Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing opioid prescribing practices after minimally invasive lung resection through a quality-improvement intervention

Background: In their 2019 guideline on the prescribing and management of opioids after elective ambulatory thoracic surgery, the Canadian Association of Thoracic Surgeons (CATS) recommended 120 morphine milligram equivalents (MME) after minimally invasive (video-assisted thoracoscopic surgery [VATS]) lung resection. We conducted a quality-improvement project to optimize opioid prescribing after VATS lung resection. Methods: We assessed baseline prescribing practices for opioid-naive patients. Using a mixed-methods approach, we selected 2 quality-improvement interventions: formal incorporation of the CATS guideline into our postoperative care pathway, and development of a patient information handout regarding opioids. The intervention was initiated on Oct. 1, 2020, and was formally implemented on Dec. 1, 2020. The outcome measure was average MME of discharge opioid prescriptions, the process measure was proportion of discharge prescriptions exceeding the recommended dosage, and the balancing measure was opioid prescription refills. We analyzed the data using control charts, and compared all measures between the pre-intervention (12 mo before) and postintervention (12 mo after) groups. Results: A total of 348 patients who underwent VATS lung resection were identified, 173 before the intervention and 175 after the intervention. Significantly less MME was prescribed after the intervention (100 v. 158, p < 0.001), and a lower proportion of prescriptions were nonadherent to the guideline (18.9% v. 50.9%, p < 0.001). Control charts showed special cause variation corresponding with the intervention, and system stability existed after the intervention. There was no statistically significant difference in the proportion or dosage of opioid prescription refills after the intervention. Conclusion: After implementation of the CATS opioid guideline, there was a significant reduction in opioids prescribed at discharge and no increase in opioid prescription refills. Control charts are a valuable resource for monitoring outcomes on an ongoing basis and for assessing the effects of an intervention.
Perspectives on relative energy deficiency in sport (RED-S): A qualitative case study of athletes, coaches and medical professionals from a super league netball club

Background Research into relative energy deficiency in sport (RED-S) has increased substantially over recent years given the impact on athletes’ health and performance. Most studies have considered sports that place emphasis on the aesthetics, endurance, or weight-restriction. Fewer studies exist in team sports. Netball is a team sport yet to be explored despite players potentially being at risk of RED-S given the high training volumes, sporting culture, internal and external pressures, and small network of coaches and medical professionals. A qualitative case study was used to explore the perspective of athletes, coaches, and medical professionals on RED-S. Methods Semi-structured interviews were conducted with 13 players, 4 coaches and 4 medical professionals affiliated to a Super League club. Interviews were recorded and transcribed verbatim. The data was analysed using thematic analysis. Results Five main themes were identified in this study. Awareness of RED-S amongst athletes and coaches was generally inadequate whereas medical professionals had some awareness of RED-S. Some athletes used contraception to reduce discomfort/pain during menstruation whilst others expressed concerns around long-term contraceptive use and previous menstrual cycle disturbance. Sporting demands, individual and contextual factors, and a preoccupation with body image were associated with nutritional restriction, whilst appearance was a source of internal and external pressure. External pressures also extended to coaches, assessments/feedback, social media, and commentary. Strategies suggested to reduce the risk of RED-S included “hard hitting cases”, multidisciplinary team involvement, and support from the governing body. Conclusion The findings of this study provide insight into factors potentially associated with the risk of RED-S from an athletes, coaches, and medical professional perspective. This insight can be used to increase overall awareness of RED-S in key stakeholders as well as improve the recognition for the pressures netball athletes face that might alter the level of risk.
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Outlier removal is a critical part of feature-based point cloud registration. In this article, we revisit the model generation and selection of the classic RANSAC approach for fast and robust point cloud registration. For the model generation, we propose a second-order spatial compatibility (SC<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="tao-ieq3-3272557.gif"/></alternatives></inline-formula>) measure to compute the similarity between correspondences. It takes into account global compatibility instead of local consistency, allowing for more distinctive clustering between inliers and outliers at an early stage. The proposed measure promises to find a certain number of outlier-free consensus sets using fewer samplings, making the model generation more efficient. For the model selection, we propose a new Feature and Spatial consistency constrained Truncated Chamfer Distance (FS-TCD) metric for evaluating the generated models. It considers the alignment quality, the feature matching properness, and the spatial consistency constraint simultaneously, enabling the correct model to be selected even when the inlier rate of the putative correspondence set is extremely low. Extensive experiments are carried out to investigate the performance of our method. In addition, we also experimentally prove that the proposed SC<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="tao-ieq4-3272557.gif"/></alternatives></inline-formula> measure and the FS-TCD metric are general and can be easily plugged into deep learning based frameworks.
Survival Analysis with Long-Term Survivors

