In vivo bioassay to test the pathogenicity of missense human AIP variants

Background Heterozygous germline loss-of-function mutations in the aryl hydrocarbon receptor-interacting protein gene (AIP) predispose to childhood-onset pituitary tumours. The pathogenicity of missense variants may pose difficulties for genetic counselling and family follow-up. Objective To develop an in vivo system to test the pathogenicity of human AIP mutations using the fruit fly Drosophila melanogaster. Methods We generated a null mutant of the Drosophila AIP orthologue, CG1847, a gene located on the Xchromosome, which displayed lethality at larval stage in hemizygous knockout male mutants (CG1847exon1_3 ). We tested human missense variants of ‘unknown significance’, with ‘pathogenic’ variants as positive control. Results We found that human AIP can functionally substitute for CG1847, as heterologous overexpression of human AIP rescued male CG1847exon1_3 lethality, while a truncated version of AIP did not restore viability. Flies harbouring patient-specific missense AIP variants (p.C238Y, p.I13N, p.W73R and p.G272D) failed to rescue CG1847exon1_3 mutants, while seven variants (p.R16H, p.Q164R, p.E293V, p.A299V, p.R304Q, p.R314W and p.R325Q) showed rescue, supporting a non-pathogenic role for these latter variants corresponding to prevalence and clinical data. Conclusion Our in vivo model represents a valuable tool to characterise putative disease-causing human AIP variants and assist the genetic counselling and management of families carrying AIP variants

A robust and active hybrid catalyst for facile oxygen reduction in solid oxide fuel cells

The sluggish oxygen reduction reaction (ORR) greatly reduces the energy efficiency of solid oxide fuel cells (SOFCs). Here we report our findings in dramatically enhancing the ORR kinetics and durability of the state-of-the-art La[subscript 0.6]Sr[subscript 0.4]Co[subscript 0.2]Fe[subscript 0.8]O[subscript 3](LSCF) cathode using a hybrid catalyst coating composed of a conformal PrNi[subscript 0.5]Mn[subscript 0.5]O[subscript 3](PNM) thin film with exsoluted PrOxnanoparticles. At 750°C, the hybrid catalyst-coated LSCF cathode shows a polarization resistance of ∼0.022 Ω cm[superscript 2], about 1/6 of that for a bare LSCF cathode (∼0.134 Ω cm[superscript 2]). Further, anode-supported cells with the hybrid catalyst-coated LSCF cathode demonstrate remarkable peak power densities (∼1.21 W cm[superscript -2]) while maintaining excellent durability (0.7 V for ∼500 h). Near Ambient X-ray Photoelectron Spectroscopy (XPS) and Near Edge X-Ray Absorption Fine Structure (NEXAFS) analyses, together with density functional theory (DFT) calculations, indicate that the oxygen-vacancy-rich surfaces of the PrOxnanoparticles greatly accelerate the rate of electron transfer in the ORR whereas the thin PNM film facilitates rapid oxide-ion transport while drastically enhancing the surface stability of the LSCF electrode

Characteristics of the Hydrogen Electrode in High Temperature Steam Electrolysis Process

YSZ-electrolyte supported solid oxide electrolyzer cells (SOECs) using LSM-YSZ oxygen electrode but with three types of hydrogen electrode, Ni–SDC, Ni–YSZ and LSCM–YSZ have been fabricated and characterized under different steam contents in the feeding gas at 850°C. Electrochemical impedance spectra results show that cell resistances increase with the increase in steam concentrations under both open circuit voltage and electrolysis conditions, suggesting that electrolysis reaction becomes more difficult in high steam content. Pt reference electrode was applied to evaluate the contributions of the hydrogen electrode and oxygen electrode in the electrolysis process. Electrochemical impedance spectra and over potential of both electrodes were measured under the same testing conditions. Experimental results show that steam contents mainly affect the behavior of the hydrogen electrode but have little influence on the oxygen electrode. Further, contribution from the hydrogen electrode is dominant in the electrolysis process for Ni–based SOECs, but this contribution decreases for LSCM–based SOECs

Ionotropic receptors (IRs) contribute to temperature synchronization in Drosophila melanogaster.

