Single-molecule RNA sizing enables quantitative analysis of alternative transcription termination.

Transcription, a critical process in molecular biology, has found many applications in RNA synthesis, including mRNA vaccines and RNA therapeutics. However, current RNA characterization technologies suffer from amplification and enzymatic biases that lead to loss of native information. Here, we introduce a strategy to quantitatively study both transcription and RNA polymerase behaviour by sizing RNA with RNA nanotechnology and nanopores. To begin, we utilize T7 RNA polymerase to transcribe linear DNA lacking termination sequences. Surprisingly, we discover alternative transcription termination in the origin of replication sequence. Next, we employ circular DNA without transcription terminators to perform rolling circle transcription. This allows us to gain valuable insights into the processivity and transcription behaviour of RNA polymerase at the single-molecule level. Our work demonstrates how RNA nanotechnology and nanopores may be used in tandem for the direct and quantitative analysis of RNA transcripts. This methodology provides a promising pathway for accurate RNA structural mapping by enabling the study of full-length RNA transcripts at the single-molecule level.

Mid-Infrared Variable Selection for Soil Organic Matter Fractions Based on Soil Model Systems and Permutation Importance Algorithm.

In this research, an attempt was made to classify soil samples according to the different fractions of soil organic matter (SOM) using model systems in which the ratio of the fractions of SOM is chemically mimicked. A mixture of starch and nicotinamide was used for the labile organic matter model, while a standard of humic acid was used for the stabile organic matter. Changing the threshold value in the selected ranges after a permutation importance algorithm is conducted using train models and test data set, a list of selected important wavelengths and their importance scores were obtained. Three regions for the classification of soil fractions within the estimated probability density function are most prominent: 800-1200 cm-1, 0.48-0.55; 1800-2000 cm-1, 0.52-0.62; and 2500-3200 cm-1, 0.48-0.62, where the first component represents the spectral range while the second component covers the range of the importance score. Obtained wavelength ranges indicate the importance of the aliphatic stretching and bending vibration region, as well as the total soil reflectance (mineral content) for the characterization of organic matter fractions. A comparative evaluation with literature data found that the obtained wavelengths have a potential for application in methods of proximal and remote detection/calibration of existing and development of new sensors for Advanced Spaceborne Thermal Emission and Reflection Radiometer satellites, specifically in the shortwave infrared and thermal infrared ranges.

Mammalian Cell-Growth Monitoring Based on an Impedimetric Sensor and Image Processing within a Microfluidic Platform.

In recent years, advancements in microfluidic and sensor technologies have led to the development of new methods for monitoring cell growth both in macro- and micro-systems. In this paper, a microfluidic (MF) platform with a microbioreactor and integrated impedimetric sensor is proposed for cell growth monitoring during the cell cultivation process in a scaled-down simulator. The impedimetric sensor with an interdigitated electrode (IDE) design was realized with inkjet printing and integrated into the custom-made MF platform, i.e., the scaled-down simulator. The proposed method, which was integrated into a simple and rapid fabrication MF system, presents an excellent candidate for the scaled-down analyses of cell growths that can be of use in, e.g., optimization of the cultivated meat bioprocess. When applied to MRC-5 cells as a model of adherent mammalian cells, the proposed sensor was able to precisely detect all phases of cell growth (the lag, exponential, stationary, and dying phases) during a 96-h cultivation period with limited available nutrients. By combining the impedimetric approach with image processing, the platform enables the real-time monitoring of biomasses and advanced control of cell growth progress in microbioreactors and scaled-down simulator systems.

Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy.

Pollen monitoring have become data-intensive in recent years as real-time detectors are deployed to classify airborne pollen grains. Machine learning models with a focus on deep learning, have an essential role in the pollen classification task. Within this study we developed an explainable framework to unveil a deep learning model for pollen classification. Model works on data coming from single particle detector (Rapid-E) that records for each particle optical fingerprint with scattered light and laser induced fluorescence. Morphological properties of a particle are sensed with the light scattering process, while chemical properties are encoded with fluorescence spectrum and fluorescence lifetime induced by high-resolution laser. By utilizing these three data modalities, scattering, spectrum, and lifetime, deep learning-based models with millions of parameters are learned to distinguish different pollen classes, but a proper understanding of such a black-box model decisions demands additional methods to employ. Our study provides the first results of applied explainable artificial intelligence (xAI) methodology on the pollen classification model. Extracted knowledge on the important features that attribute to the predicting particular pollen classes is further examined from the perspective of domain knowledge and compared to available reference data on pollen sizes, shape, and laboratory spectrofluorometer measurements.

