Miracle-dz/UniFAR-FTUs huggingface dataset:
Digging the compromise: investigating the link between limb bone histology and fossoriality in the aardvark (Orycteropus afer)

Bone microstructure has long been known as a powerful tool to investigate lifestyle-related biomechanical constraints, and many studies have focused on identifying such constraints in the limb bones of aquatic or arboreal mammals in recent years. The limb bone microstructure of fossorial mammals, however, has not been extensively described. Furthermore, so far, studies on this subject have always focused on the bone histology of small burrowers, such as subterranean rodents or true moles. Physiological constraints associated with digging, however, are known to be strongly influenced by body size, and larger burrowers are likely to exhibit a histological profile more conspicuously influenced by fossorial activity. Here, we describe for the first time the limb bone histology of the aardvark (Orycteropus afer), the largest extant burrowing mammal. The general pattern is very similar for all six sampled limb bones (i.e., humerus, radius, ulna, femur, tibia, and fibula). Most of the cortex at midshaft is comprised of compacted coarse cancellous bone (CCCB), an endosteal tissue formed in the metaphyses through the compaction of bony trabeculae. Conversely, the periosteal bone is highly resorbed in all sections, and is reduced to a thin outer layer, suggesting a pattern of strong cortical drift. This pattern contrasts with that of most large mammals, in which cortical bone is of mostly periosteal origin, and CCCB, being a very compliant bone tissue type, is usually resorbed or remodeled during ontogeny. The link between histology and muscle attachment sites, as well as the influence of the semi-arid environment and ant-eating habits of the aardvark on its bone microstructure, are discussed. We hypothesize that the unusual histological profile of the aardvark is likely the outcome of physiological constraints due to both extensive digging behavior and strong metabolic restrictions. Adaptations to fossoriality are thus the result of a physiological compromise between limited food availability, an environment with high temperature variability, and the need for biomechanical resistance during digging. These results highlight the difficulties of deciphering all factors potentially involved in bone formation in fossorial mammals. Even though the formation and maintaining of CCCB through ontogeny in the aardvark cannot be unambiguously linked with its fossorial habits, a high amount of CCCB has been observed in the limb bones of other large burrowing mammals. The inclusion of such large burrowers in future histological studies is thus likely to improve our understanding of the functional link between bone growth and fossorial lifestyle in an evolutionary context.
Overexpression of DJ-1 correlates with aggressive clinicopathological characteristics and poor prognosis in malignant tumors: a meta-analysis

Purpose A number of studies have investigated the role of DJ-1 in the development and progression of malignant tumors. This meta-analysis aims to systematically estimate the rela-tionship between the expression level of DJ-1 and the malignant biological behaviors of tumors and to assess the clinical significances of DJ-1 in the prognosis and diagnosis of cancer. Materials and methods We searched PubMed, Web of Science, China National Knowledge Infrastructure and Wanfang databases from inception to December 1, 2017. Pooled odds ratio (OR) and hazard ratio (HR) with their 95% confidence interval and the diagnostic value of DJ-1 were calculated. Results Fourteen eligible studies with a total of 1,947 subjects were enrolled in our meta-analysis. The results showed that DJ-1 was overexpressed in cancer patients compared with noncancer patients (OR = 30.72), and elevated expression of DJ-1 was demonstrated to be closely associated with high tumor-node-metastasis stage (OR = 5.52), poor differentiated degree (OR = 2.46), positive lymph node metastasis (OR = 4.12) and worse overall survival (HR = 2.23). In addition, the combined sensitivity and specificity for DJ-1 to discern malignant tumors were 0.73 and 0.93, respectively. The diagnostic OR was 34.87, and the area under the summary receiver operating characteristic curve was 0.88. Conclusion This meta-analysis demonstrated that DJ-1 was an important biomarker in tumor assessment and prognosis prediction.
Understanding health care avoidance and initial help-seeking behavior in German veterans: a theory of planned behavior

Objective To gain initial insights into salient beliefs of former German soldiers (veterans) about the use of mental health services. Data source Narrative interviews with former German soldiers (veterans) were conducted in 2016. Data collection/extraction method Forty-three interviews were transcribed and analyzed thematically based on the theoretical framework of the theory of planned behavior. From within the behavorial, normative, and control beliefs, the salient beliefs were identified. Principal findings Four groups of salient beliefs were identified, described, and named: “Autarky”, “Ineffectiveness”, “Heteronomy”, and “Incapacity”. Conclusion Interventions and campaigns addressing these four specific groups of beliefs may lead to higher health service use rates. However, as a result of methodological limitations of the study design, the conclusion remains tentative.
Silver-Mediated Double Helix: Structural Parameters for a Robust DNA Building Block

