Miracle-dz/UniFAR-FTUs huggingface dataset:
The Diacylglycerol Analogs OAG and DOG Differentially Affect Primary Events of Pheromone Transduction in the Hawkmoth Manduca sexta in a Zeitgebertime-Dependent Manner Apparently Targeting TRP Channels

For the hawkmoth Manduca sexta accumulating evidence suggests that pheromone transduction acts via a metabotropic signal transduction cascade, with G-protein-dependent phospholipase C (PLC) activations generating diacylglycerol (DAG) and inositol trisphosphate as the primary events in hawkmoth pheromone transduction. In contrast, ionotropic olfactory receptor (OR) coreceptor (Orco)-dependent mechanisms do not appear to be involved. In hawkmoths pheromones activated a specific sequence of PLC-dependent ion channels of unknown identity. In several sensory systems transient receptor potential (TRP) ion channels were found downstream of PLC as primary transduction channels. Also in the mammalian vomeronasal organ, DAG-dependent TRP channels are employed. Therefore, we hypothesized that TRPs may be downstream targets for DAG also in the hawkmoth pheromone signal transduction pathway. To test this, we employed two DAG analogs, OAG and DOG for in vivo single-sensillum tip-recordings of pheromone-sensitive sensilla. Since olfactory receptor neurons (ORNs) expressed circadian changes in sensitivity throughout the day, we recorded at two different Zeitgebertimes (ZTs), the hawkmoths activity phase at ZT 1 and its resting phase at ZT 9. We found that the DAG analogs targeted at least two different TRP-like channels that underlie the primary events of hawkmoth pheromone transduction daytime-dependently. At both ZTs OAG sped up and increased the Orco-independent phasic action potential response without affecting the Orco-dependent late, long-lasting pheromone response. Thus, OAG most likely opened a transient Ca2+ permeable TRP channel that was available at both ZTs and that opened pheromone-dependently before Orco. In contrast, DOG slowed down and decreased the sensillum potential, the phasic-, and the late, long-lasting pheromone response. Therefore, DOG appeared to activate a protein kinase C (PKC) that closed TRP-like Ca2+ permeable channels and opened Ca2+ impermeable cation channels, which have been previously described and are most abundant at ZT 9. These data support our hypothesis that hawkmoth pheromone transduction is mediated by metabotropic PLC-dependent mechanisms that activate TRP-like channels as the primary event of pheromone transduction. In addition, our data indicate that at different times of the day different second messenger-dependent ion channels are available for pheromone transduction cascades.
Turnover in local parasite populations temporarily favors host outcrossing over self‐fertilization during experimental evolution

Abstract The ubiquity of outcrossing in plants and animals is difficult to explain given its costs relative to self‐fertilization. Despite these costs, exposure to changing environmental conditions can temporarily favor outcrossing over selfing. Therefore, recurring episodes of environmental change are predicted to favor the maintenance of outcrossing. Studies of host–parasite coevolution have provided strong support for this hypothesis. However, it is unclear whether multiple exposures to novel parasite genotypes in the absence of coevolution are sufficient to favor outcrossing. Using the nematode Caenorhabditis elegans and the bacterial parasite Serratia marcescens, we studied host responses to parasite turnover. We passaged several replicates of a host population that was well‐adapted to the S. marcescens strain Sm2170 with either Sm2170 or one of three novel S. marcescens strains, each derived from Sm2170, for 18 generations. We found that hosts exposed to novel parasites maintained higher outcrossing rates than hosts exposed to Sm2170. Nonetheless, host outcrossing rates declined over time against all but the most virulent novel parasite strain. Hosts exposed to the most virulent novel strain exhibited increased outcrossing rates for approximately 12 generations, but did not maintain elevated levels of outcrossing throughout the experiment. Thus, parasite turnover can transiently increase host outcrossing. These results suggest that recurring episodes of parasite turnover have the potential to favor the maintenance of host outcrossing. However, such maintenance may require frequent exposure to novel virulent parasites, rapid rates of parasite turnover, and substantial host gene flow.
Correlation Net : spatio temporal multimodal deep learning

