Miracle-dz/UniFAR-FTUs huggingface dataset:
Different olfactory neuron classes use distinct temporal and molecular programs to complete synaptic development

Developing neurons must meet core molecular, cellular, and temporal requirements to ensure the correct formation of synapses, resulting in functional circuits. However, because of the vast diversity in neuronal class and function, it is unclear whether or not all neurons use the same organizational mechanisms to form synaptic connections and achieve functional and morphological maturation. Moreover, it remains unknown if neurons united in a common goal and comprising the same sensory circuit develop on similar timescales and using identical molecular approaches to ensure the formation of the correct number of synapses. To begin to answer these questions, we took advantage of the Drosophila antennal lobe, a model olfactory circuit with remarkable genetic access and synapse-level resolution. Using tissue-specific genetic labeling of active zones, we performed a quantitative analysis of synapse formation in multiple classes of neurons throughout development and adulthood. We found that olfactory receptor neurons (ORNs), projection neurons (PNs), and local interneurons (LNs) each have unique time-courses of synaptic development, addition, and refinement, demonstrating that each class follows a distinct developmental program. This raised the possibility that these classes may also have distinct molecular requirements for synapse formation. We genetically altered neuronal activity in each neuronal subtype and observed differing effects on synapse number based on the neuronal class examined. Silencing neuronal activity in ORNs, PNs, and LNs impaired synaptic development but only in ORNs did enhancing neuronal activity influence synapse formation. ORNs and LNs demonstrated similar impairment of synaptic development with enhanced activity of a master kinase, GSK-3β, suggesting that neuronal activity and GSK-3β kinase activity function in a common pathway. ORNs also, however, demonstrated impaired synaptic development with GSK-3β loss-of-function, suggesting additional activity-independent roles in development. Ultimately, our results suggest that the requirements for synaptic development are not uniform across all neuronal classes with considerable diversity existing in both their developmental timeframes and molecular requirements. These findings provide novel insights into the mechanisms of synaptic development and lay the foundation for future work determining their underlying etiologies.
Direct and indirect impacts of positive selection on genomic variation in Drosophila serrata

Understanding the extent to which microevolutionary adaptation relies on novel beneficial mutations, as opposed to previously neutral standing genetic variation, is an important goal of evolutionary genetics. Progress towards this goal has been enhanced during the genomic era through the study of selective sweeps. Selective sweeps fall into two categories: hard sweeps via new mutations and soft sweeps via pre-existing mutations. However, data are currently lacking on the relative frequency of these two types of selective sweep. In this study, we examined 110 whole genome sequences from Drosophila serrata sampled from eastern Australia and searched for hard and soft sweeps using a deep learning algorithm (diploS/HIC). Analyses revealed that approximately 15% of the D. serrata genome was directly impacted by soft sweeps, and that 46% of the genome was indirectly influenced via linkage to these soft sweeps. In contrast, hard sweep signatures were very rare, only accounting for 0.1% of the scanned genome. Gene ontology enrichment analysis further supported our confidence in the accuracy of sweep detection as several traits expected to be under frequent selection due to evolutionary arms races (e.g. immunity and sperm competition) were detected. Within soft sweep regions and those flanking them, there was an over-representation of SNPs with predicted deleterious effects, suggesting positive selection drags deleterious variants to higher frequency due to their linkage with beneficial loci. This study provides insight into the direct and indirect contributions of positive selection in shaping genomic variation in natural populations.
Hippocampal CA1 represents action and reward events instantly compared to the superficial and deep layers of the lateral entorhinal cortex

The entorhinal cortex (EC) is the main interface between the hippocampus and the neocortex. The EC plays a critical role in learning and memory. We investigated the neuronal representation of behavioral events during operant learning in the hippocampal-entorhinal circuit of head-fixed rats. Both CA1 and lateral entorhinal cortex (LEC) neurons develop task-related activities after learning. Among diverse task-related activities, we compared the transient peak activities that represent action and reward and found a distinct difference in the timing of behavioral event representation between CA1 and LEC. CA1 represents action and reward events in close to real-time, whereas both the superficial and deep layers of the LEC showed delayed representation of those events. Our results suggest that subpopulations exist within which CA1 and LEC neurons process the information in a different order from the anatomically defined hippocampal-entorhinal circuit.
A neural decision signal during internal sampling from working memory

