configurations. Managing IoT devices in a network can be difficult as MAC addresses are easily spoofed, creating a need for techniques to properly identify and monitor membership. Many of the proposed solutions for IoT device identification require complex feature extraction and engineering. In addition, little work has been done to identify individual devices from among identical peers. We propose a novel hashing algorithm, FlexHash, and show that we are able to identify identical devices with a very high degree of accuracy using only a single packet of network traffic. By applying hybrid locality sensitive hashing in combination with machine learning our approach achieves accuracy scores as high as 98% for identical devices and 99% for identifying device genre.
Multi-Hop Teleportation Protocols for Quantum Networks Privacy in Quantum Key Distribution

This study examines the development of wireless quantum communication network and advance quantum com- munication. In this paper we present the idea of system scheme to transfer quantum states in the wireless quantum communication network and advance. Quantum communication is a rapidly evolving field that will significantly impact the twenty-first century. Wireless quantum communication networks transport quantum states using a multi-hop teleportation quantum routing approach. This technique does not require compatibility with classical paths, as quantum states are transported directly along the designated path. The chance of successfully transferring a quantum state is determined using statistics. Researchers analyze statistics to optimize network performance and develop strategies for quantum state transfer success. Advancements in error correction techniques enhance the reliability of quantum communication networks. A quantum state is moved from the source to the destination using direct multi-hop teleportation on the chosen path. Statistics are used to determine the likelihood of successfully transmitting the quantum state.
Texturing Waste: Attachment and Identity in EveryDay Consumption and Waste Practices

Waste has often been a target of literature and policy promoting pro-environmental behaviour. However, little attention has been paid to how subjects interpret and construct waste in their daily lives. In this article we develop a synthesis of practice theory and psycho-social concepts of attachment and transitional space to explore how biographically patterned relationships and attachments to practice shape subjects’ understandings of resource consumption and disposal. Deploying biographical interview data produced by the Energy Biographies Project, we illustrate how tangible, intersubjective and interdependent experiences rub up against cultural and behavioural norms, reshaping the meanings and strategies through which subjects interpret and manage waste.
A Survey on Knowledge Graph-Based Recommender Systems

Recommender systems have emerged as indispensable tools for information filtering, and the integration of knowledge graphs for auxiliary information is becoming an increasingly popular research topic. This paper reviews recent studies, discussing the current state and practical applications of knowledge graph-based recommender systems. We summarize the strengths and weaknesses of various knowledge graph-based recommendation methods, noting that these systems significantly enhance performance in areas like accuracy, diversity, interpretability, and novelty. Furthermore, the trend of combining different knowledge graph-based methods underscores the mainstream evolution of recommender systems, warranting future exploration. We finish with an analysis of current challenges and a forward-looking perspective on future advances. This review aims to assist the reader in understanding and navigating this research field.
Lupus anticoagulant and protein S deficiency in otherwise healthy children with acute varicella infection

Acquired protein S deficiency and lupus anticoagulant have been described in children with varicella who had purpura fulminans, disseminated intravascular coagulation or thrombosis. The aim of this study was to investigate the natural anticoagulants, hypercoagulability markers, other parameters of coagulation and fibrinolytic systems, and the presence of the lupus anticoagulant in otherwise healthy children with acute varicella infection. Blood samples were obtained from 17 children with varicella without thrombosis during acute varicella infection and 1 mo after onset. Coagulation tests included determinations of the prothrombin time, the activated partial thromboplastin time, the thrombin time, the thrombin antithrombin complex, the prothrombin fragment F 1 + 2, the tissue plasminogen activator, the plasminogen activator inhibitor‐1, protein C activity and free protein S antigen. Antiphospholipid antibodies were determined in enzyme‐linked immunosorbent assays. The mean free protein S concentration in the acute phase (0.63 ±0.16U/ml) was significantly lower than that of the concentration determined 1 mo later (0.82 ± 0.17 U/ml). The children with acquired free protein S deficiency also had a lupus anticoagulant. Elevated concentrations of the prothrombin fragment F 1+2, the thrombin antithrombin complex, D‐Dimer, tissue plasminogen activator and plasminogen activator inhibitor‐1 were detected in most of the children.
Online Sparse Learning Object Tracking Based on Spatio-Temporal Convolutional Neural Network

The Convolutional Neural Network (CNN) has proven to be an effective tool for feature extraction in image processing of object tracking. However, its application in target tracking poses several challenges, including high computational complexity, insufficient training samples, limited spatiotemporal context(STC), difficulty with online learning, and more. In this work, we explore these key issues based on CNN theory and propose solutions to address them. Firstly, this study proposes an optimized spatiotemporal convolutional network that incorporates an online sparse learning method and an adaptive online sparse learning algorithm, which effectively leverages STC. Secondly, this study assesses the algorithm's effectiveness by measuring its precision and success rate against eight other known algorithms in six distinct interference scenarios using the OTB100 dataset. Finally, the comparison of experimental results on the dataset suggests that our enhanced algorithm, which is based on CNN, exhibits strong performance in terms of both tracking accuracy and realtime capabilities.
Extreme Sparsity in Hodgkin-Huxley Spiking Neural Networks

