Precise Assignment Research Articles

MIMt: a curated 16S rRNA reference database with less redundancy and higher accuracy at species-level identification

MotivationAccurate determination and quantification of the taxonomic composition of microbial communities, especially at the species level, is one of the major issues in metagenomics. This is primarily due to the limitations of commonly used 16S rRNA reference databases, which either contain a lot of redundancy or a high percentage of sequences with missing taxonomic information. This may lead to erroneous identifications and, thus, to inaccurate conclusions regarding the ecological role and importance of those microorganisms in the ecosystem.ResultsThe current study presents MIMt, a new 16S rRNA database for archaea and bacteria’s identification, encompassing 47 001 sequences, all precisely identified at species level. In addition, a MIMt2.0 version was created with only curated sequences from RefSeq Targeted loci with 32 086 sequences. MIMt aims to be updated twice a year to include all newly sequenced species. We evaluated MIMt against Greengenes, RDP, GTDB and SILVA in terms of sequence distribution and taxonomic assignments accuracy. Our results showed that MIMt contains less redundancy, and despite being 20 to 500 times smaller than existing databases, outperforms them in completeness and taxonomic accuracy, enabling more precise assignments at lower taxonomic ranks and thus, significantly improving species-level identification.

Distinct Chemical Determinants are Essential for Achieving Ligands for Superior Optical Detection of Specific Amyloid-β Deposits in Alzheimer's Disease.

Aggregated forms of different proteins are common hallmarks for several neurodegenerative diseases, including Alzheimer's disease, and ligands that selectively detect specific protein aggregates are vital. Herein, we investigate the molecular requirements of thiophene-vinyl-benzothiazole based ligands to detect a specific type of Aβ deposits found in individuals with dominantly inherited Alzheimer's disease caused by the Arctic APP E693G mutation. The staining of these Aβ deposits was alternated when switching the terminal heterocyclic moiety attached to the thiophene-vinyl-benzothiazole scaffold. The most prevalent staining was observed for ligands having a terminal 3-methyl-1H-indazole moiety or a terminal 1,2-dimethoxybenzene moiety, verifying that specific molecular interactions between these ligands and the aggregates were necessary. The synthesis of additional thiophene-vinyl-benzothiazole ligands aided in pinpointing additional crucial chemical determinants, such as positioning of nitrogen atoms and methyl substituents, for achieving optimal staining of Aβ aggregates. When combining the optimized thiophene-vinyl-benzothiazole based ligands with a conventional ligand, CN-PiB, distinct staining patterns were observed for sporadic Alzheimer's disease versus dominantly inherited Alzheimer's disease caused by the Arctic APP E693G mutation. Our findings provide chemical insights for developing novel ligands that allow for a more precise assignment of Aβ deposits, and might also aid in creating novel agents for clinical imaging of distinct Aβ aggregates in AD.

Prescribed-time multi-coalition Nash equilibrium seeking by event-triggered communication

This article investigates the event-triggered prescribed-time Nash equilibrium seeking problem among multiple coalitions of agents in noncooperative games. Each coalition acts as a virtual player in the noncooperative game, with decisions made by its member agents. Agents lack complete information about others’ decisions and instead estimate them through a communication graph. An event-triggered prescribed-time multi-coalition Nash equilibrium seeking method is developed based on the leader-following consensus protocol, dynamic average consensus protocol, and gradient play. This method ensures the Nash equilibrium of the multi-coalition game is reached within a prescribed time, even when communication between agents only occurs under specific triggering conditions—effectively conserving communication resources. Unlike existing approaches, the proposed algorithm allows precise settling time assignment without prior knowledge of system parameters. This algorithm also prevents Zeno behavior. Lastly, the efficiency of the designed algorithm is demonstrated through simulation experiments.

