Miracle-dz/UniFAR-FTUs huggingface dataset:
Diagnostic accuracy of the ultrasensitive S-PLEX SARS-CoV-2 N electrochemiluminescence immunoassay

We read with interest the recent article of Ren et al. [1], who described the accuracy of an ultrasensitive electrochemiluminescence immunoassay for saliva-based Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) nucleocapsid protein (N) detection based on the S-PLEX platform (S-PLEX SARS-CoV-2 N Kit; Meso Scale Discovery, Rockville, MD, United States). This method has been specifically developed for detecting and quantifying the SARS-CoV-2 N antigen in a variety of human specimens, including serum, plasma, saliva and nasopharyngeal swabs (NPS). Briefly, either S-PLEX 96-Well SECTOR or QuickPlex plates coated with streptavidin for binding biotin-conjugated capture anti-SARS-CoV-2 N antibodies are challenged with human samples. After this step, “TURBO-BOOST”-labeled detection antibodies react with the N antigen bound to the solid phase and, after addiction of a specific reagent, an electrochemiluminescent signal is generated and read by the specific instrument. The signal produced is proportional to the concentration of N antigen present in the test sample. A preliminary evaluation of this assay revealed that the limit of detection is 0.16 pg/mL, with a diagnostic threshold set at 0.32 pg/mL and a total imprecision ranging between 7.0 and 7.7% [2]. The sample volume is only 25 μL, with total turnaround time between 4–5 h. Since this novel technique displayed remarkable diagnostic performance in saliva samples in the hands of Ren and colleagues, exhibiting up to 100% specificity with 92% sensitivity [1], we provide here a critical literature review and pooled analysis of studies which addressed the accuracy of S-PLEX SARS-CoV-2 N Kit for diagnosing acute SARS-CoV-2 infections. We carried out a digital search in the two scientific databases Medline (PubMed interface) and Scopus, using the following keywords: “S-PLEX” AND “COVID-19” OR “SARS-CoV-2”, without no language or date (i.e., up to February 17, 2022) restrictions. The initial screening of documents was conducted by G.L. and M.M. , aimed at selecting studies were the diagnostic accuracy of S-PLEX SARS-CoV-2 N Kit was assessed against a reference molecular technique for diagnosing acute SARS-CoV-2 infections, and with sufficient extrapolable information for construction of a 2×2 table. A pooled analysis, based on the Mantel-Haenszel method and random effects model, was employed for estimating the diagnostic sensitivity, specificity and accuracy (reported as Summary Receiver Operating Characteristic Curve [SROC] and agreement) of this method. The inter-study heterogeneity was also assessed with χ test and I statistic. The statistical analysis was performed with Meta-DiSc 1.4 (Unit of Clinical Biostatistics team of the Ramón y Cajal Hospital, Madrid, Spain) [3]. The analysis was carried out in accordance with the Declaration of Helsinki and within the terms of Martina Montagnana and Mario Plebani share senior authorship of this work.
First evidence of abnormal behaviour of Mus spicilegus obtained in the wild from Slovakia.

Abstract In this study are presented six cases of abnormal behaviour based on injured individuals of Mus spicilegus from northern border of the species’ range. The material consistent of six individuals caught during autumn-winter season 2020 in field biotope on south-eastern Slovakia. Determination by morphological traits and subsequent dissection confirmed that all trapped mice were subadults. The cause of the damage found in distinctly bite was aggressive behaviour by other individuals or selfmutilation. There were three main types of damage: (1) incomplete infanticide (2) cannibalism and finally (3) self-mutilation. The findings presented in this study display the first documented abnormal behaviour in the mound-building mouse obtained in the wild.
Genome-wide base editor screen identifies regulators of protein abundance in yeast

