In-situ uniaxial drawing of poly-L-lactic acid (PLLA): Following the crystalline morphology development using time-resolved SAXS/WAXS

Simultaneous synchrotron small- and wide-angle X-ray scattering (SAXS/WAXS) was used to follow the crystalline morphology evolution of poly-L- lactic acid (PLLA) during uniaxial deformation at various draw temperatures (Td). The mechanical behaviour of PLLA, was found to be strongly dependent on Td. 2D SAXS/WAXS data taken during the draw showed that at low Tds cavitation and voiding occurred and the initial crystallites underwent ‘overdrawing’ where they slip and are partially destroyed. SEM confirmed that surface voiding and cavitation had occurred at Td = 60 and 65 °C but was absent at higher Tds. During the draw, no long-range macromolecular lamellar structure was seen in the SAXS, but small crystallites of the disordered α′ crystal form of PLLA were observed in the WAXS at all Tds. The PLLA samples were then step annealed in a second processing stage (post-draw) to develop the oriented crystalline lamellar structure and increase the amount of the stable α crystalline form. SAXS/WAXS data showed that a highly oriented lamellar stack macrostructure developed on annealing, with increased crystallite size and crystallinity at all Tds. Furthermore, step annealing drove the crystalline transition in all samples from the disordered α′ crystal form to the stable α crystal form. Therefore, varying pre- and post-processing parameters can significantly influence the mechanical properties, orientation, crystalline morphology and crystal phase transition of the final PLLA material

Air sensitivity of MoS2, MoSe2, MoTe2, HfS2 and HfSe2

A surface sensitivity study was performed on different transition-metal dichalcogenides (TMDs) under ambient conditions in order to understand which material is the most suitable for future device applications. Initially, Atomic Force Microscopy and Scanning Electron Microscopy studies were carried out over a period of 27 days on mechanically exfoliated flakes of 5 different TMDs, namely, MoS2, MoSe2, MoTe2, HfS2, and HfSe2. The most reactive were MoTe2 and HfSe2. HfSe2, in particular, showed surface protrusions after ambient exposure, reaching a height and width of approximately 60 nm after a single day. This study was later supplemented by Transmission Electron Microscopy (TEM) cross-sectional analysis, which showed hemispherical-shaped surface blisters that are amorphous in nature, approximately 180–240 nm tall and 420–540 nm wide, after 5 months of air exposure, as well as surface deformation in regions between these structures, related to surface oxidation. An X-ray photoelectron spectroscopy study of atmosphere exposed HfSe2 was conducted over various time scales, which indicated that the Hf undergoes a preferential reaction with oxygen as compared to the Se. Energy-Dispersive X-Ray Spectroscopy showed that the blisters are Se-rich; thus, it is theorised that HfO2 forms when the HfSe2 reacts in ambient, which in turn causes the Se atoms to be aggregated at the surface in the form of blisters. Overall, it is evident that air contact drastically affects the structural properties of TMD materials. This issue poses one of the biggest challenges for future TMD-based devices and technologies

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Abstract: Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts

