Miracle-dz/UniFAR-FTUs huggingface dataset:
A data-centric pipeline using convolutional neural network to select better multiple sequence alignment method

Multiple sequence alignment (MSA) is widely used to find out the evolutionary relationship of every input sequence as well as the functional or structural roles of every aligned residue. Traditionally, the MSA problem was tackled by algorithm-centric approaches which had applied many classical computer algorithms (such as dynamic programming, divide-and-conquer algorithm and so on) and proven strategies (such as progressive strategy, non-progressive strategy, consistency-based strategy, iterative refinement etc.). Different single-algorithm MSA methods have different accuracies on different similarity protein families. Therefore, to integrate the advantages of different MSA methods, we present a brand-new data-centric pipeline using the convolutional neural network (CNN) [3] to choose better MSA method for different similarity protein families. An MSA is very similar to a 2D picture, which has a good hierarchical structure. The conserved regions and the corresponding conserved columns in an MSA could be seen as boxes and lines in a picture. CNN is known to be very good at recognizing imperfect pictures which containing existed noises, which means it may perform well for recognizing draft MSAs. Briefly, the method first using a quick MSA method to construct large-scale draft MSAs from the simulated protein families produced by protein simulation tool INDELible [1]. The main point is training a classifier by CNN which employing the draft MSAs as input and giving the better MSA method as output. In our research, we simulated more than 640,000 protein families with sequence number range from 3 to 64. The fastest (but not accurate) mode of a famous MSA tool, Mafft(FFT-NS-1) [2], with default parameters used for constructing draft MSAs from those families. We regard these MSAs as two-color images, one color for the aligned residues, and the other color for the gaps. Two layers of CNN and a fully connected layer with 0.5 of dropout were used for training the decision model. The preliminary results suggest more than 85% accuracy in classification of choosing better alignment solution between the newest versions of Mafft(L-INS-i) and Mafft(G-INS-i). Currently, we are improving the performance of this pipeline by selecting better categories for protein families and fine-tuning the decision model.
Design, Modeling, Control, and Application of Everting Vine Robots

In nature, tip-localized growth allows navigation in tightly confined environments and creation of structures. Recently, this form of movement has been artificially realized through pressure-driven eversion of flexible, thin-walled tubes. Here we review recent work on robots that “grow” via pressure-driven eversion, referred to as “everting vine robots,” due to a movement pattern that is similar to that of natural vines. We break this work into four categories. First, we examine the design of everting vine robots, highlighting tradeoffs in material selection, actuation methods, and placement of sensors and tools. These tradeoffs have led to application-specific implementations. Second, we describe the state of and need for modeling everting vine robots. Quasi-static models of growth and retraction and kinematic and force-balance models of steering and environment interaction have been developed that use simplifying assumptions and limit the involved degrees of freedom. Third, we report on everting vine robot control and planning techniques that have been developed to move the robot tip to a target, using a variety of modalities to provide reference inputs to the robot. Fourth, we highlight the benefits and challenges of using this paradigm of movement for various applications. Everting vine robot applications to date include deploying and reconfiguring structures, navigating confined spaces, and applying forces on the environment. We conclude by identifying gaps in the state of the art and discussing opportunities for future research to advance everting vine robots and their usefulness in the field.
RNA-Seq Analysis of Colorectal Tumor-Infiltrating Myeloid-Derived Suppressor Cell Subsets Revealed Gene Signatures of Poor Prognosis

