Miracle-dz/UniFAR-FTUs huggingface dataset:
Why bother with sex? Answers from experiments with yeast and other organisms

This chapter concentrates on those experiments that employ fungi such as Saccharomyces cerevisiae. S. cerevisiae has the attractive microbial experimental properties mentioned earlier and further is extremely well genetically characterized, and may be genetically manipulated and dissected. These facts mean that S. cerevisiae is often the organism of choice for many researchers interested in population genetics and evolution. The spo11 spo13 system was developed in the S. cerevisiae Y55 background. Starting with a prototrophic haploid which was ho (unable to switch mating type), the URA3 gene was first precisely deleted using PCR-based gene targeting. Competition experiments were thus conducted, under a variety of conditions, to discern the relative fitness of the wild-type and asexual sporulator. Overall the spo11 spo13 system seems ideally suited to applications concerning the maintenance of sex. This system has no effect on mitotic fitness or sporulation rates (at least in the Y55 genetic background) and can hold all factors constant apart from recombination, random assortment, and syngamy. The rapid and consistent increase in fitness in the harsh environment points toward the more efficient incorporation of beneficial mutations as the most likely candidate for sex’s benefit.
Covid-19 dynamics in Albania: first estimates and projections

Albania reported the first confirmed case of COVID-19 on the 9th of March 2020. Given very close travel connections with Italy, the most affected country in Europe from this virus so far, with a fast growing epidemic over the previous month of February, the concern over a major similar outbreak in Albania was significant. Immediately following the first confirmation, country-wide measures were taken to reduce transmission such as university and school closures, closure of bars and restaurants, prohibition of public gatherings, leading to the second-quickest major social distancing measure in any European country after Italy. The parameters governing the epidemic course of COVID-19 in Albania remain unknown, in particular the initial number of imported cases from Italy or elsewhere, but as more data are becoming available, some first estimates and projections can be made. The aim of this report is to develop a mathematical epidemiological framework for data interpretation and comparison, as they accumulate, for exploring epidemiologic and demographic aspects specific to this country, and for making preliminary projections based on the current and growing understanding of COVID-19 in Albania and around the world. Using a classical SEIR epidemiological model, fitted to the initial dynamics of cases up to March 18, I show how the estimate for initial number of exposed individuals varies between 65 and 166, depending on values of symptomatic transmission (50-60%)and incubation time (6-14) days. Quantitatively, these factors together determine the future course of number of cases, and the relative effectiveness of control efforts. In model projections, the peak of the outbreak in Albania is expected towards the end of March or first week of April, with as of yet, large uncertainty with respect to its magnitude. The results highlight that maintaining transmission-reducing measures is extremely critical to control.
Molecular epidemiology of dengue viruses from complete genome sequences

The availability of the complete genetic blueprint of the dengue virus is essential in molecular epidemiological studies to uncover the role of the virus in dengue pathogenesis. During the course of this project, over two hundred complete genomes of the dengue virus were generated from clinical samples collected in three dengue-endemic Southeast Asian countries. In addition, a bioinformatics platform integrating a sequence database, sequence retrieval tools, sequence annotation data and a variety of analysis tools was developed for easy management, manipulation and analysis of dengue virus sequence data. Whereas previous studies have mostly focused on epidemiological events in the Americas and Thailand, sequence data recovered from dengue epidemics in Indonesia, Malaysia and Singapore in this study have uncovered some of the dengue virus diversity circulating in the region. The three countries appear to share similar pool of dengue viruses, with some viral lineages in sustained circulation since at least the 1970s. Sequencing of historical virus isolates prior to the 2004/2005 epidemics in Indonesia and Singapore revealed that adaptive viral evolution played little or no role in triggering those epidemics. Lastly, a method that utilised all available sequence data from Malaysia was devised to reconstruct the history of dengue virus in that country since the 1960s.
Probabilistic principal component and linear discriminant analysis of interaction of 1064 nm CW laser with ICP plasma: Anti-Stokes cooling due to analogue black hole.

