Characteristics and risk assessment of heavy metals in groundwater at a typical smelter-contaminated site in Southwest China.

To explore the characteristics and evaluate the risk of heavy metals in groundwater at a typical smelter-contaminated site, this study focuses on a representative a historical arsenic smelting plant in Southwest China, where the primary historical products were metallic arsenic (∼1000 tons/year) and arsenic trioxide (∼2000 ton/year). The results demonstrated As and Pb as the main pollutants in soil, and As and Cd as main pollutants in groundwater through soil profiling and quarterly groundwater analysis. The maximum As and Pb in the surface soil were 76800 and 2290 mg/kg, respectively, with As vertically infiltrating the deep gravel-sand layer (18-20 m). The groundwater pollution distribution progressively increased along flow direction, influenced by seasonal surface runoff and infiltration fluctuations. The groundwater pollutant concentrations during the dry season notably surpassed those during the wet season, with maximum As and Cd concentrations of 111.64 mg/L and 19.85 µg/L during the dry season, respectively. Furthermore, the analytic hierarchy process (AHP) was applied to evaluate the comprehensive risk of contaminated-site across pollution source load, regional groundwater intrinsic vulnerability, and evaluation of nearby sensitive receptors. The results revealed that the carcinogenic risk of lead in surface soil was moderate to high, while arsenic posed a high carcinogenic risk, contributing to an overall carcinogenic risk proportion of 89.6% in surface soil. Exposure through groundwater intake was identified as the primary pathway, with carcinogenic and noncarcinogenic risks exceeding those through skin contact. The final weights result demonstrated that the principal risk factors are the intrinsic arsenic load and protective target characteristics of regional groundwater at this site. This study provides a reference for comprehensive assessments of similarly contaminated industrial and smelting sites.

Assessment of retinal and choroidal microvasculature in night shift medical workers by OCT angiography.

This study evaluated retinal and choroidal microvascular changes in night shift medical workers and its correlation with melatonin level. Night shift medical workers (group A, 25 workers) and non-night shift workers (group B, 25 workers) were recruited. The images of macula and optic nerve head were obtained by swept-source OCT-angiography. Vessel density of retina, choriocapillaris (CC), choriocapillaris flow deficit (CC FD), choroidal thickness (CT) and choroidal vascularity index (CVI) were measured. 6-sulfatoxymelatonin concentration was analyzed from the morning urine. CC FD and CVI were significantly decreased and CT was significantly increased in group A (all P < 0.05). 6-sulfatoxymelatonin concentration was significantly lower in group A (P < 0.05), which was significantly positively correlated with CC FD size (r = 0.318, P = 0.024) and CVI of the most regions (maximum r-value was 0.482, P < 0.001), and was significantly negatively associated with CT of all regions (maximum r-value was - 0.477, P < 0.001). In night shift medical workers, the reduction of melatonin was significantly correlated with CT thickening, CVI reduction and CC FD reduction, which suggested that they might have a higher risk of eye diseases. CC FD could be a sensitive and accurate indicator to reflect CC perfusion.

Factor-GAN: Enhancing stock price prediction and factor investment with Generative Adversarial Networks.

Deep learning, a pivotal branch of artificial intelligence, has increasingly influenced the financial domain with its advanced data processing capabilities. This paper introduces Factor-GAN, an innovative framework that utilizes Generative Adversarial Networks (GAN) technology for factor investing. Leveraging a comprehensive factor database comprising 70 firm characteristics, Factor-GAN integrates deep learning techniques with the multi-factor pricing model, thereby elevating the precision and stability of investment strategies. To explain the economic mechanisms underlying deep learning, we conduct a subsample analysis of the Chinese stock market. The findings reveal that the deep learning-based pricing model significantly enhances return prediction accuracy and factor investment performance in comparison to linear models. Particularly noteworthy is the superior performance of the long-short portfolio under Factor-GAN, demonstrating an annualized return of 23.52% with a Sharpe ratio of 1.29. During the transition from state-owned enterprises (SOEs) to non-SOEs, our study discerns shifts in factor importance, with liquidity and volatility gaining significance while fundamental indicators diminish. Additionally, A-share listed companies display a heightened emphasis on momentum and growth indicators relative to their dual-listed counterparts. This research holds profound implications for the expansion of explainable artificial intelligence research and the exploration of financial technology applications.

