A Multi-Modal Deep Learning Approach to the Early Prediction of Mild Cognitive Impairment Conversion to Alzheimer's Disease

Mild cognitive impairment (MCI) has been described as the intermediary stage before Alzheimer's Disease - many people however remain stable or even demonstrate improvement in cognition. Early detection of progressive MCI (pMCI) therefore can be utilised in identifying at-risk individuals and directing additional medical treatment in order to revert conversion to AD as well as provide psychosocial support for the person and their family.This paper presents a novel solution in the early detection of pMCI people and classification of AD risk within MCI people. We proposed a model, MudNet, to utilise deep learning in the simultaneous prediction of progressive/stable MCI classes and time-to-AD conversion where high-risk pMCI people see conversion to AD within 24 months and low-risk people greater than 24 months. MudNet is trained and validated using baseline clinical and volumetric MRI data (n = 559 scans) from participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI). The model utilises T1-weighted structural MRIs alongside clinical data which also contains neuropsychological (RAVLT, ADAS-11, ADAS-13, ADASQ4, MMSE) tests as inputs.The averaged results of our model indicate a binary accuracy of 69.8% for conversion predictions and a categorical accuracy of 66.9% for risk classifications

GANS-based data augmentation for citrus disease severity detection using deep learning

Recently, many Deep Learning models have been employed to classify different kinds of plant diseases, but very little work has been done for disease severity detection. However, it is more important to master the severities of plant diseases accurately and timely, as it helps to make effective decisions to protect the plants from being further infected and reduce financial loss. In this paper, based on the Huanglongbing (HLB)-infected leaf images obtained from PlantVillage and crowdAI, we created a dataset with 5,406 citrus leaf images infected by HLB. Then six different kinds of popular models were trained to perform the severity detection of citrus HLB with the goal to find which types of models are more suitable to detect HLB severity with the same training circumstance. The experimental results show that the Inception_v3 model with epochs=60 can achieve higher accuracy than that of other models for severity detection with an accuracy of 74.38% due to its highly computational efficiency and small number of parameters. Additionally, aiming for evaluating whether GANs-based data augmentation can contribute to improve the model learning performance, we adopted DCGANs (Deep Convolutional Generative Adversarial Networks) to augment the original training dataset up to two times itself. Finally, a new training dataset with 14,056 leaf images composed by the original training images and the augmented ones were used to train the Inception_v3 model. As a result, we achieved an accuracy of 92.60%, about 20% higher than that of the Inception_v3 model trained by the original training dataset, which suggested that the GANs-based data augmentation is very useful to improve the model learning performance

Characterizing the IRC-based Botnet Phenomenon

Botnets, networks of compromised machines that can be remotely controlled by an attacker, are one of the most common attack platforms nowadays. They can, for example, be used to launch distributed denial-of-service (DDoS) attacks, steal sensitive information, or send spam emails. A long-term measurement study of botnet activities is useful as a basis for further research on global botnet mitigation and disruption techniques. We have built a distributed and fully-automated botnet measurement system which allows us to collect data on the botnet activity we observe in China. Based on the analysis of tracking records of 3,290 IRC-based botnets during a period of almost twelve months, this paper presents several novel results of botnet activities which can only be measured via long-term measurements. These include. amongst others, botnet lifetime, botnet discovery trends and distributions, command and control channel distributions, botnet size and end-host distributions. Furthermore, our measurements confirm and extend several previous results from this area. Our results show that the botnet problem is of global scale, with a scattered distribution of the control infrastructure and also a scattered distribution of the victims. Furthermore, the control infrastructure itself is rather flexible, with an average lifetime of a Command \& Control server of about 54 days. These results can also leverage research in the area of botnet detection, mitigation, and disruption: only by understanding the problem in detail, we can develop efficient counter measures

Studying Malicious Websites and the Underground Economy on the Chinese Web

The World Wide Web gains more and more popularity within China with more than 1.31 million websites on the Chinese Web in June 2007. Driven by the economic profits, cyber criminals are on the rise and use the Web to exploit innocent users. In fact, a real underground black market with thousand of participants has developed which brings together malicious users who trade exploits, malware, virtual assets, stolen credentials, and more. In this paper, we provide a detailed overview of this underground black market and present a model to describe the market. We substantiate our model with the help of measurement results within the Chinese Web. First, we show that the amount of virtual assets traded on this underground market is huge. Second, our research proofs that a significant amount of websites within China’s part of the Web are malicious: our measurements reveal that about 1.49% of the examined sites contain some kind of malicious content

Preparation of quinoa bran dietary fiber-based zinc complex and investigation of its antioxidant capacity in vitro

In order to improve the economic utilization of quinoa bran and develop a safe and highly available zinc ion biological supplement. In this study, a four-factor, three-level response surface optimization of quinoa bran soluble dietary fiber (SDF) complexation of zinc was studied. The effect used four factors on the chelation rate was investigated: (A) mass ratio of SDF to ZnSO4.7H2O, (B) chelation temperature, (C) chelation time, and (D) pH. Based on the results of the single-factor test, the four-factor three-level response surface method was used to optimize the reaction conditions. The optimal reaction conditions were observed as mentioned here: the mass ratio of quinoa bran SDF to ZnSO4.7H2O was 1, the reaction temperature was 65°C, the reaction time was 120 min, and the pH of the reaction system was 8.0. The average chelation rate was 25.18%, and zinc content is 465.2 μg/g under optimal conditions. The hydration method rendered a fluffy quinoa bran SDF structure. The intramolecular functional groups were less stable which made the formation of the lone pairs of electrons feasible to complex with the added divalent zinc ions to form a quinoa bran soluble dietary fiber-zinc complex [SDF-Zn(II)]. The SDF-Zn(II) chelate had higher 2,2-diphenylpicrylhydrazyl (DPPH), ABTS+, hydroxyl radical scavenging ability, and total antioxidant capacity. Therefore, metal ion chelation in dietary fiber is of biological importance

