Miracle-dz/UniFAR-FTUs huggingface dataset:
Nanomechanical Characterisation of Cells and Biocompatible Substrates

Atomic Force Microscopy (AFM) is a powerful technique that has evolved from being a purely imaging tool to a one capable of providing multifunctional information, offering exciting new possibilities for nano-biotechnology. The project focuses on the use of the AFM in order to morphologically and mechanically characterise cells and biomaterials demonstrating how versatile this instrument can be. The project is divided in the following parts:Part 1: establishment of AFM protocols for the nano-scale morphological and mechanical characterisation of soft and hard macroscopic substrates and of objects such as adsorbed nanoparticles. In particular, these techniques were tested on:Hyaluronic acid (HA)/poly(ethylene glycol) (PEG)-based hydrogels, which provide an artificial model for the mechanical behaviour of some biological tissues and organs. The elastic modulus, measured via AFM nanoindentation, of these hydrogels increased by decreasing the concentration and the molecular weight (MW) of HA in the hydrogels. We have then verified a clear relation between the mechanical properties of the hydrogels and the proliferation of cells cultured on them.Chitosan nanoparticle (popular carriers for the delivery of negatively charged macromolecular payloads, e.g. nucleic acids) cross-linked with triphosphate (TPP) and then coated with HA. We focussed on the influence of chitosan molecular weight (Mw) on nanoparticle properties. HA was able to penetrate into the more porous nanoparticles (high MW chitosan), whereas it formed a corona around the more cross-linked ones (low MW chitosan). AFM imaging was used to highlight the presence of this corona and also to estimate its apparent thickness to about 20-30 nm (in dry state). Silicone substrates modified with amphiphilic triblock copolymer (Sil-GMMA) layers. Extensive AFM (imaging and nanoindentation) provided evidence that silicone substrates are prevalently coated with Sil-GMMA thin layers that exhibit negligible hydrophobic recovery during drying and change the surface from more to less cell-adhesive. Part 2: AFM mechanical characterisation of fibroblast-to-myofibroblast differentiation process. Fibroblasts were stimulated to differentiate into myofibroblasts by Transforming Grow Factor ? 1 (TGF? 1) on hard substrate. AFM force maps performed both on fibroblasts (untreated cells) and myofibroblasts (TGF? 1-treated cells) revealed a significant increase in the elastic modulus in treated cells. Part 3: preparation and AFM characterisation of poly(ethylene glycol) diacrylate/acrylate (PEGDA/A) hydrogels. Since the mechanical properties of the substrate plays a pivotal role in fibroblast-to-myofibroblast differentiation process, hydrogels were prepared and characterised at the macro/nanoscale with AFM indentation, providing us with cell-adhesive substrates that cover a wide range of elastic modulus. These substrates are optimal candidates for future investigations to better understand and possibly control the differentiation process.
Is It Worth Responding to Reviews ? A Case Study of the Top Free Apps in the Google Play Store

The value of responding to a user review of a mobile app has never been explored. Our analysis of app reviews and responses from 10,713 top apps in the Google Play Store shows that developers of frequently-reviewed apps never respond to reviews. However, we observe that there are positive effects to responding to reviews (users change their ratings 38.7% of the time following a developer response) with a median increase of 20% in the rating.
Assisting Transfer-Enabled Machine Learning Algorithms: Leveraging Human Knowledge for Curriculum Design

Transfer learning is a successful technique that significantly improves machine learning algorithms by training on a sequence of tasks rather than a single task in isolation. However, there is currently no systematic method for deciding how to construct such a sequence of tasks. In this paper, I propose that while humans are well-suited for the task of curriculum development, significant research is still necessary to better understand how to create effective curricula for machine learning algorithms.
Synchronizing the Timestamps of Concurrent Events in Traces of Hybrid MPI/OpenMP Applications

