Miracle-dz/UniFAR-FTUs huggingface dataset:
Hospitalization and Mortality in Patients With Heart Failure Treated With Sacubitril/Valsartan vs. Enalapril: A Real-World, Population-Based Study

Background: The effect of sacubitril/valsartan on survival and hospitalization risk in older patients with heart failure has not been explored. We aimed to investigate the risk of hospitalization and mortality with the use of sacubitril/valsartan vs. enalapril in patients with heart failure. Methods: This was a population-based cohort study using the Hong Kong-wide electronic healthcare database. Patients diagnosed with heart failure and newly prescribed sacubitril/valsartan or enalapril between July 2016 and June 2019 were included. The risk of primary composite outcome of cardiovascular mortality or heart failure-related hospitalization, all-cause hospitalization, heart failure-related hospitalization, cardiovascular mortality and all-cause mortality were compared using Cox regression with inverse probability treatment weighting. Additional analysis was conducted by age stratification. Results: Of the 44,503 patients who received sacubitril/valsartan or enalapril, 3,237 new users (sacubitril/valsartan, n = 1,056; enalapril, n = 2,181) with a diagnosis of heart failure were identified. Compared with enalapril, sacubitril/valsartan users were associated with a lower risk of primary composite outcome [hazard ratio (HR) 0.58; 95% confidence interval (CI), 0.45–0.75], heart failure-related hospitalization (HR 0.59; 95% CI, 0.45–0.77), all-cause mortality (HR 0.51; 95% CI, 0.36–0.74) and borderline non-significant reductions in all-cause hospitalization (HR 0.85; 95% CI, 0.70–1.04) and cardiovascular mortality (HR 0.63; 95% CI, 0.39–1.02). The treatment effect of sacubitril/valsartan remains unaltered in the patient subgroup age ≥ 65 years (73%). Conclusions: In real-world settings, sacubitril/valsartan was associated with improved survival and reduced heart failure-related hospitalization compared to enalapril in Asian patients with heart failure. The effectiveness remains consistent in the older population.
Impact of Implementing CYP2C19 Genotype-Guided Antiplatelet Therapy on P2Y12 Inhibitor Selection and Clinical Outcomes in Acute Coronary Syndrome Patients After Percutaneous Coronary Intervention: A Real-World Study in China

Background: CYP2C19 loss-of-function (LOF) alleles reduce the effectiveness of clopidogrel in patients undergoing percutaneous coronary intervention for acute coronary syndrome. However, the clinical impact of implementing CYP2C19 gene-guided pharmacotherapy is unclear, especially among the Chinese population. The purpose of this study was to evaluate P2Y12 receptor inhibitor selection and clinical outcomes upon implementation of CYP2C19 genotype-guided pharmacotherapy in current clinical practice. Methods: This was a single-center observational cohort study. Adult percutaneous coronary intervention patients who received CYP2C19 genetic testing (*2, *3, *17 alleles) were included. Ticagrelor was recommended for patients with a LOF allele. Factors related to P2Y12 inhibitor selection were determined by logistic regression. The primary endpoint was major cardiac or cerebrovascular adverse events (MACCE) within 12 months. MACCE and clinically significant bleeding events (BARC ≥2) in the LOF-clopidogrel group, non-LOF-clopidogrel group, and non-LOF-ticagrelor group were compared with those in the LOF-ticagrelor group. The inverse probability of treatment weighting (IPTW) was adjusted in a Cox regression analysis to eliminate confounding factors. Results: Among 1,361 patients, 826 (60.7%) had a LOF allele. Patients with a LOF allele were more likely to be prescribed ticagrelor (multivariate-adjusted OR 1.349; 95% CI 1.040 to 1.751; p = 0.024). The MACCE rate was higher in the LOF-clopidogrel group than in the LOF-ticagrelor group (7.8 vs. 4.0%; log-rank p = 0.029; IPTW-adjusted HR 2.138; 95% CI 1.300–3.515). Compared with the LOF-ticagrelor group, the non-LOF-clopidogrel group showed no significant difference in MACCE rate (5.8 vs. 4.0%; log-rank p = 0.272; IPTW-adjusted HR 1.531; 95% CI 0.864–2.714). Among the patients treated with ticagrelor, there was no significant difference in the MACCE rate between the LOF group and non-LOF group (4.3 vs. 4.0%; log-rank p = 0.846; IPTW-adjusted HR 1.184; 95% CI 0.582–2.410). There was no significant difference in the incidence of clinically significant bleeding events among the four groups. Conclusion: This study confirms that efficiently returned CYP2C19 genotype results did partially guide cardiologists to prescribe ticagrelor for patients with a LOF allele, and that clopidogrel had a higher risk of MACCE than ticagrelor in these patients, which provides support for the implementation of CYP2C19 gene-guided antiplatelet therapy in clinical practice.
Contactless Multi-biometric System Using Fingerprint and Palmprint Selfies

