From explainable to interpretable deep learning for natural language processing in healthcare: How far from reality?

Deep learning (DL) has substantially enhanced natural language processing (NLP) in healthcare research. However, the increasing complexity of DL-based NLP necessitates transparent model interpretability, or at least explainability, for reliable decision-making. This work presents a thorough scoping review of explainable and interpretable DL in healthcare NLP. The term "eXplainable and Interpretable Artificial Intelligence" (XIAI) is introduced to distinguish XAI from IAI. Different models are further categorized based on their functionality (model-, input-, output-based) and scope (local, global). Our analysis shows that attention mechanisms are the most prevalent emerging IAI technique. The use of IAI is growing, distinguishing it from XAI. The major challenges identified are that most XIAI does not explore "global" modelling processes, the lack of best practices, and the lack of systematic evaluation and benchmarks. One important opportunity is to use attention mechanisms to enhance multi-modal XIAI for personalized medicine. Additionally, combining DL with causal logic holds promise. Our discussion encourages the integration of XIAI in Large Language Models (LLMs) and domain-specific smaller models. In conclusion, XIAI adoption in healthcare requires dedicated in-house expertise. Collaboration with domain experts, end-users, and policymakers can lead to ready-to-use XIAI methods across NLP and medical tasks. While challenges exist, XIAI techniques offer a valuable foundation for interpretable NLP algorithms in healthcare.

Knowledge mapping and research trends on perioperative neurocognitive disorder from 1990 to 2022: a bibliometric analysis.

Introduction: Perioperative neurocognitive disorder (PND) has attracted consistently increasing attention worldwide. However, there are few bibliometric studies that systematically evaluate this field. This study aimed to visualize the knowledge structure and research trends in PND through bibliometrics to help understand the future development of basic and clinical research. Methods: Literature related to PND in Web of Science and PubMed from 1990 to 2022 were collected through keywords retrospectively. Additionally, the source information, citation information, etc. of these publications were extracted. Finally, bibliometric analysis was performed by visualization software and statistical software. Results: There were 2837 articles and reviews in total. An exponential rise in PND-related publications was observed. China had the most publication, followed by the US and Germany. The institution with the most output and citations was Harvard University (149 papers, 8966 citations). The most prominent author was Marcantonio Edward R with 66 publications and 5721 citations. The journal with the highest productivity for PND research was Frontiers in Aging Neuroscience followed by Anesthesia and Analgesia. Keywords were identified as six topics, including postoperative delirium, postoperative neurocognitive disorder, cardiac surgery, anaesthesia, orthopedic surgery, and dementia. According to keyword analysis, the most recent popular keywords in PND research were prevention, older patients, emergence delirium, orthopedic surgery, and dexmedetomidine. Conclusions: Publications on PND are increasing at an alarming rate from 1990 to 2022. Current research and future trends will concentrate on the prevention and treatment of PND, as well as PND associated with orthopedic surgery in older adults.

Clinical Manifestations of Subjective Sleep Disorders in Chinese Patients with Parkinson's Disease and Their Relationship with Dopaminergic Drugs.

INTRODUCTION: Sleep disorders are common in Parkinson's disease (PD) and significantly impact quality of life. Herein, we surveyed the incidence and severity of sleep disorders in Chinese PD patients and observed their relationship with dopaminergic drugs. METHODS: We collected the demographic and disease information of 232 PD patients. The incidence and severity of sleep disorders were surveyed with the Parkinson's disease sleep scale (PDSS) Chinese version. Data on dopaminergic drug intake were collected and converted to levodopa equivalent doses (LED). RESULTS: The average total score of PDSS in 232 patients was 119.3 ± 19.7. There was a significant difference in PDSS scores between groups classified by the Hoehn-Yahr (H&Y) stage, but not between the groups classified by the type of dopaminergic drugs. Stepwise regression analysis revealed that the LED of dopaminergic drugs taken before bedtime (p < 0.00), LED of dopaminergic drugs taken over a 24-h period (p < 0.00), and scores of the Hamilton Rating Scale for Depression (HAMD) (p = 0.01) were determinants of PDSS. CONCLUSION: Sleep disorders in PD patients may be multifactorial. High dosage of dopaminergic drugs taken prior to sleep, daily total high dosage of dopaminergic drugs, and depression exert negative effects on subjective sleep. The timing and dosage of dopaminergic drugs taken before bedtime should be considered in PD management.

