Miracle-dz/UniFAR-FTUs huggingface dataset:
Interdependence of cellular and network properties in respiratory rhythmogenesis

How breathing is generated by the preBötzinger Complex (preBötC) remains divided between two ideological frameworks, and the persistent sodium current (INaP) lies at the heart of this debate. Although INaP is widely expressed, the pacemaker hypothesis considers it essential because it endows a small subset of neurons with intrinsic bursting or “pacemaker” activity. In contrast, burstlet theory considers INaP dispensable because rhythm emerges from “pre-inspiratory” spiking activity driven by feed-forward network interactions. Using computational modeling, we discover that changes in spike shape can dissociate INaP from intrinsic bursting. Consistent with many experimental benchmarks, conditional effects on spike shape during simulated changes in oxygenation, development, extracellular potassium, and temperature alter the prevalence of intrinsic bursting and pre-inspiratory spiking without altering the role of INaP. Our results support a unifying hypothesis where INaP and excitatory network interactions, but not intrinsic bursting or pre-inspiratory spiking, are critical interdependent features of preBötC rhythmogenesis. SIGNIFICANCE STATEMENT Breathing is a vital rhythmic process originating from the preBötzinger complex. Since its discovery in 1991, there has been a spirited debate about whether respiratory rhythm generation emerges as a network property or is driven by a subset of specialized neurons with rhythmic bursting capabilities, endowed by intrinsic currents. Here, using computational modeling, we propose a unifying data-driven model of respiratory rhythm generation which bridges the gap between these competing theories. In this model, both intrinsic cellular properties (a persistent sodium current) and network properties (recurrent excitation), but not intrinsic bursting, are essential and interdependent features of respiratory rhythm generation.
Resting state global brain activity induces bias in fMRI motion estimates

Head motion is a significant source of artifacts in resting-state fMRI (rsfMRI) studies and has been shown to affect resting-state functional connectivity (rsFC) measurements. In many rsfMRI studies, motion parameters estimated from volume registration are used to characterize head motion and to mitigate motion artifacts in rsfMRI data. While prior task-based fMRI studies have shown that task-evoked brain activations may induce temporally correlated bias in the motion estimates, resulting in artificial activations after registration, relatively little is known about neural-related bias in rsfMRI motion parameters. In this study, we demonstrate that neural-related bias exists in rsfMRI motion estimates and characterize the potential effects of the bias on rsFC estimates. Using a public multi-echo rsfMRI dataset, we use the differences between motion estimates from the first echo and second echo data as a measure of neural-induced bias. We show that the resting-state global activity of the brain, as characterized with the global signal (GS), induces bias in the motion estimates in the y- and z-translational axes. Furthermore, we demonstrate that the GS-related bias reflects superior-inferior and anterior-posterior asymmetries in the GS beta coefficient map. Finally, we demonstrate that regression with biased motion estimates can negatively bias rsFC estimates and also reduce rsFC differences between young and old subjects.
Supramolecular Interactions of Teixobactin Analogues in the Crystal State

Teixobactin is a potent peptide antibiotic against Gram-positive bacteria that binds to lipid II and related peptidoglycan precursors and disrupts the cell membrane. This paper presents the X-ray crystallographic structure of the N-methylated teixobactin analogue N-Me-D-Gln4,Lys10-teixobactin (1). N-Methylation at position 4 prevents uncontrolled aggregation and enables the crystallization of the teixobactin analogue. Lysine at position 10 replaces the non-proteinogenic amino acid allo-enduracididine, which is not commercially available. Crystallization from aqueous solution with MgCl2 and PEG3350 afforded crystals suitable for X-ray crystallography. The crystallographic phases were solved using SAD phasing on data sets collected at 2.0663 Å. Molecular replacement then enabled the determination of the structure at 1.50 Å resolution using a data set collected at 0. 9997Å (PDB 8U78). Eight peptide molecules comprise the asymmetric unit, with each peptide molecule binding a chloride anion through hydrogen bonding with the amide NH group of residues 7, 8, 10, and 11. The peptide molecules form hydrogen-bonded antiparallel β-sheet dimers in the crystal lattice, with residues 1–3 comprising the dimerization interface. The dimers further assemble end-to-end in the crystal lattice. The β-sheet dimers are amphiphilic, with the side chains of the hydrophobic residues on one surface and the side chains of the hydrophilic residues on the other surface. The dimers pack in the lattice through hydrophobic interactions between the hydrophobic surfaces. The crystal structure of teixobactin analogue 1 recapitulates several aspects of the interaction of teixobactin with the cell membrane of Grampositive bacteria, including anion binding, supramolecular assembly through β-sheet formation, and hydrophobic interactions.
Non-canonical ORFs are an important source of tumor-specific antigens in a liver cancer meta-cohort

