Differentiation of sCJD and vCJD forms by automated analysis of basal ganglia intensity distribution in multisequence MRI of the brain--definition and evaluation of new MRI-based ratios

We present a method for the analysis of basal ganglia (including the thalamus) for accurate detection of human spongiform encephalopathy in multisequence magnetic resonance imaging (MRI) of the brain. One common feature of most forms of prion protein diseases is the appearance of hyperintensities in the deep grey matter area of the brain in T2-weighted magnetic resonance (MR) images. We employ T1, T2, and Flair-T2 MR sequences for the detection of intensity deviations in the internal nuclei. First, the MR data are registered to a probabilistic atlas and normalized in intensity. Then smoothing is applied with edge enhancement. The segmentation of hyperintensities is performed using a model of the human visual system. For more accurate results, a priori anatomical data from a segmented atlas are employed to refine the registration and remove false positives. The results are robust over the patient data and in accordance with the clinical ground truth. Our method further allows the quantification of intensity distributions in basal ganglia. The caudate nuclei are highlighted as main areas of diagnosis of sporadic Creutzfeldt-Jakob Disease (sCJD), in agreement with the histological data. The algorithm permitted the classification of the intensities of abnormal signals in sCJD patient FLAIR images with a higher hypersignal in caudate nuclei (10/10) and putamen (6/10) than in thalami. Defining normalized MRI measures of the intensity relations between the internal grey nuclei of patients, we robustly differentiate sCJD and variant CJD (vCJD) patients, in an attempt to create an automatic classification tool of human spongiform encephalopathies.     

Formation of a two-dimensional electron gas in ZnO/MgZnO single heterostructures and quantum wells

The properties of ZnO/MgZnO heterostructures grown by pulsed-laser deposition on sapphire (112?0) and ZnO (0001?) have been compared. Electron accumulation layers have been observed for ZnO/MgZnO heterostructures grown on sapphire by capacitance-voltage (C-V) spectroscopy. The formation of a two-dimensional electron gas (2DEG) in these structures has been confirmed by temperature dependent Hall effect measurements. From C-V measurements the sheet carrier density in a Zn_0.8 Mg_0.2O/ZnO/Zn_0.8 Mg_0.2O quantum well (QW) structure with a well width of about 5 nm is calculated to be only about 9.0 × 10¹⁰ cm^− 2. For the films deposited on sapphire 2D growth is observed in the Burton-Cabrera-Frank mode, as confirmed by atomic force microscopy. Step flow growth mode was achieved for the homoepitaxial thin films. Quantum confinement effects have been confirmed by photoluminescence (PL) measurements. Homoepitaxial QWs are more homogeneous (smaller inhomogeneous recombination broadening) than heteroepitaxial QWs.     

Rapid On-Resin N-Formylation of Peptides as One-Pot Reaction

N-formylation is a common pre- and post-translational modification of the N-terminus or the lysine side chain of peptides and proteins that plays a role in the initiation of immune responses, gene expression, or epigenetics. Despite its high biological relevance, protocols for the chemical N-formylation of synthetic peptides are scarce. The few available methods are elaborate in their execution and the yields are highly sequence-dependent. We present a rapid, easy-to-use one-pot procedure that runs at room temperature and can be used to formylate protected peptides at both the N-terminus and the lysine side chain on the resin in near-quantitative yields. Only insensitive, storage-stable standard chemicals – formic acid, acetic anhydride, pyridine and DMF – are used. Formylation works for both short and long peptides of up to 34 amino acids and over the spectrum of canonical amino acids.     

Diffundierter Sauerstoff als dominierender flacher Akzeptor in p-Typ Kupferiodid-Dünnfilmen

The long-term stability of the optically transparent p-type semiconductor copper iodide is a current challenge. The electrical conductivity of CuI thin films depends critically on the environmental impact. Al₂O₃ cappings enhance the stability considerably. Systematic studies on Al₂O₃/CuI heterostructures in dependence of the N₂/O₂ growth pressure show the electrical conductivity of the CuI films being determined by the oxygen diffusion through Al₂O₃ und CuI. Oxygen seems to be a dominating acceptor in CuI. We traced the diffusion of atmospheric oxygen into CuI with ¹⁸O isotopes.     

