Miracle-dz/UniFAR-FTUs huggingface dataset:
Downlink Transmission in FBMC-Based Massive MIMO With Co-Located and Distributed Antennas

This paper introduces a practical precoding method for the downlink of Filter Bank Multicarrier-based (FBMC-based) massive multiple-input multiple-output (MIMO) systems. The proposed method comprises a two-stage precoder, consisting of a fractionally spaced prefilter (FSP) per subcarrier to equalize the channel across each subcarrier band. This is followed by a conventional precoder that concentrates the signals of different users at their spatial locations, ensuring each user receives only the intended information. In practical scenarios, a perfect channel reciprocity may not hold due to radio chain mismatches in the uplink and downlink. Moreover, the channel state information (CSI) may not be perfectly known at the base station. To address these issues, we theoretically analyze the performance of the proposed precoder in presence of imperfect CSI and channel reciprocity calibration errors. Our investigation covers both co-located (cell-based) and cell-free massive MIMO cases. In the cell-free massive MIMO setup, we propose an access point selection method based on the received SINRs of different users in the uplink. Finally, we conduct numerical evaluations to assess the performance of the proposed precoder. Our results demonstrate the excellent performance of the proposed precoder when compared with the orthogonal frequency division multiplexing (OFDM) method as a benchmark.
A Bridge between Dynamical Systems and Machine Learning: Engineered Ordinary Differential Equations as Classification Algorithm (EODECA)

In a world increasingly reliant on machine learning, the interpretability of these models remains a substantial challenge, with many equating their functionality to an enigmatic black box. This study seeks to bridge machine learning and dynamical systems. Recognizing the deep parallels between dense neural networks and dynamical systems, particularly in the light of non-linearities and successive transformations, this manuscript introduces the Engineered Ordinary Differential Equations as Classification Algorithms (EODECAs). Uniquely designed as neural networks underpinned by continuous ordinary differential equations, EODECAs aim to capitalize on the well-established toolkit of dynamical systems. Unlike traditional deep learning models, which often suffer from opacity, EODECAs promise both high classification performance and intrinsic interpretability. They are naturally invertible, granting them an edge in understanding and transparency over their counterparts. By bridging these domains, we hope to usher in a new era of machine learning models where genuine comprehension of data processes complements predictive prowess.
Quantum Counting on the Complete Bipartite Graph

Quantum counting is a key quantum algorithm that aims to determine the number of marked elements in a database. This algorithm is based on the quantum phase estimation algorithm and uses the evolution operator of Grover's algorithm because its non-trivial eigenvalues are dependent on the number of marked elements. Since Grover's algorithm can be viewed as a quantum walk on a complete graph, a natural way to extend quantum counting is to use the evolution operator of quantum-walk-based search on non-complete graphs instead of Grover's operator. In this paper, we explore this extension by analyzing the coined quantum walk on the complete bipartite graph with an arbitrary number of marked vertices. We show that some eigenvalues of the evolution operator depend on the number of marked vertices and using this fact we show that the quantum phase estimation can be used to obtain the number of marked vertices. The time complexity for estimating the number of marked vertices in the bipartite graph with our algorithm aligns closely with that of the original quantum counting algorithm.
Telescope: Telemetry at Terabyte Scale

Data-hungry applications that require terabytes of memory have become widespread in recent years. To meet the memory needs of these applications, data centers are embracing tiered memory architectures with near and far memory tiers. Precise, efficient, and timely identification of hot and cold data and their placement in appropriate tiers is critical for performance in such systems. Unfortunately, the existing state-of-the-art telemetry techniques for hot and cold data detection are ineffective at the terabyte scale. We propose Telescope, a novel technique that profiles different levels of the application's page table tree for fast and efficient identification of hot and cold data. Telescope is based on the observation that, for a memory- and TLB-intensive workload, higher levels of a page table tree are also frequently accessed during a hardware page table walk. Hence, the hotness of the higher levels of the page table tree essentially captures the hotness of its subtrees or address space sub-regions at a coarser granularity. We exploit this insight to quickly converge on even a few megabytes of hot data and efficiently identify several gigabytes of cold data in terabyte-scale applications. Importantly, such a technique can seamlessly scale to petabyte-scale applications. Telescope's telemetry achieves 90%+ precision and recall at just 0.009% single CPU utilization for microbenchmarks with a 5 TB memory footprint. Memory tiering based on Telescope results in 5.6% to 34% throughput improvement for real-world benchmarks with a 1-2 TB memory footprint compared to other state-of-the-art telemetry techniques.
DIISCO: A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data