Elevated levels of myeloid-derived suppressor cells (MDSCs), including polymorphonuclear MDSCs (PMN-MDSCs) and immature MDSCs (I-MDSCs), are usually associated with disease progression in cancer patients, including colorectal cancer (CRC). However, biological mechanisms and molecular pathways regulated by MDSC subpopulations in the CRC tumor microenvironment (TME) have not been fully investigated. In this study, we performed transcriptomic analysis of tumor-infiltrating I-MDSCs and PMN-MDSCs isolated from tumor tissues of six CRC patients, compared to antigen-presenting cells (APCs). We also compared the transcriptomic profiles of tumor-infiltrating PMN-MDSCs to I-MDSCs. Our results showed different molecular pathways regulated by each MDSC subset, potentially reflecting their phenotypical/molecular/functional characteristics in the CRC TME. Moreover, we identified gene signatures in PMN-MDSC and I-MDSC of poor overall survival (OS) and disease-free survival (DFS) using the Cancer Genome Atlas (TCGA) dataset from patients with colon adenocarcinoma (COAD). However, functional studies are required to validate these findings.
Influence of Lithosphere Rheology on Seismicity in an Intracontinental Rift: The Case of the Rhine Graben

The Upper Rhine Graben (URG), as a part of the wider European Cenozoic Rift System, is a tectonically active area that has been extensively investigated for its geothermal energy potential. In this study, we carry out a first investigation of the present-day thermo-mechanical stability of the area as based on a detailed 3D geological and thermal model. The overall goal is, therefore, to assess how the lithospheric strength varies within the URG in response to the natural tectonic setting as well as the internal thermal configuration, and how those variations can be related to the recorded seismicity. The results from the modeling indicate that there is a spatial correlation between the predictions for the graben-wide rheological configuration with both the deep thermal field and the configuration of the crystalline crust. We find that the regional characteristics of the long-term strength of the lithosphere match the spatial distribution of seismicity, indicating that the mechanical stability of the area is primarily controlled by resolved strength variations. By cross-plotting the modeled strength distribution with available seismicity catalogs, our results suggest that seismicity in the graben area is shallower and of lower intensity due to a hotter and weaker crust compared to its surrounding areas. In contrast, seismic energy release appears to occur at deeper levels and being of larger magnitudes east of the graben and in the adjacent Lower Rhine Graben to the north. These results demonstrate the relevance of a proper quantification of the lithospheric rheological configuration and its spatial variability in response to its tectonic inheritance as an asset to interpret the pattern and distribution of seismicity observed in the area.
Prediction of Sentinel Lymph Node Metastasis in Breast Ductal Carcinoma In Situ Diagnosed by Preoperative Core Needle Biopsy

Purpose The positivity of sentinel lymph node (SLN) metastasis is relatively low in ductal carcinoma in situ (DCIS) patients. The aim of this study was to investigate factors associated with SLN metastasis and build a model to predict the potential risk of SLN metastasis in patients with a preoperative diagnosis of DCIS. Patients and Methods Core needle biopsy-proved DCIS patients who underwent SLN biopsy and breast surgery were retrospectively reviewed and selected. Univariate analysis was used to identify the variables correlated with SLN metastasis. A model to predict SLN metastasis was developed using a multivariate logistic regression in the training set and then validated in an internal set. Results A total of 407 patients with a preoperative diagnosis of DCIS were included. Upstaging to invasive/microinvasive cancer occurred in 225 patients after surgery. SLN metastasis was found in 42 patients, including 32 patients upstaging to invasive disease, 8 to microinvasive disease, and 2 pure DCIS. Tumor size based on US examination, axillary ultrasound finding, multifocality, surgery, upstaging, and Ki-67 expression were significantly related to SLN metastasis. The model incorporating tumor size, axillary ultrasound finding and multifocality yielded an AUC of 0.805 (95% CI: 0.715–0.895, p<0.001) in the training set, and 0.729 (95% CI: 0.547–0.911, p=0.013) in the testing set. Conclusion A simple model was developed to predict SLN metastasis in patients with a preoperative diagnosis of DCIS. With good discriminatory power, this model should be helpful for surgeons to decide if SLN biopsy could be safely avoided in certain patients.
Magnetic Resonance Imaging and Histopathologic Findings From a Standard Poodle With Neonatal Encephalopathy With Seizures