The DNA double helix is a versatile building block used in DNA nanotechnology. To potentiate the discovery of new DNA nanoscale assemblies, recently, silver cations have been introduced to pair DNA strands by base–Ag+–base bonding rather than by Watson–Crick pairing. In this work, we study the classical dynamics of a parallel silver-mediated homobase double helix and compare it to the dynamics of the antiparallel double helix. Our classical simulations show that only the parallel double helix is highly stable through the 100 ns simulation time. A new type of H-bond previously proposed by our collaboration and recently observed in crystal-determined helices drives the physicochemical stabilization. Compared to the natural B-DNA form, the metal-mediated helix has a contracted axial base pair rise and smaller numbers of base pairs per turn. These results open the path for the inclusion of this robust metal-mediated building block into new nanoscale DNA assemblies.
Accessify: An ML Powered Application to Provide Accessible Images on Web Sites

Web Accessibility remains a major concern of the rapidly growing Internet community. Accessing online content is crucial for many people as they engage in a variety of tasks every day. Content can be in any form, such as audio, video, images and text. A major portion of a website's viewport generally contains images, and there are set standards & guidelines for providing information about these images while creating image elements on a webpage. Yet, most websites do not strictly follow accessibility guidelines and, as a result, either a website contains no descriptions of images or the descriptions provided are not sufficient to convey the meaning to visually impaired people who use screen readers for accessing web content. In this paper, we propose a solution that provides alternate image descriptions to all images on a website with help of Machine Learning in a completely unobtrusive environment. The application does not require any setup and can be used on static as well as dynamic websites.
Multi-objective Non-cooperative Game Model for Cost-based Task Scheduling in Computational Grid

Task scheduling is an important and complex problem in computational grid. A computational grid often covers a range of different kinds of nodes, which offers a complex environment. There is a need to develop algorithms that can capture this complexity as while as can be easily implemented and used to solve a wide range of load-balancing scenarios. In this paper, we propose a game theory based algorithm to the grid load balancing problem on the principle of minimizing the cost of the grid. The grid load-balancing problem is treated as a non-cooperative game. The experiment results demonstrate that the game based algorithm has a desirable capability to reduce the cost of the grid.
Two Odorant-Binding Proteins of the Dark Black Chafer (Holotrichia parallela) Display Preferential Binding to Biologically Active Host Plant Volatiles

The dark black chafer (DBC), Holotrichia parallela, is an important pest of multiple crops. Insect host-searching behaviors are regulated by host plant volatiles. Therefore, a better understanding of the mechanism linking the chemosensory system to plant volatiles at the molecular level will benefit DBC control strategies. Based on antenna transcriptome data, two highly expressed antenna-specific odorant-binding proteins (HparOBP20 and 49) were selected to identify novel DBC attractants using reverse chemical ecology methods. We expressed these proteins, mapped their binding specificity, and tested the activity of the plant volatiles in the field. The ligands used in the binding specificity assays included 31 host-plant-associated volatiles and two sex pheromone components. The results showed that (1) HparOBP20 and 49 are involved in odor recognition; (2) these proteins bind attractive plant volatiles strongly and can therefore be employed to develop environmentally friendly DBC management strategies; and (3) the green-leaf volatile (Z)-3-hexenyl acetate shows a high binding affinity to HparOBP20 (Ki = 18.51 μM) and HparOBP49 (Ki = 39.65 μM) and is highly attractive to DBC adults, especially females. In the field test, a (Z)-3-hexenyl acetate trap caught an average of 13 ± 1.202 females per day, which was significantly greater than the corresponding male catch (F2,6 = 74.18, P < 0.0001). (Z)-3-Hexenyl acetate may represent a useful supplement to the known sex pheromone for DBC attraction. In the present study, the binding characteristics of two HparOBPs with host plant volatiles were screened, providing behaviourally active compounds that might be useful for DBC control, based on reverse chemical ecology.
Bench4BL: reproducibility study on the performance of IR-based bug localization

In recent years, the use of Information Retrieval (IR) techniques to automate the localization of buggy files, given a bug report, has shown promising results. The abundance of approaches in the literature, however, contrasts with the reality of IR-based bug localization (IRBL) adoption by developers (or even by the research community to complement other research approaches). Presumably, this situation is due to the lack of comprehensive evaluations for state-of-the-art approaches which offer insights into the actual performance of the techniques. We report on a comprehensive reproduction study of six state-of-the-art IRBL techniques. This study applies not only subjects used in existing studies (old subjects) but also 46 new subjects (61,431 Java files and 9,459 bug reports) to the IRBL techniques. In addition, the study compares two different version matching (between bug reports and source code files) strategies to highlight some observations related to performance deterioration. We also vary test file inclusion to investigate the effectiveness of IRBL techniques on test files, or its noise impact on performance. Finally, we assess potential performance gain if duplicate bug reports are leveraged.
Geographic Variation in Larval Metabolic Rate Between Northern and Southern Populations of the Invasive Gypsy Moth

