Semi-Supervised Semantic Segmentation Methods for UW-OCTA Diabetic Retinopathy Grade Assessment

People with diabetes are more likely to develop diabetic retinopathy (DR) than healthy people. However, DR is the leading cause of blindness. At present, the diagnosis of diabetic retinopathy mainly relies on the experienced clinician to recognize the fine features in color fundus images. This is a time-consuming task. Therefore, in this paper, to promote the development of UW-OCTA DR automatic detection, we propose a novel semi-supervised semantic segmentation method for UW-OCTA DR image grade assessment. This method, first, uses the MAE algorithm to perform semi-supervised pre-training on the UW-OCTA DR grade assessment dataset to mine the supervised information in the UW-OCTA images, thereby alleviating the need for labeled data. Secondly, to more fully mine the lesion features of each region in the UW-OCTA image, this paper constructs a cross-algorithm ensemble DR tissue segmentation algorithm by deploying three algorithms with different visual feature processing strategies. The algorithm contains three sub-algorithms, namely pre-trained MAE, ConvNeXt, and SegFormer. Based on the initials of these three sub-algorithms, the algorithm can be named MCS-DRNet. Finally, we use the MCS-DRNet algorithm as an inspector to check and revise the results of the preliminary evaluation of the DR grade evaluation algorithm. The experimental results show that the mean dice similarity coefficient of MCS-DRNet v1 and v2 are 0.5161 and 0.5544, respectively. The quadratic weighted kappa of the DR grading evaluation is 0.7559. Our code will be released soon

Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks

In this work, besides improving prediction accuracy, we study whether personalization could bring robustness benefits to backdoor attacks. We conduct the first study of backdoor attacks in the pFL framework, testing 4 widely used backdoor attacks against 6 pFL methods on benchmark datasets FEMNIST and CIFAR-10, a total of 600 experiments. The study shows that pFL methods with partial model-sharing can significantly boost robustness against backdoor attacks. In contrast, pFL methods with full model-sharing do not show robustness. To analyze the reasons for varying robustness performances, we provide comprehensive ablation studies on different pFL methods. Based on our findings, we further propose a lightweight defense method, Simple-Tuning, which empirically improves defense performance against backdoor attacks. We believe that our work could provide both guidance for pFL application in terms of its robustness and offer valuable insights to design more robust FL methods in the future. We open-source our code to establish the first benchmark for black-box backdoor attacks in pFL: https://github.com/alibaba/FederatedScope/tree/backdoor-bench.Comment: KDD 202

Wpływ ekologicznych działań badawczo-rozwojowych na emisje SO2 w Chinach – dane z panelowego modelu progowego

Previous studies on the effectiveness of improving sustainable development have acknowledged the importance of domestic research and development (R&D) activities. However, these studies remain general and ambiguous because they assume that all R&D activities are related to energy-saving and sustainable development. The corresponding empirical evidence is scabrous and ambiguous. In this paper, we focus on the effect of green innovation R&D activities on SO2 emission which is an important greenhouse gas affect global climate change and eco-civilization. Considering that there is heterogeneity exists in the innovation activities, the R&D activities are divided into three performers with two purposes. The empirical results based on a Chinese inter-provincial dataset of 2000-2016 suggest that the green innovation R&D activities are crucial for the reduction of the SO2 emission. However, the innovation R&D activities of different purposes and performers show statistically differentiated effects on SO2 emission. The major positive effect of green innovation R&D activities on SO2 emissions reduction is mainly from enterprises and utility-type of R&D activities. A further study based on the panel threshold also indicates that effects of green innovation R&D activities on SO2 emissions are nonlinear, depending on the technology absorptive ability.Dotychczasowe badania nad zrównoważonym rozwojem potwierdziły znaczenie krajowych działań badawczo-rozwojowych (B + R). Jednak badania te pozostają ogólne i niejednoznaczne, ponieważ zakładają, że wszystkie działania B + R są związane z energooszczędnością i zrównoważonym rozwojem. Odpowiednie dowody empiryczne są niejednoznaczne. W artykule skupiamy się na wpływie działań badawczo-rozwojowych związanych z zielonymi innowacjami na emisję SO2, który jest ważnym gazem cieplarnianym, wpływającym na globalne zmiany klimatyczne. Biorąc pod uwagę, że istnieje heterogeniczność działań innowacyjnych, działalność B + R wskazano 3 aktorów z 2 celami. Wyniki empiryczne oparte na chińskim międzyprowincjalnym zbiorze danych z lat 2000-2016 sugerują, że działania badawczo-rozwojowe związane z zielonymi innowacjami są kluczowe dla redukcji emisji SO2. Jednak innowacyjne działania o różnych celach i różnych wykonawcach wykazują statystycznie zróżnicowany wpływ na emisję SO2. Główny pozytywny wpływ działań B + R w zakresie zielonych innowacji na redukcję emisji SO2 wynika głównie z działalności przedsiębiorstw i działalności B + R o charakterze użytkowym. Dalsze badanie oparte na panelu wskazuje również, że wpływ działań badawczo-rozwojowych związanych z zielonymi innowacjami na emisje SO2 jest nieliniowy, w zależności od zdolności absorpcyjnej technologii

