Modeling the efficiency of UV at 254 nm for disinfecting the different layers within N95 respirators

The study presented a Monte Carlo simulation of light transport in eight commonly used filtered facepiece respirators (FFRs) to assess the efficacy of UV at 254 nm for the inactivation of SARS-CoV-2. The results showed different fluence rates across the thickness of the eight different FFRs, implying that some FFR models may be more treatable than others, with the following order being (from most to least treatable): models 1512, 9105s, 1805, 9210, 1870+, 8210, 8110s and 1860, for single side illumination. The model predictions did not coincide well with some previously reported experimental data on virus inactivation when applied to FFR surfaces. The simulations predicted that FFRs should experience higher log reductions (>>6-log) than those observed experimentally (often limited to ~5-log). Possible explanations are virus shielding by aggregation or soiling, and a lack of the Monte Carlo simulations considering near-field scattering effects that can create small, localized regions of low UV photon probability on the surface of the fiber material. If the latter is the main cause in limiting practical UV viral decontamination, improvement might be achieved by exposing the FFR to UV isotropically from all directions, such as by varying the UV source to the FFR surface angle during treatment

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

PDT-SPACE: Automatic Interstitial Photodynamic Therapy Planning and Optimization

Photodynamic Therapy (PDT) is a minimally invasive oncology treatment that uses light-activated photosensitizers to destroy tumors locally. It has been approved for a variety of superficial tumor indications such as the skin. For deep-seated tumors like brain glioma, the treatment (via interstitial light illumination with fiber optic probes) is yet to be approved due to several challenges that hinder its efficacy. Much of the research to improve interstitial PDT’s (iPDT) therapeutic index has been directed towards developing targeted photosensitizers. However, another factor that highly affects iPDT efficacy is the light penetration inside the tissues, especially when the tumor is surrounded by extremelysensitive healthy tissues such as white matter in the brain. This thesis introduces PDT-SPACE, an open-source software for automatic iPDT planning. We present novel computer-aided design (CAD) algorithms to allocate powers to a set of light diffusers and determine the location and lengths of those diffusers in order to minimize the damage to surrounding organs-at-risk (OAR), while maximizing the damage to the tumor. We also generalize the power allocation algorithm to generate tailored power profiles for cylindrical diffusers to better conform the light distribution to the tumor shape and further decrease the damage. We simulate our methods onnine virtual brain tumors constructed from glioblastoma multiforme (GBM) MRI images taken from the cancer imaging archive. Through the various optimizations presented, we show that the treatment plans generated by PDT-SPACE can reduce damage to OAR by more than 70% on average compared to state-of-the-art techniques, while destroying at least 98% of the tumor. Additionally, we present several techniques utilizing machine learning to generate robust treatment plans that take into account inter-patient variations of tissue optical properties. These robust treatment plans are more adaptable to the biological uncertainties of individual patients. Finally, we show how PDT-SPACE serves as a prospective methodology to analyze the effect of different combinations of optimization parameters on iPDT efficacy for various oncological indications while avoiding the technical difficulties that come with in-vivo direct measurements. We present different treatment plans (each with its own trade-off) to treat bone metastasis resulting from colorectal cancer recurrence.Ph.D

A Fast Metal Layer Elimination Approach for Power Grid Reduction in Integrated Circuits

Simulation and verification of the on-die power delivery network (PDN) is one of the key steps in the design of integrated circuits. With the very large sizes of modern grids, verification of PDNs has become very expensive and a host of techniques for grid model approximation have been proposed. These include topological node elimination and full-blown numerical model order reduction (MOR). However, both of these traditional approaches suffer from drawbacks that limit their scalability to very large grids. In this thesis, we propose a novel technique for grid reduction that is a hybrid of both approaches -- the method is numerical but also factors in grid topology. It works by eliminating whole internal layers of the grid at a time, while aiming to preserve the dynamic behavior of the grid. Effectively, instead of traditional node-by-node topological elimination we provide a numerical layer-by-layer block-matrix approach that is both fast and accurate.M.A.S

Twelve-month observational study of children with cancer in 41 countries during the COVID-19 pandemic

Childhood cancer is a leading cause of death. It is unclear whether the COVID-19 pandemic has impacted childhood cancer mortality. In this study, we aimed to establish all-cause mortality rates for childhood cancers during the COVID-19 pandemic and determine the factors associated with mortality