Quantifying antimicrobial consumption in the Chilean salmon industry using standardized metrics and indicators: The case of florfenicol and oxytetracycline.

The adoption of standardized metrics and indicators of antimicrobial use (AMU) in the food animal industry is essential for the success of programs aimed at promoting the responsible and judicious use of antimicrobials in this activity. The objective of this study was to introduce the use of standardized AMU metrics and indicators to quantify the use of florfenicol and oxytetracycline in the Chilean salmon industry, and in this way evaluate the feasibility of their use given the type of health and production information currently managed by the National Fisheries and Aquaculture Service (SERNAPESCA), the Chilean agency responsible for regulating aquaculture in Chile. The data available from SERNAPESCA allowed the construction and evaluation of the most data-demanding AMU metrics and indicators. Consequently, the use of florfenicol and oxytetracycline administered by oral and parenteral routes was quantified using the treatment incidence based on both animal defined daily dose (TIDDDvet) and animal used daily dose (TIUDDA). To that end, the study included information from 1320 closed production cycles from farms rearing Atlantic salmon, coho salmon and rainbow trout that were active between January 2017 and December 2021. By applying standardized AMU metrics and indicators, we were able to determine that the median of TIDDDvet for florfenicol was 75.1 (80 % range, 20.0-158.0) DDDvet per ton-year at risk for oral procedures and 0.36 (80 % range, 0.07-1.19) DDDvet per ton-year at risk for parenteral procedures. For oxytetracycline, the median TIDDDvet was 3.09 (80 % range, 0.74-42.8) and 0.47 (80 % range, 0.09-1.68) DDDvet per ton-year at risk for oral and parenteral procedures, respectively. The median TIUDDA for treatments with florfenicol was 45.6 (80 % range, 10.9-96.5) UDDA per ton-year at risk for oral treatments and 0.28 (80 % range, 0.05-0.80) UDDA per ton-year at risk for parenteral treatments. For oxytetracycline, the median TIUDDA was 2.63 (80 % range, 0.61-28.2) UDDA per ton-year at risk for oral treatments and 0.41 (80 % range, 0.08-1.29) UDDA per ton-year at risk for parenteral treatments. This study demonstrates that it is feasible to move from traditional AMU metrics and indicators to standardized ones in the Chilean salmon industry. This is possible because the competent authority requires salmon farms to report detailed health and production information at a high frequency. The use of standardized AMU metrics and indicators can help the authority to have a more comprehensive view of the antimicrobial use in the Chilean salmon industry.

Promoting smartphone-based keratitis screening using meta-learning: A multicenter study.

OBJECTIVE: Keratitis is the primary cause of corneal blindness worldwide. Prompt identification and referral of patients with keratitis are fundamental measures to improve patient prognosis. Although deep learning can assist ophthalmologists in automatically detecting keratitis through a slit lamp camera, remote and underserved areas often lack this professional equipment. Smartphones, a widely available device, have recently been found to have potential in keratitis screening. However, given the limited data available from smartphones, employing traditional deep learning algorithms to construct a robust intelligent system presents a significant challenge. This study aimed to propose a meta-learning framework, cosine nearest centroid-based metric learning (CNCML), for developing a smartphone-based keratitis screening model in the case of insufficient smartphone data by leveraging the prior knowledge acquired from slit-lamp photographs. METHODS: We developed and assessed CNCML based on 13,009 slit-lamp photographs and 4,075 smartphone photographs that were obtained from 3 independent clinical centers. To mimic real-world scenarios with various degrees of sample scarcity, we used training sets of different sizes (0 to 20 photographs per class) from the HUAWEI smartphone to train CNCML. We evaluated the performance of CNCML not only on an internal test dataset but also on two external datasets that were collected by two different brands of smartphones (VIVO and XIAOMI) in another clinical center. Furthermore, we compared the performance of CNCML with that of traditional deep learning models on these smartphone datasets. The accuracy and macro-average area under the curve (macro-AUC) were utilized to evaluate the performance of models. RESULTS: With merely 15 smartphone photographs per class used for training, CNCML reached accuracies of 84.59%, 83.15%, and 89.99% on three smartphone datasets, with corresponding macro-AUCs of 0.96, 0.95, and 0.98, respectively. The accuracies of CNCML on these datasets were 0.56% to 9.65% higher than those of the most competitive traditional deep learning models. CONCLUSIONS: CNCML exhibited fast learning capabilities, attaining remarkable performance with a small number of training samples. This approach presents a potential solution for transitioning intelligent keratitis detection from professional devices (e.g., slit-lamp cameras) to more ubiquitous devices (e.g., smartphones), making keratitis screening more convenient and effective.

