Miracle-dz/UniFAR-FTUs huggingface dataset:
Space-time code design in OFDM systems

We consider a space-time coded (STC) orthogonal frequency-division multiplexing (OFDM) system in frequency-selective fading channels. By analyzing the pairwise error probability (PEP), we show that STC-OFDM systems can potentially provide a diversity order as the product of the number of transmitter antennas, the number of receiver antennas and the frequency selectivity order, and that the large effective length and the ideal interleaving are two most important principles in designing STCs for OFDM systems. Following these principles, we propose a new class of trellis-structured STCs. Compared with the conventional space-time trellis codes, our proposed STC's significantly improve the performance by efficiently exploiting both the spatial diversity and the frequency-selective-fading diversity.
Exposure to Electronic Cigarettes Impairs Pulmonary Anti-Bacterial and Anti-Viral Defenses in a Mouse Model

Electronic cigarettes (E-cigs) have experienced sharp increases in popularity over the past five years due to many factors, including aggressive marketing, increased restrictions on conventional cigarettes, and a perception that E-cigs are healthy alternatives to cigarettes. Despite this perception, studies on health effects in humans are extremely limited and in vivo animal models have not been generated. Presently, we determined that E-cig vapor contains 7x1011 free radicals per puff. To determine whether E-cig exposure impacts pulmonary responses in mice, we developed an inhalation chamber for E-cig exposure. Mice that were exposed to E-cig vapor contained serum cotinine concentrations that are comparable to human E-cig users. E-cig exposure for 2 weeks produced a significant increase in oxidative stress and moderate macrophage-mediated inflammation. Since, COPD patients are susceptible to bacterial and viral infections, we tested effects of E-cigs on immune response. Mice that were exposed to E-cig vapor showed significantly impaired pulmonary bacterial clearance, compared to air-exposed mice, following an intranasal infection with Streptococcus pneumonia. This defective bacterial clearance was partially due to reduced phagocytosis by alveolar macrophages from E-cig exposed mice. In response to Influenza A virus infection, E-cig exposed mice displayed increased lung viral titers and enhanced virus-induced illness and mortality. In summary, this study reports a murine model of E-cig exposure and demonstrates that E-cig exposure elicits impaired pulmonary anti-microbial defenses. Hence, E-cig exposure as an alternative to cigarette smoking must be rigorously tested in users for their effects on immune response and susceptibility to bacterial and viral infections.
Learning to Control Actions: Transfer Effects following a Procedural Cognitive Control Computerized Training

Few studies have addressed action control training. In the current study, participants were trained over 19 days in an adaptive training task that demanded constant switching, maintenance and updating of novel action rules. Participants completed an executive functions battery before and after training that estimated processing speed, working memory updating, set-shifting, response inhibition and fluid intelligence. Participants in the training group showed greater improvement than a no-contact control group in processing speed, indicated by reduced reaction times in speeded classification tasks. No other systematic group differences were found across the different pre-post measurements. Ex-Gaussian fitting of the reaction-time distribution revealed that the reaction time reduction observed among trained participants was restricted to the right tail of the distribution, previously shown to be related to working memory. Furthermore, training effects were only found in classification tasks that required participants to maintain novel stimulus-response rules in mind, supporting the notion that the training improved working memory abilities. Training benefits were maintained in a 10-month follow-up, indicating relatively long-lasting effects. The authors conclude that training improved action-related working memory abilities.
Characterizing Tropical Tree Species Growth Strategies: Learning from Inter-Individual Variability and Scale Invariance

Understanding how tropical tree species differ in their growth strategies is critical to predict forest dynamics and assess species coexistence. Although tree growth is highly variable in tropical forests, species maximum growth is often considered as a major axis synthesizing species strategies, with fast-growing pioneer and slow-growing shade tolerant species as emblematic representatives. We used a hierarchical linear mixed model and 21-years long tree diameter increment series in a monsoon forest of the Western Ghats, India, to characterize species growth strategies and question whether maximum growth summarizes these strategies. We quantified both species responses to biotic and abiotic factors and individual tree effects unexplained by these factors. Growth responses to competition and tree size appeared highly variable among species which led to reversals in performance ranking along those two gradients. However, species-specific responses largely overlapped due to large unexplained variability resulting mostly from inter-individual growth differences consistent over time. On average one-third of the variability captured by our model was explained by covariates. This emphasizes the high dimensionality of the tree growth process, i.e. the fact that trees differ in many dimensions (genetics, life history) influencing their growth response to environmental gradients, some being unmeasured or unmeasurable. In addition, intraspecific variability increased as a power function of species maximum growth partly as a result of higher absolute responses of fast-growing species to competition and tree size. However, covariates explained on average the same proportion of intraspecific variability for slow- and fast-growing species, which showed the same range of relative responses to competition and tree size. These results reflect a scale invariance of the growth process, underlining that slow- and fast-growing species exhibit the same range of growth strategies.
Is Yield Increase Sufficient to Achieve Food Security in China?