How humans transform sensory information into decisions that steer purposeful behaviour is a central question in psychology and neuroscience that is traditionally investigated during the sampling of external sensations. The decision-making framework of gradual information sampling toward a decision has been proposed also to apply when sampling internal sensory evidence from memory. However, neural evidence for this proposal remains lacking. Here we report that sampling internal visual representations from working memory elicits a scalp-EEG potential associated with gradual evidence accumulation – the Central Parietal Positivity (CPP). Consistent with an evolving decision process, we show how this signal (i) builds up to, and scales with, the time participants require to reach a decision about the cued memory content and (ii) is amplified when having to select (decide) among multiple contents in working memory. These results bring the electrophysiology of decision making into the domain of working memory and suggest that variability in memory-guided behaviour may be driven (at least in part) by variations in the sampling of our inner mental contents.
A Socio-Cultural Study of Pedagogical Practices inside

Abstract This paper reports on an exploratory study that explores the instructional patterns within English as a Foreign Language (EFL) secondary school classrooms in Syria. Recently, the Syrian Ministry of Education (MoE) has introduced a new national curriculum which recommends a shift in EFL teachers’ instructional practices. Despite this costly innovation, there has been no attempt to check whether it was working. Adopting a socio-cultural perspective on learning, the study looks at teacher-student interaction and the discourse taking place during teacher-fronted whole class talk. To help in the identification of teachers’ training needs, teacher beliefs and classroom practices are investigated using a mixed-methods approach comprising classroom observations and interviews. Detailed discourse analysis revealed a traditional teacher-controlled mode of teaching focusing on mechanical practices rather than meaningful interactions. Students were afforded few opportunities to participate meaningfully in classroom interactions, as teachers controlled the topics of academic learning. The study highlights the need to invest in teachers’ professional development, particularly during the critical phase of curriculum innovation, to promote interactive and dialogic teaching in the Syrian educational system.
Drug combination prediction for cancer treatment using disease-specific drug response profiles and single-cell transcriptional signatures

Developing novel cancer treatments is a challenging task that can benefit from computational techniques matching transcriptional signatures to large-scale drug response data. Here, we present ‘retriever,’ a tool that extracts robust disease-specific transcriptional drug response profiles based on cellular response profiles to hundreds of compounds from the LINCS-L1000 project. We used retriever to extract transcriptional drug response signatures of triple-negative breast cancer (TNBC) cell lines and combined these with a single-cell RNA-seq breast cancer atlas to predict drug combinations that antagonize TNBC-specific disease signatures. After systematically testing 152 drug response profiles and 11,476 drug combinations, we identified the combination of kinase inhibitors QL-XII-47 and GSK-690693 as the topmost promising candidate for TNBC treatment. Our new computational approach allows the identification of drugs and drug combinations targeting specific tumor cell types and subpopulations in individual patients. It is, therefore, highly suitable for the development of new personalized cancer treatment strategies.
Irrational choices via a curvilinear representational geometry for value

We make decisions by comparing values, but how is value represented in the brain? Many models assume, if only implicitly, that the representational geometry of value is linear. However, in part due to a historical focus on noisy single neurons, rather than neuronal populations, this hypothesis has not been rigorously tested. Here, we examined the representational geometry of value in the ventromedial prefrontal cortex (vmPFC), a part of the brain linked to economic decision-making. We found that values were encoded along a curved manifold in vmPFC. This curvilinear geometry predicted a specific pattern of irrational decision-making: that decision-makers will make worse choices when an irrelevant, decoy option is worse in value, compared to when it is better. Indeed, we observed these irrational choices in behavior. Together, these results not only suggest that the representational geometry of value is nonlinear, but that this nonlinearity could impose bounds on rational decision-making.
Topographically distinct adaptive landscapes for teeth, skeletons, and size explain the adaptive radiation of Carnivora (Mammalia)

