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The power density spectrum (PDS) is a powerful tool to study light curves of gamma-ray bursts (GRBs). We show the average PDS and individual PDS analysis with GRB data from the Hard X-ray Modulation Telescope (also named Insight-HXMT). The values of the power-law index of the average PDS ( ${\alpha }_{\bar{P}}$ ) for long GRBs (LGRBs) vary from 1.58 to 1.29 (for 100–245, 245–600, and 600–2000 keV). The Insight-HXMT data allow us to extend the energy of the LGRBs up to 2000 keV, and a relation between ${\alpha }_{\bar{P}}$ and energy E is obtained: ${\alpha }_{\bar{P}}\propto {E}^{-0.09}$ (8–2000 keV). We first systematically investigate the average PDS and individual PDSs for short GRBs (SGRBs), and obtain ${\alpha }_{\bar{P}}\propto {E}^{-0.07}$ (8–1000 keV), where the values of ${\alpha }_{\bar{P}}$ vary from 1.86 to 1.34. The distribution of the power-law index of an individual PDS ( α ) of an SGRB is consistent with that of an LGRB, and the α value for the group with a dominant timescale (the bent power law) is higher than that for the group with no dominant timescale (the single power law). Both LGRBs and SGRBs show similar α and ${\alpha }_{\bar{P}}$ , which indicates that they may be the result of similar stochastic processes. Typical values of the dominant timescale τ for LGRBs and SGRBs are 1.58 s and 0.02 s, respectively. It seems that τ varies in proportion to the duration of GRBs T _90 , with a relation $\tau \propto {T}_{90}^{0.86}$ . The GRB light curve may result from superposing a number of pulses with different timescales. No periodic or quasi-periodic signal above the 3 σ significance threshold is found in our sample.

The milestone discovery of GW170817-GRB 170817A-AT 2017gfo has shown that gravitational waves (GWs) could be produced during the merger of a neutron star–neutron star/black hole and that in electromagnetic (EM) waves, a gamma-ray burst (GRB) and a kilonova (KN) are generated in sequence after the merger. Observationally, however, EM properties before the merger phase are still unclear. Here we report a peculiar precursor in a KN-associated long-duration GRB 211211A, providing evidence of the EM before the merger. This precursor lasts ∼0.2 s, and the waiting time between the precursor and the main burst is ∼1 s, comparable to that between GW170817 and GRB 170817A. The spectrum of the precursor could be well fit with a nonthermal cutoff power-law model instead of a blackbody. In particular, a ∼22 Hz quasiperiodic oscillation candidate (∼3 σ ) is detected in the precursor. These temporal and spectral properties indicate that this precursor is probably produced by a catastrophic flare accompanied with magnetoelastic or crustal oscillations of a magnetar in a binary compact merger. The strong magnetic field of the magnetar can also account for the prolonged duration of GRB 211211A. However, it poses a challenge to reconcile the rather short lifetime of a magnetar with the rather long spiraling time of a binary neutron star system only by the GW radiation before the merger.

While mitochondria in different tissues have distinct preferences for energy sources, they are flexible in utilizing competing substrates for metabolism according to physiological and nutritional circumstances. However, the regulatory mechanisms and significance of metabolic flexibility are not completely understood. Here, we report that the deletion of Ptpmt1, a mitochondria-based phosphatase, critically alters mitochondrial fuel selection – the utilization of pyruvate, a key mitochondrial substrate derived from glucose (the major simple carbohydrate), is inhibited, whereas the fatty acid utilization is enhanced. Ptpmt1 knockout does not impact the development of the skeletal muscle or heart. However, the metabolic inflexibility ultimately leads to muscular atrophy, heart failure, and sudden death. Mechanistic analyses reveal that the prolonged substrate shift from carbohydrates to lipids causes oxidative stress and mitochondrial destruction, which in turn results in marked accumulation of lipids and profound damage in the knockout muscle cells and cardiomyocytes. Interestingly, Ptpmt1 deletion from the liver or adipose tissue does not generate any local or systemic defects. These findings suggest that Ptpmt1 plays an important role in maintaining mitochondrial flexibility and that their balanced utilization of carbohydrates and lipids is essential for both the skeletal muscle and the heart despite the two tissues having different preferred energy sources.

