Miracle-dz/UniFAR-FTUs huggingface dataset:
CRISPRs are associated with bacterial expansion in the human gut during the first year of life

The dynamics of the gut microbiome in infancy have a profound impact on health in adulthood, but the ecological mechanism underlying the dynamics between bacteria and bacteriophages remains poorly understood. CRISPR is a bacterial adaptive immune system to resist bacteriophages; however, the role they play in the dynamics in infants’ gut microbiota is unknown. In this work, using large-scale metagenomic sequencing data from 82 Sweden infants’ gut microbiomes, 1882 candidate CRISPRs were identified and their dynamics were analyzed. The results showed CRISPRs were distributed in dominant bacteria and could target distinct bacteriophages at different time points with largely alternated spacers. In the putative identical CRISPRs, we found the CRISPRs could acquire new spacers and loss old spacers during the first year. In addition, it is the first time to report that gender was a major factor to determine the bacterial richness and the number of CRISPRs and host range of bacteriophages was narrow in silico. Therefore, we concluded that CRISPRs were associated with bacterial expansion. This work improves the understanding of the ecological mechanism behind dynamics in the early life of the human gut and substantially expands the repertoire of predicted CRISPRs providing a resource to study the function of bacterial unknown genes and to enhance the performance of these beneficial bacteria by CRISPR gene-editing technology.
Predicting drug polypharmacology from cell morphology readouts using variational autoencoder latent space arithmetic

A variational autoencoder (VAE) is a machine learning algorithm, useful for generating a compressed and interpretable latent space. These representations have been generated from various biomedical data types and can be used to produce realistic-looking simulated data. However, standard vanilla VAEs suffer from entangled and uninformative latent spaces, which can be mitigated using other types of VAEs such as β-VAE and MMD-VAE. In this project, we evaluated the ability of VAEs to learn cell morphology characteristics derived from cell images. We trained and evaluated these three VAE variants—Vanilla VAE, β-VAE, and MMD-VAE—on cell morphology readouts and explored the generative capacity of each model to predict compound polypharmacology (the interactions of a drug with more than one target) using an approach called latent space arithmetic (LSA). To test the generalizability of the strategy, we also trained these VAEs using gene expression data of the same compound perturbations and found that gene expression provides complementary information. We found that the β-VAE and MMD-VAE disentangle morphology signals and reveal a more interpretable latent space. We reliably simulated morphology and gene expression readouts from certain compounds thereby predicting cell states perturbed with compounds of known polypharmacology. Inferring cell state for specific drug mechanisms could aid researchers in developing and identifying targeted therapeutics and categorizing off-target effects in the future.
Coding of social odors in the hippocampal CA2 region as a substrate for social memory

The ability to encode and update information about individuals is critical for lasting social relationships. Although the hippocampus is important for social recognition memory, its underlying neural representations remain elusive. Here we investigate the neural codes mediating social recognition and learning by examining social odor recognition and associative odor-reward learning in mice. We performed high-resolution calcium imaging from the hippocampal CA2 region of awake head-fixed mice, as CA2 is necessary for social recognition memory. We find that CA2 encodes specific neural representations of novel social odors that are further refined during associative odor-reward learning. Optogenetic silencing of CA2 impairs the formation of reward associations. Furthermore, CA2 population activity represents odors in a geometry that enables abstract representations of social versus non-social odors. Thus, CA2 distinguishes multiple forms of olfactory stimuli to enhance the learning of social odors and associations, which are poised to serve as substrates of social memory.
Protein Structure Prediction Using a Maximum Likelihood Formulation of a Recurrent Geometric Network

Only ∼40% of the human proteome has structural coordinates available from experiment (i.e., X-ray crystallography, NMR spectroscopy, or cryo-EM) or homology modeling with quality templates (i.e., 30% sequence identity or greater), leaving most of the proteome structurally unsolved. Deep learning (DL) methods for predicting protein structure can help close knowledge gaps where experimental and homology models are difficult to obtain. Recent advances in these DL methods have shown promising results in expanding structural coverage to the scale of the entire human proteome, providing researchers with more complete protein structural information. Here, we improve upon an existing DL algorithm for protein structure prediction, the Recurrent Geometric Network (RGN). We first expand the training dataset to include experimental uncertainty data in the form of atomic displacement parameters, then derive a maximum likelihood loss function that incorporates this uncertainty data into model training. Compared to the original RGN, our novel maximum likelihood model improves the rate of convergence of initial model training and ultimately results in more accurate structure prediction according to the root mean square deviation (RMSD) of backbone atoms, the Global Distance Test (GDT), the Global Distance Test High Accuracy (GDT-HA), and the Template-Modeling Score (TM-Score). Our model also predicts structures with more favorable backbone torsions, which provide more accurate starting coordinates for downstream physics-based simulations. Based on these results, our maximum likelihood reformulation provides a framework for improving existing or future machine learning algorithms for protein structure prediction. The augmented dataset, data collection scripts, reformulated RGN source code, and a series of trained models are publicly available at https://github.com/SchniedersLab/likelihood-rgn.
Neural Excursions from Low-Dimensional Manifold Structure Explain Intersubject Variation in Human Motor Learning