Neonatal encephalopathy with seizures (NEwS) is an epileptic encephalopathy with an autosomal recessive inheritance pattern found in Standard Poodle puppies. The causal genetic variant for NEwS has been identified as a homozygous missense mutation in ATF2 (c.152T>G, p.Met51Arg), and a pathological cerebellar change has been reported. Magnetic resonance imaging showed reduced whole-brain size, dilated ventricles, developmental abnormalities of the white matter of the cerebrum, white matter signal abnormalities in the occipital lobe, and abnormal morphology of the cerebellum. Histopathology included previously unrecognized irregular neuronal migration in the subventricular zone around the lateral ventricles in the frontal lobe and white matter rarefaction especially at the level of the occipital lobe in the cerebrum in addition to the cerebellar cortical dysplasia that has been previously described. The findings of this case may highlight the critical role of ATF2 in neurodevelopmental processes in the canine brain.
Influence of Freshwater Discharges on Biogeochemistry and Benthic Communities of a Coral Reef Ecosystem (La Réunion Island, Indian Ocean)

We investigated the influence of freshwater discharges on biogeochemistry and benthic communities of La Saline fringing reef, the largest coral reef system of La Réunion Island (France, Indian Ocean). Sampling was conducted in February–March 2012 along a land-ocean continuum, including rivers, groundwater, reef waters, and oceanic waters. Our results indicated the discharges of basalt groundwater (enriched in nitrates) in the southern part of the reef, and the discharges of sand groundwater and river waters (enriched in organic matter) in sites located northward. These discharges mainly impacted fore reef surface waters, while groundwater resurgences were more diffuse and not clearly detected in depth. We highlighted the relationship Si(OH)4/NO3– to identify basalt groundwater discharges. We also put forward the relationships humic-like fluorescence/NH4+ and tryptophan-like fluorescence/PO43– to identify sand groundwater discharges and wastewater pollutions, respectively. We observed the eutrophication of the southern part of the reef through higher phytoplankton biomasses, a massive presence of fleshy algal formations and a low coral coverage. The spatial homogeneity of the benthic community structures onto the entire slope contrasted with the spatial heterogeneity of chemical variables in water. This reflected the different spatial and temporal scales involved in biological and chemical processes in coral reef ecosystems.
In-depth in vitro Evaluation of the Activity and Mechanisms of Action of Organic Acids and Essential Oils Against Swine Enteropathogenic Bacteria

Alternative antimicrobials require a deep understanding of their action mechanisms by in vitro assays which support science-based field use. This study focuses on the characterization of bactericidal mechanisms of potential antimicrobial compounds, two organic acids and three single essential oil (EO) compounds against swine enteropathogenic bacteria Escherichia coli, Salmonella enterica subsp. enterica serovar Typhimurium, and Clostridium perfringens. Target concentrations of the compounds were evaluated using the inhibitory potential of the vapor phase and bacterial viability after short-term exposure, while cell targets were disclosed using flow cytometry (FC), Fourier-transform infrared (FTIR) spectroscopy, and scanning electron microscopy (SEM). All tested compounds exhibited vapor phase activity against the three bacterial species, except sodium salt of coconut fatty acid distillates against C. perfringens. Survival test results evidenced that effects on bacterial viability were concentration dependent and higher in single EO compounds than in organic acids. In detail, thymol and its isomer carvacrol were the most effective compounds. Further characterization of thymol and cinnamaldehyde activity revealed that thymol main target was the cell membrane, since it caused striking damages in the membrane permeability, integrity and composition evidenced by FC and FTIR in the three enteric pathogens. In contrast, cinnamaldehyde was more effective against enterobacteria than against C. perfringens and only caused slightly damages at the highest concentration tested. Its target at the molecular level differed between enterobacteria and C. perfringens isolates. The SEM micrographs allowed us to confirm the results previously obtained for both EO compounds by other techniques. Altogether, the study showed the straight effect of these antimicrobials, which could constitute relevant information to optimize their feed inclusion rates in field studies or field use.
Are New Gender-Neutral Pronouns Difficult to Process in Reading? The Case of Hen in SWEDISH