On Critical Care Data and Machine Learning Loss Function Landscapes

Intensive Care Units (ICUs) are constantly under strain, with many vulnerable people needing urgent critical care. Often doctors do not have the capacity to accommodate everyone, and so it is important that patients are discharged when it is safe to do so. However, discharging a patient at the wrong time may result in the patient being readmitted or simply not surviving; both of these cases are highly undesirable. Therefore, it is useful for there to be an understanding of what factors contribute most to the mortality rate of patients in intensive care units. In the present work, machine learning models are trained to predict the mortality of a given patient with certain measurements. Neural networks with one hidden layer and two outcomes (alive or deceased) are used in these models, and local minima of the neural network loss functions are found using basin-hopping methods implemented with the open source software GMIN. In this way, the landscape of the loss function defined by the neural network model can be explored. Area Under the Curve (AUC) values were used to evaluate these models. Two databases, MIMIC III and Amsterdam UMC db, are compared. There are many possible calculations to perform, and initially time-series for single variables and pairs of variables are used as inputs to the neural network. From MIMIC III, Glasgow Coma Scale (GCS) and Blood Urea Nitrogen (BUN) perform well, with AUCs just below 0.8 on their own, and an AUC above 0.8 together. From Amsterdam UMC db, Blood Pressure (BP) measurements perform well, with AUCs around 0.8. Generally the data from Amsterdam UMC db appears to outperform MIMIC III. The effect of using a model trained on one time window and evaluated on different time windows is also investigated, and we find that the AUC value decreases but not substantially for most clinical variables, suggesting the most recent data is the most useful for mortality prediction. There is a notable exception in Respiration Rate, where it is found that data from earlier times may actually provide more prognostic value than the most recent measurements. A permutational shuffling analysis is performed, which reveals patterns in the ways the data is organised, and sheds light on some innate properties of the data. The data from the two ICU databases are then applied to another model, where inputs to the neural network are the worst values of a set of pre-chosen clinical variables, inspired by a score used elsewhere in the medical prognosis picture (APACHE II). The AUCs obtained in this way are generally better than for the time-series data above, with AUCs reaching just under 0.8 for MIMIC III and 0.85 for Amsterdam UMC db. Synthetic spiral data is created to test some new machine learning methods, including an ensemble-like method where minima from the loss function landscape are combined in a process called Machine Learning Superposition (MLSUP). Minima are selected to maximise the diversity between them; pairs of minima are identified as suitable candidates for MLSUP by their misclassification index and their contributions to heat capacity peaks. We find that MLSUP outperforms a single neural network model for a larger neural network architectures, but is not as useful for a smaller ones. MLSUP is also applied to the ICU databases described above, however we find that there is little improvement in the AUCs obtained by the single neural networks. The synthetic data is further used to explore two new landscapes: the landscape defined by a loss function designed to closely resemble the AUC function, and a landscape where narrower minima are penalised in energy (Sharpness Aware Minimisation, SAM). In both cases, AUCs from synthetic data and the real data are comparable to the more conventional ``cross entropy'' loss function, but do not offer much improvement. Coupled with the fact that these new loss functions have higher order complexity, and hence take longer to evaluate, it is concluded that these landscapes are not practically useful. However they still offer great insight into the nature of machine learning models and their landscapes. This thesis concludes with an overview of the work completed, and some closing thoughts on the use of artificial intelligence (AI) in the healthcare setting, discussing how it should be used while adhering to some dangers it could present

Incorporating Cobalt Carbonyl Moieties onto Ethynylthiophene-Based Dithienylcyclopentene Switches. 1. Photochemistry

The synthesis and characterization of a series of dithienyl perhydro- and perfluorocyclopentene photochromic molecular switches appended with cobalt carbonyl binding 3-ethynylthiophene and phenyl-3-ethynylthiophene substituents are reported. Their photochromic properties, fatigue resistance, and thermal stability were examined to establish the effect of substituents on their performance as molecular photoswitches. The photochemical properties of the dithienylethene core were retained to the greatest extent by the inclusion of phenyl units and a hexafluorocyclopentene ring. The alkyne units of the switches were used to coordinate cobalt carbonyl moieties: i.e., Co-2(CO)(6) and Co-2(CO)(4)(dppm). The cobalt carbonyl moieties were found to reduce the efficiency of cyclization and cycloreversion of the dithienylethene unit. Density functional theory was used to identify the excited states responsible for cyclization.</p

Risikostories und Risikobewertung - Deutschland und Bulgarien im Vergleich

Stress oscillation has been observed in a number of linear thermoplastic polymers during the cold-drawing process, where the polymers exhibit periodic self-excited oscillatory neck propagation. However, the origin of the mechanical stress oscillation process and its relationship with the crystalline morphology of the polymer are still under debate. In this work, we revisit the stress oscillation behavior by studying a semi-crystalline polyester, poly(butylene succinate) (PBS), a biodegradable polymer suitable for biomedical and packaging applications. Stress oscillation of PBS is observed when deformed at a range of elongation rates from 10 to 200 mm min−1, and the fluctuation magnitude decays as the deformation temperature increases from 23 to 100 °C. Periodic transparent/opaque bands form during necking of PBS, which consists of alternating regions of highly oriented crystalline zones and microcavities due to crazing and voiding, although the degree of crystallinity did not change significantly in the bands. Simultaneous small- and wide-angle X-ray scattering confirms that the alternating stress increases, as shown in the stress–strain curves, correspond to the appearance of the transparent bands in the sample, and the abrupt drop of the stress is the result of voiding during the neck propagation. The voiding and cavitation are ultimately responsible for the stress oscillation process in PBS. The in-depth analysis of this work is important in understanding and controlling the occurrence of instabilities/cavitation during polymer processing such as film blowing, biaxial stretching and injection moulding of biodegradable polymer materials