Characterizing cell-cell communication and tracking its variability over time is essential for understanding the coordination of biological processes mediating normal development, progression of disease, or responses to perturbations such as therapies. Existing tools lack the ability to capture time-dependent intercellular interactions, such as those influenced by therapy, and primarily rely on existing databases compiled from limited contexts. We present DIISCO, a Bayesian framework for characterizing the temporal dynamics of cellular interactions using single-cell RNA-sequencing data from multiple time points. Our method uses structured Gaussian process regression to unveil time-resolved interactions among diverse cell types according to their co-evolution and incorporates prior knowledge of receptor-ligand complexes. We show the interpretability of DIISCO in simulated data and new data collected from CAR-T cells co-cultured with lymphoma cells, demonstrating its potential to uncover dynamic cell-cell crosstalk. Availability DIISCO is publicly accessible at https://github.com/azizilab/DIISCO_public. All data will be deposited to GEO upon publication.
Host-microbiome mutualisms emerge from community interactions among microbes

Microbiomes often benefit plants, conferring resistance to pathogens, improving stress tolerance, or promoting plant growth. As potential plant mutualists, however, microbiomes are not a single organism but a community of species with complex interactions among microbial taxa and between microbes and their shared host. The nature of ecological interactions among microbes in the microbiome can have important consequences for the net effects of microbiomes on hosts. Here, we compared the effects of individual microbial strains and 10-strain synthetic communities on microbial productivity and host growth using the common duckweed Lemna minor and a synthetic, simplified version of its native microbiome. Except for Pseudomonas protegens, which was a mutualist when tested alone, all of the single strains we tested were commensals on hosts, benefiting from plant presence but not increasing host growth relative to uninoculated controls. However, 10-strain synthetic microbial communities increased both microbial productivity and duckweed growth more than the average single-strain inoculation and uninoculated controls, meaning that host-microbiome mutualisms can emerge from community interactions among microbes on hosts. The effects of community inoculation were sub-additive, suggesting at least some competition among microbes in the duckweed microbiome. We also investigated the relationship between L. minor fitness and that of its microbes, providing some of the first empirical estimates of broad fitness alignment between plants and members of their microbiomes; hosts grew faster with more productive microbes or microbiomes.
Hybrid Adipocyte-Derived Exosome Nano Platform for Potent Chemo-Phototherapy in Targeted Hepatocellular Carcinoma