Pattern recognition and machine learning techniques are known to play an emerging role for studying underlying physics of light-matter interaction. While on the subject, photon condensation and Anti-Stokes cooling remain compelling phenomena of laser-underdense plasma interaction. In this work, the interaction of a 1064 nm continuum-wave laser with inductively-coupled plasma (ICP) of Mercury(Hg) has been studied by probabilistic pattern recognition over observed time resolved spectral data.3D vector fields obtained by the probabilistic pattern recognition over spectral database proficiently illuminates the analogue black hole (ABH) and its sonic horizon in which homoclinic orbit Whistler waves are exited. Vector spectra show the collective behavior of condensation and anti-Stokes cooling is due to the electronic transitions of Hg1 trapped by the phonon sink of ABH of laser. Modeling of subsonic phonon vector spectrum obtained by the probabilistic linear discriminant analysis(PLDA) estimates the 0.70 nanoKelvin of temperature of the sink region of ABH.
Real-Time Task Offloading for Large-Scale Mobile Edge Computing

Mobile-edge computing (MEC) is a promising technology to support computation-intensive and delay-sensitive applications at smart devices by offloading their local tasks to the network edge. In this paper, we propose a novel index based real-time task offloading policy for an asynchronous large-scale MEC system. We first formulate the policy design as a restless multi-armed bandit (RMAB) to capture the stochasticity and criticality in tasks. Based on the Whittle index theory, we then rigorously establish the indexability of our RMAB and derive a closed-form solution, making it scalable to the number of users and extremely simple to implement in practice. Simulation results show that the propose policy can achieve a significant performance improvement in term of the accumulative reward and completion ratio, compared with some existing policies.
Unsupervised Content-Preserved Adaptation Network for Classification of Pulmonary Textures from Different CT Scanners

Deep network based methods have been proposed for accurate classification of pulmonary textures on CT images. However, such methods well-trained on CT data from one scanner cannot perform well when they are directly applied to the data from other scanners. This domain shift problem is caused by different physical components and scanning protocols of different CT scanners. In this paper, we propose an unsupervised content-preserved adaptation network to address this problem. Our method can make a previously well-trained deep network to be adapted for the data of a new CT scanner and does not require the laboring annotation to delineate pulmonary texture regions on the new CT data. Extensive evaluations have been carried on images collected from GE and Toshiba CT scanners and show that the proposed method can alleviate the performance degradation problem of classifying pulmonary textures from different CT scanners.
How Attentional Guidance and Response Selection Boost Contextual Learning: Evidence from Eye Movement

The contextual cueing effect (CCE) refers to the learned association between predictive configuration and target location, speeding up response times for targets. Previous studies have examined the underlying processes (initial perceptual process, attentional guidance, and response selection) of CCE but have not reached a general consensus on their contributions to CCE. In the present study, we used eye tracking to address this question by analyzing the oculomotor correlates of context-guided learning in visual search and eliminating indefinite response factors during response priming. The results show that both attentional guidance and response selection contribute to contextual learning.
Hybrid NOMA and ZF Pre-Coding Transmission for Multi-Cell VLC Networks

Though visible-light communication (VLC) channels are contained by opaque boundaries, they present unique challenges in the development of multi-user/multi-cell scenarios. In this paper, two hybrid transmission schemes are proposed for managing multiple users in multi-cell VLC networks. The proposed schemes are based on using non-orthogonal multiple access (NOMA) in the network access points (APs), while applying zero-forcing (ZF) pre-coding to the cell edge users’ signals, which are cooperatively broadcast from the APs. The proposed approach allows a reduction of the inter-cell interference affecting the cell-edge users thanks to ZF pre-coding, while dealing with inter-user interference for cell-center users via NOMA signaling. Considering different transmission scenarios, we show the improvement in the network total achievable data rate as well as fairness, as compared to conventional NOMA. For example, for a typical scenario considered, an improvement of up to 39% in total achievable rate and up to 112% in the network fairness is achieved. The proposed approach also presents a clear advantage over the conventional ZF pre-coding, for which the maximum number of users is constrained to the number of APs.
Quantifying bioaerosol concentrations in dust clouds through online UV-LIF and mass spectrometry measurements at the Cape Verde Atmospheric Observatory

