Cryptanalysis of SPEEDY

SPEEDY is a family of ultra-lightweight block ciphers designed by Leander et al. at CHES 2021. There are three recommended variants denoted as SPEEDY-$r$-192 with $r$∈{5,6,7}. All of them support the 192-bit block and the 192-bit key. The main focus during its design is to ensure hardware-aware low latency, thus, whether it is designed to have enough security is worth to be studied. Recently, the full-round security of SPEEDY-7-192 is announced to be broken by Boura et al. at EUROCRYPT 2023 under the chosen-ciphertext setting, where a round-reduced attack on SPEEDY-6-192 is also proposed. However, no valid attack on SPEEDY-5-192 is given due to its more restricted security parameters. Up to now, the best key recovery attack on this variant only covers 3 rounds proposed by Rohit et al. at AFRICACRYPT 2022. In this paper, we give three full-round attacks on SPEEDY-7-192. Using the divide-and-conquer strategy and other new proposed techniques, we found a 5.5-round differential distinguisher which can be used to mount the first chosen-plaintext full-round key recovery attack. With a similar strategy, we also found a 5-round linear distinguisher which leads to the first full-round attack under the known-plaintext setting. Meanwhile, the 5.5-round differential distinguisher also helps us slightly improve the full-round attack in the chosen-ciphertext setting compared with the previous result. Besides, we also present a 4-round differential attack on SPEEDY-5-192, which is the best attack on this variant in terms of the number of rounds so far. A faster key recovery attack covering the same rounds is also given using a differential-linear distinguisher. Both attacks cannot threaten the full round security of SPEEDY-5-192

Remodeling of the periodontal ligament and alveolar bone during axial tooth movement in mice with type 1 diabetes

ObjectivesTo observe the elongation of the axial tooth movement in the unopposed rodent molar model with type 1 diabetes mellitus and explore the pathological changes of periodontal ligament and alveolar bone, and their correlation with tooth axial movement.MethodsThe 80 C57BL/6J mice were randomly divided into the streptozotocin(STZ)-injected group (n = 50) and the control group (n = 30). Mice in the streptozotocin(STZ)-injected group were injected intraperitoneal with streptozotocin (STZ), and mice in the control group were given intraperitoneal injection of equal doses of sodium citrate buffer. Thirty mice were randomly selected from the successful models as the T1DM group. The right maxillary molar teeth of mice were extracted under anesthesia, and allowed mandibular molars to super-erupt. Mice were sacrificed at 0, 3, 6,9, and 12 days. Tooth elongation and bone mineral density (BMD) were evaluated by micro-CT analysis(0,and 12 days mice). Conventional HE staining, Masson staining and TRAP staining were used to observe the changes in periodontal tissue(0, 3, 6, 9, and 12 days mice). The expression differences of SPARC, FGF9, BMP4, NOGGIN, and type I collagen were analyzed by RT-qPCR.ResultsAfter 12 days of tooth extraction, our data showed significant super-eruption of mandibular mouse molars of the two groups. The amount of molar super-eruption in the T1DM group was 0.055mm( ± 0.014mm), and in the control group was 0.157( ± 0.017mm). The elongation of the T1DM mice was less than that of the control mice(P<0.001). It was observed that the osteoclasts and BMD increased gradually in both groups over time. Compared with the control group, the collagen arrangement was more disordered, the number of osteoclasts was higher (P<0.05), and the increase of bone mineral density was lower(2.180 ± 0.007g/cm3 vs. 2.204 ± 0.006g/cm3, P<0.001) in the T1DM group. The relative expression of SPARC, FGF9, BMP4, and type I collagen in the two groups increased with the extension of tooth extraction time while NOGGIN decreased. The relative expression of all of SPARC, FGF9, BMP4, and type I collagen in the T1DM group were significantly lower, and the expression of NOGGIN was higher than that in the control group (P<0.05).ConclusionThe axial tooth movement was inhibited in type 1 diabetic mice. The result may be associated with the changes of periodontal ligament osteoclastogenic effects and alveolar bone remodeling regulated by the extracellular matrix and osteogenesis-related factors

Aberrant Brain Regional Homogeneity and Functional Connectivity of Entorhinal Cortex in Vascular Mild Cognitive Impairment: A Resting-State Functional MRI Study

