Miracle-dz/UniFAR-FTUs huggingface dataset:
The Plasmodium falciparum exported J domain proteins fine-tune human and malarial Hsp70s: pathological exploitation of proteostasis machinery

Cellular proteostasis requires a network of molecular chaperones and co-chaperones, which facilitate the correct folding and assembly of other proteins, or the degradation of proteins misfolded beyond repair. The function of the major chaperones, heat shock protein 70 (Hsp70) and heat shock protein 90 (Hsp90), is regulated by a cohort of co-chaperone proteins. The J domain protein (JDP) family is one of the most diverse co-chaperone families, playing an important role in functionalizing the Hsp70 chaperone system to form a powerful protein quality control network. The intracellular malaria parasite, Plasmodium falciparum, has evolved the capacity to invade and reboot mature human erythrocytes, turning them into a vehicles of pathology. This process appears to involve the harnessing of both the human and parasite chaperone machineries. It is well known that malaria parasite-infected erythrocytes are highly enriched in functional human Hsp70 (HsHsp70) and Hsp90 (HsHsp90), while recent proteomics studies have provided evidence that human JDPs (HsJDPs) may also be enriched, but at lower levels. Interestingly, P. falciparum JDPs (PfJDPs) are the most prominent and diverse family of proteins exported into the infected erythrocyte cytosol. We hypothesize that the exported PfJPDs may be an evolutionary consequence of the need to boost chaperone power for specific protein folding pathways that enable both survival and pathogenesis of the malaria parasite. The evidence suggests that there is an intricate network of PfJDP interactions with the exported malarial Hsp70 (PfHsp70-x) and HsHsp70, which appear to be important for the trafficking of key malarial virulence factors, and the proteostasis of protein complexes of human and parasite proteins associated with pathology. This review will critically evaluate the current understanding of the role of exported PfJDPs in pathological exploitation of the proteostasis machinery by fine-tuning the chaperone properties of both human and malarial Hsp70s.
Vitamin D status, sleep patterns, genetic susceptibility, and the risk of incident adult-onset asthma: a large prospective cohort study

Introduction Vitamin D has been known to be associated with asthma, particularly in children, while the evidence among adults is limited and inconclusive. This study aimed to investigate the association between serum, vitamin D concentrations, and the incidence of adult-onset asthma and also the modified effect caused by sleep patterns and genetic risks. Methods A prospective cohort study with 307,872 participants aged between 37 and 73 years was conducted based on the UK Biobank, with a median follow-up of 12 years. The Cox proportional hazard model was applied to evaluate the association between vitamin D status and incident adult-onset asthma, and the modified effect was investigated by conducting stratified analysis according to sleep pattern score and genetic risk score, and subgroup analyses were performed by sex, age, BMI, and smoking status as well. Results Individuals with optimal vitamin D concentration were associated with 11.1% reduced risk of incident asthma compared to those participants with deficient vitamin D (HR = 0.889; 95% CI: 0.820–0.964; p = 0.005). Moreover, stratification analysis demonstrated that the protective effect of vitamin D on asthma risk was modified by sleep patterns or genetic susceptibility, with the strongest protective effect being observed in the subpopulation with a moderate sleep pattern (HR = 0.883; 95% CI: 0.797–0.977; p = 0.016) and a moderate genetic risk (HR = 0.817; 95% CI: 0.711–0.938; p = 0.004). In subgroup analyses, the protective effect of optimal vitamin D levels was only significant among men, individuals younger than 60 years of age, overweight individuals, and current or previous smokers. Conclusion Increased serum vitamin D levels were associated with a lower risk of incident adult-onset asthma, and this association was modified by sleep patterns and genetic predisposition to some extent.
Deformed wing virus prevalence in solitary bees put to the test: an experimental transmission study

Virus spillover from managed to unmanaged bees and vice versa may be one mechanism driving colony losses of the former and declines of the latter. There is clear evidence that the ubiquitous Deformed wing virus (DWV) is a major driver of honey bee (Apis mellifera) colony mortality. Although DWV has been detected in the solitary bee Osmia bicornis, data on DWV infectivity and virulence from solitary bees are scarce. Here, we used microinjection to investigate whether DWV genotype A (DWV-A) obtained from honey bees can replicate in O. bicornis. DWV-A titers and intermediate strand analyses suggest that DWV-A does not replicate in O. bicornis and thus is probably not infectious for this solitary bee species. Interestingly, the data demonstrate that DWV-A recovered from O. bicornis 16 days post-microinjection remains infectious for A. mellifera. Therefore, despite the lack of apparent virulence of DWV in this solitary bee species, O. bicornis has the potential to act as a virus spillover host and may contribute to increased colony losses of managed honey bees and declines in populations of other managed or unmanaged bee species.
GReTo: Remedying dynamic graph topology-task discordance via target homophily