Recognition algorithm for laboratory protective equipment based on improved YOLOv7.

In the university laboratory environment, it is not uncommon for individual laboratory personnel to be inadequately aware of laboratory safety standards and to fail to wear protective equipment (helmets, goggles, masks) in accordance with the prescribed norms. Manual inspection is costly and prone to leakage, and there is an urgent need to develop an efficient and intelligent detection technology. Video surveillance of laboratory protective equipment reveals that these items possess the characteristics of small targets. In light of this, a laboratory protective equipment recognition method based on the improved YOLOv7 algorithm is proposed. The Global Attention Mechanism (GAM) is introduced into the Efficient Layer Aggregation Network (ELAN) structure to construct an ELAN-G module that takes both global and local features into account. The Normalized Gaussian Wasserstein Distance (NWD) metric is introduced to replace the Complete Intersection over Union (CIoU), which improves the network's ability to detect small targets of protective equipment under experimental complex scenarios. In order to evaluate the robustness of the studied algorithm and to address the current lack of personal protective Equipment (PPE) datasets, a laboratory protective equipment dataset was constructed based on multidimensionality for the detection experiments of the algorithm. The experimental results demonstrated that the improved model achieved a mAP value of 84.2 %, representing a 2.3 % improvement compared to the original model, a 5 % improvement in the detection rate, and a 2 % improvement in the Micro-F1 score. In comparison to the prevailing algorithms, the accuracy of the studied algorithm has been markedly enhanced. The approach addresses the challenge of the challenging detection of small targets of protective equipment in complex scenarios in laboratories, and plays a pivotal role in perfecting laboratory safety management system.

A Multi-Institutional Natural Language Processing Pipeline to Extract Performance Status From Electronic Health Records.

PURPOSE: Performance status (PS), an essential indicator of patients' functional abilities, is often documented in clinical notes of patients with cancer. The use of natural language processing (NLP) in extracting PS from electronic medical records (EMRs) has shown promise in enhancing clinical decision-making, patient monitoring, and research studies. We designed and validated a multi-institute NLP pipeline to automatically extract performance status from free-text patient notes. PATIENTS AND METHODS: We collected data from 19,481 patients in Harris Health System (HHS) and 333,862 patients from veteran affair's corporate data warehouse (VA-CDW) and randomly selected 400 patients from each data source to train and validate (50%) and test (50%) the proposed pipeline. We designed an NLP pipeline using an expert-derived rule-based approach in conjunction with extensive post-processing to solidify its proficiency. To demonstrate the pipeline's application, we tested the compliance of PS documentation suggested by the American Society of Clinical Oncology (ASCO) Quality Metric and investigated the potential disparity in PS reporting for stage IV non-small cell lung cancer (NSCLC). We used a logistic regression test, considering patients in terms of race/ethnicity, conversing language, marital status, and gender. RESULTS: The test results on the HHS cohort showed 92% accuracy, and on VA data demonstrated 98.5% accuracy. For stage IV NSCLC patients, the proposed pipeline achieved an accuracy of 98.5%. Furthermore, our analysis revealed a documentation rate of over 85% for PS among NSCLC patients, surpassing the ASCO Quality Metrics. No disparities were observed in the documentation of PS. CONCLUSION: Our proposed NLP pipeline shows promising results in extracting PS from free-text notes from various health institutions. It may be used in longitudinal cancer data registries.

SCIntRuler: Guiding the integration of multiple single-cell RNA-seq datasets with a novel statistical metric.

