Miracle-dz/UniFAR-FTUs huggingface dataset:
Childhood-onset asthma is characterized by airway epithelial hillock-to-squamous differentiation in early life

Childhood-onset asthma is characterized by Type 2-inflammation and airway wall remodeling, but mechanisms of asthma development in the first years of life remain unclear. Here, we investigate transcriptional changes in airway wall biopsies of 22 symptomatic one year old children and relate these to asthma at school age. We demonstrate that pre-asthmatic children (n = 10) overexpressed a gene signature characteristic for an airway epithelial differentiation trajectory via hillock cells towards squamous cells (adjusted p-value 8.06e-16), whilst there was no association with gene signatures of Type 2-inflammation or eosinophil activation. Genes expressed along this trajectory are linked to an altered epithelial barrier function, innate immune activation and extracellular matrix remodeling. Functional GWAS analysis supports a causal link between childhood-onset, but not adult-onset asthma, and the hillock-squamous cell differentiation trajectory. Next, we confirmed the presence of hillock-like cells at the RNA and protein level in pediatric upper and lower airway samples. These findings identify a novel mechanism by which an aberrant airway epithelial differentiation trajectory may contribute to a pre-asthmatic state, highlighting the difference between the early origins of childhood-onset asthma and adult asthma, and point to possible new targets for the early diagnosis and treatment of asthma in the first two years of life. One Sentence Summary RNA sequencing in bronchial biopsies from wheezing infants and children < 2 years shows evidence for an airway epithelial hillock-to-squamous differentiation pathway that marks the development of asthma.
Treatment Effect Estimation with Adjustment Feature Selection

In causal inference, it is common to select a subset of observed covariates, named the adjustment features, to be adjusted for estimating the treatment effect. For real-world applications, the abundant covariates are usually observed, which contain extra variables partially correlating to the treatment (treatment-only variables, e.g., instrumental variables) or the outcome (outcome-only variables, e.g., precision variables) besides the confounders (variables that affect both the treatment and outcome). In principle, unbiased treatment effect estimation is achieved once the adjustment features contain all the confounders. However, the performance of empirical estimations varies a lot with different extra variables. To solve this issue, variable separation/selection for treatment effect estimation has received growing attention when the extra variables contain instrumental variables and precision variables. In this paper, assuming no mediator variables exist, we consider a more general setting by allowing for the existence of post-treatment and post-outcome variables rather than instrumental and precision variables in observed covariates. Our target is to separate the treatment-only variables from the adjustment features. To this end, we establish a metric named Optimal Adjustment Features(OAF), which empirically measures the asymptotic variance of the estimation. Theoretically, we show that our OAF metric is minimized if and only if adjustment features consist of the confounders and outcome-only variables, i.e., the treatment-only variables are perfectly separated. As optimizing the OAF metric is a combinatorial optimization problem, we introduce Reinforcement Learning (RL) and adopt the policy gradient to search for the optimal adjustment set. Empirical results on both synthetic and real-world datasets demonstrate that (a) our method successfully searches the optimal adjustment features and (b) the searched adjustment features achieve a more precise estimation of the treatment effect.
Learning Balanced Tree Indexes for Large-Scale Vector Retrieval

