Machine Learning for Prostate Histopathology Assessment

Pathology reporting on radical prostatectomy (RP) specimens is essential to post-surgery patient care. However, current pathology interpretation of RP sections is typically qualitative and subject to intra- and inter-observer variability, which challenges quantitative and repeatable reporting of lesion grade, size, location, and spread. Therefore, we developed and validated a software platform that can automatically detect and grade cancerous regions on whole slide images (WSIs) of whole-mount RP sections to support quantitative and visual reporting. Our study used hæmatoxylin- and eosin-stained WSIs from 299 whole-mount RP sections from 71 patients, comprising 1.2 million 480μm×480μm regions-of-interest (ROIs) covering benign and cancerous tissues which contain all clinically relevant grade groups. Each cancerous region was annotated and graded by an expert genitourinary pathologist. We used a machine learning approach with 7 different classifiers (3 non-deep learning and 4 deep learning) to classify: 1) each ROI as cancerous vs. non-cancerous, and 2) each cancerous ROI as high- vs. low-grade. Since recent studies found some subtypes beyond Gleason grade to have independent prognostic value, we also used one deep learning method to classify each cancerous ROI from 87 RP sections of 25 patients as each of eight subtypes to support further clinical pathology research on this topic. We cross-validated each system against the expert annotations. To compensate for the staining variability across different WSIs from different patients, we computed the tissue component map (TCM) using our proposed adaptive thresholding algorithm to label nucleus pixels, global thresholding to label lumen pixels, and assigning the rest as stroma/other. Fine-tuning AlexNet with ROIs of the TCM yielded the best results for prostate cancer (PCa) detection and grading, with areas under the receiver operating characteristic curve (AUCs) of 0.98 and 0.93, respectively, followed by fine-tuned AlexNet with ROIs of the raw image. For subtype grading, fine-tuning AlexNet with ROIs of the raw image yielded AUCs ≥ 0.7 for seven of eight subtypes. To conclude, deep learning approaches outperformed non-deep learning approaches for PCa detection and grading. The TCMs provided the primary cues for PCa detection and grading. Machine learning can be used for subtype grading beyond the Gleason grading system

LSTM-Aided Hybrid Random Access Scheme for 6G Machine Type Communication Networks

In this paper, an LSTM-aided hybrid random access scheme (LSTMH-RA) is proposed to support diverse quality of service (QoS) requirements in 6G machine-type communication (MTC) networks, where massive MTC (mMTC) devices and ultra-reliable low latency communications (URLLC) devices coexist. In the proposed LSTMH-RA scheme, mMTC devices access the network via a timing advance (TA)-aided four-step procedure to meet massive access requirement, while the access procedure of the URLLC devices is completed in two steps coupled with the mMTC devices' access procedure to reduce latency. Furthermore, we propose an attention-based LSTM prediction model to predict the number of active URLLC devices, thereby determining the parameters of the multi-user detection algorithm to guarantee the latency and reliability access requirements of URLLC devices.We analyze the successful access probability of the LSTMH-RA scheme. Numerical results show that, compared with the benchmark schemes, the proposed LSTMH-RA scheme can significantly improve the successful access probability, and thus satisfy the diverse QoS requirements of URLLC and mMTC devices

Enhancing Semantic Code Search with Multimodal Contrastive Learning and Soft Data Augmentation

Code search aims to retrieve the most semantically relevant code snippet for a given natural language query. Recently, large-scale code pre-trained models such as CodeBERT and GraphCodeBERT learn generic representations of source code and have achieved substantial improvement on code search task. However, the high-quality sequence-level representations of code snippets have not been sufficiently explored. In this paper, we propose a new approach with multimodal contrastive learning and soft data augmentation for code search. Multimodal contrastive learning is used to pull together the representations of code-query pairs and push apart the unpaired code snippets and queries. Moreover, data augmentation is critical in contrastive learning for learning high-quality representations. However, only semantic-preserving augmentations for source code are considered in existing work. In this work, we propose to do soft data augmentation by dynamically masking and replacing some tokens in code sequences to generate code snippets that are similar but not necessarily semantic-preserving as positive samples for paired queries. We conduct extensive experiments to evaluate the effectiveness of our approach on a large-scale dataset with six programming languages. The experimental results show that our approach significantly outperforms the state-of-the-art methods. We also adapt our techniques to several pre-trained models such as RoBERTa and CodeBERT, and significantly boost their performance on the code search task