The aim of this study was to investigate changes in regional homogeneity (ReHo) and the functional connectivity of the entorhinal cortex (EC) in vascular mild cognitive impairment (VaMCI) and to evaluate the relationships between such changes and neuropsychological measures in VaMCI individuals. In all, 31 patients with VaMCI and 32 normal controls (NCs) underwent rs-fMRI. Differences in whole-brain ReHo and seed-based bilateral EC functional connectivity (EC-FC) were determined. Pearson's correlation was used to evaluate the relationships between regions with significant group differences and different neuropsychological measures. Vascular mild cognitive impairment (VaMCI) patients had lower scores in Mini-mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) and higher ones in Activity of Daily Living (ADL) (p < 0.05). Vascular mild cognitive impairment (VaMCI) individuals had significantly lower ReHo in the left cerebellum and right lentiform nucleus than NCs (P < 0.05, TFCE FWE correction). Vascular mild cognitive impairment (VaMCI) subjects showed significant decreases in the FC of the right EC in the right inferior frontal gyrus, right middle frontal gyrus, bilateral pre-central gyrus, and right post-central/superior parietal lobules (P < 0.05, TFCE FWE correction). Significant positive correlations were found between ReHo and MoCA scores for the right lentiform nucleus (r = 0.37, P < 0.05). The right post-central/superior parietal lobules showed a significant positive correlation between right EC-FC and MoCA scores (r = 0.37, P < 0.05). Patterns in ReHo and EC-FC changes in VaMCI patients and their correlations with neuropsychological measures may be a pathophysiological foundation of cognitive impairment, which may aid the early diagnosis of VaMCI

Action Recognition by an Attention-Aware Temporal Weighted Convolutional Neural Network

Research in human action recognition has accelerated significantly since the introduction of powerful machine learning tools such as Convolutional Neural Networks (CNNs). However, effective and efficient methods for incorporation of temporal information into CNNs are still being actively explored in the recent literature. Motivated by the popular recurrent attention models in the research area of natural language processing, we propose the Attention-aware Temporal Weighted CNN (ATW CNN) for action recognition in videos, which embeds a visual attention model into a temporal weighted multi-stream CNN. This attention model is simply implemented as temporal weighting yet it effectively boosts the recognition performance of video representations. Besides, each stream in the proposed ATW CNN framework is capable of end-to-end training, with both network parameters and temporal weights optimized by stochastic gradient descent (SGD) with back-propagation. Our experimental results on the UCF-101 and HMDB-51 datasets showed that the proposed attention mechanism contributes substantially to the performance gains with the more discriminative snippets by focusing on more relevant video segments

Quantitative tumor burden imaging parameters of the spleen at MRI for predicting treatment response in patients with acute leukemia

Objectives: To study the value of standardized volume and intravoxel incoherent motion (IVIM) parameters of the spleen based on tumor burden for predicting treatment response in newly diagnosed acute leukemia (AL). Methods: Patients with newly diagnosed AL were recruited and underwent abdominal IVIM diffusion-weighted imaging within one week before the first induction chemotherapy. Quantitative parameters of magnetic resonance imaging (MRI) included the standardized volume (representing volumetric tumor burden) and IVIM parameters (standard apparent diffusion coefficient [sADC]; pure diffusion coefficient [D]; pseudo-diffusion coefficient [D∗]; and pseudo-perfusion fraction [f], representing functional tumor burden) of the spleen. Clinical biomarkers of tumor burden were collected. Patients were divided into complete remission (CR) and non-CR groups according to the treatment response after the first standardized induction chemotherapy, and the MRI and clinical parameters were compared between the two groups. The correlations of MRI parameters with clinical biomarkers were analyzed. Multivariate logistic regression was performed to determine the independent predictors for treatment response. Receiver operating characteristic curves were used to analyze the predicted performance. Results: 76 AL patients (CR: n = 43; non-CR: n = 33) were evaluated. Standardized spleen volume, sADC, D, f, white blood cell counts, and lactate dehydrogenase were significantly different between CR and non-CR groups (all p < 0.05). Standardized spleen volume, sADC, and D were correlated with white blood cell and lactate dehydrogenase, and f was correlated with lactate dehydrogenase (all p < 0.05). Standardized spleen volume (hazard ratio = 4.055, p = 0.042), D (hazard ratio = 0.991, p = 0.027), and f (hazard ratio = 1.142, p = 0.008) were independent predictors for treatment response, and the combination of standardized spleen volume, D, and f showed more favorable discrimination (area under the curve = 0.856) than individual predictors. Conclusion: Standardized volume, D, and f of the spleen could be used to predict treatment response in newly diagnosed AL, and the combination of morphological and functional parameters would further improve the predicted performance. IVIM parameters of the spleen may be viable indicators for evaluating functional tumor burden in AL

Synthetic MRI in breast cancer: differentiating benign from malignant lesions and predicting immunohistochemical expression status