Sentiment Analysis: Comprehensive Reviews, Recent Advances, and Open Challenges.

Sentiment analysis (SA) aims to understand the attitudes and views of opinion holders with computers. Previous studies have achieved significant breakthroughs and extensive applications in the past decade, such as public opinion analysis and intelligent voice service. With the rapid development of deep learning, SA based on various modalities has become a research hotspot. However, only individual modality has been analyzed separately, lacking a systematic carding of comprehensive SA methods. Meanwhile, few surveys covering the topic of multimodal SA (MSA) have been explored yet. In this article, we first take the modality as the thread to design a novel framework of SA tasks to provide researchers with a comprehensive understanding of relevant advances in SA. Then, we introduce the general workflows and recent advances of single-modal in detail, discuss the similarities and differences of single-modal SA in data processing and modeling to guide MSA, and summarize the commonly used datasets to provide guidance on data and methods for researchers according to different task types. Next, a new taxonomy is proposed to fill the research gaps in MSA, which is divided into multimodal representation learning and multimodal data fusion. The similarities and differences between these two methods and the latest advances are described in detail, such as dynamic interaction between multimodalities, and the multimodal fusion technologies are further expanded. Moreover, we explore the advanced studies on multimodal alignment, chatbots, and Chat Generative Pre-trained Transformer (ChatGPT) in SA. Finally, we discuss the open research challenges of MSA and provide four potential aspects to improve future works, such as cross-modal contrastive learning and multimodal pretraining models.

Unveiling the liaison between financial development dimensions, energy efficiency and ecological footprint in the context of institutional frameworks: evidence from the Emerging-7 economies.

Financial development and energy efficiency can facilitate the transition towards a more environmentally sustainable and responsible economy. Simultaneously, the importance of institutional effectiveness cannot undermine the need to manage financial and energy consumption activities. To this end, the primary objective of this study is to examine the effects of financial development and energy efficiency on the ecological footprint of the Emerging-7 economies from 2000 to 2019. The study specifically focuses on the influence of these factors within the context of robust institutional mechanisms. We employ the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model as the analytical framework to accomplish this. This study takes into consideration three aspects of financial development, which are: (i) the depth of financial development, (ii) the stability of financial development, and (iii) the efficiency of financial development. In addition, this study has developed an institutional index using principal component analysis. The index comprises several crucial indicators: Control of Corruption, Government Effectiveness, Political Stability, Regulatory Quality, Rule of Law, and Voice and Accountability. The study raises the importance of energy efficiency in terms of energy intensity on ecological footprint. The study's findings suggest that without robust institutional mechanisms, the potential of financial development depth, stability, and efficiency to improve ecological well-being may not be fully realized. However, the study concludes that these institutional mechanisms positively impact mitigating the ecological footprint.

Comparison of the colonization ability of Burkholderia strain B23 in the citrus rhizoplane and rhizosphere and assessment of the underlying mechanisms using full-length 16S rDNA amplicon and metatranscriptomic analyses.