Enhancing YOLO5 for the Assessment of Irregular Pelvic Radiographs with Multimodal Information.

Developmental dysplasia of the hip (DDH) is one of the most common orthopedic disorders in infants and young children. Accurate identification and localization of anatomical landmarks are prerequisites for the diagnosis of DDH. In recent years, various works have employed deep learning algorithms on radiography images for DDH diagnosis. However, none of these works have considered the incorporation of multimodal information. The pelvis exhibits distinct structures at different developmental stages, and there are also gender-based differences. In light of this, this study proposes a method to enhance the performance of deep learning models in diagnosing DDH by incorporating age and gender information into the channels. The study utilizes YOLO5 to construct a deep learning network for detecting hip joint landmarks. Moreover, a comprehensive dataset of 7750 pelvic X-ray images is established, covering ages from 4 months to 16 years and encompassing various conditions, such as deformities and post-operative cases, which authentically capture the temporal diversity and pathological complexities of DDH. Experimental results show that the YOLO5 model with integrated multimodal information achieves a mAP0.5-0.95 of 83.1% and a diagnostic accuracy of 86.7% in test dataset. The F1 scores for diagnosing cases of normal (NM), suspected dislocation (SD), mild dislocation (MD), and heavily dislocation (HD) are 90.9%, 79.8%, 63.5%, and 97.4%, respectively. Furthermore, experiments conducted on datasets of different sizes and networks of different sizes demonstrate the beneficial impact of multimodal information in improving the effectiveness of deep learning in diagnosing DDH.

Clinically Meaningful Responses to Dupilumab Among Children Aged 6 Months to 5 Years with Moderate-to-severe Atopic Dermatitis who did Not Achieve Clear or Almost Clear Skin According to the Investigator's Global Assessment: A Post Hoc Analysis of a Phase 3 Trial.

In young children, atopic dermatitis (AD) imposes a multidimensional burden on many aspects of their quality of life (QoL) and that of their families. LIBERTY AD PRESCHOOL part B was a randomized, double- blinded, placebo-controlled phase 3 trial in 162 children (aged 6 months to 5 years) with moderate-to- severe AD receiving dupilumab or placebo, plus low-potency topical corticosteroids. Post hoc analyses were performed on the full analysis set (FAS) and a subset of patients with Investigator's Global Assessment score > 1 at week 16. The primary outcome was the proportion of patients at week 16 achieving a composite endpoint encompassing clinically meaningful changes in AD signs, symptoms and QoL: ≥ 50% improvement in Eczema Area and Severity Index; and/or ≥ 4-point reduction in worst scratch/itch numerical rating scale; and/or ≥ 6-point reduction in Children's Dermatology Life Quality Index/Infants' Dermatitis Quality of Life Index. Significantly more patients receiving dupilumab vs placebo achieved the composite endpoint in both the FAS (77.7% vs 24.6%, p < 0.0001) and subgroup (68.9% vs 21.5%, p < 0.0001). Dupilumab provided rapid and significant, clinically meaningful improvements in AD signs, symptoms, and QoL in the overall group and subgroup of patients who did not achieve clear or almost clear skin at week 16.

Encapsulation and assessment of therapeutic cargo in engineered exosomes: a systematic review.

Exosomes are nanoscale extracellular vesicles secreted by cells and enclosed by a lipid bilayer membrane containing various biologically active cargoes such as proteins, lipids, and nucleic acids. Engineered exosomes generated through genetic modification of parent cells show promise as drug delivery vehicles, and they have been demonstrated to have great therapeutic potential for treating cancer, cardiovascular, neurological, and immune diseases, but systematic knowledge is lacking regarding optimization of drug loading and assessment of delivery efficacy. This review summarizes current approaches for engineering exosomes and evaluating their drug delivery effects, and current techniques for assessing exosome drug loading and release kinetics, cell targeting, biodistribution, pharmacokinetics, and therapeutic outcomes are critically examined. Additionally, this review synthesizes the latest applications of exosome engineering and drug delivery in clinical translation. The knowledge compiled in this review provides a framework for the rational design and rigorous assessment of exosomes as therapeutics. Continued advancement of robust characterization methods and reporting standards will accelerate the development of exosome engineering technologies and pave the way for clinical studies.

