Miracle-dz/UniFAR-FTUs huggingface dataset:
INDIVIDUAL DIFFERENCES IN PROGENY VIABILITY IN LAVANDULA LATIFOLIA: A LONG‐TERM FIELD STUDY

Despite its potential importance as a source of fitness variation in natural plant populations, few studies have so far examined long-term patterns of individual dif- ferences in progeny viability under natural conditions. This paper reports on a six-year field study on the differences in progeny viability (defined here as the probability of seeds contributing an established, prereproductive juvenile to the population), and its two com- ponents (seedling emergence and seedling survivorship), for the Mediterranean evergreen shrub Lavandula latifolia. The main objectives of the study were (1) to assess whether individual plants actually differ in the viability of their progeny under natural conditions, and (2) to determine if naturally occurring levels of variation in viability may modify individual differences in fecundity. Seed progenies from 14 maternal parents belonging to one population were sown at 10 field sites differing broadly in habitat type and soil char- acteristics, and seedling emergence and survival subsequently were monitored for six years. Maternal parents differed significantly in progeny viability because of both differential seedling emergence and seedling survivorship, and variation in either of these parameters was not significantly related to maternal differences in seed size. Differences in seedling survivorship were more important than differences in seedling emergence as determinants of maternal variation in progeny viability. While individual differences in seedling emer- gence remained consistent across sowing sites, there was a strong parent 3 sowing site interaction on seedling survivorship, and the rank of maternal parents with regard to this variable varied erratically among sowing sites. A significant inverse relationship existed across maternal parents between seed production and progeny viability, which was not mediated by differences in seed size, thus suggesting a trade-off between the number and quality of the progeny produced by L. latifolia plants. Although maternal differences in seed production were about twice as important as differences in progeny viability in de- termining maternal differences in juvenile recruitment, viability differences should not be disregarded a priori as unimportant sources of fitness differences. Results of this study stress the importance of conducting long-term, direct field assessments of individual dif- ferences in progeny viability across a range of environments encompassing the conditions actually faced by the species.
Integration of genetic and metabolic features related to sialic acid metabolism distinguishes human breast cell subtypes

In this report we use ‘high-flux’ tributanoyl-modified N-acetylmannosamine (ManNAc) analogs with natural N-acetyl as well as non-natural azido- and alkyne N-acyl groups (specifically, 1,3,4-O-Bu3ManNAc, 1,3,4-O-Bu3ManNAz, and 1,3,4-O-Bu3ManNAl respectively) to probe intracellular sialic acid metabolism in the near-normal MCF10A human breast cell line in comparison with earlier stage T-47D and more advanced stage MDA-MB-231 breast cancer lines. An integrated view of sialic acid metabolism was gained by measuring intracellular sialic acid production in tandem with transcriptional profiling of genes linked to sialic acid metabolism. The transcriptional profiling showed several differences between the three lines in the absence of ManNAc analog supplementation that helps explain the different sialoglycan profiles naturally associated with cancer. Only minor changes in mRNA transcript levels occurred upon exposure to the compounds confirming that metabolic flux alone can be a key determinant of sialoglycoconjugate display in breast cancer cells; this result complements the well-established role of genetic control (e.g., the transcription of STs) of sialylation abnormalities ubiquitously associated with cancer. A notable result was that the different cell lines produced significantly different levels of sialic acid upon exogenous ManNAc supplementation, indicating that feedback inhibition of UDP-GlcNAc 2-epimerase/ManNAc kinase (GNE)—generally regarded as the ‘gatekeeper’ enzyme for titering flux into sialic acid biosynthesis—is not the only regulatory mechanism that limits production of this sugar. A notable aspect of our metabolic glycoengineering approach is its ability to discriminate cell subtype based on intracellular metabolism by illuminating otherwise hidden cell type-specific features. We believe that this strategy combined with multi-dimensional analysis of sialic acid metabolism will ultimately provide novel insights into breast cancer subtypes and provide a foundation for new methods of diagnosis.
Flexible memory controls sperm competition responses in male Drosophila melanogaster

