Miracle-dz/UniFAR-FTUs huggingface dataset:
daf-16/FOXO blocks adult cell fate in Caenorhabditis elegans dauer larvae via lin-41/TRIM71

Many tissue-specific stem cells maintain the ability to produce multiple cell types during long periods of non-division, or quiescence. FOXO transcription factors promote quiescence and stem cell maintenance, but the mechanisms by which FOXO proteins promote multipotency during quiescence are still emerging. The single FOXO ortholog in C. elegans, daf-16, promotes entry into a quiescent and stress-resistant larval stage called dauer in response to adverse environmental cues. During dauer, stem and progenitor cells maintain or re-establish multipotency to allow normal development to resume after dauer. We find that during dauer, daf-16/FOXO prevents epidermal stem cells (seam cells) from prematurely adopting differentiated, adult characteristics. In particular, dauer larvae that lack daf-16 misexpress collagens that are normally adult-enriched. Using col-19p::gfp as an adult cell fate marker, we find that all major daf-16 isoforms contribute to opposing col-19p::gfp expression during dauer. By contrast, daf-16(0) larvae that undergo non-dauer development do not misexpress col-19p::gfp. Adult cell fate and the timing of col-19p::gfp expression are regulated by the heterochronic gene network, including lin-41 and lin-29. lin-41 encodes an RNA-binding protein orthologous to LIN41/TRIM71 in mammals, and lin-29 encodes a conserved zinc finger transcription factor. In non-dauer development lin-41 opposes adult cell fate by inhibiting the translation of lin-29, which directly activates col-19 transcription and promotes adult cell fate. We find that during dauer, lin-41 blocks col-19p::gfp expression, but surprisingly, lin-29 is not required in this context. Additionally, daf-16 promotes the expression of lin-41 in dauer larvae. The col-19p::gfp misexpression phenotype observed in dauer larvae with reduced daf-16 requires the downregulation of lin-41, but does not require lin-29. Taken together, this work demonstrates a novel role for daf-16/FOXO as a heterochronic gene that promotes expression of lin-41/TRIM71 to contribute to multipotent cell fate in a quiescent stem cell model.
Spectro-temporal neural dynamics during sentence completion

This magnetoencephalography (MEG) study aimed at characterizing the spectro-temporal dynamics of brain oscillatory activity elicited by sentence completion (SC). For that purpose, we adapted a version of the SC experimental paradigm typically used in functional magnetic resonance imaging to MEG investigation constraints. Twenty right-handed healthy young adults underwent MEG recordings while they were sequentially presented with short sentences divided in three parts: the first two giving context and the last requiring completion. MEG data were then analysed using a prior-free, non-parametric statistical approach with stringent control of the family-wise error rate. We identified three successive significant neural response patterns associated with distinct spatial and spectro-temporal characteristics: (i) an early (<300 ms) bioccipital 4-10-Hz event-related synchronization (ERS); (ii) an intermediate (at about 400 ms) 8-30-Hz event-related desynchronization (ERD) in an extended semantic network involving the ventral language stream as well as bilateral posterior nodes of the default mode network (DMN) in both hemispheres; (iii) a late (>800 ms) 8-30 Hz ERD involving the left dorsal language stream. Furthermore, the left component of the ventral language stream displayed prolonged ERD after 800 ms compared to the right which showed signs of inhibition in the form of ERS. Overall, this study elucidates the dynamics of the recruitment of the language network that accompany SC and the spectro-temporal signature of an extended semantic network. This MEG adaptation of an SC paradigm also paves the way for novel approaches in presurgical language mapping and may help to understand the neural underpinnings of the alterations of sentence completion in various neurologic disorders affecting language.
Functional profiling of long intergenic non-coding RNAs in fission yeast