Increasing demand for food, driven by unprecedented population growth and increasing consumption, will keep challenging food security in China. Although cereal yields have substantially improved during the last three decades, whether it will keep thriving to meet the increasing demand is not known yet. Thus, an integrated analysis on the trends of crop yield and cultivated area is essential to better understand current state of food security in China, especially on county scale. So far, yield stagnation has extensively dominated the main cereal-growing areas across China. Rice yield is facing the most severe stagnation that 53.9% counties tracked in the study have stagnated significantly, followed by maize (42.4%) and wheat (41.9%). As another important element for production sustainability, but often neglected is the planted area patterns. It has been further demonstrated that the loss in productive arable land for rice and wheat have dramatically increased the pressure on achieving food security. Not only a great deal of the planted areas have stagnated since 1980, but also collapsed. 48.4% and 54.4% of rice- and wheat-growing counties have lost their cropland areas to varying degrees. Besides, 27.6% and 35.8% of them have retrograded below the level of the 1980s. The combined influence (both loss in yield and area) has determined the crop sustainable production in China to be pessimistic for rice and wheat, and consequently no surprise to find that more than half of counties rank a lower level of production sustainability. Therefore, given the potential yield increase in wheat and maize, as well as substantial area loss of rice and wheat, the possible targeted adaptation measures for both yield and cropping area is required at county scale. Moreover, policies on food trade, alongside advocation of low calorie diets, reducing food loss and waste can help to enhance food security.
Multi-Site Clinical Evaluation of DW-MRI as a Treatment Response Metric for Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy

Purpose To evaluate diffusion weighted MRI (DW-MR) as a response metric for assessment of neoadjuvant chemotherapy (NAC) in patients with primary breast cancer using prospective multi-center trials which provided MR scans along with clinical outcome information. Materials and Methods A total of 39 patients with locally advanced breast cancer accrued from three different prospective clinical trials underwent DW-MR examination prior to and at 3–7 days (Hull University), 8–11 days (University of Michigan) and 35 days (NeoCOMICE) post-treatment initiation. Thirteen patients, 12 of which participated in treatment response study, from UM underwent short interval (<1hr) MRI examinations, referred to as “test-retest” for examination of repeatability. To further evaluate stability in ADC measurements, a thermally controlled diffusion phantom was used to assess repeatability of diffusion measurements. MRI sequences included contrast-enhanced T1-weighted, when appropriate, and DW images acquired at b-values of 0 and 800 s/mm2. Histogram analysis and a voxel-based analytical technique, the Parametric Response Map (PRM), were used to derive diffusion response metrics for assessment of treatment response prediction. Results Mean tumor apparent diffusion coefficient (ADC) values generated from patient test-retest examinations were found to be very reproducible (|ΔADC|<0.1x10-3mm2/s). This data was used to calculate the 95% CI from the linear fit of tumor voxel ADC pairs of co-registered examinations (±0.45x10-3mm2/s) for PRM analysis of treatment response. Receiver operating characteristic analysis identified the PRM metric to be predictive of outcome at the 8–11 (AUC = 0.964, p = 0.01) and 35 day (AUC = 0.770, p = 0.05) time points (p<.05) while whole-tumor ADC changes where significant at the later 35 day time interval (AUC = 0.825, p = 0.02). Conclusion This study demonstrates the feasibility of performing a prospective analysis of DW-MRI as a predictive biomarker of NAC in breast cancer patients. In addition, we provide experimental evidence supporting the use of sensitive analytical tools, such as PRM, for evaluating ADC measurements.
Properties of Protein Drug Target Classes

