AdaptDHM: Adaptive Distribution Hierarchical Model for Multi-Domain CTR Prediction

Large-scale commercial platforms usually involve numerous business domains for diverse business strategies and expect their recommendation systems to provide click-through rate (CTR) predictions for multiple domains simultaneously. Existing promising and widely-used multi-domain models discover domain relationships by explicitly constructing domain-specific networks, but the computation and memory boost significantly with the increase of domains. To reduce computational complexity, manually grouping domains with particular business strategies is common in industrial applications. However, this pre-defined data partitioning way heavily relies on prior knowledge, and it may neglect the underlying data distribution of each domain, hence limiting the model's representation capability. Regarding the above issues, we propose an elegant and flexible multi-distribution modeling paradigm, named Adaptive Distribution Hierarchical Model (AdaptDHM), which is an end-to-end optimization hierarchical structure consisting of a clustering process and classification process. Specifically, we design a distribution adaptation module with a customized dynamic routing mechanism. Instead of introducing prior knowledge for pre-defined data allocation, this routing algorithm adaptively provides a distribution coefficient for each sample to determine which cluster it belongs to. Each cluster corresponds to a particular distribution so that the model can sufficiently capture the commonalities and distinctions between these distinct clusters. Extensive experiments on both public and large-scale Alibaba industrial datasets verify the effectiveness and efficiency of AdaptDHM: Our model achieves impressive prediction accuracy and its time cost during the training stage is more than 50% less than that of other models

Dynamic Task Scheduling in Remote Sensing Data Acquisition from Open-Access Data Using CloudSim

With the rapid development of cloud computing and network technologies, large-scale remote sensing data collection tasks are receiving more interest from individuals and small and medium-sized enterprises. Large-scale remote sensing data collection has its challenges, including less available node resources, short collection time, and lower collection efficiency. Moreover, public remote data sources have restrictions on user settings, such as access to IP, frequency, and bandwidth. In order to satisfy users’ demand for accessing public remote sensing data collection nodes and effectively increase the data collection speed, this paper proposes a TSCD-TSA dynamic task scheduling algorithm that combines the BP neural network prediction algorithm with PSO-based task scheduling algorithms. Comparative experiments were carried out using the proposed task scheduling algorithms on an acquisition task using data from Sentinel2. The experimental results show that the MAX-MAX-PSO dynamic task scheduling algorithm has a smaller fitness value and a faster convergence speed

Metagenomic next-generation sequencing in a diagnosis of Pneumocystis pneumonia in an X-linked immunodeficient child: a case report

BackgroundThe diagnosis of Pneumocystis pneumonia (PCP) remains challenging in certain specific clinical situations. Metagenomic next-generation sequencing (mNGS), as a novel diagnostic method, may help in the diagnosis of PCP.Case presentationA 6-month-old male child developed acute pneumonia and sepsis. This child had previously suffered from Escherichia coli septicemia and was cured. However, the fever and dyspnea relapsed. Blood tests revealed a low lymphocyte count (0.69 × 109/L) and acute inflammatory markers such as high-level procalcitonin (8.0 ng/ml) and C-reactive protein (19 mg/dl). Chest imaging showed inflammation and decreased translucency in both lungs but no thymus shadow. Various serology tests, the 1,3-beta-D-glucan test, culture, as well as sputum smear failed to detect any pathogens. mNGS with blood helped identify 133 specific nucleic acid sequences of Pneumocystis jirovecii, suggesting an infection with this pathogen. After treatment with trimethoprim-sulfamethoxazole for 5 days, the patient's condition improved, but the child still needed ventilator support. Unfortunately, the child died soon after because of respiratory failure after his parents decided to abandon treatment. The family declined an autopsy on the child, and therefore, an anatomical diagnosis could not be obtained. Whole-exome sequencing suggested X-linked immunodeficiency. A hemizygous mutation of c.865c > t (p.r289*) was detected in the IL2RG gene, which was inherited from the mother (heterozygous state).ConclusionThis case report highlights the value of mNGS in diagnosing PCP when conventional diagnostic methods fail to identify the agent. Early onset of recurrent infectious diseases may indicate the presence of an immunodeficiency disease, for which timely genetic analysis and diagnosis are crucial

Successive-Stage Speed Limit on Exit Ramp Upstream of Direct-Type Freeway in China

The first objective of this study is to analyze a successive-stage speed limit model developed for vehicles along the exit upstream ramp of direct-type freeway in China. This paper 1 explains the necessity to implement speed limit to the exit ramp upstream, 2 analyzes whether speed limit is related to the length of the deceleration lane, vehicle type, saturation, and turning ratio and 3 proposes a speed prediction model and calibrates speed-limit sign validity model and establishes successive-stage speed limit model. The results. Δν 85 ≥ 10 illustrates the necessity of the using speed limit on the exit ramp. Speed-deceleration lane length curve presents two trends bounded by 200 m, so the speed limit should be in accordance with the deceleration length. Speed-small vehicle curve closing to speed-large vehicle curve presents that the vehicle type is not the factor of the speed limit. After curve fitting and polynomial regression, saturation is considered to be the most influential factor of speed. Speed-saturation prediction model and calibrated speed-limit sign validity model are built through linearization. According to the above results, successivestage speed limit model is established. An exit ramp was implemented to verify the feasibility and validity of the model

