Miracle-dz/UniFAR-FTUs huggingface dataset:
Templated Synthesis of Chitosan Nanocapsules with Controllable Shell Thickness and Porosity

This study demonstrates the successful production of monodispersed, homogeneous chitosan nanocapsules with tunable properties such as controlled size, thickness and porosity. Through the use of a solid core silica template with a tunable mesoporous shell (SC/MS), hollow nanocapsules with different sizes, wall thickness, and porosity levels have been synthesized. These chitosan nanocapsules have been characterized using transmission and scanning electron microscopy. A hydrophobic drug curcumin was used as a model drug that was infiltrated in chitosan nanocapsules. The drug-encapsulation efficiency as a function of capsule size, thickness and porosity has been demonstrated using UV-Vis absorbance spectroscopy. The study provides a detailed understanding on how a control over nanocapsule synthesis parameters can provide a facile approach for attaining a controlled drug-release profile.
Spatially smooth partitioning of hyperspectral imagery using spectral/spatial measures of disparity

A Bayesian approach to partitioning hyperspectral imagery into homogeneous regions is investigated, where spatial consistency is imposed on the spectral content of sites in each partition. An energy function is investigated that models disparities in an image that are defined with respect to a local neighborhood system. This energy function uses one or certain combinations of the spectral angle, Euclidean distance, and/or Kolmogorov-Smirnov (mean-adjusted) measures. Maximum a posteriori estimates are computed using an algorithm that is implemented as a multigrid process to improve global labeling and reduce computational intensity. Both constrained and unconstrained multigrid approaches are considered. A locally extended neighborhood structure is introduced with the intention of encouraging more accurate global labeling. The present effort is focused on terrain mapping applications using hyperspectral imagery containing narrow bands throughout the 400-2500-nm spectral region. The trials of our experiment are conducted on a scene from HYDICE 210-band imagery collected over an area that contains a diverse range of terrain features and that is supported with ground truth. Quantitative measures of local consistency (smoothness) and global labeling, along with class maps, demonstrate the benefits of applying this method for unsupervised and supervised classification, where the best results are achieved with an energy function consisting of the combined spectral angle and Euclidean distance measures.
Design and Development of a Nearable Wireless System to Control Indoor Air Quality and Indoor Lighting Quality

The article describes the results of the project “open source smart lamp” aimed at designing and developing a smart object able to manage and control the indoor environmental quality (IEQ) of the built environment. A first version of this smart object, built following a do-it-yourself (DIY) approach using a microcontroller, an integrated temperature and relative humidity sensor, and techniques of additive manufacturing, allows the adjustment of the indoor thermal comfort quality (ICQ), by interacting directly with the air conditioner. As is well known, the IEQ is a holistic concept including indoor air quality (IAQ), indoor lighting quality (ILQ) and acoustic comfort, besides thermal comfort. The upgrade of the smart lamp bridges the gap of the first version of the device providing the possibility of interaction with the air exchange unit and lighting system in order to get an overview of the potential of a nearable device in the management of the IEQ. The upgraded version was tested in a real office equipped with mechanical ventilation and an air conditioning system. This office was occupied by four workers. The experiment is compared with a baseline scenario and the results show how the application of the nearable device effectively optimizes both IAQ and ILQ.
Viewpoint calibration method based on point features for point cloud fusion

Point cloud fusion is a significant process in many computer vision applications such as multi-robot maps building or multi-SLAM. When robots capture maps in different places, the maps suffer from large angle difference between respective viewpoints of the robots and spatial misalignment. These problems bring great challenges to point cloud fusion. In order to overcome such difficulty, in this study, we propose a 3D viewpoint calibration method. The method relies on the novel combination of 3D-SIFT (scale-invariant feature transform) keypoints and FPFH (fast point feature histogram) features, which are used for feature matching. Based on the feature correspondences, we compute the transformation to resolve the difference of the viewpoints. The experiments demonstrate that (1) the keypoints and features used in our method are distinctive and robust to camera viewpoint change; and (2) using viewpoint calibration can reduce the iterations of registration algorithms, and transforming different viewpoints to a close one could save computation time and improve accuracy.
Collagen VI Is a Subepithelial Adhesive Target for Human Respiratory Tract Pathogens

Bacterial engagement of specific host tissue structures can be a means of targeting a pathogen to a particular niche, establishing persistent infections and inducing invasion. In this context, primary adhesion is often the first crucial colonization step allowing pathogens to withstand the mechanical clearing mechanisms of the host. As a consequence, bacteria have evolved adhesins with the capacity to mediate interaction between microorganism and host. Here we describe collagen VI as a novel target for adherence of Streptococcuspyogenes and Streptococcus pneumoniae. In upper and lower airways this collagen was distributed in the lamina propria underneath the epithelial basement membrane. Both pathogens exhibited strong affinity to collagen VI as shown by light and electron microscopy in combination with immunodetection and in vitro binding assays. For S. pyogenes this interaction was mediated by M1 protein. The presented data provide evidence for a previously unrecognized role for collagen VI in host-pathogen interplay during respiratory tract infection.
Transcriptional variations in the wider peritumoral tissue environment of pancreatic cancer

