Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamics and self-assembly of the SARS-CoV-2 spike transmembrane domain

The spike (S) protein is a trimeric, membrane-anchored fusion protein that enables coronaviruses, such as the SARS-CoV-2, to recognize and fuse with their hosts’ cells. While the prefusion and postfusion structures of the ectomembrane domain of the spike protein are available, the corresponding organization of its transmembrane domain is obscure. Since the transmembrane and ectomembrane domains of fusion proteins are conformationally linked, an understanding of trimerization and transmembrane conformations in the viral envelope is a prerequisite to completely understand viral fusion by the spike protein. To address this, we computationally explored the self-assembly of the SARS-CoV-2 spike transmembrane domain, starting first by determining the membrane boundaries of the spike transmembrane helix. Using atomistic molecular dynamics simulations, we found the spike protein transmembrane domain to be plastic, and the transmembrane helix to be very dynamic. The observed movements of the helix changed the membrane embedded sequence, and thereby affected the conformational ensemble of the transmembrane assembly in Martini coarse grained simulations, even flipping the super-helical handedness. Analysis of the transmembrane organization of the spike transmembrane helix provided rich insights into the interfaces utilized to self-associate. Moreover, we identified two distinct cholesterol binding regions on the transmembrane helix with different affinities for the sterol. The cholesterol binding pockets overlapped with regions involved in the initiation of transmembrane protein-protein interaction. Together, the results from our multiscale simulations not only provide insight into understudied trimeric helical interfaces in biomembranes, but also enhance our understanding of the elusive transmembrane conformational dynamics of SARS-CoV-2 spike and more generally of viral fusion proteins. These insights should enable the inclusion of the conformations of the spike protein transmembrane domain into the prevalent models of virus fusion. Significance Enveloped viruses rely on fusion proteins, called spike proteins in coronaviruses, to infect cells by fusing the virus envelope with the host cell membrane. The transmembrane domain (TMD) of the coronavirus spike protein is critically involved in successful viral fusion and other aspects of the virus lifecycle, but is poorly studied. Using multiscale molecular dynamics simulations of the SARS-CoV-2 spike TMD, we explore its conformational dynamics and self-assembly in different lipid environments. The results provided here improve our understanding of transmembrane stabilization of spike trimers, which are indispensable for viral infection. Exploiting this knowledge to destabilize spike trimers should facilitate design of transmembrane domain targeted viral fusion inhibitors.
Harvesting-induced evolution of collective behavior in a fish

Many fisheries around the globe preferentially capture large individuals with implications for the evolution of exploited populations. Fisheries-induced evolution may alter collective behavioral phenotypes through individual-level adaptations that affect boldness, swimming speed and tendency to follow social vs. environmental cues. Studying the behavioural mechanisms that give rise to possible changes in shoaling and other collective outputs is challenging in the wild, but first insights into whether intensive and size-selective harvesting could alter collective phenotypes and shoaling can be gathered through experiment of size-selective harvesting conducted in the laboratory. We present a multi-generation harvest selection experiment with zebrafish (Danio rerio) as a model species and demonstrate that large size-selective harvesting typical of global fisheries decreases risk-taking behavior of individuals, and surprisingly also decreases shoal cohesion. This counter-intuitive effect at the collective level is mechanistically caused by risk-averse individuals favored under large size-selective harvest paying more attention to environmental instead of social cues. Agent-based model simulations further reveal that fisheries-induced evolution of shoaling behavior is adaptive under fishing scenarios by decreasing exploitation rate. By contrast, the same collective behavior favored by size-selective harvesting is maladaptive in the presence of natural predation and increases natural mortality. The evolutionary adaptations we document may slowly, but steadily erode the natural fitness benefits offered by shoaling in many species targeted by global fisheries. Erosion of shoal cohesion can also negatively affect catchability with consequences for human well-being.
Deconfined Global Types for Asynchronous Sessions

Multiparty sessions with asynchronous communications and global types play an important role for the modelling of interaction protocols in distributed systems. In designing such calculi the aim is to enforce, by typing, good properties for all participants, maximising, at the same time, the accepted behaviours. Our type system improves the state-of-the-art by typing all asynchronous sessions and preserving the key properties of Subject Reduction, Session Fidelity and Progress when some well-formedness conditions are satisfied. The type system comes together with a sound and complete type inference algorithm. The well-formedness conditions are undecidable, but an algorithm checking an expressive restriction of them recovers the effectiveness of typing.
Design Guidelines for the Size and Length of Chinese Characters Displayed in the Intelligent Vehicle's Central Console Interface