The high prevalence and severity of hepatocellular carcinoma (HCC) present a significant menace to human health. Despite the significant advancements in nanotechnology-driven antineoplastic agents, there remains a conspicuous gap in the development of targeted chemotherapeutic agents specifically designed for HCC. Consequently, there is an urgent need to explore potent drug delivery systems for effective HCC treatment. Here we have exploited the interplay between HCC and adipocyte to engineer a hybrid adipocyte-derived exosome platform, serving as a versatile vehicle to specifically target HCC and exsert potent antitumor effect. A lipid-like prodrug of docetaxel (DSTG) with a reactive oxygen species (ROS)-cleavable linker, and a lipid-conjugated photosensitizer (PPLA), spontaneously co-assemble into nanoparticles, functioning as the lipid cores of the hybrid exosomes (HEMPs and NEMPs). These nanoparticles are further encapsuled within adipocyte-derived exosome membranes, enhancing their affinity towards HCC cancer cells. As such, cancer cell uptakes of hybrid exosomes are increased up to 5.73-fold compared to lipid core nanoparticles. Our in vitro and in vivo experiments have demonstrated that HEMPs not only enhance the bioactivity of the prodrug and extend its circulation in the bloodstream but also effectively inhibit tumor growth by selectively targeting hepatocellular carcinoma tumor cells. Self-facilitated synergistic drug release subsequently promoting antitumor efficacy, inducing significant inhibition of tumor growth with minimal side effects. Our findings herald a promising direction for the development of targeted HCC therapeutics. Graphical abstract An adipocyte-derived exosome nanoplatform has been developed for the combined therapy of both chemotherapy and photodynamic therapy. The hybrid adipocyte-derived exosome nanoplatform integrates the advantage of targeted co-delivery and combination therapy, which can stimulate the release of drugs under the tumor microenvironment, effectively enhanced targeted co-delivery in hepatocellular carcinoma cells, prolonged blood circulation time and improve the therapeutic effect of hepatocellular carcinoma tumors model.
Genetically encoded affinity reagents (GEARs): A toolkit for visualizing and manipulating endogenous protein function in vivo

Probing endogenous protein localization and function in vivo remains challenging due to laborious gene targeting and monofunctional alleles. Here, we develop a multifunctional, universal, and adaptable toolkit based on genetically encoded affinity reagents (GEARs). GEARs use nanobodies and single chain variable fragments (scFv), which recognize small epitopes, enabling fluorescent visualization and selective degradation of protein targets in vivo. Furthermore, we delineate a CRISPR/Cas9-based epitope tagging pipeline to demonstrate its utility for producing knock-in alleles that have broad multifunctionality. We use GEARs to examine the native behaviour of the pioneer transcription factor Nanog and the planar cell polarity protein Vangl2 during early zebrafish development. Together, this toolkit provides a versatile system for probing and perturbing endogenous protein function while circumventing challenges associated with conventional gene targeting and is broadly available to the model organism community.
YAP localization mediates mechanical adaptation of human cancer cells during extravasation in vivo

Biophysical profiling of primary tumors has revealed that individual tumor cells fall along a highly heterogeneous continuum of mechanical phenotypes. One idea is that a subset of tumor cells is “softer” to facilitate detachment and escape from the primary site, a step required to initiate metastasis. However, it has also been postulated that cells must be able to deform and generate sufficient force to exit into distant sites. Here, we aimed to dissect the mechanical changes that occur during extravasation and organ colonization. Using multiplexed methods of intravital microscopy and optical tweezer based active microrheology, we obtained longitudinal images and mechanical profiles of cells during organ colonization in vivo. We determined that cells were softer, more liquid like upon exit of the vasculature but stiffened and became more solid like once in the new organ microenvironment. We also determined that a YAP mediated mechanogenotype influenced the global dissemination in our in vivo and in vitro models and that reducing mechanical heterogeneity could reduce extravasation. Moreover, our high throughput analysis of mechanical phenotypes of patient samples revealed that this mechanics was in part regulated by the external hydrodynamic forces that the cancer cells experienced within capillary mimetics. Our findings indicate that disseminated cancer cells can keep mutating with a continuum landscape of mechano-phenotypes, governed by the YAP-mediated mechanosensing of hydrodynamic flow.
Utilizing the scale-invariant feature transform algorithm to align distance matrices facilitates systematic protein structure comparison