Event traces are helpful in understanding the performance behavior of parallel applications since they allow the in-depth analysis of communication and synchronization patterns. However, the absence of synchronized clocks on most cluster systems may render the analysis ineffective because inaccurate relative event timings may misrepresent the logical event order and lead to errors when quantifying the impact of certain behaviors or confuse the users of time-line visualization tools by showing messages flowing backward in time. In our earlier work, we have developed a scalable algorithm that eliminates inconsistent inter-process timings postmortem in traces of pure MPI applications. Since hybrid programming, the combination of MPI and OpenMP in a single application, is becoming more popular on clusters in response to rising numbers of cores per chip and widening shared-memory nodes, we present an extended version of the algorithm that in addition to message-passing event semantics also preserves and restores shared-memory event semantics.
Robust transmission for simultaneous wireless information and power transfer systems with secrecy constraints

In this paper, a robust transmission scheme for simultaneous wireless information and power transfer (SWIPT) in the presence of an eavesdropper is proposed. With imperfect channel state information (CSI) at the transmitter, the optimal transmit covariance matrix is obtained based on the maximization of the worst case harvested energy for the energy receiver (ER) while guaranteeing the achievable secrecy rate constraint for the information receiver (IR). To solve such a challenging nonconvex problem, in this paper, a two-stage optimization approach is proposed. In the first stage, the original problem is transformed into a robust design problem, which can be further converted into a convex semidefinite program (SDP) problem by using S-procedure as a tool. In the second stage, the optimal transmit covariance matrix is obtained via the aid of one-dimensional search algorithm. Finally, simulation results are provided to illustrate the robustness and effectiveness of the proposed method.
Adding Value to Software Development and Maintenance by Software Architecture Evolution Visualization

Software needs to evolve overtime to cope with continuously changing requirements. “Software quickly becomes very complex when its size increases, which results in a boost in the time and effort needed to understand the system, maintain its components, extend its functionality, debug it and write tests for it”[10]. Therefore, software evolution causes burdens to both development and maintenance process. Software component organization is referred to as software architecture. A good architecture can help to satisfy the requirements, simplify the understanding of the software and provide a blueprint for development[18]. Thus, a good architecture can help to reduce the burden caused by software evolution. However, software architecture is abstract and intangible. Visualization is an effective way to display and analyze architecture by providing visual representations of software elements. Therefore, this research seeks to develop a new visualization technique for implemented software architecture[8] evolution that will be beneficial to both software development and maintenance. In order to achieve the goal, we answered the “why”, “what” and “how” aspects of visualization design: Why we need the visualization? What could be visualized? How to visualize the architecture? From a literature review and three stakeholder interviews, a list of architectural features and different stakeholders’ needs were identified. The stakeholders consist of developers, software consultants and managers of development. Based on the results, we matched stakeholder requirements with existing visualization techniques and selected features to visualize, which are component and dependency evolution. After that, a visualization prototype was designed and developed. We then validated the prototype through three case studies in which we discussed the prototype’s usefulness and applicability. In conclusion, this research has three results. First, 12 architectural features were identified as benefiting visualization. Second, requirements from developers, consultants and managers were collected and matched with some existing visualization techniques. Third, this research contributes a new visualization technique for software architecture evolution. In future research, the results can be improved by collecting the requirements from a larger group of stakeholders which consists of more diversified roles such as architects and
Hybrid modeling method for transient simulation of multilayered power/ground planes

The power integrity is one of the serious problem in the design of three-dimensional integrated circuits and packaging. Usually, the board and interposer carrying them have multilayered power/ground plane, which plays an important role in the power distribution network (PDN) of the system. Therefore, the modeling and simulation of multilayered power/ground planes become increasingly important.
Document Clustering using K-Medoids

People are always in search of matters for which they are prone to use internet, but again it has huge assemblage of data due to which it becomes difficult for the reader to get the most accurate data. To make it easier for people to gather accurate data, similar information has to be clustered at one place. There are many algorithms used for clustering of relevant information in one platform. In this paper, K-Medoids clustering algorithm has been employed for formation of clusters which is further used for document summarization.
A post-Kyoto partner: Considering the stratospheric ozone regime as a tool to manage nitrous oxide

