Miracle-dz/UniFAR-FTUs huggingface dataset:
Sex Chromosome Evolution: So Many Exceptions to the Rules

Abstract Genomic analysis of many nonmodel species has uncovered an incredible diversity of sex chromosome systems, making it possible to empirically test the rich body of evolutionary theory that describes each stage of sex chromosome evolution. Classic theory predicts that sex chromosomes originate from a pair of homologous autosomes and recombination between them is suppressed via inversions to resolve sexual conflict. The resulting degradation of the Y chromosome gene content creates the need for dosage compensation in the heterogametic sex. Sex chromosome theory also implies a linear process, starting from sex chromosome origin and progressing to heteromorphism. Despite many convergent genomic patterns exhibited by independently evolved sex chromosome systems, and many case studies supporting these theoretical predictions, emerging data provide numerous interesting exceptions to these long-standing theories, and suggest that the remarkable diversity of sex chromosomes is matched by a similar diversity in their evolution. For example, it is clear that sex chromosome pairs are not always derived from homologous autosomes. In addition, both the cause and the mechanism of recombination suppression between sex chromosome pairs remain unclear, and it may be that the spread of recombination suppression is a more gradual process than previously thought. It is also clear that dosage compensation can be achieved in many ways, and displays a range of efficacy in different systems. Finally, the remarkable turnover of sex chromosomes in many systems, as well as variation in the rate of sex chromosome divergence, suggest that assumptions about the inevitable linearity of sex chromosome evolution are not always empirically supported, and the drivers of the birth–death cycle of sex chromosome evolution remain to be elucidated. Here, we concentrate on how the diversity in sex chromosomes across taxa highlights an equal diversity in each stage of sex chromosome evolution.
Mesenchymal Stem Cell Derived Extracellular Vesicles for Tissue Engineering and Regenerative Medicine Applications

Mesenchymal stem cells (MSCs) are being extensively investigated for their potential in tissue engineering and regenerative medicine. However, recent evidence suggests that the beneficial effects of MSCs may be manifest by their released extracellular vesicles (EVs); typically not requiring the administration of MSCs. This evidence, predominantly from pre-clinical in vitro and in vivo studies, suggests that MSC-EVs may exhibit substantial therapeutic properties in many pathophysiological conditions, potentially restoring an extensive range of damaged or diseased tissues and organs. These benefits of MSC EVs are apparently found, regardless of the anatomical or body fluid origin of the MSCs (and include e.g., bone marrow, adipose tissue, umbilical cord, urine, etc). Furthermore, early indications suggest that the favourable effects of MSC-EVs could be further enhanced by modifying the way in which the donor MSCs are cultured (for example, in hypoxic compared to normoxic conditions, in 3D compared to 2D culture formats) and/or if the EVs are subsequently bio-engineered (for example, loaded with specific cargo). So far, few human clinical trials of MSC-EVs have been conducted and questions remain unanswered on whether the heterogeneous population of EVs is beneficial or some specific sub-populations, how best we can culture and scale-up MSC-EV production and isolation for clinical utility, and in what format they should be administered. However, as reviewed here, there is now substantial evidence supporting the use of MSC-EVs in tissue engineering and regenerative medicine and further research to establish how best to exploit this approach for societal and economic benefit is warranted.
Behavioral Activation and Acceptance and Commitment Therapy in the Treatment of Anxiety and Depression in Cancer Survivors: A Randomized Clinical Trial