End-to-end multiple object tracking in high-resolution optical sensors of drones with transformer models

Drone aerial imaging has become increasingly important across numerous fields as drone optical sensor technology continues to advance. One critical challenge in this domain is achieving both accurate and efficient multi-object tracking. Traditional deep learning methods often separate object identification from tracking, leading to increased complexity and potential performance degradation. Conventional approaches rely heavily on manual feature engineering and intricate algorithms, which can further limit efficiency. To overcome these limitations, we propose a novel Transformer-based end-to-end multi-object tracking framework. This innovative method leverages self-attention mechanisms to capture complex inter-object relationships, seamlessly integrating object detection and tracking into a unified process. By utilizing end-to-end training, our approach simplifies the tracking pipeline, leading to significant performance improvements. A key innovation in our system is the introduction of a trajectory detection label matching technique. This technique assigns labels based on a comprehensive assessment of object appearance, spatial characteristics, and Gaussian features, ensuring more precise and logical label assignments. Additionally, we incorporate cross-frame self-attention mechanisms to extract long-term object properties, providing robust information for stable and consistent tracking. We further enhance tracking performance through a newly developed self-characteristics module, which extracts semantic features from trajectory information across both current and previous frames. This module ensures that the long-term interaction modules maintain semantic consistency, allowing for more accurate and continuous tracking over time. The refined data and stored trajectories are then used as input for subsequent frame processing, creating a feedback loop that sustains tracking accuracy. Extensive experiments conducted on the VisDrone and UAVDT datasets demonstrate the superior performance of our approach in drone-based multi-object tracking.

Implementation of Machine Learning in the Categorization of Improvement Areas for Kaizen Submissions

This paper explores the synergy between Kaizen methodologies and Machine Learning, with the goal of improving the efficiency of categorizing areas of improvement. The implementation of Machine Learning algorithms, utilizing the ML.NET tool, has proven to be highly advantageous. These algorithms automate the categorization of Kaizen proposals based on structured data and predefined labels. This automation significantly reduces manual workload, expedites the classification process, and ultimately accelerates decision-making within the realm of continuous improvement. Furthermore, the application of machine learning has improved the categorization accuracy, reduced the risk of human errors, and ensured a more precise assignment to the relevant categories. This, in turn, enhances the quality of the collected information, facilitating well-informed decision-making. Additionally, the automated categorization has streamlined the process for users submitting Kaizen proposals, eliminating the need for manual category selection. This reduces cognitive load and promotes active engagement in the continuous improvement process. Furthermore, the system has introduced a category-based incentive structure, where users accrue points corresponding to the category assigned to their Kaizen proposals. These points can be exchanged for items provided by the company, acting as an additional incentive for active employee participation in the continuous improvement process. In conclusion, the integration of Machine Learning with the ML.NET tool has become a valuable tool for optimizing the management of improvement proposals, resulting in a marked enhancement in efficiency, accuracy, and user engagement in the categorization process.

B-Doped Graphdiynes and Several of Their Corresponding Oxides: A Theoretical Study by X-ray Spectra.

Boron doping can significantly improve the electronic structure and physical and chemical properties of graphdiyne (GDY), which also expands its application prospects in photoelectricity, catalysis, and biology. The accurate configurations characterization of B-doped graphdiyne (B-GDY) has not been achieved due to insufficient experimental and theoretical research. The current work involves the simulation of the geometries of 11 typical B-GDY and B-doped graphdiyne oxides [B(O)-GDY] as well as a pristine GDY, along with their X-ray photoelectron (XPS) and near-edge X-ray absorption fine structure (NEXAFS) spectra at the density functional theory (DFT) level. The boron and carbon spectra for various bonding types were theoretically imitated to assess the spectral contributions. Calculated outcomes indicate that there is a noticeable dependence of the NEXAFS spectra on the local structure. The simulated XPS spectra provide precise assignments and an extra supplement to the spectra peaks, in addition to the position and general peak forms of simulated spectral peaks matching the experimental spectra fairly well. The combination of XPS and NEXAFS spectra can give useful information for identifying typical B-GDY and B(O)-GDY molecules. This paper offers a comprehensive structure-spectrum relationship of B-GDY and its oxides as well as a further theoretical prediction and guidance for experimental synthesis, which is helpful to solve the challenging issue of identification of B-doped carbon-based materials.

