Destabilization of α-helical structure in solution improves bactericidal activity of antimicrobial peptides: Opposing effects on bacterial and viral targets

We have previously examined the mechanism of antimicrobial peptides on the outer membrane of vaccinia virus. Here we show that the formulation of peptides LL37 and magainin-2B amide in polysorbate 20 (Tween-20™) results in greater reductions in virus titre than formulation without detergent, and the effect is replicated by substitution of polysorbate 20 with high ionic strength buffer. In contrast, formulation with polysorbate 20 or high ionic strength buffer has the opposite effect on bactericidal activity of both peptides, resulting in lesser reductions in titre for both gram-positive and gram-negative bacteria. Circular dichroism spectroscopy shows that the differential action of polysorbate 20 and salt on the virucidal and bactericidal activities correlates with the α-helical content of peptide secondary structure in solution, suggesting that the virucidal and bactericidal activities are mediated through distinct mechanisms. The correlation of a defined structural feature with differential activity against a host-derived viral membrane and the membranes of both gram-positive and gram-negative bacteria suggests that overall helical content in solution under physiological conditions is an important feature for consideration in the design and development of candidate peptide-based antimicrobial compounds

Human-specific CpG 'beacons' identify human-specific prefrontal cortex H3K4me3 chromatin peaks

Therefore, CpG-focused comparative sequence analysis can precisely pinpoint chromatin structures that contribute to the human-specific phenotype and further supports an integrated approach in genomic and epigenomic studie

Reinforcing Additives for Ice Adhesion Reduction Coatings

Adhesion of contaminants has been identified as a ubiquitous issue for aeronautic exterior surfaces. In-flight icing is particularly hazardous for all aircraft and can be experienced throughout the year under the appropriate environmental conditions. On larger vehicles, the accretion of ice could result in loss of lift, engine failure, and potentially loss of vehicle and life were it not for active deicing or anti-icing equipment. Smaller vehicles though cannot support the mass and mechanical complexity of active ice mitigating systems and thus must rely upon passive approaches or avoid icing conditions altogether. One approach that may be applicable to all aircraft is the use of coatings. Durability remains an issue and has prevented realization of coatings for leading edge contamination mitigation. In this work, epoxy coatings were generated as a passive approach for ice adhesion mitigation and methods to improve durability were evaluated. Highly cross-linked epoxy systems can be extremely rigid, which could have deleterious consequences regarding application as a leading edge coating. Incorporation of flexible species, such as poly(ethylene glycol) may improve coating toughness.8 Additionally, core-shell rubber (CSR) particles have been utilized to improve fracture toughness of epoxies.9 Both of these more established additives are investigated in this work. An emerging additive that is also evaluated here is holey graphene. This nanomaterial possesses many of the advantageous properties of graphene (excellent mechanical properties, thermal and electrical conductivity, large surface area, etc.) while also exhibiting behaviors associated with flexible, porous materials (i.e., compressibility, increased permeation, etc.). Holey graphene, HG, was synthesized by the oxidation of defect-rich sites on graphene sheets through controlled thermal expo-sure.10 It is envisioned that the porous nature of HG would allow resin penetration through the graphitic plane, resulting in better interfacial interaction and therefore better translation of the nanomaterials properties to the surrounding matrix

An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture

A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such as autonomous plant classification will typically be coupled to just one or perhaps a few plant species. As a consequence, each crop-specific task is very likely to require its own specialized training data, and the question of how to serve this need for data now often overshadows the more routine exercise of actually training such models. To tackle this problem, we have developed an embedded robotic system to automatically generate and label large datasets of plant images for ML applications in agriculture. The system can image plants from virtually any angle, thereby ensuring a wide variety of data; and with an imaging rate of up to one image per second, it can produce lableled datasets on the scale of thousands to tens of thousands of images per day. As such, this system offers an important alternative to time- and cost-intensive methods of manual generation and labeling. Furthermore, the use of a uniform background made of blue keying fabric enables additional image processing techniques such as background replacement and plant segmentation. It also helps in the training process, essentially forcing the model to focus on the plant features and eliminating random correlations. To demonstrate the capabilities of our system, we generated a dataset of over 34,000 labeled images, with which we trained an ML-model to distinguish grasses from non-grasses in test data from a variety of sources. We now plan to generate much larger datasets of Canadian crop plants and weeds that will be made publicly available in the hope of further enabling ML applications in the agriculture sector.Comment: 35 pages, 8 figures, Preprint submitted to PLoS On

Selecting a change and evaluating its impact on the performance of a complex adaptive health care delivery system