Incorporating Cobalt Carbonyl Moieties onto Ethynylthiophene-Based Dithienylcyclopentene Switches. 2. Electro- and Spectroelectrochernical Properties

The redox behavior of dithienyl perhydro- and perfluorocyclopentene photochromic molecular switches, modified with 3-ethynylthiophene and phenyl-3-ethynylthiophene substituents, is explored by cyclic voltammetry and UV/vis-NIR and IR spectroelectrochemistry. The extent of electrochemical oxidation induced cyclization was depedent on whether a perhydro- or perfluorocyclopentene unit was present, with the former favoring ring closure, and on the nature of the substituents on the thienyl ring. The inclusion of a phenyl spacer between the alkynyl and thienyl moieties increased the stability of the molecular switches when addressed electrochemically. Binding of Co-2(CO)(6) and Co-2(CO)(4)dppm moieties to the alkyne units is shown to destabilize the cationic closed form and, in one example, inhibit oxidative cyclization for the 1,2-bis(5'(4 ''-phenyl-3 ''-ethynylthiophene)-2'-methylthien-3'-yl)perfluorocydopentene [Co-2(CO)(6)](2) complex (4Fo). However, the electrochemical cyclization observed for the Co-2(CO)(6) and Co-2(CO)(4) dppm complexes of 1,2-bis(5'-(3 ''-ethynylthiophene)2'-methylthien-3'-yl)cydopentene (3Ho and 5Ho, respectively) was induced following oxidation of the cobalt carbonyl moieties (i.e., at lower potentials than oxidation of the open form of the dithienylethene), possibly via an intramolecular electron transfer mechanism and thereby providing an alternative route to control the electrochromic behavior of the switch

Incorporating Cobalt Carbonyl Moieties onto Ethynylthiophene-Based Dithienylcyclopentene Switches. 1. Photochemistry

The synthesis and characterization of a series of dithienyl perhydro- and perfluorocyclopentene photochromic molecular switches appended with cobalt carbonyl binding 3-ethynylthiophene and phenyl-3-ethynylthiophene substituents are reported. Their photochromic properties, fatigue resistance, and thermal stability were examined to establish the effect of substituents on their performance as molecular photoswitches. The photochemical properties of the dithienylethene core were retained to the greatest extent by the inclusion of phenyl units and a hexafluorocyclopentene ring. The alkyne units of the switches were used to coordinate cobalt carbonyl moieties: i.e., Co2(CO)6 and Co2(CO)4(dppm). The cobalt carbonyl moieties were found to reduce the efficiency of cyclization and cycloreversion of the dithienylethene unit. Density functional theory was used to identify the excited states responsible for cyclization.

Enhancing Biomolecular Simulations with Hybrid Potentials Incorporating NMR Data.

Some recent advances in biomolecular simulation and global optimization have used hybrid restraint potentials, where harmonic restraints that penalize conformations inconsistent with experimental data are combined with molecular mechanics force fields. These hybrid potentials can be used to improve the performance of molecular dynamics, structure prediction, energy landscape sampling, and other computational methods that rely on the accuracy of the underlying force field. Here, we develop a hybrid restraint potential based on NapShift, an artificial neural network trained to predict protein nuclear magnetic resonance (NMR) chemical shifts from sequence and structure. In addition to providing accurate predictions of experimental chemical shifts, NapShift is fully differentiable with respect to atomic coordinates, which allows us to use it for structural refinement. By employing NapShift to predict chemical shifts from the protein conformation at each simulation step, we can compute an energy penalty and the corresponding hybrid restraint forces based on the difference between the predicted values and the experimental chemical shifts. The performance of the hybrid restraint potential was benchmarked using both basin-hopping global optimization and molecular dynamics simulations. In each case, the NapShift hybrid potential improved the accuracy, leading to better structure prediction via basin-hopping and increased local stability in molecular dynamics simulations. Our results suggest that neural network hybrid potentials based on NMR observables can enhance a broad range of molecular simulation methods, and the prediction accuracy will improve as more experimental training data become available