Partial scanning transmission electron microscopy with deep learning

Compressed sensing algorithms are used to decrease electron microscope scan time and electron beam exposure with minimal information loss. Following successful applications of deep learning to compressed sensing, we have developed a two-stage multiscale generative adversarial neural network to complete realistic 512 × 512 scanning transmission electron micrographs from spiral, jittered gridlike, and other partial scans. For spiral scans and mean squared error based pre-training, this enables electron beam coverage to be decreased by 17.9× with a 3.8% test set root mean squared intensity error, and by 87.0× with a 6.2% error. Our generator networks are trained on partial scans created from a new dataset of 16227 scanning transmission electron micrographs. High performance is achieved with adaptive learning rate clipping of loss spikes and an auxiliary trainer network. Our source code, new dataset, and pre-trained models are publicly available

Adaptive learning rate clipping stabilizes learning

Artificial neural network training with gradient descent can be destabilized by 'bad batches' with high losses. This is often problematic for training with small batch sizes, high order loss functions or unstably high learning rates. To stabilize learning, we have developed adaptive learning rate clipping (ALRC) to limit backpropagated losses to a number of standard deviations above their running means. ALRC is designed to complement existing learning algorithms: Our algorithm is computationally inexpensive, can be applied to any loss function or batch size, is robust to hyperparameter choices and does not affect backpropagated gradient distributions. Experiments with CIFAR-10 supersampling show that ALCR decreases errors for unstable mean quartic error training while stable mean squared error training is unaffected. We also show that ALRC decreases unstable mean squared errors for scanning transmission electron microscopy supersampling and partial scan completion. Our source code is available at https://github.com/Jeffrey-Ede/ALRC

Resin Distribution in Medium Density Fiberboard. Quantification of UF Resin Distribution on Blowline-and Dry-Blended MDF Fiber and Panels

A novel technique has been developed for visualizing urea formaldehyde (UF) resin distribution on fibers and within MDF panels. A fluorescent label was chemically bound to the resin, and digital images of resinated fiber, generated via confocal laser scanning microscopy (CLSM), were analyzed. Results indicate that this technique can be used to quantify UF resin coverage and distribution as well as provide information on resin film thickness on MDF fiber before pressing and in panels. The technique can distinguish between different methods of resination and was employed to determine that these processes can result in different surface coverages of UF resin on MDF fiber. Resin injected at the end of the blowline gave significantly less resin coverage of fiber than that which was injected at the start of the blowline. UF resin droplets were also relatively thicker and less dispersed when injected at the end of the blowline. Visualization of UF resin also illustrated resin distribution changes upon pressing of fiber particularly in the presence of wax. This result has important implications for future studies targeting optimization of resin deposition, since the droplet size distribution, as applied to the fiber, may not correspond to the droplet size distribution of resin in the panel

Periodontal status of rheumatoid arthritis patients in khartoum state

<p>Abstract</p> <p>Background</p> <p>Few studies have investigated the periodontal condition among Rheumatoid arthritis in Sudan. The present study described the periodontal condition among Sudanese patients suffering from rheumatoid arthritis and to compare them with those of non-rheumatic subjects.</p> <p>Methods</p> <p>A group of eighty rheumatoid arthritis patients was selected from Patient's Rheumatoid Clinics in Khartoum State during the period of January to May 2010. A control group of eighty patients with the same age and gender was selected for the study. Both Rheumatoid arthritis patients and the control group were examined for their plaque index, gingival index, and clinical attachment loss.</p> <p>Results</p> <p>The results revealed that there were no significant differences in plaque and gingival index among study and control groups, with mean plaque index of (1.25 ± 0.4) for patients and (1.17 ± 0.28) for the control group (p-value is 0.3597). The mean gingival index was (1.2 ± 0.24) for the patients and (1.2 ± 0.33) for the control (p = is 0.3049). The results showed statistically significant differences in clinical attachment loss between study and control groups, with mean clinical attachment loss of (1.03 ± 0.95) for the study group and (0.56 ± 0.63) for the control group (p = 0.0002). The study revealed that no association exists between the type of drug used to treat rheumatoid arthritis (NSAIDs & DMARDs) and the periodontal parameters (plaque index, gingival index, and clinical attachment loss).</p> <p>Conclusion</p> <p>A significant relationship between periodontal disease and Rheumatoid Arthritis does exist, but no difference between plaque and gingival index has been detected among study and control groups.</p

The 2022 world health organization reevaluation of human and mammalian toxic equivalency factors for polychlorinated dioxins, dibenzofurans and biphenyls

In October 2022, the World Health Organization (WHO) convened an expert panel in Lisbon, Portugal in which the 2005 WHO TEFs for chlorinated dioxin-like compounds were reevaluated. In contrast to earlier panels that employed expert judgement and consensus-based assignment of TEF values, the present effort employed an update to the 2006 REP database, a consensus-based weighting scheme, a Bayesian dose response modeling and meta-analysis to derive "Best-Estimate" TEFs. The updated database contains almost double the number of datasets from the earlier version and includes metadata that informs the weighting scheme. The Bayesian analysis of this dataset results in an unbiased quantitative assessment of the congener-specific potencies with uncertainty estimates. The "Best-Estimate" TEF derived from the model was used to assign 2022 WHO-TEFs for almost all congeners and these values were not rounded to half-logs as was done previously. The exception was for the mono-ortho PCBs, for which the panel agreed to retain their 2005 WHO-TEFs due to limited and heterogenous data available for these compounds. Applying these new TEFs to a limited set of dioxin-like chemical concentrations measured in human milk and seafood indicates that the total toxic equivalents will tend to be lower than when using the 2005 TEFs

International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways.

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined<5 × 10(-8)) and used pathway analysis to identify JAK-STAT/IL12/IL27 signalling and cytokine-cytokine pathways, for which relevant therapies exist