Miracle-dz/UniFAR-FTUs huggingface dataset:
Lyophilization and stability of antibody-conjugated mesoporous silica nanoparticle with cationic polymer and PEG for siRNA delivery

Introduction Long-term stability of therapeutic candidates is necessary toward their clinical applications. For most nanoparticle systems formulated in aqueous solutions, lyophilization or freeze-drying is a common method to ensure long-term stability. While lyophilization of lipid, polymeric, or inorganic nanoparticles have been studied, little has been reported on lyophilization and stability of hybrid nanoparticle systems, consisting of polymers, inorganic particles, and antibody. Lyophilization of complex nanoparticle systems can be challenging with respect to preserving physicochemical properties and the biological activities of the materials. We recently reported an effective small-interfering RNA (siRNA) nanoparticle carrier consisting of 50-nm mesoporous silica nanoparticles decorated with a copolymer of polyethylenimine and polyethyleneglycol, and antibody. Materials and methods Toward future personalized medicine, the nanoparticle carriers were lyophilized alone and loaded with siRNA upon reconstitution by a few minutes of simple mixing in phosphate-buffered saline. Herein, we optimize the lyophilization of the nanoparticles in terms of buffers, lyoprotectants, reconstitution, and time and temperature of freezing and drying steps, and monitor the physical and chemical properties (reconstitution, hydrodynamic size, charge, and siRNA loading) and biological activities (gene silencing, cancer cell killing) of the materials after storing at various temperatures and times. Results The material was best formulated in Tris-HCl buffer with 5% w/w trehalose. Freezing step was performed at −55°C for 3 h, followed by a primary drying step at −40°C (100 µBar) for 24 h and a secondary drying step at 20°C (20 µBar) for 12 h. The lyophilized material can be stored stably for 2 months at 4°C and at least 6 months at −20°C. Conclusion We successfully developed the lyophilization process that should be applicable to other similar nanoparticle systems consisting of inorganic nanoparticle cores modified with cationic polymers, PEG, and antibodies.
Challenges of implementing Mark-recapture studies on poorly marked gregarious delphinids

Population parameters of poorly marked gregarious species are difficult to estimate. This is the case for common dolphins (Delphinus sp.), a genus known for its lack of distinctive marks resulting in a low mark ratio. Furthermore, the widespread nature of common dolphins results in low recaptures. We developed reliable photo-identification protocols to ensure accurate identification of individuals in the Hauraki Gulf, New Zealand. These protocols combined the use of nicks and notches and pigmentation patterns for identification and included the development of a distinctiveness threshold. The data were further stratified by the level of distinctiveness of each individual (as distinctive or highly-distinctive). Photo-identification surveys were conducted from January 2010 to December 2013. Mark-recapture techniques were implemented through a POPAN super-population approach to estimate seasonal apparent survival, capture probability and abundance of dolphins. A total of 2,083 unique adult common dolphins were identified, 51.3% were classified as D1 (highly distinctive; n = 1,069) and 48.7% as D2 (distinctive; n = 1,014). Of all individuals identified, 34.3% (n = 704) were re-sighted over subsequent years. The proportion of marked dolphins (when compared to unmarked dolphins) was 26.3% for D1 and 46.4% for D1 & D2, respectively. Apparent survival was estimated at 0.767 (CI = 0.694–0.827) for D1 animals, and 0.796 (CI = 0.729–0.850) for D1 & D2 combined. For D1 only, seasonal abundance varied from 732 (CI = 460–1,177) in autumn 2010 to 5,304 (CI = 4,745–5,930) in spring 2013. While the inclusion of D2 individuals may offer a more precise estimate of total abundance, the inability to determine additional sources of bias (for example, arising from under or overestimated mark ratios) meant that estimates for D1 individuals were deemed the least biased for this population. The photo-identification protocol, stratification of the data and steps taken to eliminate potential model violations provided a useful and novel approach to estimate population parameters for common dolphins. These approaches could be implemented for other large gregarious populations (≥500 individuals) of animals with poor natural markings.
In Vivo Imaging of Microglia With Multiphoton Microscopy

