Miracle-dz/UniFAR-FTUs huggingface dataset:
Charged residues in the C-terminus of the P2Y1 receptor constitute a basolateral-sorting signal

The P2Y1 receptor is localized to the basolateral membrane of polarized Madin-Darby canine kidney (MDCK) cells. In the present study, we identified a 25-residue region within the C-terminal tail (C-tail) of the P2Y1 receptor that directs basolateral sorting. Deletion of this sorting signal caused redirection of the receptor to the apical membrane, indicating that the region from the N-terminus to transmembrane domain 7 (TM7) contains an apical-sorting signal that is overridden by a dominant basolateral signal in the C-tail. Location of the signal relative to TM7 is crucial, because increasing its distance from the end of TM7 resulted in loss of basolateral sorting. The basolateral-sorting signal does not use any previously established basolateral-sorting motifs, i.e. tyrosine-containing or di-hydrophobic motifs, for function, and it is functional even when inverted or when its amino acids are scrambled, indicating that the signal is sequence independent. Mutagenesis of different classes of amino acids within the signal identified charged residues (five basic and four acidic amino acids in 25 residues) as crucial determinants for sorting function, with amidated amino acids having a lesser role. Mutational analyses revealed that whereas charge balance (+1 overall) of the signal is unimportant, the total number of charged residues (nine), either positive or negative, is crucial for basolateral targeting. These data define a new class of targeting signal that relies on total charge and might provide a common mechanism for polarized trafficking of epithelial proteins.
Online Two-Section PV Array Fault Diagnosis With Optimized Voltage Sensor Locations

Photovoltaic (PV) stations have been widely built in the world to utilize solar energy directly. In order to reduce the capital and operational costs, early fault diagnosis is playing an increasingly important role by enabling the long effective operation of PV arrays. This paper analyzes the terminal characteristics of faulty PV strings and arrays, and it develops a PV array fault diagnosis technique. The terminal current-voltage curve of a faulty PV array is divided into two sections, i.e., high-voltage and low-voltage fault diagnosis sections. The corresponding working points of healthy string modules and of healthy and faulty modules in an unhealthy string are then analyzed for each section. By probing into different working points, a faulty PV module can be located. The fault information is of critical importance for the maximum power point tracking and the array dynamical reconfiguration. Furthermore, the string current sensors can be eliminated, and the number of voltage sensors can be reduced by optimizing voltage sensor locations. Typical fault scenarios including monostring, multistring, and a partial shadow for a 1.6-kW 3 $\times$ 3 PV array are presented and experimentally tested to confirm the effectiveness of the proposed fault diagnosis method.
Real-time range imaging for human-machine interfaces

Diese Arbeit befasst sich mit der automatischen kontaktlosen Echtzeiterfassung von Entfernungsbildern im Nahbereich. Dazu stellt sie ein entsprechendes neues Verfahren vor, das die klassischen Prinzipien des farbkodierten Lichtansatzes und des Stereosehens kombiniert und erweitert; daruber hinaus entwickelt sie neue Losungen fur verwandte Aufgaben wie der Kalibrierung des Aufnahmesystems. Die Evaluierung einer prototypischen Implementierung des Verfahrens ergibt eine Datenrate von bis zu 25 Entfernungsbildern der Auflosung 780 x 580 Punkten pro Sekunde. Die Standardabweichung der Messunsicherheit betragt etwa 0.2 mm bei einem Arbeitsraum von ca. 500 mm x 500 mm x 500 mm. Schlieslich zeigt diese Arbeit, dass die Erkennungssicherheit eines Gesichtserkennungs-Systems durch den Einsatz von mit dem Prototypen aufgenommenen Entfernungsbildern deutlich gesteigert werden kann.
A Recent Study of Security Techniques for IoT

IoT signifies “Internet of Things. ”. It is upgraded version of Embedded system. It is mainly used for connecting things, exchange real time data in order to improve digital intelligence to devices that boast productivity, efficiency, services, and many more. IoT Connections are between the human and sensors or sensors to sensors. From the billions of devices that are interconnected, the internet creates an intelligence system. The IoT exchanges data in the cloud or in a warehouse. It analyses and predicts information that will significantly change business and our lives. More connected devices translate to more areas for attack and openings for hackers. If we ignore these security issues, the situation will become worse, and devastation may occur. There are many types of existing security techniques that prove high security, but they are not specific to the IoT. In this work, we will concentrate on current lightweight cryptographic security solutions that are being utilised or proposed for safe data transfer in the IoT sector.
Proactive Fault Tolerant Service Management in Edge Computing

