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Time-restricted feeding (TRF) improves metabolic health. Both early TRF (eTRF, food intake restricted to the early part of the day) and mid-day TRF (mTRF, food intake restricted to the middle of the day) have been shown to have metabolic benefits. However, the two regimens have yet to be thoroughly compared. We conducted a five-week randomized trial to compare the effects of the two TRF regimens in healthy individuals without obesity (ChiCTR2000029797). The trial has completed. Ninety participants were randomized to eTRF (n=30), mTRF (n=30), or control groups (n=30) using a computer-based random-number generator. Eighty-two participants completed the entire five-week trial and were analyzed (28 in eTRF, 26 in mTRF, 28 in control groups). The primary outcome was the change in insulin resistance. Researchers who assessed the outcomes were blinded to group assignment, but participants and care givers were not. Here we show that eTRF was more effective than mTRF at improving insulin sensitivity. Furthermore, eTRF, but not mTRF, improved fasting glucose, reduced total body mass and adiposity, ameliorated inflammation, and increased gut microbial diversity. No serious adverse events were reported during the trial. In conclusion, eTRF showed greater benefits for insulin resistance and related metabolic parameters compared with mTRF. Clinical Trial Registration URL: http://www.chictr.org.cn/showproj.aspx?proj=49406 . Time-restricted eating, both early (eTRF) and mid-day (mTRF), have been shown to have metabolic benefits. Here the authors report a randomized controlled trial to compare the effects of eTRF and mTRF in healthy volunteers without obesity, and find that eTRF is more effective in improving the primary outcome insulin sensitivity.

HighlightsThe latest progress on the integration of flexible mechanical sensing platforms with machine learning (ML) is reviewed.The advantages, challenges, and future perspectives of the application of ML to intelligent flexible mechanical sensing technology are discussed.The fundamental working mechanisms and common types of flexible mechanical sensors are reviewed.To realize a hyperconnected smart society with high productivity, advances in flexible sensing technology are highly needed. Nowadays, flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device’s software. Significant research efforts have been devoted to improving materials, sensing mechanism, and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology. Meanwhile, advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors. Machine learning (ML) as an important branch of artificial intelligence can efficiently handle such complex data, which can be multi-dimensional and multi-faceted, thus providing a powerful tool for easy interpretation of sensing data. In this review, the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented. Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated, which includes health monitoring, human–machine interfaces, object/surface recognition, pressure prediction, and human posture/motion identification. Finally, the advantages, challenges, and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed. These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.

Electrochemical nitrogen reduction reaction (NRR) is considered as an alternative to the industrial Haber-Bosch process for NH 3 production due to both low energy consumption and environment friendliness. However, the major problem of electrochemical NRR is the unsatisfied efficiency and selectivity of electrocatalyst. As one group of the cheapest and most abundant transition metals, iron-group (Fe, Co, Ni and Cu) electrocatalysts show promising potential on cost and performance advantages as ideal substitute for traditional noble-metal catalysts. In this minireview, we summarize recent advances of iron-group-based materials (including their oxides, hydroxides, nitrides, sulfides and phosphides, etc.) as non-noble metal electrocatalysts towards ambient N 2 -to-NH 3 conversion in aqueous media. Strategies to boost NRR performances and perspectives for future developments are discussed to provide guidance for the field of NRR studies.

Under normal cutting conditions, the cutting tool tip passes through the spindle axis when it moves along X axis during the whole turning session, however, which is difficult to realize for the turning centers with the conventional two feed axes due to the spindle axis movement caused by the thermal deformation of machine tool structures. Addressing this problem, a novel three-axis feed system is proposed in this paper. The effect of the thermal movement of the spindle axis on the radial thermal error is analyzed. The linear and angular components of radial thermal error are derived from the proposed three-axis feed system. The whole compensation process is demonstrated using a turning center equipped with this feed system, and the needed data is mainly from simulation. The effects of the parameter l of this feed system on the two radial thermal error components are analyzed. Furthermore, the three-axis feed system can also be used as a two-axis feed system by locking the revolving feed axis, which can make the cutting tool tip pass through the spindle axis easier than the conventional two-axis feed systems, and therefore it is helpful in both facilitating the adjusting or assembling process of the turning center and reducing radial thermal errors of machined parts.

