Miracle-dz/UniFAR-FTUs huggingface dataset:
Landscape genomics provides evidence of climate‐associated genetic variation in Mexican populations of Quercus rugosa

Local adaptation is a critical evolutionary process that allows plants to grow better in their local compared to non‐native habitat and results in species‐wide geographic patterns of adaptive genetic variation. For forest tree species with a long generation time, this spatial genetic heterogeneity can shape the ability of trees to respond to rapid climate change. Here, we identify genomic variation that may confer local environmental adaptations and then predict the extent of adaptive mismatch under future climate as a tool for forest restoration or management of the widely distributed high‐elevation oak species Quercus rugosa in Mexico. Using genotyping by sequencing, we identified 5,354 single nucleotide polymorphisms (SNPs) genotyped from 103 individuals across 17 sites in the Trans‐Mexican Volcanic Belt, and, after controlling for neutral genetic structure, we detected 74 FST outlier SNPs and 97 SNPs associated with climate variation. Then, we deployed a nonlinear multivariate model, Gradient Forests, to map turnover in allele frequencies along environmental gradients and predict areas most sensitive to climate change. We found that spatial patterns of genetic variation were most strongly associated with precipitation seasonality and geographic distance. We identified regions of contemporary genetic and climatic similarities and predicted regions where future populations of Q. rugosa might be at risk due to high expected rate of climate change. Our findings provide preliminary details for future management strategies of Q. rugosa in Mexico and also illustrate how a landscape genomic approach can provide a useful tool for conservation and resource management strategies.
Oxygen Uptake Kinetics in Youth: Characteristics, Interpretation, and Application.

Pulmonary oxygen uptake ( V˙O2 ) kinetics, which describes the aerobic response to near instantaneous changes in metabolic demand, provides a valuable insight into the control and coordination of oxidative phosphorylation during exercise. Despite their applicability to the highly sporadic habitual physical activity and exercise patterns of children, relatively little is known regarding the influence of internal and external stimuli on the dynamic V˙O2 response. Although insufficient evidence is available during moderate-intensity exercise, an age-related slowing of the phase 2 time constant (τ) and augmentation of the V˙O2 slow component appears to manifest during heavy-intensity exercise, which may be related to changes in the muscle phosphate controllers of oxidative phosphorylation, muscle oxygen delivery and utilization, and/or muscle fiber type recruitment patterns. Similar to findings in adults, aerobic training is associated with a faster phase 2 τ and smaller V˙O2 slow component in youth, independent of age or maturity, indicative of an enhanced oxidative metabolism. However, a lack of longitudinal or intervention-based training studies limits our ability to attribute these changes to training per se. Further, methodologically rigorous studies are required to fully resolve the interaction(s) between age, sex, biological maturity, and external stimuli, such as exercise training and exercise intensity and the dynamic V˙O2 response at the onset and offset of exercise.
MALDI‐Imaging for Classification of Epithelial Ovarian Cancer Histotypes from a Tissue Microarray Using Machine Learning Methods

Precise histological classification of epithelial ovarian cancer (EOC) has immanent diagnostic and therapeutic consequences, but remains challenging in histological routine. The aim of this pilot study is to examine the potential of matrix‐assisted laser desorption/ionization (MALDI) imaging mass spectrometry in combination with machine learning methods to classify EOC histological subtypes from tissue microarray.
Learning to Generate the "Unseen" via Part Synthesis and Composition

Data-driven generative modeling has made remarkable progress by leveraging the power of deep neural networks. A reoccurring challenge is how to sample a rich variety of data from the entire target distribution, rather than only from the distribution of the training data. In other words, we would like the generative model to go beyond the observed training samples and learn to also generate "unseen" data. In our work, we present a generative neural network for shapes that is based on a part-based prior, where the key idea is for the network to synthesize shapes by varying both the shape parts and their compositions. Treating a shape not as an unstructured whole, but as a (re-)composable set of deformable parts, adds a combinatorial dimension to the generative process to enrich the diversity of the output, encouraging the generator to venture more into the "unseen". We show that our part-based model generates richer variety of feasible shapes compared with a baseline generative model. To this end, we introduce two quantitative metrics to evaluate the ingenuity of the generative model and assess how well generated data covers both the training data and unseen data from the same target distribution.
Comparison of Phenytoin versus Levetiracetam in Early Seizure Prophylaxis after Traumatic Brain Injury, at a Tertiary Care Hospital in Karachi, Pakistan