Neuroimaging has become an unparalleled tool to understand the central nervous system (CNS) anatomy, physiology and neurological diseases. While an altered immune function and microglia hyperactivation are common neuropathological features for many CNS disorders and neurodegenerative diseases, direct assessment of the role of microglial cells remains a challenging task. Non-invasive neuroimaging techniques, including magnetic resonance imaging (MRI), positron emission tomography (PET) and single positron emission computed tomography (SPECT) are widely used for human clinical applications, and a variety of ligands are available to detect neuroinflammation. In animal models, intravital imaging has been largely used, and minimally invasive multiphoton microcopy (MPM) provides high resolution detection of single microglia cells, longitudinally, in living brain. In this study, we review in vivo real-time MPM approaches to assess microglia in preclinical studies, including individual cell responses in surveillance, support, protection and restoration of brain tissue integrity, synapse formation, homeostasis, as well as in different pathological situations. We focus on in vivo studies that assess the role of microglia in mouse models of Alzheimer’s disease (AD), analyzing microglial motility and recruitment, as well as the role of microglia in anti-amyloid-β treatment, as a key therapeutic approach to treat AD. Altogether, MPM provides a high contrast and high spatial resolution approach to follow microglia chronically in vivo in complex models, supporting MPM as a powerful tool for deep intravital tissue imaging.
Discrepancies between Cyclic and Linear Antimicrobial Peptide Actions on the Spectrochemical and Nanomechanical Fingerprints of a Young Biofilm

Antimicrobial peptides (AMPs) are currently known for their potential as an alternative to conventional antibiotics and new weapons against drug-resistant bacteria and biofilms. In the present work, the mechanism of action of a cyclic (colistin) and a linear (catestatin) AMP on a young E. coli biofilm was deciphered from the molecular to the cellular scale. To this end, infrared spectroscopy (attenuated total reflection–Fourier transform infrared) assisted by chemometric analysis was combined with fluorescence and atomic force microscopies to address the very different behaviors of both AMPs. Indeed, the colistin dramatically damaged the bacterial cell wall and the metabolism even though its action was not homogeneous over the whole bacterial population and repopulation can be observed after peptide removal. Conversely, catestatin did not lead to major damages in the bacterial morphology but its action was homogeneous over the whole bacterial population and the cells were unable to regrow after the peptide treatment. Our results strongly suggested that contrary to the cyclic molecule, the linear one is able to cause irreversible damages in the bacterial membrane concomitantly to a strong impact on the bacterial metabolism.
Context-Aware Fusion for Continuous Biometric Authentication

Continuous authentication using biometrics is receiving renewed attention owing to recent advances in mobile technology. However, the context in which biometric inputs are acquired can affect the quality of information available for authentication. For example, in multi-speaker environments, face or gait could be better authenticators than voice. Unfortunately, existing fusion methods do not take this into account. In this paper, we propose a novel fusion method that accounts for context, and that can operate at both decision and score levels. Theoretical bounds on the proposed method are presented along with experiments on synthetic and real multi-modal biometric data. The results show that our proposed method is better than commonly used fusion methods, even when using state-of-the-art deep learners. Moreover, our method outperforms score-level fusion methods even at the decision-level, debunking the common myth that decision-level fusion is inferior, and showcasing the power of contextual learning.
Diversification, Branding, and Performance of Professional Service Firms

This article analyzes the effects of diversification and brand breadth on firm performance for professional service firms (PSFs). The research aim is two-fold. First, we test whether moving into products may put at risk the core resources that sustain PSFs’ competitive advantage. Second, we study which branding strategies best match their diversification attempts. Broad (narrow) brands characterize a branding strategy with scarce (plentiful) associations to specific product characteristics. We analyzed trademark portfolios of 47 U.S.-based management consulting firms in the 2000 to 2009 time period. Panel regression results suggest that (1) PSFs always benefit from diversification when they remain pure-service providers; (2) performance is positively related to a strategy of specialized narrow brands.
Evolution, Revolution, and Technology Scaling—The Impact on ESD and EOS Reliability

In the scaling of semiconductor devices, evolutionary and revolutionary modifications are made in the device dimension, structural changes and dimensions. The effect of MOSFET scaling on electrostatic discharge (ESD) and electrical overstress (EOS) reliability and robustness have both positive and negative implications. In this publication, the evolutionary and revolutionary technology changes on how they influence the ESD and EOS results will be discussed in full detail. The paper will discuss changes in the substrate, wells, isolation, source/drain regions, gate dielectrics, inter-level dielectrics and interconnects.
Transfer learning for high‐precision trajectory tracking through 
 L1 adaptive feedback and iterative learning