The characterization of bacterial strains with efficient root colonization ability and the mechanisms responsible for their efficient colonization is critical for the identification and application of beneficial bacteria. In this study, we found that Burkholderia strain B23 exhibited a strong niche differentiation between the rhizosphere and rhizoplane (a niche with more abundant easy-to-use nutrients but stronger selective pressures compared with the tightly adjacent rhizosphere) when inoculated into the field-grown citrus trees. Full-length 16S rDNA amplicon analysis demonstrated that the relative abundance of B23 in the rhizoplane microbiome at 3, 5, and 9 days post-inoculation (dpi) was always higher than that at 1 dpi, whereas its relative abundance in the rhizosphere microbiome was decreased continuously, as demonstrated by a 3.18-fold decrease at 9 dpi compared to 1 dpi. Time-series comparative expression profiling of B23 between the rhizoplane and rhizosphere was performed at representative time points (1, 3, and 9 dpi) through metatranscriptomic analysis, and the results demonstrated that multiple genes involved in the uptake and utilization of easy-to-use carbohydrates and amino acids and those involved in metabolism, energy production, replication, and translation were upregulated in the rhizoplane compared with the rhizosphere at 1 dpi and 3 dpi. Several genes involved in resistance to plant- and microbial competitor-derived stresses exhibited higher expression activities in the rhizoplane compared with the rhizosphere. Furthermore, gene loci responsible for the biosynthesis of the key antifungal and antibacterial metabolites occidiofungin and ornibactin were induced, and their expression levels remained relatively stable from 3 dpi to 9 dpi in the rhizoplane but not in the rhizosphere. Collectively, our findings provide novel lights into the mechanisms underlying the root colonization of the inoculated bacterial strains and serve as a basis for the identification of strains with efficient colonization ability, thus contributing to the development of beneficial bacteria applications.

In Situ Preparation of High-Performance Silicon-Based Integrated Electrodes Using Cross-Linked Cyclodextrins.

The strategy of material modification for improving the stability of silicon electrodes is laborious and costly, while the conventional binders cannot withstand the repeated massive volume variability of silicon-based materials. Hence, there is a demand to settle the silicon-based materials' problems with green and straightforward solutions. This paper presents a high-performance silicon anode with a binder obtained by in situ thermal cross-linking of citric acid (CA) and ß-cyclodextrin (ß-CD) during the electrode preparation process. The Si electrode with a binder synthesized by the one-pot method shows excellent cycling performance. It maintains a specific capacity of 1696 mAh·g-1 after 200 cycles at a high current of 0.5 C. Furthermore, the carbonylation of ß-CD to carbonyl-ß-CD (c-ß-CD) introduced better water solubility, and the c-ß-CD can generate multidimensional connections with CA and Si, which significantly enhances the specific capacity to 1941 mAh·g-1 at 0.5 C. The results demonstrate that the prepared integrated electrode facilitates the formation of a stable and controllable solid electrolyte interface layer of Si and accommodates Si's repeated giant volume variations.

Developing an Automated Assessment of In-session Patient Activation for Psychological Therapy: Codevelopment Approach.

BACKGROUND: Patient activation is defined as a patient's confidence and perceived ability to manage their own health. Patient activation has been a consistent predictor of long-term health and care costs, particularly for people with multiple long-term health conditions. However, there is currently no means of measuring patient activation from what is said in health care consultations. This may be particularly important for psychological therapy because most current methods for evaluating therapy content cannot be used routinely due to time and cost restraints. Natural language processing (NLP) has been used increasingly to classify and evaluate the contents of psychological therapy. This aims to make the routine, systematic evaluation of psychological therapy contents more accessible in terms of time and cost restraints. However, comparatively little attention has been paid to algorithmic trust and interpretability, with few studies in the field involving end users or stakeholders in algorithm development. OBJECTIVE: This study applied a responsible design to use NLP in the development of an artificial intelligence model to automate the ratings assigned by a psychological therapy process measure: the consultation interactions coding scheme (CICS). The CICS assesses the level of patient activation observable from turn-by-turn psychological therapy interactions. METHODS: With consent, 128 sessions of remotely delivered cognitive behavioral therapy from 53 participants experiencing multiple physical and mental health problems were anonymously transcribed and rated by trained human CICS coders. Using participatory methodology, a multidisciplinary team proposed candidate language features that they thought would discriminate between high and low patient activation. The team included service-user researchers, psychological therapists, applied linguists, digital research experts, artificial intelligence ethics researchers, and NLP researchers. Identified language features were extracted from the transcripts alongside demographic features, and machine learning was applied using k-nearest neighbors and bagged trees algorithms to assess whether in-session patient activation and interaction types could be accurately classified. RESULTS: The k-nearest neighbors classifier obtained 73% accuracy (82% precision and 80% recall) in a test data set. The bagged trees classifier obtained 81% accuracy for test data (87% precision and 75% recall) in differentiating between interactions rated high in patient activation and those rated low or neutral. CONCLUSIONS: Coproduced language features identified through a multidisciplinary collaboration can be used to discriminate among psychological therapy session contents based on patient activation among patients experiencing multiple long-term physical and mental health conditions.

