Multi-step Jailbreaking Privacy Attacks on ChatGPT

With the rapid progress of large language models (LLMs), many downstream NLP tasks can be well solved given good prompts. Though model developers and researchers work hard on dialog safety to avoid generating harmful content from LLMs, it is still challenging to steer AI-generated content (AIGC) for the human good. As powerful LLMs are devouring existing text data from various domains (e.g., GPT-3 is trained on 45TB texts), it is natural to doubt whether the private information is included in the training data and what privacy threats can these LLMs and their downstream applications bring. In this paper, we study the privacy threats from OpenAI's model APIs and New Bing enhanced by ChatGPT and show that application-integrated LLMs may cause more severe privacy threats ever than before. To this end, we conduct extensive experiments to support our claims and discuss LLMs' privacy implications.Comment: Work in progres

Clinical spectrum and gene mutations in a Chinese cohort with anoctaminopathy

Recessive mutations in anoctamin-5 (ANO5) are causative for limb-girdle muscular dystrophy (LGMD) 2L and non-dysferlin Miyoshi-like distal myopathy (MMD3). ANDS mutations are highly prevalent in European countries; however it is not common in patients of Asian origin, and there is no data regarding the Chinese population. We retrospectively reviewed the clinical manifestations and gene mutations of Chinese patients with anoctaminopathy. A total of five ANDS mutations including four novel mutations and one reported mutation were found in four patients from three families. No hotspot mutation was found. Three patients presented with presymptomatic hyperCKemia and one patient had limb muscle weakness. Muscle imaging of lower limbs showed preferential adductor magnus and medial gastrocnemius involvement. No hotspot mutation has been identified in Chinese patients to date. (C) 2019 Elsevier B.V. All rights reserved.Peer reviewe

The General Ensemble Biogeochemical Modeling System (GEMS) and its Applications to Agricultural Systems in the United States

The General Ensemble Biogeochemical Modeling System (GEMS) (Liu, 2009; Liu et al., 2004c) was developed to integrate well-established ecosystem biogeochemical models with various spatial databases for the simulations of biogeochemical cycles over large areas. Figure 18.1 shows the overall structure of the GEMS. Some of the key components are described below. General Ensemble Biogeochemical Modeling System (GEMS) 310 Multiple Underlying Biogeochemical Models 310 Monte Carlo Simulations 311 Model Inputs: Management Practices and Others 311 Model Outputs 311 Data Assimilation 311 Simulation of Agricultural Practices: EDCM as an Example 312 Net Primary Production (NPP) and Improvements in Crop Genetics and Agronomics 312 Soil Carbon Dynamics 312 Impacts of Soil Erosion and Deposition 313 CH4 and N2O Fluxes 313 Study Areas and Modeling Design 314 Study Areas 314 Nebraska Eddy Flux Tower Sites 314 Regional Applications: Mississippi Valley and Prairie Potholes 315 Modeling Design 315 Results 316 Impacts of Management Practices on SOC at Site Scale 316 Quantification of Regional Carbon Stocks and GHG Fluxes 317 Prairie Pothole Region 317 Mississippi Valley 319 Discussion 32

Loss-of-function mutations in Lysyl-tRNA synthetase cause various leukoencephalopathy phenotypes

Objective: To expand the clinical spectrum of lysyl-tRNA synthetase (KARS) gene–related diseases, which so far includes Charcot-Marie-Tooth disease, congenital visual impairment and microcephaly, and nonsyndromic hearing impairment. Methods: Whole-exome sequencing was performed on index patients from 4 unrelated families with leukoencephalopathy. Candidate pathogenic variants and their cosegregation were confirmed by Sanger sequencing. Effects of mutations on KARS protein function were examined by aminoacylation assays and yeast complementation assays. Results: Common clinical features of the patients in this study included impaired cognitive ability, seizure, hypotonia, ataxia, and abnormal brain imaging, suggesting that the CNS involvement is the main clinical presentation. Six previously unreported and 1 known KARS mutations were identified and cosegregated in these families. Two patients are compound heterozygous for missense mutations, 1 patient is homozygous for a missense mutation, and 1 patient harbored an insertion mutation and a missense mutation. Functional and structural analyses revealed that these mutations impair aminoacylation activity of lysyl-tRNA synthetase, indicating that de- fective KARS function is responsible for the phenotypes in these individuals. Conclusions: Our results demonstrate that patients with loss-of-function KARS mutations can manifest CNS disorders, thus broadening the phenotypic spectrum associated with KARS-related disease

IL-34 Expression Is Reduced in Hashimoto's Thyroiditis and Associated With Thyrocyte Apoptosis

Hashimoto's thyroiditis (HT) is a common autoimmune disease accompanied by lymphocyte infiltration and thyroid tissue destruction. IL-34 was first described in 2008, and its involvement in the development of many autoimmune diseases has been recently identified. However, whether IL-34 is a regulatory factor in HT is unclear. Here, we demonstrate that IL-34 is expressed on thyroid follicular epithelial cells and that IL-34 expression is significantly reduced in thyroid tissue in patients with HT and spontaneous autoimmune thyroiditis (SAT) models. Serum IL-34 levels in patients with HT are also significantly reduced. In addition, IL-34 is associated with thyroid autoantibodies in both thyroid tissue and serum. Furthermore, our data show that IL-34 participates in the apoptosis resistance of thyrocytes in HT induced by CSF-1R and may be a potential indicator for evaluating thyrocyte damage

Forest Gap Extraction Based on Convolutional Neural Networks and Sentinel-2 Images

As a type of small-scale disturbance, forest gap and its accurate extraction are of great significance to monitor forest long-term dynamics, to choose forest recovery mode and to predict forest recovery pace. Currently, airborne LiDAR and high-resolution multi-spectral data are commonly used to accurately classify forest gaps, but they are costly to acquire and have limited time and space availability. In contrast, the Sentinel-2 multi-spectral data with a 10 m spatial resolution overcomes these drawbacks in forest gap extraction. In this work, an integrated framework that combines multi-source remote sensing, machine learning and deep learning to extract forest gap in wide regions was proposed and tested in three sites. First, LiDAR, Sentinel series and random forest (RF) algorithm were synergized to produce a canopy height product in model training site. On this basis, samples for forest canopy, forest gap and non-such were identified from LiDAR-derived canopy height model (CHM) and Sentinel-based canopy height inversion (HI) data to train forest gap extraction models by applying the Deep Forest (DF) and Convolutional Neural Networks (CNN) algorithms, followed by a comparison of the accuracy and the transferability among the four models (DF-CHM, DF-HI, CNN-CHM and CNN-HI). The results indicated that the R2 and RMSE of Sentinel-based canopy height retrievals were estimated at 0.63, and 7.85 m respectively, the difference in the mean height and standard deviation between HI and CHM was 0.03 m and 4.7 m respectively. And there was a spatial agreement of about 98.60% between the HI-identified samples and the CHM-identified samples, with an agreement of 54.89% for the forest gap class. The CNN-HI model had the highest accuracy in both transfer learning test sites, with an overall accuracy (OA) of 0.85 and 0.87, Kappa coefficient at 0.78 and 0.81, respectively, proving that it has good transferability. Conversely, the DF-based models generally gave poorer accuracy and transferability. This study demonstrates that combining Sentinel-2 multi-spectral data and CNN algorithm is feasible and effective in forest gap extraction applications over wide regions