Miracle-dz/UniFAR-FTUs huggingface dataset:
Practical lower and upper bounds for the Shortest Linear Superstring

Given a set P of words, the Shortest Linear Superstring (SLS) problem is an optimisation problem that asks for a superstring of P of minimal length. SLS has applications in data compression, where a superstring is a compact representation of P, and in bioinformatics where it models the first step of genome assembly. Unfortunately SLS is hard to solve (NP-hard) and to closely approximate (MAX-SNP-hard). If numerous polynomial time approximation algorithms have been devised, few articles report on their practical performance. We lack knowledge about how closely an approximate superstring can be from an optimal one in practice. Here, we exhibit a linear time algorithm that reports an upper and a lower bound on the length of an optimal superstring. The upper bound is the length of an approximate superstring. This algorithm can be used to evaluate beforehand whether one can get an approximate superstring whose length is close to the optimum for a given instance. Experimental results suggest that its approximation performance is orders of magnitude better than previously reported practical values. Moreover, the proposed algorithm remainso efficient even on large instances and can serve to explore in practice the approximability of SLS.
The Complex Nature of Hippocampal-Striatal Interactions in Spatial Navigation

Decades of research have established the importance of the hippocampus for episodic and spatial memory. In spatial navigation tasks, the role of the hippocampus has been classically juxtaposed with the role of the dorsal striatum, the latter of which has been characterized as a system important for implementing stimulus-response and action-outcome associations. In many neuroimaging paradigms, this has been explored through contrasting way finding and route-following behavior. The distinction between the contributions of the hippocampus and striatum to spatial navigation has been supported by extensive literature. Convergent research has also underscored the fact that these different memory systems can interact in dynamic ways and contribute to a broad range of navigational scenarios. For example, although familiar routes may often be navigable based on stimulus-response associations, hippocampal episodic memory mechanisms can also contribute to egocentric route-oriented memory, enabling recall of context-dependent sequences of landmarks or the actions to be made at decision points. Additionally, the literature has stressed the importance of subdividing the striatum into functional gradients—with more ventral and medial components being important for the behavioral expression of hippocampal-dependent spatial memories. More research is needed to reveal how networks involving these regions process and respond to dynamic changes in memory and control demands over the course of navigational events. In this Perspective article, we suggest that a critical direction for navigation research is to further characterize how hippocampal and striatal subdivisions interact in different navigational contexts.
Biogeophysical Impacts of Land‐Use Change on Climate Extremes in Low‐Emission Scenarios: Results From HAPPI‐Land

The impacts of land use have been shown to have considerable influence on regional climate. With the recent international commitment to limit global warming to well below 2°C, emission reductions need to be ambitious and could involve major land‐use change (LUC). Land‐based mitigation efforts to curb emissions growth include increasing terrestrial carbon sequestration through reforestation, or the adoption of bioenergy crops. These activities influence local climate through biogeophysical feedbacks, however, it is uncertain how important they are for a 1.5° climate target. This was the motivation for HAPPI‐Land: the half a degree additional warming, prognosis, and projected impacts—land‐use scenario experiment. Using four Earth system models, we present the first multimodel results from HAPPI‐Land and demonstrate the critical role of land use for understanding the characteristics of regional climate extremes in low‐emission scenarios. In particular, our results show that changes in temperature extremes due to LUC are comparable in magnitude to changes arising from half a degree of global warming. We also demonstrate that LUC contributes to more than 20% of the change in temperature extremes for large land areas concentrated over the Northern Hemisphere. However, we also identify sources of uncertainty that influence the multimodel consensus of our results including how LUC is implemented and the corresponding biogeophysical feedbacks that perturb climate. Therefore, our results highlight the urgent need to resolve the challenges in implementing LUC across models to quantify the impacts and consider how LUC contributes to regional changes in extremes associated with sustainable development pathways.
Estimation of genetic parameters for economic traits in Mazandaran native chickens

