A Theodicy of Redemptive Suffering in African American Involvement Led by Absalom Jones and Richard Allen in the Philadelphia Yellow Fever Epidemic of 1793

This paper is a historical investigation into the involvement of African Americans during the Yellow Fever Epidemic of 1793. It explores key figures, details, medical realities, and media representation. The particular focus lies on the dilemma of suffering in the world and how the African American understanding of evil in this community led to their decision of involvement. Their understanding of theodicy will be weighed against modern philosophical and theological attempts to deal with theodicy

Ain\u27t I Cool: Investigating the Lived Experience of Cool for Black Male Collegians

The purpose of this study was to investigate the lived experiences of cool for Black male college students. Cool, defined as a means to navigate cross-cultural structures, serves as Black men’s acknowledgement of, and response to, the rules of their self-identified cultural group when confronted by dominant racial, gendered, and/or class hegemonies. Results of this study note that cool is a major part of the fabric in the identity development of Black males and is contextualized through environmental factors. For this study, four theoretical frameworks were used to understand Black male cool: Phinney’s (1989) Ethnic Development Model, 2) Performance Theory (Butler, 1988), 3) Face-Identity negotiation theory (Ting-Toomey & Kurogi, 1998) and 4) Cultural Capital (Bourdieu, 1986; Carter, 2003; Yosso, 2005). These theoretical frameworks were merged together to create a textured analysis of cool. Texturing for this academic study established a greater “feel” and connection with the topic it is analyzing. Through texturing, I was able view cool through a complex lens in order to assess how cool was learned, performed, and advanced for Black male students. In order to investigate Black male cool, I adopted a quasi-phenomenological qualitative research design, rooted in a social constructivist worldview. To obtain data for this study I conducted one to two-hour semi-structured in-depth interviews with 11 Black male students and employed a version of photo elicitation called framing. Framing is the process of identifying a visual representation of an idea, behavior, or concept, after speaking about the experience with that idea, behavior or concept. For this study the participants were asked to share their lived experiences with cool and to later frame cool. Through in-depth interviews and framing, data triangulation was accomplished, and four major themes spoke to the experiences of the participants. For the participants (1) cool is learned and influenced by one’s environment; (2) the experience of cool is an expression of self (self awareness); (3) The experience of cool provides access to multiple spaces, people, and relationships; and (4) The experience of cool creates opportunity to (re)define self

Delayed avalanches in Multi-Pixel Photon Counters

Hamamatsu Photonics introduced a new generation of their Multi-Pixel Photon Counters in 2013 with significantly reduced after-pulsing rate. In this paper, we investigate the causes of after-pulsing by testing pre-2013 and post-2013 devices using laser light ranging from 405 to 820nm. Doing so we investigate the possibility that afterpulsing is also due to optical photons produced in the avalanche rather than to impurities trapping charged carriers produced in the avalanches and releasing them at a later time. For pre-2013 devices, we observe avalanches delayed by ns to several 100~ns at 637, 777nm and 820 nm demonstrating that holes created in the zero field region of the silicon bulk can diffuse back to the high field region triggering delayed avalanches. On the other hand post-2013 exhibit no delayed avalanches beyond 100~ns at 777nm. We also confirm that post-2013 devices exhibit about 25 times lower after-pulsing. Taken together, our measurements show that the absorption of photons from the avalanche in the bulk of the silicon and the subsequent hole diffusion back to the junction was a significant source of after-pulse for the pre-2013 devices. Hamamatsu appears to have fixed this problem in 2013 following the preliminary release of our results. We also show that even at short wavelength the timing distribution exhibit tails in the sub-nanosecond range that may impair the MPPC timing performances.Comment: Submitted to JINST, 14 pages, 16 figure

Solar System-scale interferometry on fast radio bursts could measure cosmic distances with sub-percent precision