Transcriptional profiling was performed on 452 RNA preparations isolated from various types of pancreatic tissue from tumour patients and healthy donors, with a particular focus on peritumoral samples. Pancreatic ductal adenocarcinomas (PDAC) and cystic tumours were most different in these non‐tumorous tissues surrounding them, whereas the actual tumours exhibited rather similar transcript patterns. The environment of cystic tumours was transcriptionally nearly identical to normal pancreas tissue. In contrast, the tissue around PDAC behaved a lot like the tumour, indicating some kind of field defect, while showing far less molecular resemblance to both chronic pancreatitis and healthy tissue. This suggests that the major pathogenic difference between cystic and ductal tumours may be due to their cellular environment rather than the few variations between the tumours. Lack of correlation between DNA methylation and transcript levels makes it unlikely that the observed field defect in the peritumoral tissue of PDAC is controlled to a large extent by such epigenetic regulation. Functionally, a strikingly large number of autophagy‐related transcripts was changed in both PDAC and its peritumoral tissue, but not in other pancreatic tumours. A transcription signature of 15 autophagy‐related genes was established that permits a prognosis of survival with high accuracy and indicates the role of autophagy in tumour biology.
DarkMem: Fine-grained power management of local memories for accelerators in embedded systems

SRAM consumes a growing fraction of the static power in heterogeneous SoCs, as embedded memories take 70% to 90% of the area of specialized accelerators. We present Dark-Mem as a comprehensive solution for fine-grained power management of accelerator local memories. The DarkMem methodology optimizes at design time the bank configuration for each given accelerator to maximize power-gating opportunities. The DarkMem microarchitecture dynamically varies the operating mode of each memory bank according to the accelerator workload. In our experiments, DarkMem reduces the SRAM static power by more than 40% on average, which translates into a reduction of the total power by almost 18% on average with less than 1% overhead.
Application of self-learning evolutionary algorithm for optimal design of a porous polymethylmethacrylate scaffold fabricated by laser drilling process

Fabrication of scaffolds that closely mimic the biomechanical properties of the surrounding bone is one of the main issues in designing an orthopedic implant. This study is aimed to develop an optimal predefined three-dimensional polymethylmethacrylate scaffold as a potential bone substitute via laser drilling technique, which is a novel method of fabricating bone scaffold. To achieve this goal, using the obtained experimental data, the authors have tuned an adaptive neuro fuzzy inference system, which predicts the hole diameter and its depth as two process outputs for any given values of laser power and laser irradiation time. In addition to this adaptive neuro fuzzy inference system model, some experimental models were also proposed to estimate the scaffold porosity and the mechanical strength regarding the drilled hole geometry. To design an optimal scaffold with the highest amount of strength and porosity, a Pareto-based self-learning evolutionary algorithm called SOPEA was proposed. To enhance its ability in finding the optimal Pareto front, it was equipped with the adaptive self-organizing map. Based on the obtained Pareto front, a trade-off has been made in order to find a solution that yields deliberate operational properties. The scaffold morphology was observed by scanning electron microscopy images and its mechanical property was determined using a compressive test. The versatility provided by this technique allows the fabrication of scaffolds with controllable amount of porosity, pore size, and mechanical properties while providing fully interconnected channel network. The results also indicate that the proposed model predicts the process outputs with acceptable accuracy and the proposed optimization algorithm is able to find the optimal solution effectively.
Motion Artifact Quantification and Sensor Fusion for Unobtrusive Health Monitoring

Sensors integrated into objects of everyday life potentially allow unobtrusive health monitoring at home. However, since the coupling of sensors and subject is not as well-defined as compared to a clinical setting, the signal quality is much more variable and can be disturbed significantly by motion artifacts. One way of tackling this challenge is the combined evaluation of multiple channels via sensor fusion. For robust and accurate sensor fusion, analyzing the influence of motion on different modalities is crucial. In this work, a multimodal sensor setup integrated into an armchair is presented that combines capacitively coupled electrocardiography, reflective photoplethysmography, two high-frequency impedance sensors and two types of ballistocardiography sensors. To quantify motion artifacts, a motion protocol performed by healthy volunteers is recorded with a motion capture system, and reference sensors perform cardiorespiratory monitoring. The shape-based signal-to-noise ratio SNRS is introduced and used to quantify the effect on motion on different sensing modalities. Based on this analysis, an optimal combination of sensors and fusion methodology is developed and evaluated. Using the proposed approach, beat-to-beat heart-rate is estimated with a coverage of 99.5% and a mean absolute error of 7.9 ms on 425 min of data from seven volunteers in a proof-of-concept measurement scenario.
Depth upsampling by depth prediction

