Miracle-dz/UniFAR-FTUs huggingface dataset:
Recalcitrant Issues and New Frontiers in Nano-Pharmacology

Packaging of old pharma drugs into new packaging “nanoparticles” is called nano-pharmacology and the products are called nano-based drugs. The inception of nano-pharmacology research and development (R&D) is marked by the approval of the first nano-based drug Doxil® in 1995 by the Food and Drug Administration. However, even after more than two decades, today, there are only ∼20 nano-based drugs in the market to treat cancers and brain diseases. In this article we share the perspectives of nanotechnology scientists, engineers, and clinicians on the roadblocks in nano-pharmacology R&D. Also, we share our opinion on new frontiers in the field of nano-pharmacology R&D that may allow rapid and efficient transfer of nano-pharma technologies from R&D to market.
Kinematic Synergy of Multi-DoF Movement in Upper Limb and Its Application for Rehabilitation Exoskeleton Motion Planning

It is important for rehabilitation exoskeletons to move with a spatiotemporal motion patterns that well match the upper-limb joint kinematic characteristics. However, few efforts have been made to manipulate the motion control based on human kinematic synergies. This work analyzed the spatiotemporal kinematic synergies of right arm reaching movement and investigated their potential usage in upper limb assistive exoskeleton motion planning. Ten right-handed subjects were asked to reach 10 target button locations placed on a cardboard in front. The kinematic data of right arm were tracked by a motion capture system. Angular velocities over time for shoulder flexion/extension, shoulder abduction/adduction, shoulder internal/external rotation, and elbow flexion/extension were computed. Principal component analysis (PCA) was used to derive kinematic synergies from the reaching task for each subject. We found that the first four synergies can explain more than 94% of the variance. Moreover, the joint coordination patterns were dynamically regulated over time as the number of kinematic synergy (PC) increased. The synergies with different order played different roles in reaching movement. Our results indicated that the low-order synergies represented the overall trend of motion patterns, while the high-order synergies described the fine motions at specific moving phases. A 4-DoF upper limb assistive exoskeleton was modeled in SolidWorks to simulate assistive exoskeleton movement pattern based on kinematic synergy. An exoskeleton Denavit-Hartenberg (D-H) model was established to estimate the exoskeleton moving pattern in reaching tasks. The results further confirmed that kinematic synergies could be used for exoskeleton motion planning, and different principal components contributed to the motion trajectory and end-point accuracy to some extent. The findings of this study may provide novel but simplified strategies for the development of rehabilitation and assistive robotic systems approximating the motion pattern of natural upper-limb motor function.
The Role of Non-Mycorrhizal Fungi in Germination of the Mycoheterotrophic Orchid Pogoniopsis schenckii Cogn.

Endophytic fungi are those that inhabit within organs and tissues without causing damage, while mycorrhizal fungi develop hyphal complexes called pelotons within cortical cells of orchid roots. Although abundant and frequent in all plant organs, the role of endophytic fungi has been neglected in relation to orchid’s early development. Pogoniopsis schenckii Cogn. is an aclorophyllated and mycoheterotrophic (MH) orchid. This study aimed at i) investigating the endophytic fungal community in organs of P. schenckii and its mycorrhizal fungi associated; ii) evaluating the ability of isolated fungus in the in vitro germination of the seeds of the species, and iii) describing the development of P. schenckii protocorm, analyzing the ultrastructure of the infected cells. Six genera of fungi were isolated and identified through the partial sequencing of the internal transcribed spacer region, all belonging to the phylum Ascomycota. Also, Tulasnellaceae was identified through uncultured technique as potentially mycorrhizal in this MH orchid. Some isolates of the genera Trichoderma, Fusarium, and especially Clonostachys presented germinative potential on P. schenckii seeds, causing rupture of the external tegument. The protocorms showed complete absence of peloton formation, but fungal hyphae were clearly observed within living cells. This is the first report of germination of a MH and aclorophyllated orchid species stimulated by the presence of non-mycorrhizal endophytic fungi isolated from fruits and roots of the same species.
Continuous Activity Assay for HDAC11 Enabling Reevaluation of HDAC Inhibitors

