Density Information Research Articles

Predicting the translation efficiency of messenger RNA in mammalian cells.

The degree to which translational control is specified by mRNA sequence is poorly understood in mammalian cells. Here, we constructed and leveraged a compendium of 3,819 ribosomal profiling datasets, distilling them into a transcriptome-wide atlas of translation efficiency (TE) measurements encompassing >140 human and mouse cell types. We subsequently developed RiboNN, a multitask deep convolutional neural network, and classic machine learning models to predict TEs in hundreds of cell types from sequence-encoded mRNA features, achieving state-of-the-art performance (r=0.79 in human and r=0.78 in mouse for mean TE across cell types). While the majority of earlier models solely considered 5' UTR sequence, RiboNN integrates contributions from the full-length mRNA sequence, learning that the 5' UTR, CDS, and 3' UTR respectively possess ~67%, 31%, and 2% per-nucleotide information density in the specification of mammalian TEs. Interpretation of RiboNN revealed that the spatial positioning of low-level di- and tri-nucleotide features (i.e., including codons) largely explain model performance, capturing mechanistic principles such as how ribosomal processivity and tRNA abundance control translational output. RiboNN is predictive of the translational behavior of base-modified therapeutic RNA, and can explain evolutionary selection pressures in human 5' UTRs. Finally, it detects a common language governing mRNA regulatory control and highlights the interconnectedness of mRNA translation, stability, and localization in mammalian organisms.

DNA-PAINT Probe Modifications Support High-Resolution Imaging with Shorter Binding Domains.

DNA-based Points Accumulation for Imaging in Nanoscale Topography (DNA-PAINT) is an effective super resolution microscopy technique, and its optimization is key to improve nanoscale detection. The state-of-the-art improvements that are at the base of this optimization have been first routinely validated on DNA nanostructure devices before being tested on biological samples. This allows researchers to finely tune DNA-PAINT imaging features in a more controllable in vitro environment. Dye-labeled oligonucleotide probes with short hybridization domains can expand DNA-PAINT's detection by targeting short nucleotide sequences and improving resolution, speed, and multiplexing. However, developing these probes is challenging as their brief bound state makes them difficult to capture under routine imaging conditions. To extend dwell binding times and promote duplex stability, we introduced structural and chemical modifications to our imager probes. The modifications included mini-hairpins and/or Bridged Nucleic Acids (BNA); both of which increase the thermomechanical stability of a DNA duplex. Using this approach we demonstrate DNA-PAINT imaging with approximately 5 nm resolution using a 4-nucleotide hybridization domain that is 43% shorter than previously reported probes. Imager probes with such short hybridization domains are key for improving detection on DNA nanostructure devices because they have the capability to target a larger number of binding domains per localization unit. This is essential for metrology applications such as Nucleic Acid Memory (NAM) where the information density is dependent on the binding site length. The selected imager probes reported here present imaging resolution equivalent to current state-of-the-art DNA-PAINT probes, creating a strategy to image shorter DNA domains for nanoscience and nanotechnology alike.

Optics‐Enabled Highly Scalable Inverter for Multi‐Valued Logic

AbstractThe rapid advancements in machine learning have exacerbated the interconnect bottleneck inherent in binary logic‐based computing architectures. An interesting approach to tackle this problem involves increasing the information density per interconnect, i.e., by switching from a two‐valued to a multi‐valued logic (MVL) architecture. However, current MVL implementations offer limited overall performance and face challenges in scaling to process data signals with radix (number of logic levels) even just above 3. In this work, a novel concept is introduced for implementation of a highly scalable and fully passive inverter based on the frequency‐domain phase‐only linear manipulation of the input MVL data signal, which is encoded in the amplitude variations of an electromagnetic wave along the time axis. As a key advantage, this solution is entirely independent of the input radix. The proposed design is implemented using an optical fibre Bragg grating device. Inversion of quaternary signals is experimentally demonstrated, as well as binary and ternary signals, at a remarkable operation speed of 32 GBaud, with an estimated energy consumption of 24 fJ/bit. The proposed method is universal and can be applied to any system that supports transmission and detection of coherent waves, such as microwave, plasmonic, mechanical, or quantum.