Hen is a Swedish gender-neutral pronoun used for non-binary individuals and as a generic singular pronoun form. Hen was added to the Swedish Academy Glossary (SAOL) in 2015, and opponents of hen have argued that gender-neutral pronouns are difficult to process, and therefore should not be used. As of yet, this has not been empirically tested. This pre-registered study used eye-tracking to experimentally test if hen has a processing cost by measuring the process of understanding whom a pronoun refers to (i.e., pronoun resolution). Participants (N = 120) read 48 sentence pairs where the first sentence included a noun referring to a person (e.g., sister, hairdresser, person) and the second included a pronoun referring to the noun. The pronouns were either gendered (she and he) or gender-neutral (hen). The nouns were either neutral (e.g., person, colleague) or gendered, either by lexically referring to gender (e.g., sister, king), or by being associated with stereotypes based on occupational gender segregation (e.g., occupational titles like hairdresser, carpenter). We tested if hen had a greater processing cost than gendered pronouns, and whether the type of noun moderated this effect. The hypotheses were that hen referring to neutral nouns would lead to a smaller processing cost than hen referring to gendered nouns. Furthermore, we hypothesized that hen referring to lexically gendered nouns would lead to larger processing costs than stereotypically gendered role nouns. The processing cost of hen was measured by reading time spent on three regions of the sentence pairs; the pronoun, the spillover region (i.e., the words following the pronoun), and the noun. The only processing cost for hen occurred in the spillover region. The processing cost in this region was greater when hen referred to neutral nouns than when hen referred to a noun associated with gender. In contrast to the hypothesis, the type of gender information associated with the noun did not interact with these effects (i.e., the same reading time for hen following e.g., the queen or carpenter). Altogether, the results do not support that gender-neutral pronouns should be avoided because they are difficult to process.
SPOCK2 Serves as a Potential Prognostic Marker and Correlates With Immune Infiltration in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the major types of lung cancer. Tumor-infiltrating immune cells (TIICs) are positively associated with overall survival (OS) in LUAD. The SPARC/osteonectin, cwcv and kazal-like domains proteoglycan 2 (SPOCK2) is a complex type of secreted proteoglycan involved in forming a protective barrier against viral infection. The purpose of this study was to investigate the relationship between SPOCK2 and TIICs and the prognostic role of SPOCK2 in LUAD. The GEPIA2, GEO, CPTAC, and HPA databases were analyzed to examine both the mRNA and protein expression of SPOCK2 in LUAD. GEPIA2 and the Kaplan-Meier Plotter (KM Plotter) were used to evaluate the prognostic value of SPOCK2 in LUAD patients. TCGA data were examined for a correlation between SPOCK2 expression and clinical characteristics. Gene enrichment analyses were performed to explore the underlying mechanism of SPOCK2 based on LinkedOmics. RegNetwork was used to predict the regulators of SPOCK2. The correlation between SPOCK2 and TIICs, including immune infiltration level and relative proportion was investigated via TIMER. KM Plotter was also used to evaluate the prognostic role of SPOCK2 expression in LUAD with enriched and decreased TIIC subgroups. We found SPOCK2 was significantly downregulated in LUAD compared with that in non-tumor controls and was correlated with clinical parameters. Moreover, a high SPOCK2 expression level predicted better survival. The SPOCK2-associated regulatory network was constructed. SPOCK2 influenced the TIIC infiltration level and relative proportion in LUAD. Furthermore, a high SPOCK2 expression level was associated with a favorable prognosis in enriched CD4 + T cells and macrophage subgroups in LUAD. In conclusion, a high level of SPOCK2 expression predicted favorable prognosis and was significantly correlated with TIICs in LUAD. Therefore, the expression of SPOCK2 may affect the prognosis of LUAD partly due to TIICs.
Pathogenesis of Viral Hepatitis-Induced Chronic Liver Disease: Role of Extracellular Vesicles