Protein structure comparison is pivotal for deriving homological relationships, elucidating protein functions, and understanding evolutionary developments. The burgeoning field of in-silico protein structure prediction now yields billions of models with near-experimental accuracy, necessitating sophisticated tools for discerning structural similarities among proteins, particularly when sequence similarity is limited. Here, we have developed the ADAMS pipeline, which synergizes the distance matrix alignment method with the scale-invariant feature transform (SIFT) algorithm, streamlining protein structure comparison on a proteomic scale. Utilizing a computer vision-centric strategy for contrasting disparate distance matrices, ADAMS adeptly alleviates challenges associated with proteins characterized by a high degree of structural flexibility. Our findings indicate that ADAMS achieves a level of performance and accuracy on par with Foldseek, while maintaining similar speed. Crucially, ADAMS overcomes certain limitations of Foldseek in handling structurally flexible proteins, establishing it as an efficacious tool for in-depth protein structure analysis with heightened accuracy.
Deciphering the Functional Roles of Individual Cancer Alleles Across Comprehensive Cancer Genomic Studies

Cancer genome data has been growing in both size and complexity, primarily driven by advances in next-generation sequencing technologies, such as Pan-cancer data from TCGA, ICGC, and single-cell sequencing. Yet, discerning the functional role of individual genomic lesions remains a substantial challenge due to the complexity and scale of the data. Previously, we introduced REVEALER, which identifies groups of genomic alterations that significantly associate with target functional profiles or phenotypes, such as pathway activation, gene dependency, or drug response. In this paper, we present a new mathematical formulation of the algorithm. This version (REVEALER 2.0) is considerably more powerful than the original, allowing for rapid processing and analysis of much larger datasets and facilitating higher-resolution discoveries at the level of individual alleles. REVEALER 2.0 employs the Conditional Information Coefficient (CIC) to pinpoint features that are either complementary or mutually exclusive but still correlate with the target functional profile. The aggregation of these features provides a better explanation for the target functional profile than any single alteration on its own. This is indicative of scenarios where several activating genomic lesions can initiate or stimulate a key pathway or process. We replaced the initial three-dimensional kernel estimation with multiple precomputed one-dimensional kernel estimations, resulting in an approximate 150x increase in speed and efficiency. This improvement, combined with its efficient execution, makes REVEALER 2.0 suitable for much larger datasets and a more extensive range of genomic challenges.
Single-Molecule Direct RNA Sequencing Reveals the Shaping of Epitranscriptome Across Multiple Species

The significance of RNA modification in gene regulation has been widely recognized. To understand the transcriptome-wide landscape and its underlying biogenesis mechanisms, prevailing mapping strategies have been developed. However, these short-reads based techniques are primarily focused at the gene level, overlooking the nature of RNA as multiple copies within one cell. Third-generation sequencing (TGS) platforms provide direct RNA sequencing at the resolution of individual RNA molecules, offering the promise of detecting RNA modifications and RNA processing events simultaneously. In this study, we introduce SingleMod, a deep learning model tailored for the precise mapping of m6A modifications within single RNA molecules using nanopore direct RNA sequencing (DRS). We systematically dissect the transcriptome-wide m6A profiles in single-molecule and single-base resolution, refining our understanding of the genomics of m6A and revealing an additive mode through which m6A shapes the epitranscriptome. Through comparative analyses across diverse species, we identify three distinct m6A distribution patterns and unveil an exclusion-inclusion deposition mode that governs m6A biogenesis. Furthermore, we introduce a unified quantitative model that delineates this dual-mode in various species. This study pioneers single-molecule m6A landscape exploration across multiple species, deepening our understanding of m6A, including its genomics, biogenesis, mechanisms, and biological implications.
Development and Analysis of Multiscale Models for Tuberculosis: From Molecules to Populations

Although infectious disease dynamics are often analyzed at the macro-scale, increasing numbers of drug-resistant infections highlight the importance of within-host modeling that simultaneously solves across multiple scales to effectively respond to epidemics. We review multiscale modeling approaches for complex, interconnected biological systems and discuss critical steps involved in building, analyzing, and applying such models within the discipline of model credibility. We also present our two tools: CaliPro, for calibrating multiscale models (MSMs) to datasets, and tunable resolution, for fine- and coarse-graining sub-models while retaining insights. We include as an example our work simulating infection with Mycobacterium tuberculosis to demonstrate modeling choices and how predictions are made to generate new insights and test interventions. We discuss some of the current challenges of incorporating novel datasets, rigorously training computational biologists, and increasing the reach of MSMs. We also offer several promising future research directions of incorporating within-host dynamics into applications ranging from combinatorial treatment to epidemic response.
The causal roles of parietal alpha oscillations and evoked potentials in coding task-relevant information during selective attention

