Correction: Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation.

[This corrects the article DOI: 10.2196/46036.].

Synthesis of acylated derivatives of alginate oligosaccharides and evaluation of their potential antibacterial activities against Staphylococcus aureus.

Alginate oligosaccharides (AOS) are crucial carbohydrate-based biomaterial used in the synthesis of potential drugs and biological agents, but their antibacterial activities are not significant. In this study, AOS acylated derivatives were synthesized by grafting maleic anhydride (MA) onto AOS at varying ratios. Additionally, their inhibitory effects against Staphylococcus aureus were thoroughly investigated. Characterization of the AOS acylated derivatives (AOS-MA-x, where x = 1, 5, 10, and 20) was conducted using Fourier-transformed infrared spectroscopy, 1H nuclear magnetic resonance spectroscopy, and X-ray diffraction, which confirmed the successful synthesis of these derivatives. The bacteriostatic activity of the AOS-MA derivatives was assessed using growth curves and plate coating method, demonstrating significant antibacterial effects against S. aureus, as compared with AOS. Among these derivatives, AOS-MA-20 exhibited the most potent bacteriostatic activity and was selected for further investigation of its inhibitory mechanism. Scanning electron microscopy analysis revealed that treatment with AOS-MA-20 led to the lysis and rupture of S. aureus cells, expelling their intracellular contents. Moreover, AOS-MA-20 disrupted the integrity of cell wall and cell membrane, impacted ATPase activity, and inhibited the formation of biofilm to some extent, ultimately resulting in bacterial death. These findings lay a foundational framework for the development of environmentally friendly antimicrobial agents.

Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation.

BACKGROUND: A plethora of weight management apps are available, but many individuals, especially those living with overweight and obesity, still struggle to achieve adequate weight loss. An emerging area in weight management is the support for one's self-regulation over momentary eating impulses. OBJECTIVE: This study aims to examine the feasibility and effectiveness of a novel artificial intelligence-assisted weight management app in improving eating behaviors in a Southeast Asian cohort. METHODS: A single-group pretest-posttest study was conducted. Participants completed the 1-week run-in period of a 12-week app-based weight management program called the Eating Trigger-Response Inhibition Program (eTRIP). This self-monitoring system was built upon 3 main components, namely, (1) chatbot-based check-ins on eating lapse triggers, (2) food-based computer vision image recognition (system built based on local food items), and (3) automated time-based nudges and meal stopwatch. At every mealtime, participants were prompted to take a picture of their food items, which were identified by a computer vision image recognition technology, thereby triggering a set of chatbot-initiated questions on eating triggers such as who the users were eating with. Paired 2-sided t tests were used to compare the differences in the psychobehavioral constructs before and after the 7-day program, including overeating habits, snacking habits, consideration of future consequences, self-regulation of eating behaviors, anxiety, depression, and physical activity. Qualitative feedback were analyzed by content analysis according to 4 steps, namely, decontextualization, recontextualization, categorization, and compilation. RESULTS: The mean age, self-reported BMI, and waist circumference of the participants were 31.25 (SD 9.98) years, 28.86 (SD 7.02) kg/m2, and 92.60 (SD 18.24) cm, respectively. There were significant improvements in all the 7 psychobehavioral constructs, except for anxiety. After adjusting for multiple comparisons, statistically significant improvements were found for overeating habits (mean -0.32, SD 1.16; P<.001), snacking habits (mean -0.22, SD 1.12; P<.002), self-regulation of eating behavior (mean 0.08, SD 0.49; P=.007), depression (mean -0.12, SD 0.74; P=.007), and physical activity (mean 1288.60, SD 3055.20 metabolic equivalent task-min/day; P<.001). Forty-one participants reported skipping at least 1 meal (ie, breakfast, lunch, or dinner), summing to 578 (67.1%) of the 862 meals skipped. Of the 230 participants, 80 (34.8%) provided textual feedback that indicated satisfactory user experience with eTRIP. Four themes emerged, namely, (1) becoming more mindful of self-monitoring, (2) personalized reminders with prompts and chatbot, (3) food logging with image recognition, and (4) engaging with a simple, easy, and appealing user interface. The attrition rate was 8.4% (21/251). CONCLUSIONS: eTRIP is a feasible and effective weight management program to be tested in a larger population for its effectiveness and sustainability as a personalized weight management program for people with overweight and obesity. TRIAL REGISTRATION: ClinicalTrials.gov NCT04833803; https://classic.clinicaltrials.gov/ct2/show/NCT04833803.

