Phonons of Metallic Vicinal Surfaces

We present an analysis of the vibrational dynamics of metal vicinal surfaces using the embedded atom method to describe the interaction potential and both a real space Green's function method and a slab method to calculate the phonons. We report two main general characteristics : a global shift of the surface vibrational density of states resulting from a softening of the force field. The latter is a direct result of the reduction of coordination for the different type of surface atoms; and an appearance of high frequency modes above the bulk band, resulting from a stiffening of the force field near the step atom. The latter is due to a rearrangement of the atomic positions during the relaxation of the surface atoms yielding a large shortening of the nearest neighbor distances near the step atoms.Comment: 6 figures, to appear in Sur. Sci. proceedings of VAS1

SparCL: Sparse Continual Learning on the Edge

Existing work in continual learning (CL) focuses on mitigating catastrophic forgetting, i.e., model performance deterioration on past tasks when learning a new task. However, the training efficiency of a CL system is under-investigated, which limits the real-world application of CL systems under resource-limited scenarios. In this work, we propose a novel framework called Sparse Continual Learning(SparCL), which is the first study that leverages sparsity to enable cost-effective continual learning on edge devices. SparCL achieves both training acceleration and accuracy preservation through the synergy of three aspects: weight sparsity, data efficiency, and gradient sparsity. Specifically, we propose task-aware dynamic masking (TDM) to learn a sparse network throughout the entire CL process, dynamic data removal (DDR) to remove less informative training data, and dynamic gradient masking (DGM) to sparsify the gradient updates. Each of them not only improves efficiency, but also further mitigates catastrophic forgetting. SparCL consistently improves the training efficiency of existing state-of-the-art (SOTA) CL methods by at most 23X less training FLOPs, and, surprisingly, further improves the SOTA accuracy by at most 1.7%. SparCL also outperforms competitive baselines obtained from adapting SOTA sparse training methods to the CL setting in both efficiency and accuracy. We also evaluate the effectiveness of SparCL on a real mobile phone, further indicating the practical potential of our method.Comment: Published at NeurIPS 2022 as a conference pape

Additive and multiplicative hazards modeling for recurrent event data analysis

<p>Abstract</p> <p>Background</p> <p>Sequentially ordered multivariate failure time or recurrent event duration data are commonly observed in biomedical longitudinal studies. In general, standard hazard regression methods cannot be applied because of correlation between recurrent failure times within a subject and induced dependent censoring. Multiplicative and additive hazards models provide the two principal frameworks for studying the association between risk factors and recurrent event durations for the analysis of multivariate failure time data.</p> <p>Methods</p> <p>Using emergency department visits data, we illustrated and compared the additive and multiplicative hazards models for analysis of recurrent event durations under (i) a varying baseline with a common coefficient effect and (ii) a varying baseline with an order-specific coefficient effect.</p> <p>Results</p> <p>The analysis showed that both additive and multiplicative hazards models, with varying baseline and common coefficient effects, gave similar results with regard to covariates selected to remain in the model of our real dataset. The confidence intervals of the multiplicative hazards model were wider than the additive hazards model for each of the recurrent events. In addition, in both models, the confidence interval gets wider as the revisit order increased because the risk set decreased as the order of visit increased.</p> <p>Conclusions</p> <p>Due to the frequency of multiple failure times or recurrent event duration data in clinical and epidemiologic studies, the multiplicative and additive hazards models are widely applicable and present different information. Hence, it seems desirable to use them, not as alternatives to each other, but together as complementary methods, to provide a more comprehensive understanding of data.</p

Domain Adaptation using Silver Standard Labels for Ki-67 Scoring in Digital Pathology: A Step Closer to Widescale Deployment

Deep learning systems have been proposed to improve the objectivity and efficiency of Ki- 67 PI scoring. The challenge is that while very accurate, deep learning techniques suffer from reduced performance when applied to out-of-domain data. This is a critical challenge for clinical translation, as models are typically trained using data available to the vendor, which is not from the target domain. To address this challenge, this study proposes a domain adaptation pipeline that employs an unsupervised framework to generate silver standard (pseudo) labels in the target domain, which is used to augment the gold standard (GS) source domain data. Five training regimes were tested on two validated Ki-67 scoring architectures (UV-Net and piNET), (1) SS Only: trained on target silver standard (SS) labels, (2) GS Only: trained on source GS labels, (3) Mixed: trained on target SS and source GS labels, (4) GS+SS: trained on source GS labels and fine-tuned on target SS labels, and our proposed method (5) SS+GS: trained on source SS labels and fine-tuned on source GS labels. The SS+GS method yielded significantly (p < 0.05) higher PI accuracy (95.9%) and more consistent results compared to the GS Only model on target data. Analysis of t-SNE plots showed features learned by the SS+GS models are more aligned for source and target data, resulting in improved generalization. The proposed pipeline provides an efficient method for learning the target distribution without manual annotations, which are time-consuming and costly to generate for medical images. This framework can be applied to any target site as a per-laboratory calibration method, for widescale deployment.Comment: Editors: Accepted for publication at MIDL 202

Empirical study of correlated survival times for recurrent events with proportional hazards margins and the effect of correlation and censoring.