Due to the COVID-19 pandemic, automated contactless person identification based on the human hand has become very vital and an appealing biometric trait. Since, people are expected to cover their faces with masks, and advised avoiding touching surfaces. It is well-known that usually contact-based hand biometrics suffer from issues like deformation due to uneven distribution of pressure or improper placement on sensor, and hygienic concerns. Whereas, to mitigate such problems, contactless imaging is expected to collect the hand biometrics information without any deformation and leading to higher person recognition accuracy; besides maintaining hygienic and pandemic concerns. Towards this aim, in this paper, an effective multi-biometric scheme for person authentication based on contactless fingerprint and palmprint selfies has been proposed. In this study, for simplicity and efficiency, three local descriptors, i.e., local phase quantization (LPQ), local Ternary patterns (LTP), and binarized statistical image features (BSIF), have been employed to extract salient features from contactless fingerprint and palmprint selfies. The score level fusion based multi-biometric system developed in this work combines the matching scores using two different fusion techniques, i.e., transformation based-rules like triangular norms and classifier based-rules like SVM. Experimental results on two publicly available databases (i.e., PolyU contactless to contact-based fingerprint database and IIT-Delhi touchless palmprint dataset) show that the proposed contactless multi-biometric selfie system can easily outperform uni-biometrics.
Bathymetric Detection of Fluvial Environments through UASs and Machine Learning Systems

In recent decades, photogrammetric and machine learning technologies have become essential for a better understanding of environmental and anthropic issues. The present work aims to respond one of the most topical problems in environmental photogrammetry, i.e., the automatic classification of dense point clouds using the machine learning (ML) technology for the refraction correction on the fluvial water table. The applied methodology for the acquisition of multiple photogrammetric flights was made through UAV drones, also in RTK configuration, for various locations along the Orco River, sited in Piedmont (Italy) and georeferenced with GNSS—RTK topographic method. The authors considered five topographic fluvial cross-sections to set the correction methodology. The automatic classification in ML has found a valid identification of different patterns (Water, Gravel bars, Vegetation, and Ground classes), in specific hydraulic and geomatic conditions. The obtained results about the automatic classification and refraction reduction led us the definition of a new procedure, with precise conditions of validity.
Technical Advancements in SAGD Evaporative Produced Water Treatment

There has been a shift away from the use of warm lime softening (WLS) and weak acid cation (WAC) ion exchange for produced water treatment to the use of mechanical vapour compression (MVC) evaporation followed by high pressure drum-type boilers. Approximately 18 steam assisted gravity drainage (SAGD) produced water evaporators are operating or are in various stages of construction in Alberta and overseas. Since the commissioning of the first such evaporators in 2002, many technical advancements have occurred which have resulted in reduced operating costs, improved reliability, reduced scaling and fouling potential, improved distillate quality and improved boiler feed quality for steam generation. This paper provides details of the technical advancements in evaporative produced water treatment based on full-scale operating data and lessons learned. It also presents improved evaporator configurations, discusses improvements in contaminant reduction and scale prevention systems, demonstrates how capital and operating costs can be drastically reduced as compared to earlier evaporator system designs, and provides recent advancements in modularization, evaporator disposal treatment, deoiling, membrane preconcentration, and zero discharge solids drying techniques.
Hope, Stress, and Post-Divorce Child Adjustment: Development and Evaluation of the Co-Parenting for Resilience Program