The light from a source at a distance d will arrive at detectors separated by 100 AU at times that differ by as much as 120 (d/100 Mpc)^{-1} nanoseconds because of the curvature of the wavefront. At gigahertz frequencies, the arrival time difference can be determined to better than a nanosecond with interferometry. If the space-time positions of the detectors are known to a few centimeters, comparable to the accuracy to which very long baseline interferometry baselines and global navigation satellite systems (GNSS) geolocations are constrained, nanosecond timing would allow competitive cosmological constraints. We show that a four-detector constellation at Solar radii of >10 AU could measure distances to individual sources with sub-percent precision and, hence, cosmological parameters such as the Hubble constant to this precision. The precision increases quadratically with baseline length. FRBs are the only known bright extragalactic radio source that are sufficiently point-like. Galactic scattering limits the timing precision at <3 GHz, whereas at higher frequencies the precision is set by removing dispersion. Furthermore, for baselines greater than 100 AU, Shapiro time delays limit the precision, but their effect can be cleaned with two additional detectors. Accelerations that result in ~1 cm uncertainty in detector positions (from variations in the Sun's irradiance, dust collisions and gaseous drag) could be corrected for with weekly GNSS-like trilaterations. Gravitational accelerations from asteroids occur over longer timescales, and so a setup with a precise accelerometer and calibrating the detector positions off of distant FRBs may also be sufficient. The proposed interferometer would also resolve the radio emission region of Galactic pulsars, constrain the mass distribution in the outer Solar System, and reach interesting sensitivities to ~0.01-100 micro-Hz gravitational waves.Comment: 34 pages in preprint format; 3 figures; comments welcome

Combining genetic algorithm with machine learning strategies for designing potent antimicrobial peptides

Background Current methods in machine learning provide approaches for solving challenging, multiple constraint design problems. While deep learning and related neural networking methods have state-of-the-art performance, their vulnerability in decision making processes leading to irrational outcomes is a major concern for their implementation. With the rising antibiotic resistance, antimicrobial peptides (AMPs) have increasingly gained attention as novel therapeutic agents. This challenging design problem requires peptides which meet the multiple constraints of limiting drug-resistance in bacteria, preventing secondary infections from imbalanced microbial flora, and avoiding immune system suppression. AMPs offer a promising, bioinspired design space to targeting antimicrobial activity, but their versatility also requires the curated selection from a combinatorial sequence space. This space is too large for brute-force methods or currently known rational design approaches outside of machine learning. While there has been progress in using the design space to more effectively target AMP activity, a widely applicable approach has been elusive. The lack of transparency in machine learning has limited the advancement of scientific knowledge of how AMPs are related among each other, and the lack of general applicability for fully rational approaches has limited a broader understanding of the design space. Methods Here we combined an evolutionary method with rough set theory, a transparent machine learning approach, for designing antimicrobial peptides (AMPs). Our method achieves the customization of AMPs using supervised learning boundaries. Our system employs in vitro bacterial assays to measure fitness, codon-representation of peptides to gain flexibility of sequence selection in DNA-space with a genetic algorithm and machine learning to further accelerate the process. Results We use supervised machine learning and a genetic algorithm to find a peptide active against S. epidermidis, a common bacterial strain for implant infections, with an improved aggregation propensity average for an improved ease of synthesis. Conclusions Our results demonstrate that AMP design can be customized to maintain activity and simplify production. To our knowledge, this is the first time when codon-based genetic algorithms combined with rough set theory methods is used for computational search on peptide sequences

Micro Wild Initiative: An Education Through the Rejuvenation and Reclamation of Malden’s Forgotten Urban Spaces

A city’s ability to sustain a flourishing population is linked to multilayered subjective understandings of its cultural identities (Fincher and Jacobs 1998). As we understand it, Malden, Massachusetts is a rapidly densifying urban area distinguished by a near-majority immigrant population and a landscape fractured by vacated spaces (U.S. Census Bureau 2021). It has a rich socioeconomic footing but lacks public resources for its growing family population. Because current green space is inaccessible or otherwise compromised, our project explores ways that the community can transform idle lots, backyards, and urban poché into wilding spaces, agricultural learning spaces, pollinator connectivity corridors, and spaces for other forms of prefigurative activism (Kato 2020). Using an existing residential yard as a case study, we will investigate the limits of a confined urban space’s ability to host this variety of programs and test access for the growing population. We will collaborate with local schools and foundations to develop a curriculum that best complements the current Malden population through a flexible set of identifying factors specific to the community ecology, such as native food production practices, local artist initiatives, and cultural traditions (Rose 2016, 223–249). Our goal for the Micro Urban Discovery Lab, or MUD Lab, is to develop this methodology in the hopes that it could be replicated for the ongoing reactivation of other post-industrialized cities (Drake & Lawson 2014)