Agrobot Lala-An Autonomous Robotic System for Real-Time, In-Field Soil Sampling, and Analysis of Nitrates.

This paper presents an autonomous robotic system, an unmanned ground vehicle (UGV), for in-field soil sampling and analysis of nitrates. Compared to standard methods of soil analysis it has several advantages: each sample is individually analyzed compared to average sample analysis in standard methods; each sample is georeferenced, providing a map for precision base fertilizing; the process is fully autonomous; samples are analyzed in real-time, approximately 30 min per sample; and lightweight for less soil compaction. The robotic system has several modules: commercial robotic platform, anchoring module, sampling module, sample preparation module, sample analysis module, and communication module. The system is augmented with an in-house developed cloud-based platform. This platform uses satellite images, and an artificial intelligence (AI) proprietary algorithm to divide the target field into representative zones for sampling, thus, reducing and optimizing the number and locations of the samples. Based on this, a task is created for the robot to automatically sample at those locations. The user is provided with an in-house developed smartphone app enabling overview and monitoring of the task, changing the positions, removing and adding of the sampling points. The results of the measurements are uploaded to the cloud for further analysis and the creation of prescription maps for variable rate base fertilization.

Why should we care about high temporal resolution monitoring of bioaerosols in ambient air?

This is the first time that atmospheric concentrations of individual pollen types have been recorded by an automatic sampler with 1-hour and sub-hourly resolution (i.e. 1-minute and 1-second data). The data were collected by traditional Hirst type methods and state-of the art Rapid-E real-time bioaerosol detector. Airborne pollen data from 7 taxa, i.e. Acer negundo, Ambrosia, Broussonetia papyrifera, Cupressales (Taxaceae and Cupressaceae families), Platanus, Salix and Ulmus, were collected during the 2019 pollen season in Novi Sad, Serbia. Pollen data with daily, hourly and sub-hourly temporal resolution were analysed in terms of their temporal variability. The impact of turbulence kinetic energy (TKE) on pollen cloud homogeneity was investigated. Variations in Seasonal Pollen Integrals produced by Hirst and Rapid-E show that scaling factors are required to make data comparable. Daily average and hourly measurements recorded by the Rapid-E and Hirst were highly correlated and so examining Rapid-E measurements with sub-hourly resolution is assumed meaningful from the perspective of identification accuracy. Sub-hourly data provided an insight into the heterogenous nature of pollen in the air, with distinct peaks lasting ~5-10 min, and mostly single pollen grains recorded per second. Short term variations in 1-minute pollen concentrations could not be wholly explained by TKE. The new generation of automatic devices has the potential to increase our understanding of the distribution of bioaerosols in the air, provide insights into biological processes such as pollen release and dispersal mechanisms, and have the potential for us to conduct investigations into dose-response relationships and personal exposure to aeroallergens.

Fundamental Study of the Optical and Vibrational Properties of Fx-AZB@MOF systems as Functions of Dye Substitution and the Loading Amount.

Controlling the switching efficiency of photoactive hybrid systems is an obligatory key prerequisite for systematically improving the design of functional materials. By modulating the degree of fluorination and the amount being embedded into porous hosts, the E/Z ratios of fluorinated azobenzenes were adjusted as both functions of substitution and the degree of loading. Octafluoroazobenzene (F8-AZB) and perfluoroazobenzene (F10-AZB) were inserted into porous DMOF-1. Especially for perfluoroazobenzene (F10-AZB), an immense stabilization of the E isomer was observed. In complementary molecular dynamics simulations performed at the DFTB (density functional tight binding) level, an in-depth characterization of the interactions of the different photoisomers and the host structure was carried out. On the basis of the resulting structural and energetic data, the experimentally observed increase in the amount of the Z conformer for F8-AZB can be explained, while the stabilization of E-F10-AZB can be directly related to a fundamentally different interaction motif compared to its tetra- and octafluorinated counterparts.

Predicting sensitivity of recently harvested tomatoes and tomato sepals to future fungal infections.

