Miracle-dz/UniFAR-FTUs huggingface dataset:
Intermittent Hypoxic Training at Lactate Threshold Intensity Improves Aiming Performance in Well-Trained Biathletes with Little Change of Cardiovascular Variables

The main objective of this research was to evaluate the efficacy of intermittent hypoxic training (IHT) on aiming performance and aerobic capacity in biathletes. Fourteen male biathletes were randomly divided into a hypoxia group (H) (n = 7), which trained three times per week in a normobaric hypoxic environment (FiO2 = 16.5%, 2000 m a.s.l.) with lactate threshold intensity (LT) determined in hypoxia, and a control group (C) (n = 7), which exercised under normoxic conditions with LT intensity determined in normoxia. The training program included three weekly microcycles, followed by three days of recovery. The main part of the interval workout consisted of four 7 min (1st week), 8 min (2nd week), or 9 min (3rd week) running bouts at treadmill separated by 2 minutes of active recovery. After the warm-up and during the rest between the bouts, the athletes performed aiming to the target in the standing position with a sporting rifle (20 s). The results showed that the IHT caused a significant (p < 0.05) increase in retention time in the target at rest (RT9rest) by 14.4% in hypoxia, whereas RT postincremental test (RT9post) increased by 27.4% in normoxia and 26.7% in hypoxia. No significant changes in this variable were found in group C. Additionally, the capillary oxygen saturation at the end of the maximal effort (SO2capillary max) in hypoxia increased significantly (p < 0.001) by ∼4% after IHT. The maximal workload during the incremental test (WRmax) in normoxia also increased significantly (p < 0.001) by 6.3% after IHT. Furthermore, in absolute and relative values of VO2max in normoxia, there was a propensity (p < 0.07) for increasing this value by 5% in group H. In conclusion, the main findings of this study showed a significant improvement in resting and postexercise aiming performance in normoxia and hypoxia. Furthermore, the results demonstrated beneficial effects of the IHT protocol on aerobic capacity of biathletes.
Universal and distinct features of intra-population heterogeneity between differentiated cells and pluripotent stem cells

Isogenic cells cultured in the same nutrient-rich environment show heterogeneity in their proliferation rate. To understand the differences between fast and slow-proliferating cells and to identify markers for proliferation rate that can be used at the single-cell level, we developed a method to sort cells by their proliferation rate, and performed RNA sequencing (RNA-Seq) on slow, medium and fast proliferating subpopulations of pluripotent mouse embryonic stem cells (mESCs) and immortalized mouse fibroblasts. We identified a core proliferation-correlated transcriptome that is common to both cell types, to yeast, and to cancer cells: fast proliferating cells have higher expression of genes involved in both protein synthesis and protein degradation. In contrast to cells sorted by proliferation rate, RNA-seq on cells sorted by mitochondria membrane potential revealed a highly cell-type specific mitochondria-state related transcriptome. mESCs with hyperpolarized mitochondria are fast proliferating, while the opposite is true for fibroblasts. In addition, cell-to-cell variation in proliferation rate is highly predictive of pluripotency state in mESCs, with cells of more naïve pluripotent character having a slower proliferation rate. Finally, we show that the proliferation signature learned from sorted cells can predict proliferation from scRNAseq data in both mESCs and in the developing nematode. While the majority of the transcriptional-signature associated with cell-to-cell heterogeneity in proliferation rate is conserved from yeast to embryos to differentiated cells to cancer, the metabolic and energetic details of cell growth are highly cell-type specific.
Approche non supervisée de segmentation de bas niveau dans un cadre de surveillance vidéo d'environnements non contrôlés