Vector retrieval focuses on finding the k-nearest neighbors from a bunch of data points, and is widely used in a diverse set of areas such as information retrieval and recommender system. The current state-of-the-art methods represented by HNSW usually generate indexes with a big memory footprint, restricting the scale of data they can handle, except resorting to a hybrid index with external storage. The space-partitioning learned indexes, which only occupy a small memory, have made great breakthroughs in recent years. However, these methods rely on a large amount of labeled data for supervised learning, so model complexity affects the generalization. To this end, we propose a lightweight learnable hierarchical space partitioning index based on a balanced K-ary tree, called BAlanced Tree Learner (BATL), where the same bucket of data points are represented by a path from the root to the corresponding leaf. Instead of mapping each query into a bucket, BATL classifies it into a sequence of branches (i.e. a path), which drastically reduces the number of classes and potentially improves generalization. BATL updates the classifier and the balanced tree in an alternating way. When updating the classifier, we innovatively leverage the sequence-to-sequence learning paradigm for learning to route each query into the ground-truth leaf on the balanced tree. Retrieval is then boiled down into a sequence (i.e. path) generation task, which can be simply achieved by beam search on the encoder-decoder. When updating a balanced tree, we apply the classifier for navigating each data point into the tree nodes layer by layer under the balance constraints. We finally evaluate BATL with several large-scale vector datasets, where the experimental results show the superiority of the proposed method to the SOTA baselines in the tradeoff among latency, accuracy, and memory cost.
3D-Polishing for Triangular Mesh Compression of Point Cloud Data

Triangular meshes are commonly used to reconstruct the surfaces of 3-dimensional (3D) objects based on the point cloud data. With an increasing demand for high-quality approximation, the sizes of point cloud data and the generated triangular meshes continue to increase, resulting in high computational cost in data processing, visualization, analysis, transmission and storage. Motivated by the process of sculpture polishing, we develop a novel progressive mesh compression approach, called the greedy 3D-polishing algorithm, to sequentially remove redundant points and triangles in a greedy manner while maintaining the approximation quality of the surface. Based on the polishing algorithm, we propose the approximate curvature radius to evaluate the scale of features polished at each iteration. By reformulating the compression rate selection as a change-point detection problem, a rank-based procedure is developed to select the optimal compression rate so that most of the global features of the 3D object surface can be preserved with statistical guarantee. Experiments on both moderate- and large-scale 3D shape datasets show that the proposed method can substantially reduce the size of the point cloud data and the corresponding triangular mesh, so that most of the surface information can be retained by a much smaller number of points and triangles.
DNet: Distributional Network for Distributional Individualized Treatment Effects

There is a growing interest in developing methods to estimate individualized treatment effects (ITEs) for various real-world applications, such as e-commerce and public health. This paper presents a novel architecture, called DNet, to infer distributional ITEs. DNet can learn the entire outcome distribution for each treatment, whereas most existing methods primarily focus on the conditional average treatment effect and ignore the conditional variance around its expectation. Additionally, our method excels in settings with heavy-tailed outcomes and outperforms state-of-the-art methods in extensive experiments on benchmark and real-world datasets. DNet has also been successfully deployed in a widely used mobile app with millions of daily active users.
FairCod: A Fairness-aware Concurrent Dispatch System for Large-scale Instant Delivery Services

In recent years, we have been witnessing a rapid prevalence of instant delivery services (e,g., UberEats, Instacart, and Eleme) due to their convenience and timeliness. A unique characteristic of instant delivery services is the concurrent dispatch mode, where (i) one courier usually simultaneously delivers multiple orders, especially during rush hours, and (ii) couriers can receive new orders when delivering existing orders. Most existing concurrent dispatch systems are efficiency-oriented, which means they usually dispatch a group of orders that have a similar delivery route to a courier. Although this strategy may achieve high overall efficiency, it also potentially causes a huge disparity of earnings between different couriers. To address the problem, in this paper, we design a Fairness-aware Concurrent dispatch system called FairCod, which aims to optimize the overall operation efficiency and individual fairness at the same time. Specifically, in FairCod, we design a Dynamic Advantage Actor-Critic algorithm with Fairness constrain (DA2CF). The basic idea is that it includes an Actor network to make dispatch decisions based on dynamic action space and a Critic network to evaluate the dispatch decisions from the fairness perspective. More importantly, we extensively evaluate our FairCod system based on one-month real-world data consisting of 36.38 million orders from 42,000 couriers collected by one of the largest instant delivery companies in China. Experimental results show that our FairCod improves courier fairness by 30.3% without sacrificing the overall system benefit compared to state-of-the-art baselines.
Sex-specific adaptations to VTA circuits following subchronic stress