Males of many species use social cues to predict sperm competition (SC) and tailor their reproductive strategies, such as ejaculate or behavioural investment, accordingly. While these plastic strategies are widespread, the underlying mechanisms remain largely unknown. Plastic behaviour requires individuals to learn and memorize cues associated with environmental change before using this experience to modify behaviour. Drosophila melanogaster respond to an increase in SC threat by extending mating duration after exposure to a rival male. This behaviour shows lag times between environmental change and behavioural response suggestive of acquisition and loss of memory. Considering olfaction is important for a male's ability to assess the SC environment, we hypothesized that an olfactory learning and memory pathway may play a key role in controlling this plastic behaviour. We assessed the role of genes and brain structures known to be involved in learning and memory. We show that SC responses depend on anaesthesia-sensitive memory, specifically the genes rut and amn. We also show that the γ lobes of the mushroom bodies are integral to the control of plastic mating behaviour. These results reveal the genetic and neural properties required for reacting to changes in the SC environment.
IRCMS at SemEval-2018 Task 7 : Evaluating a basic CNN Method and Traditional Pipeline Method for Relation Classification

This paper presents our participation for sub-task1 (1.1 and 1.2) in SemEval 2018 task 7: Semantic Relation Extraction and Classification in Scientific Papers (Gábor et al., 2018). We experimented on this task with two methods: CNN method and traditional pipeline method. We use the context between two entities (included) as input information for both methods, which extremely reduce the noise effect. For the CNN method, we construct a simple convolution neural network to automatically learn features from raw texts without any manual processing. Moreover, we use the softmax function to classify the entity pair into a specific relation category. For the traditional pipeline method, we use the Hackabout method as a representation which is described in section3.5. The CNN method’s result is much better than traditional pipeline method (49.1% vs. 42.3% and 71.1% vs. 54.6% ).
Met polymorphism is associated with post-traumatic stress disorder and functional outcome following mild traumatic brain injury

Mild traumatic brain injury (mTBI) results in variable clinical trajectories and outcomes. The source of variability remains unclear, but may involve genetic variations, such as single nucleotide polymorphisms (SNPs). A SNP in catechol-o-methyltransferase (COMT) is suggested to influence Corresponding author at: Department of Neurological Surgery, University of California, San Francisco, 1001 Potrero Avenue, Building 1, Room 101, San Francisco, CA 94110, USA. Fax: +1 (415) 206 3948. manleyg@neurosurg.ucsf. edu. 1Authors contributed equally to the manuscript. 2The TRACK-TBI Investigators are listed in the Appendix in alphabetical order by last name Registry: ClinicalTrials.gov Identifier NCT01565551. Conflict of interest No competing financial interests exist. Disclosure statement The authors have no competing interests to disclose. HHS Public Access Author manuscript J Clin Neurosci. Author manuscript; available in PMC 2018 January 01. Published in final edited form as: J Clin Neurosci. 2017 January ; 35: 109–116. doi:10.1016/j.jocn. 2016.09. 017. A uhor M anscript
Weak Galilean invariance as a selection principle for coarse-grained diffusive models