The aims of this study were to estimate genetic parameters and genetic trends of economic traits in Iranian native chickens. The used data were collected from the Mazandaran native chickens breeding center located in Mazandaran Province during 1988 and 2011 years. The (co)variance components were estimated through an animal model using Gibbs sampling method. The estimated heritability (±SE) for body weight at hatch (BW1), body weight at age of 8 (BW8) and 12 (BW12) weeks, age at sexual maturity (ASM), body weight at sexual maturity (WSM), the number of produced eggs during the first 12 weeks of production (ENs), the first egg weight (FEW), average egg weight at age of 28 (EW28), 30 (EW30), and 32 (EW32) weeks of production, and average egg weight of 28 to 32 weeks of production (EW28-32) were 0.33(±0.02), 0.29(±0.02), 0.28(±0.02), 0.39(±0.02), 0.47(±0.02), 0.24(±0.02), 0.20(±0.02), 0.34(±0.02), 0.39(±0.02), 0.40(±0.01) and 0.51(±0.02), respectively. The average inbreeding coefficient was 2.8%, and ranged from zero to 29.8%. The results revealed significant effect of inbreeding on the BW1, BW8, ASM, ENs and EW28-32. The estimated genetic trends for BW8, BW12, WSM, FEW, EW28, EW30, EW32, ENs and EW28-32 were significant.
TcMYC2a, a Basic Helix–Loop–Helix Transcription Factor, Transduces JA-Signals and Regulates Taxol Biosynthesis in Taxus chinensis

The multitherapeutic taxol, which can be obtained from Taxus spp. , is the most widely used anticancer drug. Taxol biosynthesis is significantly regulated by jasmonate acid (JA), one of the most important endogenous hormones in land plants. Nevertheless, the JA-inducing mechanism remains poorly understood. MYC2 is one of the key regulators of JA signal transfer and the biosynthesis of various secondary metabolites. Here, TcMYC2a was identified to contain a basic helix–loop–helix (bHLH)-leucine zipper domain, a bHLH-MYC_N domain, and a BIF/ACT-like domain. TcMYC2a was also found to bind with TcJAZ3 in yeast, which was a homolog of Arabidopsis JASMONATE ZIM-domain JAZ proteins, indicating that TcMYC2a had a similar function to AtMYC2 of JA signal transduction. TcMYC2a was able to affect the expression of GUS reporter gene by binding with the T/G-box, G-box, and E-box, which were the key cis-elements of TASY and TcERF12/15 promoter. TcMYC2a overexpression also led to significantly increased expression of TASY, tat, dbtnbt, t13h, and t5h genes. Additionally, TcERF15, which played the positive role to regulate tasy gene, was up-regulated by TcMYC2a. All these results revealed that TcMYC2a can regulate taxol biosynthesis either directly or via ERF regulators depending on JA signaling transduction.
User Identification in Pinterest Through the Refinement of Cascade Fusion of Text and Images

User identiﬁcation in social media is of crucial interest for companies and organizations for purposes of marketing, e-commerce, security and demographics. In this paper, we aim to identify users from Pinterest, a platform where users post pins , a combination of an image and a short text. This type of multi-modal content is very common nowadays, since it is a natural way in which users express their interests, emotions and opinions. Thus, the goal is to identify the user that would post a particular pin. For solving the problem, we propose a two-phase classiﬁcation model. In a ﬁrst phase, we train independent classiﬁers from image data, using a deep learning representation, and from text data, using a bag-of-words representation. During testing we apply a cascade fusion of the classiﬁers. In a second phase, we reﬁne the output of the cascade for each test pin by selecting the top most likely users for the test pin and re-weighting their corresponding output in the cascade by their similarity with the test pin. Our experiments show that the problem is very hard because several reasons with the data distribution, but they also show promising results.
Accurate and Diverse Sampling of Sequences Based on a "Best of Many" Sample Objective