Histone deacetylase 11 (HDAC11) preferentially removes fatty acid residues from lysine side chains in a peptide or protein environment. Here, we report the development and validation of a continuous fluorescence-based activity assay using an internally quenched TNFα-derived peptide derivative as a substrate. The threonine residue in the +1 position was replaced by the quencher amino acid 3′-nitro-l-tyrosine and the fatty acyl moiety substituted by 2-aminobenzoylated 11-aminoundecanoic acid. The resulting peptide substrate enables fluorescence-based direct and continuous readout of HDAC11-mediated amide bond cleavage fully compatible with high-throughput screening formats. The Z′-factor is higher than 0.85 for the 15 μM substrate concentration, and the signal-to-noise ratio exceeds 150 for 384-well plates. In the absence of NAD+, this substrate is specific for HDAC11. Reevaluation of inhibitory data using our novel assay revealed limited potency and selectivity of known HDAC inhibitors, including Elevenostat, a putative HDAC11-specific inhibitor.
Experimental harvest regulations reveal that water availability during spring, not harvest, affects change in a waterfowl population

Abstract Population change is regulated by vital rates that are influenced by environmental conditions, demographic stochasticity, and, increasingly, anthropogenic effects. Habitat destruction and climate change threaten the future of many wildlife populations, and there are additional concerns regarding the effects of harvest rates on demographic components of harvested organisms. Further, many population managers strictly manage harvest of wild organisms to mediate population trends of these populations. The goal of our study was to decouple harvest and environmental variability in a closely monitored population of wild ducks in North America, where we experimentally regulated harvest independently of environmental variation over a period of 4 years. We used 9 years of capture–mark–recapture data to estimate breeding population size during the spring for a population of wood ducks in Nevada. We then assessed the effect of one environmental variable and harvest pressure on annual changes in the breeding population size. Climatic conditions influencing water availability were strongly positively related to population growth rates of wood ducks in our study system. In contrast, harvest regulations and harvest rates did not affect population growth rates. We suggest efforts to conserve waterfowl should focus on the effects of habitat loss in breeding areas and climate change, which will likely affect precipitation regimes in the future. We demonstrate the utility of capture–mark–recapture methods to estimate abundance of species which are difficult to survey and test the impacts of anthropogenic harvest and climate on populations. Finally, our results continue to add to the importance of experimentation in applied conservation biology, where we believe that continued experiments on nonthreatened species will be critically important as researchers attempt to understand how to quantify and mitigate direct anthropogenic impacts in a changing world.
Remote estimation of overwintering home ranges in an elusive, migratory nocturnal bird

Abstract Due to a long running research bias toward the breeding season, there are major gaps in knowledge on the basic nonbreeding ecology of many species, preventing a full‐annual cycle focus in ecology and conservation. Exacerbating this problem is the fact that many species are extremely difficult to detect outside of breeding. Here, we demonstrate a partial solution to this problem by using archival GPS tags to examine the overwintering ecology of a migratory nocturnal bird, the eastern whip‐poor‐will (Antrostomus vociferous). We deployed tags on 21 individuals and were able to recover 11 (52%) one year later. Tags collected high precision (approx. 10 m) points throughout the nonbreeding period. With continuous time movement models, we used these data to estimate overwintering home ranges. All individuals exhibited at least one bounded home range during this phase of the annual cycle, three of eleven had two wintering locations, and home range area ranged from 0.50 to 10.85 ha. All overwintering home ranges contained closed‐canopy forest land cover (42%–100%), and no other land cover type represented >40% of any home range. We found some evidence, with caveats, that total edge within the landscape surrounding the home range was negatively related to home range area. The prevalence of contiguous closed‐canopy forest cover in overwintering home ranges contrasts with apparent breeding habitat preferences, which includes clear‐cuts and other, more open, habitats. This study is the first to reveal key aspects of overwintering space use in this species by using archival GPS to overcome both logistical and methodological limitations. Expanded use of such technology is critical to gathering basic ecological and distributional data, necessary for achieving a more complete understanding of full‐annual cycles of animal populations.
Jasmonic acid levels decline in advance of the transition to the adult phase in maize