Nitrous oxide (N2O) is the largest known remaining anthropogenic threat to the stratospheric ozone layer. However, it is currently only regulated under the 1997 Kyoto Protocol because of its simultaneous ability to warm the climate. The threat N2O poses to the stratospheric ozone layer, coupled with the uncertain future of the international climate regime, motivates our exploration of issues that could be relevant to the Parties to the ozone regime (the 1985 Vienna Convention and its 1987 Montreal Protocol) should they decide to take measures to manage N2O in the future. There are clear legal avenues to regulate N2O under the ozone regime as well as several ways to share authority with the existing and future international climate treaties. N2O mitigation strategies exist to address the most significant anthropogenic sources, including agriculture, where behavioral practices and new technologies could contribute significantly to reducing emissions. Existing policies managing N2O and other forms of reactive nitrogen could be harnessed and built on by the ozone regime to implement N2O controls. There are several challenges and potential cobenefits to N2O control which we discuss here: food security, equity, and implications of the nitrogen cascade. The possible inclusion of N2O in the ozone regime need not be viewed as a sign of failure of the United Nations Framework Convention on Climate Change to adequately deal with climate change. Rather, it could represent an additional valuable tool in sustainable development diplomacy.
Pioglitazone improves insulin resistance and decreases blood pressure in adult patients with congenital adrenal hyperplasia.

CONTEXT Patients with congenital adrenal hyperplasia (CAH) are chronically treated with supraphysiological doses of glucocorticoids, which are known to induce insulin resistance. Thiazolidinediones might reverse this effect and improve insulin sensitivity. OBJECTIVES To assess insulin sensitivity in CAH patients and the effect of pioglitazone treatment on insulin sensitivity in CAH patients. Secondary objectives were the effects of treatment with pioglitazone on blood pressure, body fat distribution, lipid, and steroid profiles. DESIGN Randomized placebo controlled crossover trial. PARTICIPANTS Twelve CAH patients and 12 body mass and age-matched control subjects. INTERVENTION Sixteen-week treatment with pioglitazone (45 mg/day) or placebo. MAIN OUTCOME MEASURE Insulin sensitivity measured by euglycemic clamp and oral glucose tolerance test. Further measures were 24-h blood pressure profiles, body fat distribution measured by magnetic resonance imaging, dual energy x-ray absorptiometry (DEXA) and bioimpedance procedures, liver fat by magnetic resonance spectroscopy, lipid, and steroid profiles. RESULTS CAH patients were insulin resistant compared with healthy controls. Treatment with pioglitazone significantly improved insulin sensitivity in CAH patients (glucose infusion rate (GIR) from 28.5+/-11.6 to 38.9+/-11.0 micromol/kg per min, P=0.000, GIR in controls 46.2+/-23.4 micromol/kg per min, P<0.05 versus CAH). Treatment with pioglitazone decreased blood pressure (systolic: 124.0+/-13.6 vs 127.0+/-14.9 mmHg, P<0.001, diastolic: 72.8+/-11.5 vs 77.4+/-12.6 mmHg, P<0.001). No changes in body fat distribution, lipid, and steroid profiles were observed. CONCLUSIONS CAH patients are insulin resistant compared with matched control subjects. Treatment with pioglitazone improves insulin sensitivity and decreases blood pressure in CAH patients.
MicroRNA miR-98 inhibits tumor angiogenesis and invasion by targeting activin receptor-like kinase-4 and matrix metalloproteinase-11