For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence. This problem has been formalized as a sequence extrapolation problem, where a number of observations are used to predict the sequence into the future. Real-world scenarios demand a model of uncertainty of such predictions, as predictions become increasingly uncertain - in particular on long time horizons. While impressive results have been shown on point estimates, scenarios that induce multi-modal distributions over future sequences remain challenging. Our work addresses these challenges in a Gaussian Latent Variable model for sequence prediction. Our core contribution is a "Best of Many" sample objective that leads to more accurate and more diverse predictions that better capture the true variations in real-world sequence data. Beyond our analysis of improved model fit, our models also empirically outperform prior work on three diverse tasks ranging from traffic scenes to weather data.
Psychopathological Features of Bipolar Depression: Italian Validation of the Bipolar Depression Rating Scale (I-BDRS)

Background: Aim of the study was the validation of the Bipolar Disorder Rating Scale (BDRS) in an Italian population. Secondary aim was the evaluation of differences between unipolar and bipolar depression and between bipolar I and II depressed patients. Method: 125 Bipolar Disorder and 60 Major Depressive Disorder patients were administered an Italian translation of the BDRS (I-BDRS), Hamilton Depression Rating Scale (HDRS), Montgomery-Asberg Depression Rating Scale (MADRS), Young Mania Rating Scale (YMRS) and Temperament and Character Inventory-Revised (TCI-R). Results: I-BDRS showed considerable validity and reliability. Factor analysis found 3 subscales, two linked to depressive symptoms and one to mixed symptoms. Measures concerning depression (MADRS and HAM-D) were positively related to the I-BDRS's subscales, but mostly to the two subscales measuring depression. In mixed symptoms, the mean of the bipolar group was significantly higher than the unipolar group suggesting that the BDRS was able to distinguish between unipolar and bipolar depressed patients. Conclusion: I-BDRS is a valid scale for the measurement of depression in BD patients, with a notable internal consistency (Cronbach's α 0.82), a significant consistency between items/total (Cronbach's α from 0.80 to 0.82) and positive correlation with other scales (MADRS r = 0.67, p < 0.001; HDRS r = 0.81, p < 0.001; YMRS r = 0.46 p < 0.0001). The mixed state sub-scale shows usefulness in differentiating bipolar from unipolar patients. I-BDRS could be a sensitive tool, both in pure depression and in mixed states, and could be used in the everyday screening and treatment of Bipolar Disorder.
Active oxygen involvement in developmental processes in Populus

In plants, oxidative stress is result of disruption of the cellular redox metabolism and is caused by a variety of stress conditions (abiotic and/or biotic). This leads to the induction of several mechanisms that protect against disruption of the redox balance, as well as mechanisms to assist in recovery from toxicity/damage caused by increased cellular levels of reactive oxygen species (ROS). The superoxide dismutase enzymes (SODs) are key component of the reactive oxygen species gene network and represent the first line of defense against ROS, by converting superoxide radicals (O2−) to hydrogen peroxide (H2O2) and water (H2O). Therefore, SODs play an important role in protection against oxidative stress in all aerobic organisms. In this thesis, I describe the characterization of an SOD isoform from Populus, hipI-SOD, which has a high iso-electric point. The global response to oxidative stress is also discussed. Different forms of hipI-SOD transcripts were found in vascular tissue, one of which was produced by alternative splicing. HipI-SOD proteins were found to be mainly localized extracellularly, in the primary and secondary cell walls of vascular tissues. These results together with analysis of transgenic Populus trees with suppressed expression of hipI-SOD strongly indicate roles for hipI-SOD in regulating ROS levels in vascular tissue. ROS are important regulators of plant stress responses. Nevertheless, oxidative stress often affects plants growth and development. In order to understand the basis of oxidative stress tolerance, the diversity of stress responses needs to be investigated. To achieve this we first developed an O2PLS-based multivariate methodology for the integration of multiple datasets originating from three different platforms (transcriptomics, proteomics and metabolomics). Subsequently this data integration method was utilized for a comprehensive study of the overall responses to oxidative stress in Populus. The findings may facilitate the development of stress-tolerant plants with improved survival rates and yields under stressed conditions.
Nutritional Status and Psychological Impairment in Rural Adolescent Girls: Pilot Data From “KOKAN” Region of Western India

