Autophagy is a critical cellular maintenance machinery in cells, and prevents the accumulation of toxic protein aggregates, organelles or lipid droplets through degradation via the lysosome. In macro-autophagy, autophagosome first engulfs around aggregates or cellular debris and subsequently fuses with a lysosome that is sufficiently acidic (pH 4.5–5.5), where the contents are then degraded via lysosomal enzymes. Autophagy inhibition as a result of lysosomal acidification dysfunction (pH > 5.5) have been reported to play a major role in various diseases pathogenesis. Hence, there is a pressing need to target lysosomal pH to rescue autophagy. Nanoparticles are attractive materials which has been shown to be efficiently uptaken into cellular organelles and can serve as an agent to specifically localize into lysosomes and modulate its pH. Lipotoxicity, induced by chronic exposure to free fatty acids, and exposure to neurotoxins (e.g. MPP+), elevates lysosomal pH in pancreatic beta cells (Type II Diabetes, T2D) and hepatocytes (Non-alcoholic fatty liver disease, NAFLD), and PC-12 cells (Parkinson’s Disease), respectively. We first tested the lysosome acidification capability of photo-activable nanoparticles (paNPs) and poly (lactic-co-glycolic) acid nanoparticles (PLGA NPs) in a T2D model. Both NPs lowered lysosomal pH in pancreatic beta cells under lipotoxicity and improved insulin secretion function. However, paNPs only release acids upon UV trigger, limiting its applicability in vivo, while PLGA NPs degrade upon lysosome localization. We further showed that PLGA NPs are able to rescue MPP+ induced cell death in a PD model, though it has a slow degradation rate. To attain the most efficacious nanoparticle with a fast degradation and acidification rate, we synthesized acidic nanoparticles (acNPs) based on tetrafluorosuccinic and succinic acids to form optimized nanoparticles. The acNPs showed faster rescue of cellular function compared to PLGA NPs in the PD model. Finally, we tested the acNPs in NAFLD model, and where lysosomal pH reduction by acNPs restored autophagy, reduced lipid accumulation, and improved mitochondria function in high-fat diet mice. In sum, nanoparticles are of potential therapeutic interest for pathologies associated with lysosomal acidity impairment. Future studies include testing the acNPs in NASH disease model and clinical studies. / 2022-05-18T00:00:00Z

Prostate cancer (PCa) bone metastases have been reported in ~90% of patients with advanced disease. Bone metastases disrupt tissue homeostasis and weaken the skeleton, resulting in an increased risk of bone fractures and morbidity. Specifically, PCa cells disrupt the crosstalk between critical cells within the tumor/bone microenvironment (osteoblasts, osteoclasts, and immune cells), and utilize this effector-rich environment for cancer survival and growth. Therefore, a key therapeutic objective in malignant skeletal disease management is to eliminate tumors while restoring bone homeostasis. Current treatments include palliative radiotherapy, chemotherapy, or anti-RANK treatments, all of which have considerable side effects such as osteonecrosis of the jaw or enhanced tumor invasion. There remains a critical gap in therapies than can reduce tumor burden and simultaneously restore bone homeostasis. To address this gap, our work explores emerging gene therapy approaches for treating skeletal malignancies by utilizing multifunctional cytokine-based agents that can simultaneously combat tumor growth and promote bone regeneration.<div><br></div><div>We hypothesize that rationally designed cytokine-based gene therapies that can be secreted from skeletal muscle and targeted to the bone/tumor microenvironment, could effectively reduce tumor growth and restore bone cell homeostasis. To test this hypothesis, we adopted two strategies: 1) a second-generation targeted IL-27 cytokine, and 2) a de novodesign of a cytokine-like therapeutic agent (Propeptide) that includes anti-tumorigenic and pro-osteogenic domains. Both strategies share modules with overlapping therapeutic functions, rendering them complementary in their therapeutic application. In this work, we examined the proof of principle for propeptide gene therapy in muscle cells (in vitro models) and assessed the therapeutic efficacy of our cytokine-based biologics in reducing prostate tumor growth and rebalancing bone cell proliferation and differentiation. Our studies resulted in a propeptide construct representative of a cytokine structure comprised of a bundle of helices that we were able to express in cells. Additionally, our work demonstrated the targeting and anti-tumor efficacy of our therapeutic cytokines in cancer and bone cell models. Ultimately, this will provide the framework for innovative peptide and cytokine-based therapeutics that target and treat both the tumor metastases and bone. This approach will facilitate improvement of morbidity and quality of life of prostate cancer patients with bone metastases and could be applicable to other diseases with bone/tumor pathologies. <br><div><br></div></div>

