Artificial Hydrophilic Organic and Dendrite-Suppressed Inorganic Hybrid Solid Electrolyte Interface Layer for Highly Stable Zinc Anodes.

Aqueous zinc-ion batteries (AZIBs) have gained significant attentions for their inherent safety and cost-effectiveness. However, challenges, such as dendrite growth and anodic corrosion at the Zn anode, hinder their commercial viability. In this paper, an organic-inorganic coating layer (Nafion-TiO2) was introduced to protect the Zn anode and electrolyte interface. Briefly, Nafion effectively shields against the corrosion from water molecules through the hydrophobic wall of -CF3 and guided zinc deposition from the -SO3 functional group, while TiO2 particles with a higher Young's modulus (151 GPa vs 120 GPa from Zn metal) suppress the zinc dendrite formation. As a result, with the protection of Nafion-TiO2, the symmetrical Zn∥Zn battery shows an improved cycle life of 1,750 h at 0.5 mA cm-2, and the full cell based on Zn∥MnO2 shows a long cycle life over 1,500 cycles at 1 A g-1. Our research offers a novel approach for protecting zinc metal anodes, potentially applicable to other metal anodes such as those in lithium and sodium batteries.

Breaking the bottleneck of lead-free perovskite solar cells through dimensionality modulation.

The emerging perovskite solar cell (PSC) technology has attracted significant attention due to its superior power conversion efficiency (PCE) among the thin-film photovoltaic technologies. However, the toxicity of lead and poor stability of lead halide materials hinder their commercialization. In this case, after a decade of effort, various categories of lead-free perovskites and perovskite-like materials have been developed, including tin halide perovskites, double perovskites, defect-structured perovskites, and rudorffites. However, the performance of the corresponding devices still falls short of expectations, especially their PCE. The limitations mainly originate from either the unstable lattice structure of these materials, which causes the distortion of their octahedra, or their low dimensionality (e.g., structural and electronic dimensionality)-correlated poor carrier transport and self-trapping effect, accelerating nonradiative recombination. Therefore, understanding the relationship between the structures and performance in these emerging candidates and leveraging these insights to design or modify new lead-free perovskites is of great significance. Herein, we review the variety of dimensionalities in different categories of lead-free perovskites and perovskite-like materials and conclude that dimensionality is an important aspect among the crucial indexes that determine the performance of lead-free PSCs. In addition, we summarize the modulation of both structural and electronic dimensionality, and the corresponding enhanced optoelectronic properties in different categories. Finally, perspectives on the future development of lead-free perovskites and perovskite-like materials for photovoltaic applications are provided. We hope that this review will provide researchers with a concise overview of these emerging materials and help them leverage dimensionality to break the bottleneck in photovoltaic applications.

Effects of smoking cessation on plasma clozapine concentrations in male patients with schizophrenia during the COVID-19 pandemic.

Introduction: This study aimed to investigate the effect of smoking cessation on plasma clozapine (CLO) concentrations in long-term hospitalized Chinese male patients with schizophrenia treated with CLO during the COVID-19 pandemic. Methods: Therapeutic drug monitoring (TDM) data for CLO were collected at Beijing HuiLongGuan Hospital between December 1, 2019 (before smoking cessation) and January 31, 2020 (after smoking cessation) in this retrospective study. Fifty-three male smokers and inpatients with schizophrenia who were treated with CLO were included. Plasma concentrations of CLO were measured using high-performance liquid chromatography-tandem mass spectrometry. The fagerstrom test for nicotine dependence (FTND) was used to assess smoking behavior. Results: The plasma CLO concentrations and dose-corrected plasma CLO concentrations were significantly increased by 29.3 and 23.5%, respectively, after smoking cessation. Discussion: The results suggested that clinicians and pharmacists should adjust the CLO dose based on changes in smoking status in patients stabilized with CLO during the COVID-19 pandemic. Careful TDM for CLO should be performed prior to dose adjustment，to reduce the increased risk of smoking cessation induced side effects, especially for older patients receiving multiple medications.

Relationship Between Plasma Aripiprazole and Dehydroaripiprazole Concentrations and Prolactin Levels in Chinese Children and Adolescents.