Edge computing technology facilitates the deployment of real-time services near users by reducing latency and increasing efficient bandwidth utilization compared to cloud computing. The edge servers are usually heterogeneous and resource-constrained. To effectively utilize these edge resources, it is required to efficiently deploy different real-time latency-sensitive edge services so that maximum users will serve. In a pay-per-use edge environment, service providers can not deploy all services on all available edge servers, which is costly for service providers. Further, due to hardware/software issues or overloading, any edge server may fail after service deployment and hamper critical edge services’ system performance. In such a failure-prone environment, designing fault-tolerant service deployment strategies is essential to reduce the impact of the failures. In this paper, we propose a two-stage method, which includes the optimal approach for selecting a primary server to increase user coverage and a heuristic approach for impact-based service deployment within budget to choose a backup server. These stages also verify the overloading of services and user requests on the edge servers to reduce edge failures. Several experiments are conducted to analyze the proposed approach on real-world data. Compared to various baseline approaches, our solution efficiently increases user coverage (15%) and reduces failure impact by having a backup server (25%) for most users to increase availability within the deployment budget.
Giving across Borders: Philanthropy or Business as Usual?

A large literature has examined the antecedents and consequences of charitable giving by corporations, but the results have been mostly inconclusive. One reason for the mixed empirical findings is that, when measured at the firm level, both the factors associated with charitable giving and the actual level of giving can be driven by the same set of unobserved firm characteristics. This study overcomes that problem by leveraging institutional differences across countries and focusing on within-firm variations in charitable giving. In particular, we examine whether overseas giving by U.S. firms is affected by local institutional environments and by firms’ local business interests, given firm characteristics. We find that multinational enterprises (MNEs) are more likely to donate to charities in a country plagued by an ineffective and corrupt government, suggesting that MNEs use charitable giving to navigate opaque business environments. Furthermore, we find that corporations are more likely to make cross-border donations when they are new entrants to the host country—hence having stronger need to reduce information asymmetry—and when their operations require stronger connections with local stakeholders. Our results are consistent with the view that MNEs engaged in corporate philanthropy are doing good for the purpose of doing well.
Image segmentation by shape particle filtering

Statistical appearance models are valuable tools in medical image segmentation. Current methods elegantly incorporate global shape and appearance, but cannot cope with local appearance variations and rely on an assumption of Gaussian gray value distribution. Furthermore, initialization near the optimal solution is required. We propose a shape inference method that is based on pixel classification, so that local and non-linear intensity variations are dealt with naturally, while a global shape model ensures a consistent segmentation. Optimization by stochastic sampling removes the need for accurate initialization. The method is demonstrated on vertebra segmentation in spine radiographs. Segmentation errors are below 2 mm in 88 out of 91 cases, with an average error of 1.4 mm.
Genetically encoded molecular probes to visualize and perturb signaling dynamics in living biological systems

ABSTRACT In this Commentary, we discuss two sets of genetically encoded molecular tools that have significantly enhanced our ability to observe and manipulate complex biochemical processes in their native context and that have been essential in deepening our molecular understanding of how intracellular signaling networks function. In particular, genetically encoded biosensors are widely used to directly visualize signaling events in living cells, and we highlight several examples of basic biosensor designs that have enabled researchers to capture the spatial and temporal dynamics of numerous signaling molecules, including second messengers and signaling enzymes, with remarkable detail. Similarly, we discuss a number of genetically encoded biochemical perturbation techniques that are being used to manipulate the activity of various signaling molecules with far greater spatial and temporal selectivity than can be achieved using standard pharmacological or genetic techniques, focusing specifically on examples of chemically driven and light-inducible perturbation strategies. We then describe recent efforts to combine these diverse and powerful molecular tools into a unified platform that can be used to elucidate the molecular details of biological processes that may potentially extend well beyond the realm of signal transduction.
Simulation of block‐and‐ash flows and ash‐cloud surges of the 2010 eruption of Merapi volcano with a two‐layer model

A new depth‐averaged model has been developed for the simulation of both concentrated and dilute pyroclastic currents and their interactions. The capability of the model to reproduce a real event is tested for the first time with two well‐studied eruptive phases of the 2010 eruption of Merapi volcano (Indonesia). We show that the model is able to reproduce quite accurately the dynamics of the currents and the characteristics of the deposits: thickness, extent, volume, and trajectory. The model needs to be tested on other well‐studied eruptions and the equations could be refined, but this new approach is a promising tool for the understanding of pyroclastic currents and for a better prediction of volcanic hazards.
Prediction of COVID-19 using machine learning techniques