Dysregulation of the mesolimbic reward circuitry is implicated in the pathophysiology of stress-related illnesses such as depression and anxiety. These disorders are more frequently diagnosed in females, and sex differences in the response to stress are likely to be one factor that leads to enhanced vulnerability of females. In this study, we use subchronic variable stress (SCVS), a model in which females are uniquely vulnerable to behavioral disturbances, to investigate sexually divergent mechanisms of regulation of the ventral tegmental area by stress. Using slice electrophysiology, we find that female, but not male mice have a reduction in the ex vivo firing rate of VTA dopaminergic neurons following SCVS. Surprisingly, both male and female animals show an increase in inhibitory tone onto VTA dopaminergic neurons and an increase in the firing rate of VTA GABAergic neurons. In males, however, this is accompanied by a robust increase in excitatory synaptic tone onto VTA dopamine neurons. This supports a model by which SCVS recruits VTA GABA neurons to inhibit dopaminergic neurons in both male and female mice, but males are protected from diminished functioning of the dopaminergic system by a compensatory upregulation of excitatory synapses.
Identifying repurposed drugs with moderate anti-influenza virus activity through computational prioritization of drug-target pairs

Influenza A virus (IAV) causes up to five million cases of severe illness and half a million deaths worldwide each year. While there are a few clinically approved drugs for treating IAV, they are challenged by the rapid evolution of the virus leading to emergence of drug resistance and the adverse effects of the drugs. Targeting host cellular factors that support virus replication could limit resistance, increase the broad-spectrum antiviral properties of drugs, and benefit from repurposing drugs already existing against those factors. However, selecting the right drug-target pairs with low toxicity and minimal adverse effects has been challenging, even though hundreds of cellular host factors have been identified. In this study, we applied a computational and knowledge-based drug-target prioritization approach to identify promising drug-target pairs. We selected five pairs for experimental validation: telmisartan-Angiotensin II receptor, type 1 (AGTR1), metoclopramide hydrochloride-Cholinergic receptor muscarinic 1 (CHRM1), cefepime hydrochloride-phosphogluconate dehydrogenase (PGD), ranolazine dihydrochloride-sodium channel voltage-gated type v alpha subunit (SCN5A), and ofloxacin-topoisomerase II alpha 170kDa (TOP2A). Except for cefepime hydrochloride, all four drugs showed significant plaque reduction in Madin Darby canine kidney (MDCK) cells. In the immunofluorescence assay, metoclopramide hydrochloride, ranolazine dihydrochloride, and telmisartan showed antiviral activity in MDCK and/or adenocarcinoma human alveolar basal epithelial (A549) cell lines. In conclusion, our approach can prioritize and identify drugs with antiviral activity against influenza virus. Refining and strengthening such approaches could be valuable for rapid antiviral discovery and pandemic preparedness. Highlights Computational drug-target prioritization indicated drugs for validation Telmisartan showed anti-influenza virus activity in MDCK cells Metoclopramide and ranolazine showed anti-influenza activity in A549 and MDCK cells
Humoral correlates of protection against Mycobacterium tuberculosis following intravenous Bacille Calmette-Guérin vaccination in rhesus macaques

Altering the route of Bacille Calmette-Guérin (BCG) immunization from low-dose intradermal vaccination to high-dose intravenous (IV) vaccination resulted in a high level of protection against Mycobacterium tuberculosis (Mtb) infection, providing an opportunity to uncover immune correlates and mechanisms of protection. In addition to strong T cell immunity, IV BCG vaccination was associated with a robust expansion of humoral immune responses that tracked with bacterial control. However, given the near complete protection afforded by high-dose IV BCG immunization, a precise correlate of immune protection was difficult to define. Here we leveraged plasma and bronchoalveolar lavage fluid (BAL) from a cohort of rhesus macaques that received decreasing doses of IV BCG and aimed to define the correlates of immunity across macaques that experienced immune protection or breakthrough infection following Mtb challenge. We show an IV BCG dose-dependent induction of mycobacterial-specific humoral immune responses, both in the plasma and in the airways. Moreover, antibody responses at peak immunogenicity significantly predicted bacterial control following challenge. Multivariate analyses revealed antibody-mediated complement and NK cell activating humoral networks as key functional signatures associated with protective immunity. Collectively, this work extends our understanding of humoral biomarkers and potential mechanisms of IV BCG mediated protection against Mtb.
Induced pluripotent stem cell-derived human macrophages as an infection model for Leishmania donovani

