A YOLOv5-based network for the detection of a diffuse reflectance spectroscopy probe to aid surgical guidance in gastrointestinal cancer surgery

PURPOSE: A positive circumferential resection margin (CRM) for oesophageal and gastric carcinoma is associated with local recurrence and poorer long-term survival. Diffuse reflectance spectroscopy (DRS) is a non-invasive technology able to distinguish tissue type based on spectral data. The aim of this study was to develop a deep learning-based method for DRS probe detection and tracking to aid classification of tumour and non-tumour gastrointestinal (GI) tissue in real time. METHODS: Data collected from both ex vivo human tissue specimen and sold tissue phantoms were used for the training and retrospective validation of the developed neural network framework. Specifically, a neural network based on the You Only Look Once (YOLO) v5 network was developed to accurately detect and track the tip of the DRS probe on video data acquired during an ex vivo clinical study. RESULTS: Different metrics were used to analyse the performance of the proposed probe detection and tracking framework, such as precision, recall, mAP 0.5, and Euclidean distance. Overall, the developed framework achieved a 93% precision at 23 FPS for probe detection, while the average Euclidean distance error was 4.90 pixels. CONCLUSION: The use of a deep learning approach for markerless DRS probe detection and tracking system could pave the way for real-time classification of GI tissue to aid margin assessment in cancer resection surgery and has potential to be applied in routine surgical practice

Identification of phenolic compounds in Australian grown dragon fruits by LC-ESI-QTOF-MS/MS and determination of their antioxidant potential

Dragon fruit is a popular tropical fruit that has a high phenolic content which are the main contributors to the antioxidant potential and health benefits of dragon fruit pulp and peel waste. Although some phenolic compounds in dragon fruit have previously been reported, a comprehensive analysis of complete phenolic profile of the Australian varieties has not been conducted. Thus, the aim of this study was to extract, identify and quantify phenolics from dragon fruits grown in Australia. Phenolic compounds were extracted from the peels and pulps of white and red dragon fruit. Phenolic content was determined by total phenolic content (TPC), total flavonoid content (TFC) and total tannin content (TTC), while antioxidant activities were measured by 2,2-diphenyl-1-picrylhydrazyl (DPPH), ferric reducing antioxidant power (FRAP), 2,2′-Azino-bis-3-ethylbenzothiazoline-6-sulphonic acid (ABTS) and total antioxidant capacity (TAC). The results showed that dragon fruit pulp had a higher total phenolic content and stronger antioxidant capacity than peel, while the peel had a higher content of flavonoids and tannins than the pulp. Liquid chromatography electrospray ionization quadrupole time-of-flight mass spectrometry (LC-ESI-QTOF-MS/MS) was used for the characterization of phenolic compounds, a total of 80 phenolics including phenolic acids (25), flavonoids (38), lignans (6), stilbene (3) and other polyphenols (8) were characterized in all dragon fruits. High performance liquid chromatography equipped with photodiode array detector (HPLC-PDA) quantified the phenolic compounds in different portion of dragon fruit and showed that dragon peel had higher concentrations of phenolics than pulp. The results highlighted that both dragon fruit peel and pulp are potential sources of phenolic compounds, with peel in particular being a source of antioxidant phenolics with potential as ingredients for the food and pharmaceutical industries

Measuring Re-identification Risk

Compact user representations (such as embeddings) form the backbone of personalization services. In this work, we present a new theoretical framework to measure re-identification risk in such user representations. Our framework, based on hypothesis testing, formally bounds the probability that an attacker may be able to obtain the identity of a user from their representation. As an application, we show how our framework is general enough to model important real-world applications such as the Chrome's Topics API for interest-based advertising. We complement our theoretical bounds by showing provably good attack algorithms for re-identification that we use to estimate the re-identification risk in the Topics API. We believe this work provides a rigorous and interpretable notion of re-identification risk and a framework to measure it that can be used to inform real-world applications

The effects of environmental disturbances on tumor growth

In this study, the analytic expressions of the steady probability distribution of tumor cells were established based on the steady state solution to the corresponding Fokker-Planck equation. Then, the effects of two uncorrelated white noises on tumor cell growth were investigated. It was found that the predation rate plays the main role in determining whether or not the noise is favorable for tumor growth.Comment: 14 pages, 11 figures. Note: The paper will be published on volume 42 of the Brazilian Journal of Physic

Screening of Phenolic Compounds in Australian Grown Berries by LC-ESI-QTOF-MS/MS and Determination of Their Antioxidant Potential