Robust and adaptive control strategies are needed when robots or automated systems are introduced to unknown and dynamic environments where they are required to cope with disturbances, unmodeled dynamics, and parametric uncertainties. In this paper, we demonstrate the capabilities of a combined L1 adaptive control and iterative learning control (ILC) framework to achieve high‐precision trajectory tracking in the presence of unknown and changing disturbances. The L1 adaptive controller makes the system behave close to a reference model; however, it does not guarantee that perfect trajectory tracking is achieved, while ILC improves trajectory tracking performance based on previous iterations. The combined framework in this paper uses L1 adaptive control as an underlying controller that achieves a robust and repeatable behavior, while the ILC acts as a high‐level adaptation scheme that mainly compensates for systematic tracking errors. We illustrate that this framework enables transfer learning between dynamically different systems, where learned experience of one system can be shown to be beneficial for another different system. Experimental results with two different quadrotors show the superior performance of the combined L1 ‐ILC framework compared with approaches using ILC with an underlying proportional‐derivative controller or proportional‐integral‐derivative controller. Results highlight that our L1 ‐ILC framework can achieve high‐precision trajectory tracking when unknown and changing disturbances are present and can achieve transfer of learned experience between dynamically different systems. Moreover, our approach is able to achieve precise trajectory tracking in the first attempt when the initial input is generated based on the reference model of the adaptive controller.
ApproxG: Fast Approximate Parallel Graphlet Counting Through Accuracy Control

Graphlet counting is a methodology for detecting local structural properties of large graphs that has been in use for over a decade. Despite tremendous effort in optimizing its performance, even 3- and 4-node graphlet counting routines may run for hours or days on highly optimized systems. In this paper, we describe how a synergistic combination of approximate computing with parallel computing can result in multiplicative performance improvements in graphlet counting runtimes with minimal and controllable loss of accuracy. Specifically, we describe two novel techniques, multi-phased sampling for statistical accuracy guarantees and cost-aware sampling to further improve performance on multi-machine runs, which reduce the query time on large graphs from tens of hours to several minutes or seconds with only <1% relative error.
Brief Announcement: On Approximating PageRank Locally with Sublinear Query Complexity

Can one compute the PageRank score of a single, arbitrary node in a graph, exploring only a vanishing fraction of the graph? We provide a positive answer to this extensively researched open question. We develop the first algorithm that, for any n -node graph, returns a multiplicative $(1\pmε)$-approximation of the score of any given node with probability $(1-δ)$, using at most $O\big(n^2/3 łn(n)^1/3 łn(1/δ)^2/3 ε^-2/3 \big) = \tildeO (n^2/3 )$ queries which return either a node chosen uniformly at random, or the list of neighbours of a given node. Alternatively, we show that the same guarantees can be attained by fetching at most $O\big( E^4/5 d^-3/5 łn(n)^1/5 łn(1/δ)^3/5 ε^-6/5 \big) = \tildeO (E^4/5 )$ arcs, where E is the total number of arcs in the graph and d is its average degree.
Packet-Level Modeling of Cooperative Diversity: A Queueing Network Approach

Cooperative communications are expected to be a centerpiece for 5G cellular networks. Using cooperation, a wider and more uniform network coverage is attained, network capacity is enhanced, and power consumption is drastically reduced. While these latter heavily rely on collaboration among network elements, there is barely a packet-level perspective on what tangible gains cooperation is able to achieve. Motivated by this fact, different from studies on the capacity analysis of cooperative protocols, this paper tackles cooperation as a packet-level problem. The latter perspective empowers us to broaden our understanding of cooperation through characterizing high-level quantities, such as delay, throughput, fairness, and buffer length, as more tangible measures of the instant service quality that users/devices experience. In the pursuit of achieving this goal, the theory of BCMP queueing networks is leveraged. The proposed modeling approach can be used to analyze networks with an arbitrary number of relays, traffic classes, generic service time distributions, and several serving disciplines. To showcase the generality of this framework, we establish the queueing models for some of the most well-known cooperative protocols, such as amplify and forward amplify-forward, decode-forward, selection-relaying, incremental-relaying, and opportunistic-relaying, and evaluate their performances through the above-mentioned metrics. Moreover, a distributed cooperative protocol based on space–time block codes is proposed, its corresponding BCMP model is derived, and its performance is compared with other cooperative protocols.
Changes in lower extremity function and pitching performance with increasing numbers of pitches in baseball pitchers