This letter describes a network that is able to capture spatiotemporal correlations over arbitrary timestamps. The proposed scheme operates as a complementary, extended network over spatiotemporal regions. Recently, multimodal fusion has been extensively researched in deep learning. For action recognition, the spatial and temporal streams are vital components of deep Convolutional Neural Network (CNNs), but reducing the occurrence of overfitting and fusing these two streams remain open problems. The existing fusion approach is to average the two streams. To this end, we propose a correlation network with a Shannon fusion to learn a CNN that has already been trained. Long-range video may consist of spatiotemporal correlation over arbitrary times. This correlation can be captured using simple fully connected layers to form the correlation network. This is found to be complementary to the existing network fusion methods. We evaluate our approach on the UCF-101 and HMDB-51 datasets, and the resulting improvement in accuracy demonstrates the importance of multimodal correlation.
Abstractive and Extractive Text Summarization using Document Context Vector and Recurrent Neural Networks

Sequence to sequence (Seq2Seq) learning has recently been used for abstractive and extractive summarization. In current study, Seq2Seq models have been used for eBay product description summarization. We propose a novel Document-Context based Seq2Seq models using RNNs for abstractive and extractive summarizations. Intuitively, this is similar to humans reading the title, abstract or any other contextual information before reading the document. This gives humans a high-level idea of what the document is about. We use this idea and propose that Seq2Seq models should be started with contextual information at the first time-step of the input to obtain better summaries. In this manner, the output summaries are more document centric, than being generic, overcoming one of the major hurdles of using generative models. We generate document-context from user-behavior and seller provided information. We train and evaluate our models on human-extracted-golden-summaries. The document-contextual Seq2Seq models outperform standard Seq2Seq models. Moreover, generating human extracted summaries is prohibitively expensive to scale, we therefore propose a semi-supervised technique for extracting approximate summaries and using it for training Seq2Seq models at scale. Semi-supervised models are evaluated against human extracted summaries and are found to be of similar efficacy. We provide side by side comparison for abstractive and extractive summarizers (contextual and non-contextual) on same evaluation dataset. Overall, we provide methodologies to use and evaluate the proposed techniques for large document summarization. Furthermore, we found these techniques to be highly effective, which is not the case with existing techniques.
Set Cover and Vertex Cover with Delay

The set cover problem is one of the most fundamental problems in computer science. We present the problem of online set cover with delay (SCD). A family of sets with costs and a universe of elements are known in advance. Requests then arrive over time on the elements, and each request accumulates delay cost until served by the algorithm through buying a containing set. A request can only be served by sets that are bought after the request's arrival, and thus a set may be bought an unbounded number of times over the course of the algorithm. This property sets SCD apart from previous considerations of set cover in the online setting, in which there are no delays, elements are covered immediately, and sets stay bought permanently. This allows SCD to describe an unbounded process, with an unlimited number of requests for any given universe. For the SCD problem, we show an $O(\log k\cdot\log n)$-competitive randomized algorithm, where $n$ is the number of elements and $k$ is the maximum number of sets containing any single element. We also show a lower bound of $\Omega(\sqrt{\log k})$ and $\Omega(\sqrt{\log n})$ on the competitiveness of any algorithm for SCD. For the special case of Vertex Cover with Delay (VCD), we show a simple $3$-competitive deterministic algorithm. The $O(\log k\cdot\log n)$-competitive algorithm is based on exponential weights combined with the max operator (in contrast to most algorithms employing exponential weights, which use summation). The lower bound is described by a recursive construction.
Inhibition of mTOR downregulates expression of DNA repair proteins and is highly efficient against BRCA2-mutated breast cancer in combination to PARP inhibition