Formulating Tests for the Presence of Immunes and Sufficient Follow-up. Properties of the Kaplan-Meier Estimator. Nonparametric Estimation and Testing. Parametric Models for Single Samples. The Use of Concomitant Information. Large Sample Properties of Parametric Models: Single Samples. Large-Sample Properties of Parametric Models with Covariates. Further Topics. References. Statistical Tables. Index.
Non-local Means Denoising Algorithm Based on DCT

Non-local means(NLM) has been becoming one of the most useful tools for image denoising.However,the calculation for its similarity weights has limited accuracy against noise when the noise level is too high.In order to handle above-mentioned problem,NLM denoising algorithm is introducd based on discrete cosine transform(DCT). First,making use of the low frequency of DCT to reconstruct image,the part of the denoise in image is filtered while preserving the main information of the image.Second,the NLM algorithm is used for the reconstructed image to filter the additional denoise through calculation of similarity weights accurately.Compared with NLM algorithm,the experimental results demonstrate that our method gets a higher Peak Signal to Noise Ratio(PSNR) and better visual fidelity.
ChatGPT and Ophthalmology: Exploring Its Potential with Discharge Summaries and Operative Notes

ABSTRACT Purpose This study aimed to report the abilities of the large language model ChatGPTR (OpenAI, San Francisco, USA) in constructing ophthalmic discharge summaries and operative notes. Methods A set of prompts was constructed through statements incorporating common ophthalmic surgeries across the subspecialties of the cornea, retina, glaucoma, paediatric ophthalmology, neuro-ophthalmology, and ophthalmic plastics surgery. The responses of ChatGPT were assessed by three surgeons carefully and analyzed them for evidence-based content, specificity of the response, presence of generic text, disclaimers, factual inaccuracies, and its abilities to admit mistakes and challenge incorrect premises. Results A total of 24 prompts were presented to the ChatGPT. Twelve prompts assessed its ability to construct discharge summaries, and an equal number explored the potential for preparing operative notes. The response was found to be tailored based on the quality of inputs given and was provided in a matter of seconds. The ophthalmic discharge summaries had a valid but significant generic text. ChatGPT could incorporate specific medications, follow-up instructions, consultation time, and location within the discharge summaries when prompted appropriately. While the operative notes were detailed, they required significant tuning. ChatGPT routinely admits its mistakes and corrects itself immediately when confronted with factual inaccuracies. The mistakes are avoided in subsequent reports when given similar prompts. Conclusion The performance of ChatGPT in the context of ophthalmic discharge summaries and operative notes was encouraging. These are constructed rapidly in a matter of seconds. Focused training of ChatGPT on these issues with inclusion of a human verification step has an enormous potential to impact healthcare positively.
Complete Genome Sequence of Bacillus licheniformis Strain GN02, Isolated from the Root Surface of Brassica chinensis

Bacillus licheniformis GN02 was isolated from the root surface of Pak Choi Cabbage (Brassica chinensis). Here, we report the whole-genome sequence of strain GN02, which includes only a circular chromosome (4,252,022 bp; GC content, 46.08%). ABSTRACT Bacillus licheniformis GN02 was isolated from the root surface of Pak Choi Cabbage (Brassica chinensis). Here, we report the whole-genome sequence of strain GN02, which includes only a circular chromosome (4,252,022 bp; GC content, 46.08%).
Prediction of incident chronic kidney disease in a population with normal renal function and normo-proteinuria

Regarding the irreversible clinical course of chronic kidney disease, identifying high-risk subjects susceptible to Chronic Kidney Disease (CKD) has an important clinical implication. Previous studies have developed risk prediction models identifying high-risk individuals within a group, including those who may have experienced minor renal damage, to provide an opportunity for initiating therapies or interventions at earlier stages of CKD. To date, there were no other studies developed a prediction model with quantitative risk factors to detect the earliest stage of CKD that individuals with normal renal function in the general population may experience. We derived 11,495,668 individuals with an estimated glomerular filtration rate (eGFR) ≥90 mL/min/1.73 m2 and normo-proteinuria, who underwent health screening ≥2 times between 2009 and 2016 from the prospective nationwide registry cohort. The primary outcome was the incident CKD, defined by an eGFR <60 mL/min/1.73 m2. Sex-specific multivariate Cox regression models predicting the 8-year incident CKD risk were developed. The performance of developed models was assessed using Harrell’s C and the area under the receiver operating characteristics curve (AUROC) with 10-fold cross-validation. Both men and women, who met the definition of incident CKD, were older and had more medical treatment history in hypertension and diabetes. Harrell’s C and AUROC of the developed prediction models were 0.82 and 0.83 for men and 0.79 and 0.80 for women. This study developed sex-specific prediction equations with reasonable performance in a population with normal renal function.
Strategy for the Optimization of Tobacco Distribution Based on QPSO