Temporary atmospheres produced by human activities on the Moon

Outgassing from materials, whether through the ascent/descent stages of lunar vehicles, airlock depressurizing, rover or astronaut suit outgassing, may cause an effect of unwanted accumulation of volatiles at the surface and exosphere. This is especially important at (or proximal) to permanently shadowed regions (PSRs) at the lunar poles. Herein, we provide estimates of expected outgassing from various human-landed objects on the Moon, including backpacks, airlocks, rovers, landers, trash and mining operations. Astronaut suits produce some level of oxygen outgassing (Helou et al., 2022), which may transport and condense in these PSRs, even in micro- cold traps (Glavin et al., 2010).We estimate the outgassing from drill mining and trash-to-gas conversion assuming a specific technology is operating. These outgassing systems can create local, temporary atmospheres in the vicinity (∼100 km radius) of the sources. The atmosphere may be particularly high within meters of the source. To obtain column densities for these temporary atmospheres, we first bracket ranges for the gas number loss as a function of time. We then derive the maximum distance traveled and the time the released molecules remain in the exosphere for a single ballistic hop, assuming the molecules are ejected from the surface of the object at its surface temperature. In some cases, such as the astronaut backpack and the rover, the temperature is that of the source. Given this information, an average and peak local exospheric density and column density can be estimated. We find that backpacks, airlock releases, and the Starship lander can create relatively high-density local atmospheres, with local near-lander outgassing water densities exceeding 107/cm3. This local water exosphere is over 106 times greater than the LADEE-derived lower limit of the natural water exosphere at ∼3/cm3. Thus, the anthropogenic temporary water exosphere will likely dominate the environment near the lander, making an assessment of the natural exospheric water environment difficult.

Investigation of a hyperspectral Scanning Laser Optical Tomography setup for label-free cell identification

The development of non-destructive, tomographic imaging systems is a current topic of research in biomedical technologies. One of these technologies is Scanning Laser Optical Tomography (SLOT), which features a highly modular setup with various contrast mechanisms. Extending this technology with new acquisition mechanisms allows us to investigate untreated and non-stained biological samples, leaving their natural biological physiology intact. To enhance the development of SLOT, we aimed to extend the density of information with a significant increase of acquisition channels. This should allow us to investigate samples with unknown emission spectra and even allow for label-fee cell identification. We developed and integrated a hyperspectral module into an existing SLOT system. The adaptations allow for the acquisition of three-dimensional datasets containing a highly increased information density. For validation, artificial test objects were made from fluorescent acrylic and acquired with the new hyperspectral setup. In addition, measurements were made on two different human cell spheroids with an unknown spectra, to test the possibilities of label-free cell identification. The validation measurements of the artificial test target show the expected results. Furthermore, the measurements of the biological cell spheroids show small variations in their tomographic spectrum that allow for label-free cell type differentiation. The results of the biological sample demonstrate the potential of label-free cell identification of the newly developed setup.

Proximal humeral bone density assessment and prediction analysis using machine learning techniques: An innovative approach in medical research