Abstract Leaf‐derived signals drive the development of the shoot, eventually leading to flowering. In maize, transcripts of genes that facilitate jasmonic acid (JA) signaling are more abundant in juvenile compared to adult leaf primordia; exogenous application of JA both extends the juvenile phase and delays the decline in miR156 levels. To test the hypothesis that JA promotes juvenility, we measured JA and meJA levels using LC‐MS in successive stages of leaf one development and in later leaves at stages leading up to phase change in both normal maize and phase change mutants. We concurrently measured gibberellic acid (GA), required for the timely transition to the adult phase. Jasmonic acid levels increased from germination through leaf one differentiation, declining in later formed leaves as the shoot approached phase change. In contrast, levels of GA were low in leaf one after germination and increased as the shoot matured to the adult phase. Multiple doses of exogenous JA resulted in the production of as many as three additional juvenile leaves. We analyzed two transcript expression datasets to investigate when gene regulation by miR156 begins in the context of spatiotemporal patterns of JA and GA signaling. Quantifying these hormones in phase change mutants provided insight into how these two hormones control phase‐specific patterns of differentiation. We conclude that the hormone JA is a leaf‐provisioned signal that influences the duration, and possibly the initiation, of the juvenile phase of maize by controlling patterns of differentiation in successive leaf primordia.
Effective Control of the Growth and Photoluminescence Properties of CsPbBr3/Cs4PbBr6 Nanocomposites by Solvent Engineering

Metal halide perovskites exhibit small exciton binding energy, which leads to a low electron–hole capture rate for radiative recombination and accordingly decreases the luminescence efficiency. Reducing the thickness of the perovskite film or the size of the perovskite crystal is found to be an effective method to spatially confine the electrons and holes to promote the bimolecular radiative recombination. Here, we fabricate CsPbBr3/Cs4PbBr6 nanocomposites, applicable for light emission diodes, by a simple self-assembly method. We effectively reduce the critical size of the CsPbBr3 nanocrystals in the CsPbBr3/Cs4PbBr6 nanocomposites by adding a certain amount of dimethyl sulfoxide into the perovskite precursor solution. Accordingly, the photoluminescence quantum yield of the CsPbBr3/Cs4PbBr6 nanocomposites increased from 56 to 91% due to the quantum size effect. In situ observation of the growth of CsPbBr3/Cs4PbBr6 nanocomposites reveals that the reduction of the CsPbBr3 crystal size is due to the change of the chemical reaction speed during the two-step growth process of the CsPbBr3/Cs4PbBr6 nanocomposites.
Strategies to optimize modeling habitat suitability of Bertholletia excelsa in the Pan‐Amazonia

Abstract Aim Amazon‐nut (Bertholletia excelsa) is a hyperdominant and protected tree species, playing a keystone role in nutrient cycling and ecosystem service provision in Amazonia. Our main goal was to develop a robust habitat suitability model of Amazon‐nut and to identify the most important predictor variables to support conservation and tree planting decisions. Localization Amazon region, South America. Methods We collected 3,325 unique Amazon‐nut records and assembled >100 spatial predictor variables organized across climatic, edaphic, and geophysical categories. We compared suitability models using variables (a) selected through statistical techniques; (b) recommended by experts; and (c) integrating both approaches (a and b). We applied different spatial filtering scenarios to reduce overfitting. We additionally fine‐tuned MAXENT settings to our data. The best model was selected through quantitative and qualitative assessments. Results Principal component analysis based on expert recommendations was the most appropriate method for predictor selection. Elevation, coarse soil fragments, clay, slope, and annual potential evapotranspiration were the most important predictors. Their relative contribution to the best model amounted to 75%. Filtering of the presences within a radius of 10 km displayed lowest overfitting, a satisfactory omission rate and the most symmetric distribution curve. Our findings suggest that under current environmental conditions, suitable habitat for Amazon‐nut is found across 2.3 million km2, that is, 32% of the Amazon Biome. Main conclusion The combination of statistical techniques with expert knowledge improved the quality of our suitability model. Topographic and soil variables were the most important predictors. The combination of predictor variable selection, fine‐tuning of model parameters and spatial filtering was critical for the construction of a reliable habitat suitability model.
Towards Self-Organized Load Distribution over Chaotic Resources