Resistance to immune checkpoint inhibitors in advanced lung cancer: Clinical characteristics, potential prognostic factors and next strategy

BackgroundImmune checkpoint inhibitors (ICIs) have shown unprecedented clinical benefit in cancer immunotherapy and are rapidly transforming the practice of advanced lung cancer. However, resistance routinely develops in patients treated with ICIs. We conducted this retrospective study to provide an overview on clinical characteristics of ICI resistance, optimal treatment beyond disease progression after prior exposure to immunotherapy, as well as potential prognostic factors of such resistance.Methods190 patients diagnosed with unresectable lung cancer who received at least one administration of an anti-programmed cell death 1 (PD-1)/anti-programmed cell death-ligand 1(PD-L1) at any treatment line at Zhongshan Hospital Fudan University between Sep 2017 and December 2019 were enrolled in our study. Overall survival (OS) and progression-free survival (PFS) were analyzed. Levels of plasma cytokines were evaluated for the prognostic value of ICI resistance.ResultsWe found that EGFR/ALK/ROS1 mutation and receiving ICI treatment as second-line therapy were risk factors associated with ICI resistance. Patients with bone metastasis at baseline had a significantly shorter PFS1 time when receiving initial ICI treatment. Whether or not patients with oligo-progression received local treatment seemed to have no significant effect on PFS2 time. Systemic therapies including chemotherapy and anti-angiogenic therapy rather than continued immunotherapy beyond ICI resistance had significant effect on PFS2 time. TNF, IL-6 and IL-8 were significantly elevated when ICI resistance. Lower plasma TNF level and higher plasma IL-8 level seemed to be significantly associated with ICI resistance. A nomogram was established to prognosis the clinical outcome of patients treated with ICIs.ConclusionPatients with EGFR/ALK/ROS1 mutation, or those receiving ICI treatment as second-line therapy had higher risk of ICI resistance. Patients with bone metastasis had poor prognosis during immunotherapy. For those patients with oligo-progression after ICI resistance, combination with local treatment did not lead to a significantly longer PFS2 time. Chemotherapy and anti-angiogenic therapy rather than continued immunotherapy beyond ICI resistance had significant effect on PFS2 time. Levels of plasma cytokines including TNF, IL-6 and IL-8 were associated with ICI resistance

Robust Representation Learning for Unified Online Top-K Recommendation

In large-scale industrial e-commerce, the efficiency of an online recommendation system is crucial in delivering highly relevant item/content advertising that caters to diverse business scenarios. However, most existing studies focus solely on item advertising, neglecting the significance of content advertising. This oversight results in inconsistencies within the multi-entity structure and unfair retrieval. Furthermore, the challenge of retrieving top-k advertisements from multi-entity advertisements across different domains adds to the complexity. Recent research proves that user-entity behaviors within different domains exhibit characteristics of differentiation and homogeneity. Therefore, the multi-domain matching models typically rely on the hybrid-experts framework with domain-invariant and domain-specific representations. Unfortunately, most approaches primarily focus on optimizing the combination mode of different experts, failing to address the inherent difficulty in optimizing the expert modules themselves. The existence of redundant information across different domains introduces interference and competition among experts, while the distinct learning objectives of each domain lead to varying optimization challenges among experts. To tackle these issues, we propose robust representation learning for the unified online top-k recommendation. Our approach constructs unified modeling in entity space to ensure data fairness. The robust representation learning employs domain adversarial learning and multi-view wasserstein distribution learning to learn robust representations. Moreover, the proposed method balances conflicting objectives through the homoscedastic uncertainty weights and orthogonality constraints. Various experiments validate the effectiveness and rationality of our proposed method, which has been successfully deployed online to serve real business scenarios.Comment: 14 pages, 6 figures, submitted to ICD

Protective Effect of RNase on Unilateral Nephrectomy-Induced Postoperative Cognitive Dysfunction in Aged Mice

Postoperative cognitive dysfunction (POCD) is a common complication after surgery, especially for elderly patients. Administration of RNase has been reported to exhibit neuroprotective effects in acute stroke. However, the potential role of RNase on POCD is unknown. Therefore, we sought to investigate whether RNase treatment could mitigate unilateral nephrectomy induced-cognitive deficit in aged mice. In the present study, twelve-month-old mice were administered RNase or an equal amount of normal saline perioperatively. All mice underwent Morris Water Maze (MWM) training 3 times per day for 7 days to acclimatize them to the water maze before surgical operation, and testing on days 1, 3 and 7 after surgery. We found that perioperative administration of RNase: 1) attenuated unilateral nephrectomy-induced cognitive impairment at day 3 after surgery; 2) reduced the hippocampal cytokines mRNA production and serum cytokines protein production at day 1 and day 7 (for MCP-1) after surgery, and; 3) inhibited hippocampal apoptosis as indicated by cleaved caspase-3 western blot and TUNEL staining at day 1 after surgery. In addition, a trend decrease of total serum RNA levels was detected in the RNase treated group after surgery compared with the untreated group. Further, our protocol of RNase administration had no impact on the arterial blood gas analysis right after surgery, kidney function and mortality rate at the observed days postoperatively. In conclusion, perioperative RNase treatment attenuated unilateral nephrectomy-induced cognitive impairment in aged mice

