Check Me If You Can: Detecting ChatGPT-Generated Academic Writing using CheckGPT

With ChatGPT under the spotlight, utilizing large language models (LLMs) for academic writing has drawn a significant amount of discussions and concerns in the community. While substantial research efforts have been stimulated for detecting LLM-Generated Content (LLM-content), most of the attempts are still in the early stage of exploration. In this paper, we present a holistic investigation of detecting LLM-generate academic writing, by providing a dataset, evidence, and algorithms, in order to inspire more community effort to address the concern of LLM academic misuse. We first present GPABenchmark, a benchmarking dataset of 600,000 samples of human-written, GPT-written, GPT-completed, and GPT-polished abstracts of research papers in CS, physics, and humanities and social sciences (HSS). We show that existing open-source and commercial GPT detectors provide unsatisfactory performance on GPABenchmark, especially for GPT-polished text. Moreover, through a user study of 150+ participants, we show that it is highly challenging for human users, including experienced faculty members and researchers, to identify GPT-generated abstracts. We then present CheckGPT, a novel LLM-content detector consisting of a general representation module and an attentive-BiLSTM classification module, which is accurate, transferable, and interpretable. Experimental results show that CheckGPT achieves an average classification accuracy of 98% to 99% for the task-specific discipline-specific detectors and the unified detectors. CheckGPT is also highly transferable that, without tuning, it achieves ~90% accuracy in new domains, such as news articles, while a model tuned with approximately 2,000 samples in the target domain achieves ~98% accuracy. Finally, we demonstrate the explainability insights obtained from CheckGPT to reveal the key behaviors of how LLM generates texts

Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications

To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e.g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation. However, anomaly detection for these seasonal KPIs with various patterns and data quality has been a great challenge, especially without labels. In this paper, we proposed Donut, an unsupervised anomaly detection algorithm based on VAE. Thanks to a few of our key techniques, Donut greatly outperforms a state-of-arts supervised ensemble approach and a baseline VAE approach, and its best F-scores range from 0.75 to 0.9 for the studied KPIs from a top global Internet company. We come up with a novel KDE interpretation of reconstruction for Donut, making it the first VAE-based anomaly detection algorithm with solid theoretical explanation.Comment: 12 pages (including references), 17 figures, submitted to WWW 2018: The 2018 Web Conference, April 23--27, 2018, Lyon, France. The contents discarded from the conference version due to the 9-page limitation are also included in this versio

Combined Adjuvant of Poly I:C Improves Antitumor Effects of CAR-T Cells

Chimeric antigen receptor modified T cells (CAR-T) therapy is an emerging immunotherapy against malignancies. However, only limited success was obtained in solid tumors. Polyinosinic-polycytidylic acid (poly I:C), ligand of TLR3, mediates innate immune and adaptive immune and shows broad antitumor effect on many types of cancer. In the present study, we combined EGFRvIII-targeted CAR-T cells with poly I:C treatment and evaluated the synergic antitumor effect in vitro and in immunocompetent mice bearing subcutaneous colon or orthotopic breast cancer xenografts. Poly I:C significantly promoted more IL-2 and IFN γ production as well as higher lytic activity of CAR-T cells. Upon systemic administration in vivo, CAR-T cells obviously suppressed tumor growth, and poly I:C significantly enhanced the suppression. Further study showed that poly I:C exerted antitumor effect dependent on type I IFNs. In addition, poly I:C decreased myeloid-derived suppressor cells (MDSC) number in peripheral blood and spleen, and attenuated the immunosuppressive activity of MDSC on proliferation and cytolytic function of CAR-T. Depletion of MDSC with anti-Gr1 Ab further increased the antitumor effect of CAR-T cells plus poly I:C treatment. In conclusion, CAR-T treatment combined with intratumoral delivery of poly I:C resulted in synergistic antitumor activity. We thus provide a rationale to translate this immunotherapeutic strategy to solid tumors

Association of Maternal Autoimmune Diseases With Risk of Mental Disorders in Offspring in Denmark

