Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Considerations for Deploying Underwater Sensor Networks

The reliance of underwater network deployments on specialized hardware for acoustic communication and modulation has impeded wide scale deployments of underwater sensor networks. This paper examines a system that integrates software modems with generic speakers and microphones that are built-in to sensor modules for establishing underwater acoustic links. Building on our recent field experiment in a river, canal, pond, and swimming pool, this paper outlines the technical and logistical challenges for deploying software-driven underwater sensor network. The software modem design choices include methods for signal modulation at the sender, and symbol synchronization, signal filtering, and signal demodulation at the receiver. Higher layer communication protocol issues are also discussed, with a focus on cross-layer optimization. Practical solutions to logistical deployment challenges, such as waterproofing and casing, calibration, and fouling are proposed. The design guidelines in this paper lay the groundwork for further development of software-driven of underwater sensor networks.
A General Theory of Phase Noise in Transconductor-Based Harmonic Oscillators

We present a rigorous phase noise analysis of a generic harmonic oscillator where the active core can be modeled as a transconductor. Phase noise equations are derived without any specific assumption on the nature of the resonator in the oscillator; furthermore, we provide closed-form $1/f^{2}$ phase noise equations when the resonator consists of an arbitrary number of cascaded LC tanks, each tuned at a different harmonic of the oscillation frequency. We also show that the phase noise caused by transconductor white noise is always proportional to the phase noise caused by resonator losses, and that no flicker noise from the transconductor is up-converted into phase noise in an oscillator with an odd-symmetric voltage waveform. The phase noise for a number of different oscillators/resonators has been simulated, always obtaining an exceedingly good agreement between theoretical predictions and numerical results.
Hybrid Approaches for Interactive Image Segmentation Using the Live Markers Paradigm

Interactive image segmentation methods normally rely on cues about the foreground imposed by the user as region constraints (markers/brush strokes) or boundary constraints (anchor points). These paradigms often have complementary strengths and weaknesses, which can be addressed to improve the interactive experience by reducing the user's effort. We propose a novel hybrid paradigm based on a new form of interaction called live markers, where optimum boundary-tracking segments are turned into internal and external markers for region-based delineation to effectively extract the object. We present four techniques within this paradigm: 1) LiveMarkers; 2) RiverCut; 3) LiveCut; and 4) RiverMarkers. The homonym LiveMarkers couples boundary-tracking via live-wire-on-the-fly (LWOF) with optimum seed competition by the image foresting transform (IFT-SC). The IFT-SC can cope with complex object silhouettes, but presents a leaking problem on weaker parts of the boundary that is solved by the effective live markers produced by LWOF. Conversely, in RiverCut, the long boundary segments computed by Riverbed around complex shapes provide markers for Graph Cuts by the Min-Cut/Max-Flow algorithm (GCMF) to complete segmentation on poorly defined sections of the object's border. LiveCut and RiverMarkers further demonstrate that live markers can improve segmentation even when the combined approaches are not complementary (e.g., GCMFs shrinking bias is also dramatically prevented when using it with LWOF). Moreover, since delineation is always region based, our methodology subsumes both paradigms,representing a new way of extending boundary tracking to the 3D image domain, while speeding up the addition of markers close to the object's boundary - a necessary but time consuming task when done manually. We justify our claims through an extensive experimental evaluation on natural and medical images data sets, using recently proposed robot users for boundary-tracking methods.
Analysis of influenza data generated by four epidemiological surveillance laboratories in Mexico, 2010–2016

Abstract The disease caused by the influenza virus is a global public health problem due to its high rates of morbidity and mortality. Thus, analysis of the information generated by epidemiological surveillance systems has vital importance for health decision making. A retrospective analysis was performed using data generated by the four molecular diagnostic laboratories of the Mexican Social Security Institute between 2010 and 2016. Demographics, influenza positivity, seasonality, treatment choices and vaccination status analyses were performed for the vaccine according to its composition for each season. In all cases, both the different influenza subtypes and different age groups were considered separately. The circulation of A/H1N1pdm09 (48.7%), influenza A/H3N2 (21.1%), influenza B (12.6%), influenza A not subtyped (11%) and influenza A/H1N1 (6.6%) exhibited well-defined annual seasonality between November and March, and there were significant increases in the number of cases every 2 years. An inadequate use of oseltamivir was determined in 38% of cases, and the vaccination status in general varied between 12.1 and 18.5% depending on the season. Our results provide current information about influenza in Mexico and demonstrate the need to update both operational case definitions and medical practice guidelines to reduce the inappropriate use of antibiotics and antivirals.
A Simulation Study of Oxygen Vacancy-Induced Variability in  ${\rm HfO}_{2}$ /Metal Gated SOI FinFET

