Miracle-dz/UniFAR-FTUs huggingface dataset:
Size aftereffect is non-local

It is well known that prolonged exposure to a certain size stimulus alters the perceived size of a subsequently presented stimulus at the same location. How the rest of the visual space is affected by this size adaptation, however, has not been systematically studied before. Here, to fill this gap in literature, we tested size adaptation at the adapter location as well as the rest of the visual space. We used peripherally presented solid discs (Experiment 1) and rings (Experiment 2) as adapter and target (test) stimuli. Observers adapted to a mid-sized stimulus and judged the size of the subsequently presented smaller or larger target stimuli. Results showed that the perceived sizes of target stimuli were repelled away from the adapter size, not only at the adapter location but also at distant locations. These findings demonstrate that size adaptation causes widespread distortion of the visual space and alters perceived size. We discuss possible computational models that may underpin the perceptual effect.
Selection and suppression of visual information in the macaque prefrontal cortex

In everyday life, we are continuously struggling at focusing on our current goals while at the same time avoiding distractions. Attention is the neuro-cognitive process devoted to the selection of behaviorally relevant sensory information while at the same time preventing distraction by irrelevant information. Visual selection can be implemented by both long-term (learning-based spatial prioritization) and short term (dynamic spatial attention) mechanisms. On the other hand, distraction can be prevented proactively, by strategically prioritizing task-relevant information at the expense of irrelevant information, or reactively, by actively suppressing the processing of distractors. The distinctive neuronal signature of each of these four processes is largely unknown. Likewise, how selection and suppression mechanisms interact to drive perception has never been explored neither at the behavioral nor at the neuronal level. Here, we apply machine-learning decoding methods to prefrontal cortical (PFC) activity to monitor dynamic spatial attention with an unprecedented spatial and temporal resolution. This leads to several novel observations. We first identify independent behavioral and neuronal signatures for learning-based attention prioritization and dynamic attentional selection. Second, we identify distinct behavioral and neuronal signatures for proactive and reactive suppression mechanisms. We find that while distracting task-relevant information is suppressed proactively, task-irrelevant information is suppressed reactively. Critically, we show that distractor suppression, whether proactive or reactive, strongly depends on both learning-based attention prioritization and dynamic attentional selection. Overall, we thus provide a unified neuro-cognitive framework describing how the prefrontal cortex implements spatial selection and distractor suppression in order to flexibly optimize behavior in dynamic environments.
ADVANCED JOURNAL OF EMERGENCY MEDICINE. 2018; 2(1): e6

Introduction: Leaving the hospital without notice is among the problems that can inflict financial and nonfinancial burdens on the health care system of a country. Objective: The present study was carried out with the aim of evaluating the prevalence of leaving without notice cases in the emergency department (ED) of one of the major teaching hospitals of Tehran affiliated with Shahid Beheshti University of Medical Sciences and calculating the direct costs resulting from it. Methods: This study was a retrospective cross-sectional one carried out during 1 year from 2016 to 2017 in one of the teaching hospitals of Tehran affiliated with Shahid Beheshti University of Medical Sciences. Sampling was performed via census method and the study population consisted of the profiles of all the patients who had left the hospital without notice or checking out after being admitted to the hospital. To gather the required data for this study, a checklist consisting of questions regarding sex, age, insurance coverage, and the amount of money they owed the hospital was used. Statistical analysis was performed using the software IBM Statistics for Windows v22 and P-value<0.05 was considered significant. Results: Out of the total of 39946 patients visiting the ED of the studied hospital during 1 year, 1692 (4.2%) had left the hospital without checking out. Below 30 years age range was the most common age range with 46.9% (794 patients) and 72.9% of the patients leaving without notice were men. Based on the findings obtained, male patients without insurance coverage had attempted to leave the hospital without notice more than others (p<0.001). The total cost inflicted by leaving without notice throughout the studied year was 1,755,286,279 Rials, which is equal to 0.0059 of the total annual income of the ED. Conclusion: Throughout the year this study was performed, a total of 1.2% of all the visitors of the ED of a hospital affiliated with Shahid Beheshti University of Medical Sciences left the hospital without notice or checking out, which inflicted a considerable cost on the ED.
Deciphering the rules of mRNA structure differentiation in vivo and in vitro with deep neural networks in Saccharomyces cerevisiae

