Miracle-dz/UniFAR-FTUs huggingface dataset:
From dawn till dusk: Time-adaptive bayesian optimization for neurostimulation

Stimulation optimization has garnered considerable interest in recent years in order to efficiently parametrize neuromodulation-based therapies. To date, efforts focused on automatically identifying settings from parameter spaces that do not change over time. A limitation of these approaches, however, is that they lack consideration for time dependent factors that may influence therapy outcomes. Disease progression and biological rhythmicity are two sources of variation that may influence optimal stimulation settings over time. To account for this, we present a novel time-varying Bayesian optimization (TV-BayesOpt) for tracking the optimum parameter set for neuromodulation therapy. We evaluate the performance of TV-BayesOpt for tracking gradual and periodic slow variations over time. The algorithm was investigated within the context of a computational model of phase-locked deep brain stimulation for treating oscillopathies representative of common movement disorders such as Parkinson’s disease and Essential Tremor. When the optimal stimulation settings changed due to gradual and periodic sources, TV-BayesOpt outperformed standard time-invariant techniques and was able to identify the appropriate stimulation setting. Through incorporation of both a gradual “forgetting” and periodic covariance functions, the algorithm maintained robust performance when a priori knowledge differed from observed variations. This algorithm presents a broad framework that can be leveraged for the treatment of a range of neurological and psychiatric conditions and can be used to track variations in optimal stimulation settings such as amplitude, pulse-width, frequency and phase for invasive and non-invasive neuromodulation strategies.
Neonatal screening for spinal muscular atrophy: A pilot study in Brazil

Abstract Spinal muscular atrophy (SMA) is considered one of the most common autosomal recessive disorders, with an estimated incidence of 1 in 10,000 live births. Testing for SMA has been recommended for inclusion in neonatal screening (NBS) panels since there are several therapies available and there is evidence of greater efficacy when introduced in the pre/early symptomatic phases. In Brazil, the National Neonatal Screening Program tests for six diseases, with a new law issued in 2021 stating that it should incorporate more diseases, including SMA. In the present study, dried blood spot (DBS) samples collected by the Reference Services of Neonatal Screening of RS and SP, to perform the conventional test were also screened for SMA, using real-time PCR, with SALSA MC002 technique. A total of 40,000 samples were analyzed, enabling the identification of four positive cases of SMA, that were confirmed by MLPA. Considering our sampling, Brazil seems to have an incidence comparable to the described in other regions. This work demonstrated that the use of the MC002 technique in samples routinely collected for the conventional NBS program is suitable to screen for SMA in our conditions and can be included in the expansion of the neonatal screening programs.
Mechanism of Yixishu lotion in the treatment of vaginitis based on network pharmacology combined with experimental validation: an experimental research study

Objective: Yixishu lotion (YXSL) originates from the summary of traditional Chinese medicine clinical experience and constantly improves in practice in clinical validation of the exact efficacy of traditional Chinese medicine prescription. To explore the mechanism of YXSL in treating vaginitis and the potential mechanisms based on network pharmacology and experimental verification. Methods: The active components and drug-related targets of YXSL were retrieved from the TCMSP (Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform) database, and the target was predicted by the UniProt database. Searching for genes related to ‘vaginitis’ disease in the GeneCards database, a total of 2581 drug targets were obtained. The interaction between proteins (PPI – protein–protein interaction) relationship was obtained by STRING database and visualized by Cytoscape software. Finally, the ‘Bioconductor’ installation package in R software was used to analyze the GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways of the target. Results: In this study, by the method of network pharmacology, the key active components of YXSL were flavonoids such as quercetin, apigenin, kaempferol, luteolin, β-sitosterol; the main core proteins included MAPK14, TP53, FGF2, ESR1, MAPK3, MAPK1, VEGFA, JUN, IL-6, and the KEGG pathway was mainly involved in MAPK pathway, Th17 pathway, Malaria, TNF pathway, and other signaling pathways. Animal experiments showed that the clinical symptoms and vaginal tissue lesions of the YXSL group and the fluconazole group were improved, and the levels of TNF-α (tumor necrosis factor alpha), IL-6 (interleukin-6), MDA (malondialdehyde), SOD (superoxide dismutase), IL-4, and IFN-γ (interferon-γ) in vaginal tissue and serum were better than the model group. Conclusion: YXSL may achieve its therapeutic effect on vaginitis by reducing the inflammatory response, improving oxidative stress response, and improving body immunity, and it provides a theoretical basis for further research on its pharmacodynamic material basis and mechanism of action.
Uncovering the miRNA-mediated regulatory network involved in Ma bamboo (Dendrocalamus latiflorus) de novo shoot organogenesis

