Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectrum sharing for unlicensed bands

We study a spectrum sharing problem in an unlicensed band where multiple systems coexist and interfere with each other. We first analyze a cooperative setting where all the systems collaborate to achieve a common goal. Under the assumptions that the systems communicate with Gaussian signals and treat interference as noise, we study the structure of the optimal power allocations. We show that any Pareto efficient vector of rates can be achieved with piece-wise constant power allocations. Moreover, if a strong interference condition among all the systems is satisfied, we show that frequency division multiplexing is optimal. We then consider a non-cooperative situation, where the systems act in a selfish and rational way, and investigate how the lack of cooperation can affect performance. Using game theory, we first analyze the possible outcomes of a one shot game, and observe that in many cases an inefficient solution results. We show that by extending the game definition to that of a repeated game, the possibility of building reputations and applying punishments allows to enlarge the set of achievable rates. We present examples that show that in many cases, the performance loss due to lack of cooperation is small. We also provide a converse theorem that proves that our results are tight and quantify the best achievable performance in a non-cooperative scenario
Explorer SMN deficiency disrupts brain development in a mouse model of severe spinal muscular atrophy

deficiency disrupts brain development in a mouse model of severe spinal muscular atrophy' General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact openaccess@ed.ac. uk providing details, and we will remove access to the work immediately and investigate your claim. Reduced expression of the survival motor neuron (SMN) gene causes the childhood motor neuron disease spinal muscular atrophy (SMA). Low levels of ubiquitously expressed SMN protein result in the degeneration of lower motor neurons, but it remains unclear whether other regions of the nervous system are also affected. Here we show that reduced levels of SMN lead to impaired perinatal brain development in a mouse model of severe SMA. Regionally selective changes in brain morphology were apparent in areas normally associated with higher SMN levels in the healthy postnatal brain, including the hippocampus, and were associated with decreased cell density, reduced cell proliferation and impaired hippocampal neurogenesis. A comparative proteomics analysis of the hippocampus from SMA and wild-type littermate mice revealed widespread modifications in expression levels of proteins regulating cellular proliferation, migration and development when SMN levels were reduced. This study reveals novel roles for SMN protein in brain development and maintenance and provides the first insights into cellular and molecular pathways disrupted in the brain in a severe form of SMA.
Global deceleration of gene evolution following recent genome hybridizations in fungi

Polyploidization events such as whole-genome duplication and inter-species hybridization are major evolutionary forces that shape genomes. Although long-term effects of polyploidization have been well-characterized, early molecular evolutionary consequences of polyploidization remain largely unexplored. Here, we report the discovery of two recent and independent genome hybridizations within a single clade of a fungal genus, Trichosporon. Comparative genomic analyses revealed that redundant genes are experiencing decelerations, not accelerations, of evolutionary rates. We identified a relationship between gene conversion and decelerated evolution suggesting that gene conversion may improve the genome stability of young hybrids by restricting gene functional divergences. Furthermore, we detected large-scale gene losses from transcriptional and translational machineries that indicate a global compensatory mechanism against increased gene dosages. Overall, our findings illustrate counteracting mechanisms during an early phase of post-genome hybridization and fill a critical gap in existing theories on genome evolution.
Structural Basis for Prereceptor Modulation of Plant Hormones by GH3 Proteins

Plant Hormone Modulators The activity and stability of several plant hormones is modulated by conjugation with various amino acids and their derivatives. Westfall et al. (p. 1708, published online 24 May) solved the crystal structures for two acyl acid amido synthetases from Arabidopsis. The findings suggest how the enzymes might discriminate between apolar and acidic amino acids and lend insight into the reaction chemistries that add functional diversity to hormone signaling pathways. Crystal structures of plant GH3 proteins reveal how these enzymes accommodate jasmonates, auxins, and benzoates. Acyl acid amido synthetases of the GH3 family act as critical prereceptor modulators of plant hormone action; however, the molecular basis for their hormone selectivity is unclear. Here, we report the crystal structures of benzoate-specific Arabidopsis thaliana AtGH3.12/PBS3 and jasmonic acid–specific AtGH3.11/JAR1. These structures, combined with biochemical analysis, define features for the conjugation of amino acids to diverse acyl acid substrates and highlight the importance of conformational changes in the carboxyl-terminal domain for catalysis. We also identify residues forming the acyl acid binding site across the GH3 family and residues critical for amino acid recognition. Our results demonstrate how a highly adaptable three-dimensional scaffold is used for the evolution of promiscuous activity across an enzyme family for modulation of plant signaling molecules.
A Security Framework for a Drone Delivery Service

