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Code and data for training CNN-Encoder-Decoder model described in the publication 'Noise reduction in X-ray photon correlation spectroscopy with convolutional neural networks encoder-decoder models.'
Machine learning approaches to mass spectrometry (MS) data analysis require structured metadata for optimal performance. However, current MS file formats necessitate external metadata sources, creating integration challenges that impede analytical workflows. Here, we present a novel approach for encoding metadata directly within mzML files using one-hot encoding of ASCII characters mapped via Hilbert space-filling curves. This strategy embeds metadata in the first spectrum's m/z-intensity space, ensuring persistence with the primary data, eliminating the need for external metadata files, and maintaining compatibility with existing MS software. We demonstrate that the Hilbert curve mapping efficiently utilizes the two-dimensional spectral space while maintaining robust data recovery. This method offers a practical solution for machine learning applications in mass spectrometry by ensuring metadata and spectral data remain unified through all stages of analysis.
Microbial fermentation provides a sustainable method of producing valuable chemicals. Adding dynamic control to fermentations can significantly improve titers, but most systems rely on transcriptional controls of metabolic enzymes, leaving existing intracellular enzymes unregulated. This limits the ability of transcriptional controls to switch off metabolic pathways, especially when metabolic enzymes have long half-lives. We developed a two-layer transcriptional/post-translational control system for yeast fermentations. Specifically, the system uses blue light to transcriptionally activate the major pyruvate decarboxylase PDC1 , required for cell growth and concomitant ethanol production. Switching to darkness transcriptionally inactivates PDC1 and instead activates the anti-Pdc1p nanobody, NbJRI, to act as a genetically encoded inhibitor of Pdc1p accumulated during the growth phase. This dual transcriptional/post-translational control improves the production of 2,3-BDO and citramalate by up to 100 and 92% compared to using transcriptional controls alone in dynamic two-phase fermentations. This study establishes the NbJRI nanobody as an effective genetically encoded inhibitor of Pdc1p that can enhance the production of pyruvate-derived chemicals.
Potassium ion (K + ) plays a critical role as an essential electrolyte in all biological systems. Genetically-encoded fluorescent K + biosensors are promising tools to further improve our understanding of K + -dependent processes under normal and pathological conditions. Here, we report the crystal structure of a previously reported genetically-encoded fluorescent K + biosensor, GINKO1, in the K + -bound state. Using structure-guided optimization and directed evolution, we have engineered an improved K + biosensor, designated GINKO2, with higher sensitivity and specificity. We have demonstrated the utility of GINKO2 for in vivo detection and imaging of K + dynamics in multiple model organisms, including bacteria, plants, and mice.
This package contains the data and scripts used in "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning" (Jiang et al., 2022). The data.zip file contains the flux tower and automated chamber observations used for developing the deep learning model for modeling soil respiration. The scripts.zip file contains the Jupyter notebooks and python scripts for preprocessing the data, training the deep learning models, and postprocessing the results. The src.zip contains the source code for training the deep learning model, performing mutual information analysis, and plotting functions. The trained_models.zip contains multiple folders used for hosting the trained deep-learning models and the associated soil respiration predictions. The whole process is performed using python. We include the REAMD.md to document the python package requirements.Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of both Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models.
This two-year theory project focused on theoretical investigations of novel paradigms for quantum sensing, building on information encoding and techniques from quantum error correction, quantum computing and other quantum information domains. The outcomes facilitate quantum information technology development, especially at the interface of quantum computing and quantum sensing. The results of the project show new use cases and new paradigms for quantum sensing beyond what has so far been considered. One outcome shows how quantum sensing opens new opportunities for fundamental physics such as the capability of single graviton detection. Another outcome reveals a new application of NISQ quantum computers with error correction for metrology, building on recent advances in practical quantum error correction implementation. The third outcome of the project creates new paradigms of back-action-evading sensing inspired by collective quantum information encoding, which achieves quantum sensing beyond the quantum limit without the use of entanglement.