Significance Galilean invariance is a cornerstone of classical mechanics. It states that for closed systems the equations of motion of the microscopic degrees of freedom do not change under Galilean transformations to different inertial frames. However, the description of real world systems usually requires coarse-grained models integrating complex microscopic interactions indistinguishably as friction and stochastic forces, which intrinsically violate Galilean invariance. By studying the coarse-graining procedure in different frames, we show that alternative rules—denoted as “weak Galilean invariance”—need to be satisfied by these stochastic models. Our results highlight that diffusive models in general cannot be chosen arbitrarily based on the agreement with data alone but have to satisfy weak Galilean invariance for physical consistency. How does the mathematical description of a system change in different reference frames? Galilei first addressed this fundamental question by formulating the famous principle of Galilean invariance. It prescribes that the equations of motion of closed systems remain the same in different inertial frames related by Galilean transformations, thus imposing strong constraints on the dynamical rules. However, real world systems are often described by coarse-grained models integrating complex internal and external interactions indistinguishably as friction and stochastic forces. Since Galilean invariance is then violated, there is seemingly no alternative principle to assess a priori the physical consistency of a given stochastic model in different inertial frames. Here, starting from the Kac–Zwanzig Hamiltonian model generating Brownian motion, we show how Galilean invariance is broken during the coarse-graining procedure when deriving stochastic equations. Our analysis leads to a set of rules characterizing systems in different inertial frames that have to be satisfied by general stochastic models, which we call “weak Galilean invariance. ” Several well-known stochastic processes are invariant in these terms, except the continuous-time random walk for which we derive the correct invariant description. Our results are particularly relevant for the modeling of biological systems, as they provide a theoretical principle to select physically consistent stochastic models before a validation against experimental data.
Role of cardiovascular implantable electronic devices in delivering individualized disease-guided management of patients with non-valvular atrial fibrillation and high bleeding risk

Abstract Background: Many patients with non-valvular atrial fibrillation (NVAF) with high risk for thromboembolic stroke and bleeding may not wish to continue long-term oral anticoagulants (OACs) to avoid bleeding complications. We aimed to investigate whether AF burden assessment by cardiovascular implantable electronic devices (CIEDs) would allow an individualised disease-guided approach for safe withdrawal of long-term OAC in high-risk patients. Methods and results: We studied 145 patients (age 77.6 ± 10.6 years; 49.7% females) with NVAF, CHA2DS2-VASc score ≥2, HAS-BLED score ≥3, in whom CIEDs were implanted. These patients wished to stay off long-term OAC based on their previous adverse bleeding event/s or due to similar events witnessed in the family or friend circle. These patients were grouped into ‘low AF burden’ [n = 121 (83%)], or ‘high AF burden’ [n = 24 (17%)] defined as <24 hours or >24 hours cumulatively in 30 consecutive days respectively, and followed for 51.2 ± 29.8 months. All patients with ‘low AF burden’ were allowed to discontinue OAC, but OAC was resumed in 1 patient who experienced TIA. Bleeding events developed in 9 out of 24 (37.5%) patients with ‘high AF burden’ who were maintained on OAC, as compared to 3 out of 121 (2.47%) patients with ‘low AF burden’ who were off OAC (p <.05). There were 9 (6.2%) deaths unrelated to AF treatment approach. Conclusions: In NVAF patients, AF burden assessment by CIEDs allows an individualised disease-guided approach to safe withdrawal of long-term OAC in patients with high bleeding risk who do not wish to continue long-term anticoagulation.
Neuronal calcineurin transcriptional targets parallel changes observed in Alzheimer disease brain

Synaptic dysfunction and loss are core pathological features in Alzheimer disease (AD). In the vicinity of amyloid‐β plaques in animal models, synaptic toxicity occurs and is associated with chronic activation of the phosphatase calcineurin (CN). Indeed, pharmacological inhibition of CN blocks amyloid‐β synaptotoxicity. We therefore hypothesized that CN‐mediated transcriptional changes may contribute to AD neuropathology and tested this by examining the impact of CN over‐expression on neuronal gene expression in vivo. We found dramatic transcriptional down‐regulation, especially of synaptic mRNAs, in neurons chronically exposed to CN activation. Importantly, the transcriptional profile parallels the changes in human AD tissue. Bioinformatics analyses suggest that both nuclear factor of activated T cells and numerous microRNAs may all be impacted by CN, and parallel findings are observed in AD. These data and analyses support the hypothesis that at least part of the synaptic failure characterizing AD may result from aberrant CN activation leading to down‐regulation of synaptic genes, potentially via activation of specific transcription factors and expression of repressive microRNAs.
Robust and highly adaptable brain-computer interface with convolutional net architecture based on a generative model of neuromagnetic measurements

