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Owing to the consecutive occurrence of water quality incidents, such as the black and red water situation in Incheon, Mullae (Seoul), and Pohang, as well as the tap water larval events in Incheon and Jeju in 2020, South Korea's Ministry of Environment announced the new addition of Article 12-2 of the Enforcement Decree of the Water Supply and Waterworks Installation Act 2021. Additionally, the importance of intensive management for vulnerable areas where water quality incidents and complaints may occur repeatedly was specified. Therefore, this study suggests the hazard ranking of district-metered areas in the study area using the deterioration of the water distribution network, complaints (water quality, leakage), and modeling results (water pressure, residual chlorine concentration) to support the establishment of a reasonable water distribution network maintenance plan. The effective and sustainable maintenance of the water distribution network is possible if the hazard ranking is regularly determined using the suggested methodology proposed herein and the water distribution network intensive management area is managed.

Epidermal cell adhesion molecule (EpCAM) is a tumor-associated antigen (TAA), which has been considered as a cancer vaccine candidate. The EpCAM protein fused to the fragment crystallizable region of immunoglobulin G (IgG) tagged with KDEL endoplasmic reticulum (ER) retention signal (EpCAM-FcK) has been successfully expressed in transgenic tobacco (

Automated inspection systems utilizing computer vision technology are effective in managing traffic control devices (TCDs); however, they face challenges due to the limited availability of training datasets and the difficulty in generating new datasets. To address this, our study establishes a benchmark for cost-effective model training methods that achieve the desired accuracy using data from related domains and YOLOv5, a one-stage object detector known for its high accuracy and speed. In this study, three model cases were developed using distinct training approaches: (1) training with COCO-based pre-trained weights, (2) training with pre-trained weights from the source domain, and (3) training with a synthesized dataset mixed with source and target domains. Upon comparing these model cases, this study found that directly applying source domain data to the target domain is unfeasible, and a small amount of target domain data is necessary for optimal performance. A model trained with fine-tuning-based domain adaptation using pre-trained weights from the source domain and minimal target data, proved to be the most resource-efficient approach. These results contribute valuable guidance for practitioners aiming to develop TCD models with limited data, enabling them to build optimal models while conserving resources.

Abstract RE-MIND2 (NCT04697160) compared patient outcomes from the L-MIND (NCT02399085) trial of tafasitamab+lenalidomide with those of patients treated with other therapies for relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) who are autologous stem cell transplant ineligible. We present outcomes data for three pre-specified treatments not assessed in the primary analysis. Data were retrospectively collected from sites in North America, Europe, and the Asia Pacific region. Patients were aged ≥18 years with histologically confirmed DLBCL and received ≥2 systemic therapies for DLBCL (including ≥1 anti-CD20 therapy). Patients enrolled in the observational and L-MIND cohorts were matched using propensity score-based 1:1 nearest-neighbor matching, balanced for six covariates. Tafasitamab+lenalidomide was compared with polatuzumab vedotin+bendamustine+rituximab (pola-BR), rituximab+lenalidomide (R2), and CD19-chimeric antigen receptor T-cell (CAR-T) therapies. The primary endpoint was overall survival (OS). Secondary endpoints included treatment response and progression-free survival. From 200 sites, 3,454 patients were enrolled in the observational cohort. Strictly matched patient pairs consisted of tafasitamab+lenalidomide versus pola-BR (n = 24 pairs), versus R2 (n = 33 pairs), and versus CAR-T therapies (n = 37 pairs). A significant OS benefit was observed with tafasitamab+lenalidomide versus pola-BR (HR: 0.441; p = 0.034) and R2 (HR: 0.435; p = 0.012). Comparable OS was observed in tafasitamab+lenalidomide and CAR-T cohorts (HR: 0.953, p = 0.892). Tafasitamab+lenalidomide appeared to improve survival outcomes versus pola-BR and R2, and comparable outcomes were observed versus CAR-T. Although based on limited patient numbers, these data may help to contextualize emerging therapies for R/R DLBCL. Clinical trial registration NCT04697160 (January 6, 2021)