Eukaryotic genomes express numerous long intergenic non-coding RNAs (lincRNAs) that do not overlap any coding genes. Some lincRNAs function in various aspects of gene regulation, but it is not clear in general to what extent lincRNAs contribute to the information flow from genotype to phenotype. To explore this question, we systematically analyzed cellular roles of lincRNAs in Schizosaccharomyces pombe. Using seamless CRISPR/Cas9-based genome editing, we deleted 141 lincRNA genes to broadly phenotype these mutants, together with 238 diverse coding-gene mutants for functional context. We applied high-throughput colony-based assays to determine mutant growth and viability in benign conditions and in response to 145 different nutrient, drug and stress conditions. These analyses uncovered phenotypes for 47.5% of the lincRNAs and 96% of the protein-coding genes. For 110 lincRNA mutants, we also performed high-throughput microscopy and flow-cytometry assays, linking 37% of these lincRNAs with cell-size and/or cell-cycle control. With all assays combined, we detected phenotypes for 84 (59.6%) of all lincRNA deletion mutants tested. For complementary functional inference, we analyzed colony growth of strains ectopically overexpressing 113 lincRNA genes under 47 different conditions. Of these overexpression strains, 102 (90.3%) showed altered growth under certain conditions. Clustering analyses provided further functional clues and relationships for some of the lincRNAs. These rich phenomics datasets associate lincRNA mutants with hundreds of phenotypes, indicating that most of the lincRNAs analyzed exert cellular functions in specific environmental or physiological contexts. This study provides groundwork to further dissect the roles of these lincRNAs in the relevant conditions.
Classic genome-wide association methods are unlikely to identify causal variants in strongly clonal microbial populations

Since the advent of genome-wide association studies (GWAS) in human genomes, an increasing sophistication of methods has been developed for more robust association detection. Currently, the backbone of human GWAS approaches is allele-counting-based methods where the signal of association is derived from alleles that are identical-by-state. Borrowing this approach from human GWAS, allele-counting-based methods have been popularized in microbial GWAS, notably the generalized linear model using either dimension reduction for fixed covariates and/or a genetic relationship matrix as a random effect in a mixed model to control for population stratification. In this work, we show how the effects of linkage disequilibrium (LD) can potentially obscure true-positive genotype-phenotype associations (i.e., genetic variants causally associated with the phenotype of interest) and also lead to unacceptably high rates of false-positive associations when applying these classical approaches to GWAS in weakly recombining microbial genomes. We developed a GWAS method called POUTINE (https://github.com/Peter-Two-Point-O/POUTINE), which relies on homoplastic mutation to both clarify the source of putative causal variants and reduce likely false-positive associations compared to traditional allele counting methods. Using datasets of M. tuberculosis genomes and antibiotic-resistance phenotypes, we show that LD can in fact render all association signals from allele counting methods to be fully indistinguishable from hundreds to thousands of sites scattered across an entire genome. These classic GWAS methods thus fail to pinpoint likely causal genotype-phenotype associations and separate them from background noise, even after applying methods to correct for population structure. We therefore urge caution when utilizing classical approaches, particularly in populations that are strongly clonal.
Specialization of chromatin-bound nuclear pore complexes promotes yeast aging

The nuclear pore complex (NPC) mediates nearly all exchanges between nucleus and cytoplasm, and changes composition in many species as the organism ages. However, how these changes arise and whether they contribute themselves to aging is poorly understood. We show that in replicatively aging yeast cells attachment of DNA circles to NPCs drives the displacement of the NPCs’ nuclear basket and cytoplasmic complexes. Remodeling of the NPC resulted from the regulation of basket components by SAGA, rather than from damages. These changes affected NPC interaction with mRNA export factors, without affecting the residence of import factors or engaging the NPC quality control machinery. Mutations preventing NPC remodeling extended the replicative lifespan of the cells. Thus, our data indicate that DNA circles accumulating in the mother cell drive aging at least in part by triggering NPC specialization. We suggest that antagonistic pleiotropic effects of NPC specialization are key drivers of aging.
Rossmann-toolbox: a deep learning-based protocol for the prediction and design of cofactor specificity in Rossmann fold proteins