OTOv3: Automatic Architecture-Agnostic Neural Network Training and Compression from Structured Pruning to Erasing Operators

Compressing a predefined deep neural network (DNN) into a compact sub-network with competitive performance is crucial in the efficient machine learning realm. This topic spans various techniques, from structured pruning to neural architecture search, encompassing both pruning and erasing operators perspectives. Despite advancements, existing methods suffers from complex, multi-stage processes that demand substantial engineering and domain knowledge, limiting their broader applications. We introduce the third-generation Only-Train-Once (OTOv3), which first automatically trains and compresses a general DNN through pruning and erasing operations, creating a compact and competitive sub-network without the need of fine-tuning. OTOv3 simplifies and automates the training and compression process, minimizes the engineering efforts required from users. It offers key technological advancements: (i) automatic search space construction for general DNNs based on dependency graph analysis; (ii) Dual Half-Space Projected Gradient (DHSPG) and its enhanced version with hierarchical search (H2SPG) to reliably solve (hierarchical) structured sparsity problems and ensure sub-network validity; and (iii) automated sub-network construction using solutions from DHSPG/H2SPG and dependency graphs. Our empirical results demonstrate the efficacy of OTOv3 across various benchmarks in structured pruning and neural architecture search. OTOv3 produces sub-networks that match or exceed the state-of-the-arts. The source code will be available at https://github.com/tianyic/only_train_once.Comment: 39 pages. Due to the page dim limitation, the full appendix is attached here https://tinyurl.com/otov3appendix. Recommend to zoom-in for finer details. arXiv admin note: text overlap with arXiv:2305.1803

A Floating-Point Secure Implementation of the Report Noisy Max with Gap Mechanism

The Noisy Max mechanism and its variations are fundamental private selection algorithms that are used to select items from a set of candidates (such as the most common diseases in a population), while controlling the privacy leakage in the underlying data. A recently proposed extension, Noisy Top-k with Gap, provides numerical information about how much better the selected items are compared to the non-selected items (e.g., how much more common are the selected diseases). This extra information comes at no privacy cost but crucially relies on infinite precision for the privacy guarantees. In this paper, we provide a finite-precision secure implementation of this algorithm that takes advantage of integer arithmetic.Comment: 21 page

Comparison of Structural Characteristics and Major Biological Activities of Polysaccharides from Soybean and Natto