Macromolecular electron density averaging on distributed memory MIMD systems

The paper discusses algorithms and programs for electron density averaging using a distributed memory MIMD system. Electron density averaging is a computationally intensive step needed for phase refinement and extension in the computation of the 3-D structure of macromolecules like proteins and viruses. The determination of a single structure may require thousands of hours of CPU time for traditional supercomputers. The approach discussed in this paper leads to a reduction by one to two orders of magnitude of the computing time. The program runs on an Intel iPSC/860 and on the Touchstone Delta system and uses a user controlled shared virtual memory and a dynamic load-balancing mechanism.     

Polyether Cyclization Cascade Alterations in Response to Monensin Polyketide Synthase Mutations

The targeted manipulation of polyketide synthases has in recent years led to numerous new-to-nature polyketides. For type I polyketide synthases the response of post-polyketide synthases (PKS) processing enzymes onto the most frequently polyketide backbone manipulations is so far insufficiently studied. In particular, complex processes such as the polyether cyclisation in the biosynthesis of ionophores such as monensin pose interesting objects of research. We present here a study of the substrate promiscuity of the polyether cyclisation cascade enzymes in monensin biosynthesis in the conversion of redox derivatives of the nascent polyketide chain. LC-HRMS/MS²-based studies revealed a remarkable flexibility of the post-PKS enzymes. They acted on derivatized polyketide backbones based on the three possible polyketide redox states within two different modules and gave rise to an altered polyether structure. One of these monensin derivatives was isolated and characterized by 2D-NMR spectroscopy, crystallography, and bioactivity studies.     

Covalent Allosteric Inhibitors of Akt Generated Using a Click Fragment Approach

Akt is a protein kinase that has been implicated in the progression of cancerous tumours. A number of covalent allosteric Akt inhibitors are known, and based on these scaffolds, a small library of novel potential covalent allosteric imidazopyridine-based inhibitors was designed. The envisaged compounds were synthesised, with click chemistry enabling a modular approach to a number of the target compounds. The binding modes, potencies and antiproliferative activities of these synthesised compounds were explored, thereby furthering the structure activity relationship knowledge of this class of Akt inhibitors. Three novel covalent inhibitors were identified, exhibiting moderate activity against Akt1 and various cancer cell lines, potentially paving the way for future covalent allosteric inhibitors with improved properties.     

From zero-shot machine learning to zero-day attack detection

Machine learning (ML) models have proved efficient in classifying data samples into their respective categories. The standard ML evaluation methodology assumes that test data samples are derived from pre-observed classes used in the training phase. However, in applications such as Network Intrusion Detection Systems (NIDSs), obtaining data samples of all attack classes to be observed is challenging. ML-based NIDSs face new attack traffic known as zero-day attacks that are not used in training due to their non-existence at the time. Therefore, this paper proposes a novel zero-shot learning methodology to evaluate the performance of ML-based NIDSs in recognising zero-day attack scenarios. In the attribute learning stage, the learning models map network data features to semantic attributes that distinguish between known attacks and benign behaviour. In the inference stage, the models construct the relationships between known and zero-day attacks to detect them as malicious. A new evaluation metric is defined as Zero-day Detection Rate (Z-DR) to measure the effectiveness of the learning model in detecting unknown attacks. The proposed framework is evaluated using two key ML models and two modern NIDS data sets. The results demonstrate that for certain zero-day attack groups discovered in this paper, ML-based NIDSs are ineffective in detecting them as malicious. Further analysis shows that attacks with a low Z-DR have a significantly distinct feature distribution and a higher Wasserstein Distance range than the other attack classes.

     

Formation of nanoparticle aggregates and agglomerates in polymer nanocomposites and their distinct impacts on the mechanical properties

In this study, the different effects of nanoparticle aggregates and agglomerates on the mechanical properties of polymer nanocomposites are comprehensively investigated. To this end, a specific strategy, based on the equilibrium between the dispersion and cohesion energies in the mixing stage, is proposed using which the content and size of aggregates/agglomerates can be defined. The aggregated/agglomerated networks are considered to place in constrained volumes (CVs), having co-continuous morphology. An equivalent box model (EBM), corresponding to the system, is used to predict the tensile modulus of the nanocomposite. Different test results of HDPE nanocomposite samples, containing 1–3 wt.% of surface-modified silica nanoparticles, prepared by a semi-industrial single screw extruder, are applied to validate the model. Moreover, other data from the literature are also used to further evaluate the accuracy and capability of the proposed analytical method.