Humans vary greatly in their motor learning abilities, yet little is known about the neural mechanisms that underlie this variability. Recent neuroimaging and electrophysiological studies demonstrate that large-scale neural dynamics inhabit a low-dimensional subspace or manifold, and that learning is constrained by this intrinsic manifold architecture. Here we asked, using functional MRI, whether subject-level differences in neural excursion from manifold structure can explain differences in learning across participants. We had subjects perform a sensorimotor adaptation task in the MRI scanner on two consecutive days, allowing us to assess their learning performance across days, as well as continuously measure brain activity. We find that the overall neural excursion from manifold activity in both cognitive and sensorimotor brain networks is associated with differences in subjects’ patterns of learning and relearning across days. These findings suggest that off-manifold activity provides an index of the relative engagement of different neural systems during learning, and that intersubject differences in patterns of learning and relearning across days are related to reconfiguration processes in cognitive and sensorimotor networks during learning.
Bringing TrackMate into the era of machine-learning and deep-learning

TrackMate is an automated tracking software used to analyze bioimages and distributed as a Fiji plugin. Here we introduce a new version of TrackMate rewritten to improve performance and usability, and integrating several popular machine and deep learning algorithms to improve versatility. We illustrate how these new components can be used to efficiently track objects from brightfield and fluorescence microscopy images across a wide range of bio-imaging experiments.
KappaBle fluorescent reporter mice enable dynamic and low-background single-cell detection of NF-κB activity in vivo

Nuclear factor-κB (NF-κB) is a transcription factor with a key role in a great variety of cellular processes from embryonic development to immunity, the outcome of which depends on the fine-tuning of NF-κB activity. The development of sensitive and faithful reporter systems to accurately monitor the activation status of this transcription factor is therefore desirable. To address this need, over the years a number of different approaches have been used to generate NF-κB reporter mice, which can be broadly subdivided into bioluminescence- and fluorescence-based systems. While the former enables whole-body visualization of the activation status of NF-κB, the latter have the potential to allow the analysis of NF-κB activity at single cell level. However, fluorescence-based reporters frequently show poor sensitivity and excessive background or are incompatible with high-throughput flow cytometric analysis. In this work we describe the generation and analysis of ROSA26 knockin NF-κB reporter (KappaBle) mice containing a destabilized EGFP, which showed sensitive, dynamic, and faithful monitoring of NF-κB activity at the single-cell level of various cell types during inflammatory and infectious diseases.
How much the response of rice genotypes to water deficit at the reproductive phase is dependent from environmental conditions?

Rice crop is known as particularly sensitive to water deficit, especially during the reproductive phase when growth of vegetative organs and formation of spikelets are simultaneous. Many works have focused on the response of rice plants to water deficits varying in timing, duration and intensity. Oppositely, the impact of the environmental conditions on the response to a given water deficit remains largely unknown. In order to test it, two experiments under contrasted conditions of temperature, radiation and VPD were conducted on six genotypes in greenhouse in Brazil (S) and in growth chamber in France (GC). The plants were submitted to the same mild water deficit at the reproductive phase, by adjusting FTSW at 0.4. Under irrigation, plant growth rate was reduced and crop duration extended in GC in relation to S: ultimately, this trade-off resulted in similar plant height and biomass in both environments. Under water deficit and in both environments, elongation rate decreased and was associated with an increase in soluble sugars in stem and flag leaf, while starch was reduced in S and negligible in GC because of the low radiation. This common biochemical response displayed a large gradient of values across environments and genotypes, but differentially impacted the branch and spikelet formation on the developing panicle: in carbon limiting conditions (GC), the increase in soluble sugars was associated with the reduction in branch and spikelet number, and conversely in S. At the morphological level, the maintenance of spikelet number on the panicle was correlated with the maintenance of flag leaf width in all genotypes and conditions, that was discussed according to the maintenance of the apical meristem size. Genotypes were discriminated and the study underlined the global tolerance of Cirad 409 and sensitivity of IAC 25.
A spatiotemporal approach for social media sentiment analysis

