Towards load-bearing biomedical titanium-based alloys: From essential requirements to future developments

The use of biomedical metallic materials in research and clinical applications has been an important focus and a significant area of interest, primarily owing to their role in enhancing human health and extending human lifespan. This article, particularly on titanium-based alloys, explores exceptional properties that can address bone health issues amid the growing challenges posed by an aging population. Although stainless steel, magnesium-based alloys, cobalt-based alloys, and other metallic materials are commonly employed in medical applications, limitations such as toxic elements, high elastic modulus, and rapid degradation rates limit their widespread biomedical applications. Therefore, titanium-based alloys have emerged as top-performing materials, gradually replacing their counterparts in various applications. This article extensively examines and highlights titanium-based alloys, along with an in-depth discussion of currently utilized metallic biomedical materials and their inherent limitations. To begin with, the essential requirements for load-bearing biomaterials are introduced. Then, the biomedical metallic materials are summarized and compared. Afterward, the microstructure, properties, and preparations of titanium-based alloys are explored. Furthermore, various surface modification methods are discussed to enhance biocompatibility, wear resistance, and corrosion resistance. Finally, the article proposes the development path for titanium-based alloys in conjunction with additive manufacturing and the novel alloy nitinol

Response of the metastable pitting corrosion of laser powder bed fusion produced Ti–6Al–4v to H+ concentration changes

There is limited research on metastable pitting corrosion in an acidic environment, and acid is a major challenge for material corrosion. Therefore, this work investigated the metastable pitting corrosion of laser powder bed fusion (LPBF)-produced Ti–6Al–4V, in Hank’s solution, at different pH values (pH = 3, 5, and 7). This work investigated the effect of acid on the characteristics of passive films, as well as the change in metastable pitting behavior. Based on the results of electrochemical impedance spectroscopy (EIS) and X-ray photoelectron spectroscopy (XPS), the passive film will be inhibited and dissolved under the influence of H+. The higher the concentration of H+, the thinner the passive film. Potentiodynamic polarization tests reveal that LPBFed Ti–6Al–4V in Hank’s solution, at pH 3, has more obvious metastable pitting corrosion. This is because the higher the H+ concentration, the more Cl- is adsorbed on the surface of the passive film, which is prone to generate soluble chlorides by competitive adsorption with oxygen atoms and thus develop into metastable pitting corrosion

Towards Best Practice of Interpreting Deep Learning Models for EEG-based Brain Computer Interfaces

As deep learning has achieved state-of-the-art performance for many tasks of EEG-based BCI, many efforts have been made in recent years trying to understand what have been learned by the models. This is commonly done by generating a heatmap indicating to which extent each pixel of the input contributes to the final classification for a trained model. Despite the wide use, it is not yet understood to which extent the obtained interpretation results can be trusted and how accurate they can reflect the model decisions. In order to fill this research gap, we conduct a study to evaluate different deep interpretation techniques quantitatively on EEG datasets. The results reveal the importance of selecting a proper interpretation technique as the initial step. In addition, we also find that the quality of the interpretation results is inconsistent for individual samples despite when a method with an overall good performance is used. Many factors, including model structure and dataset types, could potentially affect the quality of the interpretation results. Based on the observations, we propose a set of procedures that allow the interpretation results to be presented in an understandable and trusted way. We illustrate the usefulness of our method for EEG-based BCI with instances selected from different scenarios

Objective circulation classification of rainstorm days associated with Northeast China cold vortexes in the warm seasons of 2000–19

This study conducts objective circulation classifications of rainstorm days associated with Northeast China Cold Vortexes (NECVs) in the northeast of China (NEC) during the warm seasons (May–September). To determine the optimal method and number of types, the performances of ten objective circulation classification methods are first evaluated by several evaluation indexes. Self-Organizing Maps method is then used as the optimal method to classify rainstorms into five types. The results show that the different synoptic circulation patterns are accompanied by distinctive large-scale circulation backgrounds, precipitation characteristics, thermodynamic and moisture conditions. In type 1, the strong western Pacific subtropical high extends north to connect with the mid-latitude ridge in the east of the NEC, and a shallow trough lies in the west of the NEC. This configuration brings the most daily and hourly mean precipitation of all types. A low-pressure anomaly with an obvious trough controls the NEC in type 2, which has a higher frequency. In type 3, the low-pressure anomaly shrinks to the south of the NEC, and the NEC is controlled by the cut-off low vortex. Type 4 has the strongest hourly precipitation and features a meridional high-low-high pressure anomaly, and the narrow zonal low-pressure anomaly is in the NEC. Two low-pressure anomalies and a westerly trough can be found in type 5 and are distributed in a southwest-northeast orientation. These synoptic circulation patterns and the corresponding spatial distribution of rainstorm-day precipitation indicate that the objective circulation classification is effective in helping understand the large-scale circulation and precipitation characteristics associated with NECVs

Identification of potential drug targets for varicose veins: a Mendelian randomization analysis