Deposition of a metal gate on high-K dielectric HfO2 is known to generate oxygen vacancy (OVs) defects. Positively charged OVs in the dielectric affect the gate electrostatics and modulate the effective gate workfunction (WF). Count and spatial allocation of OVs varies from device-to-device and induces significant local variability in WF and Vth. This paper presents the statistical models to simulate OV concentration and placement depending on the gate formation conditions. OV-induced variability is studied for SOI FinFET, and compared against the other sources of variability across the technologies. The implications of gate first and gate last processes to the OV concentration/distribution are studied. Simulations show that with channel length and gate dielectric thickness scaling, the OV-induced variability becomes a significant concern.
An integrated mRNA‐lncRNA signature for relapse prediction in laryngeal cancer

Patients with laryngeal cancer with early relapse usually have a poor prognosis. In this study, we aimed to identify a multi‐gene signature to improve the relapse prediction in laryngeal cancer. One microarray data set GSE27020 (training set, N = 109) and one RNA‐sequencing data set (validation set, N = 85) were included into the analysis. In the training set, the microarray expression profile was re‐annotated into an mRNA‐long noncoding RNA (lncRNA) biphasic profile. Then, LASSO Cox regression model identified nine relapse‐related RNA (eight mRNA and one lncRNA), and a risk score was calculated for each sample according to the model coefficients. Patients with high‐risk showed poorer relapse‐free survival than patients with low risk (hazard ratios (HR): 6.189, 95% confidence interval (CI): 3.075‐12.460, P < 0.0001). The risk score demonstrated good accuracy in predicting the relapse (area under time‐dependent receiver‐operating characteristic (AUC): 0.859 at 1 year, 0.822 at 3 years, and 0.815 at 5 years). The results were validated in the validation set (HR: 3.762, 95% CI: 1.594‐8.877, P = 0.011; AUC: 0.770 at 1 year, 0.769 at 3 years, and 0.728 at 5 years). The multivariate analysis reached consistent results after adjustment by multiple confounders. When compared with a 27‐gene signature, a 2‐lncRNA signature, and Tumor‐Node‐Metastasis stage, the risk score also showed better performance (P < 0.05). In conclusion, we successfully developed a robust mRNA‐lncRNA signature that can accurately predict the relapse in laryngeal cancer.
Development of R-Group Fingerprints Based on the Local Landscape from an Attachment Point of a Molecular Structure

Molecular fingerprints are indispensable in medicinal chemistry for quantifying chemical structures. Fingerprints can be calculated for substructures with attachment points, which are positions where a substructure and a corresponding core structure connect. Because structures with attachment points can be crucial for understanding structure-activity relationships, fingerprints specialized for representing this structural feature are required. R-group fingerprints and R-group descriptors were proposed previously for this purpose; however, these molecular representations have limitations. Current R-group fingerprints do not emphasize information about attachment points, and R-group descriptors are too sensitive to changes in the topological path length from an attachment point. In the present work, we developed novel R-group fingerprints, termed R-path fingerprints, which contain substituent information from an attachment point without being sensitive to small differences in topological distances. The concept of the R-path fingerprints is to describe a chemical substructure from the viewpoint of an attachment point, to distinguish atomistic information around the attachment point and other parts of the substructure. This was achieved by considering all the paths on the shortest path between the attachment point and each atom in a substituent. Benchmark testing was conducted, including comparisons of similarity distributions and potency prediction for R-group substituents. The results showed that R-path fingerprints should be useful for classifying and comparing substructures with attachment points.
A framework for resource allocation in grid computing

Grid computing is the future computing paradigm for enterprise applications. An enterprise application running on a grid is composed of a set of SLA-constrained sub-tasks demanding different types of services and resources such as processors, data storage, service providers, and network links. The paper formalizes the resource allocation problem for SLA-constrained grid applications. The paper considers a very general case in which applications are decomposed into tasks that exhibit precedence relationships. The problem consists in finding the optimal resource allocation that minimizes total cost while preserving execution time service level agreements (SLAs). The paper provides a framework for building heuristic solutions for this NP-hard problem, presents an example of such a heuristic, and provides a numerical example.
A Software Architecture for the Analysis of Large Sets of Data Streams in Cloud Infrastructures