In view of the complexities of tobacco distribution operations characterized by multitudinous nodes and complicated routes,the paper proposes to tackle the combined NP hard problem of the distribution process by the optimal distribution strategy achieved through quantum - behaved particle swarm optimization(QPSO). Aimed at minimum distribution distance and balanced distribution load,it formulates a generic mathematic model for the distribution problem and on the basis of a comparison between QPSO and particle swarm algorithm,builds the QPSO solution model to realize the simultaneous optimization of vehicle numbering and distribution sequence.A subsequent empirical study shows that QPSO - based tobacco distribution strategy can balance individual delivery loads,minimize the travel distance and cost,and effectively solve nonlinear problems of multiple objectives.
Therapeutic blocking of VEGF binding to neuropilin-2 diminishes PD-L1 expression to activate antitumor immunity in prostate cancer

Prostate cancers are largely unresponsive to immune checkpoint inhibitors (ICIs), and there is strong evidence that programmed death-ligand 1 (PD-L1) expression itself must be inhibited to activate antitumor immunity. Here, we report that neuropilin-2 (NRP2), which functions as a vascular endothelial growth factor (VEGF) receptor on tumor cells, is an attractive target to activate antitumor immunity in prostate cancer because VEGF-NRP2 signaling sustains PD-L1 expression. NRP2 depletion increased T cell activation in vitro. In a syngeneic model of prostate cancer that is resistant to ICI, inhibition of the binding of VEGF to NRP2 using a mouse-specific anti-NRP2 monoclonal antibody (mAb) resulted in necrosis and tumor regression compared with both an anti–PD-L1 mAb and control immunoglobulin G. This therapy also decreased tumor PD-L1 expression and increased immune cell infiltration. We observed that the NRP2, VEGFA, and VEGFC genes are amplified in metastatic castration-resistant and neuroendocrine prostate cancer. We also found that individuals with NRP2High PD-L1High metastatic tumors had lower androgen receptor expression and higher neuroendocrine prostate cancer scores than other individuals with prostate cancer. In organoids derived from patients with neuroendocrine prostate cancer, therapeutic inhibition of VEGF binding to NRP2 using a high-affinity humanized mAb suitable for clinical use also diminished PD-L1 expression and caused a substantial increase in immune-mediated tumor cell killing, consistent with the animal studies. These findings provide justification for the initiation of clinical trials using this function-blocking NRP2 mAb in prostate cancer, especially for patients with aggressive disease. Description The binding of VEGF to NRP2 sustains PD-L1 expression and represents a therapeutic target to activate antitumor immunity. Preventing PD-L1 expression in prostate cancer Unlike other solid tumors, prostate cancers do not respond particularly well to immune checkpoint inhibitors such as PD-L1 blockade. Thus, additional strategies are needed to promote antitumor immune responses. Here, Wang et al. demonstrated that antibody-mediated blockade of the interaction between vascular endothelial growth factor (VEGF) and neuropilin-2 (NRP2) reduced PD-L1 expression in prostate cancer and drove antitumor immune responses in mice. When the authors investigated patient samples, they found that those with more aggressive types of prostate cancer had higher expression of the genes encoding PD-L1 and NRP2, suggesting that this therapeutic axis may be especially valuable for these patients. —CM
Feature Analysis and Automatic Extraction for the 3D Point Cloud of the Sanitary Wares Body

The determination of the type of Sanitary Wares Body (SWB) is the premise for glazing robot to intelligently choose the glazing operation mode. Traditional judgement is to sample SWB by the camera with Charge Coupled Device (CCD), however, it is necessary to build a dark room to overcome the influence of light and environment, which leads to expansion of production space and increasement of cost. For the purpose of getting over the complex circumstances, a new method for automatic extraction of the feature based on three-dimensional (3D) point cloud for SWB is put forward, which is lower demand to the light environment than two-dimensional (2D) image. There are three parts for this method: Firstly, five feature parameters of the appearance of SWB were analyzed such as length-width ratio and the number of the holes. Then after the point cloud data of SWB was captured by depth camera, preprocessing, segmentation, and projection to flat space were carried out. In the end, automatic extraction of feature parameters from grey scale image was accomplished. The experiment result showed that the parameters from point cloud were basically consistent with those from the product of sanitary wares. The approach may reduce the illumination requirement and save the production cost; Therefore, it is feasible to improve the intelligent level of ceramics process.
Research on the Identification Method of Dangerous Goods in Security Inspection Images Based on Deep Learning

This paper explored the application of deep learning target detection methods in the field of X-ray security screening. Faster R-CNN is a fully supervised deep learning method that uses only abnormal images containing dangerous goods as the training set, thus making it difficult to learn the features of normal images. It results in its high false detection rate when detecting normal images. In view of the above problems, combined with the characteristics of most of the X-ray security images are normal images, the author proposed a pre-classified head X-ray security image recognition method to reduce the false detection rate, while improving the performance and efficiency of dangerous goods detection, and more suitable for real X-ray security application scenarios.
Enabling Cost-effective Wireless Data Collection by Piggybacking on Delivery Drones in Agriculture