The expression of tumor-specific antigens during cancer progression can trigger an immune response against the tumor. Antigens that have been used as cancer vaccines are those originated by non- synonymous mutations and those derived from cancer/testis antigens. However, the first class is predominantly patient-specific, preventing the development of therapies than can benefit multiple patients, and the second one offers a limited set of actionable targets. A possible alternative is the use of peptides derived from non-canonical ORFs (ncORFs). While many ncORFs have been shown to be translated in cancer cells, their tumor-specificity and patient distribution remains to be determined. Here we analyze RNA sequencing data 117 hepatocellular carcinoma (HCC) tumors and matched healthy tissue, together with ribosome profiling data from an additional 10 HCC tumors, to answer these open questions. Combining HLA-epitope binding predictions and experimental validation experiments we conclude that around 40% of the tumor-specific antigens in HCC are likely to be derived from ncORFs in lncRNAs, including two peptides that can trigger an immune response in mice. We identify a subset of 33 tumor-specific lncRNAs expressing novel cancer antigens shared by more than 10% of the HCC analyzed, which could be combined to target a large proportion of the patients. The results of the study open new avenues for extending the range of anti-cancer vaccines.
Genomic hypomethylation in cell-free DNA predicts responses to checkpoint blockade in lung and breast cancer

Genomic hypomethylation has recently been identified as a determinant of therapeutic responses to immune checkpoint blockade (ICB). However, it remains unclear whether this approach can be applied to cell-free DNA (cfDNA) and whether it can address the issue of low tumor purity encountered in tissue-based methylation profiling. In this study, we developed an assay named iMethyl, designed to estimate the genomic hypomethylation status from cfDNA. This was achieved through deep targeted sequencing of young LINE-1 elements with > 400,000 reads per sample. iMethyl was applied to a total of 653 ICB samples encompassing lung cancer (cfDNA n = 167; tissue n = 137; cfDNA early during treatment n = 40), breast cancer (cfDNA n = 91; tissue n = 50; PBMC n = 50; cfDNA at progression n = 44), and ovarian cancer (tissue n = 74). iMethyl-liquid predicted ICB responses accurately regardless of the tumor purity of tissue samples. iMethyl-liquid was also able to monitor therapeutic responses early during treatment (3 or 6 weeks after initiation of ICB) and detect progressive hypomethylation accompanying tumor progression. iMethyl-tissue had better predictive power than tumor mutation burden and PD-L1 expression. In conclusion, our iMethyl-liquid method allows for reliable noninvasive prediction, early evaluation, and monitoring of clinical responses to ICB therapy.
Nuclear deformation and dynamics of migrating cells in 3D confinement reveal adaptation of pulling and pushing forces