Effects of digital transformation and environmental resource integration capability on medical equipment suppliers' green innovation performance.

In today's digital age, the effort of medical enterprises towards green innovation has gained traction in academic and business circles. However, the current research system for medical equipment suppliers lacks a systematic study on how digital transformation can enhance the outcomes of green innovation. This research aims to develop a theoretical framework for digital transformation, environmental resource integration capability, managerial environmental concern and green innovation performance with respect to the resource-based view and conducting empirical analysis using survey data from medical equipment supply enterprises. Our findings reveal that digital transformation has a significant effect on the promotion of green innovation performance through environmental resource integration capability. Moreover, the managerial environmental concern moderates above mediation effect. These findings not only provide compelling insights into the impact of digital transformation on green innovation performance but also have important implications for sustainable development and cleaner production relevant academic research and policy-making.

The impact of insurance status on in-hospital mortality in patients with hyperglycaemic crisis: A propensity score matching analysis.

AIM: This study was designed to determine the associations between insurance status and clinical outcomes among patients with hyperglycaemic crisis. METHODS: Overall, 1668 patients with hyperglycaemic crisis were recruited from the Chongqing Medical University Medical Data Science Academy's big data platform. In-hospital mortality, length of stay and complications (i.e., hypoglycaemia, hypokalemia, pulmonary infection, multiple systemic organ failure, acute kidney injury and deep venous thrombosis) were assessed. Propensity score matching analysis was used to reduce the confounding bias, and univariate and multivariate logistic regression were used to estimate the effect of insurance status on mortality in patients with hyperglycaemic crisis. RESULTS: After matching one uninsured patient to two insured patients with a calliper of 0.02, the uninsured group suffered a higher burden of in-hospital mortality than the insured group (16.9% vs. 9.8%); the insured status (odds ratio = 0.216, 95% confidence interval = 0.079-0.587) was a potential protect factor for in-hospital mortality of patients with hyperglycaemic crisis in the multivariate logistic regression analysis. CONCLUSIONS: Insurance status is associated with the outcomes of hospitalisation for hyperglycaemic crisis; uninsured patients with hyperglycaemic crisis face a higher risk of mortality in China.

A combination predicting methodology based on T-LSTNet_Markov for short-term wind power prediction.

Wind power has been valued by countries for its renewability and cleanness and has become most of the focus of energy development in all countries. However, due to the uncertainty and volatility of wind power generation, making the grid-connected wind power system presents some serious challenges. Improving the accuracy of wind power prediction has become the focus of current research. Therefore, this paper proposes a combined short-term wind power prediction model based on T-LSTNet_markov to improve prediction accuracy. First, perform data cleaning and data preprocessing operations on the original data. Second, forecast using T-LSTNet model in original wind power data. Finally, calculate the error between the forecast value and the actual value. The k-means++ method and Weighted Markov process are used to correct errors and to get the result of the final prediction. The data that are collected from a wind farm in Inner Mongolia Autonomous Region, China, are selected as a case study to demonstrate the effectiveness of the proposed combined models. The empirical results show that the prediction accuracy is further improved after correcting errors.

Strategies of property developers in the context of carbon tax.

China needs to guide property developers in actively reducing emissions to reach carbon emission reduction targets and respond to global climate change. A carbon tax is an important policy tool. Still, to establish successful rules to steer property developers' reasonable carbon emission reduction behavior, we must first explore property developers' decision-making mechanisms. This study develops an emission reduction and price game model for property developers under the constraint of a carbon tax. It then applies reverse order induction and optimization methods to identify the game equilibrium solution for property developers. Using the game equilibria, we explore the carbon tax mechanism on emission reduction and property developer pricing strategies. We can derive the following conclusions if the carbon tax policy is not implemented: 1.House prices are related to the substitutability of the two types of competitive property developers. 2.The greater the substitutability, the greater the cost of emission reduction paid by consumers. 3.The game equilibrium carbon emission intensity is the average carbon emission intensity of the housing business. In the situation of enacting a carbon tax, we arrive at the following conclusions: 1.The profits of real estate developers who do not have the advantage of emission reduction continue to decline with the increase of carbon tax. 2. For real estate developers who have the advantage of decreasing emissions, profits declined initially and then increased as the carbon tax rate increased, and only when the carbon tax rate reaches Tm1* can they fully leverage the cost advantage and obtain ever-increasing profits. 3.Low tax rates should be adopted by the government at the start of the implementation of the carbon tax policy to provide a buffer time for real estate developers who do not have the advantage of emission reduction costs.