Aims: The aim of the study was to compare the efficacy of phenytoin and levetiracetam for seizure prophylaxis in patients with severe traumatic brain injury (TBI). Subjects and Methods: A randomized controlled trial was conducted over a period of 6 months, at a tertiary health care center in Karachi, Pakistan. Patients with TBI were divided into two groups. Patients in Group A were given phenytoin, whereas Group B patients received levetiracetam. The first dose of the drugs was given within 24 h of injury and continued for 7 days. Data were collected using a predesigned pro forma. All the patients who were in a state of persistent coma, had altered mental status, or had clinical signs of seizures underwent a 1-h electroencephalographic (EEG) recording to observe the seizures, the first EEG was done on the 1st day posttrauma and a second one was done on day 7 of drug use, both the EEGs were compared for changes. We also analyzed the patients according to their duration of antiepileptic drug therapy, length of hospital stay, and complications during therapy. Results: One hundred and forty (117 males and 23 females) patients who presented with TBI having a mean age of 29.48 ± 16.24 years were part of the study. The most prevalent cause of brain injury was road traffic accidents in 72.85% patients. There was no significant relationship between the antiepileptic drug used with the initial EEG (P = 0.313) and seizure activity (P = 0.502). However, a significant correlation of the antiepileptic drug used was found with EEG (P = 0.002) and seizure activity (P = 0.014) on follow-up. Patients who took levetiracetam had decreased the incidence of abnormal EEG and seizure activity on follow-up. There was not any correlation between GCS both initially (P = 0.845) and on follow-up (P = 0.104) with the antiepileptic drug used. Conclusion: The incidence of abnormal EEGs and seizure activity in patients with TBI is the same for both levetiracetam and phenytoin for the initial 7 days post-TBI; however, the incidence of seizures is lower for patients who used levetiracetam on the subsequent follow-up.
An Efficient Topology Refining Scheme for Apache Flink

In the past decade, there has been a boom in the volume of data and in the popularity of cloud applications with industry and academia keenly interested in big data analytics, streaming application, and social networking applications. This led to the emergence of real-time distributed stream processing systems such as Flink, Storm, Dataflow, and Samza. These systems process complex queries on streaming data sets to be distributed across multiple worker nodes in a cluster. Few of them provide adequate supports to adapt the topologies of stream processing tasks to changing input workload. We present an intelligent and efficient topology adjustment scheme which allow Flink framework to refine its topology on the basis of incoming workload. It is designed to increase the overall performance by making the refining of topology robust according to incoming workload streams on the fly, while maintaining SLA constraints. Apache Flink distributed processing engine is used as testbed in the paper. Our preliminary results indicate that the proposed system outperforms the existing default framework.
Spread Divergences

For distributions p and q with different supports, the divergence D(p|q) may not exist. We define a spread divergence on modified p and q and describe sufficient conditions for the existence of such a divergence. We demonstrate how to maximize the discriminatory power of a given divergence by parameterizing and learning the spread. We also give examples of using a spread divergence to train and improve implicit generative models, including linear models (Independent Components Analysis) and non-linear models (Deep Generative Networks).
Discourse recovery after severe traumatic brain injury: exploring the first year