In this study, crude polysaccharides from soybean and natto were prepared by water extraction followed by ethanol precipitation. The chemical compositions, structural characteristics, water solubility, water-holding capacity (WHC) and fat-binding capacity (FBC) of soybean and natto polysaccharides were analyzed. Their in vitro antioxidant, hypoglycemic, and lipid-lowering activities were compared and analyzed. The results showed that the content of uronic acid was significantly higher in natto polysaccharide than in soybean polysaccharide (P < 0.05). The molecular masses of soybean and natto polysaccharides were 5.256 and 33.532 ku, respectively, and the monosaccharide compositions of soybean and natto polysaccharides were similar in the types but different in the proportions of monosaccharide. The surface of soybean polysaccharide was rough, whereas the surface of natto polysaccharide was smooth and dense. The water solubility of natto polysaccharide was 2.04 times as high as that of soybean polysaccharide, and the FBC was 2.99 times as high as that of natto polysaccharide. Natto polysaccharide exhibited better antioxidant activity, with half maximal inhibitory concentration (IC50) of (0.049 ± 0.015) and (2.640 ± 0.072) mg/mL for scavenging of 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical and 2,2’-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) cation radical, respectively. The IC50 for the inhibition of α-amylase activity by natto polysaccharide was (3.297 ± 0.395) mg/mL. Natto polysaccharide had significantly higher in vitro hypoglycemic activity (P < 0.05), stronger cholate binding capacity and in vitro hypolipidemic activity than soybean polysaccharide. This study has provided an important theoretical basis for the structural analysis and biological activity evaluation of natto polysaccharide

Comparative efficacy and pharmacological mechanism of Chinese patent medicines against anthracycline-induced cardiotoxicity: An integrated study of network meta-analysis and network pharmacology approach

BackgroundThis study aimed to evaluate the efficacy of Chinese patent medicines (CPMs) combined with dexrazoxane (DEX) against anthracycline-induced cardiotoxicity (AIC) and further explore their pharmacological mechanism by integrating the network meta-analysis (NMA) and network pharmacology approach.MethodsWe searched for clinical trials on the efficacy of DEX + CPMs for AIC until March 10, 2023 (Database: PubMed, Embase, Cochrane Library, Chinese National Knowledge Infrastructure, China Science and Technology Journal and China Online Journals). The evaluating outcomes were cardiac troponin I (cTnI) level, creatine kinase MB (CK-MB) level, left ventricular ejection fraction (LVEF) value, and electrocardiogram (ECG) abnormal rate. Subsequently, the results of NMA were further analyzed in combination with network pharmacology.ResultsWe included 14 randomized controlled trials (RCTs) and 1 retrospective cohort study (n = 1,214), containing six CPMs: Wenxinkeli (WXKL), Cinobufotalin injection (CI), Shenqifuzheng injection (SQFZ), Shenmai injection (SM), Astragalus injection (AI) and AI + CI. The NMA was implemented in Stata (16.0) using the mvmeta package. Compared with using DEX only, DEX + SM displayed the best effective for lowering cTnI level (MD = −0.44, 95%CI [−0.56, −0.33], SUCRA 93.4%) and improving LVEF value (MD = 14.64, 95%CI [9.36, 19.91], SUCRA 98.4%). DEX + SQFZ showed the most effectiveness for lowering CK-MB level (MD = −11.57, 95%CI [−15.79, −7.35], SUCRA 97.3%). And DEX + AI + CI has the highest effectiveness for alleviating ECG abnormalities (MD = −2.51, 95%CI [−4.06, −0.96], SUCRA 96.8%). So that we recommended SM + DEX, SQFZ + DEX, and DEX + AI + CI as the top three effective interventions against AIC. Then, we explored their pharmacological mechanism respectively. The CPMs' active components and AIC-related targets were screened to construct the component-target network. The potential pathways related to CPMs against AIC were determined by KEGG. For SM, we identified 118 co-targeted genes of active components and AIC, which were significantly enriched in pathways of cancer pathways, EGFR tyrosine kinase inhibitor resistance and AGE-RAGE signaling pathway in diabetic complications. For SQFZ, 41 co-targeted genes involving pathways of microRNAs in cancer, Rap1 signaling pathway, MAPK signaling pathway, and lipid and atherosclerosis. As for AI + CI, 224 co-targeted genes were obtained, and KEGG analysis showed that the calcium signaling pathway plays an important role except for the consistent pathways of SM and SQFZ in anti-AIC.ConclusionsDEX + CPMs might be positive efficacious interventions from which patients with AIC will derive benefits. DEX + SM, DEX + SQFZ, and DEX + AI + CI might be the preferred intervention for improving LVEF value, CK-MB level, and ECG abnormalities, respectively. And these CPMs play different advantages in alleviating AIC by targeting multiple biological processes