FedDM: Federated Weakly Supervised Segmentation via Annotation Calibration and Gradient De-Conflicting.

Weakly supervised segmentation (WSS) aims to exploit weak forms of annotations to achieve the segmentation training, thereby reducing the burden on annotation. However, existing methods rely on large-scale centralized datasets, which are difficult to construct due to privacy concerns on medical data. Federated learning (FL) provides a cross-site training paradigm and shows great potential to address this problem. In this work, we represent the first effort to formulate federated weakly supervised segmentation (FedWSS) and propose a novel Federated Drift Mitigation (FedDM) framework to learn segmentation models across multiple sites without sharing their raw data. FedDM is devoted to solving two main challenges (i.e., local drift on client-side optimization and global drift on server-side aggregation) caused by weak supervision signals in FL setting via Collaborative Annotation Calibration (CAC) and Hierarchical Gradient De-conflicting (HGD). To mitigate the local drift, CAC customizes a distal peer and a proximal peer for each client via a Monte Carlo sampling strategy, and then employs inter-client knowledge agreement and disagreement to recognize clean labels and correct noisy labels, respectively. Moreover, in order to alleviate the global drift, HGD online builds a client hierarchy under the guidance of history gradient of the global model in each communication round. Through de-conflicting clients under the same parent nodes from bottom layers to top layers, HGD achieves robust gradient aggregation at the server side. Furthermore, we theoretically analyze FedDM and conduct extensive experiments on public datasets. The experimental results demonstrate the superior performance of our method compared with state-of-the-art approaches. The source code is available at https://github.com/CityU-AIM-Group/FedDM.

Assessment of cholesterol homeostasis in the living human brain.

Alterations in brain cholesterol homeostasis have been broadly implicated in neurological disorders. Notwithstanding the complexity by which cholesterol biology is governed in the mammalian brain, excess neuronal cholesterol is primarily eliminated by metabolic clearance via cytochrome P450 46A1 (CYP46A1). No methods are currently available for visualizing cholesterol metabolism in the living human brain; therefore, a noninvasive technology that quantitatively measures the extent of brain cholesterol metabolism via CYP46A1 could broadly affect disease diagnosis and treatment options using targeted therapies. Here, we describe the development and testing of a CYP46A1-targeted positron emission tomography (PET) tracer, 18F-CHL-2205 (18F-Cholestify). Our data show that PET imaging readouts correlate with CYP46A1 protein expression and with the extent to which cholesterol is metabolized in the brain, as assessed by cross-species postmortem analyses of specimens from rodents, nonhuman primates, and humans. Proof of concept of in vivo efficacy is provided in the well-established 3xTg-AD murine model of Alzheimer's disease (AD), where we show that the probe is sensitive to differences in brain cholesterol metabolism between 3xTg-AD mice and control animals. Furthermore, our clinical observations point toward a considerably higher baseline brain cholesterol clearance via CYP46A1 in women, as compared to age-matched men. These findings illustrate the vast potential of assessing brain cholesterol metabolism using PET and establish PET as a sensitive tool for noninvasive assessment of brain cholesterol homeostasis in the clinic.

Stag beetle Cyclommatus mniszechi employs both mutual- and self-assessment strategies in male-male combat.

Animals may base contest decisions on their fighting ability alone (self-assessment) or also their opponents' (mutual assessment). Many male stag beetles develop disproportionately enlarged mandibles and use them as weapons. Information on their assessment strategy is limited. To investigate their assessment strategy and whether they adopt the same strategy at different stages of contests, we used food to encourage male Cyclommatus mniszechi of different (random pairings) or similar (ML-matched pairings) mandible length (ML) to interact. For the random pairings, losers had shorter mandibles than winners and were faster to feed. Overall contest duration and the tendency to escalate to tussles associated positively with winners' ML and average ML in the random and the ML-matched pairings, respectively, consistent with self-assessment. Non-tussle phase duration associated positively with average ML in the ML-matched pairings, consistent with self-assessment. Tussle phase duration, however, positively associated with losers' ML in the random pairings and had no association with average ML in the ML-matched pairings, consistent with mutual assessment. These results show that (1) the males employ both assessment strategies, (2) winners have more control over contest intensity than losers, and (3) males with shorter mandibles are quicker to feed and also more likely to lose fights.