Crop-GPA: an integrated platform of crop gene-phenotype associations.

With the increasing availability of large-scale biology data in crop plants, there is an urgent demand for a versatile platform that fully mines and utilizes the data for modern molecular breeding. We present Crop-GPA ( https://crop-gpa.aielab.net ), a comprehensive and functional open-source platform for crop gene-phenotype association data. The current Crop-GPA provides well-curated information on genes, phenotypes, and their associations (GPAs) to researchers through an intuitive interface, dynamic graphical visualizations, and efficient online tools. Two computational tools, GPA-BERT and GPA-GCN, are specifically developed and integrated into Crop-GPA, facilitating the automatic extraction of gene-phenotype associations from bio-crop literature and predicting unknown relations based on known associations. Through usage examples, we demonstrate how our platform enables the exploration of complex correlations between genes and phenotypes in crop plants. In summary, Crop-GPA serves as a valuable multi-functional resource, empowering the crop research community to gain deeper insights into the biological mechanisms of interest.

Public acceptance of using artificial intelligence-assisted weight management apps in high-income southeast Asian adults with overweight and obesity: a cross-sectional study.

Introduction: With in increase in interest to incorporate artificial intelligence (AI) into weight management programs, we aimed to examine user perceptions of AI-based mobile apps for weight management in adults with overweight and obesity. Methods: 280 participants were recruited between May and November 2022. Participants completed a questionnaire on sociodemographic profiles, Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), and Self-Regulation of Eating Behavior Questionnaire. Structural equation modeling was performed using R. Model fit was tested using maximum-likelihood generalized unweighted least squares. Associations between influencing factors were analyzed using correlation and linear regression. Results: 271 participant responses were analyzed, representing participants with a mean age of 31.56 ± 10.75 years, median (interquartile range) BMI, and waist circumference of 27.2 kg/m2 (24.2-28.4 kg/m2) and 86.4 (80.0-94.0) cm, respectively. In total, 188 (69.4%) participants intended to use AI-assisted weight loss apps. UTAUT2 explained 63.3% of the variance in our intention of the sample to use AI-assisted weight management apps with satisfactory model fit: CMIN/df = 1.932, GFI = 0.966, AGFI = 0.954, NFI = 0.909, CFI = 0.954, RMSEA = 0.059, SRMR = 0.050. Only performance expectancy, hedonic motivation, and the habit of using AI-assisted apps were significant predictors of intention. Comparison with existing literature revealed vast variabilities in the determinants of AI- and non-AI weight loss app acceptability in adults with and without overweight and obesity. UTAUT2 produced a good fit in explaining the acceptability of AI-assisted apps among a multi-ethnic, developed, southeast Asian sample with overweight and obesity. Conclusion: UTAUT2 model is recommended to guide the development of AI-assisted weight management apps among people with overweight and obesity.

Reduced formation of brominated trihalomethanes during chlorination of bromide-containing waters in the presence of Mn(II).