Delivery drones are unmanned aerial vehicles (UAV) utilized to transport packages, food, medicine, or other goods. With high demand for a prompt and efficient delivery, a drone delivery system can be an effective solution for timely deliveries and especially for emergency management. However, current delivery drone systems lack crucial security functions. Drones may have to operate in unsupervised hostile areas, and therefore be vulnerable to physical capture in addition to conventional cyber attacks. A captured drone can be analyzed by a white-box attack model in which the attacker has full control over the execution environment of cryptographic modules in static and dynamic methods including all side-channel information. In this paper, we propose and evaluate a security framework which utilizes white-box cryptography in order to protect critical data and cryptographic keys in delivery drones from white-box attacks. The experimental results show that the proposed framework is cost effective in terms of resource usage and thus is suitable even for resource-limited UAV.
Valuing mineral stocks and depletion in green national income accounts

This paper investigates the theory and practice of adjusting national income and product accounts for the stock and depletion of mineral assets. These green income adjustments can have a significant impact on the accounts of mineral-based developing economies and the macroeconomic policies that might be derived from these accounts. We propose that the popular methods used to adjust the accounts for the impacts of mineral assets and depletion are upwardly biased, and we present alternative stock valuation and depletion formulas that are empirically supported variants of the formulas currently in use.
Image data mining and classification with DTree ensembles for linguistic tagging

Linguistic tagging of images require proper detection of language concepts from pictures, which is a challenging issue. Preparation of representative samples to demonstrate concepts is the first step; learning parameters from those training samples and setting up a classifier is the next step; proper tag set definition, extraction of relative contextual concepts, filtering and inference drawing for output tag set generation is the last step. A system based on a variant of an ensemble of decision trees classifier is developed which achieves promising results in classifying images targeted for linguistic tagging. A simple tag reconstruction strategy from classification voting information has been proposed.
Objective diagnosis of post-stroke depression using NMR-based plasma metabonomics

Background: Post-stroke depression (PSD) is a frequent and serious complication of stroke. However, the underlying molecular basis of PSD remains largely unknown, and no empirical laboratory tests were available to diagnose this disorder. Materials and methods: A proton nuclear magnetic resonance (1H NMR)-based metabonomic approach was employed to profile plasma samples from 32 PSD, 35 stroke patients and 35 healthy comparison subjects (the training set) in order to identify metabolite biomarkers for PSD. Then, 10 PSD, 11 stroke patients and 11 healthy comparison subjects (test set) were used to validate the diagnostic performance of these biomarkers. Results: The multivariate statistical analysis demonstrated that PSD group was significantly distinguishable from non-PSD groups (non-depression stroke patients and healthy comparison group). Five plasma metabolites (phenylalanine, tyrosine, 1-methylhistidine, 3-methylhistidine and LDL CH3-(CH2)n-) were identified responsible for distinguishing PSD from non-PSD subjects. These metabolites were mainly involved in neurotransmitter metabolism and oxidative stress. The biomarker panel composing of these metabolites was capable of distinguishing test samples with a sensitivity of 100.0% and a specificity of 95.5%. Conclusion: Our findings suggest that plasma disturbances of neurotransmitter levels and oxidative stress were implicated in the onset of PSD; these disturbed metabolites biomarkers facilitate to the development of diagnostic tool for PSD.
Credit Card Fraud Detection using Random Forest Algorithm

This Project is focused on credit card fraud detection in real-world scenarios. Nowadays credit card frauds are drastically increasing in number as compared to earlier times. Criminals are using fake identity and various technologies to trap the users and get the money out of them. Therefore, it is very essential to find a solution to these types of frauds. In this proposed project we designed a model to detect the fraud activity in credit card transactions. This system can provide most of the important features required to detect illegal and illicit transactions. As technology changes constantly, it is becoming difficult to track the behavior and pattern of criminal transactions. To come up with the solution one can make use of technologies with the increase of machine learning, artificial intelligence and other relevant fields of information technology, it becomes feasible to automate this process and to save some of the intensive amounts of labor that is put into detecting credit card fraud. Initially, we will collect the credit card usage data-set by users and classify it as trained and testing dataset using a random forest algorithm and decision trees. Using this feasible algorithm, we can analyze the larger data-set and user provided current data-set. Then augment the accuracy of the result data. Proceeded with the application of processing of some of the attributes provided which can find affected fraud detection in viewing the graphical model of data visualization. The performance of the techniques is gauged based on accuracy, sensitivity, and specificity, precision. The results is indicated concerning the best accuracy for Random Forest are unit 98.6% respectively.
OPRE-2011-09-480 A Fully Sequential Elimination Procedure for Indi ↵ erence-Zone Ranking and Selection with Tight Bounds on Probability of Correct Selection