THE APPLICATION OF NEURAL NETWORK TECHNOLOGY BASED ON MEA-BP ALGORITHM IN THE PREDICTION OF MICRODOSIMETRIC QUALITIES.

The most abundant products of the interaction between radiation and matter are low-energy electrons, and the collisions between these electrons and biomolecules are the main initial source of radiation-based biological damage. To facilitate the rapid and accurate quantification of low-energy electrons (0.1-10 keV) in liquid water at different site diameters (1-2000 nm), this study obtained ${\overline{y}}_{\mathrm{F}}$ and ${\overline{y}}_{\mathrm{D}}$data for low-energy electrons under these conditions. This paper proposes a back-propagation (BP) neural network optimized by the mind evolutionary algorithm (MEA) to construct a prediction model and evaluate the corresponding prediction effect. The results show that the ${\overline{y}}_{\mathrm{F}}$ and ${\overline{y}}_{\mathrm{D}}$ values predicted by the MEA-BP neural network algorithm reach a training precision on the order of ${10}^{-8}$. The relative error range between the prediction results of the validated model and the Monte Carlo calculation results is 0.03-5.98% (the error range for single-energy electrons is 0.1-5.98%, and that for spectral distribution electrons is 0.03-4.4%).

Quantitative Assessment of Three-Dimensional Choroidal Vascularity and Choriocapillaris Flow Signal Voids in Myopic Patients Using SS-OCTA.

PURPOSE: To compare the choroidal vascularity of large- and middle-sized choroidal vessels and choriocapillaris (CC) perfusion in patients with different degrees of myopia using swept-source optical coherence tomography angiography (SS-OCTA). METHODS: One hundred and thirteen people with myopia were enrolled. SS-OCTA was performed to analyze the choroidal vascularity and CC perfusion. Three-dimensional (3D) choroidal vascularity index (CVI) and choroidal luminal volumes (LV) were obtained by artificial intelligence segmentation of the choroidal lumen in Volume OCT images. CC perfusion was assessed by flow signal voids (FSVs). RESULTS: In the macular, multiple linear regression model showed that choroidal thickness (CT), total choroidal volume, LV, and choroidal stromal volume were negatively correlated with axis length (AL), respectively (all p < 0.001). Three dimensional CVI was negatively associated with AL (p < 0.05). FSV% was positively correlated with age only (p < 0.001). Additionally, after adjustment for age and AL, FSV% had a significant negative correlation with CT (p < 0.05). CONCLUSION: Choroidal vascularity decreases gradually with increasing severity of myopia. The decrease of CC blood perfusion was related to a higher severity of myopia and the thinning of choroid.

A Low-Cost Ni-Mn-Ti-B High-Temperature Shape Memory Alloy with Extraordinary Functional Properties.

The rapid development of aerospace, automotive, and energy exploration industries urgently requires high-temperature shape memory alloys (HTSMAs) which are utilized as compact solid-state actuators, sensors, and energy conversion devices at elevated temperatures. However, the currently prevailing Ni-Ti-X (X = Pd, Pt, and Hf) HTSMAs are very expensive owing to the high cost of Pd, Pt, and Hf elements, which greatly limits their widespread applications. Here, we have developed an inexpensive (Ni50Mn35.5Ti14.5)99.8B0.2 bulk polycrystalline HTSMA with extraordinary high-temperature superelasticity and a giant two-way shape memory effect (TWSME). This alloy exhibits perfect superelasticity with a fully recoverable strain of as high as 7.1% over a wide temperature range from 150 to 280 °C. Furthermore, it shows a giant TWSME with a remarkably high recoverable strain of 6.0%. Both the recoverable strain of superelasticity and the two-way shape memory strain of the present alloy are the highest among the bulk polycrystalline HTSMAs. The theoretical maximum transformation strain was calculated with energy-minimization theory using the crystal structure information of martensite and austenite obtained from in situ synchrotron high-energy X-ray diffraction experiments to help understand the superelastic behavior of the present alloy. Combining the advantages of low cost and easy fabrication, the present bulk polycrystalline (Ni50Mn35.5Ti14.5)99.8B0.2 alloy shows great potential for high-temperature shape memory applications. This work is instructive for developing cost-effective high-performance HTSMAs.