Breast cancer is a complex disease in which each patient could present several genetic alterations that are therapeutically relevant in cancers. Here we explored the therapeutic benefit of combining PARP and mTOR inhibitors in a context of DNA repair deficiency and PI3K pathway activation. The combination of everolimus and olaparib was tested in BRCA2-mutated patient-derived xenografts (PDX) carrying alterations in the PI3K/AKT/mTOR pathway. An RPPA analysis of different signalling pathways was performed in untreated and treated xenografts. Everolimus and olaparib showed marked anti-tumor activities in the monotherapy setting and high efficacy when given in combination with 100% of mice showing tumor regressions. The fraction of P-H2AX positive cells was increased in both monotherapy arms and strongly increased in the combination setting. Everolimus given as monotherapy resulted in downregulation of different proteins involved in DNA damage repair, including FANCD2, RAD50 and SUV39H1. In the combination setting, expression of these proteins was almost completely abolished, suggesting convergence of PARP and mTOR in downregulation of DNA damage repair components. In conclusion, our results suggest that combining mTOR and DNA repair inhibition could be a successful strategy to treat a subset of breast cancer with BRCA2 mutation and alterations in the PI3K/AKT/mTOR pathway.
Affordable Mobile-based Simulator for Robotic Surgery

Robotic surgery and novel surgical instrumentation present great potentials towards safer, more accurate and consistent minimally invasive surgery. However, their adoption is dependent to the access to training facilities and extensive surgical training. Robotic instruments require different dexterity skills compared to open or laparoscopic. Surgeons, therefore, are required to invest significant time by attending extensive training programs. Contrary, hands on experiences represent an additional operational cost for hospitals as the availability of robotic systems for training purposes is limited. All these technological and financial barriers for surgeons and hospitals hinder the adoption of robotic surgery. In this paper, we present a mobile dexterity training kit to develop basic surgical techniques within an affordable setting. The system could be used to train basic surgical gestures and to develop the motor skills needed for manoeuvring robotic instruments. Our work presents the architecture and components needed to create a simulated environment for training sub-tasks as well as a design for portable mobile manipulators that can be used as master controllers of different instruments. A preliminary study results demonstrate usability and skills development with this system.
Actor-Action Semantic Segmentation with Region Masks

In this paper, we study the actor-action semantic segmentation problem, which requires joint labeling of both actor and action categories in video frames. One major challenge for this task is that when an actor performs an action, different body parts of the actor provide different types of cues for the action category and may receive inconsistent action labeling when they are labeled independently. To address this issue, we propose an end-to-end region-based actor-action segmentation approach which relies on region masks from an instance segmentation algorithm. Our main novelty is to avoid labeling pixels in a region mask independently - instead we assign a single action label to these pixels to achieve consistent action labeling. When a pixel belongs to multiple region masks, max pooling is applied to resolve labeling conflicts. Our approach uses a two-stream network as the front-end (which learns features capturing both appearance and motion information), and uses two region-based segmentation networks as the back-end (which takes the fused features from the two-stream network as the input and predicts actor-action labeling). Experiments on the A2D dataset demonstrate that both the region-based segmentation strategy and the fused features from the two-stream network contribute to the performance improvements. The proposed approach outperforms the state-of-the-art results by more than 8% in mean class accuracy, and more than 5% in mean class IOU, which validates its effectiveness.
fMRI-Based Brain Responses to Quinine and Sucrose Gustatory Stimulation for Nutrition Research in the Minipig Model: A Proof-of-Concept Study