In December 2019, the world faced an unprecedented and formidable challenge in the form of COVID-19. Since its first reported case in December 2019 and subsequent classification as a pandemic by the World Health Organization (WHO), it has imposed an enormous impact, taking lives, disrupting diverse economic sectors, and introducing numerous challenges. Predicting and controlling COVID-19 precisely remains a pivotal concern for the future. To enhance the precision of COVID-19 disease prediction and alleviate the burden on healthcare systems, we explore the application of diverse machine learning classifiers. Leveraging datasets comprising confirmed cases, recoveries, and fatalities, our study seeks to improve the predictive accuracy, ensuring efficient and precise evaluation and triage of patients. This, in turn, the load on overburdened emergency departments is reduced, and mitigates the pressures faced by healthcare professionals. The pivotal role of artificial intelligence (AI) and machine learning (ML) in this context cannot be overstated. Our research endeavors to refine the accuracy and quality of results by employing advanced machine learning techniques such as Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Naïve Bayes (NB), Gradient Boosting (GB), and Extreme Gradient Boosting (XGBoost). Our dataset is sourced from Kaggle, a well-established platform for data science and machine learning. Our comprehensive analysis compares the outcomes of six distinct models. Notably, the Gradient Boosting classifier surpasses other techniques, achieving an impressive accuracy rate of 90% for confirmed cases, 90% for recoveries, and 92% for fatalities. This represents a significant improvement over the baseline paper, which achieved accuracy rates of 83% for confirmed cases, 72% for recoveries, and 81% for fatalities using the same dataset. Further our research enhances COVID-19 prediction, aiding healthcare professionals in addressing the other global epidemic for future.
Building a Bilingual ValLex Using Treebank Token Alignment: First Observations

We explore the potential and limitations of a concept of building a bilingual valency lexicon based on the alignment of nodes in a parallel treebank. Our aim is to build an electronic Czech->English Valency Lexicon by collecting equivalences from bilingual treebank data and storing them in two already existing electronic valency lexicons, PDT-VALLEX and Engvallex. For this task a special annotation interface has been built upon the TrEd editor, allowing quick and easy collecting of frame equivalences in either of the source lexicons. The issues encountered so far include limitations of technical character, theory-dependent limitations and limitations concerning the achievable degree of quality of human annotation. The issues of special interest for both linguists and MT specialists involved in the project include linguistically motivated non-balance between the frame equivalents, either in number or in type of valency participants. The first phases of annotation so far attest the assumption that there is a unique correspondence between the functors of the translation-equivalent frames. Also, hardly any linguistically significant non-balance between the frames has been found, which is partly promising considering the linguistic theory used and partly caused by little stylistic variety of the annotated corpus texts.
Dynamic prioritization of multimedia flows for improving QoS and throughput in IEEE 802.11e WLANs

In wireless LANs, QoS provisioning and multimedia traffic support are critical elements for the successful deployment of such networks. In this paper, we propose a dynamic mechanism to adapt data rate and priority of multimedia wireless stations equipped with IEEE 802.11e network cards. We show how the introduced dynamicity is able to improve performance of the enhanced distributed channel access (EDCA) of IEEE 802.11e networks.
Expression of nitric oxide synthase and effect of substrate manipulation of the nitric oxide pathway in mouse ovarian follicles.

BACKGROUND Nitric oxide (NO) is a cell messenger with multiple actions in different biological systems, implicated in the control of follicle and oocyte function. NO is formed from L-arginine by isoforms of nitric oxide synthase (NOS) via NG-hydroxy-L-arginine, with L-citrulline as a byproduct. This study aimed to show how modulation of NO by manipulating NOS substrates would affect mouse follicle growth and ovulation in vitro, where vascular effects of NO are attenuated. METHODS Immunohistochemistry [endothelial (eNOS) and inducible (iNOS)] and in situ hybridization (iNOS) were applied on mouse ovaries. Cultured follicles were also stained for iNOS by immunohistochemistry. For follicles cultured in the presence or absence of L-arginine, the ability of L-citrulline or NG-hydroxy-L-arginine to substitute for L-arginine was assessed in terms of follicle growth and ovulation. RESULTS iNOS and eNOS were localized in oocytes and theca, with some staining in granulosa. iNOS mRNA occurred predominantly in granulosa and oocyte. Omission of L-arginine significantly reduced follicle survival and ovulation. Partial compensation for L-arginine withdrawal was achieved with L-citrulline and NG-hydroxy-L- arginine. Specific abnormalities of follicle growth were noted. CONCLUSIONS NOS is present in mouse follicles, and its action is necessary at a local level for normal follicle development in vitro. Reduced growth, persistent basement membranes and reduced ovulation were associated with in vitro disruption of NO.
Bacterial entry and intracellular processing of Neisseria gonorrhoeae in epithelial cells: immunomorphological evidence for alterations in the major outer membrane protein P.IB

