Miracle-dz/UniFAR-FTUs huggingface dataset:
Therapeutic protein transduction of mammalian cells and mice by nucleic acid-free lentiviral nanoparticles

The straightforward production and dose-controlled administration of protein therapeutics remain major challenges for the biopharmaceutical manufacturing and gene therapy communities. Transgenes linked to HIV-1-derived vpr and pol-based protease cleavage (PC) sequences were co-produced as chimeric fusion proteins in a lentivirus production setting, encapsidated and processed to fusion peptide-free native protein in pseudotyped lentivirions for intracellular delivery and therapeutic action in target cells. Devoid of viral genome sequences, protein-transducing nanoparticles (PTNs) enabled transient and dose-dependent delivery of therapeutic proteins at functional quantities into a variety of mammalian cells in the absence of host chromosome modifications. PTNs delivering Manihot esculenta linamarase into rodent or human, tumor cell lines and spheroids mediated hydrolysis of the innocuous natural prodrug linamarin to cyanide and resulted in efficient cell killing. Following linamarin injection into nude mice, linamarase-transducing nanoparticles impacted solid tumor development through the bystander effect of cyanide.
Regularised k-means clustering for dimension reduction applied to supervised classification

Clustering methods provide a powerful tool for the exploratory analysis of high-dimensional, low-sample size data sets, such as gene expression microarray data. Unlike classification and regression, cluster analysis requires no response variable and thus falls into category of unsupervised learning methods. However, there are two major problems: stability of clustering and meaningfulness of centroids as cluster representatives. On the one hand, big clusters impose strong smoothing and possible loss of very essential information. On the other hand, small clusters are, usually, very unstable and noisy. Accordingly, they can not be treated as equal and independent epresentatives. To address the above problems, we propose regularisation to prevent the creation of super big clusters, and to attract data to existing small clusters. We demonstrate the effectiveness of this approach to the supervised classification of gene expression data.
An Interactive Framework for Data Cleaning and Transformation

Real world data often has discrepancies in structure and content. Traditional methods for \cleaning" the data involve many iterations of time-consuming \data quality" analysis to nd discrepancies, and long-running transformations to x them. This process requires users to endure long waits and often write complex transformation programs. We present an interactive framework for data cleaning that tightly integrates transformation and discrepancy detection. Users gradually build transformations by adding or undoing transforms, in a intuitive, graphical manner through a spreadsheet-like interface; the e ect of a transform is shown at once on records visible on screen. Discrepancy detection is done incrementally in the background on the current transformed version of data, and discrepancies are agged as they are found. This interactive combination of discrepancy detection and transformation allows users to gradually develop transformations as discrepancies are found, and clean the data without having to write complex programs or endure long waits. Balancing the goals of power, ease of speci cation, and interactive application, we develop a set of transforms that can be used for transformations within data records as well as for higher-order transformations. We also study the compilation of sequences of transforms into optimized programs.
Link-layer jamming attacks on S-MAC

We argue that among denial-of-service (DoS) attacks, link-layer jamming is a more attractive option to attackers than radio jamming is. By exploiting the semantics of the link-layer protocol (aka MAC protocol), an attacker can achieve better efficiency than blindly jamming the radio signals alone. In this paper, we investigate some jamming attacks on S-MAC, the level of effectiveness and efficiency the attacks can potentially achieve and a countermeasure that can be implemented against one of these attacks.
Analysis and design of CMOS Manchester adders with variable carry-skip

A popular VLSI adder implementation is the Manchester adder using dynamic (precharge) logic, where the ripple-carry propagation delay of a block is proportional to the square of its size. The authors examine two different CMOS implementations of the Manchester adder, analyzing them with the RC timing model, which provides a unified way of analyzing both CMOS circuits and interconnect. Based on the RC timing model, they develop efficient (polynomial) algorithms to determine near-optimal, as well as optimal, block sizes for the one-level Manchester adder with variable carry-skip. <<ETX>>
Visual and analytical mining of sales transaction data for production planning and marketing

Recent developments in information technology paved the way for the collection of large amounts of data pertaining to various aspects of an enterprise. The greatest challenge faced in processing these massive amounts of raw data gathered turns out to be the effective management of data with the ultimate purpose of deriving necessary and meaningful information out of it. The following paper presents an attempt to illustrate the combination of visual and analytical data mining techniques for planning of marketing and production activities. The primary phases of the proposed framework consist of filtering, clustering and comparison steps implemented using interactive pie charts, K-Means algorithm and parallel coordinate plots respectively. A prototype decision support system is developed and a sample analysis session is conducted to demonstrate the applicability of the framework.
Sampling Strategies in Antimicrobial Resistance Monitoring: Evaluating How Precision and Sensitivity Vary with the Number of Animals Sampled per Farm