Qualitative exploration of factors influencing the plan selection process by Medicare beneficiaries.

BACKGROUND: Because Medicare plan coverage and costs change annually and older adults, the major beneficiaries of Medicare, are faced with multiple health conditions and changing medical needs, Medicare beneficiaries should evaluate their options during open enrollment every year. However, because of the complexity of plan selection, it may be challenging for Medicare beneficiaries to make an appropriate decision from among competing options. OBJECTIVES: To (a) identify factors that beneficiaries consider having influenced their plan selection decision and (b) describe the decision-making process according to the consumer decision-making model (CDM). The 2 research questions guiding this study included (a) factors Medicare beneficiaries considered having influenced their Medicare plan selection decision and (b) characteristics of decision-making processes employed by Medicare beneficiaries. METHODS: This is a phenomenological qualitative study. Semistructured in-person or telephone interviews with Alabama residents who have Medicare as the sole insurance provider were conducted between June and August 2019. Participant recruitment continued until reaching the saturation point. Each interview session consisted of structured questions identifying characteristics of participants and open-ended questions used to elicit participant Medicare plan decisionmaking process and factors affecting their decision. Data were analyzed using content analysis with a process of qualitative inductive coding. RESULTS: Twenty participants were interviewed. Twenty codes were identified and categorized into 5 themes regarding the factors influencing plan selection decisions by beneficiaries. When making a plan selection, participants were influenced by plan attributes (including cost, coverage, access to doctors, region, quality rating, and transportation); information resources and personal assistance; knowledge about Medicare; status and changes in personal situation; and experience with Medicare. Additionally, we identified 7 codes relating to beneficiary characteristics during decision-making processes, including being proactive, setting priorities, limiting choices, evaluating plans against personal needs, acquiescing to recommendations, sticking to the status quo, and weighing trade-offs. We consulted the CDM and created a conceptual model demonstrating a 5-step Medicare plan selection decision-making process and the factors influencing that process. DISCLOSURES: This study was supported by the Auburn University Undergraduate Research Program. The authors declare no conflicts of interest. CONCLUSIONS: This study created a step-by-step decision flowchart of Medicare plan selection to illustrate the complexity of the plan selection that Medicare beneficiaries must use. We uncovered the plan selection decision-making process among Medicare beneficiaries and factors affecting that process. Drawing from the CDM and the study findings, we developed a conceptual model. Findings will help researchers and community agencies target Medicare beneficiaries with different needs for assistance and design decision-making interventions/tools to help beneficiaries make rational decisions when selecting Medicare plans. These findings suggest that health care professionals should be involved in assistance programs to maximize efficiency of Medicare plan selection and to improve monitoring and consulting mechanisms to ensure the reliability of assistance information and services.

Structural characterization and pharmacological assessment in vitro/in vivo of a new copper(II)-based derivative of enrofloxacin.

Enrofloxacin (EFX) was selected as the medicinal ligand to afford a new copper(ii)-based complex, EFX-Cu, which was structurally characterized by spectroscopic analyses including X-ray single crystal diffraction. It was also stable and could retain the coordination state in aqueous solution. The in vitro antibacterial activity of EFX-Cu against a panel of pathogenic bacteria was about the same as that of EFX, except that it was twice as active against E. coli. The in vivo test on mice gave a LD50 value of 8148 mg kg-1 for EFX-Cu, which was much lower than those for EFX (LD50, 5312 mg kg-1) and its clinically used sodium salt, EFX-Na (LD50, 1421 mg kg-1). In addition, no obvious lesions in the organs of the dead mice were found by histopathological examination. Pharmacokinetic studies on rats suggested similar pharmacokinetics between EFX-Cu and EFX. On the other hand, EFX-Cu showed higher acute toxicity than EFX-Na in zebrafish, which was inconsistent with that in mice. The ROS-related inflammation and anti-inflammatory assay of EFX-Cu, respectively, in normal cells and zebrafish could be ascribed to its ROS-related redox property. Unfortunately, the final in vivo therapeutic assay in the E. coli-infected mouse model indicated that the therapeutic effect of EFX-Cu, mainly in terms of mortality in mice, was found to be lower than that of EFX-Na at the same dosage (800 mg kg-1, continuous gavage), although the contradictory factors between toxicity and antibacterial activity could not be excluded in this trial.