Manganese(II) (Mn(II)) and bromide (Br-) are common in natural waters. This study investigated the effect of in-situ Mn(II) oxidation and preformed MnOx on the brominated trihalomethane (Br-THM) formation during chlorination of bromide-containing waters. The results showed Br-THM formation could be substantially inhibited by in-situ Mn(II) oxidation, but the addition of preformed MnOx had limited influence on Br-THM formation during chlorination of bromide-containing waters. Analysis of bromine species showed that about 30 % bromine species were incorporated into the MnOx particles and formed MnOx-Br during the in-situ Mn(II) oxidation process. Consequently, the availability of reactive bromine species for the reaction with dissolved organic matter (DOM) reduced, leading to less Br-THM formation. X-ray diffraction (XRD) analysis of in-situ Mn(II) oxidation product indicated the presence of Br- decreased the crystallinity of Mn oxides, verifying the bromine species entered MnOx crystal. However, the adsorptive uptake of bromine species by preformed MnOx was negligible and had no impact on Br-THM formation. Inhibition rate of Mn(II) oxidation on THM formation decreased with increasing specific ultraviolet absorbance (SUVA254) value of filtered water, showing SUVA254 could be a good indicator of DOM competition ability for oxidant with Mn(II). In addition, Excitation/Emission Matrix indicated that Mn(II) could form complexes with humic substances, which might also retard the reaction between humic substances and oxidant to form Br-THMs. This study highlighted the inhibiting effect of in-situ Mn(II) oxidation on Br-THM formation during chlorination of bromide-containing waters.

Brain connectome-based imaging markers for identifiable signature of migraine with and without aura.

Background: Cortical spreading depression (CSD) has been considered the prominent theory for migraine with aura (MwA). However, it is also argued that CSD can exist in patients in a silent state, and not manifest as aura. Thus, the MwA classification based on aura may be questionable. This study aimed to capture whole-brain connectome-based imaging markers with identifiable signatures for MwA and migraine without aura (MwoA). Methods: A total of 88 migraine patients (32 MwA) and 49 healthy controls (HC) underwent a diffusion tensor imaging and resting-state functional magnetic resonance imaging scan. The whole-brain structural connectivity (SC) and functional connectivity (FC) analysis was employed to extract imaging features. The extracted features were subjected to an all-relevant feature selection process within cross-validation loops to pinpoint attributes demonstrating substantial efficacy for patient categorization. Based on the identified features, the predictive ability of the random forest classifiers constructed with the 88 migraine patients' sample was tested using an independent sample of 32 migraine patients (eight MwA). Results: Compared to MwoA and HC, MwA showed two reduced SC and six FC (five increased and one reduced) features [all P<0.01, after false discovery rate (FDR) correction], involving frontal areas, temporal areas, visual areas, amygdala, and thalamus. A total of four imaging features were significantly correlated with clinical rating scales in all patients (r=-0.38 to 0.47, P<0.01, after FDR correction). The predictive ability of the random forest classifiers achieved an accuracy of 78.1% in the external sample to identify MwA. Conclusions: The whole-brain connectivity features in our results may serve as connectome-based imaging markers for MwA identification. The alterations of SC and FC strength provide possible evidence in further understanding the heterogeneity and mechanism of MwA which may help for patient-specific decision-making.

Aberrant brain functional hubs and causal effective connectivity in menstrually-related and non-menstrually-related migraine without aura.

Background: Menstrual migraine without aura (MRM) is common in female migraineurs and is closely related to cerebral functional abnormalities. However, whether the whole brain networks and directional functional connectivity of MRM patients are altered remains unclear. The purpose of this study was to detect the alterations of resting-state functional networks and directional functional connectivity between MRM and non-menstrual migraine without aura (NMM) patients using functional magnetic resonance imaging (fMRI) with degree centrality (DC) and Granger causality analysis (GCA) methods. Methods: In this retrospective and cross-sectional study, 45 MRM and 40 NMM patients (matched in age, gender, and years of education) were recruited in the study between May 2018 and June 2022. All participants had undergone resting-state fMRI scanning at the Neurology and Pain Outpatient Department of Nanjing First Hospital. Their brain functions were analyzed in terms of DC and GCA, with the significant threshold at voxel level P<0.01 and cluster level P<0.05, Gaussian random field corrected. Correlation analysis was adopted to assess the relationships between the fMRI results and clinical features (P<0.05, Bonferroni corrected). Results: Compared with those in the NMM group, MRM patients showed decreased DC in the right insula (T=-4.253). Using the right insula as the seed region, patients with MRM demonstrated enhanced effective connectivity from the right insula to the ipsilateral middle temporal gyrus (T=4.138) and contralateral superior temporal gyrus (T=3.523). Furthermore, the MRM group also showed decreased effective connectivity from several brain regions to the right insula, which included the right inferior occipital gyrus (T=-4.498), left middle frontal gyrus (T=-4.879), right precuneus (T=-4.644), and left inferior parietal gyrus (T=-4.113). The average Self-rating Anxiety Scale score of the MRM group was significantly higher than that of the NMM group [P=0.032, 95% confidence interval (CI): 0.363-7.761]. In the MRM group, disease duration was negatively correlated with the mean value of DC in right insula (r=-0.428, P=0.01). Conclusions: The present research demonstrated that patients with MRM have disruption in insula resting-state functional networks. Disrupted networks contained regions associated with cognitive processes, emotional perception, and migraine attack in MRM patients. These results may improve our comprehension of the neuromechanism of menstrually-related migraine.