Background: The authors aimed to assess outcomes with a pharmacogenetic (PGx)-informed, pharmacist-guided, personalized consult service for warfarin dosing. Methods: This retrospective cohort study included patients admitted with thromboembolic events. Eligible subjects received either PGx-informed (n = 389) or historical non-PGx pharmacist-guided warfarin dosing (Hx; n = 308) before hospital discharge. The composite of admission with bleeding or thromboembolic events over 90 days after the discharge was compared between the PGx and Hx groups. Results: The rate ratio (95% CI) of the composite of bleeding or thromboembolic admissions for PGx versus Hx was 0.32 (0.12–0.82). The estimated hazard ratio was 0.43 (0.16–1.12). Conclusion: A PGx-informed warfarin dosing service was associated with decreased bleeding and thromboembolic encounters.

Supplementary Data from Improved Efficacy of Tafasitamab plus Lenalidomide versus Systemic Therapies for Relapsed/Refractory DLBCL: RE-MIND2, an Observational Retrospective Matched Cohort Study

Supplementary Figure from Improved Efficacy of Tafasitamab plus Lenalidomide versus Systemic Therapies for Relapsed/Refractory DLBCL: RE-MIND2, an Observational Retrospective Matched Cohort Study

Purpose:In RE-MIND2 (NCT04697160), patient-level outcomes from the L-MIND study (NCT02399085) of tafasitamab plus lenalidomide were retrospectively compared with patient-level matched observational cohorts treated with National Cancer Care Network (NCCN)/European Society for Medical Oncology (ESMO)-listed systemic therapies for relapsed/refractory diffuse large B-cell lymphoma (DLBCL).Patients and Methods:Data were collected from health records of eligible patients aged ≥18 years with histologically confirmed DLBCL who had received ≥2 systemic therapies for DLBCL (including ≥1 anti-CD20 therapy). Patients from L-MIND were matched with patients from the RE-MIND2 observational cohort using estimated propensity score-based 1:1 nearest-neighbor matching, balanced for nine covariates. The primary analysis compared tafasitamab plus lenalidomide with patients who received any systemic therapy for R/R DLBCL (pooled in one cohort) or bendamustine plus rituximab (BR) or rituximab plus gemcitabine and oxaliplatin (R-GemOx; as two distinct cohorts). The primary endpoint was overall survival (OS). Secondary endpoints included treatment response and time-to-event outcomes.Results:In RE-MIND2, 3,454 patients were enrolled from 200 sites in North America, Europe, and Asia-Pacific. Strictly matched pairs of patients consisted of tafasitamab plus lenalidomide versus systemic therapies pooled (n = 76 pairs), versus BR (n = 75 pairs), and versus R­GemOx (n = 74 pairs). Significantly prolonged OS was reported with tafasitamab plus lenalidomide versus systemic pooled therapies [hazard ratios (HR): 0.55; P = 0.0068], BR (HR: 0.42; P < 0.0001), and R-GemOx (HR: 0.47; P = 0.0003).Conclusions:RE-MIND2, a retrospective observational study, met its primary endpoint, demonstrating prolonged OS with tafasitamab plus lenalidomide versus BR and R-GemOx.See related commentary by Cherng and Westin, p. 3908

Purpose: In RE-MIND2 (NCT04697160), patient-level outcomes from the L-MIND study (NCT02399085) of tafasitamab plus lenalidomide were retrospectively compared with patient-level matched observational cohorts treated with National Cancer Care Network (NCCN)/European Society for Medical Oncology (ESMO)-listed systemic therapies for relapsed/refractory diffuse large B-cell lymphoma (DLBCL). Patients and Methods: Data were collected from health records of eligible patients aged ≥18 years with histologically confirmed DLBCL who had received ≥2 systemic therapies for DLBCL (including ≥1 anti-CD20 therapy). Patients from L-MIND were matched with patients from the RE-MIND2 observational cohort using estimated propensity score-based 1:1 nearest-neighbor matching, balanced for nine covariates. The primary analysis compared tafasitamab plus lenalidomide with patients who received any systemic therapy for R/R DLBCL (pooled in one cohort) or bendamustine plus rituximab (BR) or rituximab plus gemcitabine and oxaliplatin (R-GemOx; as two distinct cohorts). The primary endpoint was overall survival (OS). Secondary endpoints included treatment response and time-to-event outcomes. Results: In RE-MIND2, 3,454 patients were enrolled from 200 sites in North America, Europe, and Asia-Pacific. Strictly matched pairs of patients consisted of tafasitamab plus lenalidomide versus systemic therapies pooled (n = 76 pairs), versus BR (n = 75 pairs), and versus R­GemOx (n = 74 pairs). Significantly prolonged OS was reported with tafasitamab plus lenalidomide versus systemic pooled therapies [hazard ratios (HR): 0.55; P = 0.0068], BR (HR: 0.42; P < 0.0001), and R-GemOx (HR: 0.47; P = 0.0003). Conclusions: RE-MIND2, a retrospective observational study, met its primary endpoint, demonstrating prolonged OS with tafasitamab plus lenalidomide versus BR and R-GemOx. See related commentary by Cherng and Westin, p. 3908