IMPORTANCE Maternal immune activation during pregnancy is associated with increased risks of several mental disorders in offspring during childhood, but little is known about how maternal autoimmune diseases during pregnancy are associated with mental health in offspring during and after childhood.OBJECTIVE To investigate the association between maternal autoimmune diseases before childbirth and risk of mental disorders among offspring up to early adulthood.DESIGN, SETTING, AND PARTICIPANTS This population-based nationwide cohort study used data from Danish national registers on singletons born in Denmark from 1978 to 2015 with up to 38 years of follow-up. Data analyses were conducted from March 1, 2020, through September 30, 2021.EXPOSURES Maternal autoimmune disease diagnosed before or during pregnancy according to the Danish National Patient Register.MAIN OUTCOMES AND MEASURES The main outcome was mental disorders, defined by hospital diagnoses, in offspring. Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% CIs for mental disorders.RESULTS Of the 2 254 234 singleton infants included in the study (median age, 16.7 years [IQR, 10.5-21.7 years]; 51.28% male), 2.26% were born to mothers with autoimmune diseases before childbirth. Exposed participants had an increased risk of overall mental disorders compared with their unexposed counterparts (HR, 1.16; 95% CI, 1.13-1.19; incidence, 9.38 vs 7.91 per 1000 person-years). Increased risks of overall mental disorders in offspring were seen in different age groups for type 1 diabetes (1-5 years: HR, 1.35 [95% CI, 1.17-1.57]; 6-18 years: HR, 1.24 [95% CI, 1.15-1.33]; >18 years: HR, 1.19 [95% CI, 1.09-1.30]) and rheumatoid arthritis (1-5 years: HR, 1.42 [95% CI, 1.16-1.74]; 6-18 years: HR, 1.19 [95% CI, 1.05436]; >18 years: HR, 1.28 [95% CI, 1.02-1.60]). Regarding specific mental disorders, increased risk after exposure to any maternal autoimmune disorder was observed for organic disorders (HR, 1.54; 95% CI, 1.21-1.94), schizophrenia (HR, 1.35; 95% CI, 1.21-1.51), obsessive-compulsive disorder (HR, 1.42; 95% CI, 1.24-1.63), mood disorders (HR, 1.12; 95% CI, 1.04-121), and a series of neurodevelopmental disorders (eg, childhood autism [HR, 1.21; 95% CI, 1.08436] and attention-deficit/hyperactivity disorder [HR, 1.19; 95% CI, 1.12-1.26]).CONCLUSIONS AND RELEVANCE In this cohort study in Denmark, prenatal exposure to maternal autoimmune diseases was associated with increased risks of overall and type-specific mental disorders in offspring. Maternal type 1 diabetes and rheumatoid arthritis during pregnancy were associated with offspring's mental health up to early adulthood. Individuals prenatally exposed to autoimmune disease may benefit from long-term surveillance for mental disorders.</p

Association of Maternal Autoimmune Diseases with Risk of Mental Disorders in Offspring in Denmark

Funding Information: László); grants 19410713500 and 2018SHZDZX01 from the Science and Technology Commission of Shanghai Municipality (Dr F. Li); grants GWV-10.1-XK07, 2020CXJQ01, and 2018YJRC03 from the Shanghai Municipal Commission of Health and Family Planning (Dr F Li); and grant 2018B030335001 from the Guangdong Key Project (Dr F Li) . Funding Information: Funding/Support: This study was supported by grant NNF18OC0052029 from the Novo Nordisk Foundation (Dr J. Li) ; grants DFF-6110-00019B, DFF-9039-00010B, and DFF-1030-00012B from the Danish Council for Independent Research (Dr J. Li); grant R275-A15770 from the Nordic Cancer Union (Dr J. Li); grant 2016 from the Karen Elise Jensens Fond (Dr J. Li); grants 81761128035, 81930095, and 82125032 (Dr F. Li) and grant 82073570 (Dr J. Li) from the National Natural Science Foundation of China; grant 20180306 from the Swedish Heart and Lung Foundation (Dr László); grant 2015-00837 from the Swedish Council for Working Life and Social Research (Dr Publisher Copyright: © 2022 American Medical Association. All rights reserved.Importance: Maternal immune activation during pregnancy is associated with increased risks of several mental disorders in offspring during childhood, but little is known about how maternal autoimmune diseases during pregnancy are associated with mental health in offspring during and after childhood. Objective: To investigate the association between maternal autoimmune diseases before childbirth and risk of mental disorders among offspring up to early adulthood. Design, Setting, and Participants: This population-based nationwide cohort study used data from Danish national registers on singletons born in Denmark from 1978 to 2015 with up to 38 years of follow-up. Data analyses were conducted from March 1, 2020, through September 30, 2021. Exposures: Maternal autoimmune disease diagnosed before or during pregnancy according to the Danish National Patient Register. Main Outcomes and Measures: The main outcome was mental disorders, defined by hospital diagnoses, in offspring. Cox proportional hazards regression was used to estimate hazard ratios (HRs) and 95% CIs for mental disorders. Results: Of the 2254234 singleton infants included in the study (median age, 16.7 years [IQR, 10.5-21.7 years]; 51.28% male), 2.26% were born to mothers with autoimmune diseases before childbirth. Exposed participants had an increased risk of overall mental disorders compared with their unexposed counterparts (HR, 1.16; 95% CI, 1.13-1.19; incidence, 9.38 vs 7.91 per 1000 person-years). Increased risks of overall mental disorders in offspring were seen in different age groups for type 1 diabetes (1-5 years: HR, 1.35 [95% CI, 1.17-1.57]; 6-18 years: HR, 1.24 [95% CI, 1.15-1.33]; >18 years: HR, 1.19 [95% CI, 1.09-1.30]) and rheumatoid arthritis (1-5 years: HR, 1.42 [95% CI, 1.16-1.74]; 6-18 years: HR, 1.19 [95% CI, 1.05-1.36]; >18 years: HR, 1.28 [95% CI, 1.02-1.60]). Regarding specific mental disorders, increased risk after exposure to any maternal autoimmune disorder was observed for organic disorders (HR, 1.54; 95% CI, 1.21-1.94), schizophrenia (HR, 1.35; 95% CI, 1.21-1.51), obsessive-compulsive disorder (HR, 1.42; 95% CI, 1.24-1.63), mood disorders (HR, 1.12; 95% CI, 1.04-1.21), and a series of neurodevelopmental disorders (eg, childhood autism [HR, 1.21; 95% CI, 1.08-1.36] and attention-deficit/hyperactivity disorder [HR, 1.19; 95% CI, 1.12-1.26]). Conclusions and Relevance: In this cohort study in Denmark, prenatal exposure to maternal autoimmune diseases was associated with increased risks of overall and type-specific mental disorders in offspring. Maternal type 1 diabetes and rheumatoid arthritis during pregnancy were associated with offspring's mental health up to early adulthood. Individuals prenatally exposed to autoimmune disease may benefit from long-term surveillance for mental disorders.Peer reviewe