The parasite Leishmania donovani is one of the species causing visceral leishmaniasis in humans, a deadly infection claiming up to 40,000 lives each year. The current drugs for leishmaniasis treatment have severe drawbacks and there is an urgent need to find new anti-leishmanial compounds. However, the search for drug candidates is complicated by the intracellular lifestyle of Leishmania. Here, we investigate the use of human induced pluripotent stem cell (iPS)-derived macrophages (iMACs) as host cells for L. donovani. iMACs obtained through embryoid body differentiation were infected with L. donovani promastigotes, and high-content imaging techniques were used to optimise the iMACs seeding density and multiplicity of infection, allowing us to reach infection rates up to 70% five days after infection. IC50 values obtained for miltefosine and amphotericin B using the infected iMACs or mouse peritoneal macrophages as host cells were comparable and in agreement with the literature, showing the potential of iMACs as an infection model for drug screening. Author Summary Yearly, up two million people in poverty-stricken areas contract leishmaniasis, a disease caused by parasites of the genus Leishmania. When an infected sandfly takes a blood meal, Leishmania parasites enter the host where they are taken up by macrophages. Inside the macrophage, Leishmania parasites establish a niche where they can proliferate. Although this infection often leads to disability or death, the drugs currently available are lacking due to toxic side effects, high expenses or difficulties in usage. Drug screening assays that are currently used for compound screening often rely on mouse peritoneal macrophages. We have generated human induced pluripotent stem cell derived macrophages and used these as new host cells for Leishmania donovani in the testing of anti-leishmanial compounds. This model has many advantages. For one, it allows us to work with human cells, mimicking the natural infection more closely than possible with murine cells. Secondly, it allows to obtain bigger batches of uniform cells for screening campaigns. Finally, this approach aligns with the principle of 3R, replacing the use of animals for cultivation of Leishmania and drug screening purposes.
Understanding the Semantics of GPS-based Trajectories for Road Closure Detection

The accurate detection of road closures is of great value for real-time updating of digital maps. The existing methods mainly follow the paradigm of detecting the drastic changes in traffic statistical values (e.g., traffic flow), but they may lead to misidentifying since 1) drastic changes of traffic statistical values are hard to be observed in low-heat roads where the passing vehicles are sparse; 2) statistical values are sensitive to noise (e.g., traffic flow for tiny roads and tunnels is prone to miscounting); and 3) statistical values are naturally delayed, and misidentifying may occur when they have not yet shown significant changes. Surprisingly, since GPS-based trajectories can also exhibit significant abnormal patterns for road closures and have the superiority in fine granularity and timeliness, they can naturally tackle the above challenges. In this paper, we present a novel road closure detection framework based on mining the semantics of trajectories, called T-Closure. We first construct a heterogeneous graph based on the trajectory and the planned route to extract the spatial-topological property of each trajectory, where a node-level auxiliary task is proposed to guide the learning of feature encoders. A multi-view heterogeneous graph neural network (MVH-GNN) with a graph-level auxiliary task is then introduced to capture the semantics of trajectories, where intra-category relevance and inter-category interaction are both considered. Finally, a sequence-level auxiliary task refines the ability of LSTM in modeling the semantic relevance among trajectories while enhancing the robustness of our framework. Experiments on four real-world road closure datasets demonstrate the superiority of T-Closure. Online performance shows that T-Closure can detect 7000+ closure events monthly, with a delay within 1.5 hours.
Linear-Quadratic Games Played over Event Trees