Accurate identification of drug targets is a crucial part of any drug development program. We mined the human proteome to discover properties of proteins that may be important in determining their suitability for pharmaceutical modulation. Data was gathered concerning each protein’s sequence, post-translational modifications, secondary structure, germline variants, expression profile and drug target status. The data was then analysed to determine features for which the target and non-target proteins had significantly different values. This analysis was repeated for subsets of the proteome consisting of all G-protein coupled receptors, ion channels, kinases and proteases, as well as proteins that are implicated in cancer. Machine learning was used to quantify the proteins in each dataset in terms of their potential to serve as a drug target. This was accomplished by first inducing a random forest that could distinguish between its targets and non-targets, and then using the random forest to quantify the drug target likeness of the non-targets. The properties that can best differentiate targets from non-targets were primarily those that are directly related to a protein’s sequence (e.g. secondary structure). Germline variants, expression levels and interactions between proteins had minimal discriminative power. Overall, the best indicators of drug target likeness were found to be the proteins’ hydrophobicities, in vivo half-lives, propensity for being membrane bound and the fraction of non-polar amino acids in their sequences. In terms of predicting potential targets, datasets of proteases, ion channels and cancer proteins were able to induce random forests that were highly capable of distinguishing between targets and non-targets. The non-target proteins predicted to be targets by these random forests comprise the set of the most suitable potential future drug targets, and should therefore be prioritised when building a drug development programme.
Enabling Big Geoscience Data Analytics with a Cloud-Based, MapReduce-Enabled and Service-Oriented Workflow Framework

Geoscience observations and model simulations are generating vast amounts of multi-dimensional data. Effectively analyzing these data are essential for geoscience studies. However, the tasks are challenging for geoscientists because processing the massive amount of data is both computing and data intensive in that data analytics requires complex procedures and multiple tools. To tackle these challenges, a scientific workflow framework is proposed for big geoscience data analytics. In this framework techniques are proposed by leveraging cloud computing, MapReduce, and Service Oriented Architecture (SOA). Specifically, HBase is adopted for storing and managing big geoscience data across distributed computers. MapReduce-based algorithm framework is developed to support parallel processing of geoscience data. And service-oriented workflow architecture is built for supporting on-demand complex data analytics in the cloud environment. A proof-of-concept prototype tests the performance of the framework. Results show that this innovative framework significantly improves the efficiency of big geoscience data analytics by reducing the data processing time as well as simplifying data analytical procedures for geoscientists.
Transcriptomic Analysis of the Primary Roots of Alhagi sparsifolia in Response to Water Stress

Background Alhagi sparsifolia is a typical desert phreatophyte and has evolved to withstand extreme dry, cold and hot weather. While A. sparsifolia represents an ideal model to study the molecular mechanism of plant adaption to abiotic stress, no research has been done in this aspect to date. Here we took advantage of Illumina platform to survey transcriptome in primary roots of A. sparsifolia under water stress conditions in aim to facilitate the exploration of its genetic basis for drought tolerance. Methodology and Principal Findings We sequenced four primary roots samples individually collected at 0, 6, 24 and 30h from the A. sparsifolia seedlings in the course of 24h of water stress following 6h of rehydration. The resulting 38,763,230, 67,511,150, 49,259,804 and 54,744,906 clean reads were pooled and assembled into 33,255 unigenes with an average length of 1,057 bp. All-unigenes were subjected to functional annotation by searching against the public databases. Based on the established transcriptome database, we further evaluated the gene expression profiles in the four different primary roots samples, and identified numbers of differently expressed genes (DEGs) reflecting the early response to water stress (6h vs. 0h), the late response to water stress (24h vs. 0h) and the response to post water stress rehydration (30h vs. 24h). Moreover, the DEGs specifically regulated at 6, 24 and 30h were captured in order to depict the dynamic changes of gene expression during water stress and subsequent rehydration. Functional categorization of the DEGs indicated the activation of oxidoreductase system, and particularly emphasized the significance of the ‘Glutathione metabolism pathway’ in response to water stress. Conclusions This is the first description of the genetic makeup of A. sparsifolia, thus providing a substantial contribution to the sequence resources for this species. The identified DEGs offer a deep insight into the molecular mechanism of A. sparsifolia in response to water stress, and merit further investigation.
Finite-Alphabet Precoding for Massive MU-MIMO With Low-Resolution DACs

Massive multiuser multiple-input multiple-output (MU-MIMO) systems are expected to be the core technology in fifth-generation wireless systems because they significantly improve spectral efficiency. However, the requirement for a large number of radio frequency (RF) chains results in high hardware costs and power consumption, which obstruct the commercial deployment of massive MIMO systems. A potential solution is to use low-resolution digital-to-analog converters (DAC)/analog-to-digital converters for each antenna and RF chain. However, using low-resolution DACs at the transmit side directly limits the degree of freedom of output signals and thus poses a challenge to the precoding design. In this paper, we develop efficient and universal algorithms for a downlink massive MU-MIMO system with finite-alphabet precodings. Our algorithms are developed based on the alternating direction method of multipliers (ADMM) framework. The original ADMM does not converge in a nonlinear discrete optimization problem. The primary cause of this problem is that the alternating (update) directions in ADMM on one side are biased, and those on the other side are unbiased. By making the two updates consistent in an unbiased manner, we develop two algorithms called iterative discrete estimation (IDE) and IDE2. IDE demonstrates excellent performance and IDE2 possesses a significantly low computational complexity. Compared with state-of-the-art techniques, the proposed precoding algorithms present significant advantages in performance and computational complexity.
Exploring Twitter to Analyze the Public’s Reaction Patterns to Recently Reported Homicides in London

