FairFed: Enabling Group Fairness in Federated Learning

As machine learning algorithms become increasingly integrated in crucial decision-making scenarios, such as healthcare, recruitment, and risk assessment, there have been increasing concerns about the privacy and fairness of such systems. Federated learning has been viewed as a promising solution for collaboratively training of machine learning models among multiple parties while maintaining the privacy of their local data. However, federated learning also poses new challenges in mitigating the potential bias against certain populations (e.g., demographic groups), as this typically requires centralized access to the sensitive information (e.g., race, gender) of each data point. Motivated by the importance and challenges of group fairness in federated learning, in this work, we propose FairFed, a novel algorithm to enhance group fairness via a fairness-aware aggregation method, which aims to provide fair model performance across different sensitive groups (e.g., racial, gender groups) while maintaining high utility. This formulation can further provide more flexibility in the customized local debiasing strategies for each client. We build our FairFed algorithm around the secure aggregation protocol of federated learning. When running federated training on widely investigated fairness datasets, we demonstrate that our proposed method outperforms the state-of-the-art fair federated learning frameworks under a high heterogeneous sensitive attribute distribution. We also investigate the performance of FairFed on naturally distributed real-life data collected from different geographical locations or departments within an organization

\u3ci\u3eIn silico\u3c/i\u3e identification of genetic mutations conferring resistance to acetohydroxyacid synthase inhibitors: A case study of \u3ci\u3eKochia scoparia\u3c/i\u3e

Mutations that confer herbicide resistance are a primary concern for herbicide-based chemical control of invasive plants and are often under-characterized structurally and functionally. As the outcome of selection pressure, resistance mutations usually result from repeated long-term applications of herbicides with the same mode of action and are discovered through extensive field trials. Here we used acetohydroxyacid synthase (AHAS) of Kochia scoparia (KsAHAS) as an example to demonstrate that, given the sequence of a target protein, the impact of genetic mutations on ligand binding could be evaluated and resistance mutations could be identified using a biophysics-based computational approach. Briefly, the 3D structures of wild-type (WT) and mutated KsAHAS-herbicide complexes were constructed by homology modeling, docking and molecular dynamics simulation. The resistance profile of two AHAS-inhibiting herbicides, tribenuron methyl and thifensulfuron methyl, was obtained by estimating their binding affinity with 29 KsAHAS (1 WT and 28 mutated) using 6 molecular mechanical (MM) and 18 hybrid quantum mechanical/molecular mechanical (QM/MM) methods in combination with three structure sampling strategies. By comparing predicted resistance with experimentally determined resistance in the 29 biotypes of K. scoparia field populations, we identified the best method (i.e., MM-PBSA with single structure) out of all tested methods for the herbicide-KsAHAS system, which exhibited the highest accuracy (up to 100%) in discerning mutations conferring resistance or susceptibility to the two AHAS inhibitors. Our results suggest that the in silico approach has the potential to be widely adopted for assessing mutation-endowed herbicide resistance on a case-by-case basis

Enhanced Interfacial Electronic Transfer of BiVO4 Coupled with 2D g‐C3N4 for Visible‐light Photocatalytic Performance

A BiVO4/2D g‐C3N4 direct dual semiconductor photocatalytic system has been fabricated via electrostatic self‐assembly method of BiVO4 microparticle and g‐C3N4 nanosheet. According to experimental measurements and first‐principle calculations, the formation of built‐in electric field and the opposite band bending around the interface region in BiVO4/2D g‐C3N4 as well as the intimate contact between BiVO4 and 2D g‐C3N4 will lead to high separation efficiency of charge carriers. More importantly, the intensity of bulid‐in electric field is greatly enhanced due to the ultrathin nanosheet structure of 2D g‐C3N4. As a result, BiVO4/2D g‐C3N4 exhibits excellent photocatalytic performance with the 93.0% Rhodamine B (RhB) removal after 40 min visible light irradiation, and the photocatalytic reaction rate is about 22.7 and 10.3 times as high as that of BiVO4 and 2D g‐C3N4, respectively. In addition, BiVO4/2D g‐C3N4 also displays enhanced photocatalytic performance in the degradation of tetracycline (TC). It is expected that this work may provide insights into the understanding the significant role of built‐in electric field in heterostructure and fabricating highly efficient direct dual semiconductor systems

SeqAssist: A Novel Toolkit For Preliminary Analysis of Next-Generation Sequencing Data