We consider the indi↵erence-zone (IZ) formulation of the ranking and selection problem with independent normal samples. In this problem, we must use stochastic simulation to select the best among several noisy simulated systems, with a statistical guarantee on solution quality. Existing IZ procedures sample excessively in problems with many alternatives, in part because loose bounds on probability of correct selection lead them to deliver solution quality much higher than requested. Consequently, existing IZ procedures are seldom considered practical for problems with more than a few hundred alternatives. To overcome this, we present a new sequential elimination IZ procedure, called BIZ (Bayes-inspired Indi↵erence Zone), whose lower bound on worst-case probability of correct selection in the preference zone is tight in continuous time, and nearly tight in discrete time. To the author’s knowledge, this is the first sequential elimination procedure with tight bounds on worst-case preference-zone probability of correct selection for more than two alternatives. Theoretical results for the discrete-time case assume that variances are known and have an integer multiple structure, but the BIZ procedure itself can be used when these assumptions are not met. In numerical experiments, the sampling e↵ort used by BIZ is significantly smaller than that of another leading IZ procedure, the KN procedure of Kim and Nelson (2001), especially on the largest problems tested (2 = 16,384 alternatives).
Excitation spectroscopy in multispectral optical fluorescence tomography: methodology, feasibility and computer simulation studies

Molecular probes used for in vivo optical fluorescence tomography (OFT) studies in small animals are typically chosen such that their emission spectra lie in the 680–850 nm wavelength range. This is because tissue attenuation in this spectral band is relatively low, allowing optical photons even from deep sites in tissue to reach the animal surface and consequently be detected by a CCD camera. The wavelength dependence of tissue optical properties within the 680–850 nm band can be exploited for emitted light by measuring fluorescent data via multispectral approaches and incorporating the spectral dependence of these optical properties into the OFT inverse problem—that of reconstructing underlying 3D fluorescent probe distributions from optical data collected on the animal surface. However, in the aforementioned spectral band, due to only small variations in the tissue optical properties, multispectral emission data, though superior for image reconstruction compared to achromatic data, tend to be somewhat redundant. A different spectral approach for OFT is to capitalize on the larger variations in the optical properties of tissue for excitation photons than for the emission photons by using excitation at multiple wavelengths as a means of decoding source depth in tissue. The full potential of spectral approaches in OFT can be realized by a synergistic combination of these two approaches, that is, exciting the underlying fluorescent probe at multiple wavelengths and measuring emission data multispectrally. In this paper, we describe a method that incorporates both excitation and emission spectral information into the OFT inverse problem. We describe a linear algebraic formulation of the multiple wavelength illumination-multispectral detection forward model for OFT and compare it to models that use only excitation at multiple wavelengths or those that use only multispectral detection techniques. This study is carried out in a realistic inhomogeneous mouse atlas using singular value decomposition and analysis of reconstructed spatial resolution versus noise. For simplicity, quantitative results have been shown for one representative fluorescent probe (Alexa 700®) and effects due to tissue autofluorescence have not been taken into account. We also demonstrate the performance of our method for 3D reconstruction of tumors in a simulated mouse model of metastatic human hepatocellular carcinoma.
Tracking of mobile users in a mobile communications system using adaptive convergence parameter

In future telecommunication systems, one of the most important ways to increase system capacity is to use adaptive antennas. They enable reception and transmission using narrow beams which drastically reduce interference for other users. The problem with the conventional adaptive tracking algorithms is that the adaptation performance is dictated by the choice of the convergence parameter or step size. We employ a multi-target tracking method and modify its tracking performance by an adaptive step size. The simulation results show that the proposed adaptive control of the convergence parameter gives fast convergence and small misadjustment errors, which cannot be combined by using fixed step size.
Performance comparison of object tracking methods on video frames using parallel computing

Moving object detection used in wide range of application especially in automated traffic surveillance and management. In defense, these techniques are used in automating threat detection and elimination, target acquisition and also in underwater object tracking. Moving object detection is carried using various methods and an analysis is made to know its precision of detection. The methods on which it is implemented is Kernel Density Estimation, Gaussian Mixture Model, Visual Background Extraction, Multi cue Background Subtraction. The experiment is carried out on different videos and Vibe is found to give better result.
ComE, an Essential Response Regulator, Negatively Regulates the Expression of the Capsular Polysaccharide Locus and Attenuates the Bacterial Virulence in Streptococcus pneumoniae

