Miracle-dz/UniFAR-FTUs huggingface dataset:
Dilation of the influenza hemagglutinin fusion pore revealed by the kinetics of individual cell-cell fusion events

We have monitored kinetics of fusion between cell pairs consisting of a single influenza hemaglutinin (HA)-expressing cell and a single erythrocyte (RBC) that had been labeled with both a fluorescent lipid (Dil) in the membrane and a fluorescent solute (calcein) in the aqueous space. Initial fusion pore opening between the RBC and HA-expressing cell produced a change in RBC membrane potential (delta psi) that was monitored by a decrease in Dil fluorescence. This event was followed by two distinct stages of fusion pore dilation: the flux of fluorescent lipid (phi L) and the flux of a large aqueous fluorescent dye (phi s). We have analyzed the kinetics of events that occur as a result of transitions between a fusion pore (FP) and a solute permissive fusion pore (FPs). Our data are consistent with a fusion pore comprising six HA trimers.
New actuator system using movable pulley for bio-mimetic system and wearable robot applications

This paper proposes a new actuator system using a movable pulley for a bio-mimetic system and wearable robot applications, which is able to store energy in advance and to release it in such a way to satisfy desired speed and force control performances, simultaneously. Since new actuator system consists of two motors (dual-motors), a movable pulley, and series spring connection for high speed/force operation characteristics, it is referred to as Dual-motor system using a Movable Pulley with a Series Elastic Actuator (DuMP-SEA) in this paper. Also, the proposed actuator system not only combines individual speed/force operation region of each motor, but also extends the combined operation region by using the energy stored in advance. Finally, we suggest the extended operation region (high speed and high force characteristics) obtained by using the DuMP-SEA through simulations and experiments.
Emerging threats in mobile communication system

In present scenario only computer system is not a medium of cyber crime, smart phones and tablets also have become targets for cyber criminals to attack. With the growing use of mobile devices, new mobile applications are developing daily and user download these apps onto their mobile devices from different sites. Attacker may utilize these sites by injecting malicious codes to mobile devices without making people realize that they could have some security risks even though downloading apps from their official sites. Mobile cyber security can be gained by avoiding third party sites to download apps for users. Using verified official tools and platforms is a better way to avoid risks and threats when downloading mobile apps.
A Priori Groups Based On Bhattacharyya Distance And Partitioning Around Medoids Algorithm (PAM) With Applications To Metagenomics

Plants, animals and humans live in close association with microbial organisms. Increasingly, biologists have come to appreciate that microbes make up an important part of an organism's phenotype. This microbial community contains a unique complexity that makes it difficult to study their diversity. However, for many questions on the structure of the microbial community one only needs to know the relative order of diversity among samples rather than the total diversity. Unfortunately the culture of microorganisms can be complex but this has prompted the development of new scientific methodologies for their study. One of these methodologies is metagenomics. An important problem in metagenomics is measuring the dissimilarity between distributions of features, such as taxons or groups. The focus of this note is the proposal of a new method based on using Bhattacharyya distance and establishing a priori groups using the partitioning around medoids algorithm (PAM). The results reveal a good reduction in the size of the dataset and an interesting way of revealing possible subgroups “a priori” or communities among the microorganisms that make up the analyzed sample.
Production Optimization of Gas Wells Using MBAL

This project focuses on optimizing production of a gas well. The reservoir in this study involves a gas condensate reservoir. Gas condensate reservoirs behave in a complex manner as it introduces liquid condensate near the wellbore region which leads to significant decrease in productivity of the well. Liquid hold up is also an important issue to consider in a gas condensate reservoir as this could impact the wells productivity. Parameters such as tubing size, skin factor, condensate gas ratio, wellhead pressure and water gas ratio are used to implement a sensitivity analysis in Prosper. Based on the Monte Carlo results from MBAL, there is a 90 percent probability of the gas condensate reservoir to produce 360 Bscf of gas and 23 MMstb of recoverable condensate. Overall, the 4.5 inch tubing pipe promotes the optimum rate based on water gas ratio, condensate gas ratio and wellhead pressure. The 4.5-inch tubing does not promote erosional velocity and liquid loading issues compared to various tubing sizes. Keyword: Well optimization, modeling, gas condensate, improve production, MBAL, PROSPER.
A Novel Money Laundering Prediction Model Based on a Dynamic Graph Convolutional Neural Network and Long Short-Term Memory