The impact of COVID-19 on the US renewable and non-renewable energy consumption: a sectoral analysis based on quantile on quantile regression approach.

The repercussions of the novel coronavirus (COVID-19) pandemic go well beyond health concerns, affecting virtually every aspect of our lives, including daily energy consumption. Therefore, this study explores the impact of COVID-19 on renewable and non-renewable energy consumption in the USA, which has been severely affected by the recent pandemic. We conducted a detailed analysis of the energy consumption demands of various sectors in response to the COVID-19 outbreak. Our in-depth analysis comprises two parts. Initially, we determine the monthly growth change by utilizing the month-on-month method. Subsequently, we used the quantile-on-quantile approach of Sim and Zhou (2015) on data spanning from December 2019 to August 2021 to explore the impact of COVID-19 on energy consumption across the whole distribution. The study's outcomes underscored that compared to renewable energy, non-renewable energy consumption was more affected by the COVID-19 lockdown, and the overall energy consumption (both renewable and non-renewable) remained low. These findings accentuate global strategic management tools to tackle COVID-19 cooperatively and restore the energy mix. Such measures are critical for energy access, security, and evenhandedness.

A Fluorescent Reporter-Based Evaluation Assay for Antibacterial Components Against Xanthomonas citri subsp. citri.

Xanthomonas citri subsp. citri (Xcc) is the agent of citrus bacterial canker (CBC) disease, which has significantly reduced citrus quantity and quality in many producing areas worldwide. Copper-based bactericides are the primary products for CBC control and management, but the problems derived from copper-resistant and environmental contamination have become issues of anxiety. Thus, there is a need to find alternative antibacterial products instead of relying on a single type of agent. This study developed a method to evaluate the inhibition of antibacterial agents using the fluorescence-labeled recombinant Xcc strain (Xcc-eYFP). The optimization of timelines and parameters for the evaluation of antibacterial agents involved the use of a Spark™ multimode microplate reader. This evaluation and screening method can be applied to bactericides, cocktail-mixture formulations, antagonistic bacteria, and derived metabolites. The results showed that the minimum inhibitory concentration (MIC) of commercial bactericides determined by fluorescence agrees with the MIC values determined by the conventional method. A screened cocktail-mixture bactericide presents more activity than the individual agents during the protective effects. Notably, this method has been further developed in the screening of Xcc-antagonistic bacterial strains. In summary, we provide a validated strategy for screening and evaluation of different antibacterial components for inhibition against Xcc for CBC control and management.

Cross-linked ß-CD-CMC as an effective aqueous binder for silicon-based anodes in rechargeable lithium-ion batteries.

As a non-active material component, the binder can effectively maintain the integrity of battery electrodes. In this work, based on the inspired structure of fishing nets, a three-dimensional mesh adhesive using widely sourced raw materials CMC and ß-CD was designed. These cross-linked cyclodextrins have the advantage of dispersing the stress at the anchor point and moderating the significant volume changes of the Si anode. The Si/ß-CD-CMC electrode maintains a reversible capacity of 1702 mA h g-1 even after 200 cycles at a high current of 0.5C. This work represents a significant step forward in Si anode binders and enables the cross-linked cyclodextrins to have potential applications in energy storage systems.

Clinical features and risk factors of severely and critically ill patients with COVID-19.

