Miracle-dz/UniFAR-FTUs huggingface dataset:
Reshaping Robot Trajectories Using Natural Language Commands: A Study of Multi-Modal Data Alignment Using Transformers

Natural language is the most intuitive medium for us to interact with other people when expressing commands and instructions. However, using language is seldom an easy task when humans need to express their intent towards robots, since most of the current language interfaces require rigid templates with a static set of action targets and commands. In this work, we provide a flexible language-based interface for human-robot collaboration, which allows a user to reshape existing trajectories for an autonomous agent. We take advantage of recent advancements in the field of large language models (BERT and CLIP) to encode the user command, and then combine these features with trajectory information using multi-modal attention transformers. We train the model using imitation learning over a dataset containing robot trajectories modified by language commands, and treat the trajectory generation process as a sequence prediction problem, analogously to how language generation architectures operate. We evaluate the system in multiple simulated trajectory scenarios, and show a significant performance increase of our model over baseline approaches. In addition, our real-world experiments with a robot arm show that users significantly prefer our natural language interface over traditional methods such as kinesthetic teaching or cost-function programming. Our study shows how the field of robotics can take advantage of large pre-trained language models towards creating more intuitive interfaces between robots and machines. Project webpage: https://arthurfenderbucker.github. io/NL_trajectory_reshaper/
Sparse bounds for maximal triangle and bilinear spherical averaging operators

We show that the method in the recent work [20] by Roncal, Shrivastava, and Shuin can be adapted to show that certain L p -improving bounds in the interior of the boundedness region for the bilinear spherical or triangle averaging operator imply sparse bounds for the corresponding lacunary maximal operator, and that L p -improving bounds in the interior of the boundedness region for the corresponding single-scale maximal operators imply sparse bounds for the correpsonding full maximal operators. More generally we show that the proof in [20] applies for bilinear convolutions with compactly supported ﬁnite Borel measures that satisfy appropriate L p -improving and continuity estimates. This shows that the method used by Roncal, Shrivastava, and Shuin in [20] can be adapted obtain sparse bounds for a general class of bilinear operators that are not of product type, for a certain range of L p exponents.
Probing as Quantifying Inductive Bias

Pre-trained contextual representations have led to dramatic performance improvements on a range of downstream tasks. Such performance improvements have motivated researchers to quantify and understand the linguistic information encoded in these representations. In general, researchers quantify the amount of linguistic information through probing, an endeavor which consists of training a supervised model to predict a linguistic property directly from the contextual representations. Unfortunately, this definition of probing has been subject to extensive criticism in the literature, and has been observed to lead to paradoxical and counter-intuitive results. In the theoretical portion of this paper, we take the position that the goal of probing ought to be measuring the amount of inductive bias that the representations encode on a specific task. We further describe a Bayesian framework that operationalizes this goal and allows us to quantify the representations’ inductive bias. In the empirical portion of the paper, we apply our framework to a variety of NLP tasks. Our results suggest that our proposed framework alleviates many previous problems found in probing. Moreover, we are able to offer concrete evidence that—for some tasks—fastText can offer a better inductive bias than BERT.
Investigation of Flow across a Symmetrical Airfoil with Backward Facing Step

Airfoil designers are in continuous pursuit of airfoils with the highest Lift Coefficient. One way to improve the Lift Coefficient is to introduce a backward facing step at the airfoil. This paper reports on a CFD studies to assess the effect of introducing backward facing step in NACA 0012 airfoil. Results show that introducing the backward facing step has improved the Lift Coefficient for Reynolds's number of 500000 over 15AoA. The CFD results were validated by comparing them to available experimental result and good agreement was reported.
An uncommon garden experiment: microenvironment has stronger influence on phenotypic variation than epigenetic memory in the clonal Lombardy poplar

