Circadian Integration of Hepatic De Novo Lipogenesis and Peripheral Energy Substrates Utilization

The liver maintains energy substrate homeostasis by synchronizing circadian or diurnal expression of metabolic genes with the feeding/fasting state. The activities of hepatic de novo lipogenic gene products peak during feeding, converting carbohydrates into fats that provide vital energy sources for peripheral tissues. Conversely, deregulated hepatic lipid synthesis leads to systemic metabolic dysfunction, establishing the importance of temporal regulation of fat synthesis/usage in metabolic homeostasis. Pharmacological activation of peroxisome proliferator-activated receptor $\delta / \beta (PPAR \delta / \beta)$improves glucose handling and systemic insulin sensitivity. However, the mechanisms of hepatic $PPAR\delta$ actions and the molecular pathways through which it is able to modulate global metabolic homeostasis remain unclear. Here we show that hepatic $PPAR\delta$ controls the diurnal expression of lipogenic genes in the dark/feeding cycle. Adenovirus mediated liver restricted activation of $PPAR\delta$ promotes glucose utilization in the liver and fat utilization in the muscle. Liver specific deletion of either $PPAR\delta$ or the $PPAR\delta$-regulated lipogenic gene acetyl-CoA carboxylase 1 (ACC1) reduces muscle fatty acid uptake. Unbiased metabolite profiling identifies 1-stearoyl-2-oleoyl-sn-glycero-3-phosphocholine (SOPC) as a serum lipid derived from the hepatic $PPAR\delta$-ACC1 activity that reduces postprandial lipid levels and increases muscle fatty acid uptake. These findings reveal a regulatory mechanism that coordinates lipid synthesis and utilization in the liver-muscle axis, providing mechanistic insights into the hepatic regulation of systemic energy substrates homeostasis

BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection

As various forms of fraud proliferate on Ethereum, it is imperative to safeguard against these malicious activities to protect susceptible users from being victimized. While current studies solely rely on graph-based fraud detection approaches, it is argued that they may not be well-suited for dealing with highly repetitive, skew-distributed and heterogeneous Ethereum transactions. To address these challenges, we propose BERT4ETH, a universal pre-trained Transformer encoder that serves as an account representation extractor for detecting various fraud behaviors on Ethereum. BERT4ETH features the superior modeling capability of Transformer to capture the dynamic sequential patterns inherent in Ethereum transactions, and addresses the challenges of pre-training a BERT model for Ethereum with three practical and effective strategies, namely repetitiveness reduction, skew alleviation and heterogeneity modeling. Our empirical evaluation demonstrates that BERT4ETH outperforms state-of-the-art methods with significant enhancements in terms of the phishing account detection and de-anonymization tasks. The code for BERT4ETH is available at: https://github.com/git-disl/BERT4ETH.Comment: the Web conference (WWW) 202

Simulation Design of a Tomato Picking Manipulator

Simulation is an important way to verify the feasibility of design parameters and schemes for robots. Through simulation, this paper analyzes the effectiveness of the design parameters selected for a tomato picking manipulator, and verifies the rationality of the manipulator in motion planning for tomato picking. Firstly, the basic parameters and workspace of the manipulator were determined based on the environment of a tomato greenhouse; the workspace of the lightweight manipulator was proved as suitable for the picking operation through MATLAB simulation. Next, the maximum theoretical torque of each joint of the manipulator was solved through analysis, the joint motors were selected reasonably, and SolidWorks simulation was performed to demonstrate the rationality of the material selected for the manipulator and the strength design of the joint connectors. After that, the trajectory control requirements of the manipulator in picking operation were determined in view of the operation environment, and the feasibility of trajectory planning was confirmed with MATLAB. Finally, a motion control system was designed for the manipulator, according to the end trajectory control requirements, followed by the manufacturing of a prototype. The prototype experiment shows that the proposed lightweight tomato picking manipulator boasts good kinematics performance, and basically meets the requirements of tomato picking operation: the manipulator takes an average of 21 s to pick a tomato, and achieves a success rate of 78.67%

Immunomodulatory glycan LNFPIII alleviates hepatosteatosis and insulin resistance through direct and indirect control of metabolic pathways