MOTIVATION: The growing number of single-cell RNA-seq (scRNA-seq) studies highlights the potential benefits of integrating multiple datasets, such as augmenting sample sizes and enhancing analytical robustness. Inherent diversity and batch discrepancies within samples or across studies continue to pose significant challenges for computational analyses. Questions persist in practice, lacking definitive answers: Should we use a specific integration method or opt for simply merging the datasets during joint analysis? Among all the existing data integration methods, which one is more suitable in specific scenarios? RESULT: To fill the gap, we introduce SCIntRuler, a novel statistical metric for guiding the integration of multiple scRNA-seq datasets. SCIntRuler helps researchers make informed decisions regarding the necessity of data integration and the selection of an appropriate integration method. Our simulations and real data applications demonstrate that SCIntRuler streamlines decision-making processes and facilitates the analysis of diverse scRNA-seq datasets under varying contexts, thereby alleviating the complexities associated with the integration of heterogeneous scRNA-seq datasets. AVAILABILITY: The implementation of our method is available on CRAN as an open-source R package with a user- friendly manual available: https://cloud.r-project.org/web/packages/SCIntRuler/index.html.

The impact of plan complexity on calculation and measurement-based pre-treatment verifications for sliding-window intensity-modulated radiotherapy.

Background and purpose: In sliding-window intensity-modulated radiotherapy, increased plan modulation often leads to increased plan complexities and dose uncertainties. Dose calculation and/or measurement checks are usually adopted for pre-treatment verification. This study aims to evaluate the relationship among plan complexities, calculated doses and measured doses. Materials and methods: A total of 53 plan complexity metrics (PCMs) were selected, emphasizing small field characteristics and leaf speed/acceleration. Doses were retrieved from two beam-matched treatment devices. The intended dose was computed employing the Anisotropic Analytical Algorithm and validated through Monte Carlo (MC) and Collapsed Cone Convolution (CCC) algorithms. To measure the delivered dose, 3D diode arrays of various geometries, encompassing helical, cross, and oblique cross shapes, were utilized. Their interrelation was assessed via Spearman correlation analysis and principal component linear regression (PCR). Results: The correlation coefficients between calculation-based (CQA) and measurement-based verification quality assurance (MQA) were below 0.53. Most PCMs showed higher correlation rpcm-QA with CQA (max: 0.84) than MQA (max: 0.65). The proportion of rpcm-QA  ≥ 0.5 was the largest in the pelvis compared to head-and-neck and chest-and-abdomen, and the highest rpcm-QA occurred at 1 %/1mm. Some modulation indices for the MLC speed and acceleration were significantly correlated with CQA and MQA. PCR's determination coefficients (R2 ) indicated PCMs had higher accuracy in predicting CQA (max: 0.75) than MQA (max: 0.42). Conclusions: CQA and MQA demonstrated a weak correlation. Compared to MQA, CQA exhibited a stronger correlation with PCMs. Certain PCMs related to MLC movement effectively indicated variations in both quality assurances.

Conceptual review of outcome metrics and measures used in clinical evaluation of artificial intelligence in radiology.

Artificial intelligence (AI) has numerous applications in radiology. Clinical research studies to evaluate the AI models are also diverse. Consequently, diverse outcome metrics and measures are employed in the clinical evaluation of AI, presenting a challenge for clinical radiologists. This review aims to provide conceptually intuitive explanations of the outcome metrics and measures that are most frequently used in clinical research, specifically tailored for clinicians. While we briefly discuss performance metrics for AI models in binary classification, detection, or segmentation tasks, our primary focus is on less frequently addressed topics in published literature. These include metrics and measures for evaluating multiclass classification; those for evaluating generative AI models, such as models used in image generation or modification and large language models; and outcome measures beyond performance metrics, including patient-centered outcome measures. Our explanations aim to guide clinicians in the appropriate use of these metrics and measures.

The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks.