By means of return-based style analysis (RBSA), heterogeneous style sub-categories were identified within the TARR category of the South African unit trust market to create a framework for sub-categorisation. The study dealt with TARR funds and their place within the investment universe. The literature review emphasised the importance of asset allocation, which supports the use of RBSA to identify asset allocation. The literature review further provided a motivation for the semi-strong form of RBSA applied to the sample data. In the study, RBSA was applied to two groups within the sample data, namely funds that have data points for the full measurement period (Group 1) and funds that have less than 75 data points (Group 2). A four-phase process was applied to the sample data. The findings suggest the following:<ul><li> in general, return-based style analysis applied to each fund identifies the asset allocation for the fund and is valid; but it is emphasised that for specific periods, the explanatory power of the regression model may become questionable; </li><li> the collective results of return-based style analysis applied to the funds can be used to create a framework for sub-categorisation. The framework proposed was the result of nine out of a potential 54 funds. The explanatory power of the regression results was less questionable. The proposed framework was applied to the remaining 45 funds (Group 2), but there were indeed inconsistencies in the application; </li><li> the framework created did not raise any concerns as a result of the Group 1 analysis. However, it was questionable when applied to the Group 2 funds in its entirety; </li><li> sub-categorisation based on only the allocation to the domestic short-term asset class was definitely a criterion that was true irrelevant of which sample group it was applied to. </li></ul> / Dissertation (MCom)--University of Pretoria, 2011. / Financial Management / unrestricted

The main aim of this study is to synthesize theranostic nanoparticles (NPs) that will drastically increase the diagnostics and therapeutic efficacy for cancer. In this research, we had prepared the NPs which constitute carbon dots (CDs), the imaging agent, Folic acid, the targeting agent, and Doxorubicin (DOX) or Gemcitabine (GEM) as the chemotherapy agents. The prepared NPs include noncovalent FA-CDs-DOX, covalent CDs-FA-DOX, and covalent FA-CDs-GEM. The spectroscopy, ultraviolet-visible spectroscopy (UV-vis), fluorescence spectroscopy, and Fourier transform-infrared spectroscopy (FT-IR), were used to confirm the successful fabrication of these complexes. Through UV-vis analysis, the drug loading capacity (DLC) and drug loading efficiency (DLE) of the complexes were determined. The noncovalent series had a higher DLE of about 83% while the covalent series showed higher DLC, 70% on average indicating high drug content. The in-vitro pH-dependent drug release shows that the noncovalent FA-CDs-DOX and the covalent FA-CDs-GEM series release more drugs into the cancer cells (pH of 5.0) than into healthy normal (pH of 7.4). The sizes of NPs were measure around 2-5 nm with Dynamic light Scattering (DLS). The toxicity of CDs, CDs-drug, and FA-CDs-drug on MDA-MB468 breast cancer cell was tested through the methylthiazolytetrazolium (MTT) assay and found that the FA bonded NPs exhibited strong therapeutic efficacy. More pharmaceutical data towards the cancer cells are investigated by our research collaborators – the pharmaceutical department at ETSU and Xavier University at Louisiana.

No description available.

The maintenance of infrastructure is critical to the well-being of society. This work focuses on a novel method for inspecting the exterior of dams using unmanned aerial vehicles (UAVs) in an automated fashion. The UAVs are equipped with optical sensors capturing still images. The resulting images are used to generate three-dimensional (3D) models using Structure from Motion (SfM) computer software. The SfM models are then used to inspect the exterior of the dam. As typical dam inspections entail completing a checklist of inspection items with varied degrees of precision (e.g. a concrete spillway may be finely inspected for cracking or joint deterioration while the general stability and water-tightness of a large embankment may be observed from a distance), a targeted inspection is also needed for the UAV method. In conjunction with the work presented in this thesis, a novel algorithm was developed which uses camera view planning across multiple proximity levels to generate a set of camera poses (positions and orientations) which can be collected in an autonomous UAV flight that facilitates generation of SfM models having tiered model quality for targeted inspection of infrastructure features. In this thesis, this novel algorithm and accompanying mobile application (referred to together as the novel advanced autonomous method) were field validated at Tibble Fork Dam, UT. The advanced autonomous method was compared to two other common image acquisition methods—basic autonomous and manual piloted—based on the SfM models produced from the collected image sets. The advanced autonomous method was found to produce models having tiered quality needed for efficient targeted inspection (25% and 50% higher resolution in medium and high priority target areas). The advanced autonomous method was found to produce models having on average 38% higher precise point accuracy (1.3cm) and 53% tighter surface reproducibility (for repeat inspections) (1.9cm) than basic autonomous and manual piloted image acquisition methods. The advanced autonomous method required on average 167% longer flight time and 38% fewer images than the other two methods, resulting in increased field time but decreased processing load. Additionally, viability of the advanced autonomous method for practical dam inspection was assessed through a case study inspection of Tibble Fork Dam using the collected SfM model and corresponding still images. The SfM model and corresponding images were found fully adequate for performing 94% of the inspection tasks and partially adequate for the remaining tasks. In consideration of this and other practical implementation factors such as time and safety, the method appears highly viable as an alternate to or supplement with traditional on-foot visual exterior inspection of dams such as Tibble Fork Dam. Suggestions for future work include adjustments to the optimization framework to improve field efficiency, development of a framework for cooperative inspection using UAV swarms, and development of a more automated workflow that would allow fully-remote dam inspections.