Parasitic worms express host-like glycans to attenuate the immune response of human hosts. The therapeutic potential of this immunomodulatory mechanism in controlling metabolic dysfunction associated with chronic inflammation remains unexplored. We demonstrate here that administration of Lacto-N-fucopentaose III (LNFPIII), a LewisX containing immunomodulatory glycan found in human milk and on parasitic helminths, improves glucose tolerance and insulin sensitivity in diet-induced obese mice. This effect is mediated partly through increased Il-10 production by LNFPIII activated macrophages and dendritic cells, which reduces white adipose tissue inflammation and sensitizes the insulin response of adipocytes. Concurrently, LNFPIII treatment up-regulates nuclear receptor Fxr-α (or Nr1h4) to suppress lipogenesis in the liver, conferring protection against hepatosteatosis. At the signaling level, the extracellular signal-regulated kinase (Erk)-Ap1 pathway appears to mediate the effects of LNFPIII on both inflammatory and metabolic pathways. Our results suggest that LNFPIII may provide novel therapeutic approaches to treat metabolic diseases

Adaptive Deep Neural Network Inference Optimization with EENet

Well-trained deep neural networks (DNNs) treat all test samples equally during prediction. Adaptive DNN inference with early exiting leverages the observation that some test examples can be easier to predict than others. This paper presents EENet, a novel early-exiting scheduling framework for multi-exit DNN models. Instead of having every sample go through all DNN layers during prediction, EENet learns an early exit scheduler, which can intelligently terminate the inference earlier for certain predictions, which the model has high confidence of early exit. As opposed to previous early-exiting solutions with heuristics-based methods, our EENet framework optimizes an early-exiting policy to maximize model accuracy while satisfying the given per-sample average inference budget. Extensive experiments are conducted on four computer vision datasets (CIFAR-10, CIFAR-100, ImageNet, Cityscapes) and two NLP datasets (SST-2, AgNews). The results demonstrate that the adaptive inference by EENet can outperform the representative existing early exit techniques. We also perform a detailed visualization analysis of the comparison results to interpret the benefits of EENet

Establishment of predictive nomogram and web-based survival risk calculator for desmoplastic small round cell tumor: A propensity score-adjusted, population-based study

Desmoplastic small round cell tumor (DSRCT) is a rare undifferentiated malignant soft tissue tumor with a poor prognosis and a lack of consensus on treatment. This study’s objective was to build a nomogram based on clinicopathologic factors and an online survival risk calculator to predict patient prognosis and support therapeutic decision-making. A retrospective cohort analysis of the Surveillance, Epidemiology and End Results (SEER) database was performed for patients diagnosed with DSRCT between 2000 and 2019. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to identify the individual variables related to overall survival (OS) and cancer-specific survival (CSS), as well as to construct online survival risk calculators and nomogram survival models. The nomogram was employed to categorize patients into different risk groups, and the Kaplan-Meier method was utilized to determine the survival rate of each risk category. Propensity score matching (PSM) was used to assess survival with different therapeutic approaches. A total of 374 patients were included, and the median OS and CSS were 25 (interquartile range 21.9-28.1) months and 27 (interquartile range 23.6-30.3) months, respectively. The nomogram models demonstrated high predictive accuracy. PSM found that patients with triple-therapy had better CSS and OS than those who received surgery plus chemotherapy (median survival times: 49 vs 34 months and 49 vs 35 months, respectively). The nomogram successfully predicted the DSRCT patients survival rate. This approach could assist doctors in evaluating prognoses, identifying high-risk populations, and implementing personalized therapy

Cosmology from weak lensing peaks and minima with Subaru Hyper Suprime-Cam survey first-year data