Thin pancake-like neuronal networks cultured on top of a planar microelectrode array have been extensively tried out in neuroengineering, as a substrate for the mobile robot's control unit, i.e., as a cyborg's brain. Most of these attempts failed due to intricate self-organizing dynamics in the neuronal systems. In particular, the networks may exhibit an emergent spatial map of steady nucleation sites ("n-sites") of spontaneous population spikes. Being unpredictable and independent of the surface electrode locations, the n-sites drastically change local ability of the network to generate spikes. Here, using a spiking neuronal network model with generative spatially-embedded connectome, we systematically show in simulations that the number, location, and relative activity of spontaneously formed n-sites ("the vitals") crucially depend on the samplings of three distributions: (1) the network distribution of neuronal excitability, (2) the distribution of connections between neurons of the network, and (3) the distribution of maximal amplitudes of a single synaptic current pulse. Moreover, blocking the dynamics of a small fraction (about 4%) of non-pacemaker neurons having the highest excitability was enough to completely suppress the occurrence of population spikes and their n-sites. This key result is explained theoretically. Remarkably, the n-sites occur taking into account only short-term synaptic plasticity, i.e., without a Hebbian-type plasticity. As the spiking network model used in this study is strictly deterministic, all simulation results can be accurately reproduced. The model, which has already demonstrated a very high richness-to-complexity ratio, can also be directly extended into the three-dimensional case, e.g., for targeting peculiarities of spiking dynamics in cerebral (or brain) organoids. We recommend the model as an excellent illustrative tool for teaching network-level computational neuroscience, complementing a few benchmark models.

Body image at the trunk: An investigation into externally referenced width perception and picture mapping.

Body image is a conscious representation of the body, encompassing how our body feels to us. Body image can be measured in a variety of ways, including metric and depictive measures. This study sought to assess body image at the trunk by investigating, and comparing, a metric and depictive measure. Sixty-nine healthy participants estimated their thorax, waist, and hip width by externally referencing mechanical calipers. Participants were also asked to select the true image of their trunk from a random display of nine images containing the true image and incrementally shrunken or enlarged images. Participants demonstrated evidence of thorax and waist width overestimation in the width perception task, with no evidence for hip misestimation. For the picture mapping task, the majority of participants were inaccurate. In participants who were inaccurate, approximately equal proportions underestimated and overestimated their trunk width. The two tasks were found to be independent of each other. Distortions, or inaccuracies, were apparent in a metric measure, and inaccuracies also present in a depictive measure, of body image at the trunk for healthy participants. An overestimation bias was apparent in the metric, but not depictive, task. No relationship was found between tasks..

Biosurfactant Production by Pseudomonas: a Systematic Review.

It is of fundamental interest to research and develop innovative biotechnologies, as well as bioproducts that replace or are alternatives to those of non-renewable origin, such as biosurfactants in relation to traditional surfactants used in various sectors. Consequently, there are a large number of experimental studies addressing different subjects, especially with the use of bacteria of the genus Pseudomonas; however, there is a lack of work that demonstrates the evaluation of this science produced to date. Therefore, this article discusses the production of biosurfactants by Pseudomonas with the aim of surveying and analyzing experimental articles on this topic. To realize this, a systematic search was carried out with well-defined temporal space, databases, and inclusion and exclusion criteria, based on metric studies that guided what information would be collected and the method of evaluation. Therefore, a large number of articles were selected, which demonstrated Pseudomonas aeruginosa as the bioagent mostly used in the tests, which aimed to improve the process in the area. Furthermore, interest in this field has increased over the years, predominantly in emerging market countries, where the most prominent authors on the topic are found. Therefore, it is necessary that there is an expansion of interest in the area to make the production of biosurfactants cheaper in areas that currently have greater development deficiencies, such as means of purifying the bioprocess and reducing foam formation in the bioprocess.

An innovative environmental tool to evaluate the sustainability of anthropogenic processes: the tetrahedron approach.

The Tetrahedron approach is a new environmental tool adapted to assess the sustainability of anthropogenic processes. This tool is based on a four-step methodology that includes (a) the identification of critical parameters, (b) evaluation through the Tetrahedron Parameter Global Evaluator, (c) construction of a tetrahedron diagram based on the final scores and (d) quantitative estimation of the global sustainability. The Tetrahedron incorporates various aspects of sustainability, including economic, social and environmental factors, and provides a comprehensive framework for evaluating the impact of human activities. This article presents the methodology and application of the Tetrahedron in determining the sustainability of five case studies: CO2 capture, unconventional methanol production, the Solvay process, CO2-alcoholic fermentation process strategy and the CO2-Rumen fermentation process strategy. The results demonstrate the Tetrahedron as an effective and reliable tool to quantify the sustainability of anthropogenic processes and to promote sustainable practices across various industries and sectors. The Tetrahedron offers several advantages over other environmental assessment tools, including holistic approach, simplicity and flexibility.