Background: In longitudinal studies where subjects experience recurrent incidents over a period of time, such as respiratory infections, fever or diarrhea, statistical methods are required to take into account the within-subject correlation. Methods: For repeated events data with censored failure, the independent increment (AG), marginal (WLW) and conditional (PWP) models are three multiple failure models that generalize Cox"s proportional hazard model. In this paper, we revise the efficiency, accuracy and robustness of all three models under simulated scenarios with varying degrees of within-subject correlation, censoring levels, maximum number of possible recurrences and sample size. We also study the methods performance on a real dataset from a cohort study with bronchial obstruction. Results: We find substantial differences between methods and there is not an optimal method. AG and PWP seem to be preferable to WLW for low correlation levels but the situation reverts for high correlations. Conclusions: All methods are stable in front of censoring, worsen with increasing recurrence levels and share a bias problem which, among other consequences, makes asymptotic normal confidence intervals not fully reliable, although they are well developed theoretically

Stage-Dependent Tolerance of the German Cockroach, Blattella germanica for Dichlorvos and Propoxur

Stage-dependent dichlorvos and propoxur tolerance in a field population of the German cockroach, Blattella germanica Linnaeus (Blatodea: Blattellidae), was investigated in the laboratory using a topical application bioassay. The results showed the 6 week-old nymphs were more tolerant to dichlorvos and propoxur than the other ages tested. LD50 values of dichlorvos and propoxur for the 6 week-old nymphs were 2.003 µµg per insect and 5.296 µµg per insect, respectively. Tolerance ratios of 18.55-fold and 4.98-fold for LD50 were obtained from 6-week-old nymphs compared to 4 week-old nymphs. The specific activity of acetylcholinesterase (AChE) from 1 week-old nymphs was the highest among all tested developmental stages of nymphs and adult males and females. The specific activity of AChE decreased significantly with increasing age. The sensitivity of AChE to dichlorvos was the highest with a ki value of 3.12××104 mol-1min-1 in the last nymphal stage of B. germanica (about 6 weeks-old). The AChE from 4 week-old nymphs was the most sensitive to propoxur, with the highest ki value being 2.63××105 mol-1min-1. These results indicated that the different developmental stages and sexes of B. germanica affected the inhibition of AChE by dichlorvos and propoxur

Microbial diversity and biogeochemical cycling in soda lakes

Soda lakes contain high concentrations of sodium carbonates resulting in a stable elevated pH, which provide a unique habitat to a rich diversity of haloalkaliphilic bacteria and archaea. Both cultivation-dependent and -independent methods have aided the identification of key processes and genes in the microbially mediated carbon, nitrogen, and sulfur biogeochemical cycles in soda lakes. In order to survive in this extreme environment, haloalkaliphiles have developed various bioenergetic and structural adaptations to maintain pH homeostasis and intracellular osmotic pressure. The cultivation of a handful of strains has led to the isolation of a number of extremozymes, which allow the cell to perform enzymatic reactions at these extreme conditions. These enzymes potentially contribute to biotechnological applications. In addition, microbial species active in the sulfur cycle can be used for sulfur remediation purposes. Future research should combine both innovative culture methods and state-of-the-art ‘meta-omic’ techniques to gain a comprehensive understanding of the microbes that flourish in these extreme environments and the processes they mediate. Coupling the biogeochemical C, N, and S cycles and identifying where each process takes place on a spatial and temporal scale could unravel the interspecies relationships and thereby reveal more about the ecosystem dynamics of these enigmatic extreme environments

Investigating the dynamics of surface-immobilized DNA nanomachines

Surface-immobilization of molecules can have a profound influence on their structure, function and dynamics. Toehold-mediated strand displacement is often used in solution to drive synthetic nanomachines made from DNA, but the effects of surface-immobilization on the mechanism and kinetics of this reaction have not yet been fully elucidated. Here we show that the kinetics of strand displacement in surface-immobilized nanomachines are significantly different to those of the solution phase reaction, and we attribute this to the effects of intermolecular interactions within the DNA layer. We demonstrate that the dynamics of strand displacement can be manipulated by changing strand length, concentration and G/C content. By inserting mismatched bases it is also possible to tune the rates of the constituent displacement processes (toehold-binding and branch migration) independently, and information can be encoded in the time-dependence of the overall reaction. Our findings will facilitate the rational design of surface-immobilized dynamic DNA nanomachines, including computing devices and track-based motors

Allergic conditions and risk of hematological malignancies in adults: a cohort study

BACKGROUND: Two contradictory hypotheses have been proposed to explain the relationship between allergic conditions and malignancies, the immune surveillance hypothesis and the antigenic stimulation hypothesis. The former advocates that allergic conditions may be protective against development of cancer, whereas the latter proposes an increased risk. This relationship has been studied in several case-control studies, but only in a few cohort studies. METHODS: The association between allergic conditions and risk of developing leukemia, Hodgkin's disease, non-Hodgkin's lymphoma and myeloma was investigated in a cohort of 16,539 Swedish twins born 1886–1925. Prospectively collected, self-reported information about allergic conditions such as asthma, hay fever or eczema was obtained through questionnaires administered in 1967. The cohort was followed 1969–99 and cancer incidence was ascertained from the Swedish Cancer Registry. RESULTS: Hives and asthma tended to increase the risk of leukemia (relative risk [RR] = 2.1, 95% Confidence Interval [CI] 1.0–4.5 and RR = 1.6, 95% CI 0.8–3.5, respectively). There was also an indication of an increased risk of non-Hodgkin's lymphoma associated with eczema during childhood (RR = 2.3, 95% CI 1.0–5.3). CONCLUSION: In contrast to most previous studies, our results do not indicate a protective effect of allergic conditions on the risk of developing hematological malignancies. Rather, they suggest that allergic conditions might increase the risk of some hematological malignancies