The minipig model is of high interest for brain research in nutrition and associated pathologies considering the similarities to human nutritional physiology, brain structures, and functions. In the context of a gustatory stimulation paradigm, fMRI can provide crucial information about the sensory, cognitive, and hedonic integration of exteroceptive stimuli in healthy and pathological nutritional conditions. Our aims were (i) to validate the experimental setup, i.e., fMRI acquisition and SPM-based statistical analysis, with a visual stimulation; (ii) to implement the fMRI procedure in order to map the brain responses to different gustatory stimulations, i.e., sucrose (5%) and quinine (10 mM), and (ii) to investigate the differential effects of potentially aversive (quinine) and appetitive/pleasant (sucrose) oral stimulation on brain responses, especially in the limbic and reward circuits. Six Yucatan minipigs were imaged on an Avanto 1.5-T MRI under isoflurane anesthesia and mechanical ventilation. BOLD signal was recorded during visual or gustatory (artificial saliva, sucrose, or quinine) stimulation with a block paradigm. With the visual stimulation, brain responses were detected in the visual cortex, thus validating our experimental and statistical setup. Quinine and sucrose stimulation promoted different cerebral activation patterns that were concordant, to some extent, to results from human studies. The insular cortex (i.e., gustatory cortex) was activated with both sucrose and quinine, but other regions were specifically activated by one or the other stimulation. Gustatory stimulation combined with fMRI analysis in large animals such as minipigs is a promising approach to investigate the integration of gustatory stimulation in healthy or pathological conditions such as obesity, eating disorders, or dysgeusia. To date, this is the first intent to describe gustatory stimulation in minipigs using fMRI.
Neural Mechanisms With Respect to Different Paradigms and Relevant Regulatory Factors in Empathy for Pain

Empathy for pain is thought to activate the affective-motivational components of the pain matrix, which includes the anterior insula and middle and anterior cingulate cortices, as indicated by functional magnetic resonance imaging and other methodologies. Activity in this core neural network reflects the affective experience that activates our responses to pain and lays the neural foundation for our understanding of our own emotions and those of others. Furthermore, although picture-based paradigms can activate somatosensory components of directly experienced pain, cue-based paradigms cannot. In addition to this difference, the two paradigms evoke other distinct neuronal responses. Although the automatic “perception-action” model has long been the dominant theory for pain empathy, a “bottom-up, top-down” mechanism seems to be more comprehensive and persuasive. Indeed, a variety of factors can regulate the intensity of empathy for pain through “top-down” processes. In this paper, we integrate and generalize knowledge regarding pain empathy and introduce the findings from recent studies. We also present ideas for future research into the neural mechanisms underlying pain empathy.
Attractant or Repellent? Behavioral Responses to Mammalian Blood Odor and to a Blood Odor Component in a Mesopredator, the Meerkat (Suricata suricatta)

It is well-established that the odor of mammalian blood is attractive to top predators such as tigers and wolves and aversive to prey species such as mice and rats. Recent studies have shown that the mammalian blood odor component trans-4,5-epoxy-(E)-2-decenal (TED) elicits corresponding behavioral responses in these two groups of mammals. Here we assess whether a mesopredator, that is, a small-bodied carnivorous mammal that is both predator and prey, is attracted to or repelled by the odor of mammalian blood and TED. To this end, we assessed the behavior of a group of 15 captive meerkats (Suricata suricatta) when presented with wooden logs that were impregnated either with horse blood or with TED, and compared it to their behavior toward a fruity odor (iso-pentyl acetate) and a near-odorless solvent (diethyl phthalate). We found that the meerkats displayed significantly more interactions with the odorized wooden logs such as sniffing and pawing when these were impregnated with the two prey-associated odors compared to the two non-prey-associated odors. Most importantly, no significant difference was found in the number of interactions with the wooden logs impregnated with horse blood and TED, respectively. These results demonstrate that meerkats, despite being small-bodied mesopredators, are clearly attracted to the odor of mammalian blood. Further, the results suggest that a single blood odor component can be as efficient as the odor of real blood in eliciting behavioral responses in this herpestid mammal, similar to previous findings in feline and canine top predators.
A Review on Differences in Effects on Normal and Malignant Cells and Tissues to Electroporation-Based Therapies: A Focus on Calcium Electroporation