The structure of mRNA in vivo is influenced by various factors involved in the translation process, resulting in significant differentiation of mRNA structure from that in vitro. Because multiple factors cause the differentiation of in vivo and in vitro mRNA structures, it was difficult to perform a more accurate analysis of mRNA structures in previous studies. In this study, we have proposed a novel application of a deep neural network (DNN) model to predict the structural stability of mRNA in vivo by fitting six quantifiable features that may affect mRNA folding: ribosome density, minimum folding free energy, GC content, mRNA abundance, ribosomal initial density and position of mRNA structure. Simulated mutations of the mRNA structure were designed and then fed into the trained DNN model to compute their structural stability. We found unique effects of these six features on mRNA structural stability in vivo. Strikingly, the ribosome density of the structural region is the most important factor affecting the structural stability of mRNA in vivo, and the strength of the mRNA structure in vitro should have a relatively small effect on its structural stability in vivo. The recruitment of DNNs provides a new paradigm to decipher the differentiation of mRNA structure in vivo and in vitro. This improved knowledge on the mechanisms of factors influencing mRNA structural stability will facilitate the design and functional analysis of mRNA structure in vivo.
Targeting CXCR1 and CXCR2 to overcome radiotherapy resistance in PTEN-deficient prostate carcinoma

Functional impairment of the tumour-suppressor PTEN is common in primary-prostate cancer and has been linked to relapse post-radiotherapy (RT). Pre-clinical modelling supports elevated CXC-chemokine signaling as a critical mediator of PTEN-depleted disease progression and therapeutic resistance. We assessed the correlation of PTEN-deficiency with CXC-chemokine signaling and its association with clinical outcomes. Gene expression analysis characterized a PTENLOW/CXCR1HIGH/CXCR2HIGH cluster of tumors that associates with earlier time-to-biochemical recurrence (HR 5.87 and HR 2.65 respectively) and development of systemic metastasis (HR 3.51). In vitro, CXCL-signaling was further amplified following exposure of PTEN-deficient prostate cancer cell lines to ionizing radiation (IR). Inhibition of CXCR1/2-signaling in PTEN-depleted cell-based models increased IR-sensitivity. In vivo, administration of a CXCR1/2-targeted pepducin (x1/2pal-i3), or CXCR2-specific antagonist (AZD5069), in combination with IR to PTEN-deficient xenografts attenuated tumor growth and progression compared to control or IR alone. Post-mortem analysis confirmed that x1/2pal-i3 administration attenuated IR-induced CXCL-signaling and anti-apoptotic protein expression. Interventions targeting CXC-chemokine signaling may provide an effective strategy to combine with radiotherapy, in both locally-advanced and oligometastatic-prostate cancers, with known presence of PTEN-deficient foci.
Primary myeloid cell proteomics and transcriptomics: importance of β-tubulin isotypes for osteoclast function

ABSTRACT Among hematopoietic cells, osteoclasts (OCs) and immature dendritic cells (DCs) are closely related myeloid cells with distinct functions: OCs participate skeleton maintenance while DCs sample the environment for foreign antigens. Such specificities rely on profound modifications of gene and protein expression during OC and DC differentiation. We provide global proteomic and transcriptomic analyses of primary mouse OCs and DCs, based on original stable isotope labeling with amino acids in cell culture (SILAC) and RNAseq data. We established specific signatures for OCs and DCs, including genes and proteins of unknown functions. In particular, we showed that OCs and DCs have the same α- and β-tubulin isotype repertoire but that OCs express much more of the β tubulin isotype Tubb6 (also known as TBB6). In both mouse and human OCs, we demonstrate that elevated expression of Tubb6 in OCs is necessary for correct podosomes organization and thus for the structure of the sealing zone, which sustains the bone resorption apparatus. Hence, lowering Tubb6 expression hinders OC resorption activity. Overall, we highlight here potential new regulators of OC and DC biology, and illustrate the functional importance of the tubulin isotype repertoire in the biology of differentiated cells. Summary: Analysis of proteomic and transcriptomic data of primary myeloid cells reveals that the β-tubulin isotype Tubb6 participates in osteoclast podosome patterning, sealing zone structure and resorption activity.
Differential encoding in prefrontal cortex projection neuron classes across cognitive tasks

Single-cell transcriptomics has been widely applied to classify neurons in the mammalian brain, while systems neuroscience has historically analyzed the encoding properties of cortical neurons without considering cell types. Here we examine how specific transcriptomic types of mouse prefrontal cortex (PFC) projection neurons relate to axonal projections and encoding properties across multiple cognitive tasks. We found that most types projected to multiple targets, and most targets received projections from multiple types, except PFC→PAG (periaqueductal gray). By comparing Ca2+-activity of the molecularly homogeneous PFC→PAG type against two heterogeneous classes in several two-alternative choice tasks in freely-moving mice, we found that all task-related signals assayed were qualitatively present in all examined classes. However, PAG-projecting neurons most potently encoded choice in cued tasks, whereas contralateral PFC-projecting neurons most potently encoded reward context in an uncued task. Thus, task signals are organized redundantly, but with clear quantitative biases across cells of specific molecular-anatomical characteristics.
Simultaneous mental updating in physical space and number space