Environmental changes can trigger phenotypic variation in plants through epigenetic mechanisms, but strong genetic influences on epigenetic variation and phenotypes make it difficult to isolate and study these effects. We investigated phenotypic plasticity using the Lombardy poplar (Populus nigra cv. ‘Italica’ Duroi), a globaly distributed clonal tree. We surveyed 14 functional traits related to tree growth, ecophysiological and phenological processes in poplar ramets collected along a wide geographical range in Europe and planted under common garden conditions. We investigated whether phenotypic variation was related to geography and historical bioclimatic data of the ramets’ sites of origin using linear mixed effect models. We found significant differences in among ramets from different geographic origins in tree height, number of stems per ramet and duration of bud flush. However, microenvironmental variation in the common garden, captured via block effects, had an even bigger impact on phenotypic variation than the environmental conditions at the sites of origin. Our results show that phenotypic variation in the ramets might be associated to the climate origin from different climates, suggesting possible epigenetic memory. However, such legacy effects might be quickly outweighed by new environmental conditions.
Differences in neuroinflammation in the olfactory bulb between D614G, Delta and Omicron BA.1 SARS-CoV-2 variants in the hamster model

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with various neurological complications. SARS-CoV-2 infection induces neuroinflammation in the central nervous system (CNS), whereat the olfactory bulb seems to be involved most frequently. Here we show differences in the neuroinvasiveness and neurovirulence among SARS-CoV-2 variants in the hamster model five days post inoculation. Replication in the olfactory mucosa was observed in all hamsters, but most prominent in D614 inoculated hamsters. We observed neuroinvasion into the CNS via the olfactory nerve in D614G-, but not Delta (B.1.617.2)- or Omicron BA.1 (B.1.1.529) inoculated hamsters. Neuroinvasion was associated with neuroinflammation in the olfactory bulb of hamsters inoculated with D614G but hardly in Delta or Omicron BA.1. Altogether, this indicates that there are differences in the neuroinvasive and neurovirulent potential among SARS-CoV-2 variants in the acute phase of the infection in the hamster model.
BNIP3 Upregulation Characterizes Cancer Cell Subpopulation With Increased Fitness and Proliferation

BNIP3 is a BH3 only protein with both pro-apoptotic and pro-survival roles depending on the cellular context. It remains unclear how BNIP3 RNA level dictates cell fate decisions of cancer cells. Here we undertook a quantitative analysis of BNIP3 expression and functions in single cell datasets of various epithelial malignancies. Our results demonstrated that BNIP3 upregulation characterizes cancer cell subpopulations with increased fitness and proliferation. We further validated the upregulation of BNIP3 in liver cancer organoids compared with 2D culture. Taken together, the combination of in silico perturbations using public single cell datasets and experimental cancer modeling using organoids ushered in a new approach to address cancer heterogeneity.
Rethinking the Methods and Algorithms for Inner Speech Decoding - and Making them Reproducible

This study focuses on the automatic decoding of inner speech using noninvasive methods, such as electroencephalography (EEG)). While inner speech has been a research topic in philosophy and psychology for half a century, recent attempts have been made to decode nonvoiced spoken words by using various brain-computer interfaces. The main shortcomings of existing work are reproducibility and the availability of data and code. In this work, we investigate various methods (using Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Long Short-Term Memory Networks (LSTM)) for the detection task of 5 vowels and 6 words on a publicly available EEG dataset. The main contributions of this work are (1) subject dependent vs. subject-independent approaches, (2) the effect of different preprocessing steps (Independent Component Analysis (ICA), down-sampling and filtering), and (3) word classification (where we achieve state-of-the-art performance on a publicly available dataset). Overall we achieve a performance accuracy of 35.20% and 29.21% when classifying 5 vowels and 6 words respectively in a publicly available dataset, using our tuned iSpeech-CNN architecture. All of our code and processed data are publicly available to ensure reproducibility. As such, this work contributes to a deeper understanding and reproducibility of experiments in the area of inner speech detection.
SARS-CoV-2 Harnesses Host Translational Shutoff and Autophagy To Optimize Virus Yields: the Role of the Envelope (E) Protein