System management algorithms in private and public cloud infrastructures have to work with literally thousands of data streams generated from resource, application and event monitors. This cloud context opens two novel issues that we address in this paper: how to design a software architecture that is able to gather and analyze all information within real-time constraints, how it is possible to reduce the analysis of the huge collected data set to the investigation of a reduced set of relevant information. The application of the proposed architecture is based on the most advanced software components, and is oriented to the classification of the statistical behavior of servers and to the analysis of significant state changes. These results guide model-driven management systems to investigate only relevant servers and to apply suitable decision models considering the deterministic or non-deterministic nature of server behaviors.
Activities of daily living and manual hand dexterity in persons with idiopathic parkinson disease

[Purpose] The purpose of this study was to evaluate the relationship between daily activities and manual dexterity in persons with Parkinson disease. [Subjects and Methods] The study participants were 25 patients with idiopathic Parkinson disease. This study used two clinical tools, the box-and-block test and Schwab and England Activities of Daily Living scale, to investigate the relationship between manual dexterity and Schwab and England Activities of Daily Living score. [Results] A positive correlation was observed between the Schwab and England Activities of Daily Living and the box-and-block test scores on the more and less affected sides. Moreover, the Schwab and England Activities of Daily Living score had a greater correlation with the box-and-block test score on the less affected side than that on the more affected side. [Conclusion] Manual dexterity and activities of daily living showed a positive correlation in individuals with Parkinson disease. The results of this study suggest that manual dexterity is an important factor for predicting physical performance in daily living in persons with Parkinson disease.
Sorafenib versus hepatic arterial infusion chemotherapy for advanced hepatocellular carcinoma: a systematic review and meta-analysis.

BACKGROUND The clinical benefits and safety of Sorafenib versus hepatic arterial infusion chemotherapy (HAIC) for advanced hepatocellular carcinoma (HCC) are inconsistent in some studies. This meta-analysis aims to evaluate the effectiveness and safety of Sorafenib versus HAIC for patients with advanced HCC. METHODS An electronic search was performed from PubMed, Embase, the Cochrane Library and Web of Science to identify comparative studies evaluating Sorafenib versus HAIC for HCC. Objective response rate, disease control rate, overall survival, progression-free survival and adverse events were evaluated using meta-analytical techniques. RESULTS Fourteen retrospective studies with 1779 patients (Sorafenib = 773, HAIC = 1006) were included in the meta-analysis. HAIC delivered favorable outcomes in objective response rate (odds ratio 0.13; 95%CI, 0.07-0.24) and disease control rate (odds ratio 0.48; 95%CI 0.26-0.87) assessed by the Response Evaluation Criteria in Solid Tumors. The pooled hazard ratio for overall survival at 0.60 (95% CI 0.39-0.91) and the pooled hazard ratio for progression-free survival at 0.69(95% CI 0.51-0.95), further indicates that HAIC was superior to Sorafenib. There was a higher incidence of adverse events, including hypertension (odds ratio 13.07; 95% CI 2.37-71.67), fatigue (odds ratio 6.72; 95% CI 2.14-21.13), dermatological disorders (odds ratio 15.87; 95% CI 5.58-45.16) and gastrointestinal disorders (odds ratio 3.20; 95% CI 2.02-5.07) in patients receiving Sorafenib than in those receiving HAIC. CONCLUSION HAIC offers a safe and effective alternative to Sorafenib with better tumor response and longer overall survival and progression-free survival, hence HAIC should be recommended for the patients with advanced HCC.
Peptosome Coadministration Improves Nanoparticle Delivery to Tumors through NRP1-Mediated Co-Endocytosis

Improving the efficacy of nanoparticles (NPs) delivery to tumors is critical for cancer diagnosis and therapy. In our previous work, amphiphilic peptide APPA self-assembled nanocarriers were designed and constructed for cargo delivery to tumors with high efficiency. In this study, we explore the use of APPA self-assembled peptosomes as a nanoparticle adjuvant to enhance the delivery of nanoparticles and antibodies to integrin αvβ3 and neuropilin-1 (NRP1) positive tumors. The enhanced tumor delivery of coadministered NPs was confirmed by better magnetosome (Mag)-based T2-weighted magnetic resonance imaging (MRI), liposome-based fluorescence imaging, as well as the improved anti-tumor efficacy of monoclonal antibodies (trastuzumab in this case) and doxorubicin (DOX)-containing liposomes. Interestingly, the improvement is most significant for the delivering of compounds that have active or passive tumor targeting ability, such as antibodies or NPs that have enhanced permeability and retention (EPR) effect. However, for non-targeting small molecules, the effect is not significant. In vitro and in vivo studies suggest that both peptosomes and the coadministered compounds might be internalized into cells through a NRP1 mediated co-endocytosis (CoE) pathway. The improved delivery of coadministered NPs and antibodies to tumors suggests that the coadministration with APPA self-assembled peptosomes could be a valuable approach for advancing αvβ3 and NRP1 positive tumors diagnosis and therapy.
Long non-coding RNA NEAT1 promotes steatosis via enhancement of estrogen receptor alpha-mediated AQP7 expression in HepG2 cells