Models of adaptive radiation were originally developed to explain the early, rapid appearance of distinct modes of life within diversifying clades. Phylogenetic tests of this hypothesis have yielded limited support for temporally declining rates of phenotypic evolution across diverse clades, but the concept of an adaptive landscape that links form to fitness, while also crucial to these models, has received more limited attention. Using methods that assess the temporal accumulation of morphological variation and estimate the topography of the underlying adaptive landscape, I found evidence of an early partitioning of mandibulo-dental morphological variation in Carnivora (Mammalia) that occurs on an adaptive landscape with multiple peaks, consistent with classic ideas about adaptive radiation. Although strong support for this mode of adaptive radiation is present in traits related to diet, its signal is not present in body mass data or for traits related to locomotor behavior and substrate use. These findings suggest that adaptive radiations may occur along some axes of ecomorphological variation without leaving a signal in others and that their dynamics are more complex than simple univariate tests might suggest.
EEG-representational geometries and psychometric distortions in approximate numerical judgment

When judging the average value of sample stimuli (e.g., numbers) people tend to either over- or underweight extreme sample values, depending on task context. In a context of overweighting, recent work has shown that extreme sample values were overly represented also in neural signals, in terms of an anti-compressed geometry of number samples in multivariate electroencephalography (EEG) patterns. Here, we asked whether neural representational geometries may also reflect underweighting of extreme values (i.e., compression) which has been observed behaviorally in a great variety of tasks. We used a simple experimental manipulation (instructions to average a single-stream or to compare dual-streams of samples) to induce compression or anti-compression in behavior when participants judged rapid number sequences. Model-based representational similarity analysis (RSA) replicated the previous finding of neural anti-compression in the dual-stream task, but failed to provide evidence for neural compression in the single-stream task, despite the evidence for compression in behavior. Instead, the results suggested enhanced neural processing of extreme values in either task, regardless of whether extremes were over- or underweighted in subsequent behavioral choice. We further observed more general differences in the neural representation of the sample information between the two tasks. The results suggest enhanced processing of extreme values as the brain’s default. Such a default raises new questions about the origin of common psychometric distortions, such as diminishing sensitivity for larger values.
Frontal Cortex Gates Distractor Stimulus Encoding in Sensory Cortex

Frontal cortex suppresses behavioral responses to distractor stimuli. One possible mechanism by which this occurs is by modulating sensory responses in sensory cortex. However, it is currently unknown how frontal cortex modulations of sensory cortex contribute to distractor response suppression. We trained mice to respond to target stimuli in one whisker field and ignore distractor stimuli in the opposite whisker field. During expert task performance, optogenetic inhibition of frontal cortex increased behavioral responses to distractor stimuli. During expert task performance, within sensory cortex we observed expanded propagation of target stimulus responses and contracted propagation of distractor stimulus responses. In contrast to current models of frontal cortex function, frontal cortex did not substantially modulate the response amplitude of preferred stimuli. Rather, frontal cortex specifically suppressed the propagation of distractor stimulus responses, thereby preventing target-preferring neurons from being activated by distractor stimuli. Single unit analyses revealed that wMC decorrelates target and distractor stimulus encoding in target-preferring S1 neurons, which likely improves selective target stimulus detection by downstream readers. Moreover, we observed proactive top-down modulation from frontal to sensory cortex, through the preferential activation of GABAergic neurons. Overall, our study provides important mechanistic details about how frontal cortex gates sensory propagation in sensory cortex to prevent behavioral responses to distractor stimuli. Highlights Pairing of frontal cortex optogenetic inhibition with sensory cortex recordings during a target-distractor Go/NoGo task. During expert task performance, we observed target stimulus response expansion and distractor stimulus response contraction. Optogenetic inhibition of frontal cortex increased false alarm rates and selectively increased the propagation of distractor evoked responses into target-aligned sensory cortex. Even before stimulus onset, frontal cortex preferentially drives GABAergic neurons in distractor-aligned sensory cortex.
Scaling Up Models and Data with t5x and seqio

Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves. Scaling can be complicated due to various factors including the need to distribute computation on supercomputer clusters (e.g., TPUs), prevent bottlenecks when infeeding data, and ensure reproducible results. In this work, we present two software libraries that ease these issues: $\texttt{t5x}$ simplifies the process of building and training large language models at scale while maintaining ease of use, and $\texttt{seqio}$ provides a task-based API for simple creation of fast and reproducible training data and evaluation pipelines. These open-source libraries have been used to train models with hundreds of billions of parameters on datasets with multiple terabytes of training data. Along with the libraries, we release configurations and instructions for T5-like encoder-decoder models as well as GPT-like decoder-only architectures. $\texttt{t5x}$ and $\texttt{seqio}$ are open source and available at https://github.com/google-research/t5x and https://github.com/google/seqio, respectively.
Therapeutic Applications of Physalins: Powerful Natural Weapons

Physalins, or 16,24-cyclo-13,14-seco steroids, are compounds belonging to the class of withanolides that can be found in plants of Solanaceae family, mainly in species belonging to the genus Physalis spp. , which are annual herbaceous plants widely distributed in tropical and subtropical regions of the world. Physalins are versatile molecules that act in several cell signaling pathways and activate different mechanisms of cell death or immunomodulation. A number of studies have shown a variety of actions of these compounds, including anticancer, anti-inflammatory, antiparasitic, antimicrobial, antinociceptive, and antiviral activities. Here we reviewed the main findings related to the anticancer, immunomodulatory, and antiparasitic activities of physalins and its mechanisms of action, highlighting the \challenges and future directions in the pharmacological application of physalins.
High-Dimensional DNA Methylation Mediates the Effect of Smoking on Crohn’s Disease

Epigenome-wide mediation analysis aims to identify high-dimensional DNA methylation at cytosine–phosphate–guanine (CpG) sites that mediate the causal effect of linking smoking with Crohn’s disease (CD) outcome. Studies have shown that smoking has significant detrimental effects on the course of CD. So we assessed whether DNA methylation mediates the association between smoking and CD. Among 103 CD cases and 174 controls, we estimated whether the effects of smoking on CD are mediated through DNA methylation CpG sites, which we referred to as causal mediation effect. Based on the causal diagram, we first implemented sure independence screening (SIS) to reduce the pool of potential mediator CpGs from a very large to a moderate number; then, we implemented variable selection with de-sparsifying the LASSO regression. Finally, we carried out a comprehensive mediation analysis and conducted sensitivity analysis, which was adjusted for potential confounders of age, sex, and blood cell type proportions to estimate the mediation effects. Smoking was significantly associated with CD under odds ratio (OR) of 2.319 (95% CI: 1.603, 3.485, p < 0.001) after adjustment for confounders. Ninety-nine mediator CpGs were selected from SIS, and then, seven candidate CpGs were obtained by de-sparsifying the LASSO regression. Four of these CpGs showed statistical significance, and the average causal mediation effects (ACME) were attenuated from 0.066 to 0.126. Notably, three significant mediator CpGs had absolute sensitivity parameters of 0.40, indicating that these mediation effects were robust even when the assumptions were slightly violated. Genes (BCL3 and FKBP5) harboring these four CpGs were related to CD. These findings suggest that changes in methylation are involved in the mechanism by which smoking increases risk of CD.
Assessment of Myocardial Microstructure in a Murine Model of Obesity-Related Cardiac Dysfunction by Diffusion Tensor Magnetic Resonance Imaging at 7T