The fate of the major outer membrane protein of the gonococcus, P.IB, during the adherence, entry, and intracellular processing of the bacteria in infected epithelial cells was investigated using post- embedding immunoelectron microscopy. Various domains of the P.IB molecule were probed at different stages in the infection. These studies revealed that P.IB epitope exposure remained unaltered during the initial attachment of the bacteria to the host cells. In contrast, upon secondary attachment of the bacteria to the eukaryotic cells, apparent zones of adhesion were formed between the gonococci and the host cell membrane, which were characterized by loss of a defined P.IB epitope. These zones of adhesion with the altered P.IB immunoreactivity continued to exist and increased in number during cellular penetration, suggesting that they were essential to bacterial invasion into the eukaryotic cells. After bacterial entry, two classes of gonococci could be recognized; morphologically intact, P.IB-positive bacteria and disintegrated organisms that showed a change in, and, in a later stage, a complete loss of P.IB immunoreactivity. The intracellular alterations in the P.IB antigen could be prevented by treatment of the host cells with the lysosomotropic agent chloroquine. These observations point to a mechanism by which a subpopulation of intracellular gonococci can escape the epithelial cell defense by preventing or resisting exposure to host cell proteolytic activity.
The SPARTACUS-Database: a Spanish Sentence Database for Offline Handwriting Recognition

In this paper we describe a database that consists of offline handwritten Spanish sentences from four different subtasks. The database includes 1 500 forms produced by the same number of writers. A total of around 100 000 word instances out of a vocabulary of around 3 300 words occur in the collection. This database is intended to be used for offline handwriting recognition tasks. However, this database is expected to be specially useful for recognition systems that may take advantage of language models of restricted-semantic tasks. The database also includes a few image-processing procedures for extraction of handwritten text images from the forms and segmentation of the images into lines and words.
Hetero-Structure Junctionless MOSFET with High-k Corner Spacer for High-Speed and Energy-Efficient Applications

In this research work, Hetero-structure Junction-less MOSFET having a Silicon-Germanium source and high-k inner corner spacer is proposed and investigated. In this article, we have shown that the introduction of a high-k dielectric material in the inner corner spacer and a low-k dielectric material in the rest of the spacer in the optimally designed device leads to a substantial reduction in parasitic capacitances, resulting in higher operating speed. It was also shown that proper doping in the drain-source underlaps regime, can improve the short channel performance (SCP) of the device by increasing the effective gate length. The optimally designed proposed device produces on current (ION) ~0.33 mA and off current (IOFF) ~ 5.55 fA along with ION/IOFF=6.08x1010, Subthreshold slope (SS)=59.6 mV/decade and drain induced barrier lowering (DIBL)=82.2 mV/V. This paper also highlights the performance improvement of the proposed device in terms of both speed and energy consumption, as compared to that of Junctionless Double Gate MOSFET when implemented as logic gates.
Communication Analysis for Multidimensional Parallel Training of Large-scale DNN Models

Multidimensional parallel training has been widely applied to train large-scale deep learning models like GPT-3. The efficiency of parameter communication among training devices/processes is often the performance bottleneck of large model training. Analysis of parameter communication mode and traffic has important reference significance for the research of interconnection network design and computing task scheduling to improve the training performance. In this paper, we analyze the parametric communication modes in typical 3D parallel training (data parallelism, pipeline parallelism, and tensor parallelism), and model the traffic in different communication modes. Finally, taking GPT-3 as an example, we present the communication in its 3D parallel training.
A mechanistic approach to methylene blue sorption on two vegetable wastes: Cork bark and grape stalks

Two vegetable wastes, cork bark and grape stalks, were investigated for the removal of methylene blue from aqueous solution. The effects of contact time, dye concentration, pH, and temperature on sorption were studied relative to adsorption on a commercially-activated carbon. The highest adsorption yield was obtained within the pH range 5 to 10 for grape stalks and 7 to 10 for cork bark. The sorption kinetics of dye onto activated carbon and grape stalks was very fast. Kinetics data were fitted to the pseudo-first and second order kinetic equations, and the values of the pseudo-second-order initial rate constants were found to be 1.69 mg g-1 min-1 for activated carbon, 2.24 mg g-1 min-1 for grape stalks, and 0.90 mg g-1 min-1 for cork bark. Langmuir maximum sorption capacities for activated carbon, grape stalks, and cork bark for methylene blue estimated by the Orthogonal Distance Regression method (ODR) were 157.5 mg g-1, 105.6 mg g-1, and 30.52 mg g-1, respectively. FTIR spectra indicated that carboxylic groups and lignin play a significant role in the sorption of methylene blue. Electrostatic forces, n-p interactions, cation-p, and p-p stacking interactions contribute to methylene blue sorption onto grape stalks and cork bark. Grape stalks can be considered an efficient biosorbent and as a viable alternative to activated carbon and ion-exchange resins for the removal of methylene blue.