Abundance of proteins is extensively regulated both transcriptionally and post-transcriptionally. To systematically characterize how regulation of protein abundance is encoded in the genome and identify protein regulators on a genome-wide scale, we developed a genetic screen that uses a CRISPR base editor. We examined the effects of 16,452 genetic perturbations on the abundance of eleven yeast proteins representing a variety of cellular functions. Thereby, we uncovered hundreds of regulatory relationships, including a novel link between the GAPDH isoenzymes Tdh1/2/3 and the Ras/PKA pathway. Many of the identified regulators are specific to one of the eleven proteins, but we also found genes that, upon perturbation, affected the abundance of most of the tested proteins. While the more specific regulators usually act transcriptionally, broad regulators often have roles in protein translation. Our results provide unprecedented insights into the components, scale and connectedness of the protein regulatory network.
Vocalization categorization behavior explained by a feature-based auditory categorization model

Vocal animals produce multiple categories of calls with high between- and within-subject variability, over which listeners must generalize to accomplish call categorization. The behavioral strategies and neural mechanisms that support this ability to generalize are largely unexplored. We previously proposed a theoretical model that accomplished call categorization by detecting features of intermediate complexity that best contrasted each call category from all other categories. We further demonstrated that some neural responses in the primary auditory cortex were consistent with such a model. Here, we asked whether a feature-based model could predict call categorization behavior. We trained both the model and guinea pigs on call categorization tasks using natural calls. We then tested categorization by the model and guinea pigs using temporally and spectrally altered calls. Both the model and guinea pigs were surprisingly resilient to temporal manipulations, but sensitive to moderate frequency shifts. Critically, model performance quantitatively matched guinea pig behavior to a remarkable degree. By adopting different model training strategies and examining features that contributed to solving specific tasks, we could gain insight into possible strategies used by animals to categorize calls. Our results validate a model that uses the detection of intermediate-complexity contrastive features to accomplish call categorization.
CD5L constraints acute and systemic inflammation and can be a novel potent therapeutic agent against sepsis

The global burden of sepsis, with an estimated 49 million cases and 11 million deaths in 2017, often passes unnoticed to the general public even though it is the direct cause of nearly 20% of all deaths worldwide. This unawareness is perhaps due to misconceptions, or miscoding in the reporting of the ultimate causes of death, as in many diseases it is not the actual infectious agent that causes the biggest harm. Rather, it is the uncontrolled inflammation leading to septic shock that is the most menacing manifestation associated with many infections, and becomes deadly serious once it has passed the stage where anti-microbial drugs no longer have any effect to inactivate or destroy the pathogen. Here we show that the combined anti-bacterial and anti-inflammatory properties of the scavenger receptor cysteine-rich (SRCR) protein CD5L contribute to a remarkable therapeutic effect of the protein to fight sepsis, such that when exogenously administered in C57BL/6 mice with induced lethal-grade sepsis, it can be a very effective curative agent to treat this condition. The resistance conferred by CD5L to polybacterial-induced sepsis using the cecal ligation and puncture (CLP) model is consistent with the reported observations that CD5L physically binds and inactivates diverse species and strains of bacteria. Accordingly, our CD5L-knockout mice are significantly more susceptible to experimentally-induced mid-grade CLP than wild-type animals. We show that CD5L is centered on promoting neutrophil recruitment and activation, overall contributing to reducing the bacteria burden of the animals. However, the dramatic susceptibility of CD5L-deficient animals is not necessarily correlated only with pathogen load, as these mice are also extremely susceptible to sterile sepsis induced by nonlethal doses of LPS. Notwithstanding the observed capacity of CD5L to directly bind to a broad range of pathogens, typical of many PRRs, our evidence suggests that the anti-inflammatory properties of the protein are at least as important as its pathogen-binding potential, and can, and should, be explored to treat the deadly inflammation storm that is sepsis.
A happy accident: a novel turfgrass reference genome

Poa pratensis, commonly known as Kentucky bluegrass, is a popular cool-season grass species used as turf in lawns and recreation areas globally. Despite its substantial economic value, a reference genome had not previously been assembled due to the genome’s relatively large size and biological complexity that includes apomixis, polyploidy, and interspecific hybridization. We report here a fortuitous de novo assembly and annotation of a P. pratensis genome. Instead of sequencing the genome of a C4 grass, we accidentally sampled and sequenced tissue from a weedy P. pratensis whose stolon was intertwined with that of the C4 grass. The draft assembly consists of 6.09 Gbp with an N50 scaffold length of 65.1 Mbp, and a total of 118 scaffolds, generated using PacBio long reads and Bionano optical map technology. We annotated 256K gene models and found 58% of the genome to be composed of transposable elements. To demonstrate the applicability of the reference genome, we evaluated population structure and estimated genetic diversity in P. pratensis collected from three North American prairies, two in Manitoba, Canada and one in Colorado, USA. Our results support previous studies that found high genetic diversity and population structure within the species. The reference genome and annotation will be an important resource for turfgrass breeding and study of bluegrasses.
Iterative deletion of gene trees detects extreme biases in distance-based phylogenomic coalescent analyses