The rapid growth of user-generated unstructured data through social media has raised several challenges and research opportunities. These data constitute a rich source of information for sentiment analysis and help the understanding of spontaneously expressed opinions. In the past few years, many scientific proposals have addressed sentiment analysis issues. However, most of them do not take into account both spatial and temporal dimensions, which would enable a more accurate analysis. To the best of our knowledge, this approach has not received much attention in the literature. In this article, we formalized a spatiotemporal sentiment analysis technique and applied this technique to a case study of tweets about the FIFA 2014 World Cup. Our approach exploits the summarization of sentiment analysis using the spatial and temporal dimensions and automatically generates opinion change flow maps through both dimensions. The results enable the tracking of opinion change flow maps through spatial and temporal analysis.
All the Polynomial Multiplication You Need on RISC-V

Polynomial multiplication is a core operation for public key cryptography, such as pre-quantum cryptography (e.g. elliptic curve cryptography) and post-quantum cryptography (e.g. code-based cryptography and multivariate-based cryptography). For this reason, the eﬃcient and secure implementation of polynomial multiplication has been actively conducted for high availability and security level in application services. In this paper, we present all polynomial multiplication methods on modern 32-bit RISC-V processors. We re-designed expensive implementations of polynomial multiplication on legacy microcontrollers (e.g. 8-bit AVR, 16-bit MSP, and 32-bit ARM) for new instruction sets of 32-bit RISC-V processors. Secondly, we suggest the optimal operand length for each polynomial multiplication on 32-bit RISC-V processors. With this implementation technique and Karatsuba algorithm, we achieved scalable features, which ensures the polynomial multiplication in any operand lengths with reasonably fast performance. Third, we propose instruction set extensions for the optimal implementation of polynomial multiplication on 32-bit RISC-V processors. This new feature introduces signiﬁcant performance enhancements. Lastly, the proposed implementation is a public domain and following researchers can easily re-produce the result.
: privacy preservation models for numerical quasi‑identifiers and multiple sensitive attributes

Rating datasets are the collection of user profile tuples such that every tuple of them is constructed from the user’s preference level to the interested artifacts and his/her personal data. Typically, rating datasets are proposed to utilize in recommendation methods, e.g., collaborative filtering, content-based filtering, knowledge-based recommendations, and hybrid recommendations. Aside from recommendation methods, rating datasets can also be provided to the data analyst with such an appropriate business objective such as business reports and researches. With a situation of utilizing rating datasets, it has been reported by several research papers that it can lead to be privacy violation issues. For this reason, there are several well-known privacy preservation models to be proposed in the recent decade years such as k-Anonymity, l-Diversity, t-Closeness, and k-Likenesses. Unfortunately, these privacy preservation models still are inefficient to address privacy violation issues in rating datasets. Therefore, an appropriate privacy preservation model for rating datasets is proposed by this work, so called as ( l p 1 , … , l p n )-privacy. With the proposed model, aside from achieving privacy preservation constraints, the data utility and the execution time are also maintained as much as possible. Moreover, the proposed model is evaluated through extensive experiments. From the experimental results, we found that the proposed model is highly effective and efficient than the compared model.
Breaching perpetual contact: Withdrawing from mobile and social media use in everyday life

This qualitative study aimed to investigate the norms and daily practices around mobile and social technology by examining what happens when mobile phones and social media on any devices are removed from one’s daily life. Most studies on technology non-use focus on one device or plat-form. In this study, participants (N = 78) relinquished not only social media but also their mobile phones for a 10-day period, and made observations on their experiences before, during, and after the “withdrawal. ” Participants initially experienced guilt and anxiety over violating a social contract by not being available and reachable anytime and anywhere, but mostly found their social capital — particularly bonding social capital — reinforced through the withdrawal. On the personal front, participants (re)discovered certain “life skills” like memory, imagination, and creativity in navigating their physical world and spending their time.
The Effects of Intensive Neurorehabilitation on Sequence Effect in Parkinson's Disease Patients With and Without Freezing of Gait

Background: The sequence effect (SE), defined as a reduction in amplitude of repetitive movements, is a common clinical feature of Parkinson's disease (PD) and is supposed to be a major contributor to freezing of gait (FOG). During walking, SE manifests as a step-by-step reduction in step length when approaching a turning point or gait destination, resulting in the so-called destination sequence effect (dSE). Previous studies explored the therapeutic effects of several strategies on SE, but none of them evaluated the role of an intensive rehabilitative program. Objectives: Here we aim to study the effects of a 4-week rehabilitative program on dSE in patients with PD with and without FOG. Methods: Forty-three patients (30 males, 70.6 ± 7.5 years old) with idiopathic PD were enrolled. The subjects were divided into two groups: patients with (PD + FOG, n = 23) and without FOG (PD – FOG, n = 20). All patients underwent a standardized 4-week intensive rehabilitation in-hospital program. At hospital admission (T0) and discharge (T1), all subjects were evaluated with an inertial gait analysis for dSE recording. Results: At T0, the dSE was more negative in the PD + FOG group (−0.80 ± 0.6) when compared to the PD – FOG group (−0.39 ± 0.3) (p = 0.007), even when controlling for several clinical and demographic features. At T1, the dSE was reduced in the overall study population (p = 0.001), with a more pronounced improvement in the PD + FOG group (T0: −0.80 ± 0.6; T1: −0.23 ± 0.4) when compared to the PD – FOG group (T0: −0.39 ± 0.3; T1: −0.22 ± 0.5) (p = 0.012). At T1, we described in the overall study population an improvement in speed, cadence, stride duration, and stride length (p = 0.001 for all variables). Conclusions: dSE is a core feature of PD gait dysfunction, specifically in patients with FOG. A 4-week intensive rehabilitative program improved dSE in PD patients, exerting a more notable beneficial effect in the PD + FOG group.
Artificial Intelligence in Obstetric Ultrasound: An Update and Future Applications