Abstract Thermal regimes can diverge considerably across the geographic range of a species, and accordingly, populations can vary in their response to changing environmental conditions. Both local adaptation and acclimatization are important mechanisms for ectotherms to maintain homeostasis as environments become thermally stressful, which organisms often experience at their geographic range limits. The spatial spread of the gypsy moth (Lymantria dispar L.) (Lepidoptera: Erebidae) after introduction to North America provides an exemplary system for studying population variation in physiological traits given the gradient of climates encompassed by its current invasive range. This study quantifies differences in resting metabolic rate (RMR) across temperature for four populations of gypsy moth, two from the northern and two from southern regions of their introduced range in North America. Gypsy moth larvae were reared at high and low thermal regimes, and then metabolic activity was monitored at four temperatures using stop-flow respirometry to test for an acclimation response. For all populations, there was a significant increase in RMR as respirometry test temperature increased. Contrary to our expectations, we did not find evidence for metabolic adaptation to colder environments based on our comparisons between northern and southern populations. We also found no evidence for an acclimation response of RMR to rearing temperature for three of the four pairwise comparisons examined. Understanding the thermal sensitivity of metabolic rate in gypsy moth, and understanding the potential for changes in physiology at range extremes, is critical for estimating continued spatial spread of this invasive species both under current and potential future climatic constraints.
Pathological Proteins Are Transported by Extracellular Vesicles of Sporadic Amyotrophic Lateral Sclerosis Patients

Amyotrophic lateral sclerosis (ALS) is a progressive adult-onset neurodegenerative disease, that affects cortical, bulbar and spinal motor neurons, and it is considered a proteinopathy, in which pathological proteins (SOD1, TDP-43, and FUS) may accumulate and interfere with neuronal functions eventually leading to cell death. These proteins can be released from cells and transported in the body fluids by extracellular vesicles (EVs). EVs are spherical vesicles, which are classified mainly in microvesicles (MVs) and exosomes (EXOs) based on their biogenesis, size and surface markers. In this study we characterized MVs and EXOs isolated from plasma of sporadic ALS patients and healthy controls and determined their number, size and SOD1, TDP-43, and FUS protein composition. No variation was found in the number of EVs between ALS patients and controls. However, the mean size both for MVs and for EXOs resulted increased in ALS patients compared to controls. MVs derived from ALS patients were enriched in SOD1, TDP-43, phospho-TDP-43, and FUS proteins compared to CTRLs. SOD1 was generally more concentrated in EXOs than in MVs, while TDP-43 and FUS protein levels were slightly higher in MVs than in EXOs. We demonstrated that MVs and EXOs size were increased in ALS patients compared to controls and that MVs of ALS patients were enriched with toxic proteins compared to CTRLs. EXOs did not show any protein changes. These data may suggest that MVs can transport toxic proteins and might play a role in prion-like propagation of ALS disease.
Kernelized Fast Algorithm for Regularized Hull-Based Face Recognition With Image Sets

Great success in face recognition has been achieved in recent years; however, complex variations and low-resolution images remain a challenge for unconstrained face recognition. Face recognition in video or image sets, which is known as image-set-based face recognition (ISFR), is one feasible solution to address this problem. Regularized nearest points (RNP) is an effective hull-based ISFR method which uses linear space as the input. However, nonlinearity usually exists when the input data contain complex structures, such as illumination and pose variations. Hence, we propose to map the input data to a higher dimensional feature space by using kernel functions, and we develop the kernel extension of the efficient iterative solver to find the regularized nearest points between two sets in higher dimensional feature space. We also exploit this kernel efficient iterative solver to improve the kernel convex hull image-set-based collaborative representation and classification method. The proposed kernelized fast algorithm improves the face recognition ability of RNP and significantly accelerates the kernel version hull-based ISFR methods. Experiments are performed on three benchmark face recognition video data sets. The experimental results illustrate the effectiveness of our proposed methods.
Container‐based cluster orchestration systems: A taxonomy and future directions