Recommendations to protect patients and health care practices from Medicare and Medicaid fraud.

Fraud is defined as knowingly submitting, or causing to be submitted, false claims or making misrepresentations of a fact to obtain a federal health care payment for which no entitlement would otherwise exist. In today's health care environment, Medicare and Medicaid fraud is not uncommon. The negative impact of fraud is vast because it diverts resources meant to care for patients in need to the benefit of fraudsters. Fraud increases the overall costs for vital health care services and can potentially be harmful to Medicare and Medicaid beneficiaries. The objectives of this commentary are to describe the types and trends of Medicare and Medicaid fraud that are committed, and provide recommendations to protect patients and health care practices. Specifically, this article identifies types of Medicare and Medicaid fraud at beneficiary (patient) and provider level, and it can be intentional or unintentional. This article also describes the 3 primary laws that prohibit fraud and gives fraud case examples relevant to each law, including the False Claims Act, Anti-Kickback Statute, and the Stark Law. We also discuss currently trending and emerging areas, including opioid and pharmacogenetic testing; both have experienced heavier and higher-profile instances of fraud in today's health care landscape. Last, the article summarizes detection methods and recommendations for health care providers and patients to protect themselves against fraud. Recommended strategies to combat fraud are discussed at policy, practice, and grassroots levels. Health care practitioners, including pharmacists, can use these strategies to protect themselves and their patients from becoming victims of fraud or unknowingly committing fraud.

Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences.

RNA post-transcriptional modifications play a crucial role in a myriad of biological processes and cellular functions. To date, more than 160 RNA modifications have been discovered; therefore, accurate identification of RNA-modification sites is fundamental for a better understanding of RNA-mediated biological functions and mechanisms. However, due to limitations in experimental methods, systematic identification of different types of RNA-modification sites remains a major challenge. Recently, more than 20 computational methods have been developed to identify RNA-modification sites in tandem with high-throughput experimental methods, with most of these capable of predicting only single types of RNA-modification sites. These methods show high diversity in their dataset size, data quality, core algorithms, features extracted and feature selection techniques and evaluation strategies. Therefore, there is an urgent need to revisit these methods and summarize their methodologies, in order to improve and further develop computational techniques to identify and characterize RNA-modification sites from the large amounts of sequence data. With this goal in mind, first, we provide a comprehensive survey on a large collection of 27 state-of-the-art approaches for predicting N1-methyladenosine and N6-methyladenosine sites. We cover a variety of important aspects that are crucial for the development of successful predictors, including the dataset quality, operating algorithms, sequence and genomic features, feature selection, model performance evaluation and software utility. In addition, we also provide our thoughts on potential strategies to improve the model performance. Second, we propose a computational approach called DeepPromise based on deep learning techniques for simultaneous prediction of N1-methyladenosine and N6-methyladenosine. To extract the sequence context surrounding the modification sites, three feature encodings, including enhanced nucleic acid composition, one-hot encoding, and RNA embedding, were used as the input to seven consecutive layers of convolutional neural networks (CNNs), respectively. Moreover, DeepPromise further combined the prediction score of the CNN-based models and achieved around 43% higher area under receiver-operating curve (AUROC) for m1A site prediction and 2-6% higher AUROC for m6A site prediction, respectively, when compared with several existing state-of-the-art approaches on the independent test. In-depth analyses of characteristic sequence motifs identified from the convolution-layer filters indicated that nucleotide presentation at proximal positions surrounding the modification sites contributed most to the classification, whereas those at distal positions also affected classification but to different extents. To maximize user convenience, a web server was developed as an implementation of DeepPromise and made publicly available at http://DeepPromise.erc.monash.edu/, with the server accepting both RNA sequences and genomic sequences to allow prediction of two types of putative RNA-modification sites.