Compared to upper extremity function, the changes in lower extremity function with increasing numbers of pitches have not fully been investigated in baseball pitchers. In addition, little is known about the correlation between lower extremity function and pitching performance. The purpose of this study was to evaluate the effect of fatigue on lower extremity function and pitching performance in a simulated baseball game. Eighteen collegiate baseball pitchers threw 117 pitches in 9 innings (13 pitches per inning) with 5-min rest between innings at an official pitching distance in a simulated game. Isometric hip muscle strength (abduction and adduction) and squat jump performance (height, mean/peak power, and mean/peak velocity) were measured before and after the game. The mean ball velocity and pitching accuracy were assessed per inning. Ball velocity significantly decreased in the 7th (P=0.026) and 9th (P=0.001) innings compared to the 1st inning, but pitching accuracy did not change significantly. Hip abduction (P=0.009) and adduction (P=0.001) strength significantly decreased after the game, but squat jump performance did not significantly change before and after the game. A significant correlation between decreased ball velocity in the 9th inning and decreased hip adduction strength was found (P=0.011, r=0.583). Our findings suggest that hip abduction and adduction strength are susceptible to fatigue owing to repetitive throwing motions and that hip adduction strength, especially, is an important physical fitness factor for maintaining ball velocity during a game in baseball pitchers.
Who Watches the Watchmen

Malicious software, a threat users face on a daily basis, have evolved from simple bankers based on social engineering to advanced persistent threats. Recent research and discoveries reveal that malware developers have been using a wide range of anti-analysis and evasion techniques, in-memory attacks, and system subversion, including BIOS and hypervisors. In addition, code-reuse attacks like Returned Oriented Programming emerge as highly potential remote code execution threats. To counteract the broadness of malicious codes, distinct techniques and tools have been proposed, such as transparent malware tracers, system-wide debuggers, live forensics tools, and isolated execution rings. In this work, we present a survey on state-of-the-art techniques that detect, mitigate, and analyze the aforementioned attacks. We show approaches based on Hardware Virtual Machines introspection, System Management Mode instrumentation, Hardware Performance Counters, isolated rings (e.g., Software Guard eXtensions), as well as others based on external hardware. We also discuss upcoming threats based on the very same technologies used for defense. Our main goal is to provide the reader with a broader, more comprehensive understanding of recently surfaced tools and techniques aiming at binary analysis for modern platforms.
On the Semantics of Distributed Reactive Programming: The Cost of Consistency

The reactive programming paradigm aims to simplify the development of reactive systems. It provides abstractions to define time-changing values that are automatically updated by the runtime according to their dependencies. The benefits of reactive programming in distributed settings have been recognized for long. Yet, existing solutions for distributed reactive programming enforce the same semantics as in single processes, introducing communication and synchronization costs that hamper scalability. Establishing suitable abstractions for distributed reactive programming demands for a deeper investigation of the semantics of change propagation. This paper takes a foundational approach and defines precise propagation semantics in terms of consistency guarantees that constrain the order and isolation of value updates. We study the benefits and costs of these consistency guarantees both theoretically and empirically, using case studies and synthetic benchmarks. We show that different applications require different levels of consistency and that manually implementing the required level on a middleware that provides a lower one annuls the abstraction improvements of reactive programming. This motivates a framework that enables the developers to select the best trade-off between consistency and overhead for the problem at hand. To this end, we present $\mathsf{DREAM}$ , a distributed reactive programming middleware with flexible consistency guarantees.
Reinforcement Learning for LTLf/LDLf Goals

MDPs extended with LTLf/LDLf non-Markovian rewards have recently attracted interest as a way to specify rewards declaratively. In this paper, we discuss how a reinforcement learning agent can learn policies fulfilling LTLf/LDLf goals. In particular we focus on the case where we have two separate representations of the world: one for the agent, using the (predefined, possibly low-level) features available to it, and one for the goal, expressed in terms of high-level (human-understandable) fluents. We formally define the problem and show how it can be solved. Moreover, we provide experimental evidence that keeping the RL agent feature space separated from the goal's can work in practice, showing interesting cases where the agent can indeed learn a policy that fulfills the LTLf/LDLf goal using only its features (augmented with additional memory).
Performance Evaluation of Routing Protocols in Electromagnetic Nanonetworks

Revolutionary advancement in realizing nano-sensors promises unprecedented enhancement of applications in several fields, such as health, industry, agriculture, environment, and sport. The small size of nano-sensors and their THz band leads to significant constraints in energy, memory, processing, and transmission range. Recent progress and active research in nano-sensing technology have tackled these constraint and led to increasing interest in connecting these nano-sensors in a new network technology, the nano-network. Communication in nano-networks still poses a non-trivial challenge owing to the constraint of processing, storage, energy, and communication range capabilities of nano-nodes. Short communication range in the THz band renders direct communication in nano-networks infeasible most of the time. Hence, multihop communication among nano-nodes is currently regarded as the viable solution for nano-network realization. In this paper, we investigate three routing protocols: controlled flooding, coordinate/routing for nanonetworks, and hierarchical ad hoc on demand distance vector. We evaluate the performance of the three protocols with respect to energy consumption and network delay against transmission range and network density.