Block Encoding (BE) is a crucial subroutine in many modern quantum algorithms, including those with near-optimal scaling for simulating quantum many-body systems, which often rely on Quantum Signal Processing (QSP). Currently, the primary methods for constructing BEs are the Linear Combination of Unitaries (LCU) and the sparse oracle approach. In this work, we demonstrate that QSP-based techniques, such as Quantum Singular Value Transformation (QSVT) and Quantum Eigenvalue Transformation for Unitary Matrices (QETU), can themselves be efficiently utilized for BE implementation. Specifically, we present several examples of using QSVT and QETU algorithms, along with their combinations, to block encode Hamiltonians for lattice bosons, an essential ingredient in simulations of high-energy physics. We also introduce a straightforward approach to BE based on the exact implementation of Linear Operators Via Exponentiation and LCU (LOVE-LCU). We find that, while using QSVT for BE results in the best asymptotic gate count scaling with the number of qubits per site, LOVE-LCU outperforms all other methods for operators acting on up to qubits, highlighting the importance of concrete circuit constructions over mere comparisons of asymptotic scalings. Using LOVE-LCU to implement the BE, we simulate the time evolution of single-site and two-site systems in the lattice theory using the Generalized QSP algorithm and compare the gate counts to those required for Trotter simulation.
Viruses modulate the function(s) of environmentally relevant microbial populations, yet considerations of the metabolic capabilities of individual virus particles themselves are rare. We used shotgun proteomics to quantitatively identify 43 virus-encoded proteins packaged within purified Aureococcus anophagefferens Virus (AaV) particles, normalizing data to the per-virion level using a 9.5-Å-resolution molecular reconstruction of the 1900-Å (AaV) particle that we generated with cryogenic electron microscopy. This packaged proteome was used to determine similarities and differences between members of different giant virus families. We noted that proteins involved in sugar degradation and binding (e.g., carbohydrate lyases) were unique to AaV among characterized giant viruses. To determine the extent to which this virally encoded metabolic capability was ecologically relevant, we examined the TARA Oceans dataset and identified genes and transcripts of viral origin. Our analyses demonstrated that putative giant virus carbohydrate lyases represented up to 17% of the marine pool for this function. In total, our observations suggest that the AaV particle has potential prepackaged metabolic capabilities and that these may be found in other giant viruses that are widespread and abundant in global oceans.
The orange carotenoid protein (OCP) family of proteins are light-activated proteins that function in dissipating excess energy absorbed by accessory light-harvesting complexes, i.e., phycobilisomes (PBSs), in cyanobacteria. Some cyanobacteria contain multiple homologs of the OCP-encoding gene ( ocp ). Fremyella diplosiphon , a cyanobacterium studied for light-dependent regulation of PBSs during complementary chromatic acclimation (CCA), contains several OCP homologs – two full-length OCPs, three Helical Carotenoid Proteins (HCPs) with homology to the N-terminus of OCP, and one C-terminal domain-like carotenoid protein (CCP) with homology to the C-terminus of OCP. We examined whether these homologs are distinctly regulated in response to different environmental factors, which could indicate distinct functions. We observed distinct patterns of expression for some OCP, HCP, and CCP encoding genes, and have evidence that light-dependent aspects of ocp homolog expression are regulated by photoreceptor RcaE which controls CCA. RcaE-dependent transcriptional regulator RcaC is also involved in the photoregulation of some hcp genes. Apart from light, additional environmental factors associated with cellular redox regulation impact the mRNA levels of ocp homologs, including salt, cold, and disruption of electron transport. Analyses of conserved sequences in the promoters of ocp homologs were conducted to gain additional insight into regulation of these genes. Several conserved regulatory elements were found across multiple ocp homolog promoters that potentially control differential transcriptional regulation in response to a range of environmental cues. The impact of distinct environmental cues on differential accumulation of ocp homolog transcripts indicates potential functional diversification of this gene family in cyanobacteria. These genes likely enable dynamic cellular protection in response to diverse environmental stress conditions in F . diplosiphon .