BackgroundPatients with fractures of the proximal humerus often local complications and failures attributed to osteoporosis. Currently, there is a lack of straight forward screening methods for assessing the extent of local osteoporosis in the proximal humerus. This study utilizes machine learning techniques to establish a diagnostic approach for evaluating local osteoporosis by analyzing the patient's demographic data, bone density, and X-ray ratio of the proximal humerus. MethodsA cohort comprising a total of 102 hospitalized patients admitted during the period spanning from 2021 to 2023 underwent random selection procedures. Resulting in exclusion of 5 patients while enrolling 97 patients for analysis encompassing patient demographics, shoulder joint anteroposterior radiographs, and bone density information. Using the modified Tingart index methodology involving multiple measurements denoted as M1 through M4 obtained from humeral shafts. Within this cohort comprised 76 females (78.4 %) and 21 males (21.6 %), with an average age of 73.0 years (range: 43–98 years). There were 25 cases with normal bone density, 35 with osteopenia, and 37 with osteoporosis. Machine learning techniques were used to randomly divide the 97 cases into training (n = 59) and validation (n = 38) sets with a ratio of 6:4 using stratified random sampling. A decision tree model was built in the training set, and significant diagnostic indicators were selected, with the performance of the decision tree evaluated using the validation set. Multinomial logistic regression methods were used to verify the strength of the relationship between the selected indicators and osteoporosis. ResultsThe decision tree identified significant diagnostic indicators as the humeral shaft medullary cavity ratio M2/M4, age, and gender. M2/M4 ≥ 1.13 can be used as an important screening criterion; M2/M4 < 1.13 was predicted as local osteoporosis; M2/M4 ≥ 1.13 and age ≥83 years were also predicted as osteoporosis. M2/M4 ≥ 1.13 and age <64 years or males aged between 64 and 83 years were predicted as the normal population; M2/M4 ≥ 1.13 and females aged between 64 and 83 years were predicted as having osteopenia. The decision tree's accuracy in the training set was 0.7627 (95 % CI (0.6341, 0.8638)), and its accuracy in the test set was 0.7895 (95 % CI (0.6268, 0.9045)). Multinomial logistic regression results showed that humeral shaft medullary cavity ratios M2/M4, age, and gender in X-ray images were significantly associated with the occurrence of osteoporosis. ConclusionUtilizing X-ray data of the proximal humerus in conjunction with demographic information such as gender and age enable the prediction of localized osteoporosis, facilitating physicians' rapid comprehension of osteoporosis in patients and optimization of clinical treatment plans. Level of evidenceLevel IV retrospective case study.

Generating a Multi-Material Lattice Structure through a Modified Relative Density Mapping Algorithm

Multi-material lattice structures demonstrate superior mechanical advantages and lightweight potential, enabling it preferable in many engineering fields, especially with the rise of additive manufacturing. However, employing multi-scale topology optimization inherently involves computational complexities of designing the macromaterial distribution and microlattice geometric size simultaneously. In this paper, a modified relative density mapping (MRDM) method is proposed, which generates a multi-material lattice structure by projecting the continuum multi-material topology optimization results. The proposed method extends the standard relative density mapping (RDM) method to multi-material problem and improves the performance. To achieve such purpose, a new mapping relation considering multi-material density and stress information is introduced to generate mapping weight of materials, which is the most novel contribution. Second, to avoid intersection of different materials, two types of material mapping strategies, namely continuous mapping strategy, and discrete mapping strategy, are introduced to generate multi-material lattice structure using the mapping weight of materials. Several numerical examples are exhibited, which demonstrate that the proposed method is capable of generating a multi-material lattice structure with clear material interface and good performance.

Towards more reliable photovoltaic energy conversion systems: A weakly-supervised learning perspective on anomaly detection

With the increasing popularity of photovoltaic (PV) systems, both academia and industry have been paying growing attention to fault prediction and health management. Although deep learning has achieved remarkable results in this field, the high cost of labeled data acquisition has become a bottleneck for its application. While unsupervised methods can reduce this cost, they fail to effectively utilize the prior knowledge of anomalous samples. To address this issue, this paper innovatively proposes a weakly-supervised anomaly detection network based on feature map conversion and hypersphere transformation (FMC-HT). Firstly, PV electroluminescence (EL) images are input into the feature extraction layer based on feature map conversion, which can reduce redundancy, enhance information density, and achieve efficient feature extraction through feature map conversion. Subsequently, inverse squared norm loss is set to utilize the knowledge of unlabeled sample features and labeled anomalous sample features, and backpropagation is performed separately to train the hypersphere transformation network, ultimately achieving effective distinction between normal and anomalous samples. On real PV EL datasets, this model exhibits impressive performance and remains stable under different prior knowledge and anomaly rates. This study not only provides a new solution for anomaly detection in PV systems but also expands new directions for the application of deep learning in scenarios with limited labeled data.