In late 2019, a new β-coronavirus, SARS-CoV-2, entered the human population causing a pandemic that has resulted in over 6 million deaths worldwide. Although closely related to SARS-CoV, the mechanisms of SARS-CoV-2 pathogenesis are not fully understood. ABSTRACT The SARS-CoV-2 virion is composed of four structural proteins: spike (S), nucleocapsid (N), membrane (M), and envelope (E). E spans the membrane a single time and is the smallest, yet most enigmatic of the structural proteins. E is conserved among coronaviruses and has an essential role in virus-mediated pathogenesis. We found that ectopic expression of E had deleterious effects on the host cell as it activated stress responses, leading to LC3 lipidation and phosphorylation of the translation initiation factor eIF2α that resulted in host translational shutoff. During infection E is highly expressed, although only a small fraction is incorporated into virions, suggesting that E activity is regulated and harnessed by the virus to its benefit. Consistently, we found that proteins from heterologous viruses, such as the γ1 34.5 protein of herpes simplex virus 1, prevented deleterious effects of E on the host cell and allowed for E protein accumulation. This observation prompted us to investigate whether other SARS-CoV-2 structural proteins regulate E. We found that the N and M proteins enabled E protein accumulation, whereas S did not. While γ1 34.5 protein prevented deleterious effects of E on the host cells, it had a negative effect on SARS-CoV-2 replication. The negative effect of γ1 34.5 was most likely associated with failure of SARS-CoV-2 to divert the translational machinery and with deregulation of autophagy. Overall, our data suggest that SARS-CoV-2 causes stress responses and subjugates these pathways, including host protein synthesis (phosphorylated eIF2α) and autophagy, to support optimal virus replication. IMPORTANCE In late 2019, a new β-coronavirus, SARS-CoV-2, entered the human population causing a pandemic that has resulted in over 6 million deaths worldwide. Although closely related to SARS-CoV, the mechanisms of SARS-CoV-2 pathogenesis are not fully understood. We found that ectopic expression of the SARS-CoV-2 E protein had detrimental effects on the host cell, causing metabolic alterations, including shutoff of protein synthesis and mobilization of cellular resources through autophagy activation. Coexpression of E with viral proteins known to subvert host antiviral responses such as autophagy and translational inhibition, either from SARS-CoV-2 or from heterologous viruses, increased cell survival and E protein accumulation. However, such factors were found to negatively impact SARS-CoV-2 infection, as autophagy contributes to formation of viral membrane factories and translational control offers an advantage for viral gene expression. Overall, SARS-CoV-2 has evolved mechanisms to harness host functions that are essential for virus replication.
Targeting Reductive Metabolic Shifts by T315I Mutation in BCR-ABL Myeloid Leukemia for Therapy

T315I mutation of Bcr-Abl in chronic myeloid leukemia (CML) leads to therapeutic resistance. It is known that Bcr-Abl transformation causes ROS-induced DNA damages and replication stress, which can be exploited for anti-nucleotide therapy. We developed a small compound, JMF4073, which inhibited pyrimidylate kinases and selectively eliminated Bcr-Abl-transformed, but not untransformed myeloid cells, due to dTTP exhaustion and ROS-induced replication stress. However, T315I-Bcr-Abl-transformed cells were less vulnerable to JMF4073 because of higher dTTP pool and low replication stress. Unlike WT-Bcr-Abl-transformed cells, T315I-Bcr-Abl cells lacked Sirt1- regulated OXPHOS with increased glutamine flux to reductive carboxylation in TCA cycle and glutathione synthesis. Blocking mitochondrial pyruvate carrier (MPC) by UK-5099 reduced NADH and glutathione levels with replication stress induction, thereby converting T315I-Bcr-Abl cells sensitive to JMF4073 with dTTP and dCTP depletion. The combination of JMF4073 with UK-5099 showed in vivo eradication of T315I-Bcr-Abl-CML. These data reveal that T315I mutation causes reductive metabolic shifts in Bcr-Abl-CML, and demonstrate the therapeutic option by co-targeting MPC and pyrimidylate kinases.
The Effects of Antibiotic Combination Treatments on Pseudomonas aeruginosa Tolerance Evolution and Coexistence with Stenotrophomonas maltophilia