Tomato is an important commercial product which is perishable by nature and highly susceptible to fungal incidence once it is harvested. Not all tomatoes are equally vulnerable to pathogenic fungi, and an early detection of the vulnerable ones can help in taking timely preventive actions, ranging from isolating tomato batches to adjusting storage conditions, but also in making right business decisions like dynamic pricing based on quality or better shelf life estimate. More importantly, early detection of vulnerable produce can help in taking timely actions to minimize potential post-harvest losses. This paper investigates Near-infrared (NIR) hyperspectral imaging (1000-1700 nm) and machine learning to build models to automatically predict the susceptibility of sepals of recently harvested tomatoes to future fungal infections. Hyperspectral images of newly harvested tomatoes (cultivar Brioso) from 5 different growers were acquired before the onset of any visible fungal infection. After imaging, the tomatoes were placed under controlled conditions suited for fungal germination and growth for a 4-day period, and then imaged using normal color cameras. All sepals in the color images were ranked for fungal severity using crowdsourcing, and the final severity of each sepal was fused using principal component analysis. A novel hyperspectral data processing pipeline is presented which was used to automatically segment the tomato sepals from spectral images with multiple tomatoes connected via a truss. The key modelling question addressed in this research is whether there is a correlation between the hyperspectral data captured at harvest and the fungal infection observed 4 days later. Using 10-fold and group k-fold cross-validation, XG-Boost and Random Forest based regression models were trained on the features derived from the hyperspectral data corresponding to each sepal in the training set and tested on hold out test set. The best model found a Pearson correlation of 0.837, showing that there is strong linear correlation between the NIR spectra and the future fungal severity of the sepal. The sepal specific predictions were aggregated to predict the susceptibility of individual tomatoes, and a correlation of 0.92 was found. Besides modelling, focus is also on model interpretation, particularly to understand which spectral features are most relevant to model prediction. Two approaches to model interpretation were explored, feature importance and SHAP (SHapley Additive exPlanations), resulting in similar conclusions that the NIR range between 1390-1420 nm contributes most to the model's final decision.

MRI Reconstruction Using Markov Random Field and Total Variation as Composite Prior.

Reconstruction of magnetic resonance images (MRI) benefits from incorporating a priori knowledge about statistical dependencies among the representation coefficients. Recent results demonstrate that modeling intraband dependencies with Markov Random Field (MRF) models enable superior reconstructions compared to inter-scale models. In this paper, we develop a novel reconstruction method, which includes a composite prior based on an MRF model and Total Variation (TV). We use an anisotropic MRF model and propose an original data-driven method for the adaptive estimation of its parameters. From a Bayesian perspective, we define a new position-dependent type of regularization and derive a compact reconstruction algorithm with a novel soft-thresholding rule. Experimental results show the effectiveness of this method compared to the state of the art in the field.

Engineering Meteorological Features to Select Stress Tolerant Hybrids in Maize.

In this study we used meteorological parameters and predictive modelling interpreted by model explanation to develop stress metrics that indicate the presence of drought and heat stress at the specific environment. We started from the extreme temperature and precipitation indices, modified some of them and introduced additional drought indices relevant to the analysis. Based on maize's sensitivity to stress, the growing season was divided into four stages. The features were calculated throughout the growing season and split in two groups, one for the drought and the other for heat stress. Generated meteorological features were combined with soil features and fed to random forest regression model for the yield prediction. Model explanation gave us the contribution of features to yield decrease, from which we estimated total amount of stress at the environments, which represents new environmental index. Using this index we ranked the environments according to the level of stress. More than 2400 hybrids were tested across the environments where they were grown and based on the yield stability they were marked as either tolerant or susceptible to heat, drought or combined heat and drought stress. Presented methodology and results were produced within the Syngenta Crop Challenge 2019.

The balance between KIFC3 and EG5 tetrameric kinesins controls the onset of mitotic spindle assembly.