Money laundering is an illicit activity that seeks to conceal the nature and origins of criminal proceeds, posing a substantial threat to the national economy, the political order, and social stability. To scientifically and reasonably predict money laundering risks, this paper focuses on the “layering” stage of the money laundering process in the field of supervised learning for money laundering fraud prediction. A money laundering and fraud prediction model based on deep learning, referred to as MDGC-LSTM, is proposed. The model combines the use of a dynamic graph convolutional network (MDGC) and a long short-term memory (LSTM) network to efficiently identify illegal money laundering activities within financial transactions. MDGC-LSTM constructs dynamic graph snapshots with symmetrical spatiotemporal structures based on transaction information, representing transaction nodes and currency flows as graph nodes and edges, respectively, and effectively captures the relationships between temporal and spatial structures, thus achieving the dynamic prediction of fraudulent transactions. The experimental results demonstrate that compared with traditional algorithms and other deep learning models, MDGC-LSTM achieves significant advantages in comprehensive spatiotemporal feature modeling. Specifically, based on the Elliptic dataset, MDGC-LSTM improves the Macro-F1 score by 0.25 compared to that of the anti-money laundering fraud prediction model currently considered optimal.
Investigation of local and global features for face detection

This work is concerned with the empirical evaluation of a set of local and global features under the context of frontal (including semi-profile) and full profile face classification. Integral LBP, Integral Histograms, PCA and Optimized Face Ratios features have been evaluated using SVM classifiers. A data set of about 14,000 face and 300,000 non face images has been used in the experiments. Face images were obtained from well known public face research databases, such as BioID, Color Feret, CMU PIE, among others. The PCA-SVM classifier presented best overall results for both frontal and full profile faces whereas the classifier based on Face Ratios presented the lowest classification rates. A weighted combination of all classifiers yielded high True Positive (TPR) and True Negative (TNR) rates: 91.7% and 100%, respectively, for the frontal face experiments; 99.59% and 99.62%, respectively, for the profile face experiments. These results indicate that the evaluated features are very suitable to the problem of face detection and that a simple classifier combination improves individual classifiers performance.
On Backup Battery Data in Base Stations of Mobile Networks: Measurement, Analysis, and Optimization

Base stations have been massively deployed nowadays to afford the explosive demand to infrastructure-based mobile networking services, including both cellular networks and commercial WiFi access points. To maintain high service availability, backup battery groups are usually installed on base stations and serve as the only power source during power outages, which can be prevalent in rural areas or during severe weather conditions such as hurricanes or snow storms. Therefore, being able to understand and predict the battery group working condition is of immense technical and commercial importance as the first step towards a cost-effective battery maintenance on minimizing service interruptions. In this paper, we conduct a systematical analysis on a real world dataset collected from the battery groups installed on the base stations of China Mobile, with totally 1,550,032,984 records from July 28th, 2014 to February 17th, 2016. We find that the working condition degradation of a battery group may be accelerated under various situations and can cause premature failures on batteries in the group, which can hardly be captured by nowadays maintenance procedure and easily lead to a power-outage-triggered service interruption to a base station. To this end, we propose BatPro, a battery profiling framework, to precisely extract the features that cause the working condition degradation of the battery group. We formulate the prediction models for both battery voltage and lifetime and develop a series of solutions to yield accurate outputs. By real world trace-driven evaluations, we demonstrate that our BatPro approach can precisely predict the battery voltage and lifetime with the RMS error less than 0.01 v.
Hemoglobin H disease: not necessarily a benign disorder.