BACKGROUND: As of June 1, 2020, over 370000 coronavirus disease 2019 (COVID-19) deaths have been reported to the World Health Organization. However, the risk factors for patients with moderate-to-severe or severe-to-critical COVID-19 remain unclear. AIM: To explore the characteristics and predictive markers of severely and critically ill patients with COVID-19. METHODS: A retrospective study was conducted at the B11 Zhongfaxincheng campus and E1-3 Guanggu campus of Tongji Hospital affiliated with Huazhong University of Science and Technology in Wuhan. Patients with COVID-19 admitted from 1st February 2020 to 8th March 2020 were enrolled and categorized into 3 groups: The moderate group, severe group and critically ill group. Epidemiological data, demographic data, clinical symptoms and outcomes, complications, laboratory tests and radiographic examinations were collected retrospectively from the hospital information system and then compared between groups. RESULTS: A total of 126 patients were enrolled. There were 59 in the moderate group, 49 in the severe group, and 18 in the critically ill group. Multivariate logistic regression analysis showed that age [odd ratio (OR) = 1.055, 95% (confidence interval) CI: 1.099-1.104], elevated neutrophil-to-lymphocyte ratios (OR = 4.019, 95%CI: 1.045-15.467) and elevated high-sensitivity cardiac troponin I (OR = 10.126, 95%CI: 1.088 -94.247) were high-risk factors. CONCLUSION: The following indicators can help clinicians identify patients with severe COVID-19 at an early stage: age, an elevated neutrophil-to-lymphocyte ratio and high sensitivity cardiac troponin I.

A persistent luminescence resonance energy transfer-based molecular beacon probe for the highly sensitive detection of microRNA in biological samples.

Herein, a time-resolved luminescence resonance energy transfer (TR-LRET) molecular beacon (MB) probe employing persistent luminescence nanoparticles (PLNPs) as the energy donors was first constructed, and further designed for microRNA21 (miR21) sensing. This probe (named as PLNPs-MB) was facilely fabricated by covalent bioconjugation between poly-(acrylic acid) (PAA) modified near-infrared (NIR) emissive PLNPs i.e. ZnGa2O4:Cr3+ and functionalized MB oligonucleotide (5'-NH2 and 3'-BHQ3). Accordingly, PLNPs and BHQ3 were in close proximity to each other, leading to the occurrence of LRET and obvious persistent luminescence (PL) quenching. In the presence of miR21, loop of the PLNP-MB was hybridized, accompanying BHQ3 away from PLNPs and the restraint of LRET process. As a result, PL of the PLNPs was recovered, which built the foundation of miR21 quantification. The probe provided a linear response range from 0.1 to 10 nM for miR21 detection. Quantification limit of this probe was competitive and about 1-2 orders of magnitude lower than that of other reported MB probes for nucleic acid. Moreover, the proposed probe was successfully adopted for miR21 detection in biological fluids (human serum, cell extraction). This work also provided a sensitive detection nanoplatform for other targets through modifying diverse MBs onto the surface of PLNPs.

Digital video interventions and mental health literacy among young people: a scoping review.

BACKGROUND: Mental health literacy is important as it relates to understanding mental illness, increasing help-seeking efficacy, and reducing mental illness-related stigma. One method to improve the mental health literacy of young people is a digital video intervention. AIMS: A scoping review was conducted to map existing research in the area of digital video interventions for mental health literacy among young people. METHODS: The scoping review was conducted following the PRISMA-ScR checklist. All results were screened based on our inclusion criteria. RESULTS: Seventeen studies were selected for analysis. In most studies (n = 14), a digital video was the only intervention whereas three studies took a multi-intervention approach. Only two of the digital video interventions were co-created with people with mental illness or university students. All studies showed positive results in favor of digital video interventions in at least one component of mental health literacy or compared to one of the comparison conditions. CONCLUSIONS: Digital video interventions represent effective tools for enhancing mental health literacy. However, there is a need for active involvement of end-users in co-creation and to attend to the production quality so that the digital video intervention is as relevant, informed, and effective as possible.