Angiogenesis and invasion are essential processes for solid tumor growth and dissemination. The tumor development process can be dependent on the activation of a series of signaling pathways, including growth factor-activated pathways. MicroRNAs have been shown to be critical for tumorigenesis, but their roles in cancer angiogenesis, invasion and other signaling pathways important for tumor development are still unclear in the context of tumor biology. We investigated the role of microRNA miR-98 in regulating tumor growth, invasion, and angiogenesis using a highly aggressive breast cancer model in vitro and in vitro. We found that the expression of miR-98 inhibited breast cancer cell proliferation, survival, tumor growth, invasion, and angiogenesis. Conversely, inhibition of endogenous miR-98 promoted cell proliferation, survival, tumor growth, invasion, and angiogenesis. It appeared that miR-98 inhibited angiogenesis by modulating endothelial cell activities including cell spreading, cell invasion and tubule formation. Interestingly, miR-98 reduced the expression of ALK4 and MMP11, both of which were potential targets of miR-98. Transfection of an anti-miR-98 construct increased the expression of both targets. We confirmed that mir-98 targeted the 3'-untranslated regions of ALK4 and MMP11. Finally, ALK4- and MMP11-specific siRNAs inhibited breast cancer cell proliferation, survival, and angiogenesis. Rescue experiments with ALK4 and MMP11 constructs reversed the anti-proliferative, anti-invasive and anti-angiogenic effects of miR-98. Our findings define a regulatory role of miR-98 in tumor angiogenesis and invasion through repressed ALK4 and MMP11 expression.
Privacy Preservation in Social Networks with Sensitive Edge Weights

With the development of emerging social networks, such as Facebook and MySpace, security and privacy threats arising from social network analysis bring a risk of disclosure of confidential knowledge when the social network data is shared or made public. In addition to the current social network anonymity de-identification techniques, we study a situation, such as in a business transaction network, in which weights are attached to network edges that are considered to be confidential (e.g., transactions). We consider perturbing the weights of some edges to preserve data privacy when the network is published, while retaining the shortest path and the approximate cost of the path between some pairs of nodes in the original network. We develop two privacypreserving strategies for this application. The first strategy is based on a Gaussian randomization multiplication, the second one is a greedy perturbation algorithm based on graph theory. In particular, the second strategy not only yields an approximate length of the shortest path while maintaining the shortest path between selected pairs of nodes, but also maximizes privacy preservation of the original weights. We present experimental results to support our mathematical analysis.
A Machine Learning Framework to Detect and Document Text-Based Cyberstalking

Cyberstalking is becoming a social and international problem, where cyberstalkers utilise the Internet to target individuals and disguise themselves without fear of any consequences. Several technologies, methods, and techniques are used by perpetrators to terrorise victims. While spam email filtering systems have been effective by applying various statistical and machine learning algorithms, utilising text categorization and filtering to detect textand email-based cyberstalking is an interesting new application. There is also the need to gather evidence by the victim. To this end we discuss a framework to detect cyberstalking in messages; short message service, multimedia messaging service, chat, instance messaging and emails, and as well as to support documenting evidence. Our framework consists of five main modules: a detection module which detects cyberstalking using message categorisation; an attacker identification module based on cyberstalkers’ previous messages history, personalisation module, aggregator module and messages and evidence collection module. We discuss our ongoing work and how different text categorization and machine learning approaches can be applied to identify cyberstalkers.
GoDec: Randomized Lowrank & Sparse Matrix Decomposition in Noisy Case

Low-rank and sparse structures have been profoundly studied in matrix completion and compressed sensing. In this paper, we develop "Go Decomposition" (GoDec) to efficiently and robustly estimate the low-rank part L and the sparse part S of a matrix X = L + S + G with noise G. GoDec alternatively assigns the low-rank approximation of X - S to L and the sparse approximation of X - L to S. The algorithm can be significantly accelerated by bilateral random projections (BRP). We also propose GoDec for matrix completion as an important variant. We prove that the objective value ||X - L - S||2F converges to a local minimum, while L and S linearly converge to local optimums. Theoretically, we analyze the influence of L, S and G to the asymptotic/convergence speeds in order to discover the robustness of GoDec. Empirical studies suggest the efficiency, robustness and effectiveness of GoDec comparing with representative matrix decomposition and completion tools, e.g., Robust PCA and OptSpace.
Ldiff: An enhanced line differencing tool