Introduction The hemoglobin molecule is a tetramer consisting of 2 pairs of globin chains, each of which contains a heme group. During fetal development, the major hemoglobin is Hb F (α2γ2). In a normal adult, the major hemoglobin is Hb A (α2β2). The α-globin gene cluster is located on
Waste Conversion into n-Caprylate and n-Caproate: Resource Recovery from Wine Lees Using Anaerobic Reactor Microbiomes and In-line Extraction

To convert wastes into sustainable liquid fuels and chemicals, new resource recovery technologies are required. Chain elongation is a carboxylate-platform bioprocess that converts short-chain carboxylates (SCCs) (e.g., acetate [C2] and n-butyrate [C4]) into medium-chain carboxylates (MCCs) (e.g., n-caprylate [C8] and n-caproate [C6]) with hydrogen gas as a side product. Ethanol or another electron donor (e.g., lactate, carbohydrate) is required. Competitive MCC productivities, yields (product vs. substrate fed), and specificities (product vs. all products) were only achieved previously from an organic waste material when exogenous ethanol had been added. Here, we converted a real organic waste, which inherently contains ethanol, into MCCs with n-caprylate as the target product. We used wine lees, which consisted primarily of settled yeast cells and ethanol from wine fermentation, and produced MCCs with a reactor microbiome. We operated the bioreactor at a pH of 5.2 and with continuous in-line extraction and achieved a MCC productivity of 3.9 g COD/L-d at an organic loading rate of 5.8 g COD/L-d, resulting in a promising MCC yield of 67% and specificities of 36% for each n-caprylate and n-caproate (72% for both). Compared to all other studies that used complex organic substrates, we achieved the highest n-caprylate-to-ncaproate product ratio of 1.0 (COD basis), because we used increased broth-recycle rates through the forward membrane contactor, which improved in-line extraction rates. Increased recycle rates also allowed us to achieve the highest reported MCC production flux per membrane surface area thus far (20.1 g COD/m2-d). Through microbial community analyses, we determined that an operational taxonomic unit (OTU) for Bacteroides spp. was dominant and was positively correlated with increased MCC productivities. Our data also suggested that the microbiome may have been shaped for improved MCC production by the high broth-recycle rates. Comparable abiotic studies suggest that further increases in the broth-recycle rates could improve the overall mass transfer coefficient and its corresponding MCC production flux by almost 30 times beyond the maximum that we achieved. With improved in-line extraction, the chain-elongation biotechnology production platform offers new opportunities for resource recovery and sustainable production of liquid fuels and chemicals.
A range-free 3-D localization method for RFID tags based on virtual landmarks

RFID is an automatic identification technique that stores and remotely retrieves data on small devices called tags. This technology enables tracking of people and objects and could support the conception of pervasive networks if identities are linked in real-time to their locations. We propose a scalable and robust 3-D localization method for RFID tags based only on connectivity information. Virtual landmarks are used to define inclusive and exclusive constraints, localize tags and estimate the accuracy of the calculations. Simulation results show that our algorithm provides fine-grained localization accuracy.
The Sea Level

The sea level changes that affect coastal systems involve more than just expanding oceans, however, because the Earth’s continents can also rise and fall relative to the oceans. Land can rise through processes such as sediment accumulation (the process that built the Mississippi River delta) and geological uplift (for example, as glaciers melt and the land below is no longer weighed down by heavy ice). In other areas, land can sink because of erosion, sediment compaction, natural subsidence (sinking due to geologic changes), groundwater withdrawal, or engineering projects that prevent rivers from naturally depositing sediments along their banks. Changes in ocean currents such as the Gulf Stream can also affect sea levels by pushing more water against some coastlines and pulling it away from others, raising or lowering sea levels accordingly.
Trajectory Planning and Optimized Adaptive Control for a Class of Wheeled Inverted Pendulum Vehicle Models