Selective attention is a fundamental cognitive mechanism that allows people to prioritise task-relevant information while ignoring irrelevant information. Previous research has suggested key roles of parietal evoked potentials like N2pc, and parietal oscillatory responses like alpha power, in spatial attention tasks. However, the informational content of these signals is less clear. Spatial attention tasks require not only representation of spatial information (i.e., where to attend) but also task (e.g., what to attend to) and visual feature information (e.g., details of visual stimuli). Whether all these features are represented in both evoked and oscillatory responses, and with what dynamics, is unknown. Moreover, the question of whether parietal evoked potentials and/or alpha power causally influence the coding of these different task elements is yet unresolved. Here, we used concurrent TMS-EEG to causally manipulate parietal alpha power and evoked potentials and investigate their roles in coding task-relevant information in a selective attention task. First, using EEG-only data, we investigated the decodability and temporal dynamics of evoked potentials and alpha power in coding different aspects of a selective attention task (where to attend, what to attend to, and visual feature information). We found that evoked potentials coded all three types of task-relevant information with distinct temporal dynamics, and alpha oscillations carried information regarding both where to attend and what to attend to. Then, we applied rhythmic-TMS (rTMS) at individual alpha frequency over the right intraparietal sulcus (IPS), to examine the causal roles of evoked potentials and alpha oscillations in selective attention. Compared with the control arrhythmic-TMS, alpha rTMS increased power and inter-trial phase coherence in alpha band and induced more negative N2pc amplitudes. Moreover, alpha rTMS causally and specifically improved multivariate decoding accuracy of the information about where to attend but not what to attend to or feature information during task performance. This TMS-induced change in decoding information about where to attend, encoded in both evoked potentials and alpha power, predicted TMS-induced changes in response errors and reaction time. These findings illuminate the dynamics with which the complementary aspects of a selective attention task are encoded in evoked and oscillatory brain activity. Moreover, they reveal a specific and causal role of IPS-controlled evoked and oscillatory activity in carrying behaviour-driving information about where to focus attention.
Counter-gradient variation in gene expression between fish populations facilitates colonization of low-dissolved oxygen environments

The role of phenotypic plasticity during colonization remains unclear due to the shifting importance of plasticity across timescales. Over time, genetic responses can reduce plasticity such that species in a novel environment show higher levels of plasticity than those with a longer evolutionary timescale in the environment. Therefore, comparing species in the early stages of colonization to long-established species provides a powerful approach for uncovering the role of phenotypic plasticity during different stages of colonization. We compared gene expression between the cyprinid fish Enteromius apleurogramma, a species that has undergone a recent range expansion, and E. neumayeri, a long-established native species in the same region, caught from low-dissolved oxygen (DO) and high-DO habitats. We sampled tissue either immediately after capture from the field or after a two-week acclimation under high-DO conditions, allowing us to test for both evolved and plastic differences in low-DO vs high-DO populations of each species. We found that most genes showing evolved differences in gene expression did not overlap with those showing plastic differences in gene expression. However, in the genes that did overlap, there was counter-gradient variation such that plastic and evolved gene expression responses were in opposite directions in both species. Additionally, E. apleurogramma had higher levels of plasticity and evolved divergence in gene expression between field populations. We suggest that the higher level of plasticity and counter-gradient variation may have allowed rapid genetic adaptation in E. apleurogramma and facilitated colonization. This study shows how counter-gradient variation may impact colonization of divergent oxygen environments.