Efficient Multi-key FHE with short extended ciphertexts and less public parameters

Multi-Key Full Homomorphic Encryption (MKFHE) can perform arbitrary operations on encrypted data under different public keys (users), and the final ciphertext can be jointly decrypted by all involved users. Therefore, MKFHE has natural advantages and application value in security multi-party computation (MPC). The MKFHE scheme based on Brakerski-Gentry-Vaikuntanathan (BGV) inherits the advantages of BGV FHE scheme in aspects of encrypting a ring element, the ciphertext/plaintext ratio, and supporting the Chinese Remainder Theorem (CRT)-based ciphertexts packing technique. However some weaknesses also exist such as large ciphertexts and keys, and complicated process of generating evaluation keys. In this paper, we present an efficient BGV-type MKFHE scheme. Firstly, we construct a nested ciphertext extension for BGV and separable ciphertext extension for Gentry-Sahai-Waters (GSW), which can reduce the size of the extended ciphertexts about a half. Secondly, we apply the hybrid homomorphic multiplication between RBGV ciphertext and RGSW ciphertext to the generation process of evaluation keys, which can significantly reduce the amount of input/output ciphertexts and improve the efficiency. Finally, we construct a directed decryption protocol which allows the evaluated ciphertext to be decrypted by any target user, thereby enhancing the ability of data owner to control their own plaintext, and abolish the limitation in current MKFHE schemes that the evaluated ciphertext can only be decrypted by users involved in homomorphic evaluation

Intelligent Reflecting Surface Aided Power Control for Physical-Layer Broadcasting

Reconfigurable intelligent surface (RIS), a recently introduced technology for future wireless com-munication systems, enhances the spectral and energy efficiency by intelligently adjusting the propaga-tion conditions between a base station (BS) and mobile equipments (MEs). An RIS consists of manylow-cost passive reflecting elements to improve the quality of the received signal. In this paper, westudy the problem of power control at the BS for the RIS aided physical-layer broadcasting. Our goalis to minimize the transmit power at the BS by jointly designing the transmit beamforming at the BSand the phase shifts of the passive elements at the RIS. Furthermore, to help validate the proposedoptimization methods, we derive lower bounds to quantify the average transmit power at the BS as afunction of the number of MEs, the number of RIS elements, and the number of antennas at the BS.The simulation results demonstrated that the average transmit power at the BS is close to the lowerbound in an RIS aided system, and is significantly lower than the average transmit power in conventionalschemes without the RIS

Two round multiparty computation via Multi-key fully homomorphic encryption with faster homomorphic evaluations