Abstract Bamboo is an important non-timber forest product and is well-known for its reluctance to regenerate. Recently we have established a de novo shoot organogenesis (DNSO) protocol in Ma bamboo (Dendrocalamus latiflorus) and revealed the transcriptomic dynamics during Ma bamboo regeneration, which suggested the potential roles of Ma bamboo microRNAs (DlamiRNAs) in this process. However, how DlamiRNAs regulate bamboo DNSO is poorly understood. Here we performed integrated analysis with sRNAome, degradome, and transcriptome sequencing by using samples covering the four stages of the bamboo DNSO process. A total of 727 DlamiRNAs showed differential expression during the bamboo DNSO process, and the core DlamiRNA–DlamRNA- mediated regulatory networks for bamboo DNSO were constructed. Based on the results, DlamiR156 was selected for further functional characterization of its potential roles in bamboo DNSO. Transgenic bamboos with increased DlamiR156 levels exhibited an enhancement in their regeneration efficiency. Conversely, when DlamiR156 levels were downregulated, the regeneration efficiencies of transgenic bamboos decreased. Our findings show that the DlamiRNA-mediated regulatory pathways are significant in the process of bamboo regeneration and will contribute to our understanding of the molecular mechanisms governing plant organogenesis in a more comprehensive manner.
Small peptide fingerprint (peptidomics) of Amyotrophic Lateral Sclerosis in CSF

Background: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by abnormal protein aggregates in motor neurons. Present and earlier proteomic studies to characterize peptides in cerebrospinal fluid (CSF) associated with motoneuron pathology did not target the low molecular weight proteins and peptides. We generated the hypothesis that specific changes in CSF peptides or low molecular weight proteins are significantly changed in ALS, and that these changes may support deciphering molecular pathophysiology and even guide approaches towards therapeutic interventions. Methods: Cerebrospinal fluid (CSF) from 50 ALS patients and 50 non-ALS controls was collected, centrifuged immediately after collection, aliquoted into polypropylene test tubes, frozen within 30–40 min after the puncture and stored at −80°C until use. Peptides were sequenced using capillary electrophoresis or liquid chromatography / mass spectrometry (CE-MS/MS or LC-MS/MS) analyses. Findings: In cerebrospinal CSF from 50 patients and 50 non-ALS controls 33 peptides were found, of which 14 could be sequenced using a non-lytic single pot proteomic detection method, CE/MS. ALS deregulated peptides vs. controls included Integral membrane protein 2B, Neurosecretory protein VGF, Osteopontin, Neuroendocrine protein 7B2 (Secretogranin-V), EGF-containing fibulin-like extracellular matrix protein 1, Xylosyltransferase 1 XT-1, Chromogranin-A, Superoxide dismutase SOD-1, Secretogranin-1 (Chromogranin B), NR2F2 Nuclear Receptor Subfamily 2 Group F Member 2 and Collagen alpha-1(VII) chain. Interpretation: Most striking deregulations in CSF from ALS patients were found in VGF, Osteopontin, SOD-1 and EFEMP1 peptides. No associations of disease severity, duration and region of onset with sequenced peptides were found.
Deforestation alters rainfall: a myth or reality