Emission of PAHs, NPAHs and OPAHs from residential honeycomb coal briquette combustion

Coal combustion is one of the most significant sources of air pollution in China. In this study, emission factors (EFs) of 15 polycyclic aromatic hydrocarbons (PAHs), 26 nitrated PAHs (NPAHs) and 6 oxygenated PAHs (OPAHs) were determined in five different coals with different geological maturity (vitrinite reflectance <i>R</i>_O = 0.77–1.88%) burned in the form of honeycomb briquettes. The total EFs ranged from 9.82 to 215 mg kg^–1 for PAHs, 0.14 to 1.88 mg kg^–1 for NPAHs and 4.47 to 20.8 mg kg^–1 for OPAHs. Measured EFs and gas-particle partitioning varied depending on the geological maturity. The lowest EFs were found in anthracite. The proportion of PAHs, NPAHs and OPAHs in gaseous phase increasing with increased geological maturity. The coal with higher geological maturity produced more 3-ring PAHs. On the basis of the statistical analysis for the residential sector of China in 2008, PAHs, NPAHs and OPAHs emitted from residential honeycomb coal briquettes were 4.36 Gg, 0.03 Gg and 0.47 Gg in 2007, respectively. By 2020, the emission would decrease to 2.18 Gg, 0.02 Gg and 0.24 Gg for PAHs, NPAHs and OPAHs due to the increasing usage of new energy resources. If only anthracite is used as the residential coal, 93% PAHs, 87% NPAHs and 71% OPAHs would be reduced in 2020

3D printing high interfacial bonding polyether ether ketone components via pyrolysis reactions

Recently, 3D-printed polyether-ether-ketone (PEEK) components have been shown to offer many applications in state-of-the-art electronics, 5G wireless communications, medical implantations, and aerospace components. Nevertheless, a critical barrier that limits the application of 3D printed PEEK components is their weak interfacial bonding strength. Herein, we propose a novel method to improve this unsatisfied situation via the interface plasticizing effect of benzene derivatives obtained from the thermal pyrolysis of trisilanolphenyl polyhedral oligomeric silsequioxane (POSS). Based on this method, the bonding strength of the filaments and interlayers of 3D-printed POSS/PEEK components can reach 82.9 MPa and 59.8 MPa, respectively. Moreover, the enhancing mechanism of the pyrolysis products derived from the POSS is characterized using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS), Fourier transform infrared spectroscopy (FTIR), and X-ray computed tomography (X-CT). Our proposed strategy broadens the novel design space for developing additional 3D-printed materials with satisfactory interfacial bonding strength

Effective microorganisms input efficiently improves the vegetation and microbial community of degraded alpine grassland

Soil beneficial microorganism deficiency in the degraded grasslands have emerged as the major factors negatively impacting soil quality and vegetation productivity. EM (effective microorganisms) has been regarded as a good ameliorant in improving microbial communities and restoring degraded soil of agricultural systems. However, knowledge was inadequate regarding the effects of adding EM on the degraded alpine grassland. Four levels of EM addition (0, 150, 200, 250 mL m–2) were conducted to investigate the effects of EM addition on soil properties and microorganisms of degraded alpine grassland. The addition of EM increased aboveground biomass, soil organic carbon, total nitrogen, available phosphorus, and microbial biomass, but decreased soil electric conductivity. Meanwhile, the relative biomasses of gram-negative bacteria decreased, while the ectomycorrhizal fungi and arbuscular mycorrhizal fungi increased after EM addition. The relationship between microbial communities and environmental factors has been changed. The restore effect of EM increased with the increase of addition time. These results indicated that EM addition could be a good practice to restore the health of the degraded alpine grassland ecosystem

In Situ Study the Dynamics of Blade-Coated All-Polymer Bulk Heterojunction Formation and Impact on Photovoltaic Performance of Solar Cells

All-polymer solar cells (all-PSCs) have achieved impressive progress by employing acceptors polymerized from well performing small-molecule non-fullerene acceptors. Herein, the device performance and morphology evolution in blade-coated all-PSCs based on PBDBT:PF5–Y5 blends prepared from two different solvents, chlorobenzene (CB), and ortho-xylene (o-XY) are studied. The absorption spectra in CB solution indicate more ordered conformation for PF5–Y5. The drying process of PBDBT:PF5–Y5 blends is monitored by in situ multifunctional spectroscopy and the final film morphology is characterized with ex situ techniques. Finer-mixed donor/acceptor nanostructures are obtained in CB-cast film than that in o-XY-cast ones, corresponding to more efficient charge generation in the solar cells. More importantly, the conformation of polymers in solution determines the overall film morphology and the device performance. The relatively more ordered structure in CB-cast films is beneficial for charge transport and reduced non-radiative energy loss. Therefore, to achieve high-performance all-PSCs with small energy loss, it is crucial to gain favorable aggregation in the initial stage in solution