Named entity recognition of rice genes and phenotypes based on BiGRU neural networks.

Named Entity Recognition (NER) is a fundamental but crucial task in natural language processing (NLP) and big data analysis, with wide application range. NER for rice genes and phenotypes is a technique to identify genes and phenotypes from a large amount of text. NER for rice genes and phenotypes can facilitate the acquisition of information in the field of crops and provide references for our research on higher quality crops. At the same time, named entity recognition still faces many challenges. In this paper, we propose an improved bidirectional gated recurrent unit neural network (BI-GRU) method, which is used to automatically identify the required entities (i.e. gene names, rice phenotypes) from relevant rice literature and patents. The neural network model is combined with the Softmax function to directly output the probabilities of labels, forming the BI-GRU-SF model. With the ability of deep learning methods, the semantic information in the context can be learned without the need for feature engineering. Finally, we conducted experiments, and the results showed that our proposed model provided better performance compared to other models. All datasets and resource codes of BI-GRU-SF are available at https://github.com/qqeeqq/NER for academic use.

Recent development of piezoelectric biosensors for physiological signal detection and machine learning assisted cardiovascular disease diagnosis.

As cardiovascular disease stands as a global primary cause of mortality, there has been an urgent need for continuous and real-time heart monitoring to effectively identify irregular heart rhythms and to offer timely patient alerts. However, conventional cardiac monitoring systems encounter challenges due to inflexible interfaces and discomfort during prolonged monitoring. In this review article, we address these issues by emphasizing the recent development of the flexible, wearable, and comfortable piezoelectric passive sensor assisted by machine learning technology for diagnosis. This innovative device not only harmonizes with the dynamic mechanical properties of human skin but also facilitates continuous and real-time collection of physiological signals. Addressing identified challenges and constraints, this review provides insights into recent advances in piezoelectric cardiac sensors, from devices to circuit systems. Furthermore, this review delves into the integration of machine learning technologies, showcasing their pivotal role in facilitating continuous and real-time assessment of cardiac status. The synergistic combination of flexible piezoelectric sensor design and machine learning holds substantial potential in automating the detection of cardiac irregularities with minimal human intervention. This transformative approach has the power to revolutionize patient care paradigms.

Iron particles lower than 10 µm in drinking water dominate particle catalysis effect on disinfection byproduct formation.

Iron particles could catalyze disinfection by-product (DBP) formation in drinking water distribution systems (DWDS), but the catalytic effects of iron particles considering size effects have not been focused. Here, we first found that fine particles (lower than 10 µm) dominated the particle catalysis effect of the iron particles on the formation of DBPs containing multiple Cl atoms (DBP-3Cl), especially those with aromatic structure and containing multiple N atoms (DBP-3N). The loose deposit particles were filtered through 50 µm (F50), 10 µm (F10) and 1 µm (F10) membranes, and their turbidity values were 231.6, 53.4 and 1.1 NTU, respectively. In mass ratio, F50, F10 and F1 accounted for 84 %, 15 % and 1 % of unfiltered samples. Notably, the lower mass F10 generated more DBP-3Cl and DBP-3N than F50. Metal crystals and natural organic matters showed little difference among different sizes. The high catalytic activity of particles in F10 due to size effect was proved to be the essential mechanism. F1 contained few particles to affect DBP formation. In toxicity evaluation, the toxicity of F10 was even higher than F50. Therefore, fine particles with sizes lower than 10 µm may play a dominate role in the catalytic effect on DBP transformation in DWDS.