Accurate self-motion estimation is critical for various navigational tasks in mobile robotics. Optic flow provides a means to estimate self-motion using a camera sensor and is particularly valuable in GPS- and radio-denied environments. The present study investigates the influence of different activation functions—ReLU, leaky ReLU, GELU, and Mish—on the accuracy, robustness, and encoding properties of convolutional neural networks (CNNs) and multi-layer perceptrons (MLPs) trained to estimate self-motion from optic flow. Our results demonstrate that networks with ReLU and leaky ReLU activation functions not only achieved superior accuracy in self-motion estimation from novel optic flow patterns but also exhibited greater robustness under challenging conditions. The advantages offered by ReLU and leaky ReLU may stem from their ability to induce sparser representations than GELU and Mish do. Our work characterizes the encoding of optic flow in neural networks and highlights how the sparseness induced by ReLU may enhance robust and accurate self-motion estimation from optic flow.
African swine fever virus (ASFV) causes a devastating disease of swine that has caused outbreaks in Central Europe since 2007, spreading into Asia in 2018. ASFV is a large, structurally complex virus with a large dsDNA genome encoding for more than 160 genes, most of them still uncharacterized. p22, encoded by the ASFV gene KP177R, is an early transcribed, structural virus protein located in the ASFV particle. Although its exact function is unknown, p22 has recently been identified as an interacting partner of several host proteins. Here, we describe the development of a recombinant ASFV (ASFV-G-ΔKP177R) lacking the KP177R gene as a tool to evaluate the role of p22 in virus replication and virulence in swine. The recombinant ASFV-G-ΔKP177R demonstrated that the KP177R gene is non-essential for ASFV replication in primary swine macrophages, with virus yields similar to those of the parental, highly virulent field isolate Georgia2010 (ASFV-G). In addition, experimental infection of domestic pigs with ASFV-G-ΔKP177R produced a clinical disease similar to that caused by the parental ASFV-G. Therefore, and surprisingly, p22 does not seem to be involved in virus replication or virulence in swine.
Source encoding in presence of random disturbance related to optimum code for encoder operating on random variable
Encoding mechanism integral to the motor and using the permanent magnets embedded in the rotor eliminates the need for external devices to encode information relating the position and velocity of the rotating member.
The Langley Research Center uses radio-controlled, scaled aircraft models to study the spin-entry characteristics of aircraft. Recent spin-entry studies required the use of an electronic proportional-control system for manipulating model control surfaces. In order to meet control system requirements, a special-purpose analog computer was designed to simulate the coupling between roll and pitch controls. A digital encoder was designed to encode the voltage analogs of control-surface position into a special pulse format for transmission to the model. This paper describes the two special developments and their relationship to the functions of the overall control system.
In this paper we discuss the correlation properties of the output of a binary convolutional encoder. We consider both random statistically independent inputs and first-order Markov inputs. It is shown that for random inputs, the ensemble average of the product of the outputs of a convolutional encoder taken two at a time is always either 0 or 1, and that for almost all good codes the ensemble average is always 0.
Realization of a bit-serial multiplication algorithm for the encoding of Reed-Solomon (RS) codes on a single VLSI chip using NMOS technology is demonstrated to be feasible. A dual basis (255, 223) over a Galois field is used. The conventional RS encoder for long codes ofter requires look-up tables to perform the multiplication of two field elements. Berlekamp's algorithm requires only shifting and exclusive-OR operations.
Circuit compensates for timing, amplitude and symmetry perturbations. Data asymmetry corrector and convolutional encoder regenerate data and clock signals in spite of signal variations such as data or clock asymmetry, phase errors, and amplitude variations, then encode data for transmission.
Concatenated coding was adopted for interplanetary space missions. Concatenated coding was employed with a convolutional inner code and a Reed-Solomon (RS) outer code for spacecraft telemetry. Conventional RS encoders are compared with those that incorporate two architectural features which approximately halve the number of multiplications of a set of fixed arguments by any RS codeword symbol. The fixed arguments and the RS symbols are taken from a nonbinary finite field. Each set of multiplications is bit-serially performed and completed during one (bit-serial) symbol shift. All firmware employed by conventional RS encoders is eliminated.