Summary coalescent methods offer an alternative to the concatenation (supermatrix) approach for inferring phylogenetic relationships from genome-scale datasets. Given huge datasets, broad congruence between contrasting phylogenomic paradigms is often obtained, but empirical studies commonly show some well supported conflicts between concatenation and coalescence results and also between species trees estimated from alternative coalescent methods. Partitioned support indices can help arbitrate these discrepancies by pinpointing outlier loci that are unjustifiably influential at conflicting nodes. Partitioned coalescence support (PCS) recently was developed for summary coalescent methods, such as ASTRAL and MP-EST, that use the summed fits of individual gene trees to estimate the species tree. However, PCS cannot be implemented when distance-based coalescent methods (e.g., STAR, NJst, ASTRID, STEAC) are applied. Here, this deficiency is addressed by automating computation of ‘partitioned coalescent branch length’ (PCBL), a novel index that uses iterative removal of individual gene trees to assess the impact of each gene on every clade in a distance-based coalescent tree. Reanalyses of five phylogenomic datasets show that PCBL for STAR and NJst trees helps quantify the overall stability/instability of clades and clarifies disagreements with results from optimality-based coalescent analyses. PCBL scores reveal severe ‘missing taxa’, ‘apical nesting’, ‘misrooting’, and ‘basal dragdown’ biases. Contrived examples demonstrate the gross overweighting of outlier gene trees that drives these biases. Because of interrelated biases revealed by PCBL scores, caution should be exercised when using STAR and NJst, in particular when many taxa are analyzed, missing data are non-randomly distributed, and widespread gene-tree reconstruction error is suspected. Similar biases in the optimality-based coalescent method MP-EST indicate that congruence among species trees estimated via STAR, NJst, and MP-EST should not be interpreted as independent corroboration for phylogenetic relationships. Such agreements among methods instead might be due to the common defects of all three summary coalescent methods.
Thiopurines inhibit coronavirus Spike protein processing and incorporation into progeny virions

There is an outstanding need for broadly acting antiviral drugs to combat emerging viral diseases. Here, we report that thiopurines inhibit the replication of the betacoronaviruses HCoV-OC43 and SARS-CoV-2, and to a lesser extent, the alphacoronavirus HCoV-229E. 6-Thioguanine (6-TG) disrupted early stages of infection, limiting synthesis of full-length and subgenomic HCoV RNAs. Furthermore, consistent with our previous report on the effects of thiopurines on influenza A virus (IAV) glycoproteins, we observed that 6-TG inhibited accumulation of Spike glycoproteins from diverse HCoVs. Specifically, 6-TG treatment decreased the accumulation of Spike proteins and increased their electrophoretic mobility to match the properties of Spike following enzymatic removal of N-linked oligosaccharides with Peptide:N-glycosidase F (PNGaseF). SARS-CoV-2 virus-like particles (VLPs) harvested from 6-TG-treated cells were deficient in Spike. 6-TG treatment had a similar effect on lentiviruses pseudotyped with SARS-CoV-2 Spike; lentiviruses could be harvested from cell supernatants, but they were deficient in Spike and unable to infect human cells bearing ACE2 receptors. Together, these findings from complementary ectopic expression and infection models strongly indicate that defective Spike trafficking and processing is an outcome of 6-TG treatment. At low micromolar doses, the primary known mode of action of 6-TG is selective inhibition of the small GTPase Rac1. However, we show that selective chemical inhibitors of the small GTPases Rac1, CDC42 and Rho had no effect on Spike processing and accumulation, whereas the broad GTPase agonist ML099 was able to counter the effects of 6-TG, suggesting that an unknown GTPase could be the relevant 6-TG-target protein involved in regulating Spike processing and accumulation. Overall, these findings provide important clues about the mechanism of action of a candidate antiviral that can broadly target HCoVs and suggest that small GTPases may be promising targets for host-targeted antivirals.
Non-literal language processing is jointly supported by the language and theory of mind networks: Evidence from a novel meta-analytic fMRI approach