Behavioural Activation (BA) and Acceptance and Commitment Therapy (ACT) aim to reduce the inflexible avoidance of painful thoughts, feelings and memories and to encourage involvement in relevant activities, objectives which are clearly relevant to the situation of cancer survivors with emotional problems. With a view to evaluating and comparing the efficacy of both therapies, applied on a group basis, a randomized controlled trial was developed. Cancer survivors (age 18–65 years) with anxiety and/or depression were assigned at random to two experimental groups (BA; ACT) and a waiting list control group (WL). Of the 66 cancer survivors randomized to trial (intention-to-treat sample), 46 participants (M = 51.49; SD = 6.88) completed the intervention (BA, n = 17; ACT, n = 12; WL, n = 17) (per-protocol sample). The emotional state, experiential avoidance and behavioural activation of the participants was evaluated in the pre- and post-treatment and in a 3-month follow-up using standardized instruments. Both treatment groups showed statistically significant changes, indicating an improvement in all the result variables in the post-treatment and follow-up as compared to the pre-treatment. BA showed better results than ACT regarding impact on anxiety and activation. This greater efficacy may have been due to factors such as the emphasis placed in BA on behavioural activation and the central role played in it by functional analysis. The key role played by experiential avoidance and behavioral activation in the maintenance and treatment of emotional problems in cancer survivors is discussed. Raw data are available online (http://dx.doi.org/10.17632/m7w688khs8.1)
Understanding the impact of road design characteristic on teen driver’s fatality

Abstract Objective: There is little research simultaneously examining both teen drivers’ characteristics and the driving environment. This study aimed to investigate how traffic engineering factors (road designs, traffic operations, and surface conditions) contribute to risks of teen driver’s fatality. Understanding the role of traffic engineering factors can further improve efforts to reduce teen diving-related fatal crashes. Methods: This study used the Fatality Analysis Reporting System and General Estimate System for years 2014 to 2015. We explored numerous engineering variables: horizontal/vertical alignment, intersections types, interchange areas, traffic ways types, posted speed limit, work zone areas, types of traffic control devices and road surface conditions. Traditional variables considered in this study included sex, age, presence of passengers in relation to age group, time of day, lighting condition, season, vehicle model year, vehicle type, seatbelt use, crash type and movement prior to a crash. We ran cross-tabulations and unadjusted analyses to report unadjusted odds ratios of teen driver’s fatality. A multiple logistic regression model was used to identify which variables related to road design and traffic operations remained significant after being adjusted by other variables. Results: For traditional factors, results corroborated prior findings. All the engineering factors, except work zone area, were significant in the unadjusted analyses; four variables – horizontal alignments, posted speed limit, traffic control device type, and traffic way type – remained statistically significant in the final model. The adjusted odds ratio (AOR) of curved high-speed segment was nearly seven times (6.94; 95% CI = 3.58, 13.46) that of a straight segment with a low posted speed limit (reference group). The AOR of no traffic control was four times (4.17; 95% CI = 2.95, 5.88) that of roads with traffic controls. The AOR of a two-way undivided road was almost three times (2.75, 95% CI = 1.43, 5.29) that of a two-way median barrier road (reference group). Conclusions: This study suggests teen driver education programs consider addressing road design characteristics. For example, incorporating focused lessons on traffic signs and traffic control types may better prepare young people for their driving experiences. Additionally, more supervised driving time is needed on various road types, especially those that present greater risks (e.g., roads with grades/curves, intersections with no traffic signal, undivided roads, and unpaved roads).
Fault diagnostics and reporting in mobile services

With the increase of enterprise network resources, performing management functions has become an extremely complex process. The widespread of di erent equipment and the non standardization of network event reporting has originated the creation of large repositories of data that don not follow any prede ned structure. This article discusses the problems of developing a platform capable of launching management actions based on the analysis of unstructured and heterogeneous network events. It then presents a possible architecture constituted by various distinct nodes diveded into layers, each one implementing a di erent functionality, that have the objective of solving the enunciated problems.
Waiting for the Right One: The Role of Social Proximity and Location in the Decision to Use Cannabis for the First Time