We present cosmological constraints derived from peak counts, minimum counts, and the angular power spectrum of the Subaru Hyper Suprime-Cam first-year (HSC Y1) weak lensing shear catalog. Weak lensing peak and minimum counts contain non-Gaussian information and hence are complementary to the conventional two-point statistics in constraining cosmology. In this work, we forward-model the three summary statistics and their dependence on cosmology, using a suite of $N$-body simulations tailored to the HSC Y1 data. We investigate systematic and astrophysical effects including intrinsic alignments, baryon feedback, multiplicative bias, and photometric redshift uncertainties. We mitigate the impact of these systematics by applying cuts on angular scales, smoothing scales, statistic bins, and tomographic redshift bins. By combining peaks, minima, and the power spectrum, assuming a flat-$\Lambda$CDM model, we obtain $S_{8} \equiv \sigma_8\sqrt{\Omega_m/0.3}= 0.810^{+0.022}_{-0.026}$, a 35\% tighter constraint than that obtained from the angular power spectrum alone. Our results are in agreement with other studies using HSC weak lensing shear data, as well as with Planck 2018 cosmology and recent CMB lensing constraints from the Atacama Cosmology Telescope and the South Pole Telescope

A multidimensional framework to quantify the effects of urbanization on avian breeding fitness

Urbanization has dramatically altered Earth's landscapes and changed a multitude of environmental factors. This has resulted in intense land-use change, and adverse consequences such as the urban heat island effect (UHI), noise pollution, and artificial light at night (ALAN). However, there is a lack of research on the combined effects of these environmental factors on life-history traits and fitness, and on how these interactions shape food resources and drive patterns of species persistence. Here, we systematically reviewed the literature and created a comprehensive framework of the mechanistic pathways by which urbanization affects fitness and thus favors certain species. We found that urbanization-induced changes in urban vegetation, habitat quality, spring temperature, resource availability, acoustic environment, nighttime light, and species behaviors (e.g., laying, foraging, and communicating) influence breeding choices, optimal time windows that reduce phenological mismatch, and breeding success. Insectivorous and omnivorous species that are especially sensitive to temperature often experience advanced laying behaviors and smaller clutch sizes in urban areas. By contrast, some granivorous and omnivorous species experience little difference in clutch size and number of fledglings because urban areas make it easier to access anthropogenic food resources and to avoid predation. Furthermore, the interactive effect of land-use change and UHI on species could be synergistic in locations where habitat loss and fragmentation are greatest and when extreme-hot weather events take place in urban areas. However, in some instances, UHI may mitigate the impact of land-use changes at local scales and provide suitable breeding conditions by shifting the environment to be more favorable for species' thermal limits and by extending the time window in which food resources are available in urban areas. As a result, we determined five broad directions for further research to highlight that urbanization provides a great opportunity to study environmental filtering processes and population dynamics

A diurnal serum lipid integrates hepatic lipogenesis and peripheral fatty acid utilization

Food intake increases the activity of hepatic de novo lipogenesis, which mediates the conversion of glucose to fats for storage or utilization. In mice, this program follows a circadian rhythm that peaks with nocturnal feeding1,2 and is repressed by Rev-erbα/β and an HDAC3-containing complex3–5 during the day. The transcriptional activators controlling rhythmic lipid synthesis in the dark cycle remain poorly defined. Disturbances in hepatic lipogenesis are also associated with systemic metabolic phenotypes6–8, suggesting that lipogenesis in the liver communicates with peripheral tissues to control energy substrate homeostasis. Here we identify a PPARδ-dependent de novo lipogenic pathway in the liver that modulates fat utilization by muscle via a circulating lipid. The nuclear receptor PPARδ controls diurnal expression of lipogenic genes in the dark/feeding cycle. Liver-specific PPARδ activation increases, while hepatocyte-Ppard deletion reduces, muscle fatty acid (FA) uptake. Unbiased metabolite profiling identifies PC(18:0/18:1), or 1-stearoyl-2-oleoyl-sn-glycero-3-phosphocholine (SOPC), as a serum lipid regulated by diurnal hepatic PPARδ activity. PC(18:0/18:1) reduces postprandial lipid levels and increases FA utilization through muscle PPARα. High fat feeding diminishes rhythmic production of PC(18:0/18:1), whereas PC(18:0/18:1) administration in db/db mice improves metabolic homeostasis. These findings reveal an integrated regulatory circuit coupling lipid synthesis in the liver to energy utilization in muscle by coordinating the activity of two closely related nuclear receptors. These data implicate alterations in diurnal hepatic PPARδ-PC(18:0/18:1) signaling in metabolic disorders including obesity