Calcium electroporation is a potential novel anticancer treatment, where high concentrations of calcium are introduced into the cell cytosol by electroporation. This is a method where short, high-voltage pulses induce a transient permeabilization of the cell membrane and thereby allow influx and efflux of ions and molecules. Electroporation is used in combination with chemotherapeutic drugs (electrochemotherapy) as a standard treatment for cutaneous metastases, and electroporation using a higher electric field and number of pulses (irreversible electroporation) is increasingly being used as an anticancer treatment. In this review, calcium electroporation is described with emphasis on the investigations of differences in the effect on normal and malignant cells and tissues in vitro and in vivo. Calcium electroporation has been shown to induce cell death in vitro and tumor necrosis in vivo with a difference in sensitivity between different tumor types. Normal cells treated in vitro are significantly less affected than cancer cells, and a similar trend is shown in vivo where muscle and skin tissue surrounding a treated tumor as well as muscle and skin directly treated with calcium electroporation were less affected than tumors. The mechanism behind this difference in sensitivity is not fully understood but might be affected by differences in electric impedance, membrane repair, and expression of plasma membrane calcium ATPases in normal and malignant cells. The research on calcium electroporation shows a potential novel anticancer treatment with significant effect on cancer cells and tissues while normal cells and tissues are clearly less affected.
Understanding the Modeling of Computer Network Delays using Neural Networks

Recent trends in networking are proposing the use of Machine Learning (ML) techniques for the control and operation of the network. In this context, ML can be used as a computer network modeling technique to build models that estimate the network performance. Indeed, network modeling is a central technique to many networking functions, for instance in the field of optimization, in which the model is used to search a configuration that satisfies the target policy. In this paper, we aim to provide an answer to the following question: Can neural networks accurately model the delay of a computer network as a function of the input traffic? For this, we assume the network as a black-box that has as input a traffic matrix and as output delays. Then we train different neural networks models and evaluate its accuracy under different fundamental network characteristics: topology, size, traffic intensity and routing. With this, we aim to have a better understanding of computer network modeling with neural nets and ultimately provide practical guidelines on how such models need to be trained.
Effects of Soil Organisms on Aboveground Plant-Insect Interactions in the Field: Patterns, Mechanisms and the Role of Methodology

Soil biota-plant interactions play a dominant role in terrestrial ecosystems. Through nutrient mineralization and mutualistic or antagonistic interactions with plants soil biota can affect plant performance and physiology and via this affect plant-associated aboveground insects. There is a large body of work in this field that has already been synthesized in various review papers. However, most of the studies have been carried out under highly controlled laboratory or greenhouse conditions. Here, we review studies that manipulate soil organisms of four dominant taxa (i.e. bacteria, fungi, nematodes and soil arthropods) in the field and assess the effects on the growth of plants and interactions with associated aboveground insects. We show that soil organisms play an important role in shaping plant-insect interactions in the field and that general patterns can be found for some taxa. Plant growth-promoting rhizobacteria generally have negative effects on herbivore performance or abundance, most likely through priming of defenses in the host plant. Addition of arbuscular mycorrhizal fungi (AMF) has positive effects on sap sucking herbivores, which is likely due to positive effects of AMF on nutrient levels in the phloem. The majority of AMF effects on chewers were neutral but when present, AMF effects were positive for specialist and negative for generalist chewing herbivores. AMF addition has negative effects on natural enemies in the field, suggesting that AMF may affect plant attractiveness for natural enemies, e.g. through volatile profiles. Alternatively, AMF may affect the quality of prey or host insects mediated by plant quality, which may in turn affect the performance and density of natural enemies. Nematodes negatively affect the performance of sap sucking herbivores (generally through phloem quality) but have no effect on chewing herbivores. For soil arthropods there are no clear patterns yet. We further show that the methodology used plays an important role in influencing the outcomes of field studies. Studies using potted plants in the field and studies that remove target soil taxa by means of pesticides are most likely to detect significant results. Lastly, we discuss suggestions for future research that could increase our understanding of soil biota-plant-insect interactions in the field.