Abstract. Observations of the long-range transport of biological particles in the tropics via dust vectors are now seen as fundamental to the understanding of many global atmosphere–ocean biogeochemical cycles, changes in air quality, human health, ecosystem impacts, and climate. However, there is a lack of long-term measurements quantifying their presence in such conditions. Here, we present annual observations of bioaerosol concentrations based on online ultraviolet laser-induced fluorescence (UV-LIF) spectrometry from the World Meteorological Organization – Global Atmospheric Watch (WMO-GAW) Cape Verde Atmospheric Observatory on São Vicente. We observe the expected strong seasonal changes in absolute concentrations of bioaerosols with significant enhancements during winter due to the strong island inflow of air mass, originating from the African continent. Monthly median bioaerosol concentrations as high as 45 L−1 were found with 95th percentile values exceeding 130 L−1 during strong dust events. However, in contrast, the relative fraction of bioaerosol numbers compared to total dust number concentration shows little seasonal variation. Mean bioaerosol contributions accounted for 0.4 ± 0.2 % of total coarse aerosol concentrations, only rarely exceeding 1 % during particularly strong events under appropriate conditions. Although enhancements in the median bioaerosol fraction do occur in winter, they also occur at other times of the year, likely due to the enhanced Aeolian activity driving dust events at this time from different sources. We hypothesise that this indicates the relative contribution of bioaerosol material in dust transported across the tropical Atlantic throughout the year is relatively uniform, comprised mainly of mixtures of dust and bacteria and/or bacterial fragments. We argue that this hypothesis is supported from analysis of measurements also at Cabo Verde just prior to the long-term monitoring experiment where UV-LIF single particle measurements were compared with laser ablation aerosol particle time-of-flight mass spectrometer (LAAP-ToF) measurements. These clearly show a very high correlation between particles with mixed biosilicate mass spectral signatures and UV-LIF biofluorescent signatures suggesting the bioaerosol concentrations are dominated by these mixtures. These observations should assist with constraining bioaerosol concentrations for tropical global climate model (GCM) simulations. Note that here we use the term “bioaerosol” to include mixtures of dust and bacterial material.
Teachers’ Job Satisfaction in Developing Countries: A Literature Review

This study determines, explores and examines the factors affecting the job satisfaction of teachers and teacher educators in developing countries of the world. The literature review was employed to collect and select papers for this study. The quantitative, qualitative and mixed research papers were included in this study. Most of the factors of job satisfaction of teachers were found similar such as demographic and socio-economic factors, promotional opportunities, fair remuneration, good working environment, community support, colleagues and friendships. Management and administration, mistrust, non-transparent system, work life imbalance, ineffective teaching-learning environment, unavailability of resources and interference of political parties and pressure groups were found the main factors of job dissatisfaction.
Disentangled Multidimensional Metric Learning for Music Similarity

Music similarity search is useful for a variety of creative tasks such as replacing one music recording with another recording with a similar "feel", a common task in video editing. For this task, it is typically necessary to define a similarity metric to compare one recording to another. Music similarity, however, is hard to define and depends on multiple simultaneous notions of similarity (i.e. genre, mood, instrument, tempo). While prior work ignore this issue, we embrace this idea and introduce the concept of multidimensional similarity and unify both global and specialized similarity metrics into a single, semantically disentangled multidimensional similarity metric. To do so, we adapt a variant of deep metric learning called conditional similarity networks to the audio domain and extend it using track-based information to control the specificity of our model. We evaluate our method and show that our single, multidimensional model outperforms both specialized similarity spaces and alternative baselines. We also run a user-study and show that our approach is favored by human annotators as well.
Delayed Presentation of Rectal Trauma - Is Colostomy Necessary?

Background : Presentation of rectal injuries in the civilian setting is often delayed due to patient denial or assault and requires a high index of suspicion for diagnosis. The standard of care in dealing with such injuries has evolved from mandatory fecal diversion to emphasis on avoidance of colostomy whenever possible. Case Report : A 17 year old male presented to the emergency department with complaints of severe lower abdominal pain, high grade fever and bloody stool following a fall at swimming facility. CT scan abdomen and pelvis confirmed presence of a well organized hematoma in right side of vesicorectal pouch with no active leak. Proctoscopy under anesthesia confirmed rectal perforation on the anterior wall on the right side and a small ulcer after blood clots were evacuated. This was sutured in the same setting and patient was kept on oral liquids and responded well to treatment with follow up ultrasound scans confirming reduction in hematoma size. He was discharged on 7 th day and remained healthy on immediate follow up. Conclusion : Primary repair of low rectal perforation during proctoscopy under anesthesia can be safely carried out without colostomy as an alternative in patients presenting with hemodynamic stability and limited sepsis. The optimum approach should be individualized and fecal diversion should be avoided where possible to reduce morbidity.
Immune-checkpoint engagement as predictive biomarkers in clear cell renal cell carcinoma and melanoma