ABSTRACT Objectives: Although much is known about discourse impairment, little is known about discourse recovery after severe traumatic brain injury (TBI). This paper explores discourse recovery across the critical first year, controlling for pre-injury, injury and post-injury variables. Design and methods: An inception cohort comprising 57 participants with severe TBI was examined at 3, 6, 9 and 12 months post-injury and compared to a cross-section of matched healthy control participants. A narrative discourse task was analyzed with main concept analysis (MCA). A mixed linear model approach was used to track recovery controlling for pre-injury, injury and post-injury variables. Results: An upward trajectory of recovery was observed, with peak periods of improvement between 3–6 and 9–12 months and all time points were significantly below controls. Years of education and PTA duration were significant covariates in the recovery model. Presence of aphasia also influenced the recovery model. Conclusions: Individuals with TBI typically improve over the first year, however many will continue to have discourse deficits at 12 months. Years of education, PTA duration and aphasia should be considered when planning services. The 3–6- and 9–12-month periods may offer optimal periods for discourse recovery and increased supports may be beneficial between 6-9 months.
Contention-Based Transmission Coordination for Interfering BSs in Dense mmWave Networks

A contention-based collision avoidance protocol for dense millimeter-wave wireless networks in which base stations (BSs) use directional beamforming to provide downlink data streaming services to mobile users is proposed. The proposed protocol allows the BSs to contend with interfering BSs successfully transmit their downlink packets. Under the assumption that the inter-BS packet collisions incurred by inter-BS beamforming interferences are statistically predictable, a random number mechanism was used to decide whether to transmit the downlink packets. The proposed protocol’s effectiveness was then evaluated through extensive numerical simulations.
Cationic vesicles for efficient shRNA transfection in the MCF-7 breast cancer cell line

Introduction Novel and safe delivery solutions for RNAi therapeutics are essential to obtain the full potential of cancer gene therapy. Methods In this study, cationic vesicular nanocarrier was applied for delivering lnc urothelial carcinoma-associated 1 (lnc UCA1) shRNA expression vector to MCF-7 cells. The physicochemical characteristics, cytotoxicity, and transfection efficiency of cationic vesicles prepared from various molar ratios of amphiphilic surfactant Tween 80 (T), squalene (S), cationic charge lipid didodecyldimethylammonium bromide, and polyethylenimine were investigated. The particle sizes of the vesicles in the nanosize range were determined by dynamic light scattering and transmission electron microscopy. Results Gel protection assay with agarose gel electrophoresis showed cationic vesicles can protect the shRNA plasmid from DNase 1 enzyme. 3-(4,5-Dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H tetrazolium, inner salt result showed no significant cytotoxicity was caused in MCF-7 cancer cell line by (T:S):polyethylenimine cationic vesicles. 3-(4,5-Dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H tetrazolium, inner salt assay, fluorescence microscope images, and flow cytometry analyses confirmed that (T:S)1,040 μM with 4.3 μg/mL of PEI vesicles provided effective transfection without significant cytotoxicity. Furthermore, we found efficient UCA1 shRNA transfection and significant (P<0.05) cell cycle arrest and apoptosis in MCF-7 cancer cells. Conclusion The novel nonviral vesicular nanocarrier, (T:S)1,040 μM with 4.3 μg/mL of PEI, might be safe and efficient for cancer gene therapy and can be used in further in vitro and in vivo studies.
The ER morphology-regulating lunapark protein induces the formation of stacked bilayer discs

Lunapark, an ER protein implicated in stabilizing junctions of the tubular network and regulating morphology changes during mitosis, forms stacked bilayer discs upon reconstitution with phospholipids. Lunapark (Lnp) is a conserved membrane protein that localizes to and stabilizes three-way junctions of the tubular ER network. In higher eukaryotes, phosphorylation of Lnp may contribute to the conversion of the ER from tubules to sheets during mitosis. Here, we report on the reconstitution of purified Lnp with phospholipids. Surprisingly, Lnp induces the formation of stacked membrane discs. Each disc is a bicelle, with Lnp sitting in the bilayer facing both directions. The interaction between bicelles is mediated by the cytosolic domains of Lnp, resulting in a constant distance between the discs. A phosphomimetic Lnp mutant shows reduced bicelle stacking. Based on these results, we propose that Lnp tethers ER membranes in vivo in a cell cycle–dependent manner. Lnp appears to be the first membrane protein that induces the formation of stacked bicelles.
Recurrent Neural Network to Forecast Sprint Performance