Retained stone retrieval basket causing chronic pancreatitis: a case report.

Background: Endoscopic retrograde cholangiopancreatography is a common procedure performed for choledocholithiasis and gallstone pancreatitis. Although a relatively low risk procedure, it is not without its complications. Cases of impacted Dormia baskets during stone retrieval have been reported, but these are usually retrieved surgically during the same setting. Case summary: A 40-year-old man presented to our hospital with an episode of epigastric pain and discomfort. He has a prior background of recurrent episodes of pancreatitis of which he underwent prior endoscopic therapy in his home country. Initial investigations revealed a metallic object seen on abdominal x-ray, computer tomographic scan of the abdomen and pelvis, and magnetic resonance imaging of the pancreas. Further evaluation was done with endoscopy, which revealed a retained stone extraction basket from a previous endoscopic retrograde pancreatography, resulting in recurrent episodes of acute chronic pancreatitis. Although the retained foreign body was removed, he subsequently developed further complications of portal vein thrombosis as a result of recurrent acute chronic pancreatitis, which required anticoagulation. Conclusion: This case highlights the importance of retrieving any foreign body from the pancreas, especially on the head, to prevent the development of further complications.

Enhanced disinfection byproducts formation by fine iron particles intercepted in household point-of-use facilities.

To cope with the demand for good-quality potable water, household point-of-use (POU) facilities such as polypropylene cotton filters (PCFs) are widely used. However, the behaviors of new and used PCFs under discoloration are unclear. In this study, we found that new PCF did not effectively intercept particles under discoloration within the initial 5 d of inflow. In addition, the particles, especially the fine ones, accumulated in the long-used PCF exacerbated the risks of disinfection byproducts (DBPs) and microbes. The concentrations of trihalomethanes (THMs) and haloacetonitriles (HANs) in the effluent run through the PCF all increased over time; interestingly, all sharply increased after 5 d in accordance with the decrease in effluent iron particles. During this stage, maximum increases rate of 117.89% in THMs and 75.12% in HANs were observed. For haloacetic acids (HAAs), it served as the dominant contaminants, with concentrations approximately 10-fold greater than those of THMs and HANs. The increase showed that used PCFs could exacerbate the risks in DBPs exposure. Adenosine triphosphate (ATP) also showed a similar trend, with a maximum increase from 0.0033 to 0.0055 nmol/mL. Thus, PCFs can act only as pretreatment units and should be replaced after yellow water events. This study offers important guidance for PCF usage in drinking water purification, especially under discoloration.

Bandgap Engineering of Two-Dimensional Double Perovskite Cs4AgBiBr8/WSe2 Heterostructure from Indirect Bandgap to Direct Bandgap by Introducing Se Vacancy.

Heterostructures based on layered materials are considered next-generation photocatalysts due to their unique mechanical, physical, and chemical properties. In this work, we conducted a systematic first-principles study on the structure, stability, and electronic properties of a 2D monolayer WSe2/Cs4AgBiBr8 heterostructure. We found that the heterostructure is not only a type-II heterostructure with a high optical absorption coefficient, but also shows better optoelectronic properties, changing from an indirect bandgap semiconductor (about 1.70 eV) to a direct bandgap semiconductor (about 1.23 eV) by introducing an appropriate Se vacancy. Moreover, we investigated the stability of the heterostructure with Se atomic vacancy in different positions and found that the heterostructure was more stable when the Se vacancy is near the vertical direction of the upper Br atoms from the 2D double perovskite layer. The insightful understanding of WSe2/Cs4AgBiBr8 heterostructure and the defect engineering will offer useful strategies to design superior layered photodetectors.