Objective: To investigate the relationship between plasma aripiprazole (ARI) and its metabolite dehydroaripiprazole (DARI) concentrations and prolactin (PRL) levels in Chinese children and adolescents. Methods: This was a retrospective cross-sectional study and the data were collected at Beijing HuiLongGuan Hospital, a Beijing City owned psychiatric hospital, between January 1 and December 31, 2021. Fifty-two child and adolescent inpatients (17 males, 35 females) aged 13-18 years and received ARI regardless of diagnosis were included. The steady-state ARI and DARI plasma concentrations were measured using high-performance liquid chromatography-tandem mass spectrometry. The serum PRL levels were measured by chemiluminescence immunoassay. Results: The plasma concentrations of ARI, DARI, and the total of ARI and DARI were negatively correlated with serum PRL levels in female children and adolescents. Approximately 15% of child and adolescent inpatients treated with ARI exhibited subnormal PRL serum levels. Conclusions: The results suggest that in addition to regularly monitoring PRL levels, therapeutic drug monitoring for ARI and its main metabolite DARI can help to mitigate the adverse medical consequences associated with PRL reduction. Thus, clinicians should consider the ARI-induced reduction of PRL levels when prescribing ARI to child and adolescent patients, particularly among females.

Relationship between the tryptophan-kynurenine pathway and painful physical symptoms in patients with major depressive disorder.

OBJECTIVES: To investigate the relationship between the tryptophan-kynurenine (TRP-KYN) pathway and painful physical symptoms (PPS) in major depressive disorder (MDD). METHODS: Eighty-four patients with MDD (40 patients with PPS and 44 without PPS) and forty-six healthy controls (HC) were recruited. The serum levels of tryptophan (TRP), kynurenine(KYN), kynurenic acid (KA), quinolinic acid (QA), 3-hydroxy-kynurenine (3-HK), serotonin (5-HT) were measured using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Depression, anxiety and pain were assessed using Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA) and Short-form McGill pain questionnaire (SFMPQ) respectively. RESULTS: Patients in the MDD group exhibited significantly lower KA and 5-HT levels than HC, whereas MDD patients with PPS showed higher KYN and QA levels, and a higher KYN/TRP ratio than those without. There was a positive correlation between the scores of SFMPQ and QA levels and a negative correlation between the scores of SFMPQ and TRP levels or KA/QA ratios in MDD patients with PPS group. Stepwise multiple regression analysis showed that the KYN/TRP ratios, the KA/QA ratios, and the HAMD scores were significant predictor factors for SFMPQ scores. CONCLUSIONS: These results demonstrated that the TRP-KYN pathway may play a role in the pathophysiology of pain in patients with major depressive disorder, suggesting that further studies of this pathway as a potential biomarker or therapeutic target are required.

Relationship Between Plasma Olanzapine and N-Desmethyl-Olanzapine Concentration and Metabolic Parameters in Patients With Schizophrenia.

Objectives: The aim of the present study was to investigate a potential relationship between metabolic parameters and steady-state plasma concentrations of olanzapine (OLA) and its metabolite, 4-N'-desmethyl-olanzapine (DMO) in patients with schizophrenia taking therapeutic doses. Methods: A total of 352 inpatients, diagnosed with schizophrenia according to the DSM-V criteria and treated with OLA, were investigated. The plasma concentrations of OLA and DMO were measured by high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS). Fasting blood samples were measured for insulin, glucose, total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), C-reactive protein (CRP) and homocysteine, and differences in these parameters were investigated in relation to plasma concentrations of OLA and DMO. Results: Lower plasma DMO concentrations were associated with higher glucose and TG levels and homeostasis model assessment of insulin resistance (HOMA-IR), while higher plasma OLA concentrations were associated with higher CRP and homocysteine levels in the OLA-treated patients with schizophrenia. Conclusion: These results demonstrate that OLA and its metabolite DMO may have different effects on OLA-induced metabolic abnormalities. DMO might have a counteracting effects on glucose-insulin homeostasis and lipid metabolic abnormalities, which suggests that regular measure of various metabolic parameters and drug monitoring on both OLA and DMO are recommended in OLA-treated patients with schizophrenia.