Containers, enabling lightweight environment and performance isolation, fast and flexible deployment, and fine‐grained resource sharing, have gained popularity in better application management and deployment in addition to hardware virtualization. They are being widely used by organizations to deploy their increasingly diverse workloads derived from modern‐day applications such as web services, big data, and internet of things in either proprietary clusters or private and public cloud data centers. This has led to the emergence of container orchestration platforms, which are designed to manage the deployment of containerized applications in large‐scale clusters. These systems are capable of running hundreds of thousands of jobs across thousands of machines. To do so efficiently, they must address several important challenges including scalability, fault tolerance and availability, efficient resource utilization, and request throughput maximization among others. This paper studies these management systems and proposes a taxonomy that identifies different mechanisms that can be used to meet the aforementioned challenges. The proposed classification is then applied to various state‐of‐the‐art systems leading to the identification of open research challenges and gaps in the literature intended as future directions for researchers.
Delay Minimization for NOMA-MEC Offloading

This letter considers the minimization of the offloading delay for nonorthogonal multiple access assisted mobile edge computing (NOMA-MEC). By transforming the delay minimization problem into a form of fractional programming, two iterative algorithms based on, respectively, Dinkelbach's method and Newton's method are proposed. The optimality of both methods is proved and their convergence is compared. Furthermore, criteria for choosing between three possible modes, namely orthogonal multiple access, pure NOMA, and hybrid NOMA, for MEC offloading are established.
Optimal Algorithms for Right-Sizing Data Centers

Electricity cost is a dominant and rapidly growing expense in data centers. Unfortunately, much of the consumed energy is wasted because servers are idle for extended periods of time. We study a capacity management problem that dynamically right-sizes a data center, matching the number of active servers with the varying demand for computing capacity. We resort to a data-center optimization problem introduced by Lin, Wierman, Andrew and Thereska~\citeW1a,W1 that, over a time horizon, minimizes a combined objective function consisting of operating cost, modeled by a sequence of convex functions, and server switching cost. All prior work addresses a continuous setting in which the number of active servers, at any time, may take a fractional value. In this paper, we investigate for the first time the discrete data-center optimization problem where the number of active servers, at any time, must be integer valued. Thereby we seek truly feasible solutions. First, we show that the offline problem can be solved in polynomial time. Our algorithm relies on a new, yet intuitive graph theoretic model of the optimization problem and performs binary search in a layered graph. Second, we study the online problem and extend the algorithm \em Lazy Capacity Provisioning (LCP) by Lin et al. \citeW1a,W1 to the discrete setting. We prove that LCP is 3-competitive. Moreover, we show that no deterministic online algorithm can achieve a competitive ratio smaller than~3. Hence, while LCP does not attain an optimal competitiveness in the continuous setting, it does so in the discrete problem examined here. We prove that the lower bound of~3 also holds in a problem variant with more restricted operating cost functions, introduced by Lin et al. \citeW1a. Finally, we address the continuous setting and give a lower bound of~2 on the best competitiveness of online algorithms. This matches an upper bound by Bansal et al. \citeB+. A lower bound of~2 was also recently shown by Antoniadis and Schewior~\citeA2. We develop an independent proof that extends to the scenario with more restricted operating cost.
Joint Localization and Data Gathering Over a Small-World WSN With Optimal Data MULE Allocation

Localization of sensor nodes and efficient data gathering over a wireless sensor network (WSN) is vital in applications such as cyber-physical systems, Internet of Things, and context-aware pervasive systems. In WSNs, sensor nodes transfer the data cooperatively using multiple hops over a network. The large number of hops required for data transmission leads to erroneous distance estimation between node pairs, resulting in a large localization error. In this paper, we utilize a recent development in social networks called small-world characteristics for proposing a novel method of joint localization and data gathering over a WSN. A small-world WSN is developed by introducing data mobile ubiquitous LAN extensions (MULEs) into a conventional WSN. A small-world WSN exhibits low average path length and high average clustering coefficient. Such a small-world WSN when designed with novel routing strategies leads to reduced hop counts in sensor data transmission. Additionally, a method for optimal data MULE allocation is also developed. This method minimizes an objective function, which is a normalized weighted sum of network parameters such as bandwidth requirement and localization error. The optimal data MULE allocation method computes both the optimal number of data MULEs and their placement in the network. On the other hand, the joint localization and data gathering method that utilizes a multidimensional-scaling-based cooperative localization method is also developed for this purpose. Experiments are conducted using simulations and real node deployments over a WSN testbed. The performance of the proposed method is evaluated by conducting exhaustive analysis of power consumption, bandwidth required, localization error, data gathering efficiency, and throughput. The obtained experimental results indicate a significant improvement on several evaluation parameters when compared to results obtained on the conventional WSN. The obtained results also illustrate reasonable improvement when compared to results obtained using conventional small-world characteristic introduction and localization methods. The results are motivating enough for the proposed method to be used in large- and medium-scale network applications.