PhDLike most organisms, Drosophila melanogaster can synchronize its physiological and behavioural processes by possessing internal circadian clock that regulates. Naturally fluctuating timing cues, like light and temperature (also known as Zeitgebers), synchronize these endogenous and self‐sustained clocks with external time. In Drosophila, synchronization of the circadian clock by light has been studied in detail, but much less is known about the molecular mechanisms underlying temperature entrainment. Previous data from our lab shows that Nocte, a Chordotonal organ (Ch organ) located protein, is required for normal temperature entrainment in Drosophila. However, neither the function of Nocte in temperature entrainment nor the molecular underlying mechanisms are clear. To address these issues, a proteomics strategy of combing co‐immunoprecipitation and MS/MS sequencing was applied to isolate potential interactors of Nocte. IR25a was one of the most promising candidates, which was later confirmed by behavioural tests using RNA interference: Reducing IR25a expression in Chorgan resulted in abnormal behaviour during temperature cycles, similar to what had been described for Nocte mutant. To further confirm the interaction between Nocte and IR25a, I showed that IR25a physically interacts with Nocte in vivo. Moreover, using an IR25a‐gal4 line, I was able to show that IR25a is expressed in subsets of chordotonal organs (Ch organ) including Johnston’s Organs (JO), where Nocte is also highly expressed. These results, along with the behavioural data mentioned above are consistent with the proteomics results and suggest that Nocte and IR25a physically and functionally interact.IR25a mutants were employed to further investigate the function of IR25a in temperature entrainment. First of all, I found that both central and peripheral clocks in wild type flies can be synchronized to temperature cycles with only two degree differences (12h: 12h, 27 °C: 25 °C). In contrast, synchronization of locomotor activity rhythms in the IR25a null mutants to the same temperature cycles and other TC’s with 2°C amplitude was eliminated. Under the same conditions, the oscillations of the core clock proteins TIMLESS (TIM) and PERIOD (PER) that normally occur in fly heads were completely abolished inIR25a null mutants, suggesting that IR25a is required for temperature entrainment of peripheral clocks. In the central brain pacemaker neurons, the oscillations of TIM in dorsal and lateral neurons were also affected by the IR25a mutants. On the contrary, IR25a is not required for light entrainment and temperature compensation, suggesting that IR25a is specifically involved in temperature synchronization. Moreover, temperature entrainment of the IR25a null mutants can be partially restored by applying larger temperature intervals (29°C: 25°C) indicating that IR25amay function as amplitude detector independent of absolute temperature values. Finally, neuronal activity in IR25a+ neurons is crucial for the synchronization of circadian clocks to low amplitude temperature cycles.Re‐constitution of functional olfactory receptors required the assembly of IR25a with IR76a and IR76b. Interestingly, IR76a and IR76b are neither required for temperature entrainment at the behavioural level nor expressed in the Ch organs. To check if other potential IRs interacting with IR25a exist, I screened the expression pattern of most divergent IRs using IR‐gal4/UAS‐GFP flies. IR56a was isolated as a potential partner of IR25a because it is also expressed in the femur chordotonal organs. To investigate the function of IR56a in temperature entrainment, I generated a null mutant of IR56a.Surprisingly, this gene is not required for synchronizing clocks to a temperature cycle (27°C: 25°C) at the behavioural level. However, the behavioural and molecular phenotypes of IR56a mutant under different temperature cycles need to be further characterized

Progress and challenges in biomaterials used for bone tissue engineering: bioactive glasses and elastomeric composites