In this paper, we investigate optimized adaptive control and trajectory generation for a class of wheeled inverted pendulum (WIP) models of vehicle systems. Aiming at shaping the controlled vehicle dynamics to be of minimized motion tracking errors as well as angular accelerations, we employ the linear quadratic regulation optimization technique to obtain an optimal reference model. Adaptive control has then been developed using variable structure method to ensure the reference model to be exactly matched in a finite-time horizon, even in the presence of various internal and external uncertainties. The minimized yaw and tilt angular accelerations help to enhance the vehicle rider's comfort. In addition, due to the underactuated mechanism of WIP, the vehicle forward velocity dynamics cannot be controlled separately from the pendulum tilt angle dynamics. Inspired by the control strategy of human drivers, who usually manipulate the tilt angle to control the forward velocity, we design a neural-network-based adaptive generator of implicit control trajectory (AGICT) of the tilt angle which indirectly “controls” the forward velocity such that it tracks the desired velocity asymptotically. The stability and optimal tracking performance have been rigorously established by theoretic analysis. In addition, simulation studies have been carried out to demonstrate the efficiency of the developed AGICT and optimized adaptive controller.
Optimal allocation of virtual optical networks for the future internet

Optical network infrastructures can be partitioned into multiple parallel, dedicated virtual networks for a physical infrastructure sharing purpose. However, different transport technologies may impact in both the amount and the characteristics of the different virtual instances that can be built on top of a single physical infrastructure. To analyse the impact of the transport technology in this regard, we present exact Integer Linear Programming (ILP) formulations that address the off-line problem of optimally allocate a set of virtual networks in two kind of substrates: wavelength switching and spectrum switching. Both formulations serve the purpose to provide opaque transport services from the virtual network point of view, where electronic terminations are assumed in the virtual network nodes. We carry out a series of experiments to validate the presented formulations and determine which is the impact of both substrates in the number of virtual networks that can be optimally allocated in the transport network.
Lexically-Based Terminology Structuring: Some Inherent Limits

Terminology structuring has been the subject of much work in the context of terms extracted from corpora: given a set of terms, obtained from an existing resource or extracted from a corpus, it consists in identifying hierarchical (or other types of) relations between these terms. The present work aims at assessing the feasibility of such structuring by studying it on an existing hierarchically structured terminology. Our overall goal is to test various structuring methods proposed in the literature and to check how they fare on this task. The specific goal at the present stage of our work, which we report here, is focussed on lexical methods that match terms on the basis on their content words, taking morphological variants and synonyms into account. We describe experiments performed on the French version of the US National Library of Medicine MeSH thesaurus. We compare the lexically-induced relations with the original MeSH relations and measure recall and precision metrics, taking two different views on the task: relation recovery and term placement. This method proposes correct term placement for up to 26% of the MeSH concepts, and its precision can reach 58%. After this quantitative evaluation, we perform a qualitative, human analysis of the ‘new’ relations not present in the MeSH. This analysis shows, on the one hand, the limits of the lexical structuring method. On the other hand, it reveals some specific structuring choices and naming conventions made by the MeSH designers, and emphasizes ontological commitments that cannot be left to automatic structuring.
192-channel CMOS neurochemical microarray

A 16×12-channel neurochemical microarray is presented. Each channel acquires bidirectional currents down to pico-amperes proportional to the concentration of a neurochemical. By combining the current-to-frequency and the single-slope analog-to-digital converter (ADC) 110dB of dynamic range is achieved. The ADC in each channel generates a 16-bit output in less than a millisecond. The microarray with flat and 3D gold electrodes and an on-chip microfluidic network is experimentally validated in in-situ recording of neurotransmitter dopamine.
Bone destruction in arthritis