Drones have drawn considerable attention as the agents in wireless data collection for agricultural applications, by virtue of their three-dimensional mobility and dominant line-of-sight communication channels. Existing works mainly exploit dedicated drones via deployment and maintenance, which is insufficient regarding resource and cost-efficiency. In contrast, leveraging existing delivery drones for the data collection on their way of delivery, called delivery drones’ piggybacking, is a promising solution. For achieving such cost-efficiency, drone scheduling inevitably stands in front, but the delivery missions involved have escalated it to a wholly different and unexplored problem. As an attempt, we first survey 514 delivery workers and conduct field experiments; noticeably, the collection cost, which mostly comes from the energy consumption of drones’ piggybacking, is determined by the decisions on package-route scheduling and data collection time distribution. Based on such findings, we build a new model that jointly optimizes these two decisions to maximize data collection amount, subject to the collection budget and delivery constraints. Further model analysis finds it a Mixed Integer Non-Linear Programming problem, which is NP-hard. The major challenge stems from interdependence entangling the two decisions. For this point, we propose Delta, a \(\frac{1}{9+\delta } \) -approximation delivery drone scheduling algorithm. The key idea is to devise an approximate collection time distribution scheme leveraging energy slicing, which transforms the complex problem with two interdependent variables into a submodular function maximization problem only with one variable. The theoretical proofs and extensive evaluations verify the effectiveness and the near-optimal performance of Delta.
Empirical Evaluation of Language Agnostic Filtering of Parallel Data for Low Resource Languages

Most of the available resources for low resource languages are crawled from the web. In order to obtain reasonable machine translation performance with such datasets, it is important to filter low quality samples from the training data. In this paper we explore the use of language agnostic sentence representations for filtering parallel data for low resource language pairs: Pashto-English, Khmer-English, Nepali-English and Sinhalese-English. We determine the quality of the samples based on embedding similarity be-tween source and target sentences. Our experiments show that when preceded by language filtering using language agnostic embeddings significantly improves the performance of neural machine translation (NMT) and achieve performance competitive to language specific approaches.
From Frying to Speculating: Google Ngram evidence to the meaning development of ‘?’ in Mandarin Chinese

This paper explores semantic change simply using the Ngram information, with the intuition that Ngrams as the direct neighbors offer collocation cues that further signal meaning changes. We specifically investigate the case of ‘ 炒 ’ in Mandarin Chinese on the basis of unigrams extracted from Google Books Ngrams Corpus and reconstruct the meaning development of ‘ 炒 ’ with specific stages. The results indicated that the major meaning changes that occurred to ‘ 炒 ’ is from ‘frying’ to ‘speculating’, which roughly started in the 1970s. The attested word types related to the latter sense denote economic events, such as stocks, foreign currencies, and speculators. It further re-flects that social context plays an essential role in the process of semantic change.
A comparison of the validity of measurement methods of the general English proficiency by dictation and read-aloud performance

This paper compares three classroom-based measurement methods of the general proficiency of English as a second language (GEP): one measures the GEP by dictation performance; another by read-aloud performance; and the other by both dictation performance and read-aloud performance. A learner’s GEP has been measured by commercially tests because the reliability and validity of the tests have been well-acknowledged, but the use of the tests has ineffective regarding test-administration cost and test materials. By contrast, measurement of dictation and/or read-aloud needs only a sound file or a text file, and a teacher of English as a second language can choose test materials according to the class contents. This study developed the three GEP-measurement methods using a learner corpus data involving GEP, dictation and read-aloud performance of 50 learners of English as a second language. The experimental results suggested that the proficiency should be measured with both dictation and read-aloud performance.
Contextual-Boosted Deep Neural Collaborative Filtering Approach for Arabic Textual Documents Recommendation

The technological advancement and the expansion of using the internet has made it possible to communicate between people and machines. Many exchanged textual resources have been given simultaneously make users have difficulty in choosing the most appropriate items that will improve their knowledge. To deal with data sparsity problem, review-based recommendation systems have shown potential in a wide range of Natural Language Processing (NLP) tasks. Due to the high-dimensionality and the complex semantics of Arabic textual data, review-based approach is proposed for documents recommendation. Two parallel neural networks are introduced to learn items properties and users’ behaviors. Indeed, contextualized word representation model is used. Then, Gated Recurrent Units (GRU) is attached for extracting high-level-semantic features. After that, a shared layer modeled complex interactions between the latent vectors of users and items to improve subsequently rating prediction. Experiments showed the superiority of our proposed model compared with state-of-the-art methods.