T his thesis proposes a unified low-level segmentation algorithm adapted to complex real-life scenarios like crowded or highly dynamic environments. Based on the learning of video sequences obtained with a standing non-moving camera, our system is able to correctly label regions in the image belonging to foreground pedestrians or vehicles in an environment where the background is time-varying due to cast shadows, weather elements or illumination conditions. We propose a statistical framework based on Kernel Density Estimators (KDE). K D E are well known for their versatility for background subtraction in complex environments where background statistics at the pixel level cannot be described parametrically. These methods, however, typically require a training sequence free or mostly free of foreground activity in order to get a good initial estimate of the background distribution. To extend the flexibility of these approaches, we exploit the spatial coherence of the scene foreground activity in order to estimate foreground and background priors via the minimization of a global energy function using graph-cuts. To do so, cues such as the scatterdness of the data, the probability associated to modes in the RGB space, the spatial constancy of priors and the relative entropy of regions in the image are embedded in a coherent framework. Cast shadows detected during the background subtraction process induces silhouette distortions and object fusions, hindering the performance of high level algorithms in scene monitoring applications. Two strategies are proposed in order to remove cast shadows from the foreground region of interest. The first method uses the learning capacity of the Gaussians Mixture Model algorithm (GMM) to model the cast shadow behaviour on background surfaces. The second method is grounded on cast shadow physical properties as well as a spatial gradient measure, both integrated in a non-parametric statistical framework. With this approach, we model the posterior distribution of lighting attenuation under cast shadows and foreground objects, which allows differentiation of foreground and cast shadow values with similar chromaticity. Results show that our approach performs well in a variety of complex scenarios.
Characterization of Organic Compounds Using Moma Flight-Like Instrumentation in Preparation of the Upcoming Exomars Rover Mission

After four decades of Mars exploration, one of the driving questions, if Mars was ever inhabited by some form of life, still remains unanswered. Recently, traces of chlorinated and sulfur-bearing organic compounds were detected in martian soils by the Sample Analysis at Mars (SAM) instrument onboard the Curiosity rover (Freissinet et al., 2015; Eigenbrode et al., 2018). However, it remains unclear if these organic traces can be associated with (microbial) life and further investigations of the martian surface and near subsurface are needed. The Mars Organic Molecule Analyzer (MOMA) instrument onboard ESA’s ExoMars rover (launch in 2020) will be searching for potential organic biosignatures in samples from up to 2 m depth (Goesmann et al. 2017; Vago et al., 2017). Analog experiments are an essential prerequisite to appraise analytical limits and support data interpretation for this upcoming mission.
Genetic factor for twisting legume pods identified by fine-mapping of shattering-related traits in azuki bean and yard-long bean

Legumes have evolved a unique manner of seed dispersal in that the seed pods explosively split open with helical tension generated by sclerenchyma on the endocarp. During domestication, azuki bean (Vigna angularis) and yard-long bean (Vigna unguiculata cv-gr. Sesquipedalis) have reduced or lost the sclerenchyma and lost the shattering behavior of seed pods. Here we performed fine-mapping with back-crossed populations and narrowed the candidate genomic region down to 4 kbp in azuki bean and 13 kbp in yard-long bean. Among genes located in these regions, we found MYB26 genes encoded truncated proteins in both the domesticated species. We also found in azuki bean and other legumes that MYB26 is duplicated and only the duplicated copy is expressed in seed pods. Interestingly, in Arabidopsis MYB26 is single copy and is specifically expressed in anther to initiate secondary wall thickening that is required for anther dehiscence. These facts indicated that, in legumes, MYB26 has been duplicated and acquired a new role in development of pod sclerenchyma. However, pod shattering is unfavorable phenotype for harvesting and thus has been selected against by human.
Sentiment Classification with Gated CNN and Spatial Pyramid Pooling