In order to ensure the driver’s safe driving, the human–computer interaction interface of an intelligent vehicle needs to convey important information. The text is an important carrier of this kind of information. The design criteria of English characters have been widely discussed, including the color, meaning, size and length. However, design guidelines for Chinese characters in central consoles of vehicles have rarely been discussed from a human–computer interaction perspective. In this paper, we investigated the size and the length of Chinese characters in the intelligent vehicle’s central control screen, based on international design guidelines and standards. The experiment involved 30 participants performing simulated in-vehicle secondary tasks. The result from the experiments shows that the usability of characters increases and the driver’s workload decreases as the characters get larger and shorter. We also propose a set of recommended values for the size and length of Chinese characters in this context. Future work will focus on providing design guidelines for other aspects of HMI design in intelligent vehicles.
Understanding the Fake Removal Information Advertisement Sites

: Fake antivirus (AV) software is a type of malware that disguises as legitimate antivirus software and causes harm to users and their devices. Fake removal information advertisement (FRAD) sites, which introduce fake removal information for cyber threats, have emerged as platforms for distributing fake AV software. Although FRAD sites seriously threaten users who have been su ﬀ ering from cyber threats and need information for removing them, little attention has been given to revealing these sites. In this paper, we propose a system to automatically crawl the web and identify FRAD sites. To shed light on the pervasiveness of this type of attack, we performed a comprehensive analysis of both passively and actively collected data. Our system collected 2,913 FRAD sites in 31 languages, which have 73.5 million visits per month in total. We show that FRAD sites occupy search results when users search for cyber threats, thus preventing the users from obtaining the correct information.
Real-World Data on Apatinib Efficacy - Results of a Retrospective Study in Metastatic Breast Cancer Patients Pretreated With Multiline Treatment

Objectives The NCCN guidelines recommend that the addition of bevacizumab should be considered in metastatic breast cancers in some circumstances, but there are no recommendations for the similar antiangiogenic drug apatinib. The aim of this study was to evaluate the safety and efficacy of apatinib in metastatic breast cancer patients pretreated with multiline treatment in a real-world setting. Materials and Methods Metastatic breast cancer patients pretreated with multiline treatment who had apatinib treatment initiated from September 2015 to August 2019 at Shandong Cancer Hospital and Institute were included. The primary endpoints included PFS and OS, and the secondary endpoint was treatment-related toxicity. Results A total of 66 patients with metastatic breast cancer received apatinib treatment after failure of multiline chemotherapy in this study. The median PFS and OS of all 66 patients were 6.0 months and 10.0 months, respectively. The clinical beneficial rate was 40.9%. All patients tolerated treatment well, and no patients died of toxicity. The common toxicities of apatinib were hand and foot syndrome, secondary hypertension and fatigue events. The number of prior chemotherapy regimens was significantly associated with DFS and OS. Capecitabine may be a better choice for combination with a longer median OS of 19 months, while apatinib combined with other drugs was 9 months, and the apatinib monotherapy was 10 months. Conclusion Apatinib produced moderate efficacy in metastatic breast cancer patients pretreated with multiline treatment with no significant treatment-related adverse events. Apatinib might be a choice for women as a maintenance salvage therapy following multiline chemotherapy failure.
Polyphenol Enriched Diet Administration During Pregnancy and Lactation Prevents Dysbiosis in Ulcerative Colitis Predisposed Littermates

Neonatal colonization of the gastrointestinal tract depends on mother microbiome, thus mother microbiota dysbiosis is transmitted to the offspring during the delivery and shaped by breastmilk characteristics. Here we used a murine model of UC predisposition (Winnie-/-) to evaluate the effects of maternal diet during pregnancy and lactation. Using heterozygous breeders, we obtained both Winnie-/- and C57BL/6 littermates from the same mother and compared their microbiota at weaning and adult age, using a diet enriched with 1% tomato fruit of a line – named Bronze – highly enriched in bioactive polyphenols, or Control tomato. Females received enriched diets two weeks before the beginning of the breeding and never stopped for the following six months. No significant effect was observed in regard to the percentage of Winnie-/- offspring, as with both diets the percentage was about 25% as expected. Winnie littermates from breeders fed with the Bronze-enriched diet showed reduced dysbiosis at 4 weeks of age if compared with Winnie under the Control tomato diet. This effect was then reduced when mice reached adult age. Conversely, the microbiota of C57BL/6 does not change significantly, indicating that fortified mothers-diet significantly contribute to preventing dysbiosis in genetically predisposed offspring, but has mild effects on healthy littermates and adult mice. An overall tendency towards reduced inflammation was underlined by the colon weight and the percentage of Foxp3+ cells reduction in Winnie mice fed with Bronze diet. Control diet did not show similar tendency.
Unexpected Gating Behaviour of an Engineered Potassium Channel Kir