Because antimicrobial resistance in food-producing animals is a major public health concern, many countries have implemented antimicrobial monitoring systems at a national level. When designing a sampling scheme for antimicrobial resistance monitoring, it is necessary to consider both cost effectiveness and statistical plausibility. In this study, we examined how sampling scheme precision and sensitivity can vary with the number of animals sampled from each farm, while keeping the overall sample size constant to avoid additional sampling costs. Five sampling strategies were investigated. These employed 1, 2, 3, 4 or 6 animal samples per farm, with a total of 12 animals sampled in each strategy. A total of 1,500 Escherichia coli isolates from 300 fattening pigs on 30 farms were tested for resistance against 12 antimicrobials. The performance of each sampling strategy was evaluated by bootstrap resampling from the observational data. In the bootstrapping procedure, farms, animals, and isolates were selected randomly with replacement, and a total of 10,000 replications were conducted. For each antimicrobial, we observed that the standard deviation and 2.5–97.5 percentile interval of resistance prevalence were smallest in the sampling strategy that employed 1 animal per farm. The proportion of bootstrap samples that included at least 1 isolate with resistance was also evaluated as an indicator of the sensitivity of the sampling strategy to previously unidentified antimicrobial resistance. The proportion was greatest with 1 sample per farm and decreased with larger samples per farm. We concluded that when the total number of samples is pre-specified, the most precise and sensitive sampling strategy involves collecting 1 sample per farm.
Age-specific prevalence, incidence, and duration of human papillomavirus infections in a cohort of 290 US men.

BACKGROUND Human papillomavirus (HPV) infections cause disease in men and women, and male-to-female HPV transmission influences the risk of cancer in females. The purpose of the present study was to describe the overall and age-specific incidence and clearance of HPV infections in men. METHODS In a prospective cohort study of 290 men aged 18-44 years, participants were examined at baseline and every 6 months, with a mean duration of follow-up of 15.5 months. RESULTS The period prevalence was 52.8% for any, 31.7% for oncogenic, and 30.0% for nononcogenic HPV infection. The 12-month cumulative risk of acquiring a new HPV infection was 29.2%. Incidences of HPV types 6, 11, 16, and 18 were 2.8, 0.5, 4.8, and 0.8 per 1000 person-months, respectively. The median time to clearance of any HPV infection was 5.9 months (95% confidence interval, 5.7-6.1 months), with comparable times to clearance for oncogenic and nononcogenic infections. Approximately 75% of men tested negative for any HPV 12 months after initial HPV detection. Age was not significantly associated with HPV incidence or duration of infection in men. CONCLUSION HPV infection in men was common, with relatively rapid rates of acquisition and clearance.
Nonuniform Compression in Databases with Haar Wavelet

Data synopsis is a lossy compressed representation of data stored into databases that helps the query optimizer to speed up the query process, e.g. time to retrieve the data from the database. An efficient data synopsis must provide accurate information about the distribution of data to the query optimizer at any point in time. Due to the fact that some data will be queried more often than others, a good data synopsis should consider the use of nonuniform accuracy, e.g. provide better approximation of data that are queried the most. Although, the generation of data synopsis is a critical step to achieve a good approximation of the initial data representation, data synopsis must be updated over time when dealing with time varying data. In this paper, we introduce new Haar wavelet synopses for nonuniform accuracy and time-varying data that can be generated in linear time and space, and updated in sublinear time. The efficiency of our new data synopses is validated against other linear methods by using both synthetic and real data sets
Scale Free in Software Metrics

Software has become a complex piece of work by the collective efforts of many. And it is often hard to predict what the final outcome will be. This transition poses new challenge to the software engineering (SE) community. By employing methods from the study of complex network, we investigate the object oriented (OO) software metrics from a different perspective. We incorporate the weighted methods per class (WMC) metric into our definition of the weighted OO software coupling network as the node weight. Empirical results from four open source OO software demonstrate power law distribution of weight and a clear correlation between the weight and the out degree. According to its definition, it suggests uneven distribution of function among classes and a close correlation between the functionality of a class and the number of classes it depending on. Further experiment shows similar distribution also exists between average LCOM and WMC as well as out degree. These discoveries will help uncover the underlying mechanisms of software evolution and will be useful for SE to cope with the emerged complexity in software as well as efficient test cases design
Isolation of Polycyclic Aromatic Hydrocarbon Degrading Bacterial Strains Isolated from Indigenous Microbial Communities of Petroleum Contaminated Soils