Many cancers are termed immuno-evasive due to expression of immuno-modulatory ligands. Programmed death ligand-1 (PD-L1) and cluster of differentiation 80/86 (CD80/86) interact with their receptors, programmed death receptor-1 (PD-1) and cytotoxic T-lymphocyte associated protein-4 (CTLA-4), on tumour infiltrating leukocytes, thus eliciting immunosuppression. Immunotherapies aimed at blocking these interactions are revolutionising cancer treatments, albeit in an inadequately described patient subset.Our prognostic assay, utilising amplified two-site time-resolved Förster resonance energy transfer (iFRET), quantifies PD-1/ PD-L1 and CTLA-4/ CD80 cell-cell interactions in single cell assays and tumour biopsies. iFRET efficiencies demonstrate, in cell-cell engagement models, that receptor-ligand interactions are significantly lower with anti-PD-1 or anti-CTLA-4 blocking antibodies. In patient samples, iFRET detects immune-cell/tumour-cell interaction variance in different cancers. These results revealed inter-cancer, inter-patient and intra-tumoural heterogeneity of engaged immune-checkpoints, contradicting their ligand expression patterns. Exploiting spatio-temporal interactions of immune-checkpoint proteins defined biomarker functionality for determining whether checkpoint inhibitors are appropriate treatments.Quantitative photophysics exploitation in determining immune-checkpoint engagement, as predictive biomarkers in cancers led to revealing inter-cancer, inter-patient and intra-tumoural heterogeneity of the engaged immune-checkpoints. This receptor-ligand interaction did not reflect simple expression patterns of these immuno-modulatory proteins. Our findings may affect immunotherapies aimed at blocking these intercellular interactions in patients.
Modeling the Impact of Urbanization on Land-Use Change in Bahir Dar City, Ethiopia: An Integrated Cellular Automata–Markov Chain Approach

The fast-paced urbanization of recent decades entails that many regions are facing seemingly uncontrolled land-use changes (LUCs) that go hand in hand with a range of environmental and socio-economic challenges. In this paper, we use an integrated cellular automata–Markov chain (CA–MC) model to analyze and predict the urban expansion of and its impact on LUC in the city of Bahir Dar, Ethiopia. To this end, the research marshals high-resolution Landsat images of 1991, 2002, 2011, and 2018. An analytical hierarchy process (AHP) method is then used to identify the biophysical and socioeconomic factors underlying the expansion in the research area. It is shown that, during the period of study, built-up areas are rapidly expanding in the face of an overall decline of the farmland and vegetation cover. Drawing on a model calibration for 2018, the research predicts the possible geographies of LUC in the Bahir Dar area for 2025, 2034, and 2045. It is predicted that the conversions of other land-use types into built-up areas will persist in the southern, southwestern, and northeastern areas of the sprawling city, which can mainly be traced back to the uneven geographies of road accessibility, proximity to the city center, and slope variables. We reflect on how our findings can be used to facilitate sustainable urban development and land-use policies in the Bahir Dar area.
The Influence of Audit Quality on Earnings Management among Listed Oil and Gas Companies in Nigeria

The primary aim of this study is to evaluate the impact of audit quality on earnings management practices among oil and gas companies listed in the Nigeria Stock Exchange (NSE) for period of 5 years (2014-2018). The focus of the research is on oil and gas companies due to recent allegations of large scale financial misrepresentations associated with the Nigerian oil sector. Similarly, over the past decades, the global markets have witnessed several accounting scandals mainly because of the manipulation of the accounting figures reported in the financial statements. To achieve the objectives of this study, the corporate annual reports of twelve (12) listed oil and gas companies for the period under consideration were used for the study. In testing the research hypotheses, the study adopted the use of both descriptive statistics and Ordinary Least Square (OLS) regression analysis is used to explore the relationship between Audit quality proxies which are Audit firm size, auditor industry specialization and auditor independence and Earnings management in listed companies. The management of earnings is measured using Discretionary Accruals (a modified Jones model) while Eviews 8.1as the tool of data analysis of OLS regression. The results suggest that there is a relationship between audit quality and earnings manipulation; auditors’ independence has a significant positive relationship with earnings management, while the rest of the hypotheses indicate that the other variables remain to have an insignificant relationship with earnings management.
A Hybrid Technique for the Multiple Imputation of Survey Data

Abstract Most of the background variables in MICS (Multiple Indicator Cluster Surveys) are categorical with many categories. Like many other survey data, the MICS 2014 women’s data suffers from a large number of missing values. Additionally, complex dependencies may be existent among a large number of categorical variables in such surveys. The most commonly used parametric multiple imputation (MI) approaches based on log linear models or chained Equations (MICE) become problematic in these situations and often the implemented algorithms fail. On the other hand, nonparametric MI techniques based on Bayesian latent class models worked very well if only categorical variables are considered. This article describes how chained equations MI for continuous variables can be made dependent on categorical variables which have been imputed beforehand by using latent class models. Root mean square errors (RMSEs) and coverage rates of 95% confidence intervals (CI) for generalized linear models (GLM’s) with binary response are estimated in a simulation study and a comparison is made among proposed and various existing MI methods. The proposed method outperforms the MICE algorithms in most of the cases with less computational time. The results obtained by the simulation study are supported by a real data example.