In this study, we investigated the dynamics and functional characteristics of the KirBac3.1 S129R, a mutated bacterial potassium channel for which the inner pore-lining helix (TM2) was engineered so that the bundle crossing is trapped in an open conformation. The structure of this channel has been previously determined at high atomic resolution. We explored the dynamical characteristics of this open state channel using an in silico method MDeNM that combines molecular dynamics simulations and normal modes. We captured the global and local motions at the mutation level and compared these data with HDX-MS experiments. MDeNM provided also an estimation of the probability of the different opening states that are in agreement with our electrophysiological experiments. In the S129R mutant, the Arg129 mutation releases the two constriction points in the channel that existed in the wild type but interestingly creates another restriction point.
Cross-Subject EEG Emotion Recognition With Self-Organized Graph Neural Network

As a physiological process and high-level cognitive behavior, emotion is an important subarea in neuroscience research. Emotion recognition across subjects based on brain signals has attracted much attention. Due to individual differences across subjects and the low signal-to-noise ratio of EEG signals, the performance of conventional emotion recognition methods is relatively poor. In this paper, we propose a self-organized graph neural network (SOGNN) for cross-subject EEG emotion recognition. Unlike the previous studies based on pre-constructed and fixed graph structure, the graph structure of SOGNN are dynamically constructed by self-organized module for each signal. To evaluate the cross-subject EEG emotion recognition performance of our model, leave-one-subject-out experiments are conducted on two public emotion recognition datasets, SEED and SEED-IV. The SOGNN is able to achieve state-of-the-art emotion recognition performance. Moreover, we investigated the performance variances of the models with different graph construction techniques or features in different frequency bands. Furthermore, we visualized the graph structure learned by the proposed model and found that part of the structure coincided with previous neuroscience research. The experiments demonstrated the effectiveness of the proposed model for cross-subject EEG emotion recognition.
Cannabinoid Modulation of Dopamine Release During Motivation, Periodic Reinforcement, Exploratory Behavior, Habit Formation, and Attention

Motivational and attentional processes energize action sequences to facilitate evolutionary competition and promote behavioral fitness. Decades of neuropharmacology, electrophysiology and electrochemistry research indicate that the mesocorticolimbic DA pathway modulates both motivation and attention. More recently, it was realized that mesocorticolimbic DA function is tightly regulated by the brain’s endocannabinoid system and greatly influenced by exogenous cannabinoids—which have been harnessed by humanity for medicinal, ritualistic, and recreational uses for 12,000 years. Exogenous cannabinoids, like the primary psychoactive component of cannabis, delta-9-tetrahydrocannabinol, produce their effects by acting at binding sites for naturally occurring endocannabinoids. The brain’s endocannabinoid system consists of two G-protein coupled receptors, endogenous lipid ligands for these receptor targets, and several synthetic and metabolic enzymes involved in their production and degradation. Emerging evidence indicates that the endocannabinoid 2-arachidonoylglycerol is necessary to observe concurrent increases in DA release and motivated behavior. And the historical pharmacology literature indicates a role for cannabinoid signaling in both motivational and attentional processes. While both types of behaviors have been scrutinized under manipulation by either DA or cannabinoid agents, there is considerably less insight into prospective interactions between these two important signaling systems. This review attempts to summate the relevance of cannabinoid modulation of DA release during operant tasks designed to investigate either motivational or attentional control of behavior. We first describe how cannabinoids influence DA release and goal-directed action under a variety of reinforcement contingencies. Then we consider the role that endocannabinoids might play in switching an animal’s motivation from a goal-directed action to the search for an alternative outcome, in addition to the formation of long-term habits. Finally, dissociable features of attentional behavior using both the 5-choice serial reaction time task and the attentional set-shifting task are discussed along with their distinct influences by DA and cannabinoids. We end with discussing potential targets for further research regarding DA-cannabinoid interactions within key substrates involved in motivation and attention.
TRP Channels Interactome as a Novel Therapeutic Target in Breast Cancer

Breast cancer is one of the most frequent cancer types worldwide and the first cause of cancer-related deaths in women. Although significant therapeutic advances have been achieved with drugs such as tamoxifen and trastuzumab, breast cancer still caused 627,000 deaths in 2018. Since cancer is a multifactorial disease, it has become necessary to develop new molecular therapies that can target several relevant cellular processes at once. Ion channels are versatile regulators of several physiological- and pathophysiological-related mechanisms, including cancer-relevant processes such as tumor progression, apoptosis inhibition, proliferation, migration, invasion, and chemoresistance. Ion channels are the main regulators of cellular functions, conducting ions selectively through a pore-forming structure located in the plasma membrane, protein–protein interactions one of their main regulatory mechanisms. Among the different ion channel families, the Transient Receptor Potential (TRP) family stands out in the context of breast cancer since several members have been proposed as prognostic markers in this pathology. However, only a few approaches exist to block their specific activity during tumoral progress. In this article, we describe several TRP channels that have been involved in breast cancer progress with a particular focus on their binding partners that have also been described as drivers of breast cancer progression. Here, we propose disrupting these interactions as attractive and potential new therapeutic targets for treating this neoplastic disease.
Cellular Metabolomics Profiles Associated With Drug Chemosensitivity in AML