Spectroscopic characterization of black N- and P-doped zeolite templated carbons

The incorporation of heteroatoms (X) into the zeolite-templated carbon (ZTC) structure is a widely used technique to tailor its properties for specific applications such as gas adsorption, methane and hydrogen storage, and catalysis. However, the literature lacks sufficient data on Fourier-transform infrared spectroscopy (FTIR) analysis of ZTCs due to the challenge in obtaining high-quality FTIR spectra required for accurate assignment of the C–X bands, primarily caused by the black mass effect. In this work we prepared nitrogen (N) and phosphorus (P) doped ZTC samples using FAU type zeolite as a template and furfuryl alcohol as a carbon precursor. The Raman results confirmed the presence of medium-sized aromatic structures and also showed that doping these structures with N and P leads to some defects, although the overall conformation remains structurally intact. The FTIR spectra of the ZTC materials were obtained by controlling the preparation procedure and humidity, enabling clear analysis of the black samples. A Density Functional Theory (DFT) model based on the dimeric buckybowl structure was developed and complemented by experimental results obtained from X-ray photoelectron spectroscopy (XPS), nuclear magnetic resonance (NMR), and FTIR. The proposed DFT model was then used for deconvolution and precise band assignment of the experimental FTIR spectra. The FTIR deconvolution study also supported the incorporation of N and P into the ZTC as well as the presence of primarily two types of nitrogen species, amide (primary and secondary) and pyridine-like, while P was mainly incorporated as triphenylphosphine oxide and phosphonic acid.

Evaluation of experimental and computational studies and docking studies of (2E)-1-(anthracen-9-yl)-3-(4-bromophenyl) prop-2-en-1-one using a DFT method

This study presents both experimental and computational analyses of the molecular structure, electronic properties, and other characteristics of (2E)-1-(anthracen-9-yl)-3-(4-bromophenyl)prop-2-en-1-one (ANC2). The density functional theory (DFT) method, known for its cost-effectiveness, was employed to optimize the molecular structure and calculate structural properties using the B3LYP functional with the 6–31+G and 6–31 G basis sets. Vibrational spectra were analyzed, with precise assignments made based on potential energy distribution, showing high agreement between measured and computed spectra. Additionally, the study explores the chemopreventive properties of the compound and includes spectral and quantum chemical calculations, as well as tests such as RDG, HOMO-LUMO, ELF, MEP, ADMET, and MAC.

Fourier-Transform Infrared Spectral Library of MXenes.

Fourier-transform infrared (FTIR) spectroscopy characterization is a powerful and easy-to-use technique frequently employed for the characterization and fingerprinting of materials. Although MXenes are a large and fastest growing family of inorganic 2D materials, the lack of systematic FTIR spectroscopy studies hinders its application to MXenes and often leads to misinterpretation of the results. In this study, we report experimental and calculated FTIR spectra of 12 most typical carbide and carbonitride MXenes with different compositions (5 transition metals) and all four basic structures, including Ti2CT x , Nb2CT x , Mo2CT x , V2CT x , Ti3C2T x , Ti3CNT x , Mo2TiC2T x , Mo2Ti2C3T x , Nb4C3T x , V4C3T x , Ta4C3T x , and Mo4VC4T x . The measurements were performed on delaminated MXene flakes incorporated in KBr pellets in the 4000-400 cm-1 range. We provide detailed instructions for sample preparation, data collection, and interpretation of FTIR spectra of MXenes. Background correction and spectra smoothing are applied to obtain clear FTIR peaks corresponding to bond vibrations in MXenes. Density functional theory calculations were used for the precise assignment of all characteristic FTIR peaks and an in-depth analysis of the vibration modes. This work aims to provide the 2D material community with the FTIR spectroscopy technique as a reliable method for identifying and analyzing MXenes.