ABSTRACT Two studies examine the effectiveness of Co-Parenting for Resilience, a program targeting divorcing or separating parents with a minor child in common. Study-one (N = 132) uses a within-group design to assess whether parent scores on key constructs improve across time. Study-two (N = 330) employs a control group to assess whether change can be attributed to the program. Within-group results indicate significant increases on parental hope and child adjustment, and decreases on parental stress and conflict with a co-parent. Between-group analyses find significant differences in parental hope, stress, conflict with a co-parent, collaborative co-parenting, and child adjustment. Implications for policy are discussed.
Deep Learning-Based Haptic Guidance for Surgical Skills Transfer

Having a trusted and useful system that helps to diminish the risk of medical errors and facilitate the improvement of quality in the medical education is indispensable. Thousands of surgical errors are occurred annually with high adverse event rate, despite inordinate number of devised patients safety initiatives. Inadvertently or otherwise, surgeons play a critical role in the aforementioned errors. Training surgeons is one of the most crucial and delicate parts of medical education and needs more attention due to its practical intrinsic. In contrast to engineering, dealing with mortal alive creatures provides a minuscule chance of trial and error for trainees. Training in operative rooms, on the other hand, is extremely expensive in terms of not only equipment but also hiring professional trainers. In addition, the COVID-19 pandemic has caused to establish initiatives such as social distancing in order to mitigate the rate of outbreak. This leads surgeons to postpone some non-urgent surgeries or operate with restrictions in terms of safety. Subsequently, educational systems are affected by the limitations due to the pandemic. Skill transfer systems in cooperation with a virtual training environment is thought as a solution to address aforesaid issues. This enables not only novice surgeons to enrich their proficiency but also helps expert surgeons to be supervised during the operation. This paper focuses on devising a solution based on deep leaning algorithms to model the behavior of experts during the operation. In other words, the proposed solution is a skill transfer method that learns professional demonstrations using different effective factors from the body of experts. The trained model then provides a real-time haptic guidance signal for either instructing trainees or supervising expert surgeons. A simulation is utilized to emulate an operating room for femur drilling surgery, which is a common invasive treatment for osteoporosis. This helps us with both collecting the essential data and assessing the obtained models. Experimental results show that the proposed method is capable of emitting guidance force haptic signal with an acceptable error rate.
Raman-Based Diagnostics of Biotic and Abiotic Stresses in Plants. A Review

Digital farming is a novel agricultural philosophy that aims to maximize a crop yield with the minimal environmental impact. Digital farming requires the development of technologies that can work directly in the field providing information about a plant health. Raman spectroscopy (RS) is an emerging analytical technique that can be used for non-invasive, non-destructive, and confirmatory diagnostics of diseases, as well as the nutrient deficiencies in plants. RS is also capable of probing nutritional content of grains, as well as highly accurate identification plant species and their varieties. This allows for Raman-based phenotyping and digital selection of plants. These pieces of evidence suggest that RS can be used for chemical-free surveillance of plant health directly in the field. High selectivity and specificity of this technique show that RS may transform the agriculture in the US. This review critically discusses the most recent research articles that demonstrate the use of RS in diagnostics of abiotic and abiotic stresses in plants, as well as the identification of plant species and their nutritional analysis.
Volumetric Deficit Within the Fronto-Limbic-Striatal Circuit in First-Episode Drug Naïve Patients With Major Depression Disorder