The Rossmann fold enzymes are involved in essential biochemical pathways such as nucleotide and amino acid metabolism. Their functioning relies on interaction with cofactors, small nucleoside-based compounds specifically recognized by a conserved βαβ motif shared by all Rossmann fold proteins. While Rossmann methyltransferases recognize only a single cofactor type, the S-Adenosylmethionine (SAM), the oxidoreductases, depending on the family, bind nicotinamide (NAD, NADP) or flavin-based (FAD) cofactors. In this study, we show that despite its short length, the βαβ motif unambiguously defines the specificity towards the cofactor. Following this observation, we trained two complementary deep learning models for the prediction of the cofactor specificity based on the sequence and structural features of the βαβ motif. A benchmark on two independent test sets, one containing βαβ motifs bearing no resemblance to those of the training set, and the other comprising 38 experimentally confirmed cases of rational design of the cofactor specificity, revealed the nearly perfect performance of the two methods. The Rossmann-toolbox protocols can be accessed via the webserver at https://lbs.cent. uw.edu. pl/rossmann-toolbox and are available as a Python package at https://github.com/labstructbioinf/rossmann-toolbox. Key points The Rossmann fold encompasses a multitude of diverse enzymes involved in most of the essential cellular pathways Proteins belonging to the Rossmann fold co-evolved with their nucleoside-based cofactors and require them for the functioning Manipulating the cofactor specificity is an important step in the process of enzyme engineering We developed an end-to-end pipeline for the prediction and design of the cofactor specificity of the Rossmann fold proteins Owing to the utilization of deep learning approaches the pipeline achieved nearly perfect accuracy
Teachers’ Impact on Psychosocial Treatment for Latinx Youth with ADHD

Abstract This study examined the impact of teacher engagement in a psychosocial treatment for Latinx youth with attention-deficit/hyperactivity disorder (ADHD). Results revealed that teachers were equally engaged in treatment regardless of the source of the referral to treatment, indicating that teachers were motivated to work with students and families. Additionally, results indicated that referral source and teacher engagement in treatment were related to treatment outcomes. These findings indicate that higher-quality teacher intervention implementation is related to enhanced child and parent treatment outcomes in a Latinx sample. Results additionally revealed significant relationships among parental acculturation and treatment outcomes. Clinical implications are discussed.
Clustering Genomic Data in the Membership Embedding Space

In this paper we proposed a method to face clustering problems due to small cardinality and high dimensionality that are typical of many genomic data. We use an embedding space where each data point is represented by a vector containing memberships to fuzzy sets centered on a set of probes selected from the data base. The proposed approach leads to significant improvements with respect the application of clustering algorithms in the original space and in the distance embedding space
Decoding delay in network coded multipath transmissions

We investigate the decoding delay performance of a communication network in which a single source is transmitting data packets to a single receiver via multiple routers. Network coding is applied to all data packets at the source at each transmission opportunity. Receiver receives network coded packets from routers and decodes them. We deﬁne the delay as the time between arrival of a data packet at the source and decoding of all the packets served in the busy period of the source queue starting from the arrival of that data packet. We show that the delay can be expressed in closed-form.
Keypoint-graph-driven learning framework for object pose estimation

Since our method needs to crop the objects in images based on bounding boxes of objects predicted by object detection network, we present the detailed quantitative evaluation in this section. In our work, we use Faster-RCNN [10] with Resnet-101 [3] backbone to crop the objects in images for 6D pose estimation. To improve the performance of Faster-RCNN on real images, we pre-train Resnet-101 on a classification task on ImageNet [2] and freeze the Resnet-101 after block3 for follow-up training like in Hinterstoisser [4]. Besides, we follow the structure of domain adaptive Faster-RCNN [1] that uses two domain adaption components to reduce the domain discrepancy on image and instance level. The training images are same as the training images for DAKDN, where only the synthetic images have labels. To evaluate the object detection, we calculated IoU between the predicted bounding boxes and the ground-truth bounding boxes on LineMOD test set. From Table 1, we can see that the mean average percentage of correctly predicted 2D bounding boxes (IoU>0.5) is 84.3%. It shows that the object detection network can provide precise cropped objects for pose estimation Table 1: Detailed evaluation of predicted bounding boxes. We report the mean average percentages of correct 2D bounding boxes (IoU>0.5) on LINEMOD test set
Learning Scalable ℓ∞-constrained Near-lossless Image Compression via Joint Lossy Image and Residual Compression