One of the first steps in mitotic spindle assembly is the dissolution of the centrosome linker followed by centrosome separation driven by EG5, a tetrameric plus-end-directed member of the kinesin-5 family. However, even in the absence of the centrosome linker, the two centrosomes are kept together by an ill-defined microtubule-dependent mechanism. Here we show that KIFC3, a minus-end-directed kinesin-14, provides microtubule-based centrosome cohesion. KIFC3 forms a homotetramer that pulls the two centrosomes together via a specific microtubule network. At mitotic onset, KIFC3 activity becomes the main driving force of centrosome cohesion to prevent premature spindle formation after linker dissolution as it counteracts the increasing EG5-driven pushing forces. KIFC3 is eventually inactivated by NEver in mitosis-related Kinase 2 (NEK2) to enable EG5-driven bipolar spindle assembly. We further show that persistent centrosome cohesion in mitosis leads to chromosome mis-segregation. Our findings reveal a mechanism of spindle assembly that is evolutionary conserved from yeast to humans.

STED nanoscopy of the centrosome linker reveals a CEP68-organized, periodic rootletin network anchored to a C-Nap1 ring at centrioles.

The centrosome linker proteins C-Nap1, rootletin, and CEP68 connect the two centrosomes of a cell during interphase into one microtubule-organizing center. This coupling is important for cell migration, cilia formation, and timing of mitotic spindle formation. Very little is known about the structure of the centrosome linker. Here, we used stimulated emission depletion (STED) microscopy to show that each C-Nap1 ring at the proximal end of the two centrioles organizes a rootletin ring and, in addition, multiple rootletin/CEP68 fibers. Rootletin/CEP68 fibers originating from the two centrosomes form a web-like, interdigitating network, explaining the flexible nature of the centrosome linker. The rootletin/CEP68 filaments are repetitive and highly ordered. Staggered rootletin molecules (N-to-N and C-to-C) within the filaments are 75 nm apart. Rootletin binds CEP68 via its C-terminal spectrin repeat-containing region in 75-nm intervals. The N-to-C distance of two rootletin molecules is ∼35 to 40 nm, leading to an estimated minimal rootletin length of ∼110 nm. CEP68 is important in forming rootletin filaments that branch off centrioles and to modulate the thickness of rootletin fibers. Thus, the centrosome linker consists of a vast network of repeating rootletin units with C-Nap1 as ring organizer and CEP68 as filament modulator.

Portfolio optimization for seed selection in diverse weather scenarios.

The aim of this work was to develop a method for selection of optimal soybean varieties for the American Midwest using data analytics. We extracted the knowledge about 174 varieties from the dataset, which contained information about weather, soil, yield and regional statistical parameters. Next, we predicted the yield of each variety in each of 6,490 observed subregions of the Midwest. Furthermore, yield was predicted for all the possible weather scenarios approximated by 15 historical weather instances contained in the dataset. Using predicted yields and covariance between varieties through different weather scenarios, we performed portfolio optimisation. In this way, for each subregion, we obtained a selection of varieties, that proved superior to others in terms of the amount and stability of yield. According to the rules of Syngenta Crop Challenge, for which this research was conducted, we aggregated the results across all subregions and selected up to five soybean varieties that should be distributed across the network of seed retailers. The work presented in this paper was the winning solution for Syngenta Crop Challenge 2017.

Sparse Recovery in Magnetic Resonance Imaging With a Markov Random Field Prior.

Recent research in compressed sensing of magnetic resonance imaging (CS-MRI) emphasizes the importance of modeling structured sparsity, either in the acquisition or in the reconstruction stages. Subband coefficients of typical images show certain structural patterns, which can be viewed in terms of fixed groups (like wavelet trees) or statistically (certain configurations are more likely than others). Wavelet tree models have already demonstrated excellent performance in MRI recovery from partial data. However, much less attention has been given in CS-MRI to modeling statistically spatial clustering of subband data, although the potentials of such models have been indicated. In this paper, we propose a practical CS-MRI reconstruction algorithm making use of a Markov random field prior model for spatial clustering of subband coefficients and an efficient optimization approach based on proximal splitting. The results demonstrate an improved reconstruction performance compared with both the standard CS-MRI methods and the recent related methods.

Human phosphatase CDC14A regulates actin organization through dephosphorylation of epithelial protein lost in neoplasm.