Eukaryotic cells show an astounding ability to migrate through pores and constrictions smaller than their nuclear diameter. However, the forces engaged in nuclear deformation and their effect on confined cell dynamics remain unclear. Here, we study the mechanics and dynamics of nuclei of mesenchymal cancer cells as they spontaneously and repeatedly transition through 3D compliant hydrogel channels. We find a biphasic dependence of migration speed and transition frequency on channel width, revealing maximal transition rates at widths comparable to the nuclear diameter. Using confocal imaging and hydrogel bead displacement, we determine the nuclear deformation and corresponding forces during spontaneous confined migration. We find the nucleus to reversibly deform with an elastic modulus not adapting to the confinement. Instead, with decreasing channel width, the nuclear shape during transmigration changes biphasically concomitant with the transitioning dynamics. The nucleus exhibits a prolate form along the migration direction in wide channels and a more compressed oblate shape in narrow channels. We propose a physical model for confined cell migration that explains the observed nuclear shapes and slowing down in terms of the cytoskeletal force-generation adapting from a pulling-to a pushing-dominated mechanism with increasing nuclear confinement.
A general prediction model for substrates of transport proteins

Transport proteins play a crucial role in cellular metabolism and are central to many aspects of molecular biology and medicine. Determining the function of transport proteins experimentally is challenging, as they become unstable when isolated from cell membranes. Machine learning-based predictions could provide an efficient alternative. However, existing methods are limited to predicting a small number of specific substrates or broad transporter classes. These limitations stem partly from using small datasets for model training and a choice of input features that lack sufficient information about the prediction problem. Here, we present SPOT, the first general machine learning model that can successfully predict specific substrates for arbitrary transport proteins, achieving an accuracy above 92% on independent and diverse test data covering widely different transporters and a broad range of metabolites. SPOT uses Transformer Networks to represent transporters and substrates numerically. To overcome the problem of missing negative data for training, it augments a large dataset of known transporter-substrate pairs with carefully sampled random molecules as non-substrates. SPOT not only predicts specific transporter-substrate pairs, but also outperforms previously published models designed to predict broad substrate classes for individual transport proteins. We provide a web server and Python function that allows users to explore the substrate scope of arbitrary transporters.
Enabling population protein dynamics through Bayesian modeling

The knowledge of protein dynamics or turnover in patients provides invaluable information related to certain diseases, drug efficacy, or biological processes. A great corpus of experimental and computational methods has been developed, including by us, in the case of human patients followed in vivo. Moving one step further, we propose here a new modeling approach to capture the highly relevant notion of population protein dynamics. Using two data sets, we show that models inspired by population pharmacokinetics can accurately capture protein turnover within a cohort of individuals, even in presence of substantial inter-individual variability. Such models pave the way for comparative studies searching for altered dynamics or biomarkers in diseases.
Conspicuous shell shape plasticity across lake-stream habitats in a freshwater mussel (Pyganodon grandis)

Hydrodynamic forces and their absence appears to exert differential selection pressure between lake and stream populations, creating a tight fit between organismal phenotypes and their environments. Ecophenotypic variants may be the result from isolated genetic evolution or phenotypic plasticity, where a widespread genotype can produce multiple phenotypes dependent on the environment. Freshwater mussels possess a wide degree of morphological variation that frequently covaries with the environment, making them a good system to understand the mechanisms of ecophenotypic variation across hydrological conditions. We designed a two-year experiment where individuals from the same Pyganodon grandis maternal brood (half-full siblings) were reared at a controlled site and four natural sites involving one lake and three streams. At the end of the experiment, shell shape was quantified for recaptured (N=70), wild (N=206), and zoo-reared (N=305) mussels. Analysis of covariance found significant differences in shell shape between rearing sites, particularly between stream and lake habitats, but no shape differences were detected across the three stream sites. At two of the four sites, shell shape of recaptured individuals matched the morphology of wild populations. Genomic sequencing and parentage analysis identified 27 different fathers among recaptured individuals. Yet, no genetic differences were present between stream and lake habitats and there was no effect of parentage on shell shape. Taken together, phenotypic plasticity, over genetic differentiation, is identified as the primary mechanism of shell shape ecophenotypy. Plasticity may be a key adaptation for freshwater mussels and possibly provide a buffer against their imperilment in degraded habitats. 1. Summary statement Streamlined and obese freshwater mussel shell shapes in stream-lake environments are developed from the same maternal brood and may reflect adaptations to the presence and absence of streamflow.
Driver lines for studying associative learning in Drosophila 1