The study examines the social context of a cannabis offer, an outcome rarely examined in research on substance use. Drawing from a survey conducted among 15-year-old students in a mid-sized Canadian city, we examine (a) the differences between three types of users (immediate, late, and nonusers) and (b) the factors associated with accepting a cannabis offer more quickly. The findings show that 40% of the sample received an offer, that 25% of those who accept an offer do so on the first occasion, and that among the others, it takes up to seven offers before accepting. The social context of the offer distinguishes between the types of users, and offers are accepted more quickly when adolescents are first offered by a close social contact, and when the offer occurs in familiar settings. The study also identifies a type of nonuser, those who are exposed to drugs but decide not to participate.
Subjective Uncertainty over Behavior Strategies: A Correction

In order to model the subjective uncertainty of a player over the behavior strategies of an opponent, one must consider the player's beliefs about the opponent's play at information sets that the player thinks have probability zero. This corregendum uses â€œtremblesâ€ to provide a definition of the convex hull of a set of behavior strategies. This corrects a definition we gave in [E. Dekel, D. Fudenberg, and D. K. Levine, 1999, J. Econ. Theory 89, 165â€“185], which led to two of the solution concepts we defined there not having the properties we intended.
University of Birmingham Parameterized approximation algorithms for bidirected steiner network problems

The Directed Steiner Network (DSN) problem takes as input a directed edge-weighted graph G = ( V, E ) and a set D ⊆ V × V of k demand pairs. The aim is to compute the cheapest network N ⊆ G for which there is an s → t path for each ( s, t ) ∈ D . It is known that this problem is notoriously hard as there is no k 1 / 4 − o (1) -approximation algorithm under Gap-ETH, even when parameterizing the runtime by k [Dinur & Manurangsi, ITCS 2018]. In light of this, we systematically study several special cases of DSN and determine their parameterized approximability for the parameter k . For the bi-DSN Planar problem, the aim is to compute a planar optimum solution N ⊆ G in a bidirected graph G , i.e. for every edge uv of G the reverse edge vu exists and has the same weight. This problem is a generalization of several well-studied special cases. Our main result is that this problem admits a parameterized approximation scheme (PAS) for k . We also prove that our result is tight in the sense that (a) the runtime of our PAS cannot be signiﬁcantly improved, and (b) it is unlikely that a PAS exists for any generalization of bi-DSN Planar , unless FPT=W[1]. Additionally we study several generalizations of bi-DSN Planar and obtain upper and lower bounds on obtainable runtimes parameterized by k . One important special case of DSN is the Strongly Connected Steiner Subgraph (SCSS) problem, for which the solution network N ⊆ G needs to strongly connect a given set of k terminals. It has been observed before that for SCSS a parameterized 2-approximation exists when parameterized by k [Chitnis et al., IPEC 2013]. We show a tight inapproximability result: under Gap-ETH there is no (2 − ε )-approximation algorithm parameterized by k (for any (cid:15) > 0). To the best of our knowledge, this is the ﬁrst example of a W[1]-hard problem admitting a non-trivial parameterized approximation factor which is also known to be tight! Additionally we show that when restricting the input of SCSS to bidirected graphs, the problem remains NP-hard but becomes FPT for k .
Antioxidant and Cytotoxic Activities of Sterculia setigera Del

Antioxidant evaluation of Sterculia setigera plant, used in Sudan traditional medicine, was carried out using 2,2-Di(4-tert-octylphenyl)-1-picrylhydrazyl (DPPH) assay on both ethanolic and petroleum ether extracts of seed, fruits, leaves, stem bark, and roots in comparison to propyl gallate as standard level. The ethanolic extract of the fruits showed the highest scavenging activity 91.81% ±0.02 followed by ethanolic extract of stem bark 86% ±0.09 then ethanolic extract of leaves 85%±0.03 at 500 μg mL.While petroleum ether extracts of seeds, fruits and leaves exhibit low scavenging activity. In the ethanolic extract of the leaves, stem bark, and fruits, the results of the DPPH scavenging activity indicate a concentration dependent antioxidant activity at 500, 250, 125, 62.50, and 31.25 μgml with those of the standard propyl gallate. The tested extracts reduced DPPH free radical in a concentration dependent manner. They showed maximum activities at highest concentrations. The cytotoxic effect of ethanolic extracts was determined against African green monkey kidney cells (Vero cells). The extracts were non-toxic to Vero cells.
News Recommendation