Abstract Hepatic steatosis is one of the most important features of the pathogenesis for non-alcoholic fatty liver disease. Fat deposition in liver cells can influence hepatic lipogenesis along with other metabolic pathways and further lead to the irreversible liver cirrhosis and injury. However, the underlying mechanism of steatosis remains largely unexplored. Our previous study revealed that AQP7 played an important role in liver steatosis. In this study, we determined that the transcriptional level of AQP7 was up-regulated by estrogen receptor alpha (ERα) upon 17β-estradiol (E2) and oleic acids treated HepG2 cells. Furthermore, we identified long non-coding RNA nuclear enriched abundant transcript 1 (NEAT1) as a potential hallmark which was down-regulated in ERα silencing HepG2 cells by RNA-Seq. Finally, we validated that the 3’ terminal nucleotides of NEAT1 were contributed for the interaction with ERα to facilitate AQP7 transcription to suppress liver steatosis. Overall, our study gave evidence that NEAT1 played an important role in the activation of ERα to regulate AQP7-mediated hepatic steatosis.
Cell Biology International

Renal cell carcinoma (RCC) is one of the most lethal urogenital cancers and effective treatment of metastatic RCC remains an elusive target. Cell lines enable the in vitro investigation of molecular and genetic changes leading to renal carcinogenesis and are important for evaluating cellular drug response or toxicity. This study details a fast and easy protocol of establishing epithelial and fibroblast cell cultures or cell lines concurrently from renal cancer nephrectomy tissue. The protocol involves mechanical disaggregation, collagenase digestion and cell sieving for establishing epithelial cells while fibroblast cells were grown from explants. This protocol has been modified from previous published reports with additional antibiotics and washing steps added to eliminate microbial contamination from the surgical source. Cell characterisation was carried out using immunofluorescence and quantitative polymerase chain reaction. Eleven stable epithelial renal tumour cell lines of various subtypes, including rare subtypes, were established with a spontaneous immortalisation rate of 21.6% using this protocol. Eight fibroblast cell cultures grew successfully but did not achieve spontaneous immortalisation. Cells of epithelial origin expressed higher expressions of epithelial markers such as pan‐cytokeratin, cytokeratin 8 and E‐cadherin whereas fibroblast cells expressed high α‐smooth muscle actin. Further mutational analysis is needed to evaluate the genetic or molecular characteristics of the cell lines.
A VEHICULAR ACCIDENT DETECTION AND AVOIDANCE SYSTEM FOR PROTECTING PASSENGERS AND VEHICLES

Considering the increasing number of traffic accidents in recent years, it is being accepted that traffic accidents have assumed exceptional dimension of a serious problem. Of the major causes of accidents, the driver has been recognised as the main cause of accident especially in Nigeria. As regards to human factor, it is concluded that not so much can be done to improve the level of alertness of drivers or reduce the level of stress experienced by drivers. Hence, Intelligent System is being deployed to vehicles to aid drivers to avoid collision. Existing Collision Detection and Avoidance systems have grown in complexity that the goal of easy integration and maintainability is elusive. This Research has developed a system of Robust Sensors and Actuators coupled to a Rugged Microprocessor to achieve a Low Cost but highly reliable Vehicular Accident Detection and Avoidance System using Ultrasonic Sensors installed in the front end of the vehicle. Analog proximity measurements for various collision hazards were taken and at 40 kilometer per hour and above, the system will be activated. The use of microcontroller for the generation of ultrasonic signal and the control logic enables the prediction of imminent collision when the vehicle approaches an obstacle within 7meters range, thereby enabling some controls which ensure a warning alarm and an activation signal to the automatic brake system when the collision range of 5meters is reached. With the embedded C program in the microcontroller, a visual liquid crystal display is equally incorporated to give situation report of the device surveillance activities. Based on Proteus 7.0 simulation software, the performance of the system is extensively evaluated and the results record satisfactory performance.
Input Vector Comparison for Hourly Load Forecast of Small Load Area Using Artificial Neural Network

This paper presents an hourly load forecast of small load area using Artificial Neural Network (ANN). For this case-study duration of February-2010 to Januray-2011 is considered. In this study ANN is trained and tested for by providing two different input vectors. In this paper the input vector design and the data is mainly focused. Also, suitable ANN topology is also discussed. Further the training and testing process for ANNs of these months are explained. Back-propagation algorithm is employed in this process. Finally by comparing network performances for these two input vectors for each of the considered month, optimum vector is selected.