IntroductionVaricose veins are a common chronic disease that creates a significant economic burden on the healthcare system. Current treatment options, including pharmacological treatments, are not always effective, and there is a need for more targeted therapies. A Mendelian randomization (MR) method uses genetic variants as instrumental variables to estimate the causal effect of an exposure on an outcome, and it has been successful in identifying therapeutic targets in other diseases. However, few studies have used MR to explore potential protein drug targets for varicose veins.MethodsTo identify potential drug targets for varicose veins of lower extremities, we undertook a comprehensive screen of plasma protein with a two-sample MR method. We used recently reported cis-variants as genetic instruments of 2,004 plasma proteins, then applied MR to a recent meta-analysis of genome-wide association study on varicose veins (22,037 cases and 437,665 controls). Furthermore, pleiotropy detection, reverse causality testing, colocalization analysis, and external replication were utilized to strengthen the causal effects of prioritized proteins. Phenome-wide MR (PheW-MR) of the prioritized proteins for the risk of 525 diseases was conducted to screen potential side effects.ResultsWe identified eight plasma proteins that are significantly associated with the risk of varicose veins after Bonferroni correction (P < 2.495 × 10−5), with five being protective (LUM, POSTN, RPN1, RSPO3, and VAT1) and three harmful (COLEC11, IRF3, and SARS2). Most identified proteins showed no pleiotropic effects except for COLLEC11. Bidirectional MR and MR Steiger testing excluded reverse causal relationship between varicose veins and prioritized proteins. The colocalization analysis indicated that COLEC11, IRF3, LUM, POSTN, RSPO3, and SARS2 shared the same causal variant with varicose veins. Finally, seven identified proteins replicated with alternative instruments except for VAT1. Furthermore, PheW-MR revealed that only IRF3 had potential harmful adverse side effects.ConclusionsWe identified eight potential causal proteins for varicose veins with MR. A comprehensive analysis indicated that IRF3, LUM, POSTN, RSPO3, and SARS2 might be potential drug targets for varicose veins

Reversible Zn metal anodes enabled by trace amounts of underpotential deposition initiators

Routine electrolyte additives are not effective enough for uniform zinc (Zn) deposition, because they are hard to proactively guide atomic-level Zn deposition. Here, based on underpotential deposition (UPD), we propose an "escort effect" of electrolyte additives for uniform Zn deposition at the atomic level. With nickel ion (Ni2+) additives, we found that metallic Ni deposits preferentially and triggers the UPD of Zn on Ni. This facilitates firm nucleation and uniform growth of Zn while suppressing side reactions. Besides, Ni dissolves back into the electrolyte after Zn stripping with no influence on interfacial charge transfer resistance. Consequently, the optimized cell operates for over 900 h at 1 mA cm-2 (more than 4 times longer than the blank one). Moreover, the universality of "escort effect" is identified by using Cr3+ and Co2+ additives. This work would inspire a wide range of atomic-level principles by controlling interfacial electrochemistry for various metal batteries

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Short- and Long-Term Biochar Cadmium and Lead Immobilization Mechanisms

The mechanisms of soil Cd and Pb alterations and distribution following biochar (BC; 0 to 40 t ha−1) amendments applied (in either 2009 [long-term] or in 2016 [short-term]) to a contaminated rice paddy soil, and subsequent plant Cd and Pb tissue distribution over time was investigated. Water-soluble Cd and Pb concentrations decreased by 6.7–76.0% (short-term) and 10.3–88.1% (long-term) with biochar application compared to the control. The soil exchangeable metal fractions (i.e., considered more available) decreased, and the residual metal fractions (i.e., considered less available) increased with short- and long-term biochar amendments, the latter likely a function of biochar increasing pH and forcing Cd and Pb to form crystal mineral lattice associations. Biochar application reduced Cd (16.1–84.1%) and Pb (4.1–40.0%) transfer from root to rice grain, with rice Cd and Pb concentrations lowered to nearly Chinese national food safety standards. Concomitantly, soil organic matter (SOM), pH and soil water content increased by 3.9–49.3%, 0.05–0.35 pH units, and 3.8–77.4%, respectively, with increasing biochar application rate. Following biochar applications, soil microbial diversity (Shannon index) also increased (0.8–46.2%) and soil enzymatic activities were enhanced. Biochar appears to play a pivotal role in forcing Cd and Pb sequestration in contaminated paddy soils, reducing heavy metal transfer to rice grain, and potentially leading to reduced heavy metal consumption by humans

Augmented Collaborative Filtering for Sparseness Reduction in Personalized POI Recommendation

As mobile device penetration increases, it has become pervasive for images to be associated with locations in the form of geotags. Geotags bridge the gap between the physical world and the cyberspace, giving rise to new opportunities to extract further insights into user preferences and behaviors. In this article, we aim to exploit geotagged photos from online photo-sharing sites for the purpose of personalized Point-of-Interest (POI) recommendation. Owing to the fact that most users have only very limited travel experiences, data sparseness poses a formidable challenge to personalized POI recommendation. To alleviate data sparseness, we propose to augment current collaborative filtering algorithms along from multiple perspectives. Specifically, hybrid preference cues comprising user-uploaded and user-favored photos are harvested to study users’ tastes. Moreover, heterogeneous high-order relationship information is jointly captured from user social networks and POI multimodal contents with hypergraph models. We also build upon the matrix factorization algorithm to integrate the disparate sources of preference and relationship information, and apply our approach to directly optimize user preference rankings. Extensive experiments on a large and publicly accessible dataset well verified the potential of our approach for addressing data sparseness and offering quality recommendations to users, especially for those who have only limited travel experiences