People can track moving objects after they disappear or become occluded. For instance, they can estimate the position of a car after it goes behind a truck. People can also track through feature space after occlusion. For instance, they can estimate the current number on a hidden stopwatch. Previous work has suggest that a common rate control module paces mental updating in both physical space and number-space. We tested this by comparing single and dual task conditions. On every trial, participants observed a moving target-counter travel clockwise round a circular track. In the then disappeared for 2, 4, 6 or 8 seconds, and beep sounded. In the single task blocks, participants either estimated the final position or the final number. In the dual task block, they estimated final position and final number. Performance was very similar in single and dual task blocks. This suggests people can extrapolate through physical space and number space simultaneously. This is a challenge to the common rate control model, which implies there should be some dual task interference. We conclude that if a common rate controller exists, it is capable of pacing simultaneous mental simulations in different dimensions.
TBtools, a Toolkit for Biologists integrating various HTS-data handling tools with a user-friendly interface

Various softwares or pipelines have been developed for biological information mining from high-throughput sequencing (HTS) data, and most of them relies on programming and command-line environment with which most biologists are unfamiliar. Bioinformatic tools with an user-friendly interface are preferred by wet-lab biologists. Here, we describe TBtools, a Toolkit for Biologists integrating various HTS-data handling tools with a user-friendly interface. It includes a large collection of functions, which facilitate many simple, routine but elaborate tasks working on HTS data, such as bulk sequence extraction, gene set functional enrichment, venn diagram and etc. TBtools can run under all operating systems with JRE1.6 and is freely available at github.com/CJ-Chen/TBtools. Since its development, it has been used by many researchers. It will be a useful toolkit for wet-lab biologists to work on all kinds of high-throughput data.
Learning to control the brain through adaptive closed-loop patterned stimulation

Objective. Stimulation of neural activity is an important scientific and clinical tool, causally testing hypotheses and treating neurodegenerative and neuropsychiatric diseases. However, current stimulation approaches cannot flexibly control the pattern of activity in populations of neurons. To address this, we developed a model-free, adaptive, closed-loop stimulation (ACLS) system that learns to use multi-site electrical stimulation to control the pattern of activity of a population of neurons. Approach. The ACLS system combined multi-electrode electrophysiological recordings with multi-site electrical stimulation to simultaneously record the activity of a population of 5–15 multiunit neurons and deliver spatially-patterned electrical stimulation across 4–16 sites. Using a closed-loop learning system, ACLS iteratively updated the pattern of stimulation to reduce the difference between the observed neural response and a specific target pattern of firing rates in the recorded multiunits. Main results. In silico and in vivo experiments showed ACLS learns to produce specific patterns of neural activity (in ∼15 min) and was robust to noise and drift in neural responses. In visual cortex of awake mice, ACLS learned electrical stimulation patterns that produced responses similar to the natural response evoked by visual stimuli. Similar to how repetition of a visual stimulus causes an adaptation in the neural response, the response to electrical stimulation was adapted when it was preceded by the associated visual stimulus. Significance. Our results show an ACLS system that can learn, in real-time, to generate specific patterns of neural activity. This work provides a framework for using model-free closed-loop learning to control neural activity.
Semantic context enhances neural envelope tracking

The speech envelope is known to be essential for speech understanding and can be reconstructed from the electroencephalography (EEG) signal in response to running speech. Today, the factors influencing this neural tracking of the speech envelope are still under debate. Is envelope tracking mainly related to the encoding of acoustic speech information or is it influenced by top-down processing of speech understanding and the availability of semantic context in the stimulus? We recorded the EEG in 19 normal-hearing participants while they listened to two types of stimuli: concatenated Matrix sentences without contextual information and a coherent story. Each stimulus was presented with varying levels of background noise to vary speech understanding. The speech envelope was reconstructed from the EEG in both the delta (0.5-4 Hz) and the theta (4-8 Hz) band with the use of a linear decoder and then correlated with the real speech envelope in that band. We also conducted a spatiotemporal analysis using temporal response functions (TRFs). For both stimulus types and filter bands the correlation between the speech envelope and the reconstructed envelope increased with increasing speech understanding. In addition, correlations were higher for the story compared to the Matrix sentences, indicating that neural envelope tracking may be enhanced by the availability of semantic context in the stimulus and speech understanding.
Left prefrontal impact links subthalamic stimulation with depressive symptoms