Rapid Evaluation of the Resistance of Citrus Germplasms Against Xanthomonas citri subsp. citri.

Xanthomonas citri subsp. citri (Xcc) is the causal agent of citrus bacterial canker (CBC), one of the most devastating citrus diseases. Most commercial citrus varieties are susceptible to CBC. However, some citrus varieties and wild citrus germplasms are CBC resistant and are promising in genetic increases in citrus resistance against CBC. We aimed to evaluate citrus germplasms for resistance against CBC. First, we developed a rapid evaluation method based on enhanced yellow fluorescent protein (eYFP)-labeled Xcc. The results demonstrated that eYFP does not affect the growth and virulence of Xcc. Xcc-eYFP allows measurement of bacterial titers but is more efficient and rapid than the plate colony counting method. Next, we evaluated citrus germplasms collected in China. Based on symptoms and bacterial titers, we identified that two citrus germplasms ('Ichang' papeda and 'Huapi' kumquat) are resistant, whereas eight citrus germplasms ('Chongyi' wild mandarin, 'Mangshan' wild mandarin, 'Ledong' kumquat, 'Dali' citron, 'Yiliang' citron, 'Longyan' kumquat, 'Bawang' kumquat, and 'Daoxian' wild mandarin) are tolerant. In summary, we have developed a rapid evaluation method to test the resistance of citrus plants against CBC. This method was successfully used to identify two highly canker-resistant citrus germplasms and eight citrus germplasms with canker tolerance. These results could be leveraged in traditional breeding contexts or be used to identify canker resistance genes to increase the disease resistance of commercial citrus varieties via biotechnological approaches.

Seed coat color genetics and genotype × environment effects in yellow beans via machine-learning and genome-wide association.

Common bean (Phaseolus vulgaris L.) is consumed worldwide, with strong regional preferences for seed appearance characteristics. Colors of the seed coat, hilum ring, and corona are all important, along with susceptibility to postharvest darkening, which decreases seed value. This study aimed to characterize a collection of 295 yellow bean genotypes for seed appearance and postharvest darkening, evaluate genotype × environment (G × E) effects and map those traits via genome-wide association analysis. Yellow bean germplasm were grown for 2 yr in Michigan and Nebraska and seed were evaluated for L*a*b* color values, postharvest darkening, and hilum ring and corona colors. A model to exclude the hilum ring and corona of the seeds, black background, and light reflection was developed by using machine learning, allowing for targeted and efficient L*a*b* value extraction from the seed coat. The G × E effects were significant for the color values, and Michigan-grown seeds were darker than Nebraska-grown seeds. Single-nucleotide polymorphisms (SNPs) were associated with L* and hilum ring color on Pv10 near the J gene involved in mature seed coat color and hilum ring color. A SNP on Pv07 associated with L*, a*, postharvest darkening, and hilum ring and corona colors was near the P gene, the ground factor gene for seed coat color expression. The machine-learning-aided model used to extract color values from the seed coat, the wide variability in seed morphology traits, and the associated SNPs provide tools for future breeding and research efforts to meet consumers' expectations for bean seed appearance.

Field-Deployable Candidatus Liberibacter asiaticus Detection Using Recombinase Polymerase Amplification Combined with CRISPR-Cas12a.

The early detection of Candidatus Liberibacter asiaticus (CLas) by citrus growers facilitates early intervention and prevents the spread of disease. A simple method for rapid and portable Huanglongbing (HLB) diagnosis is presented here that combines recombinase polymerase amplification and a fluorescent reporter utilizing the nuclease activity of the clustered regularly interspaced short palindromic repeats/CRISPR-associated 12a (CRISPR-Cas12a) system. The sensitivity of this technique is much higher than PCR. Furthermore, this method showed similar results to qPCR when leaf samples were used. Compared with conventional CLas detection methods, the detection method presented here can be completed in 90 min and works in an isothermal condition that does not require the use of PCR machines. In addition, the results can be visualized through a handheld fluorescent detection device in the field.