Using a measurement type-independent metric to compare patterns of determinants between patient-reported versus performance-based physical function in hemodialysis patients.

PURPOSE: We applied a previously established common T-score metric for patient-reported and performance-based physical function (PF), offering the unique opportunity to directly compare measurement type-specific patterns of associations with potential laboratory-based, psychosocial, sociodemographic, and health-related determinants in hemodialysis patients. METHODS: We analyzed baseline data from the CONVINCE trial (N = 1,360), a multinational randomized controlled trial comparing high-flux hemodialysis with high-dose hemodiafiltration. To explore the associations of potential determinants with performance-based versus patient-reported PF, we conducted multiple linear regression (backward elimination with cross-validation and Lasso regression). We used standardized T-scores as estimated from the PROMIS PF short-form 4a (patient-reported PF) and the Physical Performance Test (performance-based PF) as dependent variables. RESULTS: Performance-based and patient-reported PF were both significantly associated with a laboratory marker-based indicator of muscle mass (simplified creatinine index), although the effects were relatively small (partial f2 = 0.04). Age was negatively associated with PF; the effect size was larger for performance-based (partial f2 = 0.12) than for patient-reported PF (partial f2 = 0.08). Compared to performance-based PF, patient-reported PF showed a stronger association with self-reported health domains, particularly pain interference and fatigue. When using the individual difference between patient-reported and performance-based T-scores as outcome, we found that younger age and more fatigue were associated with lower patient-reported PF compared to performance-based PF (small effect size). CONCLUSION: Patient-reported and performance-based assessments were similarly associated with an objective marker of physical impairment in hemodialysis patients. Age and fatigue may result in discrepancies when comparing performance-based and patient-reported scores on the common PF scale. Trial Registration CONVINCE is registered in the Dutch Trial Register (Register ID: NL64750.041.18). The registration can be accessed at: https://onderzoekmetmensen.nl/en/trial/52958 .

Results on fixed points in b-metric space by altering distance functions.

We discuss the existence of a fixed point for a self mapping and its uniqueness satisfying ( ϕ ˙ , η ˙ ) -generalized contractive condition including altering distance functions of rational terms in an ordered b-metric space. It is also discussed whether the two self-maps under the same contraction condition can be coincident and coupled coincident. The results are backed up by a dearth of numerical examples and application to nonlinear quadratic integral equation.

Impact of 1.5†T Magnetic Field on Treatment Plan Quality in MR-Guided Radiotherapy: Typical Phantom Test Cases.

PURPOSE: This study aims to investigate the influence of the magnetic field on treatment plan quality using typical phantom test cases, which encompass a circle target test case, AAPM TG119 test cases (prostate, head-and-neck, C-shape, multi-target test cases), and a lung test case. MATERIALS AND METHODS: For the typical phantom test cases, two plans were formulated. The first plan underwent optimization in the presence of a 1.5 Tesla magnetic field (1.5 T plan). The second plan was re-optimized without a magnetic field (0 T plan), utilizing the same optimization conditions as the first plan. The two plans were compared based on various parameters, including con-formity index (CI), homogeneity index (HI), fit index (FI) and dose coverage of the planning target volume (PTV), dose delivered to organs at risk (OARs) and normal tissue (NT), monitor unit (MU). A plan-quality metric (PQM) scoring procedure was employed. For the 1.5 T plans, dose verifications were performed using an MR-compatible ArcCHECK phantom. RESULTS: A smaller dose influence of the magnetic field was found for the circle target, prostate, head-and-neck, and C-shape test cases, compared with the multi-target and lung test cases. In the multi-target test case, the significant dose influence was on the inferior PTV, followed by the superior PTV. There was a relatively large dose influence on the PTV and OARs for lung test case. No statistically significant differences in PQM and MUs were observed. For the 1.5 T plans, gamma passing rates were all higher than 95% with criteria of 2 mm/3% and 2 mm/2%. CONCLUSION: The presence of a 1.5 T magnetic field had a relatively large impact on dose parameters in the multi-target and lung test cases compared with other test cases. However, there were no significant influences on the plan-quality metric, MU and dose accuracy for all test cases.