Cystic fibrosis (CF) is a genetic condition that results in thick mucus secretions in the lungs that are susceptible to chronic bacterial infections. The bacterial pathogen Pseudomonas aeruginosa is often associated with morbidity in CF and is difficult to treat due to its high resistance to antibiotics. ABSTRACT The Pseudomonas aeruginosa bacterium is a common pathogen of cystic fibrosis (CF) patients due to its ability to evolve resistance to antibiotics during treatments. While P. aeruginosa resistance evolution is well-characterized in monocultures, it is less well-understood in polymicrobial CF infections. Here, we investigated how exposure to ciprofloxacin, colistin, or tobramycin antibiotics, administered at sub-minimum inhibitory concentration (MIC) doses, both alone and in combination, shaped the tolerance evolution of P. aeruginosa (PAO1 lab and clinical CF LESB58 strains) in the absence and presence of a commonly co-occurring species, Stenotrophomonas maltophilia. The increases in antibiotic tolerances were primarily driven by the presence of that antibiotic in the treatment. We observed a reciprocal cross-tolerance between ciprofloxacin and tobramycin, and, when combined, the selected antibiotics increased the MICs for all of the antibiotics. Though the presence of S. maltophilia did not affect the tolerance or the MIC evolution, it drove P. aeruginosa into extinction more frequently in the presence of tobramycin due to its relatively greater innate tobramycin tolerance. In contrast, P. aeruginosa dominated and drove S. maltophilia extinct in most other treatments. Together, our findings suggest that besides driving high-level antibiotic tolerance evolution, sub-MIC antibiotic exposure can alter competitive bacterial interactions, leading to target pathogen extinctions in multispecies communities. IMPORTANCE Cystic fibrosis (CF) is a genetic condition that results in thick mucus secretions in the lungs that are susceptible to chronic bacterial infections. The bacterial pathogen Pseudomonas aeruginosa is often associated with morbidity in CF and is difficult to treat due to its high resistance to antibiotics. The resistance evolution of Pseudomonas aeruginosa is poorly understood in polymicrobial infections that are typical of CF. To study this, we exposed P. aeruginosa to sublethal concentrations of ciprofloxacin, colistin, or tobramycin antibiotics in the absence and presence of a commonly co-occurring CF species, Stenotrophomonas maltophilia. We found that low-level antibiotic concentrations selected for high-level antibiotic resistance. While P. aeruginosa dominated in most antibiotic treatments, S. maltophilia drove it into extinction in the presence of tobramycin due to an innately higher tobramycin resistance. Our findings suggest that, besides driving high-level antibiotic tolerance evolution, sublethal antibiotic exposure can magnify competition in bacterial communities, which can lead to target pathogen extinctions in multispecies communities.
Multiple-timescale dynamics, mixed mode oscillations and mixed affective states in a model of Bipolar Disorder

Mixed affective states in bipolar disorder is a common psychiatric condition that occurs when symptoms of the two opposite poles coexist during an episode of mania or depression. There exist many strategies to attempt to model the mood variations of patients with bipolar disorder (BD) as recently reviewed in [14]. A four-dimensional model by A. Goldbeter [28, 29] rests upon the notion that manic and depressive symptoms are produced by two competing and auto-inhibited neural networks. Some of the rich dynamics that this model can produce, include complex rhythms formed by both small-amplitude (subthreshold) and large-amplitude (suprathreshold) oscillations and could correspond to mixed bipolar states. These rhythms are commonly referred to as mixed mode oscillations (MMOs) and they have already been studied in different contexts ranging from neurons’ electrical activity [7] to oscillatory chemical reactions [51]. In order to accurately explain these dynamics one has to apply a mathematical apparatus that is making full use of the timescale separation between variables. Here we apply this very framework to the model of BD from [28, 29] in order to understand the mathematical mechanisms underpinning the observed dynamics of changing mood using multiple-timescale dynamics. We show that the observed complex oscillations can be understood as MMOs due to a so-called folded-node singularity. Moreover we explore the bifurcation structure of the system with respect to two meaningful parameters and we provide possible biological interpretations of our findings. Furthermore, we show the possibility of deterministic chaos in this system and the robustness of the MMOs regime to stochastic noise. Finally, we propose a reduction of the original 4D model to a minimal three-dimensional model which, with the addition of noise, exhibits similar yet purely noise-driven dynamics. The broader significance of this work is to introduce mathematical tools that could be used to analyse and potentially control future, more biologically grounded models of BD.
Temporal coordination between chromosome mobility and homologous recombination