ABSTRACT The present paper demonstrates that the performance of an elite track and field sprinter can be predicted by means of the dynamic, nonlinear mathematical method of recurrent neural networks (RNNs). Dataset considers three years of National Collegiate Athletics Association (NCAA) Division I competitions where the student-athlete recorded heart rate variability two days precedent to each competition. Input parameters were selected by transfer entropy via permutation tests. Subsequently, two RNN topologies, Elman and Jordan, were trained with 32 competitions, validated with 7 competitions, and tested against 6 held-out competitions. Resultant RNNs, which possess a sense of time and memory, were able to learn time-dependent sequence of acute adaptation and predict race times with an error of 0.09–0.16 s on held-out test data. Root mean sum of differences of successive R-R intervals (RMSSD), an indicator of parasympathetic tone, and direct current biopotentials, indicator of active wakefulness, were most predictive toward competitive performance for an NCAA Division I male sprinter.
Residual Convolutional Neural Network Revisited with Active Weighted Mapping

In visual recognition, the key to the performance improvement of ResNet is the success in establishing the stack of deep sequential convolutional layers using identical mapping by a shortcut connection. It results in multiple paths of data flow under a network and the paths are merged with the equal weights. However, it is questionable whether it is correct to use the fixed and predefined weights at the mapping units of all paths. In this paper, we introduce the active weighted mapping method which infers proper weight values based on the characteristic of input data on the fly. The weight values of each mapping unit are not fixed but changed as the input image is changed, and the most proper weight values for each mapping unit are derived according to the input image. For this purpose, channel-wise information is embedded from both the shortcut connection and convolutional block, and then the fully connected layers are used to estimate the weight values for the mapping units. We train the backbone network and the proposed module alternately for a more stable learning of the proposed method. Results of the extensive experiments show that the proposed method works successfully on the various backbone architectures from ResNet to DenseNet. We also verify the superiority and generality of the proposed method on various datasets in comparison with the baseline.
Quantification of sulfated polysaccharides in urine by the Heparin Red mix-and-read fluorescence assay

Quantification of sulfated polysaccharides in urine samples is relevant to pharmacokinetic studies in drug development projects and to the non-invasive diagnosis and therapy monitoring of mucopolysaccharidoses. The Heparin Red Kit is a particularly simple and user friendly fluorescence assay for the quantification of sulfated polysaccharides and has recently emerged as a novel tool for the monitoring of their blood levels during pharmacokinetic studies in clinical trials. The standard protocol for the blood plasma matrix is, however, not suited for urine samples due to matrix interference. The present study identifies inorganic sulfate as the interfering component in urine. The sulfate level of urine is typically 1-2 orders of magnitude higher compared with the blood plasma level. Addition of either hydrochloric acid or magnesium chloride counteracts sulfate interference but still enables sensitive detection of sulfated polysaccharides such as heparin, heparan sulfate and dermatan sulfate at low microgramm per milliliter levels. This study extends the application range of the Heparin Red Kit by a simple modification of the assay protocol to the direct quantification of various sulfated polysaccharides in human urine.
Analysis of Dual Connectivity Gain in Terms of Delay and Throughput

Dual connectivity is very important for gradual transition from LTE to 5G. Release 12 of the 3GPP specification introduced DC scheme, but what advantage we can get from DC is not evaluated yet. In this paper, we simulate the DC schemes (LTE – LTE DC, LTE-mmWave DC, mmWave – mmWave DC) using newly modified network simulator (NS3) for 5G and evaluate the DC gains in terms of throughput and delay. We demonstrate the DC would increase the throughput under the fast switching scheme and decrease the end-to-end delay a lot.
Response Inhibition as a Function of Movement Complexity and Movement Type Selection