Going beyond the literal meaning of utterances is key to communicative success. However, the mechanisms that support non-literal inferences remain debated. Using a novel meta-analytic approach, we evaluate the contribution of linguistic, social-cognitive, and executive mechanisms to non-literal interpretation. We identified 74 fMRI experiments (n=1,430 participants) from 2001-2021 that contrasted non-literal language comprehension with a literal control condition, spanning ten phenomena (e.g., metaphor, irony, indirect speech). Applying the activation likelihood estimation approach to the 825 activation peaks yielded six left-lateralized clusters. We then evaluated the locations of both the individual-study peaks and the clusters against probabilistic functional atlases (cf. macroanatomy, as is typically done) for three candidate brain networks—the language-selective network (Fedorenko et al., 2011), which supports language processing, the Theory of Mind (ToM) network (Saxe & Kanwisher, 2003), which supports social inferences, and the domain-general Multiple-Demand (MD) network (Duncan, 2010), which supports executive control. These atlases were created by overlaying individual activation maps of participants who performed robust and extensively validated ‘localizer’ tasks that target each network in question (n=806 for language; n=198 for ToM; n=691 for MD). We found that both the individual-study peaks and the ALE clusters fell primarily within the language network and the ToM network. These results suggest that non-literal processing is supported by both i) mechanisms that process literal linguistic meaning, and ii) mechanisms that support general social inference. They thus undermine a strong divide between literal and non-literal aspects of language and challenge the claim that non-literal processing requires additional executive resources.
Lingering Neural Representations of Past Task Features Adversely Affect Future Behavior

During goal-directed behavior, humans purportedly form and retrieve so-called event files, conjunctive representations that link context-specific information about stimuli, their associated actions, and the expected action outcomes. The automatic formation, and later retrieval, of such conjunctive representations can substantially facilitate efficient action selection. However, recent behavioral work suggests that these event files may also adversely affect future behavior, especially when action requirements have changed between successive instances of the same task context (e.g., during task switching). Here, we directly tested this hypothesis with a recently developed method for measuring the strength of the neural representations of context-specific stimulus–action conjunctions (i.e., event files). Thirty-five male and female adult humans performed a task switching paradigm while undergoing EEG recordings. Replicating previous behavioral work, we found that changes in action requirements between two spaced repetitions of the same task incurred a significant reaction time cost. By combining multivariate pattern analysis and representational similarity analysis of the EEG recordings with linear mixed-effects modeling of trial-to-trial behavior, we then found that the magnitude of this behavioral cost was directly proportional to the strength of the conjunctive representation formed during the most recent previous exposure to the same task, that is, the most recent event file. This confirms that the formation of conjunctive representations of specific task contexts, stimuli, and actions in the brain can indeed adversely affect future behavior. Moreover, these findings demonstrate the potential of neural decoding of complex task set representations toward the prediction of behavior beyond the current trial. SIGNIFICANCE STATEMENT Understanding how the human brain organizes individual components of complex tasks is paramount for understanding higher-order cognition. During complex tasks, the brain forms conjunctive representations that link individual task features (contexts, stimuli, actions), which aids future performance of the same task. However, this can have adverse effects when the required sequence of actions within a task changes. We decoded conjunctive representations from electroencephalographic recordings during a task that included frequent changes to the rules determining the response. Indeed, stronger initial conjunctive representations predicted significant future response-time costs when task contexts repeated with changed response requirements. Showing that the formation of conjunctive task representations can have negative future effects generates novel insights into complex behavior and cognition, including task switching, planning, and problem solving.
Neuroendocrinology of the lung revealed by single-cell RNA sequencing