Dynamic graphs are ubiquitous across disciplines where observations usually change over time. Regressions on dynamic graphs often contribute to diverse critical tasks, such as climate early-warning and traffic controlling. Existing ho-mophily Graph Neural Networks (GNNs) adopt physical connections or feature similarity as adjacent matrix to perform node-level aggregations. However, on dynamic graphs with diverse node-wise relations, exploiting a pre-defined fixed topology for message passing inevitably leads to the aggregations of target-deviated neighbors. We designate such phenomenon as the topology-task discordance, which naturally challenges the homophily assumption. In this work, we revisit node-wise relationships and explore novel homophily measurements on dynamic graphs with both signs and distances, capturing multiple node-level spatial relations and temporal evolutions. We discover that advancing homophily ag-gregations to signed target-oriented message passing can effectively resolve the discordance and promote aggregation capacity. Therefore, a GReTo is proposed, which performs signed message passing in immediate neighborhood, and exploits both local environments and target awareness to realize high-order message propagation. Empirically, our solution achieves significant improvements against best baselines, notably improving 24.79% on KnowAir and 3.60% on Metr-LA.
Performance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems

In this work we investigate the performance of various lower-fidelity models of seawater intrusion in coastal aquifer management problems. The variable density model is considered as the high-fidelity model and a pumping optimization framework is applied on a hypothetical coastal aquifer system in order to calculate the optimal pumping rates which are used as a benchmark for the lower-fidelity approaches. The examined lower-fidelity models could be classified in two categories: (1) physics-based models, which include several widely used variations of the sharp-interface approximation and (2) machine learning assisted models, which aim to improve the efficiency of the SI approach. The Random Forest method was utilized to create a spatially adaptive correction factor for the original sharp-interface model, which improves its accuracy without compromising its efficiency as a lower-fidelity model. Both the original sharp-interface and Machine Learning assisted model are then tested in a single-fidelity optimization method. The optimal pumping rated which were calculated using the Machine Learning based SI model sufficiently approximate the solution from the variable density model. The Machine Learning assisted approximation seems to be a promising surrogate for the high-fidelity, variable density model and could be utilized in multi-fidelity groundwater management frameworks.
Advantages of FDM and gamma irradiation to manufacture personalized medical devices for airway obstructions

In the early childhood population, congenital airway conditions like bronchomalacia (BM) can pose a life-threatening threat. A breakthrough technology called additive manufacturing, or 3D printing, makes it feasible to create a biomedical device that aids in the treatment of airway obstruction. This article describes how a polycaprolactone (PCL) splint for the upper airways can be created using the fusion deposition technique (FDM) and sterilized using gamma radiation. It is presented as a simple, accessible, and cost-reduced alternative that complements other techniques using more expensive and sophisticated printing methods. Thermomechanical and morphological analysis proved that FDM and sterilizing by gamma irradiation are both appropriate methods for producing splints to treat life-threatening airway blockages. Additionally, the 3D-printed splints’ effectiveness in treating a young patient with BM that was life-threatening was assessed by medical professionals. In this regard, the case report of a patient with 34 months of follow-up is presented. Splints manufactured by this affordable 3D printing method successfully surpass breathing arrest in life-threatening airway obstruction in pediatric patients. The success of this procedure represents a fundamental contribution to the treatment of the population in countries where access to expensive and complex technologies is not available.
Self-Supervised Set Representation Learning for Unsupervised Meta-Learning

Unsupervised meta-learning (UML) essentially shares the spirit of self-supervised learning (SSL) in that their goal aims at learning models without any human supervision so that the models can be adapted to downstream tasks. Further, the learning objective of self-supervised learning, which pulls positive pairs closer and repels negative pairs, also resembles metric-based meta-learning. Metric-based meta-learning is one of the most successful meta-learning methods, which learns to minimize the distance between representations from the same class. One notable aspect of metric-based meta-learning, however, is that it is widely interpreted as a set-level problem since the inference of discriminative class prototypes (or set representations) from few examples is crucial for the performance of downstream tasks. Motivated by this, we propose Set-SimCLR, a novel self-supervised set representation learning framework for targeting UML problem. Specifically, our Set-SimCLR learns a set encoder on top of instance representations to maximize the agreement between two sets of augmented samples, which are generated by applying stochastic augmentations to a given image. We theoretically analyze how our proposed set representation learning can potentially improve the generalization performance at the meta-test. We also empirically validate its effectiveness on various benchmark datasets, showing that Set-SimCLR largely outperforms both UML and instance-level self-supervised learning baselines.
Molecular epidemiology of Candida albicans infections revealed dominant genotypes in waterfowls diagnosed with esophageal mycosis