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, previously known as 2019-nCoV) outbreak has engulfed an unprepared world amidst a festive season. The zoonotic SARS-CoV-2, believed to have originated from infected bats, is the seventh member of enveloped RNA coronavirus. Specifically, the overall genome sequence of the SARS-CoV-2 is 96.2% identical to that of bat coronavirus termed BatCoV RaTG13. Although the current mortality rate of 2% is significantly lower than that of SARS (9.6%) and Middle East respiratory syndrome (MERS) (35%), SARS-CoV-2 is highly contagious and transmissible from human to human with an incubation period of up to 24 days. Some statistical studies have shown that, on average, one infected patient may lead to a subsequent 5.7 confirmed cases. Since the first reported case of coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 on December 1, 2019, in Wuhan, China, there has been a total of 60,412 confirmed cases with 1370 fatalities reported in 25 different countries as of February 13, 2020. The outbreak has led to severe impacts on social health and the economy at various levels. This paper is a review of the significant, continuous global effort that was made to respond to the outbreak in the first 75 days. Although no vaccines have been discovered yet, a series of containment measures have been implemented by various governments, especially in China, in the effort to prevent further outbreak, whilst various medical treatment approaches have been used to successfully treat infected patients. On the basis of current studies, it would appear that the combined antiviral treatment has shown the highest success rate. This review aims to critically summarize the most recent advances in understanding the coronavirus, as well as the strategies in prevention and treatment.

ObjectivesRheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial inflammation and joint destruction. Abnormal T-cell ubiquitination has been implicated in RA pathogenesis, yet its molecular mechanisms remain unclear.MethodsTranscriptomic data from RA blood and synovial tissue were analyzed to identify differentially expressed genes (DEGs). Ubiquitination-related module genes were obtained using weighted gene co-expression network analysis (WGCNA), and their overlap with DEGs yielded blood-synovial ubiquitination-related genes (BS-UGs). Single-cell datasets were used to extract T-cell marker genes, and intersection analysis identified T-cell-specific ubiquitination genes (BS-TUGs). Machine learning algorithms (SVM-RFE and Boruta) screened key BS-TUGs. Immune infiltration, transcription factor (TF) regulation, and master regulators were explored. Finally, two-sample Mendelian randomization (MR) was performed to assess causal relationships between key genes and RA.ResultsA total of 521 BS-UGs and 21 candidate BS-TUGs were identified, from which six key genes (DOCK10, DGKA, NOP58, JAK3, GCC2, ANO9) were selected. These genes exhibited significant immune-cell correlations and were regulated by multiple TFs. MR analysis demonstrated a positive causal association between NOP58 (OR = 1.074, p = 0.001) and RA, and a negative association between GCC2 (OR = 0.928, p < 0.001) and RA, without heterogeneity or pleiotropy.ConclusionIntegrative dual-omics and MR analyses identified key ubiquitination-related T-cell genes driving RA pathogenesis. NOP58 and GCC2 represent potential causal biomarkers and therapeutic targets, offering novel insights into immune regulation and precision intervention in RA.