A genome-wide association study revealed the genetic variation and candidate genes for grain copper content in bread wheat (Triticum aestivum L.).

As an essential microelement, copper plays a crucial role in the human body. However, the grains of bread wheat, a major crop food, contain a low copper content. Here, a diversity panel of 443 wheat accessions cultivated in four environments was used to analyse grain copper content by ICAP-7000, and the genetic variation in grain copper content was examined using a 660 K single nucleotide polymorphism chip. Phenotypic analysis indicated that the grain copper content varied between 2.58 mg kg-1 and 13.65 mg kg-1. A genome-wide association study identified 12 QTLs associated with grain copper content that showed significance in at least two environments on chromosomes 1A, 1D, 3D, 4A, 5A, 5D, 6B, 6D, 7A and 7D. Through haplotype analysis, the phenotypic difference between the haplotypes of three genes, TraesCS5D01G282300, TraesCS6B01G052900 and TraesCS7D01G146600, showed significance (P ⩽ 0.05) in four environments. They were considered to be important candidate genes for grain copper content in wheat. In addition, we detected that the grain copper content gradually decreased with release years among wheat accessions in China, and the percentage of favourable alleles showed a similar trend. Analysing the changes in grain copper content with yield factors, we found that the dilute effect was mainly caused by thousand kernel weight. This study provides useful information on the genetic basis for grain copper content, and thus helps in improving the wheat grain quality.

Cascaded Parsing of Human-Object Interaction Recognition.

This paper addresses the task of detecting and recognizing human-object interactions (HOI) in images. Considering the intrinsic complexity and structural nature of the task, we introduce a cascaded parsing network (CP-HOI) for a multi-stage, structured HOI understanding. At each cascade stage, an instance detection module progressively refines HOI proposals and feeds them into a structured interaction reasoning module. Each of the two modules is also connected to its predecessor in the previous stage, enabling efficient cross-stage information propagation. The structured interaction reasoning module is built upon a graph parsing neural network (GPNN), which efficiently models potential HOI structures as graphs and mines rich context for comprehensive relation understanding. In particular, GPNN infers a parse graph that i) interprets meaningful HOI structures by a learnable adjacency matrix, and ii) predicts action (edge) labels. Within an end-to-end, message-passing framework, GPNN blends learning and inference, iteratively parsing HOI structures and reasoning HOI representations (i.e., instance and relation features). Further beyond relation detection at a bounding-box level, we make our framework flexible to perform fine-grained pixel-wise relation segmentation; this provides a new glimpse into better relation modeling. A preliminary version of our CP-HOI model reached 1st place in the ICCV2019 Person in Context Challenge, on both relation detection and segmentation. In addition, our CP-HOI shows promising results on two popular HOI recognition benchmarks, i.e., V-COCO and HICO-DET.

Hierarchical Human Semantic Parsing With Comprehensive Part-Relation Modeling.

Modeling the human structure is central for human parsing that extracts pixel-wise semantic information from images. We start with analyzing three types of inference processes over the hierarchical structure of human bodies: direct inference (directly predicting human semantic parts using image information), bottom-up inference (assembling knowledge from constituent parts), and top-down inference (leveraging context from parent nodes). We then formulate the problem as a compositional neural information fusion (CNIF) framework, which assembles the information from the three inference processes in a conditional manner, i.e., considering the confidence of the sources. Based on CNIF, we further present a part-relation-aware human parser (PRHP), which precisely describes three kinds of human part relations, i.e., decomposition, composition, and dependency, by three distinct relation networks. Expressive relation information can be captured by imposing the parameters in the relation networks to satisfy specific geometric characteristics of different relations. By assimilating generic message-passing networks with their edge-typed, convolutional counterparts, PRHP performs iterative reasoning over the human body hierarchy. With these efforts, PRHP provides a more general and powerful form of CNIF, and lays the foundation for more sophisticated and flexible human relation patterns of reasoning. Experiments on five datasets demonstrate that our two human parsers outperform the state-of-the-arts in all cases.