Deep Neural Networks have been applied very successfully in image recognition and natural language processing. Recently these powerful methods have received attention also in the brain-computer interface (BCI) community. Here, we introduce a convolutional neural network (CNN) architecture optimized for classification of brain states from non-invasive magnetoencephalographic (MEG) measurements. The model structure is motivated by a state-of-the-art generative model of the MEG signal and is thus readily interpretable in neurophysiological terms. We demonstrate that the proposed model is highly accurate in decoding event-related responses as well as modulations of oscillatory brain activity, and is robust with respect to inter-individual differences. Importantly, the model generalizes well across users: when trained on data pooled from previous users, it can successfully perform on new users. Thus, the time-consuming BCI calibration can be omitted. Moreover, the model can be incrementally updated, resulting in +8.9% average accuracy improvement in offline experiments and +17.0% in a real-time BCI. We argue that this model can be used in practical BCIs and basic neuroscience research.
Male lifespan extension with 17‐α estradiol is linked to a sex‐specific metabolomic response modulated by gonadal hormones in mice

Longevity in mammals is influenced by sex, and lifespan extension in response to anti‐aging interventions is often sex‐specific, although the mechanisms underlying these sexual dimorphisms are largely unknown. Treatment of mice with 17‐α estradiol (17aE2) results in sex‐specific lifespan extension, with an increase in median survival in males of 19% and no survival effect in females. Given the links between lifespan extension and metabolism, we performed untargeted metabolomics analysis of liver, skeletal muscle and plasma from male and female mice treated with 17aE2 for eight months. We find that 17aE2 generates distinct sex‐specific changes in the metabolomic profile of liver and plasma. In males, 17aE2 treatment raised the abundance of several amino acids in the liver, and this was further associated with elevations in metabolites involved in urea cycling, suggesting altered amino acid metabolism. In females, amino acids and urea cycling metabolites were unaffected by 17aE2. 17aE2 also results in male‐specific elevations in a second estrogenic steroid—estriol‐3‐sulfate—suggesting different metabolism of this drug in males and females. To understand the underlying endocrine causes for these sexual dimorphisms, we castrated males and ovariectomized females prior to 17aE2 treatment, and found that virtually all the male‐specific metabolite responses to 17aE2 are inhibited or reduced by male castration. These results suggest novel metabolic pathways linked to male‐specific lifespan extension and show that the male‐specific metabolomic response to 17aE2 depends on the production of testicular hormones in adult life.
False-Accept/False-Reject Trade-offs in Biometric Authentication Systems

Biometric authentication systems, based on secret key generation, work as follows. In the enrollment stage, an individual provides a biometric signal that is mapped into a secret key and a helper message, the former being prepared to become available to the system at a later time (for authentication), and the latter is stored in a public database. When an authorized user requests authentication, claiming his/her identity as one of the subscribers, he/she has to provide a biometric signal again, and then the system, which retrieves also the helper message of the claimed subscriber, produces an estimate of the secret key, that is finally compared to the secret key of the claimed user. In case of a match, the authentication request is approved, otherwise, it is rejected. Evidently, there is an inherent tension between two desired, but conflicting, properties of the helper message encoder: on the one hand, the encoding should be informative enough concerning the identity of the real subscriber, in order to approve him/her in the authentication stage, but on the other hand, it should not be too informative, as otherwise, unauthorized imposters could easily fool the system and gain access. A good encoder should then trade off the two kinds of errors: the false reject (FR) error and the false accept (FA) error. In this work, we investigate trade-offs between the random coding FR error exponent and the best achievable FA error exponent. We compare two types of ensembles of codes: fixed-rate codes and variable-rate codes, and we show that the latter class of codes offers considerable improvement compared to the former. In doing this, we characterize the optimal rate functions for both types of codes. We also examine privacy leakage constraints for both fixed-rate codes and variable-rate codes.
ACE‐FTS measurements across the edge of the winter 2004 Arctic vortex

Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE‐FTS) solar occultation measurements have been used to create volume mixing ratio (VMR) profiles of H2O, CH4, and N2O inside and outside the 2004 Arctic vortex. Using derived meteorological quantities such as potential vorticity, we have classified 450 occultations (from February and March 2004 spanning 0 to 79.8°N) as vortex, vortex edge, or extravortex. We plot [CH4] versus [N2O] correlations to display the distinct patterns observed for measurements of different air masses and use these correlations to further classify the extravortex occultations as tropical, subtropical, or midlatitude. Using comparisons between high latitude profiles of [N2O], [CH4], and [H2O] inside and outside the Arctic vortex, we estimate upper stratospheric and lower mesospheric descent rates and find that descent in the winter 2004 Arctic vortex was rapid, with evidence of descent at higher altitudes than in past years.
An allosteric propofol-binding site in kinesin disrupts kinesin-mediated processive movement on microtubules

Microtubule-based molecular motors mediate transport of intracellular cargo to subdomains in neurons. Previous evidence has suggested that the anesthetic propofol decreases the average run-length potential of the major anterograde transporters kinesin-1 and kinesin-2 without altering their velocity. This effect on kinesin has not been observed with other inhibitors, stimulating considerable interest in the underlying mechanism. Here, we used a photoactive derivative of propofol, meta-azipropofol (AziPm), to search for potential propofol-binding sites in kinesin. Single-molecule motility assays confirmed that AziPm and propofol similarly inhibit kinesin-1 and kinesin-2. We then applied AziPm in semiquantitative radiolabeling and MS microsequencing assays to identify propofol-binding sites within microtubule–kinesin complexes. The radiolabeling experiments suggested preferential AziPm binding to the ATP-bound microtubule–kinesin complex. The photolabeled residues were contained within the kinesin motor domain rather than at the motor domain–β-tubulin interface. No residues within the P-loop of kinesin were photolabeled, indicating an inhibitory mechanism that does not directly affect ATPase activity and has an effect on run length without changing velocity. Our results also indicated that when the kinesin motor interacts with the microtubule during its processive run, a site forms in kinesin to which propofol can then bind and allosterically disrupt the kinesin–microtubule interaction, resulting in kinesin detachment and run termination. The discovery of the propofol-binding allosteric site in kinesin may improve our understanding of the strict coordination of the motor heads during the processive run. We hypothesize that propofol's potent effect on intracellular transport contributes to various components of its anesthetic action.
Synergistic Reconstruction and Synthesis via Generative Adversarial Networks for Accelerated Multi-Contrast MRI

Multi-contrast MRI acquisitions of an anatomy enrich the magnitude of information available for diagnosis. Yet, excessive scan times associated with additional contrasts may be a limiting factor. Two mainstream approaches for enhanced scan efficiency are reconstruction of undersampled acquisitions and synthesis of missing acquisitions. In reconstruction, performance decreases towards higher acceleration factors with diminished sampling density particularly at high-spatial-frequencies. In synthesis, the absence of data samples from the target contrast can lead to artefactual sensitivity or insensitivity to image features. Here we propose a new approach for synergistic reconstruction-synthesis of multi-contrast MRI based on conditional generative adversarial networks. The proposed method preserves high-frequency details of the target contrast by relying on the shared high-frequency information available from the source contrast, and prevents feature leakage or loss by relying on the undersampled acquisitions of the target contrast. Demonstrations on brain MRI datasets from healthy subjects and patients indicate the superior performance of the proposed method compared to previous state-of-the-art. The proposed method can help improve the quality and scan efficiency of multi-contrast MRI exams.
Introducing Hypertension FACT: Vital Sign Ontology Annotations in the Florida Annotated Corpus for Translational Science

We introduce the Florida Annotated Corpus for Translational Science (FACTS), which currently consists of 20 case reports about hypertension that we annotated with Vital Sign Ontology (VSO) classes. We describe the annotation method, the annotation results, interannotator agreement measure, and the availability of the corpus and supporting tools for annotating corpora with OWL ontologies. We also discuss issues and limitations of VSO for annotating vital sign data in case reports.