Recurrent neural networks(RNNs) have been applied to sentiment classification but RNNs is usually heavier than convolutional neural networks (CNNs), turning more interest in the application of CNNs to language tasks. In this paper we propose a new method to apply gated CNN (gCNN) with Maxpooling via Spatial Pyramid Pooling (SPP) to sentiment classification of customer reviews. Our proposal consists of two parts. One is the application of gCNN to sentiment classification, instead of constructing a language model. The other is to incorporate SPP as pooling instead of a simple Maxpooling, which is originally proposed for image processing. Two experiments were conducted to evaluate our proposal. The result shows that gCNN works better than Support Vector Machines, which suggests that advanced CNNs can be effectively employed for various language processing tasks. On the other hand, SPP did not work well for our taks and the simple Maxpooling to the whole convolutional output performed the best. This suggests that Maxpooling works fine also with document classification tasks while the simple application of SPP for document classification is not so appropriate.
A New Rate-Complexity-Distortion Model for Fast Motion Estimation Algorithm in HEVC

In the high efficiency video coding (HEVC) standard, motion estimation (ME) adopts a quadtree coding structure and a larger search range to improve the coding performance. These advanced coding tools, however, dramatically increase the computational complexity. To accelerate ME, fast methods have been proposed that reduce ME complexity at the expense of rate-distortion (R-D) performance. However, none of these methods can claim their tradeoff to be optimal. In this paper, we propose an optimal fast motion estimation (FME) algorithm based on an analytical model of rate-complexity-distortion (R-C-D). We extend the traditional R-D model by introducing the ME complexity into it, which enables us to explicitly express the R-D performance under different complexity budgets. Based on the R-C-D model, the proposed FME finds the R-C-D optimized search ranges for some representative prediction units (PUs), which are then extended or refined dynamically according to motion characteristics for neighboring PUs. The proposed FME enables an R-D performance close to that of full search. When compared with the default FME method in the reference software of HEVC, the proposed fast algorithm can reduce the complexity by over $80\%$ while improving the R-D performance. Furthermore, our proposed FME algorithm is hardware friendly, as regular data flow enables high data reuse efficiency.
Design on routing optimization algorithm based on complex network

In order to solve the problem of information flow network transmission and congestion control in complex internet networks,A new and complicated routing network algorithm based on network evolution simulation model is proposed,An improved method is proposed for a typical static local routing algorithm in an SRTD network. This method adds a node message queue length as a main parameter. The variable parameters can be used to adjust the node processing capacity and the weight of node packet queue length in the routing policy. And to a certain extent, the efficiency of simulation is improved and the purpose of network optimization is achieved.
Population dynamics of wheat aphid on different landraces of wheat under field conditions

Wheat (Triticum aestivum L.) is the leading food grain and staple diet in Pakistan. Cereal aphids are a severe problem in wheat growing areas. Among environmentally safe and economical methods for the control of aphids, the use of resistant wheat cultivars is one of the promising approach in pest management programs which can overcome the economic damage caused by aphid infestation. In the present study, the population dynamics of wheat aphid was observed on 114 landraces of wheat. The results showed that during March, nine landraces showed the highest population of Rhopalosiphum padi while the lowest R. padi population was recorded on fourteen different landraces with aphid populations ranging from 3.25 to 4.08. In the month of April, the highest population of R. padi was recorded on 81 landraces while the lowest population was recorded on 33 landraces with mean aphid numbers ranging from 2.91 to 6.9. The peak population was recorded during the month of May on 78 landraces of wheat and proved to be the susceptible while the remaining thirty eight landraces of wheat proved to be resistant with low aphid densities. The mean aphid population varied from 4.25 to 6.95. The results obtained from the month of March were non-significant while those obtained during the months of April and May were significant
Using microphone arrays to investigate microhabitat selection by declining breeding birds