Fungal infections of animals could yield significant economic losses, especially in the poultry industry, due to their adverse effects on growth, feed intake, digestion, and reproduction. Previous investigations showed that Candida albicans plays the main etiological role in the esophageal mycosis of birds. In this study, we used multilocus sequence typing (MLST) to determine the population structure and molecular epidemiology of C. albicans isolated from geese and ducks in Hungary. Interestingly, only three known genotypes were identified among investigated flocks, namely, diploid sequence type (DST) 840, DST 656, and DST 605, suggesting the intra-species transmission of these genotypes. Additionally, two novel allele combinations (new DSTs) were found that have not been previously submitted to the MLST database. Phylogenetic analysis of isolates revealed a close relationship between DST 656 and DST 605 as well as between the two newly identified genotypes (designated DST 3670 and DST 3671). Although isolates from birds belonged to minor clades in contrast with most human isolates, no species-specificity was observed. Poultry-derived isolates were group founders or closely related to group founders of clonal complexes, suggesting that C. albicans is exposed to lesser selective pressure in animal hosts. The increasing number of genetic information in the C. albicans MLST database could help to reveal the epidemiological characteristics and evolutionary pathways that are essential for disease prevention strategies.
Domain Generalization via Heckman-type Selection Models

The domain generalization (DG) setup considers the problem where models are trained on data sampled from multiple domains and evaluated on test domains unseen during training. In this paper, we formulate DG as a sample selection problem where each domain is sampled from a common underlying population through non-random sampling probabilities that correlate with both the features and the outcome. Under this setting, the fundamental iid assumption of the empirical risk minimization (ERM) is violated, so it often performs worse on test domains whose non-random sampling probabilities differ from the domains in the training dataset. We propose a Selection-Guided DG (SGDG) framework to learn the selection probability of each domain and the joint distribution of the outcome and domain selection variables. The proposed SGDG is domain generalizable as it intends to minimize the risk under the population distribution. We theoretically prove that, under certain regular conditions, SGDG can achieve smaller risk than ERM. Furthermore, we present a class of parametric SGDG (HeckmanDG) estimators applicable to continuous, binary, and multinomial outcomes. We also demonstrate its efficacy empirically through simulations and experiments on a set of benchmark datasets comparing with other well-known DG methods.
Case report: Molecular characterisation of adipose-tissue derived cells from a patient with ROHHAD syndrome

There have been over 100 cases of Rapid-onset obesity with hypothalamic dysfunction, hypoventilation, and autonomic dysregulation (ROHHAD) syndrome reported, but there is currently no curative treatment for children with this condition. We aimed to better characterise adipose cells from a child with ROHHAD syndrome. We isolated pre-adipocytes from a 4 year-old female patient with ROHHAD syndrome and assessed proliferation rate of these cells. We evaluated levels of DLP-Pref-1(pre-adipocyte marker) using western blotting, and concentrations of interleukin-6(IL-6) using ELISA. We performed next-generation sequencing (NGS) and bioinformatic analyses on these cells compared to tissue from an age/sex-matched control. The two most up-/down-regulated genes were validated using QPCR. We successfully isolated pre-adipocytes from a fat biopsy, by confirming the presence of Pref-1 and differentiated them to mature adipocytes. Interleukin 6, (Il-6) levels were 5.6-fold higher in ROHHAD cells compared to a control age/sex-matched biopsy. NGS revealed 25,703 differentially expressed genes (DEGs) from ROHHAD cells vs. control of which 2,237 genes were significantly altered. The 20 most significantly up/down-regulated genes were selected for discussion. This paper describes the first transcriptomic analysis of adipose cells from a child with ROHHAD vs. normal control adipose tissue as a first step in identifying targetable pathways/mechanisms underlying this condition with novel therapeutic interventions.
Generalized Newman's Lemma for Discrete and Continuous Systems

We propose a generalization of Newman’s lemma which gives a criterion of confluence for a wide class of not-necessarily-terminating abstract rewriting systems. We show that ordinary Newman’s lemma for terminating systems can be considered as a corollary of this criterion. We describe a formalization of the proposed generalized Newman’s lemma in Isabelle proof assistant using HOL logic. 2012 ACM Subject Classification Theory of computation → Equational logic and rewriting; Theory of computation → Logic and verification; Theory of computation → Timed and hybrid models
On Compositional Uncertainty Quantification for Seq2seq Graph Parsing