Self-supervised monocular depth estimation can exhibit excellent performance in static environments due to the multi-view consistency assumption during the training process. However, it is hard to maintain depth consistency in dynamic scenes when considering the occlusion problem caused by moving objects. For this reason, we propose a method of self-supervised self-distillation for monocular depth estimation (SS-MDE) in dynamic scenes, where a deep network with a multi-scale decoder and a lightweight pose network are designed to predict depth in a self-supervised manner via the disparity, motion information, and the association between two adjacent frames in the image sequence. Meanwhile, in order to improve the depth estimation accuracy of static areas, the pseudo-depth images generated by the LeReS network are used to provide the pseudo-supervision information, enhancing the effect of depth refinement in static areas. Furthermore, a forgetting factor is leveraged to alleviate the dependency on the pseudo-supervision. In addition, a teacher model is introduced to generate depth prior information, and a multi-view mask filter module is designed to implement feature extraction and noise filtering. This can enable the student model to better learn the deep structure of dynamic scenes, enhancing the generalization and robustness of the entire model in a self-distillation manner. Finally, on four public data datasets, the performance of the proposed SS-MDE method outperformed several state-of-the-art monocular depth estimation techniques, achieving an accuracy (δ1) of 89% while minimizing the error (AbsRel) by 0.102 in NYU-Depth V2 and achieving an accuracy (δ1) of 87% while minimizing the error (AbsRel) by 0.111 in KITTI.

Semantic communication is a promising technology used to overcome the challenges of large bandwidth and power requirements caused by the data explosion. Semantic representation is an important issue in semantic communication. The knowledge graph, powered by deep learning, can improve the accuracy of semantic representation while removing semantic ambiguity. Therefore, we propose a semantic communication system based on the knowledge graph. Specifically, in our system, the transmitted sentences are converted into triplets by using the knowledge graph. Triplets can be viewed as basic semantic symbols for semantic extraction and restoration and can be sorted based on semantic importance. Moreover, the proposed communication system adaptively adjusts the transmitted contents according to channel quality and allocates more transmission resources to important triplets to enhance communication reliability. Simulation results show that the proposed system significantly enhances the reliability of the communication in the low signal-to-noise regime compared to the traditional schemes.

Despite intense interests in developing blood measurements of Alzheimer’s disease (AD), the progress has been confounded by limited sensitivity and poor correlation to brain pathology. Here, we present a dedicated analytical platform for measuring different populations of circulating amyloid β (Aβ) proteins – exosome-bound vs. unbound – directly from blood. The technology, termed a mplified p lasmonic ex osome (APEX), leverages in situ enzymatic conversion of localized optical deposits and double-layered plasmonic nanostructures to enable sensitive, multiplexed population analysis. It demonstrates superior sensitivity (~200 exosomes), and enables diverse target co-localization in exosomes. Employing the platform, we find that prefibrillar Aβ aggregates preferentially bind with exosomes. We thus define a population of Aβ as exosome-bound (Aβ42+ CD63+) and measure its abundance directly from AD and control blood samples. As compared to the unbound or total circulating Aβ, the exosome-bound Aβ measurement could better reflect PET imaging of brain amyloid plaques and differentiate various clinical groups. Detecting Alzheimer’s disease from blood samples is challenging because amyloid β blood levels are lower than the ELISA detection limit. Here the authors capture amyloid β bound to circulating exosomes on a plasmonic nanosensor, followed by enzymatic amplification to improve detection sensitivity.

Graphene, an allotrope of carbon in 2D structure, has revolutionised research, development and application in various disciplines since its successful isolation 16 years ago. The single layer of sp 2 -hybridised carbon atoms brings with it a string of unrivalled characteristics at a fraction of the price of its competitors, including platinum, gold and silver. More recently, there has been a growing trend in the application of graphene in catalysis, either as metal-free catalysts, composite catalysts or as catalyst supports. The unique and extraordinary properties of graphene have rendered it useful in increasing the reactivity and selectivity of some reactions. Owing to its large surface area, outstanding adsorptivity and high compatibility with various functional groups, graphene is able to provide a whole new level of possibilities and flexibilities to design and synthesise fit-for-purpose graphene-based catalysts for specific applications. This review is focussed on the progress, mechanisms and challenges of graphene application in four main reactions, i.e., oxygen reduction reaction, water splitting, water treatment and Fischer–Tropsch synthesis. This review also summarises the advantages and drawbacks of graphene over other commonly used catalysts. Given the inherent nature of graphene, coupled with its recent accelerated advancement in the synthesis and modification processes, it is anticipated that the application of graphene in catalysis will grow exponentially from its current stage of infancy.