Complexity science suggests that our current health care delivery system acts as a complex adaptive system (CAS). Such systems represent a dynamic and flexible network of individuals who can coevolve with their ever changing environment. The CAS performance fluctuates and its members’ interactions continuously change over time in response to the stress generated by its surrounding environment. This paper will review the challenges of intervening and introducing a planned change into a complex adaptive health care delivery system. We explore the role of the “reflective adaptive process” in developing delivery interventions and suggest different evaluation methodologies to study the impact of such interventions on the performance of the entire system. We finally describe the implementation of a new program, the Aging Brain Care Medical Home as a case study of our proposed evaluation process

A Micro Molecular Bipolar Outflow From HL Tau

We present detailed geometry and kinematics of the inner outflow toward HL Tau observed using Near Infrared Integral Field Spectograph (NIFS) at the Gemini-North 8-m Observatory. We analyzed H2 2.122 um emission and [Fe II] 1.644 um line emission as well as the adjacent continuum observed at a <0".2 resolution. The H2 emission shows (1) a bubble-like geometry to the northeast of the star, as briefly reported in the previous paper, and (2) faint emission in the southwest counterflow, which has been revealed through careful analysis. The emission on both sides of the star show an arc 1".0 away from the star, exhibiting a bipolar symmetry. Different brightness and morphologies in the northeast and southwest flows are attributed to absorption and obscuration of the latter by a flattened envelope and a circumstellar disk. The H2 emission shows a remarkably different morphology from the collimated jet seen in [Fe II] emission. The positions of some features coincide with scattering continuum, indicating that these are associated with cavities in the dusty envelope. Such properties are similar to millimeter CO outflows, although the spatial scale of the H2 outflow in our image (~150 AU) is strikingly smaller than the mm outflows, which often extend over 1000-10000 AU scales. The position-velocity diagram of the H2 and [Fe II] emission do not show any evidence for kinematic interaction between these flows. All results described above support the scenario that the jet is surrounded by an unseen wide-angled wind, which interacts with the ambient gas and produce the bipolar cavity and shocked H2 emission.Comment: 13 pages, 4 figures, accepted for publication in ApJ

On the Efficacy of Isolating Shoulder and Elbow Movements with a Soft, Portable, and Wearable Robotic Device

Treatment intensity has a profound effect on motor recovery following neurological injury. The use of robotics has potential to automate these labor-intensive therapy procedures that are typically performed by physical therapists. Further, the use of wearable robotics offers an aspect of portability that may allow for rehabilitation outside the clinic. The authors have developed a soft, portable, lightweight upper extremity wearable robotic device to provide motor rehabilitation of patients with affected upper limbs due to traumatic brain injury (TBI). A key feature of the device demonstrated in this paper is the isolation of shoulder and elbow movements necessary for effective rehabilitation interventions. Herein is presented a feasibility study with one subject and demonstration of the device's ability to provide safe, comfortable, and controlled upper extremity movements. Moreover, it is shown that by decoupling shoulder and elbow motions, desired isolated joint actuation can be achieved

A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images

Hyperspectral imaging (HSI) is a non-destructive and contactless technology that provides valuable information about the structure and composition of an object. It has the ability to capture detailed information about the chemical and physical properties of agricultural crops. Due to its wide spectral range, compared with multispectral-or RGB-based imaging methods, HSI can be a more effective tool for monitoring crop health and productivity. With the advent of this imaging tool in agrotechnology, researchers can more accurately address issues related to the detection of diseased and defective crops in the agriculture industry. This allows to implement the most suitable and accurate farming solutions, such as irrigation and fertilization, before crops enter a damaged and difficult-to-recover phase of growth in the field. While HSI provides valuable insights into the object under investigation, the limited number of HSI datasets for crop evaluation presently poses a bottleneck. Dealing with the curse of dimensionality presents another challenge due to the abundance of spectral and spatial information in each hyperspectral cube. State-of-the-art methods based on 1D and 2D convolutional neural networks (CNNs) struggle to efficiently extract spectral and spatial information. On the other hand, 3D-CNN-based models have shown significant promise in achieving better classification and detection results by leveraging spectral and spatial features simultaneously. Despite the apparent benefits of 3D-CNN-based models, their usage for classification purposes in this area of research has remained limited. This paper seeks to address this gap by reviewing 3D-CNN-based architectures and the typical deep learning pipeline, including preprocessing and visualization of results, for the classification of hyperspectral images of diseased and defective crops. Furthermore, we discuss open research areas and challenges when utilizing 3D-CNNs with HSI data."This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors."https://www.sciencedirect.com/science/article/pii/S277237552300145