Abstract To evaluate and compare the performance of synthetic magnetic resonance imaging (SyMRI) in classifying benign and malignant breast lesions and predicting the expression status of immunohistochemistry (IHC) markers. We retrospectively analysed 121 patients with breast lesions who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and SyMRI before surgery in our hospital. DCE-MRI was used to assess the lesions, and then regions of interest (ROIs) were outlined on SyMRI (before and after enhancement), and apparent diffusion coefficient (ADC) maps to obtain quantitative values. After being grouped according to benign and malignant status, the malignant lesions were divided into high and low expression groups according to the expression status of IHC markers. Logistic regression was used to analyse the differences in independent variables between groups. The performance of the modalities in classification and prediction was evaluated by receiver operating characteristic (ROC) curves. In total, 57 of 121 lesions were benign, the other 64 were malignant, and 56 malignant lesions performed immunohistochemical staining. Quantitative values from proton density-weighted imaging prior to an injection of the contrast agent (PD-Pre) and T2-weighted imaging (T2WI) after the injection (T2-Gd), as well as its standard deviation (SD of T2-Gd), were valuable SyMRI parameters for the classification of benign and malignant breast lesions, but the performance of SyMRI (area under the curve, AUC = 0.716) was not as good as that of ADC values (AUC = 0.853). However, ADC values could not predict the expression status of breast cancer markers, for which SyMRI had excellent performance. The AUCs of androgen receptor (AR), estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), p53 and Ki-67 were 0.687, 0.890, 0.852, 0.746, 0.813 and 0.774, respectively. SyMRI had certain value in distinguishing between benign and malignant breast lesions, and ADC values were still the ideal method. However, to predict the expression status of IHC markers, SyMRI had an incomparable value compared with ADC values

Irrigation expansion has kept pace with the CO2 fertilization effect on vegetation growth in a typical arid region

Abstract Dynamics of vegetation in arid areas have drawn worldwide attention. The expansion of irrigated cropland (ICE) in arid regions contributes to increased food security and impacts on the extent and development of regional vegetation. However, the quantitative attribution of vegetation growth variation from ICE and biogeochemical factors (e.g., atmospheric CO2 concentration, climatic factors) is still lacking. Here, we assessed key drivers of vegetation growth in the inland arid region of Northwest China (IANC) from 1982 to 2018, including ICE, increased nitrogen rates, elevated atmospheric CO2 concentration (eCO2) and climate drivers, using normalized difference vegetation index (NDVI) and ecosystem gross primary productivity (GPP) as measures. These variables were quantified through trend decomposition, machine learning algorithms, and a satellite-based model. The results show that vegetation growth was increased in IANC mainly due to eCO2 and ICE. After 1995, as the regional climatic aridity intensified, the CO2 fertilization effect on vegetation growth decreased, as the atmospheric CO2 concentration continued to increase. Meanwhile, irrigated cropland area increased sharply, and ICE-driven GPP variation exceeded that driven by eCO2 in the whole region, while the ICE-driven NDVI variation exceeded that due to eCO2 when the ICE reached 6.38%. The ICE effect on regional vegetation growth rather than the CO2 fertilization effect has mitigated the slowdown of the rate of vegetation growth caused by climate changes. Although the ICE is conducive to food security and continuous greening of arid areas, further reclamation will exacerbate water scarcity. Our results provide research base for identifying the scale of sustainable agricultural development

DataSheet_1_Diffusion-weighted imaging-based radiomics in epithelial ovarian tumors: Assessment of histologic subtype.docx

BackgroundEpithelial ovarian tumors (EOTs) are a group of heterogeneous neoplasms. It is importance to preoperatively differentiate the histologic subtypes of EOTs. Our study aims to investigate the potential of radiomics signatures based on diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps for categorizing EOTs.MethodsThis retrospectively enrolled 146 EOTs patients [34 with borderline EOT(BEOT), 30 with type I and 82 with type II epithelial ovarian cancer (EOC)]. A total of 390 radiomics features were extracted from DWI and ADC maps. Subsequently, the LASSO algorithm was used to reduce the feature dimensions. A radiomics signature was established using multivariable logistic regression method with 3-fold cross-validation and repeated 50 times. Patients with bilateral lesions were included in the validation cohort and a heuristic selection method was established to select the tumor with maximum probability for final consideration. A nomogram incorporating the radiomics signature and clinical characteristics was also developed. Receiver operator characteristic, decision curve analysis (DCA), and net reclassification index (NRI) were applied to compare the diagnostic performance and clinical net benefit of predictive model.ResultsFor distinguishing BEOT from EOC, the radiomics signature and nomogram showed more favorable discrimination than the clinical model (0.915 vs. 0.852 and 0.954 vs. 0.852, respectively) in the training cohort. In classifying early-stage type I and type II EOC, the radiomics signature exhibited superior diagnostic performance over the clinical model (AUC 0.905 vs. 0.735). The diagnostic efficacy of the nomogram was the same as that of the radiomics model with NRI value of -0.1591 (P = 0.7268). DCA also showed that the radiomics model and combined model had higher net benefits than the clinical model.ConclusionRadiomics analysis based on DWI, and ADC maps serve as an effective quantitative approach to categorize EOTs.</p