Background Acute myeloid leukemia (AML) is a hematological malignancy with a dismal prognosis. For over four decades, AML has primarily been treated by cytarabine combined with an anthracycline. Although a significant proportion of patients achieve remission with this regimen, roughly 40% of children and 70% of adults relapse. Over 90% of patients with resistant or relapsed AML die within 3 years. Thus, relapsed and resistant disease following treatment with standard therapy are the most common clinical failures that occur in treating this disease. In this study, we evaluated the relationship between AML cell line global metabolomes and variation in chemosensitivity. Methods We performed global metabolomics on seven AML cell lines with varying chemosensitivity to cytarabine and the anthracycline doxorubicin (MV4.11, KG-1, HL-60, Kasumi-1, AML-193, ME1, THP-1) using ultra-high performance liquid chromatography – mass spectrometry (UHPLC-MS). Univariate and multivariate analyses were performed on the metabolite peak intensity values from UHPLC-MS using MetaboAnalyst to identify cellular metabolites associated with drug chemosensitivity. Results A total of 1,624 metabolic features were detected across the leukemic cell lines. Of these, 187 were annotated to known metabolites. With respect to doxorubicin, we observed significantly greater abundance of a carboxylic acid (1-aminocyclopropane-1-carboxylate) and several amino acids in resistant cell lines. Pathway analysis found enrichment of several amino acid biosynthesis and metabolic pathways. For cytarabine resistance, nine annotated metabolites were significantly different in resistance vs. sensitive cell lines, including D-raffinose, guanosine, inosine, guanine, aldopentose, two xenobiotics (allopurinol and 4-hydroxy-L-phenylglycine) and glucosamine/mannosamine. Pathway analysis associated these metabolites with the purine metabolic pathway. Conclusion Overall, our results demonstrate that metabolomics differences contribute toward drug resistance. In addition, it could potentially identify predictive biomarkers for chemosensitivity to various anti-leukemic drugs. Our results provide opportunity to further explore these metabolites in patient samples for association with clinical response.
Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts

Large-scale neuroimaging data acquired and shared by multiple institutions are essential to advance neuroscientific understanding of pathophysiological mechanisms in psychiatric disorders, such as major depressive disorder (MDD). About 75% of studies that have applied machine learning technique to neuroimaging have been based on diagnoses by clinicians. However, an increasing number of studies have highlighted the difficulty in finding a clear association between existing clinical diagnostic categories and neurobiological abnormalities. Here, using resting-state functional magnetic resonance imaging, we determined and validated resting-state functional connectivity related to depression symptoms that were thought to be directly related to neurobiological abnormalities. We then compared the resting-state functional connectivity related to depression symptoms with that related to depression diagnosis that we recently identified. In particular, for the discovery dataset with 477 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a brain network prediction model of depression symptoms (Beck Depression Inventory-II [BDI] score). The prediction model significantly predicted BDI score for an independent validation dataset with 439 participants from 4 different imaging sites. Finally, we found 3 common functional connections between those related to depression symptoms and those related to MDD diagnosis. These findings contribute to a deeper understanding of the neural circuitry of depressive symptoms in MDD, a hetero-symptomatic population, revealing the neural basis of MDD.
Classifying Medulloblastoma Subgroups Based on Small, Clinically Achievable Gene Sets

As treatment protocols for medulloblastoma (MB) are becoming subgroup-specific, means for reliably distinguishing between its subgroups are a timely need. Currently available methods include immunohistochemical stains, which are subjective and often inconclusive, and molecular techniques—e.g., NanoString, microarrays, or DNA methylation assays—which are time-consuming, expensive and not widely available. Quantitative PCR (qPCR) provides a good alternative for these methods, but the current NanoString panel which includes 22 genes is impractical for qPCR. Here, we applied machine-learning–based classifiers to extract reliable, concise gene sets for distinguishing between the four MB subgroups, and we compared the accuracy of these gene sets to that of the known NanoString 22-gene set. We validated our results using an independent microarray-based dataset of 92 samples of all four subgroups. In addition, we performed a qPCR validation on a cohort of 18 patients diagnosed with SHH, Group 3 and Group 4 MB. We found that the 22-gene set can be reduced to only six genes (IMPG2, NPR3, KHDRBS2, RBM24, WIF1, and EMX2) without compromising accuracy. The identified gene set is sufficiently small to make a qPCR-based MB subgroup classification easily accessible to clinicians, even in developing, poorly equipped countries.