This paper addresses a question of whether resources suffering nonlinear fluctuations can maintain their stability as a system expands for computing tasks in a distributed manner. To this end, we suggest that by evolving individual resources following the self-organized criticality of sandpile model, the whole load distribution system can reach a stable state after a small but extremely local overhead occurs, leading to lots of avalanches. The proposed load balancing approach is evaluated in terms of latency minimization.
Bayesian Optimisation for Planning And Reinforcement Learning

Philippe Morere Doctor of Philosophy The University of Sydney October 2019 Bayesian Optimisation for Planning and Reinforcement Learning This thesis addresses the problem of achieving efficient non-myopic decision making in continuous spaces by explicitly balancing exploration and exploitation. Decision making, both in planning and reinforcement learning, enables agents or robots to complete tasks by acting on their environments. Complexity arises when completing objectives requires sacrificing short-term performance in order to achieve better long-term performance. Decision making algorithms with this characteristic are known as non-myopic and require long sequences of actions and their performance to be evaluated, thereby greatly increasing the search space size. Decision making computes optimal behaviours by balancing two key quantities: exploration and exploitation. Exploitation takes advantage of previously acquired information or high performing solutions, whereas exploration focuses on acquiring more informative data or investigating potential solutions. The balance between these quantities can be found in planning when searching over the space of future sequences of actions, or in reinforcement learning (RL) when deciding whether to acquire new data to refine underlying models. This thesis brings the following contributions: Firstly, a reward function trading off exploration and exploitation of gradients for sequential planning is proposed. This reward function is based on Bayesian optimisation (BO) and is combined to a non-myopic planner to achieve efficient spatial monitoring. Secondly, the previous planning algorithm is extended to continuous actions spaces. The resulting solution, called continuous belief tree search (CBTS) uses BO to dynamically sample actions within a tree search, balancing high-performing actions and novelty. The continuous planner is combined with a kernel-based trajectory generation technique into kCBTS. These trajectories display consistent properties such as smoothness and differentiability, enforced by kernel functions. Finally, the framework is extended to reinforcement learning, for which a multiobjective methodology for explicit exploration and exploitation balance is proposed. The two objectives are modelled explicitly and balanced at a policy level, as in BO acquisition functions. This allows for online exploration balancing strategies, as well as a data-efficient model-free RL algorithm achieving exploration by minimising the uncertainty of Q-values (EMU-Q). The proposed algorithms are evaluated on different simulated and real-world robotics problems. Experiments show superior performance in terms of sample efficiency and exploration for all algorithms. Results underline the advantages of techniques trading off exploration and exploitation.
Behavioral vs drug treatment for urge urinary incontinence in older women: a randomized controlled trial