Sodium Polysulfides As Catholytes for Redox Flow Batteries

Redox flow batteries based on sodium polysulfide (Na2Sx) show promise due to sodium's high solubility in organic solvents (e.g., diglyme. However, when x is lower than 5, sodium polysulfide exhibits low solubility (less than 0.1 molal), limiting the nominal capacity of this redox-active material. Sodium phosphorothioate complexes, Na2P2Sx, have been reported as an alternative to Na2Sx. Incorporating phosphorus into the polysulfide structure improves their solubility in organic solvents, specifically in diglyme.This presentation will describe the synthesis and characterization of sodium phosphorothioate complexes (Na2P2Sx, where x = 6, 7, 8, and 9) for nonaqueous redox flow batteries. The structure of these materials is explored using 31P NMR and 23Na NMR spectroscopy, Raman spectroscopy, and X-ray diffraction analysis. Overall, 31P NMR results indicate that P5+ is tetrahedrally coordinated in all structures and that Na-P-S compounds have a common PS3- moiety. Ongoing efforts are aimed at providing more precise assignments to the 31P bands and correlating these findings with complementary structural information obtained from 23Na NMR and Raman spectroscopy.Electrochemical properties of sodium polysulfide and sodium thiophosphate catholytes, specifically Na2S8 and Na2P2S8, were also compared using CV. The voltammogram of Na2S8 revealed two clear redox reactions in the 1.5-3 V range vs. Na/Na+ due to a multi-step electron transfer process involving the S2-/S0 redox center. In contrast, Na2P2S8 exhibited a larger peak separation (~2 V) and lower peak currents at a given scan rate, potentially indicating a significant activation barrier associated with the rearrangement of the thiophosphate’s polyanionic structure during reduction/oxidation. Despite the substantial voltage hysteresis, Na2P2S8 displayed stable cycling performance, with a slight increase in peak current after 100 cycles. This behavior is consistent with measurements on RFBs containing a Na2P2Sx catholyte, where an electrode activation process increased the cell’s reversible capacity, likely due to subtle changes in the electrode’s surface chemistry during repeated oxidation/reduction. Finally, preliminary flow cell measurements on Na2P2S7 catholyte show reversible voltage profiles after the formation step with deep discharge. Overall, this presentation demonstrates that Na-P-S catholytes represent a promising material for high energy, low-cost redox flow batteries.AcknowledgementsThis research was conducted at Oak Ridge National Laboratory, managed by UT Battelle, LLC, for the U.S. Department of Energy (DOE) and is sponsored by the U.S. Department of Energy through the Energy Storage Program in the Office of Electricity.

Gaussian-kernel weighted neighborhood preserving embedding algorithm and its application in fault detection

Fault detection in industrial processes is essential for enhancing production safety. Despite the application of the neighborhood preserving embedding (NPE) algorithm in fault detection as a manifold learning technique, a notable limitation exists-NPE overlooks local geometric structure, leading to suboptimal fault detection and occasional false alarms. This paper introduces the Gaussian kernel weighted NPE (KW-NPE) algorithm to address this challenge. Specifically designed for precise weight assignment in local structures, KW-NPE strategically employs the Gaussian kernel method to project the spatial neighborhood set and capture comprehensive local structural characteristics. The weight assignment, dependent on feature values, enhances the retention of intrinsic structure during dimensionality reduction. A novel objective function further augments this process.To assess performance, a comprehensive composite index is introduced in a case study, amalgamating the false alarm rate and fault detection rate. The effectiveness of the KW-NPE algorithm is demonstrated through extensive simulations and its application to the Tennessee Eastman process dataset, highlighting its superiority over conventional approaches.

Resonant Tip-Enhanced Raman Spectroscopy of a Single-Molecule Kondo System.

Tip-enhanced Raman spectroscopy (TERS) under ultrahigh vacuum and cryogenic conditions enables exploration of the relations between the adsorption geometry, electronic state, and vibrational fingerprints of individual molecules. TERS capability of reflecting spin states in open-shell molecular configurations is yet unexplored. Here, we use the tip of a scanning probe microscope to lift a perylene-3,4,9,10-tetracarboxylic dianhydride (PTCDA) molecule from a metal surface to bring it into an open-shell spin one-half anionic state. We reveal a correlation between the appearance of a Kondo resonance in differential conductance spectroscopy and concurrent characteristic changes captured by the TERS measurements. Through a detailed investigation of various adsorbed and tip-contacted PTCDA scenarios, we infer that the Raman scattering on suspended PTCDA is resonant with a higher excited state. Theoretical simulation of the vibrational spectra enables a precise assignment of the individual TERS peaks to high-symmetry Ag modes, including the fingerprints of the observed spin state. These findings highlight the potential of TERS in capturing complex interactions between charge, spin, and photophysical properties in nanoscale molecular systems and suggest a pathway for designing single-molecule spin-optical devices.