Extracellular vesicles are encapsulated lipid nanoparticles secreted by a variety of cell types in living organisms. They are known to carry proteins, metabolites, nucleic acids, and lipids as their cargoes and are important mediators of intercellular communication. The role of extracellular vesicles in chronic liver disease has been reported. Chronic liver disease such as viral hepatitis accounts for a significant mortality and morbidity burden worldwide. Hepatic fibrosis has been commonly associated with the chronic form of viral hepatitis, which results in end-stage liver disease, including cirrhosis, liver failure, and carcinoma in some patients. In this review, we discuss the potential role of extracellular vesicles in mediating communication between infectious agents (hepatitis B and C viruses) and host cells, and how these complex cell-cell interactions may facilitate the development of chronic liver disease. We will further discuss how understanding their biological mechanism of action might be beneficial for developing therapeutic strategies to treat chronic liver disease.
What Determines the Order of Verbal Dependents in Hindi? Effects of Efficiency in Comprehension and Production

Word order flexibility is one of the distinctive features of SOV languages. In this work, we investigate whether the order and relative distance of preverbal dependents in Hindi, an SOV language, is affected by factors motivated by efficiency considerations during comprehension/production. We investigate the influence of Head–Dependent Mutual Information (HDMI), similarity-based interference, accessibility and case-marking. Results show that preverbal dependents remain close to the verbal head when the HDMI between the verb and its dependent is high. This demonstrates the influence of locality constraints on dependency distance and word order in an SOV language. Additionally, dependency distance were found to be longer when the dependent was animate, when it was case-marked and when it was semantically similar to other preverbal dependents. Together the results highlight the crosslinguistic generalizability of these factors and provide evidence for a functionally motivated account of word order in SOV languages such as Hindi.
HIT-SCIR at MRP 2020: Transition-based Parser and Iterative Inference Parser

This paper describes our submission system (HIT-SCIR) for the CoNLL 2020 shared task: Cross-Framework and Cross-Lingual Meaning Representation Parsing. The task includes five frameworks for graph-based meaning representations, i.e., UCCA, EDS, PTG, AMR, and DRG. Our solution consists of two sub-systems: transition-based parser for Flavor (1) frameworks (UCCA, EDS, PTG) and iterative inference parser for Flavor (2) frameworks (DRG, AMR). In the final evaluation, our system is ranked 3rd among the seven team both in Cross-Framework Track and Cross-Lingual Track, with the macro-averaged MRP F1 score of 0.81/0.69.
Zero-Shot Rationalization by Multi-Task Transfer Learning from Question Answering

Extracting rationales can help human understand which information the model utilizes and how it makes the prediction towards better interpretability. However, annotating rationales requires much effort and only few datasets contain such labeled rationales, making supervised learning for rationalization difficult. In this paper, we propose a novel approach that leverages the benefits of both multi-task learning and transfer learning for generating rationales through question answering in a zero-shot fashion. For two benchmark rationalization datasets, the proposed method achieves comparable or even better performance of rationalization without any supervised signal, demonstrating the great potential of zero-shot rationalization for better interpretability.
Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing

This paper studies a novel model that simplifies the disambiguation of connectives for explicit discourse relations. We use a neural approach that integrates contextualized word embeddings and predicts whether a connective candidate is part of a discourse relation or not. We study the influence of those context-specific embeddings. Further, we show the benefit of training the tasks of connective disambiguation and sense classification together at the same time. The success of our approach is supported by state-of-the-art results.
Do Models of Mental Health Based on Social Media Data Generalize?

Proxy-based methods for annotating mental health status in social media have grown popular in computational research due to their ability to gather large training samples. However, an emerging body of literature has raised new concerns regarding the validity of these types of methods for use in clinical applications. To further understand the robustness of distantly supervised mental health models, we explore the generalization ability of machine learning classifiers trained to detect depression in individuals across multiple social media platforms. Our experiments not only reveal that substantial loss occurs when transferring between platforms, but also that there exist several unreliable confounding factors that may enable researchers to overestimate classification performance. Based on these results, we enumerate recommendations for future mental health dataset construction.