Objective Subthalamic nucleus deep brain stimulation (STN-DBS) in Parkinson’s Disease (PD) not only stimulates focal target structures but also affects distributed brain networks. The impact this network modulation has on non-motor DBS effects is not well characterized. By focusing on the affective domain, we systematically investigate the impact of electrode placement and associated structural connectivity on changes in depressive symptoms following STN-DBS which have been reported to improve, worsen or remain unchanged. Methods Depressive symptoms before and after STN-DBS surgery were documented in 116 PD patients from three DBS centers (Berlin, Queensland, Cologne). Based on individual electrode reconstructions, the volumes of tissue activated (VTA) were estimated and combined with normative connectome data to identify structural connections passing through VTAs. Berlin and Queensland cohorts formed a training and cross-validation dataset used to identify structural connectivity explaining change in depressive symptoms. The Cologne data served as test-set for which depressive symptom change was predicted. Results Structural connectivity was linked to depressive symptom change under STN-DBS. An optimal connectivity map trained on the Berlin cohort could predict changes in depressive symptoms in Queensland patients and vice versa. Furthermore, the joint training-set map predicted changes in depressive symptoms in the independent test-set. Worsening of depressive symptoms was associated with left prefrontal connectivity. Interpretation Fibers linking the STN electrode with left prefrontal areas predicted worsening of depressive symptoms. Our results suggest that for the left STN-DBS lead, placement impacting fibers to left prefrontal areas should be avoided to maximize improvement of depressive symptoms.
Limited correspondence in visual representation between the human brain and convolutional neural networks

Convolutional neural networks (CNNs) have achieved very high object categorization performance recently. It has increasingly become a common practice in human fMRI research to regard CNNs as working model of the human visual system. Here we reevaluate this approach by comparing fMRI responses from the human brain in three experiments with those from 14 different CNNs. Our visual stimuli included original and filtered versions of real-world object images and images of artificial objects. Replicating previous findings, we found a brain-CNN correspondence in a number of CNNs with lower and higher levels of visual representations in the human brain better resembling those of lower and higher CNN layers, respectively. Moreover, the lower layers of some CNNs could fully capture the representational structure of human early visual areas for both the original and filtered real-world object images. Despite these successes, no CNN examined could fully capture the representational structure of higher human visual processing areas. They also failed to capture that of artificial object images in all levels of visual processing. The latter is particularly troublesome, as decades of vision research has demonstrated that the same algorithms used in the processing of natural images would support the processing of artificial visual stimuli in the primate brain. Similar results were obtained when a CNN was trained with stylized object images that emphasized shape representation. CNNs likely represent visual information in fundamentally different ways from the human brain. Current CNNs thus may not serve as sound working models of the human visual system. Significance Statement Recent CNNs have achieved very high object categorization performance, with some even exceeding human performance. It has become common practice in recent neuroscience research to regard CNNs as working models of the human visual system. Here we evaluate this approach by comparing fMRI responses from the human brain with those from 14 different CNNs. Despite CNNs’ ability to successfully perform visual object categorization like the human visual system, they appear to represent visual information in fundamentally different ways from the human brain. Current CNNs thus may not serve as sound working models of the human visual system. Given the current dominating trend of incorporating CNN modeling in visual neuroscience research, our results question the validity of such an approach.
Gesture’s body orientation modulates the N400 during semantic integration of gesture and visual sentence

Body orientation of gesture entails social-communicative intention, and may thus influence how gestures are perceived and comprehended together with auditory speech during face-to-face communication. To date, despite the emergence of neuroscientific literature on the role of body orientation on hand action perception, limited studies have directly investigated the role of body orientation in the interaction between gesture and language. To address this research question, we carried out an EEG experiment presenting to participants (n=21) videos of frontal and lateral hand gestures of five-seconds (e.g., raising a hand), followed by visually presented sentences that are either congruent or incongruent with the hand gesture (e.g., ‘the mountain is high/low…’). All participants underwent a semantic-probe task, judging whether a target word is related or unrelated to the gesture-speech event. EEG results suggest that, during the perception phase of hand-gestures, while both frontal and lateral gestures elicited power decrease in both the alpha (8-12Hz) and the beta (16-24Hz) bands, lateral gestures elicited reduced power decrease in the beta band when compared with frontal gestures. For sentence comprehension, at the critical word whose meaning is congruent/incongruent with the gesture, frontal gestures elicited an N400 effect for gesture-sentence incongruency. More importantly, this incongruency effect was significantly reduced for lateral gestures. The findings suggest that body orientation plays a crucial role in gesture perception, and that its inferred social-communicative intention influences gesture-sentence semantic integration in an interactive manner.