Pinpointing pigs: performance and challenges of an ultra-wideband real-time location system for tracking growing-finishing pigs under practical conditions

Real-Time Location Systems (RTLSs) are promising precision livestock farming tools and have been employed in behavioural studies across various farm animal species. However, their application in research with fattening pigs is so far unexplored. The implementation of these systems has great potential to gain insight into pigs’ spatial behaviour such as the use of functional areas and pigs’ proximity to each other as indicators for social relationships. The aim of this study was therefore to validate the accuracy, precision, and data quality of the commercial Noldus Information Technology BV TrackLab system. We conducted different measurement sets: first, we performed static measurements in 12 pens at four different locations in each pen at three heights each using a single ultra-wideband tag (UWB). We recorded unfiltered x- and y-coordinates at 1 Hz. We repeated these measurements with six tags aligned in a 2 × 3 grid with varied spacing to test interference between the tags. We also tested dynamic performance by moving the tags along the centre line of the pens. Finally, we measured the data quality with 55 growing pigs in six pens, including the identification of location 'jumps' from the inside to the outside of the pen. Each pen housed ten animals fitted with a UWB tag attached to their farm ear tag. We collected data for 10 days and analysed seven 24-h periods of raw and filtered data. The mean accuracy of the RTLS measurements was 0.53 m (precision: 0.14 m) for single and 0.46 m (precision: 0.07 m) for grouped tags. Accuracy improved with increasing measurement height for single tags but less clearly for grouped tags (P [height single] = 0.01; P [height grouped] = 0.22). When tags were fitted to animals, 63.3% of the filtered data was lost and 21.8% of the filtered location estimates were outside the pens. Altogether, the TrackLab system was capable of fairly accurate and precise assignment of the functional areas where individual animals were located, but was insufficient for the analysis of social relationships. Furthermore, the frequent occurrence of gaps in signal transmission and the overall high data loss rates presented significant limitations. Additionally, the challenging hardware requirements for attaching sensors to the animals underline the need for further technological advances in RTLS for the application with growing-finishing pigs.

Synergistic approach of PCR-based fragment length analysis and amplicon deep sequencing reveals rich diversity of S-alleles in sweet cherries from the Caucasian region of origin.

The self-incompatibility system in sweet cherry (Prunus avium L.) prevents fertilization with own or genetically related pollen, and is genetically determined by the multi-allelic S-locus. Therefore, determining S-alleles is crucial for plant breeding and fruit production, as it enables the selection of compatible combinations of S-genotypes for successful pollination. In this study, S-alleles were identified in a total of 260 genotypes from the Caucasian region, the species' center of origin. S-allele genotyping was conducted using PCR fragment length analysis with the standard marker PaConsI-F/R2 and reference genotypes, complemented by sequence analysis through amplicon deep sequencing. The genotypes collected from Azerbaijan and Turkey exhibit a high allelic richness at the S-locus, particularly compared to modern sweet cherry cultivars worldwide. Nine previously undescribed S-alleles were identified and designated as S45, S46, S47, S48, S49, S50, S51, S52 and S53. Given the expected high diversity for other traits, this plant material represents a valuable resource for further breeding research and introgression of new traits in future breeding programs. Furthermore, our results underscore that fragment length alone may not be sufficient for unambiguous assignment of S-alleles due to minimal length differences between different alleles. To address this issue, an S-allele reference ladder was developed using the rich diversity for precise assignment of the S-alleles. This tool can be applied in future experiments as a robust and cost-effective method for accurate S-genotyping across different runs and laboratories. Additionally, several selected S-genotypes were planted in a trial field and will be maintained as an S-allele reference collection.

Extended Conjugation Strategy Enabling Red-Shifted and Efficient Emission of Orange-Red Thermally Activated Delayed Fluorescence Materials.

In account of the energy gap law, the development of efficient narrow-band gap thermally activated delayed fluorescence (TADF) materials remains a major challenge for the application of organic light-emitting diodes (OLEDs). The orange-red TADF materials are commonly designed with either large π-conjugated systems or strong intramolecular donor-acceptor (D-A) interactions for red-shift emission and small singlet-triplet energy gap (ΔEST). There are rare reports on the simultaneous incorporation of these two strategies on the same material systems. Herein, two orange-red emitters named 1P2D-BP and 2P2D-DQ have been designed by extending the conjugation degree of the center acceptor DQ and increasing the number distribution of the peripheral donor PXZ units, respectively. The emission peak of 1P2D-BP is red-shifted to 615 nm compared to 580 nm for 2P2D-DQ, revealing the pronounced effect of the conjugation extension on the emission band gap. In addition, the distorted molecular structure yields a small ΔEST of 0.02 eV, favoring the acquisition of a high exciton utilization through an efficient reverse intersystem crossing process. As a result, orange-red OLEDs with both 1P2D-BP and 2P2D-DQ have achieved an external quantum efficiency (EQE) of more than 17%. In addition, the efficient white OLED based on 1P2D-BP is realized through precise exciton assignment and energy transport modulation, showing an EQE of 23.6% and a color rendering index of 82. The present work provides an important reference for the design of high-efficiency narrow-band gap materials in the field of solid-state lighting.