Crime is an ubiquitous part of society. The way people express their concerns about crimes has been of particular interest to the scientific community. Over time, the numbers and kinds of available communication channels have increased. Today, social media services, such Twitter, present a convenient way to express opinions and concerns about crimes. The main objective of this study is to explore people’s perception of homicides, specifically, how the characteristics and proximity of the event affect the public’s concern about it. The analysis explores Twitter messages that refer to homicides that occurred in London in 2012. In particular, the dependence of tweeting propensity on the proximity, in space and time, of a crime incident and of people being concerned about that particular incident are examined. Furthermore, the crime characteristics of the homicides are analysed using logistic regression analysis. The results show that the proximity of the Twitter users’ estimated home locations to the homicides’ locations impacts on whether the associated crime news is spread or not and how quickly. More than half of the homicide related tweets are sent within the first week and the majority of them are sent within a month of the incident’s occurrence. Certain crime characteristics, including the presence of a knife, a young victim, a British victim, or a homicide committed by a gang are predictors of the crime-tweets posting frequency.
Effect of Monospecific and Mixed Sea-Buckthorn (Hippophae rhamnoides) Plantations on the Structure and Activity of Soil Microbial Communities

This study aims to evaluate the effect of different afforestation models on soil microbial composition in the Loess Plateau in China. In particular, we determined soil physicochemical properties, enzyme activities, and microbial community structures in the top 0 cm to 10 cm soil underneath a pure Hippophae rhamnoides (SS) stand and three mixed stands, namely, H. rhamnoides and Robinia pseucdoacacia (SC), H. rhamnoides and Pinus tabulaeformis (SY), and H. rhamnoides and Platycladus orientalis (SB). Results showed that total organic carbon (TOC), total nitrogen, and ammonium (NH4 +) contents were higher in SY and SB than in SS. The total microbial biomass, bacterial biomass, and Gram+ biomass of the three mixed stands were significantly higher than those of the pure stand. However, no significant difference was found in fungal biomass. Correlation analysis suggested that soil microbial communities are significantly and positively correlated with some chemical parameters of soil, such as TOC, total phosphorus, total potassium, available phosphorus, NH4 + content, nitrate content (NH3 −), and the enzyme activities of urease, peroxidase, and phosphatase. Principal component analysis showed that the microbial community structures of SB and SS could clearly be discriminated from each other and from the others, whereas SY and SC were similar. In conclusion, tree species indirectly but significantly affect soil microbial communities and enzyme activities through soil physicochemical properties. In addition, mixing P. tabulaeformis or P. orientalis in H. rhamnoides plantations is a suitable afforestation model in the Loess Plateau, because of significant positive effects on soil nutrient conditions, microbial community, and enzyme activities over pure plantations.
Transgenic and Mutation-Based Suppression of a Berberine Bridge Enzyme-Like (BBL) Gene Family Reduces Alkaloid Content in Field-Grown Tobacco

Motivation exists to develop tobacco cultivars with reduced nicotine content for the purpose of facilitating compliance with expected tobacco product regulations that could mandate the lowering of nicotine levels per se, or the reduction of carcinogenic alkaloid-derived tobacco specific nitrosamines (TSNAs). A berberine bridge enzyme-like (BBL) gene family was recently characterized for N. tabacum and found to catalyze one of the final steps in pyridine alkaloid synthesis for this species. Because this gene family acts downstream in the nicotine biosynthetic pathway, it may represent an attractive target for genetic strategies with the objective of reducing alkaloid content in field-grown tobacco. In this research, we produced transgenic doubled haploid lines of tobacco cultivar K326 carrying an RNAi construct designed to reduce expression of the BBL gene family. Field-grown transgenic lines carrying functional RNAi constructs exhibited average cured leaf nicotine levels of 0.684%, in comparison to 2.454% for the untransformed control. Since numerous barriers would need to be overcome to commercialize transgenic tobacco cultivars, we subsequently pursued a mutation breeding approach to identify EMS-induced mutations in the three most highly expressed isoforms of the BBL gene family. Field evaluation of individuals possessing different homozygous combinations of truncation mutations in BBLa, BBLb, and BBLc indicated that a range of alkaloid phenotypes could be produced, with the triple homozygous knockout genotype exhibiting greater than a 13-fold reduction in percent total alkaloids. The novel source of genetic variability described here may be useful in future tobacco breeding for varied alkaloid levels.