Six-channel colorimetric sensing of metal ions and advanced molecular information security based on fish scale-derived carbon–gold–silver nanocomposites

Due to its high flexibility and expansibility, nanomaterials-based systems have been constructed and applied from sensing, and medicine to molecular information technology. However, the current challenges include tedious and harsh nanomaterials preparation, the need for a wider range of response channels, new information paradigms, higher information density and length. Herein, fish-scale derived carbon nanomaterials (FSCN) were used as reducing and stabilizing agents for direct, green, and facile synthesis of carbon-bimetallic nanocomposites and applications in six-channel colorimetric sensing of Hg2+, and multi-combination and multi-round information encoding and protection for long text. First, the reaction sequence of three precursors (FSCN, Au3+, Ag+) was optimized to prepare FSCN–Au–Ag nanocomposites with double plasmonic absorption peaks of Au/Ag nanoparticles (NPs). The addition of Hg2+ causes the aggregation of nanoparticles and the formation of Ag/Hg amalgam or Au/Hg amalgam, FSCN–Au–Ag NPs were capable of multi-channel sensing (color, absorbance, and peak shift) of Hg2+. Especially, the detection limit of Hg2+ was 8.9 nM based on absorption response in wide wavelength range (456–546 nm), which was at least 6 times more sensitive than that at single wavelength. Moreover, different long text information can be encrypted and hidden in six-channel selective responses of FSCN–Au–Ag NPs-based colorimetric sensing system. By setting the threshold, these selective responses can be abstracted as a series of binary strings and then divided into shorter strings. Utilizing multi-combination and multi-round information coding, these short binary strings can be decrypted and merged into various long text content. This research promotes preparation and multi-purpose application of carbon-bimetallic nanomaterials, which provides new way to improve the sensing sensitivity of nanosystems and also offers new direction for molecular digitization.

Generating synthetic computed tomography for radiotherapy: SynthRAD2023 challenge report

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning.Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: (1) MRI-to-CT and (2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans.The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥0.87/0.90) and gamma pass rates for photon (≥98.1%/99.0%) and proton (≥97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT.SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning.

Curation of historical phenotypic wheat data from the Czech Genebank for research and breeding

Climate change and population growth are putting increasing pressure on global food security. The development of high-yielding varieties for important crops such as wheat is crucial to meet these challenges. The basis for this is extensive exploitation of beneficial genetic variation resting in genebanks around the world. Selecting suitable donor genotypes from the vast number of wheat accessions stored in genebanks is a difficult task and depends critically on the density of information on the performance of individual accessions. Therefore, this study aimed to access phenotypic data from the Czech genebank, storing over 13,000 wheat accessions. We curated and analyzed data on heading date, plant height, and thousand grain weight for more than one-third of all available accessions regenerated across 70 years. The data underwent analysis using a linear mixed model, revealing high quality of curated data with heritability reaching 99%. The raw data, but also derived data such as the best linear unbiased estimations, are now available for the wheat collection of the Czech genebank for research and breeding.