Understanding the microhabitat preferences of animals can help managers to develop better conservation and recovery strategies but this is challenging. Traditional methods are limited by cost, accuracy and human resources. In this study, we investigated avian microhabitat preferences using microphone arrays that are capable of accurately locating vocalizing birds. Our objective was to identify the microhabitat associations of two common species in steep population decline, the Boreal Chickadee Poecile hudsonicus and the Cape May Warbler Setophaga tigrina. We deployed 68 eight‐channel arrays at random locations in Labrador, Canada, during the 2016 avian breeding season. We returned in 2017 to the 18 array locations where the target species had been detected the previous year and characterized the microhabitat at the exact locations where they had been detected. We also characterized the microhabitat at randomly determined control locations. Results show that Boreal Chickadees select trees with greater diameter‐at‐breast‐height that are surrounded by greater stem density. We did not find evidence that Cape May Warblers exhibit microhabitat selection during song production. The study shows that microphone arrays are an effective tool for identifying preferred microhabitat that could be incorporated into future conservation or recovery strategies.
A Novel Multi-Objective Moth-Flame Optimization Algorithm for Feature Selection

Objectives: A novel hybrid multi-objective swarm system is proposed in order to find the optimal feature subset that achieves data description with minor redundancy as well as keeps classification performance. Methods/Statistical Analysis: The advantages of filter and wrapper approaches characteristics’ are merged simultaneously via different phases of optimization. The proposed system based on Modified Moth Flame Optimization algorithm is assessed against the original algorithm in different experimentations with a single objective based on using MFO and with a multi-objective MFO. The proposed system has been tested over 21 data sets derived from UCI data repository under set of statistical assessment indicators. Findings: The experimental results proved the capability of the hybrid multi-objective MFO-2 to adaptively search the feature space to find optimal feature subset with highest mutual information and achieves maximum classification accuracy and tolerates the problems that are common on both wrapper-based feature selection as well as filter based ones. In addition to, the results obtained from using Random Forests ensemble classifier within the proposed system proved that the performance of hybrid system based on the modified MFO-2 is superior to the hybrid system based on original MFO algorithm in 62% from the data sets, while in case of using the K-Nearest Neighbor classifier we can highlight that the proposed hybrid multi-objective MFO-2 outperforms the other single objective MFO-2 as well as the hybrid multi-objective MFO algorithm in classification performance but also has a comparable ratio of features selected, which confirms that the hybrid multi-objective MFO-2 can select the optimal feature combination with comparable size. Application/Improvements: The novel hybrid multi-objective MFO-2 proved the capability to adaptively search the feature space and its ability to avoid premature convergence caused by falling in local minima.
Extreme resistance to late blight disease by transferring 3 R genes from wild relatives into African farmer-preferred potato varieties

Three late blight resistance (R) genes from wild potato species were transferred as a stack into the farmer-preferred varieties ‘Tigoni’ and ‘Shangi’. Transgenic events were tested by detached-leaf assays (DLA) and whole-plant assays (WPA) with isolates of Phytophthora infestans using 20,000 sporangia / mL inoculum. For the first DLA, 9 out of 13 ‘Tigoni’ and 10 out of 12 ‘Shangi’ transgenic events had scores below 3% of leaf affected area. 17 of the 19 transgenic events with low scores were subjected to a second DLA using two different P. infestans isolates. 8 of them exhibited extreme LB resistance while the remaining 9 transgenic events showed hypersensitive response (HR). 6 transgenic events (2 ‘Tigoni’ and 4 ‘Shangi’) with extreme resistance by DLA were subjected to WPA and they all exhibited extreme resistance to LB. Hence, this study demonstrates that a simple DLA predicts high level of resistance to late blight. R gene expression analysis in 18 transgenic events showed different transgenic events exhibiting different levels of expression in the three genes. However, that pathogen induction and / or high R gene expression are necessary for extreme resistance when transgenic events bear a stack of three R genes was not observed. Key words: Detached-leaf assay, late blight, Phytophthora infestans, resistance.
Canopy effects on abundance and leaf traits of a spring ephemeral: Erythronium americanum