13 14 The mushroom body (MB) is the center for associative learning in insects. In Drosophila, 15 intersectional split-GAL4 drivers and electron microscopy (EM) connectomes have laid the 16 foundation for precise interrogation of the MB neural circuits. However, many cell types 17 upstream and downstream of the MB remained to be investigated due to lack of driver lines. 18 Here we describe a new collection of over 800 split-GAL4 and split-LexA drivers that cover 19 approximately 300 cell types, including sugar sensory neurons, putative nociceptive ascending 20 neurons, olfactory and thermo-/hygro-sensory projection neurons, interneurons connected with 21 the MB-extrinsic neurons, and various other cell types. We characterized activation phenotypes 22 for a subset of these lines and identified the sugar sensory neuron line most suitable for reward 23 substitution. Leveraging the thousands of confocal microscopy images associated with the 24 collection, we analyzed neuronal morphological stereotypy and discovered that one set of 25 mushroom body output neurons, MBON08/MBON09, exhibits striking individuality and 26 asymmetry across animals. In conjunction with the EM connectome maps, the driver lines 27 reported here offer a powerful resource for functional dissection of neural circuits for associative 28 learning in adult Drosophila. 29
Lossless Speedup of Autoregressive Translation with Generalized Aggressive Decoding

In this paper, we propose Generalized Aggressive Decoding (GAD) – a novel decoding paradigm for speeding up autoregressive translation without quality loss, through the collaboration of autoregressive and non-autoregressive translation (NAT) of the Transformer. At each decoding iteration, GAD aggressively decodes a number of tokens in parallel as a draft with NAT and then veriﬁes them in the autoregressive manner, where only the tokens that pass the veriﬁcation are kept as decoded tokens. GAD can achieve the same performance as autoregressive translation but much more efﬁciently because both NAT drafting and autoregressive veriﬁcation are fast due to parallel computing. We conduct experiments in the WMT14 English-German translation task and conﬁrm that the vanilla GAD yields exactly the same results as greedy decoding with an around 3 × speedup, and that its variant (GAD++) with an advanced veriﬁcation strategy not only outperforms the greedy translation and even achieves the comparable translation quality with the beam search result, but also further improves the decoding speed, resulting in an around 5 × speedup over autoregressive translation. Our models and codes are available at https://github.com/hemingkx/ Generalized-Aggressive-Decoding .
Anatomy of regional phases and source characterization of the Soviet Joint Verification Experiment, underground nuclear explosion

Broadband data from the NRDC deployment of stations in Kazakhstan at Karkaralinsk (KKL) and Bayanual (BAY) are subjected to detailed waveform modeling to understand the nature of regional wave propagation and to determine the nature of the explosion time function and sources of low- and high-frequency “tectonic release. ” Minor modification of a crustal model inferred from DSS studies in the region is very effective in explaining the arrival times, amplitudes, and waveshapes of the Pn and PmP arrivals at both stations. The SH -wave data are remarkable, however, for showing a large, high-frequency arrival after the expected and observed arrival of SmS from the explosion source. These secondary S arrivals imply a secondary source 1 to a few kilometers to the south of the shot point. Shear waves from this source dominate the seismic data at high frequency but are minor at low frequency where shear waves and surface waves from the inferred relaxation of the explosion cavity dominate the wave field. The secondary S -wave source is inferred to represent explosion-driven faulting with no net moment. Significant overshoot in the RDP is required to match the P -wave data. It is found that von Seggern-Blandford and Haskell source functions are incapable of producing the observed overshoot. However, the Mueller-Murphy source model is very effective in matching the clear overshoot effects seen in the P waves. Spall effects are also considered but appear to be minor and serve to require even more overshoot in the RDP. The excitation and dispersion of Rayleigh waves in the period band of 1 to 10 sec is seen to be a sensitive, but nonunique, function of both VS and VP in the upper crustal layer. High Poisson ratios in this layer are needed to explain the Rayleigh waveform.
Hydrothermal Alteration of Oman Ophiolite Extrusives in Ghuzayn Area