Homologous recombination (HR), a principal cellular pathway for double-strand break (DSB) repair, is linked to changes in chromosome movement. Although increased chromosome mobility in response to a DSB has been observed in a variety of species, its precise role in HR remains controversial. Here, we find that end resection, the recruitment of recombination proteins, increased chromosome mobility, the pairing of homologs and gene conversion are temporally linked in response to a DSB. In mre11Δ mutant cells, which exhibit a delay in the initial processing of a DSB, chromosome mobility and all subsequent recombination events are also delayed. Overexpression of the Dna2 nuclease suppresses the mre11Δ delay in end resection and restores the original timing of chromosome mobility and all subsequent downstream HR events. Thus, changing the timing of chromosome mobility results in a corresponding change in essential downstream HR events, reinforcing its mechanistic role in the DNA repair process.
Successor-like representation guides the prediction of future events in human visual cortex and hippocampus

Human agents build models of their environment, which enable them to anticipate and plan upcoming events. However, little is known about the properties of such predictive models. Recently, it has been proposed that hippocampal representations take the form of a predictive map-like structure, the so-called successor representation. Here we used human fMRI to probe whether activity in the early visual cortex (V1) and hippocampus adhere to the postulated properties of the successor representation after visual sequence learning. Participants were exposed to an arbitrary spatiotemporal sequence consisting of four items (A-B-C-D). We found that after repeated exposure to the sequence, merely presenting single sequence items (e.g., - B - -) resulted in V1 activation at the successor locations of the full sequence (e.g., C-D), but not at the predecessor locations (e.g., A). This highlights that visual representations are skewed toward future states, in line with the successor representation. Similar results were also found in the hippocampus. Moreover, the hippocampus developed a tuning profile that showed sensitivity to the temporal distance in sequence-space, with fading representations for sequence events in the more distant past and future. V1, in contrast, showed a tuning profile that was only sensitive to spatial distance in stimulus-space. Together, these results provide empirical evidence for the proposition that both visual and hippocampal cortex represent a predictive map of the visual world akin to the successor representation.
Oscillatory dynamics underlying emotion-cognition integration: differential role of theta and alpha oscillations

A fundamental aspect of human mental life is the seamless ability for integration of emotion and cognition. Despite progress regarding the spatial architecture of Emotion-Cognition Integration (ECI), the time course of ECI processes remains unclear. To examine the temporal organization of brain oscillations underpinning ECI, we simultaneously manipulated emotional valence of stimuli and cognitive task demand while recording electrophysiological responses of 61 participants. They were asked to complete tasks with low (body-part judgement) and high (laterality judgement) cognitive demand while viewing other people photographs that varied on dimensions of laterality (left or right), body-part (hand or foot), and emotional valence (pain or no pain). We found increased reaction times and error rates in pain versus no pain during laterality judgement relative to body-part judgement, suggesting reciprocal inhibition between emotion and cognition. EEG results showed that 1) emotion processing (valence) occurred first in the theta band from 144 to 372 ms; 2) cognitive processing (laterality) took place in the theta band from 332 to 608 ms; 3) emotional and cognitive processes were integrated in the alpha band from 268 ms and lasted to 800 ms. These findings reveal oscillatory dynamics of the processing and integration of emotion and cognition, providing further insights into the underlying neurophysiology. This may ultimately contribute to our understanding of ECI processing in psychopathology.