Metagenomics-based exploration of key soil microorganisms contributing to continuously planted Casuarina equisetifolia growth inhibition and their interactions with soil nutrient transformation

Casuarina equisetifolia (C. equisetifolia) is an economically important forest tree species, often cultivated in continuous monoculture as a coastal protection forest. Continuous planting has gradually affected growth and severely restricted the sustainable development of the C. equisetifolia industry. In this study, we analyzed the effects of continuous planting on C. equisetifolia growth and explored the rhizosphere soil microecological mechanism from a metagenomic perspective. The results showed that continuous planting resulted in dwarfing, shorter root length, and reduced C. equisetifolia seedling root system. Metagenomics analysis showed that 10 key characteristic microorganisms, mainly Actinoallomurus, Actinomadura, and Mycobacterium, were responsible for continuously planted C. equisetifolia trees. Quantitative analysis showed that the number of microorganisms in these three genera decreased significantly with the increase of continuous planting. Gene function analysis showed that continuous planting led to the weakening of the environmental information processing-signal transduction ability of soil characteristic microorganisms, and the decrease of C. equisetifolia trees against stress. Reduced capacity for metabolism, genetic information processing-replication and repair resulted in reduced microbial propagation and reduced microbial quantity in the rhizosphere soil of C. equisetifolia trees. Secondly, amino acid metabolism, carbohydrate metabolism, glycan biosynthesis and metabolism, lipid metabolism, metabolism of cofactors and vitamins were all significantly reduced, resulting in a decrease in the ability of the soil to synthesize and metabolize carbon and nitrogen. These reduced capacities further led to reduced soil microbial quantity, microbial carbon and nitrogen, microbial respiration intensity, reduced soil enzyme nutrient cycling and resistance-related enzyme activities, a significant reduction in available nutrient content of rhizosphere soils, a reduction in the ion exchange capacity, and an impediment to C. equisetifolia growth. This study provides an important basis for the management of continuously planted C. equisetifolia plantations

On the Validation of a Multiple-Network Poroelastic Model Using Arterial Spin Labeling MRI Data

The Multiple-Network Poroelastic Theory (MPET) is a numerical model to characterize the transport of multiple fluid networks in the brain, which overcomes the problem of conducting separate analyses on individual fluid compartments and losing the interactions between tissue and fluids, in addition to the interaction between the different fluids themselves. In this paper, the blood perfusion results from MPET modeling are partially validated using cerebral blood flow (CBF) data obtained from arterial spin labeling (ASL) magnetic resonance imaging (MRI), which uses arterial blood water as an endogenous tracer to measure CBF. Two subjects—one healthy control and one patient with unilateral middle cerebral artery (MCA) stenosis are included in the validation test. The comparison shows several similarities between CBF data from ASL and blood perfusion results from MPET modeling, such as higher blood perfusion in the gray matter than in the white matter, higher perfusion in the periventricular region for both the healthy control and the patient, and asymmetric distribution of blood perfusion for the patient. Although the partial validation is mainly conducted in a qualitative way, it is one important step toward the full validation of the MPET model, which has the potential to be used as a testing bed for hypotheses and new theories in neuroscience research