Background Diminished immune defense plays an important role in cancer development. Cancer risk in immunocompromised patients may differ. Identifying individuals with elevated cancer risk can inform strategies for routine cancer screening. This study aimed to understand and compare cancer incidence and risk in three patient groups: recipients of solid organ transplant (SOT) or hematopoietic stem cell transplant (HSCT); diagnosis of primary or secondary immunodeficiency disorder (PID/SID); and recipients of tumor necrosis factor inhibitor (TNF-i) therapy. Methods This retrospective cohort study used the University of Utah Health System database and Huntsman Cancer Institute tumor registry. Patients aged ≥18 years with SOT/HSCT, PID/SID or ≥ 3 months of TNF-i therapy were included. The date of transplant, diagnosis of PID/SID, or 1st TNF-i medication order date was defined as the index date. We calculated cumulative cancer incidence by Kaplan-Meier method. A Cox-proportional hazard regression model with a stepwise variable selection process was used to identify independent risk factors associated with the time to onset of a new primary cancer. Results In total, 13,887 patients were included which comprised of 2982 (21%) SOT/HSCT, 7542 (54%) PID/SID and 3363 (24%) patients receiving TNF-i. The mean (SD) age ranged from 46.8 (15) years - 50.4 (18.2) years. The proportion of white patients ranged from 72.3–84.8%. The estimated cumulative cancer incidence was 11.5% in the SOT/HSCT cohort, 14.3% in the PID/SID cohort, and 8.8% in the TNF-i cohort. The multivariable model adjusted for age, benign in-situ disease, Charlson Comorbidity Index, hypertension/cardiovascular disease/end stage renal disease, gender, race/ethnicity, and renal cyst as significant risk factors. The adjusted hazard ratios for cancer development in SOT/HSCT and PID/SID cohorts compared to the TNF-i cohort over the full follow-up period were 1.57 (95% CI: 1.16–2.13) and 2.14 (95% CI: 1.65–2.77), respectively. Conclusion A significantly increased risk of cancer was observed in PID/SID patients and SOT/HSCT patients compared to TNF-i patients. Age ≥ 50 years, male gender, and clinical comorbidities were additional factors impacting cancer risk. PID/SID and SOT/HSCT patients may benefit from more intensive cancer screening.

Background: Multiple studies have investigated the epidemic of persistent opioid use as a common postsurgical complication. However, there exists a knowledge gap in the association between the level of opioid exposure in the peri-surgical setting and post-discharge adverse outcomes to patients and healthcare settings. We analyzed the association between peri-surgical opioid exposure use and post-discharge outcomes, including persistent postsurgical opioid prescription, opioid-related symptoms (ORS), and healthcare resource utilization (HCRU). Methods: A retrospective cohort study included patients undergoing cesarean delivery, hysterectomy, spine surgery, total hip arthroplasty, or total knee arthroplasty in an academic healthcare system between January 2015 and June 2018. Peri-surgical opioid exposure was converted into morphine milligram equivalents (MME), then grouped into two categories: high (>median MME of each surgery cohort) or low (≤median MME of each surgery cohort) MME groups. The rates of persistent opioid use 30 and 90 days after discharge were compared using logistic regression. Secondary outcomes, including ORS and HCRU during the 180-day follow-up, were descriptively compared between the high and low MME groups. Results: The odds ratios (95% CI) of high vs. low MME for persistent opioid use after 30 and 90 days of discharge were 1.38 (1.24–1.54) and 1.41 (1.24–1.61), respectively. The proportion of patients with one or more ORS diagnoses was greater among the high-MME group than the low-MME group (27.2% vs. 21.2%, p < 0.01). High vs. low MME was positively associated with the rate of inpatient admission, emergency department admissions, and outpatient visits. Conclusions: Greater peri-surgical opioid exposure correlates with a statistically and clinically significant increase in post-discharge adverse opioid-related outcomes. The study findings warrant intensive monitoring for patients receiving greater peri-surgical opioid exposure.