To cope with the issue of food safety and human shelter, natural landscape has gone through a number of alterations. In the coming future, the expansion of urban land and agricultural farms will likely disrupt the natural environment. Researchers have claimed that land use change may become the most serious issue of the current century. Thus, it is necessary to understand the consequences of land use change on the climatic variables, e.g., rainfall. This study investigated the impact of deforestation on local rainfall. An integrated methodology was adopted to achieve the objectives. Above ground biomass was considered as the indicator of forest areas. Time series data of a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor were obtained for the year of 2000, 2005, and 2010. Rainfall data were collected from the Department of Irrigation and Drainage, Malaysia. The MODIS time series data were classified and four major classes were developed based on the Normalised Difference Vegetation Index (NDVI) ranges. The results of the classification showed that water, and urban and agricultural lands have increased in their area by 2, 3, and 6%, respectively. On the other hand, the area of forest has decreased 10% collectively from 2000 to 2010. The results of NDVI and rainfall data were analysed by using a linear regression analysis. The results showed a significant relationship at a 90% confidence interval between rainfall and deforestation (t = 1.92, p = 0.06). The results of this study may provide information about the consequences of land use on the climate on the local scale.
M3T: Multi-Scale Memory Matching for Video Object Segmentation and Tracking

Video Object Segmentation (VOS) has became increasingly important with availability of larger datasets and more complex and realistic settings, which involve long videos with global motion (e.g, in egocentric settings), depicting small objects undergoing both rigid and non-rigid (including state) deformations. While a number of recent approaches have been explored for this task, these data characteristics still present challenges. In this work we propose a novel, DETR-style encoder-decoder architecture, which focuses on systematically analyzing and addressing aforementioned challenges. Specifically, our model enables on-line inference with long videos in a windowed fashion, by breaking the video into clips and propagating context among them using time-coded memory. We illustrate that short clip length and longer memory with learned time-coding are important design choices for achieving state-of-the-art (SoTA) performance. Further, we propose multi-scale matching and decoding to ensure sensitivity and accuracy for small objects. Finally, we propose a novel training strategy that focuses learning on portions of the video where an object undergoes significant deformations -- a form of"soft"hard-negative mining, implemented as loss-reweighting. Collectively, these technical contributions allow our model to achieve SoTA performance on two complex datasets -- VISOR and VOST. A series of detailed ablations validate our design choices as well as provide insights into the importance of parameter choices and their impact on performance.
Quantum Fluctuations and Multifractally-Enhanced Superconductivity in Disordered Thin Films

The interplay between electron-electron interactions and weak localization (or anti-localization) phenomena in two-dimensional systems can significantly enhance the superconducting transition temperature. We develop the theory of quantum fluctuations within such multifractally-enhanced superconducting states in thin films. In conditions of weak disorder, we employ the Finkel'stein nonlinear sigma model to derive an effective action for the superconducting order parameter and the quasiclassical Green's function, meticulously accounting for the influence of quantum fluctuations. This effective action, applicable for interactions of any strength, reveals the critical role of well-known collective modes in a dirty superconductor, and its saddle point analysis leads to modified Usadel and gap equations. These equations comprehensively incorporate the renormalizations stemming from the interplay between interactions and disorder, resulting in the non-trivial energy dependence of the gap function. Notably, our analysis establishes a direct relation between the self-consistent gap equation at the superconducting transition temperature and the known renormalization group equations for interaction parameters in the normal state.
Support theories for non-Noetherian tensor triangulated categories

We extend the support theory of Benson--Iyengar--Krause to the non-Noetherian setting by introducing a new notion of small support for modules. This enables us to prove that the stable module category of a finite group is canonically stratified by the action of the Tate cohomology ring, despite the fact that this ring is rarely Noetherian. In the tensor triangular context, we compare the support theory proposed by W. Sanders (which extends the Balmer--Favi support theory beyond the weakly Noetherian setting) with our generalized BIK support theory. When the Balmer spectrum is homeomorphic to the Zariski spectrum of the endomorphism ring of the unit, the two support theories coincide as do their associated theories of stratification. We also prove a negative result which states that the Balmer--Favi--Sanders support theory can only stratify categories whose spectra are weakly Noetherian. This provides additional justification for the weakly Noetherian hypothesis in the work of Barthel, Heard and B. Sanders. On the other hand, the detection property and the local-to-global principle remain interesting in the general setting.
RdimKD: Generic Distillation Paradigm by Dimensionality Reduction