Most engineering curricula in the United States include some form of major design project experiences for students, such as capstone courses or design-build-fly projects. Such courses are examples of project-based learning (PBL). Part of PBL is to prepare students—and future engineers—to deal with and prevent common project failures such as missing requirements, overspending, and schedule delays. <i>But how well are students performing once they join the workforce?</i> Unfortunately, despite our best efforts to prepare future engineers as best we can, the frequency of failures of complex projects shows no signs of decreasing. In 2020 only 53% of projects were on time, 59% within budget, and 69% met their goal, as reported by the Project Management Institute. If we want to improve success rates in industry projects, letting students get the most out of their PBL experience and be better prepared to deal with project failures before they join the workforce may be a viable starting point. <br><br>The overarching goal of this dissertation is to identify and suggest improvements to areas that PBL lacks when it comes to preparing students for failure, to investigate student behaviors that lead to project failures, and to improve these behaviors by providing helpful feedback to students. <br><br>To investigate the actions and behaviors that lead to events that cause failures in student projects, I introduced “crowd signals”, which are data collected directly from the students that are part of a project team. In total, I developed 49 survey questions that collect these crowd signals. To complete the first part of the dissertation, I conducted a first experiment with 28 student teams and their instructors in two aerospace engineering PBL courses at Purdue University. The student teams were working on aircraft designs or low-gravity experiments.<br><br><i>Does PBL provide sufficient opportunities for students to fail safely, and learn from the experience? How can we improve?</i> To identify areas that PBL may lack, I compared industry failure cause occurrence rates with similar rates from student teams in PBL courses, and then provided recommendations to PBL instructors. Failure causes refer to events that frequently preceded budget, schedule, or requirements failures in industry, and are identified from the literature. Through this analysis, I found that PBL does not prepare students sufficiently for situations where the failure cause missing a design aspect occurs. The failure cause is fundamentally linked to proper systems engineering: it represents a scenario where, for example, students failed to consider an important requirement during system development, or did not detect a design flaw, or component incompatibility. I provided four recommendations to instructors who want to give their students more opportunities to learn from this failure cause, so they are better prepared to tackle it as engineers. <br><br><i>Is crowdsourced information from project team members a good indicator of future failure occurrences in student projects?</i> I developed models that predict the occurrence of future budget, schedule, or requirements failures, using crowd signals and other information as inputs, and interpreted those models to get an insight on which student actions are likely to lead to project failures. The final models correctly predict, on average, 73.11±6.92% of budget outcomes, 75.27%±9.21% of schedule outcomes, and 76.71±6.90% of technical requirements outcomes. The previous status of the project is the only input variable that appeared to be important in all three final predictive models for all three metrics. Overall, crowdsourced information is a useful source of knowledge to assess likelihood of future failures in student projects. <br><br><i>Does targeted feedback that addresses the failure causes help reduce failures in student projects?</i> To improve student behaviors that lead to project failures, I used correlations between failure measures and the crowd signals as a guide to generate 35 feedback statements. To evaluate whether the feedback statements help reduce project failures in the student teams, I conducted a second experiment at Purdue University with 14 student teams and their instructors. The student teams were enrolled in aircraft design, satellite design, or propulsion DBT courses. The student teams were split in two treatment groups: teams that received targeted feedback (i.e., feedback that aimed to address the failure causes that the specific team is most prone to) and teams that received non-targeted feedback (i.e., feedback that is positive, but does not necessarily address the failure causes the specific team is most prone to). Through my analysis, I found that my targeted feedback does not reduce the failure occurrences in terms of any metrics, compared to the non-targeted feedback. However, qualitative evaluations from the students indicated that student teams who received targeted feedback made more changes to their behaviors and thought the feedback was more helpful, compared to the student teams who received non-targeted feedback.<br><br>

During the Second Intifada, Israel shocked the international community by becoming the first country in the world to publically announce an overt policy of targeted-killing. While utilized by Israel in previous conflicts, the Second Intifada was a turning point in Israeli history due to a series of dramatic changes introduced to its targeting policy which would sharply contrast those which were previously utilized. This diploma thesis analyzed thirty-eight cases of Israeli targeting operations conducted both before and during the Second Intifada to determine if the changes made to its policy during the Second Intifada resulted in more or less successful targeting operations compared to those conducted prior to this time period. The results of this study indicated that, following the introduction of the aforementioned policy changes, Israeli targeting operations during the Second Intifada were less successful than those conducted prior to this time period.