A Higher Correlation of HCV Core Antigen with CD4+ T Cell Counts Compared with HCV RNA in HCV/HIV-1 Coinfected Patients

Development of HCV infection is typically followed by chronic hepatitis C (CHC) in most patients, while spontaneous HCV viral clearance (SVC) occurs in only a minority of subjects. Compared with the widespread application of HCV RNA testing by quantitative RT-PCR technique, HCV core antigen detection may be an alternative indicator in the diagnosis of hepatitis C virus infections and in monitoring the status of infectious individuals. However, the correlation and differences between these two indicators in HCV infection need more investigation, especially in patients coinfected by HIV-1. In this study, a total of 354 anti-HCV and/or anti-HIV serum positive residents from a village of central China were enrolled. Besides HCV-related hepatopathic variables including clinical status, ALT, AST, anti-HCV Abs, as well as the altered CD4+/CD8+ T cell counts, HCV core antigen and HCV viral load were also measured. The concentration of serum HCV core antigen was highly correlated with level of HCV RNA in CHC patients with or without HIV-1 coinfection. Of note, HCV core antigen concentration was negatively correlated with CD4+ T cell count, while no correlation was found between HCV RNA level and CD4+ T cell count. Our findings suggested that quantitative detection of plasma HCV core antigen may be an alternative indicator of HCV RNA qPCR assay when evaluating the association between HCV replication and host immune status in HCV/HIV-1 coinfected patients

Anesthetic Propofol Attenuates the Isoflurane-Induced Caspase-3 Activation and Aβ Oligomerization

Accumulation and deposition of β-amyloid protein (Aβ) are the hallmark features of Alzheimer's disease. The inhalation anesthetic isoflurane has been shown to induce caspase activation and increase Aβ accumulation. In addition, recent studies suggest that isoflurane may directly promote the formation of cytotoxic soluble Aβ oligomers, which are thought to be the key pathological species in AD. In contrast, propofol, the most commonly used intravenous anesthetic, has been reported to have neuroprotective effects. We therefore set out to compare the effects of isoflurane and propofol alone and in combination on caspase-3 activation and Aβ oligomerization in vitro and in vivo. Naïve and stably-transfected H4 human neuroglioma cells that express human amyloid precursor protein, the precursor for Aβ; neonatal mice; and conditioned cell culture media containing secreted human Aβ40 or Aβ42 were treated with isoflurane and/or propofol. Here we show for the first time that propofol can attenuate isoflurane-induced caspase-3 activation in cultured cells and in the brain tissues of neonatal mice. Furthermore, propofol-mediated caspase inhibition occurred when there were elevated levels of Aβ. Finally, isoflurane alone induces Aβ42, but not Aβ40, oligomerization, and propofol can inhibit the isoflurane-mediated oligomerization of Aβ42. These data suggest that propofol may mitigate the caspase-3 activation by attenuating the isoflurane-induced Aβ42 oligomerization. Our findings provide novel insights into the possible mechanisms of isoflurane-induced neurotoxicity that may aid in the development of strategies to minimize potential adverse effects associated with the administration of anesthetics to patients

Introduction to Special Issue - In-depth study of air pollution sources and processes within Beijing and its surrounding region (APHH-2 Beijing)

Abstract. The Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-Beijing) programme is an international collaborative project focusing on understanding the sources, processes and health effects of air pollution in the Beijing megacity. APHH-Beijing brings together leading China and UK research groups, state-of-the-art infrastructure and air quality models to work on four research themes: (1) sources and emissions of air pollutants; (2) atmospheric processes affecting urban air pollution; (3) air pollution exposure and health impacts; and (4) interventions and solutions. Themes 1 and 2 are closely integrated and support Theme 3, while Themes 1-3 provide scientific data for Theme 4 to develop cost-effective air pollution mitigation solutions. This paper provides an introduction to (i) the rationale of the APHH-Beijing programme, and (ii) the measurement and modelling activities performed as part of it. In addition, this paper introduces the meteorology and air quality conditions during two joint intensive field campaigns - a core integration activity in APHH-Beijing. The coordinated campaigns provided observations of the atmospheric chemistry and physics at two sites: (i) the Institute of Atmospheric Physics in central Beijing, and (ii) Pinggu in rural Beijing during 10 November – 10 December 2016 (winter) and 21 May- 22 June 2017 (summer). The campaigns were complemented by numerical modelling and automatic air quality and low-cost sensor observations in the Beijing megacity. In summary, the paper provides background information on the APHH-Beijing programme, and sets the scene for more focussed papers addressing specific aspects, processes and effects of air pollution in Beijing