Multi-key fully homomorphic encryption (MKFHE) allows computations on ciphertexts encrypted by different users (public keys), and the results can be jointly decrypted using the secret keys of all the users involved. The NTRU-based scheme is an important alternative to post-quantum cryptography, but the NTRU-based MKFHE has the following drawbacks, which cause it inefficient in scenarios such as secure multi-party computing (MPC). One is the relinearization technique used for key switching takes up most of the time of the scheme’s homomorphic evaluation, the other is that each user needs to decrypt in sequence, which makes the decryption process complicated. We propose an efficient leveled MKFHE scheme, which improves the efficiency of homomorphic evaluations, and constructs a two-round (MPC) protocol based on this. Firstly, we construct an efficient single key FHE with less relinearization operations. We greatly reduces the number of relinearization operations in homomorphic evaluations process by separating the homomorphic multiplication and relinearization techniques. Furthermore, the batching technique and a specialization of modulus can be applied to our scheme to improve the efficiency. Secondly, the efficient single-key homomorphic encryption scheme proposed in this paper is transformed into a multi-key vision according to the method in LTV12 scheme. Finally, we construct a distributed decryption process which can be implemented independently for all participating users, and reduce the number of interactions between users in the decryption process. Based on this, a two-round MPC protocol is proposed. Experimental analysis shows that the homomorphic evaluation of the single-key FHE scheme constructed in this paper is 2.4 times faster than DHS16, and the MKFHE scheme constructed in this paper can be used to implement a two-round MPC protocol effectively, which can be applied to secure MPC between multiple users under the cloud computing environment

Ultra-Conformal Skin Electrodes With Synergistically Enhanced Conductivity For Long-Time and Low-Motion Artifact Epidermal Electrophysiology

Accurate and imperceptible monitoring of electrophysiological signals is of primary importance for wearable healthcare. Stiff and bulky pregelled electrodes are now commonly used in clinical diagnosis, causing severe discomfort to users for long-time using as well as artifact signals in motion. Here, we report a ~100 nm ultra-thin dry epidermal electrode that is able to conformably adhere to skin and accurately measure electrophysiological signals. It showed low sheet resistance (~24 Ω/sq, 4142 S/cm), high transparency, and mechano-electrical stability. The enhanced optoelectronic performance was due to the synergistic effect between graphene and poly (3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS), which induced a high degree of molecular ordering on PEDOT and charge transfer on graphene by strong π-π interaction. Together with ultra-thin nature, this dry epidermal electrode is able to accurately monitor electrophysiological signals such as facial skin and brain activity with low-motion artifact, enabling human-machine interfacing and long-time mental/physical health monitoring

Genome-wide identification and comprehensive analysis of tubby-like protein gene family in multiple crops

IntroductionThe highly conserved tubby-like proteins (TLPs) play key roles in animal neuronal development and plant growth. The abiotic stress tolerance function of TLPs has been widely explored in plants, however, little is known about comparative studies of TLPs within crops.MethodsBioinformatic identification, phylogenetic analysis, Cis-element analysis, expression analysis, Cis-element analysis, expression analysis and so on were explored to analysis the TLP gene family of multiple crops.ResultsIn this study, a comprehensive analysis of TLP genes were carried out in seven crops to explore whether similar function of TLPs in rice could be achieved in other crops. We identified 20, 9, 14, 11, 12, 35, 14 and 13 TLP genes in Glycine max, Hordeum vulgare, Sorghum bicolor, Arabidopsis thaliana, Oryza sativa Japonica, Triticum aestivum, Setaria italic and Zea mays, respectively. All of them were divided into two groups and ten orthogroups (Ors) based on amino acids. A majority of TLP genes had two domains, tubby-like domain and F-box domain, while members of Or5 only had tubby-like domain. In addition, Or5 had more exons and shorter DNA sequences, showing that characteristics of different Ors reflected the differentiated function and feature of TLP genes in evolutionary process, and Or5 was the most different from the other Ors. Besides, we recognized 25 cis-elements in the promoter of TLP genes and explored multiple new regulation pathway of TLPs including light and hormone response. The bioinformatic and transcriptomic analysis implied the stresses induced expression and possible functional redundancy of TLP genes. We detected the expression level of 6 OsTLP genes at 1 to 6 days after seed germination in rice, and the most obvious changes in these days were appeared in OsTLP10 and OsTLP12.DiscussionCombined yeast two-hybrid system and pull down assay, we suggested that the TLP genes of Or1 may have similar function during seed germination in different species. In general, the results of comprehensive analysis of TLP gene family in multiple species provide valuable evolutionary and functional information of TLP gene family which are useful for further application and study of TLP genes