Mycelium-wood composites as a circular material for building insulation

In Europe, buildings account for 40% of the energy consumption and produce 36% of CO2 emissions. Renovation could be a great tool to decarbonize the building stock since it allows for a decrease in the operational energy required for buildings and is less material-consuming than new construction. Further benefits are brought by the usage of bio-based insulation materials that can drastically reduce embodied emissions and transform structures into factual carbon sinks. This study focuses on a particular kind of biogenic material, mycelium-wood composites, consisting of organic matter bound by the root structure of fungal organisms. This innovative insulation material was compared with traditional ones for the renovation of the building stock, with a focus on vertical components like walls in the Helsinki metropolitan area. To characterize mycelium-wood composites, density and carbon content information were gathered from the samples realized in the Politecnico di Milano MaBa.SAPERLab, while the production processes were included in a SimaPro model to obtain the GWP value. Different scenarios were then defined by two variables: the renovation rate of the building stock and the market penetration of mycelium-wood composites. For each scenario, the overall GWP and CO2 stored values were calculated. Results show the great potential of the innovative material that grants carbon storage in the building stock that could even surpass the amount stored in the 32,500 ha of forest in the area. However, this possibility is heavily influenced by factors independent of the type of insulation used that should be further investigated.

Relative numerosity is constructed from size and density information: Evidence from adaptation.

To dissociate aftereffects of size and density in the perception of relative numerosity, large or small adapter sizes were crossed with high or low adapter densities. A total of 48 participants were included in this preregistered design. To adapt the same retinotopic region as the large adapters, the small adapters were flashed in a sequence so as to "paint" the adapting density across the large region. Perceived numerosities and sizes in the adapted region were then compared to those in an unadapted region in separate blocks of trials, so that changes in density could be inferred. These density changes were found to be bidirectional and roughly symmetric, whereas the aftereffects of size and number were not symmetric. A simple account of these findings is that local adaptations to retinotopic density as well as global adaptations to size combine in producing numerosity aftereffects measured by assessing perceived relative number. Accounts based on number adaptation are contraindicated, in particular, by the result of adapting to a large, sparse adapter and testing with a stimulus with a double the density but half number of dots.

Concatenated Bragg grating fiber-optic sensors for simultaneous measurement of curvature, temperature, and axial pressure

Abstract. This paper presents the development and evaluation of four sensors based on multiple fiber Bragg grating (FBG) constellations embedded in a silicon dioxide single-mode fiber (SMF) for simultaneous measurement of pressure, temperature, and bending curvature. We applied dimension and material variations – including core, cladding, and coating dimensions; coating material; and the number and arrangement of the FBGs – to optimize the reflected signal response and increase information density. A bootstrap-aggregated ensemble of decision trees was used to evaluate the sensor signal. The results show that adjusting the cladding-to-coating ratio led to significant improvements in pressure and bending prediction performance. Additionally, two combined FBGs were fabricated to form a fiber Bragg grating Fabry–Pérot interferometer, which enabled the detection of curvature with a root-mean-square error (RMSE) of 0.0034 L mm−1 (R2=1), axial pressure with an RMSE of 0.0564 bar (R2=0.99), and temperature with an RMSE of 0.0265 °C (R2=1). At the time of writing, there is no commercially available instrument that can perform these measurements simultaneously.

Quantum computer-enabled receivers for optical communication

Optical communication is the standard for high-bandwidth information transfer in today’s digital age. The increasing demand for bandwidth has led to the maturation of coherent transceivers that use phase- and amplitude-modulated optical signals to encode more bits of information per transmitted pulse. Such encoding schemes achieve higher information density, but also require more complicated receivers to discriminate the signaling states. In fact, achieving the ultimate limit of optical communication capacity, especially in the low light regime, requires coherent joint detection of multiple pulses. Despite their superiority, such joint detection receivers are not in widespread use because of the difficulty of constructing them in the optical domain. In this work we describe how optomechanical transduction of phase information from coherent optical pulses to superconducting qubit states followed by the execution of trained short-depth variational quantum circuits can perform joint detection of communication codewords with error probabilities that surpass all classical, individual pulse detection receivers. Importantly, we utilize a model of optomechanical transduction that captures non-idealities such as thermal noise and loss in order to understand the transduction performance necessary to achieve a quantum advantage with such a scheme. We also execute the trained variational circuits on an IBM-Q device with the modeled transduced states as input to demonstrate that a quantum advantage is possible even with current levels of quantum computing hardware noise.