Berries are grown worldwide with the most consumed berries being blackberries (Rubus spp.), blueberries (Vaccinium corymbosum), red raspberries (Rubus idaeus) and strawberries (Fragaria spp.). Berries are either consumed fresh, frozen, or processed into wines, juices, and jams. In recent times, researchers have focused their attention on berries due to their abundance in phenolic compounds. The current study aimed to evaluate the phenolic content and their antioxidant potential followed by characterization and quantification using LC-ESI-QTOF-MS/MS and HPLC-PDA. Blueberries were highest in TPC (2.93 ± 0.07 mg GAE/gf.w.) and TFC (70.31 ± 1.21 µg QE/gf.w.), whereas the blackberries had the highest content in TTC (11.32 ± 0.13 mg CE/gf.w.). Blueberries had the highest radical scavenging capacities for the DPPH (1.69 ± 0.09 mg AAE/gf.w.), FRAP (367.43 ± 3.09 µg AAE/gf.w.), TAC (1.47 ± 0.20 mg AAE/gf.w.) and ABTS was highest in strawberries (3.67 ± 0.14 mg AAE/gf.w.). LC-ESI-QTOF-MS/MS study identified a total of 65 compounds including 42 compounds in strawberries, 30 compounds in raspberries, 28 compounds in blueberries and 21 compounds in blackberries. The HPLC-PDA quantification observed phenolic acid (p-hydroxybenzoic) and flavonoid (quercetin-3-rhamnoside) higher in blueberries compared to other berries. Our study showed the presence of phenolic acids and provides information to be utilized as an ingredient in food, pharmaceutical and nutraceutical industries

Acute rejection is associated with antibodies to non-Gal antigens in baboons using Gal-knockout pig kidneys

We transplanted kidneys from α1,3-galactosyltransferase knockout (GalT-KO) pigs into six baboons using two different immunosuppressive regimens, but most of the baboons died from severe acute humoral xenograft rejection. Circulating induced antibodies to non-Gal antigens were markedly elevated at rejection, which mediated strong complement-dependent cytotoxicity against GalT-KO porcine target cells. These data suggest that antibodies to non-Gal antigens will present an additional barrier to transplantation of organs from GalT-KO pigs to humans. © 2005 Nature Publishing Group

A QM/MM approach for the study of monolayer-protected gold clusters

We report the development and implementation of hybrid methods that combine quantum mechanics (QM) with molecular mechanics (MM) to theoretically characterize thiolated gold clusters. We use, as training systems, structures such as Au25(SCH2-R)18 and Au38(SCH2-R)24, which can be readily compared with recent crystallographic data. We envision that such an approach will lead to an accurate description of key structural and electronic signatures at a fraction of the cost of a full quantum chemical treatment. As an example, we demonstrate that calculations of the 1H and 13C NMR shielding constants with our proposed QM/MM model maintain the qualitative features of a full DFT calculation, with an order-of-magnitude increase in computational efficiency.Comment: Journal of Materials Science, 201

Effect of ZnCdTe-Alloyed Nanocrystals on Polymer–Fullerene Bulk Heterojunction Solar Cells

The photovoltaic properties of solar cell based on the blends of poly[2-methoxy-5-(2-ethylhexoxy-1,4-phenylenevinylene) (MEH-PPV), fullerene (C60), and ZnCdTe-alloyed nanocrystals were investigated. Comparing the spectral response of photocurrent of the MEH-PPV:C60(+ZnCdTe) nanocomposite device with that of the devices based on MEH-PPV:C60and pristine MEH-PPV, one can find that the nanocomposite device exhibits an enhanced photocurrent. In comparing the composite devices with different ZnCdTe:[MEH-PPV + C60] weight ratios of 10 wt% (D1–1), 20 wt% (D1–2), 40 wt% (D1–3), and 70 wt% (D1–4), it was found that the device D1–3exhibits the best performance. The power conversion efficiency (η) is improved doubly compared with that of the MEH-PPV:C60device

Massive End-to-end Models for Short Search Queries

In this work, we investigate two popular end-to-end automatic speech recognition (ASR) models, namely Connectionist Temporal Classification (CTC) and RNN-Transducer (RNN-T), for offline recognition of voice search queries, with up to 2B model parameters. The encoders of our models use the neural architecture of Google's universal speech model (USM), with additional funnel pooling layers to significantly reduce the frame rate and speed up training and inference. We perform extensive studies on vocabulary size, time reduction strategy, and its generalization performance on long-form test sets. Despite the speculation that, as the model size increases, CTC can be as good as RNN-T which builds label dependency into the prediction, we observe that a 900M RNN-T clearly outperforms a 1.8B CTC and is more tolerant to severe time reduction, although the WER gap can be largely removed by LM shallow fusion