The capsular polysaccharide (CPS) of Streptococcus pneumoniae is the main virulence factors required for effective colonization and invasive disease. The capacity to regulate CPS production at the transcriptional level is critical for the survival of S. pneumoniae in different host niches, but little is known about the transcription regulators of cps locus. In the present study, we isolated and identified the response regulator ComE, the master competence switch in transformation of S. pneumoniae, as a transcriptional regulator of cps locus by DNA affinity chromatography-pulldown, MALDI-TOF mass spectrometry (MS) and electrophoretic mobility shift assay (EMSA). Our results showed that phosphorylated mimetic of ComE (ComED58E) bound specifically to the cps locus prompter in vitro, and phosphorylated ComE negatively impacted both cps locus transcription and CPS production attenuating the pneumococcal virulence in vivo. Compared with D39-WT strain, D39ΔcomE mutant exhibited much thicker capsule, attenuated nasopharyngeal colonization and enhanced virulence in both pneumonia and bacteremia models of Balb/c mice. Furthermore, it was demonstrated that CSP-ComD/E competence system involved in regulating negatively the CPS production during the progress of transformation in D39. Our CSP1 induction experiment results showed that the expression of ComE in D39-WT strain increased powerfully by 120% after 10 min of CSP1 induction, but the CPS production in D39-WT strain decreased sharply by 67.1% after 15 min of CSP1 induction. However, the CPS production in D39ΔcomE mutant was almost constant during the whole stage of induction. Additionally, we found that extracellular glucose concentration could affect both the expression of ComE and CPS production of D39 in vitro. Taken together, for the first time, we report that ComE, as a transcriptional regulator of cps locus, plays an important role in transcriptional regulation of cps locus and capsular production level.
Gender Diversity and Women in Software Teams: How Do They Affect Community Smells?

As social as software engineers are, there is a known and established gender imbalance in our community structures, regardless of their open-or closed-source nature. To shed light on the actual benefits of achieving such balance, this empirical study looks into the relations between such balance and the occurrence of community smells, that is, sub-optimal circumstances and patterns across the software organizational structure. Examples of community smells are Organizational Silo effects (overly disconnected sub-groups) or Lone Wolves (defiant community members). Results indicate that the presence of women generally reduces the amount of community smells. We conclude that women are instrumental to reducing community smells in software development teams.
Functional dissection of YabA, a negative regulator of DNA replication initiation in Bacillus subtilis

The regulation of initiation of DNA replication is crucial to ensure that the genome is replicated only once per cell cycle. In the Gram-positive bacterium Bacillus subtilis, the function of the YabA protein in initiation control was assigned based on its interaction with the DnaA initiator and the DnaN sliding clamp in the yeast two-hybrid and on the overinitiation phenotype observed in a yabA null strain. However, YabA is unrelated to known regulators of initiation and interacts with several additional proteins that could also be involved directly or not in initiation control. Here, we investigated the specific role of YabA interactions with DnaA and DnaN in initiation control by identifying single amino acid changes in YabA that disrupted solely the interaction with DnaA or DnaN. These disruptive mutations delineated specific interacting surfaces involving a Zn2+-cluster structure in YabA. In B. subtilis, these YabA interaction mutations abolished both initiation control and the formation of YabA foci at the replication factory. Upon coexpression of deficient YabA mutants, mixed oligomers formed foci at the replisome and restored initiation control, indicating that YabA acts within a heterocomplex with DnaA and DnaN. In agreement, purified YabA oligomerized and formed complexes with DnaA and DnaN. These findings underscore the functional association of YabA with the replication machinery, indicating that YabA regulates initiation through coupling with the elongation of replication.
The HALTED ROOT gene encoding the 26S proteasome subunit RPT2a is essential for the maintenance of Arabidopsis meristems

In higher plants, post-embryonic development is dependent on the activity of the root and shoot apical meristem (RAM and SAM). The quiescent center (QC) in the RAM and the organizing center (OC) in the SAM are known to be essential for the maintenance of meristematic activity. To understand the mechanism that maintains post-embryonic meristems, we isolated an Arabidopsis mutant, halted root (hlr). In this mutant, the cellular organization was disrupted in post-embryonic meristems both in the root and in the shoot, and their meristematic activity was reduced or became abnormal. We showed that the mutant RAM lost its QC identity after germination, which was specified during embryogenesis, whereas the identity of differentiated tissues was maintained. In the post-embryonic SAM, the expression pattern of a typical OC marker gene, WUSCHEL, was disturbed in the mutant. These observations indicate that the HLR gene is essential to maintain the cellular organization and normal nature of the RAM and SAM. The HLR gene encodes RPT2a, which is a subunit of the 26S proteasome that degrades key proteins in diverse cellular processes. We showed that the HLR gene was expressed both in the RAM and in the SAM, including in the QC and the OC, respectively, and that the activity of proteasomes were reduced in the mutant. We propose that proteasome-dependent programmed proteolysis is required to maintain the meristem integrity both in the shoot and in the root.