Safely Implementing a Program of Pure Laparoscopic Donor Right Hepatectomy: The Experience From a Southeast Asian Center.

Laparoscopic donor right hepatectomy (LDRH) is a technically challenging procedure. There is increasing evidence demonstrating the safety of LDRH in high-volume expert centers. We report our center's experience in implementing an LDRH program in a small- to medium-sized transplantation program. Methods: Our center systematically introduced a laparoscopic hepatectomy program in 2006. We started with minor wedge resections followed by major hepatectomies with increasing complexities. In 2017, we performed our first laparoscopic living donor left lateral sectionectomy. Since 2018, we have performed 8 cases of right lobe living donor hepatectomy (laparoscopy-assisted: 4 and pure laparoscopic: 4). Results: The median operative time was 418 (298-540) min, whereas the median blood loss was 300 (150-900) mL. Two patients (25%) had surgical drain placed intraoperatively. The median length of stay was 5 (3-8) d, and the median time to return to work was 55 (24-90) d. None of the donors sustained any long-term morbidity or mortality. Conclusions: Small- to medium-sized transplant programs face unique challenges in adopting LDRH. Progressive introduction of complex laparoscopic surgery, a mature living donor liver transplantation program, appropriate patient selection, and the invitation of an expert to proctor the LDRH are necessary to ensure success.

Synthesis of acylated derivatives of chitosan oligosaccharide and evaluation of their potential antifungal agents on Fusarium oxysporum.

Chitosan oligosaccharide (COS) is an important carbohydrate-based biomaterial for synthesizing candidate drugs and biological agents. This study synthesized COS derivatives by grafting acyl chlorides of different alkyl chain lengths (C8, C10, and C12) onto COS molecules and further investigated their physicochemical properties and antimicrobial activity. The COS acylated derivatives were characterized using Fourier transform infrared spectroscopy, 1H nuclear magnetic resonance spectroscopy, X-ray diffraction, and thermogravimetric analysis. COS acylated derivatives were successfully synthesized and possessed high solubility and thermal stability. As for the evaluation of antibacterial activity, COS acylated derivatives did not significantly inhibit Escherichia coli and Staphylococcus aureus, but they significantly inhibited Fusarium oxysporum, which was superior to that of COS. Transcriptomic analysis revealed that COS acylated derivatives exerted antifungal activity mainly by downregulating the expression of efflux pumps, disrupting cell wall integrity, and impeding normal cell metabolism. Our findings provided a fundamental theory for the development of environmentally friendly antifungal agents.

Socio-Demographic, Behavioral and Psychological Factors Associated with High BMI among Adults in a Southeast Asian Multi-Ethnic Society: A Structural Equation Model.

While various influencing factors of overweight and obesity have been identified, the underlying mechanism remains unclear. We examined the relationships among sociodemographic, behavioral, and psychological factors on anthropometry in a multi-ethnic population with overweight and obesity. Participants (N = 251) were recruited from January to October 2022. Mean age and self-reported BMI were 31.7 ± 10.1 years and 29.2 ± 7.2 kg/m2. Participants were mostly female (52.4%) and overweight (58.2%). Multivariate multiple regression was performed using maximum likelihood estimation. Body mass index was associated with waist circumference, age, sex, race, marital status, education level, residential region, overeating habit, immediate thinking, self-regulation, and physical activity, but not anxiety, depression, or the intention to change eating habits. Final model indicated good fit: χ2 (30, N = 250) = 33.5, p = 0.32, CFI = 0.993, TLI = 0.988, RMSEA = 0.022, and SRMR = 0.041. Direct effects were found between BMI and overeating (ß = 0.10, p = 0.004), race (ß = -0.82, p < 0.001), marital status (ß = -0.42, p = 0.001), and education level (ß = -0.28, p = 0.019). Crisps (68.8%), cake (66.8%) and chocolate (65.6%) were identified as the most tempting foods. Immediate thinking indirectly increased overeating habits through poor self-regulation, although sociodemographic characteristics better predicted anthropometry than psycho-behavioral constructs.