We propose a method for depth upsampling with the aid of high-resolution color image. The key novelty of our method is to exploit a spatio-temporal coherency between the color and depth image sequences. It allows us to perform a depth prediction using the responses from motion estimation in the color image sequence. The predicted depth image is able to estimate the scene boundary regions in the color image; it enables to suppress the influences of color image textures in depth upsampling. We synthesize high-resolution depth images with the help of the estimated scene boundary. Our experiments demonstrate the effectiveness of our method.
Modeling, simulation and measurements of queuing delay under long-tail internet traffic

In this paper we describe an analytical approach for estimating the queuing delay distribution on an Internet link carrying realistic TCP traffic, such as that produced by a large number of finite-size connections transferring files whose sizes are taken from a long-tail distribution. The analytical predictions are validated against detailed simulation experiments and real network measurements. Despite its simplicity, our model proves to be accurate and robust under a variety of operating conditions, and offers novel insights into the impact on the network of long-tail flow length distributions. Our contribution is a performance evaluation methodology that could be usefully employed in network dimensioning and engineering.
Transcriptional Organization and Physiological Contributions of the relQ Operon of Streptococcus mutans

ABSTRACT The molecular alarmone (p)ppGpp functions as a global regulator of gene expression in bacteria. In Streptococcus mutans, (p)ppGpp synthesis is catalyzed by three gene products: RelA, RelP, and RelQ. RelA is responsible for (p)ppGpp production during a stringent response, and RelP is the primary source of (p)ppGpp during exponential growth, but the role of RelQ has not been thoroughly investigated. In this study, we analyzed the four-gene relQ operon to establish how these gene products may affect homeostasis and stress tolerance in the dental caries pathogen S. mutans. Northern blotting and reverse transcriptase PCR demonstrated that relQ is cotranscribed with the downstream genes ppnK (NAD kinase), rluE (pseudouridine synthase), and pta (phosphotransacetylase). In addition, a promoter located within the rluE gene was shown to drive transcription of pta. Inactivation of relQ, ppnK, or rluE did not significantly affect growth of or stress tolerance by S. mutans, whereas strains lacking pta were more sensitive to acid and oxidative stresses. Interestingly, introduction of an rluE deletion into the pta mutant reversed the deleterious effects of the pta mutation on growth and stress tolerance. Accumulation of (p)ppGpp was also decreased in a pta mutant strain, whereas inactivation of relQ caused enhanced (p)ppGpp synthesis in exponential-phase cells. The results reveal an important role for the relQ operon in the expression of traits that are essential for persistence and pathogenesis by S. mutans and provide evidence for a molecular connection of acetate and (p)ppGpp metabolism with tolerance of acid and oxidative stresses.
Drosophila PLP assembles pericentriolar clouds that promote centriole stability, cohesion and MT nucleation

Pericentrin is a conserved centrosomal protein whose dysfunction has been linked to several human diseases. It has been implicated in many aspects of centrosome and cilia function, but its precise role is unclear. Here, we examine Drosophila Pericentrin-like-protein (PLP) function in vivo in tissues that form both centrosomes and cilia. Plp mutant centrioles exhibit four major defects: (1) They are short and have subtle structural abnormalities; (2) They disengage prematurely, and so overduplicate; (3) They organise fewer cytoplasmic MTs during interphase; (4) When forming cilia, they fail to establish and/or maintain a proper connection to the plasma membrane—although, surprisingly, they can still form an axoneme-like structure that can recruit transition zone (TZ) proteins. We show that PLP helps assemble “pericentriolar clouds” of electron-dense material that emanate from the central cartwheel spokes and spread outward to surround the mother centriole. We propose that the partial loss of these structures may largely explain the complex centriole, centrosome and cilium defects we observe in Plp mutant cells.
Delivering personalised insoles to the high street using additive manufacturing

Research was undertaken to explore a process that delivers insoles for personalised footwear to the high street using additive manufacturing (AM) and to evaluate such insoles in terms of discomfort and lower extremity biomechanics. During the development of the process, five elements were identified: foot capture, anthropometric measurements, insole design, manufacture of the parts and physical biomechanical evaluation. In order to evaluate the process, recreational runners were recruited. They had 15 anthropometric measurements of the foot taken and both feet scanned. Scan data enabled personalised insoles to be designed and manufactured. Participants were fitted with footwear under two conditions: personalised and control insoles. Both conditions were evaluated in terms of discomfort ratings and biomechanics. The main advantage of the process is that it is possible to design footwear based on the foot scans, without the need to construct a last and mould for every consumer. In fact, statistical analysis indicated no significant differences between the two conditions for discomfort ratings and only one biomechanical parameter, midfoot peak mean pressure, was found to be significantly higher for the personalised condition for a short period of usage. The results suggest that producing comfortable personalised insoles using AM technique shows potential.
Can sharing stories change practice? A qualitative study of an interprofessional narrative-based palliative care course