Effects of Dose, Age, Sex, Body Weight, and Smoking on Plasma Concentrations of Olanzapine and N-desmethyl Olanzapine in Inpatients With Schizophrenia.

PURPOSE: This study aimed to investigate the combined effects of dose, age, sex, body weight, and smoking on plasma concentrations of olanzapine (OLA) and N-desmethyl olanzapine (DMO) in Chinese inpatients with schizophrenia. METHODS: A retrospective study including 185 inpatients was conducted. The steady-state plasma concentrations of OLA (COLA) and DMO (CDMO) were measured using high-performance liquid chromatography-tandem mass spectrometry. The combined effects of dose, age, sex, body weight, and smoking on COLA and CDMO were evaluated. FINDINGS: Multiple linear regression analyses revealed that dose, age, body weight, and smoking had significant effects on COLA and CDMO in inpatients with schizophrenia treated with OLA. The dose was the most important determinant of COLA and CDMO and was positively correlated with both. Furthermore, smokers exhibited a significantly lower COLA and COLA + DMO, whereas higher body weight led to the reduction of COLA, CDMO, and COLA + DMO. Advanced age was associated with lower CDMO. IMPLICATIONS: These results suggest that dose, age, body weight, and smoking have a significant influence on the plasma concentration of OLA and its metabolite DMO. Clinicians should consider the combined effects when prescribing OLA to patients with schizophrenia.

A Generalized Earley Parser for Human Activity Parsing and Prediction.

Detection, parsing, and future predictions on sequence data (e.g., videos) require the algorithms to capture non-Markovian and compositional properties of high-level semantics. Context-free grammars are natural choices to capture such properties, but traditional grammar parsers (e.g., Earley parser) only take symbolic sentences as inputs. In this paper, we generalize the Earley parser to parse sequence data which is neither segmented nor labeled. Given the output of an arbitrary probabilistic classifier, this generalized Earley parser finds the optimal segmentation and labels in the language defined by the input grammar. Based on the parsing results, it makes top-down future predictions. The proposed method is generic, principled, and widely applicable. Experiment results clearly show the benefit of our method for both human activity parsing and prediction on three video datasets.

A tale of two explanations: Enhancing human trust by explaining robot behavior.

The ability to provide comprehensive explanations of chosen actions is a hallmark of intelligence. Lack of this ability impedes the general acceptance of AI and robot systems in critical tasks. This paper examines what forms of explanations best foster human trust in machines and proposes a framework in which explanations are generated from both functional and mechanistic perspectives. The robot system learns from human demonstrations to open medicine bottles using (i) an embodied haptic prediction model to extract knowledge from sensory feedback, (ii) a stochastic grammar model induced to capture the compositional structure of a multistep task, and (iii) an improved Earley parsing algorithm to jointly leverage both the haptic and grammar models. The robot system not only shows the ability to learn from human demonstrators but also succeeds in opening new, unseen bottles. Using different forms of explanations generated by the robot system, we conducted a psychological experiment to examine what forms of explanations best foster human trust in the robot. We found that comprehensive and real-time visualizations of the robot's internal decisions were more effective in promoting human trust than explanations based on summary text descriptions. In addition, forms of explanation that are best suited to foster trust do not necessarily correspond to the model components contributing to the best task performance. This divergence shows a need for the robotics community to integrate model components to enhance both task execution and human trust in machines.

The Martian: Examining Human Physical Judgments across Virtual Gravity Fields.

This paper examines how humans adapt to novel physical situations with unknown gravitational acceleration in immersive virtual environments. We designed four virtual reality experiments with different tasks for participants to complete: strike a ball to hit a target, trigger a ball to hit a target, predict the landing location of a projectile, and estimate the flight duration of a projectile. The first two experiments compared human behavior in the virtual environment with real-world performance reported in the literature. The last two experiments aimed to test the human ability to adapt to novel gravity fields by measuring their performance in trajectory prediction and time estimation tasks. The experiment results show that: 1) based on brief observation of a projectile's initial trajectory, humans are accurate at predicting the landing location even under novel gravity fields, and 2) humans' time estimation in a familiar earth environment fluctuates around the ground truth flight duration, although the time estimation in unknown gravity fields indicates a bias toward earth's gravity.