Stepwise Crystalline Structural Transformation in 0D Hybrid Antimony Halides with Triplet Turn-on and Color-Adjustable Luminescence Switching.

Intelligent stimuli-responsive fluorescence materials are extremely pivotal for fabricating luminescent turn-on switching in solid-state photonic integration technology, but it remains a challenging objective for typical 3-dimensional (3D) perovskite nanocrystals. Herein, by fine-tuning the accumulation modes of metal halide components to dynamically control the carrier characteristics, a novel triple-mode photoluminescence (PL) switching was realized in 0D metal halide through stepwise single-crystal to single-crystal (SC-SC) transformation. Specifically, a family of 0D hybrid antimony halides was designed to exhibit three distinct types of PL performance including nonluminescent [Ph3EtP]2Sb2Cl8 (1), yellow-emissive [Ph3EtP]2SbCl5·EtOH (2), and red-emissive [Ph3EtP]2SbCl5 (3). Upon stimulus of ethanol, 1 was successfully converted to 2 through SC-SC transformation with enhanced PL quantum yield from ~0% to 91.50% acting as "turn-on" luminescent switching. Meanwhile, reversible SC-SC and luminescence transformation between 2 and 3 can be also achieved in the ethanol impregnation-heating process as luminescence vapochromism switching. As a consequence, a new triple-model turn-on and color-adjustable luminescent switching of off-onI-onII was realized in 0D hybrid halides. Simultaneously, wide advanced applications were also achieved in anti-counterfeiting, information security, and optical logic gates. This novel photon engineering strategy is expected to deepen the understanding of dynamic PL switching mechanism and guide development of new smart luminescence materials in cutting-edge optical switchable device.

Use of Mixed Reality in Neuro-Oncology: A Single Centre Experience.

(1) Background: Intra-operative neuronavigation is currently an essential component to most neurosurgical operations. Recent progress in mixed reality (MR) technology has attempted to overcome the disadvantages of the neuronavigation systems. We present our experience using the HoloLens 2 in neuro-oncology for both intra- and extra-axial tumours. (2) Results: We describe our experience with three patients who underwent tumour resection. We evaluated surgeon experience, accuracy of superimposed 3D image in tumour localisation with standard neuronavigation both pre- and intra-operatively. Surgeon training and usage for HoloLens 2 was short and easy. The process of image overlay was relatively straightforward for the three cases. Registration in prone position with a conventional neuronavigation system is often difficult, which was easily overcome during use of HoloLens 2. (3) Conclusion: Although certain limitations were identified, the authors feel that this system is a feasible alternative device for intra-operative visualization of neurosurgical pathology. Further studies are being planned to assess its accuracy and suitability across various surgical disciplines.

Remarkable Stability and Optoelectronic Properties of an All-Inorganic CsSn0.5Ge0.5I3 Perovskite Solar Cell.

Sn-Ge mixed perovskites have been proposed as promising lead-free candidates in the photovoltaics (PV) field. In this work, we discovered a stable P1 phase Sn-Ge mixed structure (CsSn0.5Ge0.5I3) with an appropriate band gap value of 1.19 eV, which manifests its unique structural stability and physics properties. The thermodynamic stability of this mixed structure under different growth conditions and all possible native defects are depicted in detail. The formation energies and dominant native point defects indicate that P1 phase CsSn0.5Ge0.5I3 exhibits unipolar self-doping behavior (p-type conductivity) and good defect tolerance while the growth condition changes. In addition, the calculation of light absorption confirmed that the P1 phase has a higher light absorption coefficient than that of MAPbI3 in the visible light range, showing excellent light absorption. Our work not only provides theoretical guidance for unraveling the unusual structural stability of Sn-Ge mixed perovskites, but also offers a useful scheme to modulate the stability and optoelectronic properties of Ge-based perovskites through alloy engineering.