We propose a novel joint lossy image and residual compression framework for learning ℓ∞-constrained near-lossless image compression. Specifically, we obtain a lossy reconstruction of the raw image through lossy image compression and uniformly quantize the corresponding residual to satisfy a given tight ℓ∞ error bound. Suppose that the error bound is zero, i.e., lossless image compression, we formulate the joint optimization problem of compressing both the lossy image and the original residual in terms of variational auto-encoders and solve it with end-to-end training. To achieve scalable compression with the error bound larger than zero, we derive the probability model of the quantized residual by quantizing the learned probability model of the original residual, instead of training multiple networks. We further correct the bias of the derived probability model caused by the context mismatch between training and inference. Finally, the quantized residual is encoded according to the bias-corrected probability model and is concatenated with the bitstream of the compressed lossy image. Experimental results demonstrate that our near-lossless codec achieves the state-of-the-art performance for lossless and near-lossless image compression, and achieves competitive PSNR while much smaller ℓ∞ error compared with lossy image codecs at high bit rates.
UWS Academic Portal Is Virtual Reality sickness elicited by illusory motion affected by gender and prior video gaming experience? Pöhlmann,

Gaming using VR headsets is becoming increasingly popular; however, these displays can cause VR sickness. To investigate the effects of gender and gamer type on VR sickness motion illusions are used as stimuli, being a novel method of inducing the perception of motion whilst minimising the “accommodation vergence conflict”. Females and those who do not play action games experienced more severe VR sickness symptoms compared to males and experienced action gamers. The interaction of the gender and gamer type revealed that prior video gaming experience was beneficial for females, however, for males, it did not show the same positive effects.
Age affects white matter microstructure and episodic memory across the older adult lifespan

Diffusion imaging studies have observed age-related degradation of white matter that contributes to cognitive deficits separately in younger-old (ages 65-89) and oldest-old (ages 90+) adults. But it remains unclear whether these age effects are magnified in advanced age groups, which may reflect disease-related pathology. Here, we tested whether age-related differences in white matter microstructure followed linear or nonlinear patterns across the entire older adult lifespan (65-98 years), these patterns were influenced by oldest-old adults at increased risk of dementia (cognitive impairment no dementia, CIND), and they explained age effects on episodic memory. Results revealed nonlinear microstructure declines across fiber classes (medial temporal, callosal, association, projection and/or thalamic) that were largest for medial temporal fibers. These patterns remained after excluding oldest-old participants with CIND, indicating that aging of white matter microstructure cannot solely be explained by pathology associated with early cognitive impairment. Moreover, finding that the effect of age on episodic memory was mediated by medial temporal fiber microstructure suggests it is essential for facilitating memory-related neural signals across the older adult lifespan.
Discussion of the paper ’Riemann manifold Langevin and Hamiltonian Monte Carlo methods’ by M. Girolami and B. Calderhead

with ε ∼ N (0, σ). The binomial variable is Y (W ) = 0 if W < 0 and Y (W ) = 1 if W > 0. The model in this form is known to be non-identifiable as the likelihood is constant along straight lines out of the origin of the β, σ plane and therefore is only informative about their ratio (Nobile, 1998, 2000; McCulloch et al., 2000; Imai and van Dyk, 2005). This poses significant challenges to the MCMC methods discussed in this paper since the Fisher information (FI) matrix is not positive definite. The problem can be resolved by considering an informative prior Nobile (1998) and by adding the negative of its Hessian to the FI as suggested by the Authors in Section 4.2. The resulting posterior, however, is strongly skewed and, as we discuss here, this can lead to very poor mixing of the chains. For the experiments presented here, we generated a synthetic dataset for the binomial model as described in Nobile (1998) and used the priors p(β) = N (0, 100) and p(1/σ) = G(3/2, 1/6) which ensure weak identifiability. Furthermore, we re-parameterise σ, such that ψ = log(σ), and sample ψ. The log-likelihood is given by:
DMD066316 1938..1945