Background: Depression is a major psychiatric disorder and the leading cause of disability worldwide. Previous evidence suggested certain pattern of structural alterations were induced by major depression disorder (MDD) with heterogeneity due to patients' clinical characteristics and proposed that early impairment of fronto-limbic-striatal circuit was involved. Yet the hypothesis couldn't be replicated fully. Accordingly, this study aimed to validate this hypothesis in a new set of first-episode, drug naïve MDD patients and further explore the neuroimaging biomarker of illness severity using whole-brain voxel-based morphometry (VBM). Materials and Methods: A total of 93 participants, 30 patients with first-episode medication-naïve MDD, and 63 healthy controls were enrolled in the study. VBM was applied to analyze differences in the gray matter volume (GMV) between these two groups. The correlation between the GMV of the identified brain regions and the severity of clinical symptoms quantified by the Hamilton Depression Scale (HAMD) was further conducted in the post-hoc analysis to confirm the role of GMV structural alteration in clinical symptoms. Results: Our results revealed that the brain gray matter volume of the prefrontal lobe, limbic system, striatum, cerebellum, temporal lobe, and bilateral lingual gyri were significantly decreased in MDD patients compared with healthy controls. Besides, the HAMD scores were negatively correlated with GMV of the right insula and positively correlated with that of the right lingual gyrus. Conclusions: Our findings provide robust evidence that gray matter structural abnormalities within the prefronto-limbic-striatal circuit are implicated in the pathophysiology of MDD at an early stage without confounding influence of medication status. Besides, our data suggest that the cerebellum, lingual gyrus, and fusiform gyrus should also be integrated into the brain alterations in MDD. Future synthesis of individual neuroimaging studies and more advanced statistical analysis comparing subfields of the aforementioned regions are warranted to further shed light on the neurobiology of the disease and assist in the diagnosis of this burdensome disorder.
Encapsulation of Au nanoparticles with well-crystallized anatase TiO2 mesoporous hollow spheres for increased thermal stability

Uniform Auencap/TiO2 hollow microspheres, in which sub-10 nm Au nanoparticles are coated with a mesoporous anatase TiO2 shell, are prepared by a protected-calcinating process. The method involves the preparation of SiO2@Au@TiO2 colloidal composites, sequential deposition of carbon and then SiO2 layers through solvothermal and sol–gel processes, crystallization of TiO2 by calcination and finally etching of the inner and outer silica layers to produce the hollow structure. The protected crystallization process suppresses the excessive growth of TiO2 and eventually produces mesoporous anatase shells with high surface area. Additionally, by simply controlling the crystallization temperature, it can be convenient to tune the porosity and crystallinity of the TiO2 shell, meanwhile maintaining the small size of Au nanoparticles. When used as the catalyst for the reduction of 4-nitrophenol, the synthesized Auencap/TiO2 catalysts exhibit significantly enhanced catalytic performance. Moreover, the obtained Auencap/TiO2 hollow spheres show a superior thermal stability, as it resists sintering during the additional calcination at 500 °C, whereas the sample prepared by deposited Au nanoparticles on commercial P25(Au/P25) was found to sinter severely.
Tracking Major Sources of Water Contamination Using Machine Learning

Current microbial source tracking techniques that rely on grab samples analyzed by individual endpoint assays are inadequate to explain microbial sources across space and time. Modeling and predicting host sources of microbial contamination could add a useful tool for watershed management. In this study, we tested and evaluated machine learning models to predict the major sources of microbial contamination in a watershed. We examined the relationship between microbial sources, land cover, weather, and hydrologic variables in a watershed in Northern California, United States. Six models, including K-nearest neighbors (KNN), Naïve Bayes, Support vector machine (SVM), simple neural network (NN), Random Forest, and XGBoost, were built to predict major microbial sources using land cover, weather and hydrologic variables. The results showed that these models successfully predicted microbial sources classified into two categories (human and non-human), with the average accuracy ranging from 69% (Naïve Bayes) to 88% (XGBoost). The area under curve (AUC) of the receiver operating characteristic (ROC) illustrated XGBoost had the best performance (average AUC = 0.88), followed by Random Forest (average AUC = 0.84), and KNN (average AUC = 0.74). The importance index obtained from Random Forest indicated that precipitation and temperature were the two most important factors to predict the dominant microbial source. These results suggest that machine learning models, particularly XGBoost, can predict the dominant sources of microbial contamination based on the relationship of microbial contaminants with daily weather and land cover, providing a powerful tool to understand microbial sources in water.
The Periodic Table of Industries: Detection of Collaboration Opportunities Based on an Imitation of the Mendeleev Periodic Table of Elements