News recommendation systems allow selecting, from an immense oﬀer, only the news the users are interest on. Nowadays state-of-art distinguishes three types of recommendation systems: systems based on content, collaborative systems and hybrid systems. The Weighted Slope (WSO) is an algorithm that follows the collaborative approach that has been used in several domains. This article proposes an extension of the WSO that will improve the algorithm performance, in order to recommend news, through the introduction of new features, related to the similarity and to the news source. The general performances of both, proposed and WSO algorithms, were evaluated. Other from that we have also analyzed the performances on critical cases like new users, new news and multimedia formatted news. Every experiment reveals that the proposed algorithm allows improving the performance of original WSO. All tests were performed on a collection of news developed on the current project.
MobUser: A new user-centric publish-subscribe service for mobile devices

—Mobile devices with wireless communication capabilities are a constant in our daily lives. This offers the possibility for such devices to exchange information for their users to assist in their daily lives. Consider for instance the exchange of information about points of interest in the locations that the user usually frequents. One should expect those devices to support such an application in a decentralized fashion, through pairwise interactions, without the interaction of the user, and avoiding to disclose information that might be sensitive to a central entity. Current solutions are not suitable for supporting such applications since most rely on some kind of centralized architecture (either brokers or rendezvous nodes), other solutions that don’t rely on centralized solutions present a high overhead. To address this challenge, in this paper we propose MobUser, a novel topic-based publish-subscribe service specially tailored for supporting location-aware operation for mobile devices in a decentralized fashion. Extensive experimental work shows that MobUser offers not only better performance but also superior delivery rates when compared with a state-of-the-art mobile publish subscribe solution and a state-of-the-art gossip solution. We also show through a prototype implementation deployed on Android 4.0 devices, that the overhead and energy consumption of MobUser is acceptably low.
Can machine learning automatically choose your best model checking strategy?

State space methods provide an automated way of verifying models. However, these methods suﬀer from the state space explosion problem. Many methods have been developed to cope with this problem. As a result, a user has many diﬀerent strategies to apply state space methods to models. Because there is such a large amount of diﬀerent strategies, it is hard for a user to select an appropriate strategy. If a bad strategy is selected, the model may be unsolvable, or waste resources. Additionally, the necessary intervention of a user makes the process of validation less automated. Therefore, it would be useful if the model checker itself is able to select an appropriate strategy. This would make the process fully automated and ensure that no resources are wasted. To allow the model checker to predict a suitable strategy, it needs to use information present in the model itself. This research investigates to what extent the characteristics of Petri Net models can be used to predict an appropriate strategy. The performance of 532 diﬀerent PNML models was determined using LTSMin for 20 selected strategies. This data was used to create a regressor for every strategy, which predicts the expected runtime when that strategy is applied to a given model. These regressors were then combined into a single classiﬁer which could predict an appropriate strategy. The classiﬁer was compared to the best single strategy, and was shown to predict a strategy which outperforms this ﬁxed strategy in %17 of the predictions, and results in an average time loss of 12.95 seconds.
Process Analysis in Issue Tracking Environment

Issue tracking systems are well-known for the recording, tracking, solving and archiving the issues. Due to its nature, they serve as a link between the stakeholders in the supported process. Traditionally, the researchers have been focusing on the system’s operational level, i.e. on the understanding of how they are being used in practice. Thus, the information intrinsic in the process was forgotten. This paper proposes the uncovering of the stored processes in the issue tracking environment with process mining techniques to address the lack of awareness of the stored information from process perspective. Design Science Research Methodology was used to conduct the present work. In terms of proposed solution, three applications were performed on one of the most used issue trackers and these were evaluated according to a holistic view of criteria and standard methods. It was composed by logical reasoning and analysis and unstructured interviews.
Designing Heavy-Hitter Detection Algorithms for Programmable Switches