Soil and sludge samples polluted with petroleum waste from Cairo Oil Refining Company Mostorod, Qalyubia, Egypt for more than 41 years were used for isolation of indigenous microbial communities. These communities were grown on seven polycyclic aromatic hydrocarbon compounds (naphthalene- (Naph.), phenanthrene (Phen.), anthracene (Anth.), fluorancene (Flu.), acenaphthene (Acen.), pyrene (Pyr.) and benzo-a- anthracene (B-a-Anth.)) as a sole carbon and energy source. The growth of the seven different bacterial communities on the seven different PAHs had been investigated by recording their growth (O.D) and secretion of extracellular protein at zero time (initial) and after 7, 15, 21 and 28 days incubation. The growth of community these seven communities after 28 days incubation revealed an increase in bacterial count ranging from 0.8-1.0 ; 0.9-1.3 ; 0.9- 1.2 ;0.7 -1.0 ; 0.9-1.2 ; 0.9-1.3 and 0.6-1.2 log cycles on Naph. Phen. Anth. Flu. Acen. Pyr. And B-a- Anth. respectively. The highest total bacterial count at zero time (initial) was 1.6 x 10 CFU/ml for community (4), 7
Collective bargaining and its effect on the central bank conservatism : theory and some evidence

The primary purpose of this paper is to investigate, both theoretically and empirically, the relationship between the socially optimal degree of central bank conservatism and the level of unionization. The theoretical framework suggests a clear and negative relationship between the two, such that the lower the level of unionization, the higher the socially optimal degree of central bank conservatism. This result is derived from the combination of two effects that operate when the level of unionization changes. These effects can be identified as the "competition effect" and the "strategic effect". An increase in the proportion of unionized labor force reduces competition in the labor market and, as a result, induces the central bank to choose higher inflation in order to erode real wages ("competition effect"). Simultaneously, an increase in unionization leads to real wage moderation by the union as it desires to avoid inflation ("strategic effect"). Because this moderation will be greater the greater the weight that the central bank ascribes to unemployment stabilization, a more liberal central bank is socially preferred. In case the union's inflation-aversion is sufficiently high, the combination of these two effects produces a hump shaped relationship between the level of union density and both unemployment and inflation (a Calmfors-Driffill relationship). The paper also presents an empirical examination based on panel data from over 48 countries. The analysis provides some support for the theoretical model's prediction about the negative relationship between collective bargaining and central bank conservatism as measured by legal independence.
Power allocation in cognitive spectrum sharing area using effective capacity in imperfect fading channel

In this paper we propose a power control policy over an imperfect fading channel within a cognitive spectrum sharing area. In particular, our proposed scheme maximizes the secondary user effective capacity subject to the average interference power and the secondary user transmit power constraints. We also investigate the minimization of average interference power at the Primary Receiver (PR) under required effective capacity of the secondary user with respect to imperfect Channel State Information (CSI) between cognitive radio transmitter and the PR link. Furthermore the impact of the Quality of Service (QoS) exponent on effective capacity in imperfect channel is studied. The analytical results reveal that the effective capacity of the Secondary User (SU) under given average interference power and peak power constraints increases due to growing channel state error of the secondary transmitter-primary receiver link. Moreover, results show that cross channel error increases the average interference power at the primary receiver with regards to the changing QoS exponent. The numerical analysis confirms our theoretical results.
Wireless Sensor-Networks Conditions Monitoring and Fault Diagnosis Using Neighborhood Hidden Conditional Random Field

This paper formulates wireless sensor networks (WSNs) fault diagnosis problem as a pattern-classification problem and introduces a newly developed algorithm, neighborhood hidden conditional random field (NHCRF), for determining hidden states between sensors. The health conditions of WSN are determined by using the NHCRF model to estimate the posterior probability of different faulty scenarios. The NHCRF model can improve the WSN fault diagnosis, because it has relaxed the independence assumption of the hidden Markov model. To enhance the robustness and antinoise ability of the NHCRF, the concept of nearest neighbors is used when estimating dependencies. In this paper, a 200-sensor-node WSN is used to show that the proposed NHCRF method can deliver excellent and effective results for WSN-health diagnosis. Our study also presents thorough results on different types of WSN traffic, the free traffic, light traffic, and heavy traffic. Comparative results indicate that our method can deliver superior classification performance compared with other methods.
A Noise-Shaping Algorithm of Multi-bit DAC Nonlinearities in Complex Bandpass ∆ Σ AD Modulators