Machine learning-based active control for lightweight antenna with force density method and nested genetic algorithm

This work proposes the integration of a tensegrity structure composed of struts and cables into an antenna, thus achieving a lightweight and optimized design for the large-scale system. Active control is introduced to ensure the deformation accuracy of the cable-strut antenna, while a shape sensing method based on force density information is adopted to obtain the deformation field of actual wind cases from monitoring the cable forces. Numerical simulations are performed to verify the accuracy of the method, and a multi-objective optimization framework is employed to determine the optimal positions and control of actuators for active control. A machine learning-based framework is proposed to achieve rapid response of the active control system under actual wind cases. The framework is composed of a two-stage control system that combines the shape sensing method with the multi-objective optimization approach. The machine learning framework establishes a quick response system that monitors changes in the cable forces, thus ensuring structural accuracy control under wind load without structural analysis.

The Genome Explorer genome browser.

Genome browsers provide graphical depictions of genome information to speed the uptake of complex genome data by scientists. They provide search operations to help scientists find information and zoom operations to enable scientists to view genome features at different resolutions. We introduce the Genome Explorer browser, which provides extremely fast zooming and panning of genome visualizations and displays with high information density.

Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information

The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we propose a neural network model, RMSF-net, which outperforms previous methods and produces the best results in a large-scale protein dynamics dataset; this model can accurately infer the dynamic information of a protein in only a few seconds. By learning effectively from experimental protein structure data and cryo-electron microscopy (cryo-EM) data integration, our approach is able to accurately identify the interactive bidirectional constraints and supervision between cryo-EM maps and PDB models in maximizing the dynamic prediction efficacy. Rigorous 5-fold cross-validation on the dataset demonstrates that RMSF-net achieves test correlation coefficients of 0.746 ± 0.127 at the voxel level and 0.765 ± 0.109 at the residue level, showcasing its ability to deliver dynamic predictions closely approximating molecular dynamics simulations. Additionally, it offers real-time dynamic inference with minimal storage overhead on the order of megabytes. RMSF-net is a freely accessible tool and is anticipated to play an essential role in the study of protein dynamics.

Growth Monitoring of Greenhouse Tomatoes Based on Context Recognition

To alleviate social problems in agriculture such as aging and labor force shortages, automatic growth monitoring based on image measurement has been introduced to tomato cultivation in greenhouses. The overlap of leaves and fruits makes precise observations challenging. In this study, we applied context recognition to tomato growth monitoring by using a Bayesian network. The proposed method combines image recognition using convolutional networks and context recognition using Bayesian networks. It enables not only the recognition of individual tomatoes but also the evaluation of tomato plants. An accurate number of tomatoes and the condition of the stocks can be estimated based on the number of ripe and unripened tomatoes in addition to their density information. The verification experiments clarified that a more accurate number of tomatoes could be estimated than with simple tomato detection, and the stock states could also be evaluated correctly. Compared to conventional methods, the method used in this study has improved tomato decision accuracy by 23%.

Does presentation size of instructional materials influence the split‐attention effect?

AbstractThe split‐attention effect posits that learning outcomes are negatively impacted when interrelated text and graphics are spatially segregated rather than cohesively integrated. This study explored how the instructional material's presentation size influences the manifestation of the split‐attention effect. Based on cognitive load theory and perceptual load theory, we hypothesized that elevated information density in a compact presentation format would attenuate the advantage of integrated text and graphics, thereby diminishing the salience of the split‐attention effect relative to a more expansive presentation size. University students (n = 146) studied a split‐attention format or integrated format in either large or small presentation size. Results on retention and comprehension tests and extraneous cognitive load ratings revealed no effects of instructional format, presentation size or their interaction. The present results call for a more nuanced understanding of the split‐attention effect and suggest additional research to explore its cognitive foundations.