Background: Adolescence is a period during which psychological foundations are laid down as well as consolidated. Not much information is available on rural Indian adolescent girls and their psychological health. Methods: We did a pilot survey of psychological health of 80 adolescent girls residing at KOKAN region of western India. Psychological health was evaluated using Youth Paediatric Symptom Checklist (Y-PSC) consisting of 35 items with maximum score of 70. Girls with a score >30 were classified as psychologically impaired. In addition we also collected random blood sample and measured the micronutrients. Macronutrient intake was estimated by 24 h recall. Results: The mean age of the girls was 14 years with a standard deviation of 1.5. In all 35/76 (46.1%) could be classified as psychologically impaired. There was a high prevalence of micronutrient deficiencies with varying degrees. More than 65% were deficient in calcium, zinc and folic acid. About 22% were anemic and 36% were vitamin B1 deficient. More than 75% had a low recommended dietary allowance (RDA) of macronutrients. Those with poor serum calcium concentration had higher psychological score (p < 0.05). Fat and calcium intakes were inversely associated with psychological score (p < 0.05 and p < 0.001 respectively). Odds ratios for psychological impairment were significant for those with low calcium levels [1.47 (95% CI 1.21, 4.31)], and for those with low calcium intake 1.43 (1.08, 3.19) and low iron intake 3.04 (1.02, 9.26). Conclusion: Our pilot data has shown the urgent need to improve the nutrition of adolescent girls, which could improve their psychological health.
Supervised extractive summarisation of news events

This thesis investigates whether the summarisation of news-worthy events can be improved by using evidence about entities (i.e.\ people, places, and organisations) involved in the events. More effective event summaries, that better assist people with their news-based information access requirements, can help to reduce information overload in today's 24-hour news culture. Summaries are based on sentences extracted verbatim from news articles about the events. Within a supervised machine learning framework, we propose a series of entity-focused event summarisation features. Computed over multiple news articles discussing a given event, such entity-focused evidence estimates: the importance of entities within events; the significance of interactions between entities within events; and the topical relevance of entities to events. The statement of this research work is that augmenting supervised summarisation models, which are trained on discriminative multi-document newswire summarisation features, with evidence about the named entities involved in the events, by integrating entity-focused event summarisation features, we will obtain more effective summaries of news-worthy events. The proposed entity-focused event summarisation features are thoroughly evaluated over two multi-document newswire summarisation scenarios. The first scenario is used to evaluate the retrospective event summarisation task, where the goal is to summarise an event to-date, based on a static set of news articles discussing the event. The second scenario is used to evaluate the temporal event summarisation task, where the goal is to summarise the changes in an ongoing event, based on a time-stamped stream of news articles discussing the event. The contributions of this thesis are two-fold. First, this thesis investigates the utility of entity-focused event evidence for identifying important and salient event summary sentences, and as a means to perform anti-redundancy filtering to control the volume of content emitted as a summary of an evolving event. Second, this thesis also investigates the validity of automatic summarisation evaluation metrics, the effectiveness of standard summarisation baselines, and the effective training of supervised machine learned summarisation models.
The use of infrared thermography for welfare assessment during the application of ear tags to lambs