Aims: Treatment intensification (TI) may help patients with type 2 diabetes mellitus (T2DM) achieve target hemoglobin A1c (A1c) < 7.0%. This study aimed to measure the influence of TI on A1c outcome in patients who insufficiently responded to two classes of oral antidiabetic drugs (2OADs).

Pancreatic ductal adenocarcinoma (PDAC) exhibits severe hypoxia, which is associated with chemoresistance and worse patient outcome. It has been reported that hypoxia induces metabolic reprogramming in cancer cells. However, it is not well known whether metabolic reprogramming contributes to hypoxia. Here, we established that increased glutamine catabolism is a fundamental mechanism inducing hypoxia, and thus chemoresistance, in PDAC cells. An extracellular matrix component–based in vitro three-dimensional cell printing model with patient-derived PDAC cells that recapitulate the hypoxic status in PDAC tumors showed that chemoresistant PDAC cells exhibit markedly enhanced glutamine catabolism compared with chemoresponsive PDAC cells. The augmented glutamine metabolic flux increased the oxygen consumption rate via mitochondrial oxidative phosphorylation (OXPHOS), promoting hypoxia and hypoxia-induced chemoresistance. Targeting glutaminolysis relieved hypoxia and improved chemotherapy efficacy in vitro and in vivo. This work suggests that targeting the glutaminolysis–OXPHOS–hypoxia axis is a novel therapeutic target for treating patients with chemoresistant PDAC.Significance:Increased glutaminolysis induces hypoxia via oxidative phosphorylation-mediated oxygen consumption and drives chemoresistance in pancreatic cancer, revealing a potential therapeutic strategy of combining glutaminolysis inhibition and chemotherapy to overcome resistance.

Recent studies on fuel cell design have showed that the use of simulation tools is beneficial in terms of saving time and money. Current density management is still a key research problem for several technologies, including Direct Borohydride Fuel Cell (DBFC). This paper describes a systematic machine learning technique for estimating the cell current density for DBFC as a function of various input factors. Artificial Neural Networks (ANN) and Decision Tree Regressor (DTR) are two popular machine learning models that were trained and evaluated for the current density simulation using a conducted fuel cell experiments presented in previous research. The ANN model performed the better than the DTR model in the simulation, with a mean absolute error of 3.00015 for training and 5.57614 for testing. The simulation exhibits very small error values, indicating that the suggested approaches accurately mirror real-world DBFC process.

Virtual reality (VR) has recently been used as a clinical treatment because it can efficiently simulate situations that are difficult to control in real-world settings. In our study, we assessed the potential of VR in patients with chronic subjective tinnitus. An evaluation of its clinical benefits was performed based on analyses of patient electroencephalograms (EEGs) and by questionnaire responses after 6–8 weeks of patient involvement in our VR-based alleviation program. Clinical trials were performed at a tertiary academic hospital. Nineteen patients (aged 33–64 years) who visited our hospital with chronic subjective tinnitus over 3 months were enrolled in the study. The intervention consisted of trashing the tinnitus avatar in VR. We expected that the patients would have the subjective feeling of controlling tinnitus through our intervention. The VR environment comprised four different sessions in four different settings: a bedroom, a living room, a restaurant, and a city street. We analyzed changes in the source activities of the prefrontal regions related to tinnitus in these patients using standardized low-resolution brain electromagnetic tomography. The Tinnitus Handicap Inventory (THI), the total score (from 50.11 to 44.21, P = 0.046) and the grade (from 3.16 to 2.79, P = 0.035) were significantly improved after the VR-based tinnitus treatment program (P P = 0.025). On the other hand, a Tinnitus Handicap Questionnaire, Quality of Life Assessment (WHO-QOL), Hospital Anxiety and Depression Scale, Profile of Mood States revealed no significant change after the intervention. The baseline EEG data showed that brain activity in the orbitofrontal cortex significantly increased in the alpha and theta frequency bands. Furthermore, patients who showed a THI score improvement after the intervention showed specific increases in brain activity for the theta and high beta bands in the orbitofrontal cortex. Our findings suggest that the virtual reality-based program, as in parts of cognitive behavioral treatment, may help to alleviate tinnitus-related distress in patients with chronic subjective tinnitus.