Recent years have witnessed the success of applying seq2seq models to graph parsing tasks, where the outputs are compositionally structured (e.g., a graph or a tree). However, these seq2seq approaches pose a challenge in quantifying the model’s compositional uncertainty on graph structures due to the gap between seq2seq output probability and structural probability on the graph. This work is the first to quantify and evaluate compositional uncertainty for seq2seq graph parsing tasks. First, we proposed a generic, probabilistically interpretable framework that allows correspondences between seq2seq output probability to structural probability on the graph. This framework serves as a powerful medium for quantifying a seq2seq model’s compositional uncertainty on graph elements (i.e., nodes or edges). Second, to evaluate uncertainty quality in terms of calibration, we propose a novel metric called Compositional Expected Calibration Error (CECE) which can measure a model’s calibration behavior in predicting graph structures. By a thorough evaluation for compositional uncertainty on three different tasks across ten domains, we demonstrate that CECE is a better reflection for distributional shift compared to vanilla sequence ECE. Finally, we validate the effectiveness of compositional uncertainty considering the task of collaborative semantic parsing, where the model is allowed to send limited subgraphs for human review. The results show that the collaborative performance based on uncertain subgraph selection consistently outperforms random subgraph selection (30% average error reduction rate) and performs comparably to oracle subgraph selection (only 0.33 difference in average prediction error), indicating that compositional uncertainty is an ideal signal for model errors and can benefit various downstream tasks. 1
Bridge the Inference Gaps of Neural Processes via Expectation Maximization

The neural process (NP) is a family of computationally efficient models for learning distributions over functions. However, it suffers from under-fitting and shows suboptimal performance in practice. Researchers have primarily focused on incorporating diverse structural inductive biases, e.g. attention or convolution, in modeling. The topic of inference suboptimality and an analysis of the NP from the optimization objective perspective has hardly been studied in earlier work. To fix this issue, we propose a surrogate objective of the target log-likelihood of the meta dataset within the expectation maximization framework. The resulting model, referred to as the Self-normalized Importance weighted Neural Process (SI-NP), can learn a more accurate functional prior and has an improvement guarantee concerning the target log-likelihood. Experimental results show the competitive performance of SI-NP over other NPs objectives and illustrate that structural inductive biases, such as attention modules, can also augment our method to achieve SOTA performance.
Structure by Architecture: Structured Representations without Regularization

We study the problem of self-supervised structured representation learning using autoencoders for downstream tasks such as generative modeling. Unlike most methods which rely on matching an arbitrary, relatively unstructured, prior distribution for sampling, we propose a sampling technique that relies solely on the independence of latent variables, thereby avoiding the trade-off between reconstruction quality and generative performance typically observed in VAEs. We design a novel autoencoder architecture capable of learning a structured representation without the need for aggressive regularization. Our structural decoders learn a hierarchy of latent variables, thereby ordering the information without any additional regularization or supervision. We demonstrate how these models learn a representation that improves results in a variety of downstream tasks including generation, disentanglement, and extrapolation using several challenging and natural image datasets.
The Tilted Variational Autoencoder: Improving Out-of-Distribution Detection

A problem with using the Gaussian distribution as a prior for a variational autoencoder (VAE) is that the set on which Gaussians have high probability density is small as the latent dimension increases. This is an issue because VAEs aim to achieve both a high likelihood with respect to a prior distribution and at the same time, separation between points for better reconstruction. Therefore, a small volume in the high-density region of the prior is problematic because it restricts the separation of latent points. To address this, we propose a simple generalization of the Gaussian distribution, the tilted Gaussian, whose maximum probability density occurs on a sphere instead of a single point. The tilted Gaussian has exponentially more volume in high-density regions than the standard Gaussian as a function of the distribution dimension. We empirically demonstrate that this simple change in the prior distribution improves VAE performance on the task of detecting unsupervised out-of-distribution (OOD) samples. We also introduce a new OOD testing procedure, called the Will-It-Move test, where the tilted Gaussian achieves remarkable OOD performance.
Learning to Jointly Share and Prune Weights for Grounding Based Vision and Language Models

Transformers have been successful in processing different data modalities, such as language and image data, which could use transformers with similar architectures to achieve good performance. Leveraging this observation, we propose weight sharing across two transformer backbones and within the same transformer backbone and pruning across two backbones in a unified framework. More specifically, we investigate weight sharing and pruning for two components of the transformers: (1) Multi-Head Attention (MSA) and (2) Feed-Forward Network (FFN) layers. To jointly perform weight sharing and pruning, we propose to use a regularization term to align model weights and the desired structure during the multimodal pre-training step. The structure vectors of sharing and pruning are generated by using a hypernetwork, which can capture complex interactions between pruning and sharing across layers and modalities. We train the hypernetwork and model weights iteratively so that the learned structure evolves along with model weights. After minimizing the proposed objective in pre-training step, we perform weight sharing and pruning and fine-tune the compressed model on downstream tasks. Finally, we perform experiments on vision and language tasks, including Referring Expression Comprehension (REC), Visual Question Answering (VQA), and Object Detection using the state-of-the-art grounding based models: MDETR and GLIP. Our experiments show that we can compress these models by 35− 40% by sharing and pruning MSA and FFN weights without almost any loss in accuracy.