Pulmonary neuroendocrine cells (PNECs) are sensory epithelial cells that transmit airway status to the brain via sensory neurons and locally via CGRP and GABA. Several other neuropeptides and neurotransmitters have been detected in various species, but the number, targets, functions, and conservation of PNEC signals are largely unknown. We used scRNAseq to profile hundreds of the rare mouse and human PNECs. This revealed >40 PNEC neuropeptide and peptide hormone genes, most cells expressing unique combinations of 5-18 genes. Peptides are packaged in separate vesicles, release presumably regulated by the distinct, multimodal combinations of sensors expressed by each PNEC. Expression of cognate receptors predicts an array of local targets, and we show the new PNEC signal angiotensin directly activates innervating sensory neurons. Many signals lack lung targets so may have endocrine activity like those of PNEC- derived carcinoid tumors. PNECs are an extraordinarily rich and diverse signaling hub rivaling the enteroendocrine system.
A Dynamic Bottom-Up Saliency Detection Method for Still Images

Introduction Existing saliency detection algorithms in the literature have ignored the importance of time. They create a static saliency map for the whole recording time. However, bottom-up and top-down attention continuously compete and the salient regions change through time. In this paper, we propose an unsupervised algorithm to predict the dynamic evolution of bottom-up saliency in images. Method We compute the variation of low-level features within non-overlapping patches of the input image. A patch with higher variation is considered more salient. We use a threshold to ignore less salient parts and create a map. A weighted sum of this map and its center of mass is calculated to provide the saliency map. The threshold and weights are set dynamically. We use the MIT1003 and DOVES datasets for evaluation and break the recording to multiple 100ms or 500ms-time intervals. A separate ground-truth is created for each interval. Then, the predicted dynamic saliency map is compared to the ground-truth using Normalized Scanpath Saliency, Kullback-Leibler divergence, Similarity, and Linear Correlation Coefficient metrics. Results The proposed method outperformed the competitors on DOVES dataset. It also had an acceptable performance on MIT1003 especially within 0-400ms after stimulus onset. Conclusion This dynamic algorithm can predict an image’s salient regions better than the static methods as saliency detection is inherently a dynamic process. This method is biologically-plausible and in-line with the recent findings of the creation of a bottom-up saliency map in the primary visual cortex or superior colliculus.
Altered excitatory and inhibitory neuronal subpopulation parameters are distinctly associated with tau and amyloid in Alzheimer’s disease

Background Neuronal and circuit level abnormalities of excitation and inhibition are shown to be associated with tau and amyloid-beta (Aβ) in preclinical models of Alzheimer’s disease (AD). These relationships remain poorly understood in patients with AD. Methods Using empirical spectra from magnetoencephalography (MEG) and computational modeling (neural mass model; NMM) we examined excitatory and inhibitory parameters of neuronal subpopulations and investigated their specific associations to regional tau and Aβ, measured by positron emission tomography (PET), in patients with AD. Results Patients with AD showed abnormal excitatory and inhibitory time-constants and neural gains compared to age-matched controls. Increased excitatory time-constants distinctly correlated with higher tau depositions while increased inhibitory time-constants distinctly correlated with higher Aβ depositions. Conclusions Our results provide critical insights about potential mechanistic links between abnormal neural oscillations and cellular correlates of impaired excitatory and inhibitory synaptic functions associated with tau and Aβ in patients with AD. Funding This study was supported by the National Institutes of Health grants: K08AG058749 (KGR), F32AG050434-01A1 (KGR), K23 AG038357 (KAV), P50 AG023501, P01 AG19724 (BLM), P50-AG023501 (BLM & GDR), R01 AG045611 (GDR); AG034570, AG062542 (WJ); NS100440 (SSN), DC176960 (SSN), DC017091 (SSN), AG062196 (SSN); a grant from John Douglas French Alzheimer’s Foundation (KAV); grants from Larry L. Hillblom Foundation: 2015-A-034-FEL and (KGR); 2019-A-013-SUP (KGR); a grant from the Alzheimer’s Association: (PCTRB-13-288476) (KAV), and made possible by Part the CloudTM, (ETAC-09-133596); a grant from Tau Consortium (GDR & WJJ), and a gift from the S. D. Bechtel Jr. Foundation.