CDC14 is an essential dual-specificity phosphatase that counteracts CDK1 activity during anaphase to promote mitotic exit in Saccharomyces cerevisiae Surprisingly, human CDC14A is not essential for cell cycle progression. Instead, it regulates cell migration and cell adhesion. Little is known about the substrates of hCDC14A and the counteracting kinases. Here, we combine phospho-proteome profiling and proximity-dependent biotin identification to identify hCDC14A substrates. Among these targets were actin regulators, including the tumor suppressor eplin. hCDC14A counteracts EGF-induced rearrangements of actin cytoskeleton by dephosphorylating eplin at two known extracellular signal-regulated kinase sites, serine 362 and 604. hCDC14APD and eplin knockout cell lines exhibited down-regulation of E-cadherin and a reduction in α/ß-catenin at cell-cell adhesions. Reduction in the levels of hCDC14A and eplin mRNA is frequently associated with colorectal carcinoma and is correlated with poor prognosis. We therefore propose that eplin dephosphorylation by hCDC14A reduces actin dynamics to restrict tumor malignancy.

Genome editing through large insertion leads to the skipping of targeted exon.

BACKGROUND: Highly efficient genome editing can be achieved through targeting an endonuclease to specific locus of interest. Engineered zinc-finger nuclease (ZFN) and CRISPR-associated protein-9 nuclease (Cas9) offer such an elegant approach for genome editing in vertebrate cells. In this study, we have utilized ZFN and Cas9-catalyzed double strand break followed by homologous recombination-mediated incorporation of premature stop codon and selection marker to target human cell division cycle 14A (hCDC14A) and cell division cycle 14B (hCDC14B) genes. RESULTS: Targeting of the hCDC14A and hCDC14B loci in telomerase immortalized human retinal pigment epithelium (hTERT-RPE1) and human colon cancer (HCT116) cells were confirmed by Southern blot hybridization. Nevertheless, DNA sequence analysis of reverse transcription polymerase chain reaction (RT-PCR) products confirmed that in all the single/double allele ablations, the targeted exon was spliced out. The phenomenon of exon skipping was independent of the genome editing approaches exploited, Cas9 or ZFN. Because the exons had a nucleotide number that could be divided by 3, the reading frame of the exon deletion was maintained. This indicates an exon-skipping event possibly due to the insertion of large DNA fragment (1.7 to 2.5 Kb) within the targeted exons. As a proof-of-principle, we have used gene disruption followed by non-homologous end joining (NHEJ) approach. Small alterations in the exon (one to fifteen bases) were transcribed to mRNA without exon skipping. Furthermore, loxP site-mediated removal of selection markers left a 45 bp scar within the targeted exon that can be traced in mRNA without exon skipping. CONCLUSION: From this study, we conclude that insertion of a large DNA fragment into an exon by genome editing can lead to its skipping from the final transcript. Hence, more cautious approach needs to be taken while designing target sites in such that the possible skipping of targeted exon causes a frame-shift mediated incorporation of pre-mature stop codon. On the other hand, exon skipping may be a useful strategy for the introduction of protein deletions.

The centrosomal linker and microtubules provide dual levels of spatial coordination of centrosomes.

The centrosome is the principal microtubule organizing center in most animal cells. It consists of a pair of centrioles surrounded by pericentriolar material. The centrosome, like DNA, duplicates exactly once per cell cycle. During interphase duplicated centrosomes remain closely linked by a proteinaceous linker. This centrosomal linker is composed of rootletin filaments that are anchored to the centrioles via the protein C-Nap1. At the onset of mitosis the linker is dissolved by Nek2A kinase to support the formation of the bipolar mitotic spindle. The importance of the centrosomal linker for cell function during interphase awaits characterization. Here we assessed the phenotype of human RPE1 C-Nap1 knockout (KO) cells. The absence of the linker led to a modest increase in the average centrosome separation from 1 to 2.5 µm. This small impact on the degree of separation is indicative of a second level of spatial organization of centrosomes. Microtubule depolymerisation or stabilization in C-Nap1 KO cells dramatically increased the inter-centrosomal separation (> 8 µm). Thus, microtubules position centrosomes relatively close to one another in the absence of linker function. C-Nap1 KO cells had a Golgi organization defect with a two-fold expansion of the area occupied by the Golgi. When the centrosomes of C-Nap1 KO cells showed considerable separation, two spatially distinct Golgi stacks could be observed. Furthermore, migration of C-Nap1 KO cells was slower than their wild type RPE1 counterparts. These data show that the spatial organization of centrosomes is modulated by a combination of centrosomal cohesion and microtubule forces. Furthermore a modest increase in centrosome separation has major impact on Golgi organization and cell migration.