This study aims to determine whether response inhibition shows the same degree of effectiveness for two sources of motor complexity: (1) Movement complexity, which is measured through two actions with different motor requirements (simple lifting action vs. complex reaching action), and (2) Movement type selection, which is measured in movements performed separately (no active-movement type selection) vs. selectively (active-movement type selection). Activation–suppression model was tested in three experiments to measure activation of the preponderant responses and subsequent suppression in a Simon task. More errors and higher magnitude of congruence effect (which reflects greater effectiveness of response suppression) were expected for more difficult motor conditions. Reaction time, movement time, kinematic errors, and movement errors were recorded. Results of Experiment 1, in which movement type selection was not active, showed that both movements did not differ in their activation and suppression, as they presented similar kinematic error rates and Simon effects. Experiment 2, in which movement type selection was active, resulted in a higher kinematic error rate and higher magnitude of Simon effect in lifting. These results were confirmed in Experiment 3, in which participants performed all experimental motor complexity conditions. Finally, Experiment 4 showed that responses with similar movement complexity did not differ in their activation and suppression, even when movement type selection was active. Thus, the present study provides evidence on the varying effectiveness of response inhibition as a function of movement complexity, but only in demanding situations in which movement type selection is active. These results can be attributed to a top-down strategy to minimize error for actions most prone to develop kinematic error.
Assessment of histopathology of wounds based on protein distribution detected by wound blotting

Background: Shortening the duration of healing based on an accurate assessment is important in pressure ulcer management. This study focused on the peroxidase and alkaline phosphatase activity detected by wound blotting, a non-invasive method of collecting wound exudate, to establish a non-invasive and point-of-care assessment method for analyzing the histopathology of wounds using an animal model. Methods: Wounds were created on the dorsal skin of rats. Peroxidase and alkaline phosphatase activities in the wound exudate were detected by wound blotting on post-wounding days 1, 4, 7, and 10. Wound tissue was collected on the same sampling days. Peroxidase and alkaline phosphatase activity within the tissue and myeloperoxidase were visualized. Two types of peroxidase activities were detected by wound blotting: ring and non-ring signals. The histopathological features were compared between wounds with ring and non-ring signals. Results: The wounds with ring signals showed a high level of peroxidase activity, and histological analysis demonstrated that the secreted or deviated peroxidase activity originated from myeloperoxidase, indicating a strong inflammation reaction within the tissue. The histopathology of wounds related to the alkaline phosphatase signals was not identified. Conclusion: The results suggested that ring signals indicated a strong inflammatory reaction and that they could be used to assess non-visible inflammation.
Trendrapport bewegen en gezondheid 1996/1997

In het kader van het VWS doelfinancieringsprogramma 'Bewegen en Gezondheid' wordt door TNO-PG en NIA TNO een 'Monitor Bewegen en Gezondheid' ontwikkeld. Doel van deze monitor is het systematisch en periodiek verzamelen, opslaan en evalueren van gegevens over (1) bewegen van mensen in relatie tot gezondheid, zowel in de thuis- en werksituatie als tijdens sportbeoefening en (2) de positieve en negatieve gevolgen hiervan. Met deze gegevens moet het beleid op zowel het niveau van lokale, regionale en nationale overheid als van andere organisaties die zich bezig houden met het stimuleren van gezond bewegen. In het verlengde van deze monitor wordt periodiek een 'Trendrapport Bewegen en gezondheid' uitgebracht waarin een aantal kerngegevens over bewegen en gevolgen van bewegen in relatie tot de gezondheid zijn samengebracht. Dit rapport is de eerste in deze reeks, waarin antwoord wordt gegeven op de volgende vragen: (1) Wat is er bekend over de sport- en bewegingsparticipatie van de Nederlandse bevolking? (2) Wat is er bekend over de positieve gezondheidseffecten van lichamelijke activiteit? (3) Wat is er bekend over de negatieve gezondheidseffecten van lichamelijke activiteit, toegespitst op sport- en arbeidsblessures? (4) Welke gezondheidskundige richtlijnen bestaan er voor gezond bewegen?