Mechanistic Insight into the Mechanical Unfolding of the Integral Membrane Diacylglycerol Kinase.

The essential forces stabilizing membrane proteins and governing their folding and unfolding are difficult to decipher. Single-molecule atomic force spectroscopy mechanically unfolds individual membrane proteins and quantifies their dynamics and energetics. However, it remains challenging to structurally assign unfolding intermediates precisely and to deduce dominant interactions between specific residues that facilitate either the localized stabilization of these intermediates or the global assembly of membrane proteins. Here, we performed force spectroscopy experiments and multiscale molecular dynamics simulations to study the unfolding pathway of diacylglycerol kinase (DGK), a small trimeric multispan transmembrane enzyme. The remarkable agreement between experiments and simulations allowed precise structural assignment and interaction analysis of unfolding intermediates, bypassing existing limitations on structural mapping, and thus provided mechanistic explanations for the formation of these states. DGK unfolding was found to proceed with structural segments varying in size that do not correlate with its secondary structure. We identified intermolecular side-chain packing interactions as one of the major contributions to the stability of unfolding intermediates. Mutagenesis creating packing defects induced a dramatic decrease in the mechano-stability of corresponding intermediates and also in the thermo-stability of DGK trimer, in good agreement with predictions from simulations. Hence, the molecular determinants of the mechano- and thermo-stability of a membrane protein can be identified at residue resolution. The accurate structural assignment established and microscopic mechanism revealed in this work may substantially expand the scope of single-molecule studies of membrane proteins.

Combined optically stimulated luminescence and radiocarbon dating of aeolian dunes in Arctic Sweden

AbstractMultiple parabolic sand dune fields formed in Arctic Sweden after the last deglaciation, facilitated by an abundance of loose glaciofluvial sediment, limited vegetation cover and strong winds. Following initial stabilisation, these dunes underwent repeated reworking after fire events, as evidenced by the presence of buried soils, charcoal layers and redeposited sands in the dune stratigraphy. These reworking events may be driven by wider climate forcing; however, to date, no chronological framework exists for this activity in Sweden. As such, here, we apply quartz optically stimulated luminescence (OSL) dating of Arctic Swedish sand dunes using two dunes at the sites of Vastakielinen and Jorggástat. Resultant double‐SAR (single aliquot regenerative dose) quartz OSL ages are in good agreement with independent ages provided by 14C dating of charcoal fragments recovered from charcoal layers within the dunes, and we conclude that the chosen protocol is generally well suited for dating aeolian reworking of dune sediments in Arctic Sweden. While feldspar contamination limits precise age assignment for initial dune movement, our results nonetheless suggest repeated and long‐lasting aeolian activity in Arctic Sweden throughout the Holocene and, although there are differences in detail, further suggest some general trends in terms of dune stability and reworking over Arctic Fennoscandia.

Profiling native pulmonary basement membrane stiffness using atomic force microscopy.

Mammalian cells sense and react to the mechanics of their immediate microenvironment. Therefore, the characterization of the biomechanical properties of tissues with high spatial resolution provides valuable insights into a broad variety of developmental, homeostatic and pathological processes within living organisms. The biomechanical properties of the basement membrane (BM), an extracellular matrix (ECM) substructure measuring only ∼100-400 nm across, are, among other things, pivotal to tumor progression and metastasis formation. Although the precise assignment of the Young's modulus E of such a thin ECM substructure especially in between two cell layers is still challenging, biomechanical data of the BM can provide information of eminent diagnostic potential. Here we present a detailed protocol to quantify the elastic modulus of the BM in murine and human lung tissue, which is one of the major organs prone to metastasis. This protocol describes a streamlined workflow to determine the Young's modulus E of the BM between the endothelial and epithelial cell layers shaping the alveolar wall in lung tissues using atomic force microscopy (AFM). Our step-by-step protocol provides instructions for murine and human lung tissue extraction, inflation of these tissues with cryogenic cutting medium, freezing and cryosectioning of the tissue samples, and AFM force-map recording. In addition, it guides the reader through a semi-automatic data analysis procedure to identify the pulmonary BM and extract its Young's modulus E using an in-house tailored user-friendly AFM data analysis software, the Center for Applied Tissue Engineering and Regenerative Medicine processing toolbox, which enables automatic loading of the recorded force maps, conversion of the force versus piezo-extension curves to force versus indentation curves, calculation of Young's moduli and generation of Young's modulus maps, where the pulmonary BM can be identified using a semi-automatic spatial filtering tool. The entire protocol takes 1-2 d.