Differencing tools are highly relevant for a series of software engineering tasks, including analyzing developers' activities, assessing the changeability of software artifacts, and monitoring the maintenance of critical assets such as source clones and vulnerable instructions. This tool demonstration shows the features of ldiff, an enhanced, language-independent line differencing tool. L-diff builds upon the Unix diff and overcomes its limitations in determining whether an artifact line has been changed or is the result of additions and removals, and in tracking artifact fragments that have been moved upward or downward within the file. The paper describes the tool and shows its capability of analyzing changes on different kinds of software artifacts, including use cases, code developed with different programming languages, and test cases.
Efficient Spatial Keyword Search in Trajectory Databases

An increasing amount of trajectory data is being annotated with text descriptions to better capture the semantics associated with locations. The fusion of spatial locations and text descriptions in trajectories engenders a new type of top-$k$ queries that take into account both aspects. Each trajectory in consideration consists of a sequence of geo-spatial locations associated with text descriptions. Given a user location $\lambda$ and a keyword set $\psi$, a top-$k$ query returns $k$ trajectories whose text descriptions cover the keywords $\psi$ and that have the shortest match distance. To the best of our knowledge, previous research on querying trajectory databases has focused on trajectory data without any text description, and no existing work has studied such kind of top-$k$ queries on trajectories. This paper proposes one novel method for efficiently computing top-$k$ trajectories. The method is developed based on a new hybrid index, cell-keyword conscious B$^+$-tree, denoted by \cellbtree, which enables us to exploit both text relevance and location proximity to facilitate efficient and effective query processing. The results of our extensive empirical studies with an implementation of the proposed algorithms on BerkeleyDB demonstrate that our proposed methods are capable of achieving excellent performance and good scalability.
CARBON NANOTUBE: A FLEXIBLE APPROACH FOR NANOMEDICINE AND DRUG DELIVERY

Nanostructures of carbon were first observed in 1952, which gained worldwide interest due to their various physicochemical properties. Carbon nanotubes (CNTs) have found wide applications in the delivery of therapeutic agents such as peptides, proteins, siRNA, nucleic acids, genes, vaccines and also in bone and neural tissue regeneration. Functionalized CNTs have found to be biocompatible. The eye-catching features of these structures are their electronic, mechanical, optical and chemical characteristics, which open a way to future applications and make them good candidates for a wide variety of applications, including drug transporters, new therapeutics, delivery systems and diagnostics. Their unique surface area, stiffness, strength, and resilience have led to much excitement in the field of pharmacy. They can pass through membranes, carrying therapeutic drugs, vaccines, and nucleic acids deep into the cell to targets that are previously unreachable. The applications of carbon nanotubes are in tissue engineering, drug carrier release system, wound healing, in cancer treatment and as biosensor. The successful realization of CNT-based biosensors requires proper control of their chemical and physical properties, as well as their functionalization and surface immobilization. Real applications are still under development. The modifications are done to improve efficiency of carbon nanotubes by formulating luminescent carbon nanotubes, ultrathin carbon nano-needles, magnetically guided nanotubes. Researchers have recently developed a new approach to boron neutron capture therapy in the treatment of cancer using substituted carborane-appended water-Soluble single-wall carbon nanotubes. This article provides an overview of current nanotube technology, with a special focus on synthesis and purification, properties, benefits, and applications. Keywords: Carbon nanotubes, Biosensors, Tissue engineering, Biocompatible.
Oil and the Great Moderation

We assess the extent to which the great US macroeconomic stability since the mid-1980s can be accounted for by changes in oil shocks and the oil share in GDP. To do this we estimate a DSGE model with an oil-producing sector before and after 1984 and perform counterfactual simulations. We nest two popular explanations for the Great Moderation: (1) smaller (non-oil) real shocks; and (2) better monetary policy. We find that the reduced oil share accounted for as much as one-third of the inflation moderation, and 13% of the growth moderation, while smaller oil shocks accounted for 11% of the inflation moderation and 7% of the growth moderation. This notwithstanding, better monetary policy explains the bulk of the inflation moderation, while most of the growth moderation is explained by smaller TFP shocks.