Knowledge Distillation (KD) emerges as one of the most promising compression technologies to run advanced deep neural networks on resource-limited devices. In order to train a small network (student) under the guidance of a large network (teacher), the intuitive method is regularizing the feature maps or logits of the student using the teacher's information. However, existing methods either over-restrict the student to learn all information from the teacher, which lead to some bad local minimum, or use various fancy and elaborate modules to process and align features, which are complex and lack generality. In this work, we proposed an abstract and general paradigm for the KD task, referred to as DIMensionality Reduction KD (RdimKD), which solely relies on dimensionality reduction, with a very minor modification to naive L2 loss. RdimKD straightforwardly utilizes a projection matrix to project both the teacher's and student's feature maps onto a low-dimensional subspace, which are then optimized during training. RdimKD achieves the goal in the simplest way that not only does the student get valuable information from the teacher, but it also ensures sufficient flexibility to adapt to the student's low-capacity reality. Our extensive empirical findings indicate the effectiveness of RdimKD across various learning tasks and diverse network architectures.
The Cosmological Dynamics of String Theory Axion Strings

The quantum chromodynamics (QCD) axion may solve the strong CP problem and explain the dark matter (DM) abundance of our Universe. The axion was originally proposed to arise as the pseudo-Nambu Goldstone boson of global $\mathrm{U}(1)_{\rm PQ}$ Peccei-Quinn (PQ) symmetry breaking, but axions also arise generically in string theory as zero modes of higher-dimensional gauge fields. In this work we show that string theory axions behave fundamentally differently from field theory axions in the early Universe. Field theory axions may form axion strings if the PQ phase transition takes place after inflation. In contrast, we show that string theory axions do not generically form axion strings. In special inflationary paradigms, such as D-brane inflation, string theory axion strings may form; however, their tension is parametrically larger than that of field theory axion strings. We then show that such QCD axion strings overproduce the DM abundance for all allowed QCD axion masses and are thus ruled out, except in scenarios with large warping. A loop-hole to this conclusion arises in the axiverse, where an axion string could be composed of multiple different axion mass eigenstates; a heavier eigenstate could collapse the network earlier, allowing for the QCD axion to produce the correct DM abundance and also generating observable gravitational wave signals.
The Origins of Gas Accreted by Supermassive Black Holes: The Importance of Recycled Gas

We investigate the fueling mechanisms of supermassive black holes (SMBHs) by analyzing 10 zoom-in cosmological simulations of massive galaxies, with stellar masses 1011–12 M ⊙ and SMBH masses 108.9–9.7 M ⊙ at z = 0, featuring various major and minor merger events. By tracing the gas history in these simulations, we categorize the gas accreted by the central SMBHs based on its origin. Gas that belonged to a different galaxy before accretion onto the BH is labeled as (i) “external,” while smoothly accreted cosmic gas is classified as (ii) “smooth. ” Gas produced within the primary halo through stellar evolution and subsequently accreted by the SMBH is classified as (iii) “recycled. ” Our analysis, which includes stellar feedback, reveals that the primary fuel source for SMBHs is the recycled gas from dying stars. This recycled gas from stars in the inner region of the galaxy readily collapses toward the center, triggering starbursts and simultaneously fueling the SMBH. Galaxy mergers also play a crucial role in fueling SMBHs in massive galaxies, as SMBHs in massive halos tend to accrete a higher fraction of external gas from mergers compared to smoothly accreted gas. However, on average, it takes approximately 1.85 Gyr for external gas to enter the main galaxy and accrete onto the SMBH. Considering the presence of various other gas triggers for active galactic nucleus (AGN) activity alongside this time delay, the association between AGNs and mergers may not always be obvious.
On the Uplink and Downlink EMF Exposure and Coverage in Dense Cellular Networks: A Stochastic Geometry Approach