Abstract. The aim of this study was to determine the effect of metal-tipped, plastic-tipped, and electronic ear tags on the likelihood of infection using infrared thermography and to evaluate the effect on welfare by examining the behavioral responses of the lambs. Ninety lambs with an average age of 6 months were used in this study. Before the application of the ear tags, infrared images of the right and left ears of the lambs were taken with the thermal camera and the infrared temperatures were recorded. Measurements were repeated on days 1, 4, 10, and 30 after the ear tag application. The mean of infrared ear temperatures on days 0, 1, 4, 10, and 30 were found to be 14.60, 23.20, 23.77, 20.36, and 17.61 °C for the metal-tipped ear tag group; 15.69, 21.50, 20.42, 18.00, and 16.39 °C for the plastic-tipped ear tag group; and 15.17, 29.47, 27.49, 25.93, and 20.61 °C for the electronic ear tag group. The infrared temperatures on days 1, 4, and 10 in the control and experimental groups were significantly different from each other (P
Bioinformatic screening for key miRNAs and genes associated with myocardial infarction

Despite significant advances in understanding of the causes of and treatment of myocardial infarction (MI) in recent years, morbidity and mortality is still high. The aim of this study was to identify miRNA and genes potentially associated with MI. mRNA and miRNA expression datasets were downloaded from the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/). Interactions between miRNA and the expression and function of target genes were analyzed, and a protein–protein interaction network was constructed. The diagnostic value of identified miRNA and genes was assessed. Quantitative RT‐PCR was applied to validate the results of the bioinformatics analysis. MiR‐27a, miR‐31*, miR‐1291, miR‐139‐5p, miR‐204, miR‐375, and target genes including CX3CR1, HSPA6, and TPM3 had potential diagnostic value. The genes TFEB, IRS2, GRB2, FASLG, LIMS1, CX3CR1, HSPA6, TPM3, LAT2, CEBPD, AQP9, and MAPKAPK2 were associated with recovery from MI. In conclusion, the identified miRNA and genes might be associated with the pathology of MI.
Extracting news events from microblogs

Abstract Twitter stream has become a large source of information, but the magnitude of tweets posted and the noisy nature of its content makes harvesting of knowledge from Twitter has challenged researchers for long time. Aiming at overcoming some of the main challenges of extracting hidden information from tweet streams, this work proposes a new approach for real-time detection of news events from the Twitter stream. We divide our approach into three steps. The first step is to use a neural network or deep learning to detect news-relevant tweets from the stream. The second step is to apply a novel streaming data clustering algorithm to the detected news tweets to form news events. The third and final step is to rank the detected events based on the size of the event clusters and growth speed of the tweet frequencies. We evaluate the proposed system on a large, publicly available corpus of annotated news events from Twitter. As part of the evaluation, we compare our approach with a related state-of-theart solution. Overall, our experiments and user-based evaluation show that our approach on detecting current (real) news events delivers a state-of-the-art performance.
Awareness and practice concerning osteoporosis and its prevention among a sample of Kurdish women

Background and objective Osteoporosis (OP) is a chronic progressive disease, it is a major public health problem; it is estimated to affect 200 million women worldwide and causes more than 8.9 million fractures annually. This study aimed to assess the knowledge of women regarding risk factors and prevention of osteoporosis, to find out the women's practice for osteoporosis prevention and determine the association between their level of knowledge and practice with their Sociodemographic characteristics. Methods: A cross sectional study was conducted on 220 women at different age group who lived in Erbil cit/ Iraq and attending to Hawler Teaching Hospital for seeking treatment for their complains. Data were collected through interview with them. Frequency, percentage, and chi squire test were used for analyzing and interpreting the data. Results: The majority of samples were between age group 20-34 years. They had fair to poor knowledge regarding risk factors and prevention methods for osteoporosis and mass media was the main source of information regarding that (85.4 %). Almost half of the study sample (42.7%) had poor practice for prevention from osteoporosis. There was statistically significantly association between age, marital status and educational level with the level of knowledge. Educational level of the study sample had significant association with preventive practice of women regarding osteoporosis. Conclusion: The Kurdish women have not enough and correct knowledge regarding osteoporosis and its prevention as well as have poor practice for its prevention. Further studies are needed to find out the barriers of preventive osteoporosis practice among Kurdish women.