Spring ephemerals take advantage of the high light levels available in the spring by completing the aboveground portion of their lifecycle before the canopy develops and while few other understory plant species are growing. The spring is marked by high resource availability, yet spring ephemerals are variably abundant throughout forests. Research indicates that canopy conditions can influence the growth of spring ephemerals; consequently, we tested whether the variation in canopy conditions predicted variation in the abundance of Erythronium americanum Ker Gawl. across 50 forest plots. We also tested whether the specific leaf area (SLA) of E. americanum in plots was predicted by variation in plot-level canopy conditions, reflecting E. americanum‘s ability to adapt to different canopy conditions. The abundance of E. americanum was significantly lower in the plots with greater hard canopy closure (i.e., permanent cover: tree architecture + evergreen leaf cover), and significantly higher under canopies that reached full development earlier. Canopies with greater hard canopy cover at the start of the growing season were associated with significantly higher SLA, quantifying local adaptation by E. americanum to variable canopy conditions. Erythronium americanum takes advantage of the high light levels available in the spring. It is unclear at this time why higher abundance of E. americanum is associated with canopies that close earlier.
Increasing the robustness of autonomous systems to hardware degradation using machine learning

Autonomous systems perform predetermined tasks (missions) with minimum supervision. In most applications, the state of the world changes with time. Sensors are employed to measure part or whole of the world’s state. However, sensors often fail amidst operation; feeding as such decision-making with wrong information about the world. Moreover, hardware degradation may alter dynamic behaviour, and subsequently the capabilities, of an autonomous system; rendering the original mission infeasible. This thesis applies machine learning to yield powerful and robust tools that can facilitate autonomy in modern systems. Incremental kernel regression is used for dynamic modelling. Algorithms of this sort are easy to train and are highly adaptive. Adaptivity allows for model adjustments, whenever the environment of operation changes. Bayesian reasoning provides a rigorous framework for addressing uncertainty. Moreover, using Bayesian Networks, complex inference regarding hardware degradation can be answered. Specifically, adaptive modelling is combined with Bayesian reasoning to yield recursive estimation algorithms that are robust to sensor failures. Two solutions are presented by extending existing recursive estimation algorithms from the robotics literature. The algorithms are deployed on an underwater vehicle and the performance is assessed in real-world experiments. A comparison against standard filters is also provided. Next, the previous algorithms are extended to consider sensor and actuator failures jointly. An algorithm that can detect thruster failures in an Autonomous Underwater Vehicle has been developed. Moreover, the algorithm adapts the dynamic model online to compensate for the detected fault. The performance of this algorithm was also tested in a real-world application. One step further than hardware fault detection, prognostics predict how much longer can a particular hardware component operate normally. Ubiquitous sensors in modern systems render data-driven prognostics a viable solution. However, training is based on skewed datasets; datasets where the samples from the faulty region of operation are much fewer than the ones from the healthy region of operation. This thesis presents a prognostic algorithm that tackles the problem of imbalanced (skewed) datasets.
Assessment of damages on a RC building after a big fire

This paper presents a case study about the damages on the structural elements of a cast in place reinforced concrete (RC) building after a big fire which was able to be controlled after six hours. The fire broke off at the 2 nd basement floor of the building, which has five basements, one ground, and two normal floors. As a result of intensely stocked ignitable materials, it spread out to the all of the upstairs. In visual inspection, most of the typical fire damages were observed (such as spalling, net-like cracks, crumbled plasters, bared or visible reinforcement). Also, failures of the 2 nd basement columns were encountered. It has been concluded that the severity failures of the columns at the 2 nd basement caused utterly deformation of the building, which is responsible for the massive damages on the beam-column connections. All of the observed damages were categorized related to the types and presented separated regarding the floors. Besides to the visual inspection, the numerical analysis was run to verify the observed damaged on the building for columns, beams, and the connection regions. It is concluded from the study that several parameters such as duration of the fire, level of the temperature influence on the damages to the RC building. Also, it is highlighted by the study that if the damaged building is considered on the overall structural system, it is not able to satisfy the minimum service requirements neither gravity loads nor earthquake conditions.