Existing studies analyzing electromagnetic field (EMF) exposure in wireless networks have primarily considered downlink (DL) communications. In the uplink (UL), the EMF exposure caused by the user's smartphone is usually the only considered source of radiation, thereby ignoring contributions caused by other active neighboring devices. In addition, the network coverage and EMF exposure are typically analyzed independently for both the UL and DL, while a joint analysis would be necessary to fully understand the network performance. This paper aims at bridging the resulting gaps by presenting a comprehensive stochastic geometry framework including the above aspects. The proposed topology features base stations (BS) modeled via a homogeneous Poisson point process as well as a user process of type II (with users uniformly distributed in the Voronoi cell of each BS). In addition to the UL to DL exposure ratio, we derive joint probability metrics considering the UL and DL coverage and EMF exposure. These metrics are evaluated in two scenarios considering BS and/or user densifications. Our numerical results highlight the existence of optimal node densities maximizing these joint probabilities.
The connectivity degree controls the difficulty of RBN reservoir design

Reservoir Computing (RC) is a paradigm in artificial intelligence where a recurrent neural network (RNN) is used to process temporal data, leveraging the inherent dynamical properties of the reservoir to perform complex computations. In the realm of RC, the excitatory-inhibitory balance b has been shown to be pivotal for driving the dynamics and performance of Echo State Networks (ESN) and, more recently, Random Boolean Network (RBN). However, the relationship between b and other parameters of the network is still poorly understood. This article explores how the interplay of the balance b, the connectivity degree K (i.e., the number of synapses per neuron) and the size of the network (i.e., the number of neurons N) influences the dynamics and performance (memory and prediction) of an RBN reservoir. Our findings reveal that K and b are strongly tied in optimal reservoirs. Reservoirs with high K have two optimal balances, one for globally inhibitory networks (b<0), and the other one for excitatory networks (b>0). Both show asymmetric performances about a zero balance. In contrast, for moderate K, the optimal value being K=4, best reservoirs are obtained when excitation and inhibition almost, but not exactly, balance each other. For almost all K, the influence of the size is such that increasing N leads to better performance, even with large values of N. Our investigation provides clear directions to generate optimal reservoirs or reservoirs with constraints on size or connectivity.
Multi-CMGAN+/+: Leveraging Multi-Objective Speech Quality Metric Prediction for Speech Enhancement

Neural network based approaches to speech enhancement have shown to be particularly powerful, being able to leverage a data-driven approach to result in a significant performance gain versus other approaches. Such approaches are reliant on artificially created labelled training data such that the neural model can be trained using intrusive loss functions which compare the output of the model with clean reference speech. Performance of such systems when enhancing real-world audio often suffers relative to their performance on simulated test data. In this work, a non-intrusive multi-metric prediction approach is introduced, wherein a model trained on artificial labelled data using inference of an adversarially trained metric prediction neural network. The proposed approach shows improved performance versus state-of-the-art systems on the recent CHiME-7 challenge \ac{UDASE} task evaluation sets.
Living Scenes: Multi-object Relocalization and Reconstruction in Changing 3D Environments

Research into dynamic 3D scene understanding has primarily focused on short-term change tracking from dense observations, while little attention has been paid to long-term changes with sparse observations. We address this gap with MoRE, a novel approach for multi-object relocalization and reconstruction in evolving environments. We view these environments as"living scenes"and consider the problem of transforming scans taken at different points in time into a 3D reconstruction of the object instances, whose accuracy and completeness increase over time. At the core of our method lies an SE(3)-equivariant representation in a single encoder-decoder network, trained on synthetic data. This representation enables us to seamlessly tackle instance matching, registration, and reconstruction. We also introduce a joint optimization algorithm that facilitates the accumulation of point clouds originating from the same instance across multiple scans taken at different points in time. We validate our method on synthetic and real-world data and demonstrate state-of-the-art performance in both end-to-end performance and individual subtasks.