Simple Preparation of Carbon Dots and Application in Cephalosporin Detection.

Carbon dots have good biocompatibility, low toxicity, excellent photoluminescence properties, and good light stability, endowing them good application prospects in drug detection, chemical analysis, drug delivery, and other fields. In this study, p-phenylenediamine was used as the carbon source, and carbon dots were synthesized in hydrochloric acid medium using microwave method. When the excitation wavelength is about 300 nm, a strong emission peak of 689 nm is detected for the synthesized carbon dots. Carbon dots' size is about 4.0±0.2 nm, and the carbon dots with spherical shape are uniformly distributed. The quantum yield of carbon dots is 8.07%. In addition, cephalosporins. were detected and analyzed using synthetic carbon dots. The results show that the presence of cephalosporins reduced the fluorescence intensity of carbon dots, and the reduced fluorescence intensity of the synthesized carbon dots showed a linear correlation with the cephalosporins' concentration. Cephalosporins' detection scope is 0.2 µmol/L to 80 µ mol/L, and the detection limit is 0.084 µ mol/L. A mechanism study shows that the effect of cephalosporins on carbon dot's fluorescence intensity can be attributed to the inner filter effect of cephalosporins. On this basis, a sensitive and 0selective cephalosporins detection method was established. Furthermore, this established method for cephalosporins detection was applied to real samples, resulting in a low relative standard deviation (RSD) and good recoveries.

Measuring Online Wellbeing: A Scoping Review of Subjective Wellbeing Measures.

With the increasing importance of the internet to our everyday lives, questions are rightly being asked about how its' use affects our wellbeing. It is important to be able to effectively measure the effects of the online context, as it allows us to assess the impact of specific online contexts on wellbeing that may not apply to offline wellbeing. This paper describes a scoping review of English language, peer-reviewed articles published in MEDLINE, EMBASE, and PsychInfo between 1st January 2015 and 31st December 2019 to identify what measures are used to assess subjective wellbeing and in particular to identify any measures used in the online context. Two hundred forty studies were identified; 160 studies were removed by abstract screening, and 17 studies were removed by full-text screening, leaving 63 included studies. Fifty-six subjective wellbeing scales were identified with 18 excluded and 38 included for further analysis. Only one study was identified researching online wellbeing, and no specific online wellbeing scale was found. Therefore, common features of the existing scales, such as the number and type of questions, are compared to offer recommendations for building an online wellbeing scale. Such a scale is recommended to be between 3 and 20 questions, using mainly 5-point Likert or Likert-like scales to measure at least positive and negative affect, and ideally life satisfaction, and to use mainly subjective evaluation. Further research is needed to establish how these findings for the offline world effectively translate into an online measure of wellbeing.

Proton solvent-controllable synthesis of manganese oxalate anode material for lithium-ion batteries.

Manganese oxalates with different structures and morphologies were prepared by the precipitation method in a mixture of dimethyl sulfoxide (DMSO) and proton solvents. The proton solvents play a key role in determining the structures and morphologies of manganese oxalate. Monoclinic MnC2O4·2H2O microrods are prepared in H2O-DMSO, while MnC2O4·H2O nanorods and nanosheets with low crystallinity are synthesized in ethylene glycol-DMSO and ethanol-DMSO, respectively. The corresponding dehydrated products are mesoporous MnC2O4 microrods, nanorods, and nanosheets, respectively. When used as anode material for Li-ion batteries, mesoporous MnC2O4 microrods, nanorods, and nanosheets deliver a capacity of 800, 838, and 548 mA h g-1 after 120 cycles at 8C, respectively. Even when charged/discharged at 20C, mesoporous MnC2O4 nanorods still provide a reversible capacity of 647 mA h g-1 after 600 cycles, exhibiting better rater performance and cycling stability. The electrochemical performance is greatly influenced by the synergistic effect of surface area, morphology, and size. Therefore, the mesoporous MnC2O4 nanorods are a promising anode material for Li-ion batteries due to their good cycle stability and rate performance.