Abstract Amino acid-mediated metabolism is one of the key catabolic and anabolic processes involved in diverse cellular functions. However, the role of the semi-essential amino acid arginine in normal and malignant hematopoietic cell development is poorly understood. Here we report that a continuous supply of exogenous arginine is required for the maintenance/function of normal hematopoietic stem cells (HSCs). Surprisingly, knockout of Slc7a3 (CAT3), a major L-arginine transporter, does not affect HSCs in steady-state or under stress. Although Slc7a3 is highly expressed in naïve and activated CD8 T cells, neither T cell development nor activation/proliferation is impacted by Slc7a3 depletion. Furthermore, the Slc7a3 deletion does not attenuate leukemia development driven by Pten loss or the oncogenic Ptpn11 E76K mutation. Arginine uptake assays reveal that L-arginine uptake is not disrupted in Slc7a3 knockout cells. These data suggest that extracellular arginine is critically important for HSCs, but CAT3 is dispensable for normal hematopoiesis and leukemogenesis.

Myelodysplastic syndromes (MDS) are heterogeneous hematopoietic disorders that are incurable with conventional therapy. Their incidence is increasing with global population aging. Although many genetic, epigenetic, splicing, and metabolic aberrations have been identified in patients with MDS, their clinical features are quite similar. Here, we show that hypoxia-independent activation of hypoxia-inducible factor 1α (HIF1A) signaling is both necessary and sufficient to induce dysplastic and cytopenic MDS phenotypes. The HIF1A transcriptional signature is generally activated in MDS patient bone marrow stem/progenitors. Major MDS-associated mutations (Dnmt3a, Tet2, Asxl1, Runx1, and Mll1) activate the HIF1A signature. Although inducible activation of HIF1A signaling in hematopoietic cells is sufficient to induce MDS phenotypes, both genetic and chemical inhibition of HIF1A signaling rescues MDS phenotypes in a mouse model of MDS. These findings reveal HIF1A as a central pathobiologic mediator of MDS and as an effective therapeutic target for a broad spectrum of patients with MDS.Significance: We showed that dysregulation of HIF1A signaling could generate the clinically relevant diversity of MDS phenotypes by functioning as a signaling funnel for MDS driver mutations. This could resolve the disconnection between genotypes and phenotypes and provide a new clue as to how a variety of driver mutations cause common MDS phenotypes. Cancer Discov; 8(11); 1438-57. ©2018 AACR. See related commentary by Chen and Steidl, p. 1355 This article is highlighted in the In This Issue feature, p. 1333.

As a distributed machine learning method, federated learning (FL) has the advantage of naturally protecting data privacy. It keeps data locally and trains local models through local data to protect the privacy of local data. The federated learning method effectively solves the problem of artificial Smart data islands and privacy protection issues. However, existing research shows that attackers may still steal user information by analyzing the parameters in the federated learning training process and the aggregation parameters on the server side. To solve this problem, differential privacy (DP) techniques are widely used for privacy protection in federated learning. However, adding Gaussian noise perturbations to the data degrades the model learning performance. To address these issues, this paper proposes a differential privacy federated learning scheme based on adaptive Gaussian noise (DPFL-AGN). To protect the data privacy and security of the federated learning training process, adaptive Gaussian noise is specifically added in the training process to hide the real parameters uploaded by the client. In addition, this paper proposes an adaptive noise reduction method. With the convergence of the model, the Gaussian noise in the later stage of the federated learning training process is reduced adaptively. This paper conducts a series of simulation experiments on real MNIST and CIFAR-10 datasets, and the results show that the DPFL-AGN algorithm performs better compared to the other algorithms. Graphic Abstract [ABSTRACT FROM AUTHOR]

Incorrect decision-making in financial institutions is very likely to cause financial crises. In recent years, many studies have demonstrated that artificial intelligence techniques can be used as alternative methods for credit scoring. Previous studies showed that prediction models built using hybrid approaches perform better than single approaches. In addition, feature selection or instance selection techniques should be incorporated into building prediction models to improve the prediction performance. In this study, we integrate feature selection, instance selection, and decision tree techniques to propose a new approach to predicting credit approval. Experimental results obtained using the survey data show that our proposed approach is superior to the other five traditional machine learning approaches in the measures. In addition, our approach has a lower cost effect than the traditional five methods. That is, the proposed approach generates fewer costs, such as money loss, than the traditional five approaches.