Context. —Urinary incontinence is a common condition caused by many factors with several treatment options.Objective. —To compare the effectiveness of biofeedback-assisted behavioral treatment with drug treatment and a placebo control condition for the treatment of urge and mixed urinary incontinence in older community-dwelling women.Design. —Randomized placebo-controlled trial conducted from 1989 to 1995.Setting. —University-based outpatient geriatric medicine clinic.Patients. —A volunteer sample of 197 women aged 55 to 92 years with urge urinary incontinence or mixed incontinence with urge as the predominant pattern. Subjects had to have urodynamic evidence of bladder dysfunction, be ambulatory, and not have dementia.Intervention. —Subjects were randomized to 4 sessions (8 weeks) of biofeedback-assisted behavioral treatment, drug treatment (with oxybutynin chloride, possible range of doses, 2.5 mg daily to 5.0 mg 3 times daily), or a placebo control condition.Main Outcome Measures. —Reduction in the frequency of incontinent episodes as determined by bladder diaries, and patients' perceptions of improvement and their comfort and satisfaction with treatment.Results. —For all 3 treatment groups, reduction of incontinence was most pronounced early in treatment and progressed more gradually thereafter. Behavioral treatment, which yielded a mean 80.7% reduction of incontinence episodes, was significantly more effective than drug treatment (mean 68.5% reduction; P=.04) and both were more effective than the placebo control condition (mean 39.4% reduction; P<.001 and P=.009, respectively). Patient-perceived improvement was greatest for behavioral treatment (74.1% "much better" vs 50.9% and 26.9% for drug treatment and placebo, respectively). Only 14.0% of patients receiving behavioral treatment wanted to change to another treatment vs 75.5% in each of the other groups.Conclusion. —Behavioral treatment is a safe and effective conservative intervention that should be made more readily available to patients as a first-line treatment for urge and mixed incontinence.
MEML: Resource-aware MQTT-based Machine Learning for Network Attacks Detection on IoT Edge Devices

Growing number of Smart Applications in recent years bring a completely new landscape of cyber-attacks and exploitation scenario that have not been seen in wild before. Devices in Edge commonly have very limited computational resources and corresponding power source reducing the number of conventional cybersecurity measures available for deployment. This also puts strict requirements on how the signatures of malicious actions can be updated and actualized. It has been proved efficiency of Machine Learning models, Neural Networks in particular, in multiple tasks related to cybersecurity due to the high-abstract precise models and training from historical data. However, when it comes to the devices in Edge, it is clear that the extensive training of the model is not possible, while testing of new unseen data can be successfully done. In addition to the conventional understanding of off-line and on-line model training, this contribution looks into how the Machine Learning can be successfully deployed on IoT while putting unnecessary computations off-chip through parameters transfer over MQTT network, reducing computational footprint on micro-controllers. We believe that proposed approach will be beneficial for many applications in resource-constrained environment.
3D Visualisation of Diffusion Flame Structure and Dynamics under Acoustic Excitation

Laminar gas jet diffusion flames have been intensively studied in combustion science. The understanding of diffusion flame stabilization mechanisms is essential for industrial applications, such as engine and furnace designs, safety consideration, costs cutting, combustion efficiency improvement and pollutant emissions reduction. It is found that laminar jet diffusion flames show a typical phenomenon of flickering/oscillating at a low frequency of 10-20 Hz [1]. In a flickering flame, the flame and flow interactions show periodic and reproducible characteristics. It was found that the flame oscillation frequency is correlated with the large toroidal vortex structures outside the visible flame zone due to buoyancy-driven instability [2-4]. Buckmaster et al. [5] employed a linear instability theory and showed that the flame buoyancy could result in a modified Kelvin-Helmholtz type instability.
Neuromuscular reflex based hopping control for a two-segmented robotic leg

Human legs are capable of achieving different locomotion tasks (e.g. hopping, walking, running, etc.) with different ground conditions (e.g. flat ground, uneven terrains, soft ground, etc.). Understanding human leg functions are one of the keys to improve the performance of exoskeletons, prosthesis and bipedal robots. It has been shown that compliant leg behavior can explain basic dynamics of hopping, walking and running gaits [1, 2]. Human compliant leg behavior can be generated by neuromuscular properties and neural control circuitries. Simulation studies from [3, 4] have shown that diverse behaviors of human (e.g. dynamic walking and running gait) can be achieved by a neural circuitry that emphasizes spinal feedback. Schumacher and Seyfarth showed that different reflex feedback pathways lead to different behaviors during hopping with a point mass simulation model [5]. However, it remains unclear if this knowledge is applicable to a real robotic system.