Background Obesity exerts multiple deleterious effects on the heart that may ultimately lead to cardiac failure. This study sought to characterize myocardial microstructure and function in an experimental model of obesity-related cardiac dysfunction. Methods Male C57BL/6N mice were fed either a high-fat diet (HFD; 60 kcal% fat, n = 12) or standard control diet (9 kcal% fat, n = 10) for 15 weeks. At the end of the study period, cardiac function was assessed by ultra-high frequency echocardiography, and hearts were processed for further analyses. The three-dimensional myocardial microstructure was examined ex vivo at a spatial resolution of 100 × 100 × 100 μm3 by diffusion tensor magnetic resonance imaging (DT-MRI) at 7T. Myocardial deformation, diffusion metrics and fiber tract geometry were analyzed with respect to the different myocardial layers (subendocardium/subepicardium) and segments (base/mid-cavity/apex). Results were correlated with blood sample analyses, histopathology, and gene expression data. Results HFD feeding induced significantly increased body weight combined with a pronounced accumulation of visceral fat (body weight 42.3 ± 5.7 vs. 31.5 ± 2.2 g, body weight change 73.7 ± 14.8 vs. 31.1 ± 6.6%, both P < 0.001). Obese mice showed signs of diastolic dysfunction, whereas left-ventricular ejection fraction and fractional shortening remained unchanged (E/e’ 41.6 ± 16.6 vs. 24.8 ± 6.0, P < 0.01; isovolumic relaxation time 19 ± 4 vs. 14 ± 4 ms, P < 0.05). Additionally, global longitudinal strain was reduced in the HFD group (−15.1 ± 3.0 vs. −20.0 ± 4.6%, P = 0.01), which was mainly driven by an impairment in basal segments. However, histopathology and gene expression analyses revealed no myocardial fibrosis or differences in cardiomyocyte morphology. Mean diffusivity and eigenvalues of the diffusion tensor were lower in the basal subepicardium of obese mice as assessed by DT-MRI (P < 0.05). The three-dimensional fiber tract arrangement of the left ventricle (LV) remained preserved. Conclusion Fifteen weeks of high-fat diet induced alterations in myocardial diffusion properties in mice, whereas no remodeling of the three-dimensional myofiber arrangement of the LV was observed. Obese mice showed reduced longitudinal strain and lower mean diffusivity predominantly in the left-ventricular base, and further investigation into the significance of this regional pattern is required.
Deep Learning Approach for Assessing Air Quality During COVID-19 Lockdown in Quito

Weather Normalized Models (WNMs) are modeling methods used for assessing air contaminants under a business-as-usual (BAU) assumption. Therefore, WNMs are used to assess the impact of many events on urban pollution. Recently, different approaches have been implemented to develop WNMs and quantify the lockdown effects of COVID-19 on air quality, including Machine Learning (ML). However, more advanced methods, such as Deep Learning (DL), have never been applied for developing WNMs. In this study, we proposed WNMs based on DL algorithms, aiming to test five DL architectures and compare their performances to a recent ML approach, namely Gradient Boosting Machine (GBM). The concentrations of five air pollutants (CO, NO2, PM2.5, SO2, and O3) are studied in the city of Quito, Ecuador. The results show that Long-Short Term Memory (LSTM) and Bidirectional Recurrent Neural Network (BiRNN) outperform the other algorithms and, consequently, are recommended as appropriate WNMs to quantify the effects of the lockdowns on air pollution. Furthermore, examining the variable importance in the LSTM and BiRNN models, we identify that the most relevant temporal and meteorological features for predicting air quality are Hours (time of day), Index (1 is the first collected data and increases by one after each instance), Julian Day (day of the year), Relative Humidity, Wind Speed, and Solar Radiation. During the full lockdown, the concentration of most pollutants has decreased drastically: −48.75%, for CO, −45.76%, for SO2, −42.17%, for PM2.5, and −63.98%, for NO2. The reduction of this latter gas has induced an increase of O3 by +26.54%.