Rheumatoid arthritis (RA) is characterised by the presence of an inflammatory synovitis accompanied by destruction of joint cartilage and bone. Destruction of cartilage matrix results predominantly from the action of connective tissue proteinases released by RA synovial tissues, chondrocytes, and pannus tissue. Several lines of evidence in RA and in animal models of arthritis support a role for osteoclasts in the pathogenesis of bone erosions. RA synovial tissues produce a variety of cytokines and growth factors that may increase osteoclast formation, activity, and/or survival. These include interleukin 1α (IL1α) and β, tumour necrosis factor α (TNFα), IL11, IL17, and macrophage colony stimulating factor (M-CSF). Receptor activator of NFκB ligand (RANKL) is an essential factor for osteoclast differentiation and also functions to augment T cell-dendritic cell cooperative interactions. CD4+ T cells and synovial fibroblasts derived from RA synovium are sources of RANKL. Furthermore, in collagen induced arthritis (CIA), blockade with osteoprotegerin (OPG), a decoy receptor for RANKL, results in protection from bone destruction. To further evaluate the role of osteoclasts in focal bone erosion in arthritis, arthritis was generated in the RANKL knockout mouse using a serum transfer model. Despite ongoing inflammation, the degree of bone erosion in arthritic RANKL knockout mice, as assessed by microcomputed tomography and correlated histopathological analysis, was dramatically reduced compared with that seen in arthritic control mice. Cartilage damage was present in both the arthritic RANKL knockout mice and in arthritic control littermates, with a trend toward milder cartilage damage in the RANKL knockout mice. This study supports the hypothesis that osteoclasts play an important part in the pathogenesis of focal bone erosion in arthritis, and reveals distinct mechanisms of cartilage destruction and bone erosion in this animal model of arthritis. Future directions for research in this area include the further investigation of a possible direct role for the RANKL/RANK/OPG system in cartilage metabolism, and the possible role of other cell types and cytokines in bone erosion in arthritis.
Evolution of Conversations in the Age of Email Overload

Email is a ubiquitous communications tool in the workplace and plays an important role in social interactions. Previous studies of email were largely based on surveys and limited to relatively small populations of email users within organizations. In this paper, we report results of a large-scale study of more than 2 million users exchanging 16 billion emails over several months. We quantitatively characterize the replying behavior in conversations within pairs of users. In particular, we study the time it takes the user to reply to a received message and the length of the reply sent. We consider a variety of factors that affect the reply time and length, such as the stage of the conversation, user demographics, and use of portable devices. In addition, we study how increasing load affects emailing behavior. We find that as users receive more email messages in a day, they reply to a smaller fraction of them, using shorter replies. However, their responsiveness remains intact, and they may even reply to emails faster. Finally, we predict the time to reply, length of reply, and whether the reply ends a conversation. We demonstrate considerable improvement over the baseline in all three prediction tasks, showing the significant role that the factors that we uncover play, in determining replying behavior. We rank these factors based on their predictive power. Our findings have important implications for understanding human behavior and designing better email management applications for tasks like ranking unread emails.
Modulation of HIV Binding to Epithelial Cells and HIV Transfer from Immature Dendritic Cells to CD4 T Lymphocytes by Human Lactoferrin and its Major Exposed LF-33 Peptide

Lactoferrin (LF), a multifunctional molecule present in human secretions, has potent inhibitory activities against human immunodeficiency virus (HIV). The aim of the study was to evaluate whether human LF (hLF) and its exposed domain LF-33 represented by the peptide (LF-33-GRRRRSVQWCAVSQPEATKCFQWQRNMRKVRGP) involved in LF-HIV gag binding and endotoxines neutralization, may inhibit early steps of HIV mucosal transmission. Human LF and the peptide LF-33 inhibited the attachment of primary X4-tropic HIV-1NDK and R5-tropic HIV-1JR-CSF strains to human endometrial (HEC-1) and colorectal (HT-29) CD4-negative epithelial cells, the purified hLF being more potent (up to 80%) than the LF-33 peptide. In addition, the hLF, but not the LF-33 peptide, inhibited up to 40% the transfer in trans of HIV-1JR-CSF and HIV-1NDK, from immature dendritic cells to CD4 T lymphocytes, likely in a DC-SIGN-dependent manner. Altogether, these findings demonstrate that hLF can interfere with HIV-1 mucosal transmission by blocking virus attachment to epithelial cells and by inhibiting virus transfer from dendritic cells to CD4 T cells, two crucial steps of HIV dissemination from mucosae to lymphoid tissue.