Spiking neural networks present a new generation of artificial neural networks utilizing binary data representation for large energy, memory, and latency savings due to widespread sparsity. At the same time, they are at the focal point for neuroscientific communities, enabling biologically plausible simulations of systems with a learning capacity. In this work, we present spiking neural networks based on an implementation of a biologically highly plausible neuron model, the Hodgkin-Huxley model. The networks are trained via backpropagation through time using surrogate gradients. They show competitive performances on various neuromorphic datasets while displaying a spiking activity lower by up to two orders of magnitude compared to similar spiking neural networks based on the conventional leaky integrate-and-fire neuron model. Additionally, this work paves the way toward higher biological plausibility in high-performance computational neuroscience by enabling in-depth looks at the underlying dynamics of Hodgkin-Huxley neurons as part of larger operating networks.
Adrenocortical Status Predicts the Degree of Age-Related Deficits in Prefrontal Structural Plasticity and Working Memory

Cognitive decline in aging is marked by considerable variability, with some individuals experiencing significant impairments and others retaining intact functioning. Whereas previous studies have linked elevated hypothalamo-pituitary-adrenal (HPA) axis activity with impaired hippocampal function during aging, the idea has languished regarding whether such differences may underlie the deterioration of other cognitive functions. Here we investigate whether endogenous differences in HPA activity are predictive of age-related impairments in prefrontal structural and behavioral plasticity. Young and aged rats (4 and 21 months, respectively) were partitioned into low or high HPA activity, based upon averaged values of corticosterone release from each animal obtained from repeated sampling across a 24 h period. Pyramidal neurons in the prelimbic area of medial prefrontal cortex were selected for intracellular dye filling, followed by 3D imaging and analysis of dendritic spine morphometry. Aged animals displayed dendritic spine loss and altered geometric characteristics; however, these decrements were largely accounted for by the subgroup bearing elevated corticosterone. Moreover, high adrenocortical activity in aging was associated with downward shifts in frequency distributions for spine head diameter and length, whereas aged animals with low corticosterone showed an upward shift in these indices. Follow-up behavioral experiments revealed that age-related spatial working memory deficits were exacerbated by increased HPA activity. By contrast, variations in HPA activity in young animals failed to impact structural or behavioral plasticity. These data implicate the cumulative exposure to glucocorticoids as a central underlying process in age-related prefrontal impairment and define synaptic features accounting for different trajectories in age-related cognitive function.
Data Prefetching On The HP PA-8000

Memory latency is a major issue for many modern microprocessor based systems, including the Hewlett-Packard PA-8000. Due to its fast clock rate and wide issue capability, cache misses in the PA-8000 are very expensive. The PA-8000 combines out-of-order execution with multiple outstanding memory requests to tolerate memory latency; however, this approach has its limitations. In order to substantially reduce much of the memory latency penalty, the PA-8000 uses software-based data cache prefetching. In this paper, we discuss the implementation of the data prefetch generation algorithm in the Hewlett-Packard Precision Architecture (HP-PA) compiler. We present performance results for SPECfp95 on a PA-8000 system that show speedups, due to data prefetching, of up to 100%.
Identification of Bona Fide Alternative Renin Transcripts Expressed Along Cortical Tubules and Potential Roles in Promoting Insulin Resistance In Vivo Without Significant Plasma Renin Activity Elevation

Renin belongs to a family of aspartyl proteases and is the rate-limiting enzyme in the synthesis of the potent vasoactive peptide angiotensin II. Processing of renal renin has been extensively investigated in juxtaglomerular granular cells, in which prorenin and active renin are present in secretory condensed granules. Previous studies demonstrated alternative renin transcription in rat adrenal glands. Different studies reported novel intracellular forms of renin deduced from novel 5′ variants derived from renin mRNA in both mice and humans. Comprehensive detailed studies in genetically engineered mice showed that both a secreted and an intracellular form of renin plays divergent mechanism regulating fluid intake and metabolism by the brain renin–angiotensin system; however, the presence, regulation, and functions of these renin isoforms in kidney and adrenal gland are not fully understood in mice. To investigate the characteristics of renin isoforms in mice, we performed a systematic inventory of renin transcripts of mice with and without a duplication of the renin gene alternatively from previous studies. We discovered a novel isoform of renin of the Ren2 gene, which conserved functionally important residues of the prosegment and incomplete isoforms of the Ren1C/D gene lacking a pre-pro segment. In situ hybridization assays revealed alternative renin isoforms expressed along cortical tubules. Newly generated transgenic mice with systemic overexpression of alternative renin transcript showed enhanced local angiotensin II generation without elevation of plasma renin activity and systemic insulin resistance in vivo, providing new pathophysiological insights into insulin resistance exaggerated by bona fide renin isoform.
Codes for magnetic recorders

The development of signal processing and coding techniques for magnetic recording channels has taken place in an environment of escalating demand for higher data transfer rates and storage density. The error probability of many magnetic-recording systems may be characterized in terms of the differences between the sequences that may be recorded. In fact, the bit-error rate is often dominated by a small set of potential difference patterns. Binary codes have been proposed in the literature which exploits this fact. The codes designed to avoid the difference patterns by constraining the set of allowed recorded sequences, have been shown to improve system performance. We propose here a new algorithm for determining a code that avoids the specified difference patterns.