A series of six interprofessional palliative care meetings used narrative, with participants sharing stories from their professional experience in facilitated small groups. The course was attended by doctors, nurses, social workers and emergency care practitioners. The course was evaluated by telephone interview with 19 of the 28 participants. Respondents reported effects including changed behaviours and benefit to patients. The use of narrative, as a starting point for shared learning, discussion and evaluation is unusual. Five months after the end of the course, many participants described changed professional behaviour which they believed led to improved patient outcomes.
Local Maximal Homogeneous Region Search for SAR Speckle Reduction With Sketch-Based Geometrical Kernel Function

With the flourish of the nonlocal mean method, the neighborwise similarity metric is widely applied in speckle reduction for its robust performance on the search of similar samples. In this metric, an isotropic kernel function is usually chosen to aggregate the corresponding pixels' distance between two neighborhoods. It means that the kernel function is considered as the explanation of the local spatial relationship at each pixel. However, for anisotropic features (such as edges and lines), a strong relationship exists along their directions rather than across them, so the isotropic kernel is not suitable to explain the spatial relationship around these features. Meanwhile, due to the inherent speckle in synthetic aperture radar (SAR) images, the discrimination and exploration of the geometrical properties of anisotropic features are important for the construction of adaptive kernel function. In this paper, the sketch map which is a representation of the sketch information of SAR images is extracted as the criterion for designing the kernel function. Meanwhile, due to the properties of symmetric and maximal self-similarity, a modified ratio distance is proposed and used jointly with the constructed kernel function as a similarity metric. Then, under the local stationary assumption, the local maximal homogeneous region of each pixel is searched by using the region growing method with the proposed metric. Moreover, maximal likelihood rule is used within the region for the estimation of true value. From the experiments on the synthetic and real SAR images, a promising performance in terms of speckle reduction and preservation of the details is achieved by our proposed method.
MetaSelf: an architecture and a development method for dependable self-* systems

This paper proposes a software architecture and a development process for engineering dependable and controllable self-organising (SO) systems. Our approach addresses dependability by exploiting metadata to support decision making and adaptation based on the dynamic enforcement of explicitly defined policies. Control is obtained by actively modifying metadata, policies or components. We show how this applies to two different systems: (1) a dynamically resilient Web service system; and (2) an industrial assembly system with self-adaptive and SO capabilities.
Improving robot grasping plans with affordance

The quality of grasping is supported by leveraging the concept of affordance in this paper to improve grasping operations. When introducing flexibility in handling different objects in a dynamically changing environment, uncertainty in grasping areas and operations may cause problems. We have studied the relationship between the objects, their associated operations, and the effect of performing such operations. With such information, the robot arm may be able to limit some improper grasping operations and angles in order to introduce better plans more efficiently. We use OpenRAVE as the simulation environment to carry out the plans generated. The experiments show that better plans were generated with affordance.
Globally homogeneous, locally adaptive sparse matrix-vector multiplication on the GPU

The rising popularity of the graphics processing unit (GPU) across various numerical computing applications triggered a breakneck race to optimize key numerical kernels and in particular, the sparse matrix-vector product (SpMV). Despite great strides, most existing GPU-SpMV approaches trade off one aspect of performance against another. They either require preprocessing, exhibit inconsistent behavior, lead to execution divergence, suffer load imbalance or induce detrimental memory access patterns. In this paper, we present an uncompromising approach for SpMV on the GPU. Our approach requires no separate preprocessing or knowledge of the matrix structure and works directly on the standard compressed sparse rows (CSR) data format. From a global perspective, it exhibits a homogeneous behavior reflected in efficient memory access patterns and steady per-thread workload. From a local perspective, it avoids heterogeneous execution paths by adapting its behavior to the work load at hand, it uses an efficient encoding to keep temporary data requirements for on-chip memory low, and leads to divergence-free execution. We evaluate our approach on more than 2500 matrices comparing to vendor provided, and state-of-the-art SpMV implementations. Our approach not only significantly outperforms approaches directly operating on the CSR format ( 20% average performance increase), but also outperforms approaches that preprocess the matrix even when preprocessing time is discarded. Additionally, the same strategies lead to significant performance increase when adapted for transpose SpMV.