Abstract. The hydrothermal alteration in Ghuzayn Volcanics was associated with mineralization and accumulation of three massive sulfide deposits. The Ghuzayn Volcanics were discriminated into basaltic and andesitic lavas. The crossplots of Zr versus Nb, Y, Hf, La and Lu show that they fall in the same linear fractionation trends with more evolved affinities in the andesitic lavas compared to the less evolved affinities in the basaltic lavas. The immobile trace element contents of the Ghuzayn Volcanics show that they are of oceanic to continental tholeiitic affinity which fall in the fields of back‐arc basin basalt, N‐MORB and island arc tholeiite all meet and match with the field of the Lau‐Tonga back‐arc basin lavas.
A low-profile dual-polarization programmable dual-beam scanning antenna array

In this paper, a dual-polarization dual-beam scanning array antenna based on holographic control theory is proposed. The antenna element integrates four PIN diodes, two in each of the horizonal and vertical directions, to achieve reconfigurable polarization. By varying the states of two PIN diodes in the same direction to change the radiation phase of the element, the simulation and measurement of the 2-element subarray verify that the proposed element is capable of manipulating the radiation pattern. Based on digital coding theory, the phase-adjustable structure integrated with the PIN diodes can act as a holographic element, and the desired object wave can be accurately and stably obtained by modulating the reference wave excited by the feed and the holographic element. The simulation verifies that the antenna array can achieve satisfactory 2D beam scanning. To verify the simulation results, a 2-element subarray prototype and a 6×12 array prototype are fabricated and subjected to measurement. The results of measurement and simulation are in good agreement, which proves the feasibility of the dual-polarization dual-beam scanning antenna system. Due to its low profile, low cost, and easy integration, this antenna system excels in applications in fields such as radar systems and smart antennas. 提出一种基于全息控制理论的低剖面双极化双波束扫描天线阵列. 巧妙设计辐射单元, 以通过控制集成在每个单元上的PIN二极管的状态实现可重构极化和辐射相位调制. 将一个72通道的串并联等幅同相馈电网络与辐射阵列集成, 实现阵列的低剖面特性. 通过设计直流偏置电路, 利用单片机对天线阵列数字编码, 实现波束的二维动态精确偏转. 加工制作了一个2单元子阵列和一个6×12阵列, 并对该天线系统的数字可控辐射方向图特性进行实验验证. 该天线系统可以在11 GHz实现−30°至30°的波束扫描, 步进扫描角为5°. 该天线系统具有体积小、成本低、易于集成、波束控制准确等特点, 在雷达系统、智能天线等领域具有广阔的应用前景.
Improved classification of Landsat Thematic Mapper data using modified prior probabilities in large and complex landscapes

The use of modified prior probabilities to exploit ancillary data and increase classification accuracy has been proposed before. However, this method has not been widely applied because it has heavy computing requirements and because obtaining prior probability estimates has presented practical problems. This article presents a procedure that generates large sets of prior probability estimates from class frequencies modelled with ancillary data and a Mahalanobis Distance selection of previously classified pixels. The method produces a pixel sample size that is large enough to estimate class frequencies in numerous strata, which is particularly desirable for the study of large and complex landscapes. A case study is presented in which the procedure made it possible to estimate 537 sets of prior probabilities for an entire Landsat Thematic Mapper (TM) scene of central Costa Rica. After modifying the class prior probabilities, the overall classification consistency of the training sites improved from 74.6% to 91.9%, while the overall classification accuracy of sites controlled in the field by independent studies improved from 68.7% to 89.0%. The classification accuracy was most improved for spectrally similar classes. The method improves classification accuracy in large and complex landscapes with spectrally mixed land-cover categories.