Artificial intelligence (AI) can support clinical decisions and provide quality assurance for images. Although ultrasonography is commonly used in the field of obstetrics and gynecology, the use of AI is still in a stage of infancy. Nevertheless, in repetitive ultrasound examinations, such as those involving automatic positioning and identification of fetal structures, prediction of gestational age (GA), and real-time image quality assurance, AI has great potential. To realize its application, it is necessary to promote interdisciplinary communication between AI developers and sonographers. In this review, we outlined the benefits of AI technology in obstetric ultrasound diagnosis by optimizing image acquisition, quantification, segmentation, and location identification, which can be helpful for obstetric ultrasound diagnosis in different periods of pregnancy.
Overexpression of Circular RNA circ_0013587 Reverses Erlotinib Resistance in Pancreatic Cancer Cells Through Regulating the miR-1227/E-Cadherin Pathway

Background Erlotinib, a small-molecule epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor, demonstrated therapeutic efficacy against pancreatic cancer. However, acquired resistance to erlotinib in pancreatic cancer is widely observed, and the exact mechanisms have not been fully explored until now. We examined the role of circular RNA circ_0013587 in the acquired resistance to erlotinib in pancreatic cancer cells and explored the underlying mechanisms. Methods We selected erlotinib-resistant pancreatic cancer cells from the AsPC-1 cell line. The expression of circ_0013587 was examined by qRT-PCR assays. The effects of circ_0013587 on pancreatic cancer cell proliferation, invasion, and erlotinib resistance were assessed by cell functional assays. Bioinformatic analysis and dual-luciferase reporter assays identified circ_0013587 and E-cadherin as direct targets of miR-1227. Mouse xenograft models were employed to investigate the function of circ_0013587 in erlotinib resistance of tumors in vivo. Results Circ_0013587 expression was significantly reduced in erlotinib-resistant AsPC-1 cells. We found that increasing circ_0013587 levels in erlotinib-resistant AsPC-1 cells re-sensitized them, whereas reducing circ_0013587 levels in erlotinib-sensitive AsPC-1 cells made them resistant. Mechanically, circ_0013587 released E-cadherin from the suppression of miR-1227, leading to E-cadherin up-regulation. Rescue assays highlighted that circ_0013587 reversed erlotinib resistance in pancreatic cancer cells by increasing E-cadherin levels through reducing the expression of miR-1227. Furthermore, circ_0013587 overexpression sensitized erlotinib-resistant AsPC-1 cells to erlotinib in xenograft models. Conclusions Our results demonstrated that down-regulation of circ_0013587 contributes to acquired resistance to erlotinib in pancreatic cancer cells through mediating the miR-1227/E-cadherin pathway and that circ_0013587 is a potential target molecular to overcome erlotinib resistance.
Precise Monitoring of Pore Pressure at Boreholes Around Nankai Trough Toward Early Detecting Crustal Deformation

In our recent study, we detected the pore pressure change due to the slow slip event (SSE) in March 2020 at the two borehole stations (C0002 and C0010), where the other borehole (C0006) close to the Nankai Trough seems not because of instrumental drift for the reference pressure on the seafloor to remove non-crustal deformation such as tidal and oceanic fluctuations. To overcome this problem, we use the seafloor pressure gauges of cabled network Dense Oceanfloor Network System for Earthquakes and Tsunamis (DONET) stations nearby boreholes instead of the reference by introducing time lag between them. We confirm that the time lag is explained from superposition of theoretical tide modes. By applying this method to the pore pressure during the SSE, we find pore pressure change at C0006 about 0.6 hPa. We also investigate the impact of seafloor pressure due to ocean fluctuation on the basis of ocean modeling, which suggests that the decrease of effective normal stress from the onset to the termination of the SSE is explained by Kuroshio meander and may promote updip slip migration, and that the increase of effective normal stress for the short-term ocean fluctuation may terminate the SSE as observed in the Hikurangi subduction zone.