CSUID - Comprehensive synthetic underwater image dataset.

The underwater environment is characterized by complex light traversal, encompassing effects such as color loss, contrast loss, water distortion, backscatter, light attenuation, and color cast, which vary depending on water purity, depth, and other factors. The dataset presented in this paper is prepared with 100 ground-truth images and 1,50,000 synthetic underwater images. This dataset approximates the effects of underwater environment with implementable combinations of color cast, blurring, low-light, and contrast reduction. These effects and their combinations, with different severity levels are applied to each ground-truth image to generate as many as 150 synthetic underwater images. In addition to the dataset of 1,50,100 images, a comprehensive set of 21 focus metrics, including the average contrast measure operator, Brenner's gradient-based metric, and many others, are calculated for each image.

The cyclic flats of a q-matroid.

In this paper we develop the theory of cyclic flats of q-matroids. We show that the cyclic flats, together with their ranks, uniquely determine a q-matroid and hence derive a new q-cryptomorphism. We introduce the notion of F q m -independence of an F q -subspace of F q n and we show that q-matroids generalize this concept, in the same way that matroids generalize the notion of linear independence of vectors over a given field.

Sleep quality in cancer patients: a common metric for several instruments measuring sleep quality.

PURPOSE: Sleep problems are frequently observed in cancer patients. Multiple questionnaires for assessing sleep quality have been developed. The aim of this study was to present transfer rules that allow the conversion of the patients' scores from one questionnaire to another. In addition, we anchored this common metric to the general population. METHODS: A sample of 1,733 cancer patients completed the following questionnaires: Pittsburgh Sleep Quality Index, Insomnia Sleep Index, Jenkins Sleep Scale, EORTC QLQ-C30, and the sleep scale of the EORTC QLQ-SURV100. The methods for establishing a common metric were based on Item Response Theory. RESULTS: The main result of the study is a figure that allows the conversion from one of the above-mentioned sleep scales into another. Furthermore, the scores of the questionnaires can be transferred to theta scores that indicate the position within the group of cancer patients and also to T scores that indicate the position in relation to the general population. The correlations between the sleep scales ranged between 0.70 and 0.85. CONCLUSIONS: The conversion rules presented in the study enable researchers and clinicians to directly compare single scores or mean scores across studies using different sleep scales, to assess the degree of sleep problems with regard to the general population, and to relate cutoff scores from one questionnaire to another.

Green highly sensitive and selective spectroscopic detection of guaifenesin in multiple dosage forms and spiked human plasma.

Guaifenesin (GUA) is determined in dosage forms and plasma using two methods. The spectrofluorimetric technique relies on the measurement of native fluorescence intensity at 302 nm upon excitation wavelength "223 nm". The method was validated according to ICH and FDA guidelines. A concentration range of 0.1-1.1 µg/mL was used, with limit of detection (LOD) and quantification (LOQ) values 0.03 and 0.08 µg/mL, respectively. This method was used to measure GUA in tablets and plasma, with %recovery of 100.44% ± 0.037 and 101.03% ± 0.751. Furthermore, multivariate chemometric-assisted spectrophotometric methods are used for the determination of GUA, paracetamol (PARA), oxomemazine (OXO), and sodium benzoate (SB) in their lab mixtures. The concentration ranges of 2.0-10.0, 4.0-16.0, 2.0-10.0, and 3.0-10.0 µg/mL for OXO, GUA, PARA, and SB; respectively, were used. LOD and LOQ were 0.33, 0.68, 0.28, and 0.29 µg/mL, and 1.00, 2.06, 0.84, and 0.87 µg/mL for PARA, GUA, OXO, and SB. For the suppository application, the partial least square (PLS) model was used with %recovery 98.49% ± 0.5, 98.51% ± 0.64, 100.21% ± 0.36 & 98.13% ± 0.51, although the multivariate curve resolution alternating least-squares (MCR-ALS) model was used with %recovery 101.39 ± 0.45, 99.19 ± 0.2, 100.24 ± 0.12, and 98.61 ± 0.32 for OXO, GUA, PARA, and SB. Analytical Eco-scale and Analytical Greenness Assessment were used to assess the greenness level of our techniques.