Drug treatment of neonates and infants and its long-term consequences on drug responses have emerged in recent years as amajor challenge for health care professionals. In the current study, we use phenobarbital as a model drug and mouse as an in vivo model to demonstrate that the dose of phenobarbital and age of treatment are two key factors for the persistent induction of gene expression and consequential increases of enzyme activities of Cyp2b, Cyp2c, and Cyp3a in adult livers. We show that phenobarbital treatment at early life of day 5 after birth with a low dose (<100mg/kg) does not change expression and enzyme activities of Cyp2b, Cyp2c, and Cyp3a in adult mouse liver, whereas phenobarbital treatment with a high dose (>200 mg/kg) significantly increases expression and enzyme activities of these P450s in adult liver. We also demonstrate that phenobarbital treatment before day 10 after birth, but not at later ages, significantly increases mRNAs, proteins, and enzyme activities of the tested P450s. Such persistent induction of P450 gene expression and enzyme activities in adult livers by phenobarbital treatment only occurs within a sensitive age window early in life. The persistent induction in gene expression and enzyme activities is higher in female mice than in male mice for Cyp2b10 but not for Cyp2c29 and Cyp3a11. These results will stimulate studies to evaluate the long-term impacts of drug treatment with different doses at neonatal and infant ages on drug metabolism, therapeutic efficacy, and drug-induced toxicity throughout the rest of life.
AIS Transactions on Human-Computer Interaction AIS Transactions on Human-Computer Interaction

: Mobile health (mHealth) systems hold great potential for supporting users in self-managing disease and engaging in a healthier life. However, given the mobile context and the multiple factors that affect a person’s health, designing mHealth systems involves much complexity and a range of pitfalls. To overcome these pitfalls, scholars have called on system designers to employ a co-design approach; that is, to involve stakeholders in all phases of the design process. However, the literature on how, when, and why designers use co-design in mHealth remains scant. To address this gap, we systematically reviewed 61 studies that co-designed mHealth systems. Our results show that co-designing mHealth systems constitutes a fragmented and rapidly evolving research field with only limited overlaps and a strong focus on the early design phases (i.e., pre-design, generative). Thereby, the co-designed artifacts cover various application contexts in disease management (e.g., heart disease, diabetes) and health promotion (e.g., physical activity, nutrition) and a diverse group of involved users, healthcare professionals, and system designers. Finally, guided by Sanders and Stappers’ (2014) co-design framework, we provide a concise overview of the most widely used methods in the different co-design phases.
Parameter Solving of Probability Integral Method Based on Improved Genetic Algorithm

: The probability integral method (PIM) is the main method for mining subsidence prediction in China. Parameter errors and model errors are the main sources of error in the application of the probability integral method. There are many surface subsidence problems caused by coal mining. In order to improve the accuracy and operating efficiency of the genetic algorithm (GA) in calculating the parameters of the PIM, this paper proposes an improved genetic algorithm (IGA) by adding the dynamic crossover and mutation rate to the traditional GA. Made improvements to the shortcomings of random crossover and mutation rate of all individuals in the population in the original algorithm.Through simulation experiments, it is confirmed that the IGA improves the calculation efficiency and accuracy of the traditional GA under the same conditions.The IGA has higher accuracy, reliability, resistance to gross interference and resistance to missing observation points. This method is obviously superior to direct inversion and conventional optimization inversion algorithms, and effectively avoids the dependence on the initial value of the probabilistic integral method parameter.