Background: While next-generation sequencing (NGS) technologies are rapidly advancing, an area that lags behind is the development of efficient and user-friendly tools for preliminary analysis of massive NGS data. As an effort to fill this gap to keep up with the fast pace of technological advancement and to accelerate data-to-results turnaround, we developed a novel software package named SeqAssist ( Sequencing Assistant or SA). Results: SeqAssist takes NGS-generated FASTQ files as the input, employs the BWA-MEM aligner for sequence alignment, and aims to provide a quick overview and basic statistics of NGS data. It consists of three separate workflows: (1) the SA_RunStats workflow generates basic statistics about an NGS dataset, including numbers of raw, cleaned, redundant and unique reads, redundancy rate, and a list of unique sequences with length and read count; (2) the SA_Run2Ref workflow estimates the breadth, depth and evenness of genome-wide coverage of the NGS dataset at a nucleotide resolution; and (3) the SA_Run2Run workflow compares two NGS datasets to determine the redundancy (overlapping rate) between the two NGS runs. Statistics produced by SeqAssist or derived from SeqAssist output files are designed to inform the user: whether, what percentage, how many times and how evenly a genomic locus (i.e., gene, scaffold, chromosome or genome) is covered by sequencing reads, how redundant the sequencing reads are in a single run or between two runs. These statistics can guide the user in evaluating the quality of a DNA library prepared for RNA-Seq or genome (re-)sequencing and in deciding the number of sequencing runs required for the library. We have tested SeqAssist using a synthetic dataset and demonstrated its main features using multiple NGS datasets generated from genome re-sequencing experiments. Conclusions: SeqAssist is a useful and informative tool that can serve as a valuable assistant to a broad range of investigators who conduct genome re-sequencing, RNA-Seq, or de novo genome sequencing and assembly experiments

Ultrasound characteristics of abdominal vascular compression syndromes

Abdominal vascular compression syndrome (AVCS) is caused by the compression of abdominal blood vessels by adjacent structures or the compression of abdominal organs by neighboring blood vessels. Such compressions can result in a variety of clinical symptoms. They are not commonly seen in ultrasound practices, and their presence may have been underrecognized and underdiagnosed. This article reviews the clinical features, ultrasound characteristics, and diagnostic criteria of four types of AVCS, namely, celiac artery compression syndrome, renal vein compression syndrome, iliac vein compression syndrome, and superior mesenteric artery syndrome to increase awareness of these conditions among ultrasound practitioners. The ultrasound criteria for AVCS are primarily based on studies with small sample sizes, and therefore, it is important to exercise caution if these criteria are used

Occurrence of multi-mycotoxin in paddy rice in Guangdong Province

Objective To understand the contamination pattern of mycotoxin in paddy rice in the main rice-growing regions of Guangdong Province, and analyze the distribution difference of mycotoxin in different areas, so as to provide basis for the implementation of precise prevention and control measures. Methods A total of 120 paddy rice samples were collected from eight cities in the Pearl River Delta, northern, eastern and western Guangdong during 2018 and 2019, and were analyzed for 16 mycotoxins by multiple reaction monitoring mode of ultra performance liquid chromatography-tandem mass spectrometer. Results Among the 120 paddy rice samples, 19.17% (23/120) were positive for mycotoxins, and the main polluants were aflatoxins and fumonisins. FB1 were detected in 9.17% (11/120) of the samples, followed by 8.33% (10/120) for AFB1. Two samples had the AFB1 concentrations above the tolerance limit of 10 μg/kg. The detection values were 73.90 and 18.80 μg/kg, respectively. Among 6 trichothecene mycotoxins, only deoxynivalenol (1.67%, 2/120) and its acetyl derivatives[0.83% (1/120) for 3-Ac-DON and 0.83% (1/120) for 15-Ac-DON] were found. ZEN was found in 3.33% (4/120) of the samples. Additionally, 1.67% (2/120) of the paddy rice samples were positive for sterigmatocystin. The ochratoxin A, nivalenol, T-2 and HT-2 mycotoxins were not found in the paddy rice samples. The co-occurrence of two or more mycotoxins was confirmed in 8.33% (10/120) of the paddy rice samples, mainly combination was AFB1 and other mycotoxins. The contamination patterns were different in the eight cities. The paddy rice samples from Zhanjiang was mainly contaminated by FB1, FB2, DON and 3-Ac-DON. Samples from Heyuan were mainly contaminated by AFB1, AFB2, sterigmatocystin, FB1 and FB2. The concentration levels of ZEN, DON and 3-Ac-DON were relatively higher in samples from Shaoguan. Conclusion The paddy rice samples from Guangdong Province were contaminated by multiple mycotoxins, and the pollution patterns were different in different areas. In terms of the co-occurence of mycotoxins, some measures should be conducted to assess the exposure risk, reduce the damage, and protect the consumers food safety