Driven by the increasing economic burden associated with bone injury and disease, biomaterial development for bone repair represents the most active research area in the field of tissue engineering. This article provides an update on recent advances in the development of bioactive biomaterials for bone regeneration. Special attention is paid to the recent developments of sintered Na-containing bioactive glasses, borate-based bioactive glasses, those doped with trace elements (such as Cu, Zn, and Sr), and novel elastomeric composites. Although bioactive glasses are not new to bone tissue engineering, their tunable mechanical properties, biodegradation rates, and ability to support bone and vascular tissue regeneration, as well as osteoblast differentiation from stem and progenitor cells, are superior to other bioceramics. Recent progresses on the development of borate bioactive glasses and trace element-doped bioactive glasses expand the repertoire of bioactive glasses. Although boride and other trace elements have beneficial effects on bone remodeling and/or associated angiogenesis, the risk of toxicity at high levels must be highly regarded in the design of new composition of bioactive biomaterials so that the release of these elements must be satisfactorily lower than their biologically safe levels. Elastomeric composites are superior to the more commonly used thermoplastic-matrix composites, owing to the well-defined elastic properties of elastomers which are ideal for the replacement of collagen, a key elastic protein within the bone tissue. Artificial bone matrix made from elastomeric composites can, therefore, offer both sound mechanical integrity and flexibility in the dynamic environment of injured bone. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/2194-0517-1-2) contains supplementary material, which is available to authorized users

Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-term Utility Demand Forecasting in Electricity Wholesale Markets

Short-term load forecasting (STLF) plays a significant role in the operation of electricity trading markets. Considering the growing concern of data privacy, federated learning (FL) is increasingly adopted to train STLF models for utility companies (UCs) in recent research. Inspiringly, in wholesale markets, as it is not realistic for power plants (PPs) to access UCs' data directly, FL is definitely a feasible solution of obtaining an accurate STLF model for PPs. However, due to FL's distributed nature and intense competition among UCs, defects increasingly occur and lead to poor performance of the STLF model, indicating that simply adopting FL is not enough. In this paper, we propose a DRL-assisted FL approach, DEfect-AwaRe federated soft actor-critic (DearFSAC), to robustly train an accurate STLF model for PPs to forecast precise short-term utility electricity demand. Firstly. we design a STLF model based on long short-term memory (LSTM) using just historical load data and time data. Furthermore, considering the uncertainty of defects occurrence, a deep reinforcement learning (DRL) algorithm is adopted to assist FL by alleviating model degradation caused by defects. In addition, for faster convergence of FL training, an auto-encoder is designed for both dimension reduction and quality evaluation of uploaded models. In the simulations, we validate our approach on real data of Helsinki's UCs in 2019. The results show that DearFSAC outperforms all the other approaches no matter if defects occur or not

Mitigate Replication and Copying in Diffusion Models with Generalized Caption and Dual Fusion Enhancement

While diffusion models demonstrate a remarkable capability for generating high-quality images, their tendency to `replicate' training data raises privacy concerns. Although recent research suggests that this replication may stem from the insufficient generalization of training data captions and duplication of training images, effective mitigation strategies remain elusive. To address this gap, our paper first introduces a generality score that measures the caption generality and employ large language model (LLM) to generalize training captions. Subsequently, we leverage generalized captions and propose a novel dual fusion enhancement approach to mitigate the replication of diffusion models. Our empirical results demonstrate that our proposed methods can significantly reduce replication by 43.5% compared to the original diffusion model while maintaining the diversity and quality of generations

Effective Distillation of Table-based Reasoning Ability from LLMs

Large Language Models (LLMs) have demonstrated remarkable performance across a wide range of natural language processing tasks. However, their remarkable parameter size and their impressive high requirement of computing resources pose challenges for their practical deployment. Recent research has revealed that specific capabilities of LLMs, such as numerical reasoning, can be transferred to smaller models through distillation. Some studies explore the potential of leveraging LLMs to perform table-based reasoning. Nevertheless, prior to our work, there has been no investigation into the prospect of specialising table reasoning skills in smaller models specifically tailored for table-to-text generation tasks. In this paper, we propose a novel table-based reasoning distillation, with the aim of distilling distilling LLMs into tailored, smaller models specifically designed for table-based reasoning task. Experimental results have shown that a 0.22 billion parameter model (Flan-T5-base) fine-tuned using distilled data, not only achieves a significant improvement compared to traditionally fine-tuned baselines but also surpasses specific LLMs like gpt-3.5-turbo on the scientific table-to-text generation dataset (SciGen). The code and data are released in https://github.com/Bernard-Yang/TableDistill