This paper presents a technique for improving the SNR and resolution of complex bandpass ∆ΣADCs which are used for wireless communication systems such as cellular phone, wireless LAN and Bluetooth. Oversampling and noise-shaping are used to achieve high accuracy of a ∆ΣAD modulator. However when a multi-bit internal DAC is used inside a modulator, nonlinearities of the DAC are not noise-shaped and the SNR of the ∆ΣADC degrades. For the conversion of complex intermediate frequency (IF) input signals, a complex bandpass ∆ΣAD modulator can provide superior performance to a pair of real bandpass ∆ΣAD modulators of the same order. This paper proposes a new noise-shaping algorithm— implemented by adding simple digital circuitry—to reduce the effects of nonlinearities in multi-bit DACs of complex bandpass ∆ΣAD modulators. We have performed simulation with MATLAB to verify the effectiveness of the algorithm, and the results show that the proposed algorithm can improve the SNR of a complex bandpass ∆ΣADC with nonlinear internal multi-bit DACs. key words: complex bandpass ∆ΣAD modulator, bandpass filter, noiseshaping, ADC, DAC, element rotation, data-weighted averaging, low-IF receiver
Adaptive control method of network data flow for QoS providing

Problem of Quality of Service (QoS) providing in computer networks in case of uncertainty of data transferring processes is examined. Method of random early detection (RED) for congestion avoidance in networks is analyzed. Modification of adaptive RED (ARED) is offered. This modification consists in adjusting parameter, that are used during determination of average queue length in router's buffer, on a base of queue stability condition and dynamic adjustment of maximum probability of packets drop in case of congestions. Reducing of networks parameters amount, that are used for adjusting of active queue management algorithms, on a base of their ranking towards availability and importance, is offered.
Optimized engine transients

Recent development has renewed the interest in drivetrain concepts which give a higher degree of freedom by disconnecting the engine and vehicle speeds. This freedom raises the demand for active control, which especially during transients is not trivial but of which the quality is crucial for the success of the drivetrain concept. This work attempts to analyze and explain the fuel optimal solution for the simplest drivetrain setup, which is an engine connected to a load which does not restrict the engine speed. This is made by using a Willan's model for the engine and deriving the fuel optimal solution during output power transients. The analysis is made with dynamic programming, Pontryagin's maximum principle and backward simulation under a static optimal line restriction. The analysis show that the optimal transients can be explained, visualized and, in simple cases, derived from phase planes of the engine speed and the Lagrange multiplier. In these cases the time needed for computation was reduced a factor > 1000 compared to dynamic programming. Restricting the engine to the static optimal line turns out to be very close to optimal, even during highly transient operation, while reducing the time needed for computation a factor ≫ 1000.
Clinical Efficacy and Meta-Analysis of Stem Cell Therapies for Patients with Brain Ischemia

Objective. Systematic review and meta-analysis to observe the efficacy and safety of stem cell transplantation therapy in patients with brain ischemia. Methods. We searched Cochrane Library, PubMed, Ovid, CBM, CNKI, WanFang, and VIP Data from its inception to December 2015, to collect randomized controlled trials (RCT) of stem cell transplantation for the ischemic stroke. Two authors independently screened the literature according to the inclusion and exclusion criteria, extracted data, and assessed the risk of bias. Thereafter, meta-analysis was performed. Results. Sixteen studies and eighteen independent treatments were included in the current meta-analysis. The results based upon the pooled mean difference from baseline to follow-up points showed that the stem cell transplantation group was superior to the control group with statistical significance in the neurologic deficits score (NIHSS, MD = 1.57; 95% CI, 0.64–2.51; I 2 = 57%; p = 0.001), motor function (FMA, MD = 4.23; 95% CI, 3.08–5.38; I 2 = 0%; p < 0.00001), daily life ability (Barthel, MD = 8.37; 95% CI, 4.83–11.91; I 2 = 63%; p < 0.00001), and functional independence (FIM, MD = 8.89; 95% CI, 4.70–13.08; I 2 = 79%; p < 0.0001). Conclusions. It is suggested that the stem cell transplantation therapy for patients with brain ischemic stroke can significantly improve the neurological deficits and daily life quality, with no serious adverse events. However, higher quality and larger data studies are required for further investigation to support clinical application of stem cell transplantation.
Image processing using one-dimensional processor arrays

The first half of this paper presents the design rationale for CNAPS, a specialized one-dimensional (1-D) processor array developed by Adaptive Solutions Inc. In this context, we discuss the problem of Amdahl's law which severely constrains special-purpose architectures. We also discuss specific architectural decisions such as the kind of parallelism, the computational precision of the processors, on-chip versus off-chip processor memory, and-most importantly-the interprocessor communication architecture. We argue that, for our particular set of applications, a 1-D architecture gives the best "bang for the buck", even when compared to the more traditional two-dimensional (2-D) architecture. The second half of this paper describes how several simple algorithms map to the CNAPS array. Our results show that the CNAPS 1-D array offers excellent performance over a range of IP algorithms. We also briefly look at the performance of CNAPS as a pattern recognition engine because many image processing and pattern recognition problems are intimately related.