Automated human posture estimation (A-HPE) systems need delicate methods for detecting body parts and selecting cues based on marker-less sensors to effectively recognize complex activity motions. Recognition of human activities using vision sensors is a challenging issue due to variations in illumination conditions and complex movements during the monitoring of sports and fitness exercises. In this paper, we propose a novel A-HPE method that intelligently identifies human behaviours by utilizing saliency silhouette detection, robust body parts model and multidimensional cues from full-body silhouettes followed by an entropy Markov model. Initially, images are pre-processed and noise is removed to obtain a robust silhouette. Body parts models are then used to extract twelve key body parts. These key body parts are further optimized to assist the generation of multidimensional cues. These cues include energy, optical flow and distinctive values that are fed into quadratic discriminant analysis to discriminate cues which help in the recognition of actions. Finally, these optimized patterns are further processed by a maximum entropy Markov model as a recognizer engine based on transition and emission probability values for activity recognition. For evaluation, we used a leave-one-out cross validation scheme and the results outperformed existing well-known statistical state-of-the-art methods by achieving better body parts detection and higher recognition accuracy over four benchmark datasets. The proposed method will be useful for man-machine interactions such as 3D interactive games, virtual reality, service robots, e-health fitness, and security surveillance.

Background: Through actions of calcium channel trafficking inhibition and sodium/water retention, pregabalin may increase the risk of acute heart failure (AHF). Objective: The objective of this study was to determine the prevalence of heart failure (HF) acute exacerbations, measured by a composite of emergency department (ED) visits, per-patient per-year (PPPY) hospitalizations, time-to first ED admission, and time-to hospitalizations in pre-existing HF patients taking pregabalin compared with those who were pregabalin-naive. Methods: A retrospective cohort study of pregabalin users with HF were propensity score–matched to pregabalin-naïve patients with HF to evaluate the composite of ED admissions or PPPY hospitalizations, time-to first ED admission, and time-to hospitalizations during the 365 days post-index. Doubly robust generalized linear regression and Cox-proportional hazard regression modeling were undertaken for analysis of differences between groups. Results: The matched cohort of 385 pregabalin users and 3460 pregabalin nonusers were principally middle-aged, equally gender distributed, and primary Caucasian. Most patients were on guideline-directed HF medical therapy. The estimated cumulative incidence of the primary outcome was a hazard ratio of 1.099 (95% CI: 0.789-1.530; P = 0.58). Conclusion and Relevance: This large, single-center, cohort study shows pregabalin is not associated with an increased risk of AHF events in patients with pre-existing HF.

A global energy shift to a carbon-neutral society requires clean energy. Hydrogen can accelerate the process of expanding clean and renewable energy sources. However, conventional hydrogen compression and storage technology still suffers from inefficiencies, high costs, and safety concerns. An electrochemical hydrogen compressor (EHC) is a device similar in structure to a water electrolyzer. Its most significant advantage is that it can accomplish hydrogen separation and compression at the same time. With no mechanical motion and low energy consumption, the EHC is the key to future hydrogen compression and purification technology breakthroughs. In this study, the compression performance, efficiency, and other related parameters of EHC are investigated through experiments and simulation calculations. The experimental results show that under the same experimental conditions, increasing the supply voltage and the pressure in the anode chamber can improve the reaction rate of EHC and balance the pressure difference between the cathode and anode. The presence of residual air in the anode can impede the interaction between hydrogen and the catalyst, as well as the proton exchange membrane (PEM), resulting in a decrease in performance. In addition, it was found that a single EHC has a better compression ratio and reaction rate than a double EHC. The experimental results were compatible with the theoretical calculations within less than a 7% deviation. Finally, the conditions required to reach commercialization were evaluated using the theoretical model.