Study of the Crystal Structure and Hydrogen Bonding during Cold Crystallization of Poly(trimethylene 2,5-furandicarboxylate).

Here, we present a detailed description of the in situ isothermal crystallization of poly(trimethylene 2,5-furandicarboxylate)(PTF) as revealed by real-time Fourier transform infrared spectroscopy (FTIR) and grazing incidence wide-angle X-ray scattering (GIWAXS). From FTIR experiments, the evolution of hydrogen bonding with crystallization time can be monitored in real time, while from GIWAXS, crystal formation can be followed. Density functional theory (DFT) calculations have been used to simulate FTIR spectra for different theoretical structures, enabling a precise band assignment. In addition, based on DFT ab initio calculations, the influence of hydrogen bonding on the evolution with crystallization time can be understood. Moreover, from DFT calculations and comparison with both FTIR and GIWAXS experiments, a crystalline structure of poly(trimethylene 2,5-furandicarboxylate) is proposed. Our results demonstrate that hydrogen bonding is present in both the crystalline and the amorphous phases and its rearrangement can be considered as a significant driving force for crystallization of poly(alkylene 2,5-furanoate)s.

Spectral information content of Compton scattering events in silicon photon counting detectors.

Silicon (Si) is a possible sensor material for photon counting detectors (PCDs). A major drawback of Si is that roughly two-thirds of x-ray interactions in the diagnostic energy range are Compton scattering. Because Compton scattering is an energy-insensitive process, it is commonly assumed that Compton events retain little spectral information. To quantify how much information can be recovered from Compton scattering events in models of Si PCDs. We built a simplified model of Si interactions including two interaction mechanisms: photoelectric effect and Compton scattering. We considered three different binning options that represent strategies for handling Compton events: in Compton censoring, all events under 38keV (the maximum energy possible from Compton scattering for a 120keV incident photon) were discarded; in Compton counting, all events between 1 and 38keV were placed into a single bin; in Compton binning, all events were placed into energy bins of uniform width. These were compared to the ideal detector, which always recorded the correct energy (i.e., 100% photoelectric effect). Every photon was assumed to interact once and only once with Si, and the energy bin width was 5keV. In the primary analysis, the Si detector was irradiated with a 120kV spectrum filtered by 30cm of water, with 99.5% of the arriving spectrum above 38keV so that there was good separation between photoelectric effect and Compton scattering, and the figures of merit were the Cramér-Rao lower bound (CRLB) of the variance of iodine and water basis material decomposition images, as well as the CRLB of virtual monoenergetic images (i.e., linear combinations of material images) that maximize iodine CNR or water CNR. We also constructed a local linear estimator that attains the CRLB. In secondary analyses, we applied other sources of spectral distortion: (1) a nonzero minimum energy threshold; (2) coarser, 10keV energy bins; and (3) a model of charge sharing. With our chosen spectrum, 67% of the interactions were Compton scattering. Consistent with this, the material decomposition variance for the Compton censoring model, averaged over both basis materials, was 258% greater than the ideal detector. If Compton events carried no spectral information, the Compton counting model would show similar variance. Instead, its basis material variance was 103% greater than the ideal detector, implying that Compton counts indeed carry significant spectral information. The Compton binning model had a basis material variance 60% greater than the ideal detector. The Compton binning model was not affected by a 5keV minimum energy threshold, but the variance increased from 60% to 107% when charge sharing was included and to 78% with coarser energy bins. For optimized CNR images, the average variance was 149%, 12%, and 10% higher than the ideal detector for the Compton censoring, counting, and binning models, reinforcing the hypothesis that Compton counts are useful for detection tasks and that precise energy assignments are not necessary. Substantial spectral information remains after Compton scattering events in silicon PCDs.