In this paper, we study N -player finite-horizon discrete-time dynamic stochastic games where the uncertainty is described by an event tree. We consider linear-state dynamics, with a one-period lag structure, and quadratic costs. We derive necessary and sufficient conditions for the existence of S -adapted Nash equilibria with open-loop and closed-loop (no-memory) information structures. We observe that the existence of these equilibria is related to the solvability of a generalized backward Riccati recursion defined on the event tree. Next, we consider these games with (node-specific) linear constraints. We show that the S -adapted Nash equilibria (both open-loop and closed-loop) can be obtained by solving a parametric linear-complementarity problem defined on the entire event tree.
IN VIVO ISOLATION OF A QUIESCENT MELANOMA POPULATION WITH INVASIVE PROPERTIES UNVEILS A TRANSCRIPTIONAL REPROGRAMMING DRIVEN BY THE TUMOR NICHE

Melanoma is a heterogeneous tumor composed of many interacting cellular populations and highly plastic melanoma cells that pass through distinct cell states to adapt to the surrounding microenvironment. Slow cycling is a transient state that defines a minor population of cells with cancer-initiating features. These cells are enriched upon drug therapy and can trigger cancer relapse and metastasis dissemination when they acquire proliferative potential. This population is still not entirely characterized. Here we provide evidence of the existence of a slow cycling melanoma population isolated in vivo from melanoma PDXs using the H2B-GFP system. These cells display a highly invasive phenotype and are able to dynamically respond to cancer microenvironmental stimuli. Single cell transcriptomic analysis unveils a significant transcriptional heterogeneity of GFP-retaining slow cycling cells, defining a quiescent subpopulation of cells. These cells show a different phenotype in primary tumors and matched metastases, suggesting that tumor niche pressure drives a transcriptional reprogramming of quiescent cells during melanoma progression.
un-xPass: Measuring Soccer Player's Creativity

Creativity is highly valued in soccer players. It contributes to exciting and unpredictable play, which can help teams to overcome defensive strategies and create scoring opportunities. Consequently, evaluating the creative abilities of players is an important aspect of the player recruitment process. However, there is currently no clear way to measure creativity in soccer. It is not captured by the typical result-based performance indicators, as being creative entails going beyond just doing something useful, to accomplishing something useful but in a unique or atypical way. Therefore in this paper, we define a novel metric to quantify the level of creativity involved in a player's passes. Our Creative Decision Rating (CDR) utilizes machine learning techniques to assess two important factors: the originality of a pass, and its value in terms of increasing the team's chances of scoring a goal. We validated our metric on StatsBomb 360 contextual event stream data of the 2021/22 English Premier League season and show through a number of use cases that it provides another angle on a player's skill, complementing existing player evaluation metrics. Overall, our metric provides a concise method for capturing and quantifying the creativity of soccer players and could have important implications for player recruitment and talent development in the sport.
Quantifying Node Importance over Network Structural Stability

Quantifying node importance on engagement dynamics is critical to support network stability. We can motivate or retain the users in a social platform according to their importance s.t. the network is more sustainable. Existing studies validate that the coreness of a node is the "best practice" on network topology to estimate the engagement of the node. In this paper, the importance of a node is the effect on the engagement of other nodes when its engagement is strengthened or weakened. Specifically, the importance of a node is quantified via two novel concepts: the anchor power to measure the engagement effect of node strengthening (i.e., the overall coreness gain) and the collapse power to measure the engagement effect of node weakening (i.e., the overall coreness loss). We find the computation of the two concepts can be naturally integrated into a shell component-based framework, and propose a unified static algorithm to compute both the anchored and collapsed followers. For evolving networks, efficient maintenance techniques are designed to update the follower sets of each node, which is faster than redoing the static algorithm by around 3 orders of magnitude. Extensive experiments on real-life data demonstrate the effectiveness of our model and the efficiency of our algorithms.