Separation of 225Ra (t1/2 = 15 d) from its daughter isotope 225Ac (t1/2 = 10 d) is necessary to obtain pure 225Ac for cancer alpha-therapy. In this study, microscale separation of 225Ra from its daughter 225Ac using BioRad AG50X4 cation exchange resin was achieved with good reproducibility across microdevices, and ≥90% purity was achieved for 225Ac, which is comparable to conventional chromatography. These results indicate the potential for greater use of microfluidics for biomedical radiochemistry. The modularity of the system and its compatibility with different resins allows for quick and easy adaptation to the various needs of a separation campaign.

In the light of the ongoing loss of biodiversity at the global scale, monitoring grasslands is nowadays of utmost importance considering their functional relevance in terms of the ecosystem services that they provide. Here, guidelines of the European Union like the Fauna-Flora-Habitat Directive and the European Agricultural fund for Rural Development with its HNV indicators are crucial. Indeed, they form the legal framework for nature conservation and define grasslands as one of their conservation targets, whose status needs to be assessed and reported by all member states on a regular basis. In the light of these reporting requirements, the need for a harmonised and thorough grassland monitoring is highly demanding since most member states are still currently adopting intensive field surveys or photointerpretation with differing levels of detail for mapping habitat distribution. To this purpose, a cost-effective solution is offered by Earth Observation data for which specific grassland monitoring methodologies shall be then implemented which are capable of processing multitemporal acquisitions collected throughout the entire growing season. Although optical data are most suited for characterising vegetation in terms of spectral information content, they are actually subject to weather conditions (especially cloud coverage), which hinder the possibility of collecting enough information over the full phenological cycle. Furthermore, so far only few studies started employing high and very high resolution optical time series for grassland habitat monitoring since they have become available e.g., from the RapidEye satellites, only in the recent past. To overcome this limitation, SAR systems can be employed which provide imagery independent from weather or daytime conditions, hence enabling vegetation analysis by means of complete time series. Compared to optical data, radar imagery is less affected by the physical-chemical characteristics of the surface, but rather it is sensitive to structural features like geometry and roughness. However, in this context presently only very few techniques have been implemented, which are anyhow not suitable to be employed in an operational framework. Furthermore, to address the classification task, supervised approaches (which require in situ information for all the land-cover classes present in the study area) represent the most accurate methodological solution; nevertheless, collecting an exhaustive ground truth is generally expensive both in terms of time and economic costs and is not even feasible when the test site is remote. However, in many applications the end-users are generally only interested in very few specific targeted land-cover classes which, for instance, have high ecological value or are associated with support actions, subsidies or benefits from national or international institutions. The categorisation of specific grasslands and habitat types as those addressed in this thesis falls within such category of problems, which is defined in the literature as targeted land-cover classification. In this framework, a robust and effective targeted classification system for the automatic identification of grassland types by means of multi-temporal and multi-polarised SAR data has been developed within this thesis. In particular, the proposed system is composed of three main blocks: the preprocessing of the SAR image time series including the Kennaugh decomposition, the feature extraction including multi-temporal filtering and texture analysis, and the hierarchical targeted classification, which consist of two phases where first a one-class classifier is employed to outline the merger of all the grassland types of interest considered as a single information class and then a multi-class classifier is applied for discriminating the specific targeted classes within the areas identified as positive by the one-class classifier. To evaluate the capabilities of the proposed methodology, several experimental trials have been carried out over two test sites located in Southern Bavaria (Germany) and Mecklenburg Western-Pomerania (Germany) for which six diverse datasets have been derived from multitemporal series of dualpol TerraSAR-X as well as dual-/quadpol Radarsat-2 images. Four among the Natura 2000 habitat types of the Fauna-Flora-Habitat Directive as well all High Nature Value grassland types have been considered as targeted classes for this study. Overall, the proposed system proved to be robust and confirmed the effectiveness of employing multitemporal and multi-polarisation VHR SAR data for discriminating habitat types and High Nature Value grassland types, exhibiting high potential for future employment even at larger scales. In particular, it could be demonstrated that the proposed hierarchical targeted classification approach outperforms the available state-of-the-art methods and has a clear advantage with respect to the standard approaches in terms of robustness, reliability and transferability.