The concept of collaborative networks has been encountered very often lately as the answer when trying to adapt and improve enterprises in these highly competitive business environments, therefore the urge for constantly addressing this topic. A lot of work-related to collaborative networks has been done so far, from defining network types to leveling partnerships and proposing models for partnership developments. But the lack of tackling a very important obstacle, which is the difficulty of detecting and anticipating collaboration opportunities between enterprises, inspired this research. In this article, a new theoretical opportunity detection approach is proposed based on enterprise characterization concept, KPI classification as well as collaboration types. This detection approach is a table of industrial classifications that imitates the Mendeleev periodic table from the concept point of view. A fictional example from an industrial context is shown to explain the usage of this approach accompanied by discussion about future work and limitations.
Genetics and Clinical Features of Noncompaction Cardiomyopathy in the Fetal Population

Objectives: Noncompaction Cardiomyopathy (NCCM) has been classified as primary genetic cardiomyopathy and has gained increasing clinical awareness; however, little is known about NCCM in the fetal population. We aimed to investigate the clinical characteristics and genetic spectrum of a fetal population with NCCM. Methods: We retrospectively reviewed all fetuses with a prenatal diagnosis of NCCM at a single center between October 2010 and December 2019. These cases were investigated for gestational age at diagnosis, gender, left or biventricular involvement, associated cardiac phenotypes, outcomes, and genetic testing data. Results: We identified 37 fetuses with NCCM out of 49,898 fetuses, indicating that the incidence of NCCM in the fetal population was 0.07%. Of the 37 fetuses, 26 were male, ten were female and one was of unknown gender. NCCM involvement biventricle is the most common (n = 16, 43%), followed by confined to the left ventricle (n = 14, 38%). Nineteen (51%) had additional congenital heart defects, with right-sided lesions being the most common (n = 14, 74%), followed by ventricular septal defects (n = 10, 53%). Hydrops fetalis was present in 12 cases (32%), of which four were atypical (pericardial effusion only). Sequencing analysis was performed at autopsy (n = 19) or postnatally (n = 1) on 20 fetuses. Of the 20 fetuses undergoing copy number variation sequencing and whole-exome sequencing, nine (47%) had positive genetic results, including one with a pathogenic copy number variant and eight with pathogenic/likely pathogenic variants. Non-sarcomere gene mutations accounted for the vast majority (n = 7). In contrast, sarcomere gene mutations occurred in only one case (TPM1), and no mutations were identified in the three most common sarcomere genes (MYH7, TTN, and MYBPC3) of pediatric and adult patients. Pathogenic/likely pathogenic variants were significantly more frequent in fetuses with congenital heart defects than those without congenital heart defects. Conclusions: Our data demonstrate that fetal NCCM is a unique entity. Compared with pediatric and adult NCCM, fetal NCCM is more prone to biventricle involvement, more likely to be complicated with congenital heart defects, and has a distinct genetic spectrum.
Bcl9 Depletion Modulates Endothelial Cell in Tumor Immune Microenvironment in Colorectal Cancer Tumor

Tumor endothelial cells are an important part of the tumor microenvironment, and angiogenesis inhibitory therapy has shown potential in tumor treatment. However, which subtypes of tumor endothelial cells are distributed in tumors, what are the differences between tumor endothelial cells and normal endothelial cells, and what is the mechanism of angiogenesis inhibitory therapy at the histological level, are all need to be resolved urgently. Using single-cell mRNA sequencing, we analyzed 12 CT26 colon cancer samples from mice, and found that knockdown of the downstream factor BCL9 in the Wnt signaling pathway or inhibitor-mediated functional inhibition can modulate tumor endothelial cells at a relatively primitive stage, inhibiting their differentiation into further extracellular matrix construction and angiogenesis functions. Furthermore, we propose a BCL9-endo-Score based on the differential expression of cells related to different states of BCL9 functions. Using published data sets with normal endothelial cells, we found that this score can characterize endothelial cells at different stages of differentiation. Finally, in the The Cancer Genome Atlas (TCGA) pan-cancer database, we found that BCL9-endo-Score can well predict the prognosis of diseases including colon cancer, kidney cancer and breast cancer, and identified the markers of these tumor subtypes, provide a basis for the prognosis prediction of patients with such types of tumor. Our data also contributed knowledge for tumor precision treatment with angiogenesis inhibitory therapy by targeting the Wnt signaling pathway.