In the recent days, scene understanding has become hot research topic due to its real usage at perceiving, analyzing and recognizing different dynamic scenes coverage during GPS monitoring system, drone’s targets, auto-driving and tourist guide. The goal of scene understanding is to make machines look at like humans do, which means the accurate recognition of the contents in scenes and during location observations. Then, we perform two operations such as (1) to perfectly describe the whole environment and (2) to describe what action is going on in the environment. Due to complex scene analysis, recognition of multiple objects and the relation between the objects remain as a challenging part of the research. In this paper, we have proposed a novel approach for the scene understanding that integrates multiple objects detection/segmentation and scene labeling using Geometric features, Histogram of oriented gradient and scale invariant feature transform descriptors. The complete procedure of the purposed model includes resizing and noise removing of images from the dataset, multiple object segmentation and detection, feature extraction and multiple object recognition using multi-layer kernel sliding perceptron. After that, scene recognition is achieved by using multi-class logistic regression. Finally, two datasets such as MSRC and UIUC sports are used for the experimental evaluation of our proposed method. Our purposed method accurately handles the complex objects physical exclusion and objects occlusion. Therefore, it outperforms in term of accuracy compared with other state-of-the-art approaches.

Human-Object Interaction (HOI) recognition, due to its significance in many computer vision-based applications, requires in-depth and meaningful details from image sequences. Incorporating semantics in scene understanding has led to a deep understanding of human-centric actions. Therefore, in this research work, we propose a semantic HOI recognition system based on multi-vision sensors. In the proposed system, the de-noised RGB and depth images, via Bilateral Filtering (BLF), are segmented into multiple clusters using a Simple Linear Iterative Clustering (SLIC) algorithm. The skeleton is then extracted from segmented RGB and depth images via Euclidean Distance Transform (EDT). Human joints, extracted from the skeleton, provide the annotations for accurate pixel-level labeling. An elliptical human model is then generated via a Gaussian Mixture Model (GMM). A Conditional Random Field (CRF) model is trained to allocate a specific label to each pixel of different human body parts and an interaction object. Two semantic feature types that are extracted from each labeled body part of the human and labelled objects are: Fiducial points and 3D point cloud. Features descriptors are quantized using Fisher’s Linear Discriminant Analysis (FLDA) and classified using K-ary Tree Hashing (KATH). In experimentation phase the recognition accuracy achieved with the Sports dataset is 92.88%, with the Sun Yat-Sen University (SYSU) 3D HOI dataset is 93.5% and with the Nanyang Technological University (NTU) RGB+D dataset it is 94.16%. The proposed system is validated via extensive experimentation and should be applicable to many computer-vision based applications such as healthcare monitoring, security systems and assisted living etc.

With advances in machine vision systems (e.g., artificial eye, unmanned aerial vehicles, surveillance monitoring) scene semantic recognition (SSR) technology has attracted much attention due to its related applications such as autonomous driving, tourist navigation, intelligent traffic and remote aerial sensing. Although tremendous progress has been made in visual interpretation, several challenges remain (i.e., dynamic backgrounds, occlusion, lack of labeled data, changes in illumination, direction, and size). Therefore, we have proposed a novel SSR framework that intelligently segments the locations of objects, generates a novel Bag of Features, and recognizes scenes via Maximum Entropy. First, denoising and smoothing are applied on scene data. Second, modified Fuzzy C-Means integrates with super-pixels and Random Forest for the segmentation of objects. Third, these segmented objects are used to extract a novel Bag of Features that concatenate different blobs, multiple orientations, Fourier transform and geometrical points over the objects. An Artificial Neural Network recognizes the multiple objects using the different patterns of objects. Finally, labels are estimated via Maximum Entropy model. During experimental evaluation, our proposed system illustrated a remarkable mean accuracy rate of 90.07% over the MSRC dataset and 89.26% over the Caltech 101 for object recognition, and 93.53% over the Pascal-VOC12 dataset for scene recognition, respectively. The proposed system should be applicable to various emerging technologies, such as augmented reality, to represent the real-world environment for military training and engineering design, as well as for entertainment, artificial eyes for visually impaired people and traffic monitoring to avoid congestion or road accidents.