Programmable network switches promise flexibility and high throughput, enabling applications such as load balancing and traffic engineering. Network measurement is a fundamental building block for such applications, including tasks such as the identification of heavy hitters (largest flows) or the detection of traffic changes. However, high-throughput packet processing architectures place certain limitations on the programming model, such as restricted branching, limited capability for memory access, and a limited number of processing stages. These limitations restrict the types of measurement algorithms that can run on programmable switches. In this paper, we focus on the Reconfigurable Match Tables (RMT) programmable high-throughput switch architecture, and carefully examine its constraints on designing measurement algorithms. We demonstrate our findings while solving the heavy hitter problem. We introduce PRECISION, an algorithm that uses Partial Recirculation to find top flows on a programmable switch. By recirculating a small fraction of packets, PRECISION simplifies the access to stateful memory to conform with RMT limitations and achieves higher accuracy than previous heavy hitter detection algorithms that avoid recirculation. We also evaluate each of the adaptations made by PRECISION and analyze its effect on the measurement accuracy. Finally, we suggest two algorithms for the hierarchical heavy hitters detection problem in which the goal is identifying the subnets that send excessive traffic and are potentially malicious. To the best of our knowledge, our work is the first to do so on RMT switches.
Differential Equations as a Model Prior for Deep Learning and its Applications in Robotics

For many decades, much of the scientific knowledge of physics and engineering has been expressed via differential equations. These differential equations describe the underlying phenomena and the relations between different interpretable quantities. Therefore, differential equations are a promising approach to incorporate prior knowledge in machine learning models to obtain robust and interpretable models. Especially, deep networks and differential equations fit naturally as deep networks are differentiable and enable the computation of the partial derivatives in closed form at machine precision (Raissi & Karniadakis, 2018). Therefore, combining deep networks and differential equations is a promising approach to constrain deep networks to learn meaningful representations.
Novel diagnostics of respiratory infection in the intensive care unit

Pneumonia is one the leading causes of a critical care admission and is associated with substantial mortality and morbidity. Despite this, the diagnosis of pneumonia and identifying the causative pathogen is not always straight forward, particularly in the immunocompromised or those already receiving antibiotics. Medical belief today encourages the use of broad spectrum antimicrobials in all patients that may have pneumonia. This approach invariably leads to overtreatment of many patients, with consequent side effects. These side effects may lead to an insidious impact on organ function or overgrowth of harmful organisms (e.g., Clostridium difficile ) which in turn can adversely affect outcome, reduce institutional efficiency and increase healthcare costs. At a society level, it is this over-use of broad spectrum antimicrobials that is helping drive the spread of anti-microbial resistance. Clinicians, urgently need reliable diagnostic aids to inform them that their patient does or does not have an infection and to reliably and rapidly identify the pathogen with any associated resistance genes. Only then can we confidently withhold or prescribe the appropriate antimicrobial.
Capturing Unattended Demand In Bike-Sharing Systems

The BSS consists in an platform that allows people to use bicycles as a way of transportation without having to bring their own personal bicycles. The main concept is to pick up a bicycle on one of the many stations spread across the city, and then drop it off on another station for somebody else to use it after [1,2]. Bicycles can be manual or have electric assistance [3]. Paris, London and New York are just a few examples of cities that already deployed an extensive bikesharing station network [4,5].
Use of the foot in 3D interactions with mobile devices

—With this paper, we report a novel wearable interface dedicated to provide new types of 3D interactions with mobile devices. Proposed interface is based on the fact that the foot can be exploited in the interaction with a virtual 3D world. By using several force sensors incorporated in the sole and an accelerometer attached to the shoe; gestures performed with the foot are interpreted in order to let the user interact with a 3D virtual environment. Being located inside a shoe this interface is fully compatible to constraints related to mobile devices. Indeed as a wearable and transparent device it can be carried everywhere and therefore can be exploited everywhere.