Bionano-Interfaces through Peptide Design

The clinical success of restoring bone and tooth function through implants critically depends on the maintenance of an infection-free, integrated interface between the host tissue and the biomaterial surface. The surgical site infections, which are the infections within one year of surgery, occur in approximately 160,000-300,000 cases in the US annually. Antibiotics are the conventional treatment for the prevention of infections. They are becoming ineffective due to bacterial antibiotic-resistance from their wide-spread use. There is an urgent need both to combat bacterial drug resistance through new antimicrobial agents and to limit the spread of drug resistance by limiting their delivery to the implant site. This work aims to reduce surgical site infections from implants by designing of chimeric antimicrobial peptides to integrate a novel and effective delivery method. In recent years, antimicrobial peptides (AMPs) have attracted interest as natural sources for new antimicrobial agents. By being part of the immune system in all life forms, they are examples of antibacterial agents with successfully maintained efficacy across evolutionary time. Both natural and synthetic AMPs show significant promise for solving the antibiotic resistance problems. In this work, AMP1 and AMP2 was shown to be active against three different strains of pathogens in Chapter 4. In the literature, these peptides have been shown to be effective against multi-drug resistant bacteria. However, their effective delivery to the implantation site limits their clinical use. In recent years, different groups adapted covalent chemistry-based or non-specific physical adsorption methods for antimicrobial peptide coatings on implant surfaces. Many of these procedures use harsh chemical conditions requiring multiple reaction steps. Furthermore, none of these methods allow the orientation control of these molecules on the surfaces, which is an essential consideration for biomolecules. In the last few decades, solid binding peptides attracted high interest due to their material specificity and self-assembly properties. These peptides offer robust surface adsorption and assembly in diverse applications. In this work, a design method for chimeric antimicrobial peptides that can self-assemble and self-orient onto biomaterial surfaces was demonstrated. Three specific aims used to address this two-fold strategy of self-assembly and self-orientation are: 1) Develop classification and design methods using rough set theory and genetic algorithm search to customize antibacterial peptides; 2) Develop chimeric peptides by designing spacer sequences to improve the activity of antimicrobial peptides on titanium surfaces; 3) Verify the approach as an enabling technology by expanding the chimeric design approach to other biomaterials. In Aim 1, a peptide classification tool was developed because the selection of an antimicrobial peptide for an application was difficult among the thousands of peptide sequences available. A rule-based rough-set theory classification algorithm was developed to group antimicrobial peptides by chemical properties. This work is the first time that rough set theory has been applied to peptide activity analysis. The classification method on benchmark data sets resulted in low false discovery rates. The novel rough set theory method was combined with a novel genetic algorithm search, resulting in a method for customizing active antibacterial peptides using sequence-based relationships. Inspired by the fact that spacer sequences play critical roles between functional protein domains, in Aim 2, chimeric peptides were designed to combine solid binding functionality with antimicrobial functionality. To improve how these functions worked together in the same peptide sequence, new spacer sequences were engineered. The rough set theory method from Aim 1 was used to find structure-based relationships to discover new spacer sequences which improved the antimicrobial activity of the chimeric peptides. In Aim 3, the proposed approach is demonstrated as an enabling technology. In this work, calcium phosphate was tested and verified the modularity of the chimeric antimicrobial self-assembling peptide approach. Other chimeric peptides were designed for common biomaterials zirconia and urethane polymer. Finally, an antimicrobial peptide was engineered for a dental adhesive system toward applying spacer design concepts to optimize the antimicrobial activity

Improving Cosmological Distance Measurements with Type Ia Supernovae: From Pixels to Dark Energy

In the late 1990s, precise distance measurements with Type Ia supernovae (SNe Ia) were used to show that the expansion of the universe is accelerating. One possibility is that this accelerated expansion is due to an additional form of energy referred to as “dark energy” which contributes roughly 70% of the total energy in the present day universe. The properties of dark energy are not currently well-constrained, and a wide range of different cosmological probes are currently being designed to explore the fundamental nature of the accelerated expansion of the universe. SNe Ia have remained one of the strongest cosmological probes, and upcoming experiments such as the Large Synoptic Survey Telescope (LSST) are expected to discover over 100,000 SNe Ia that can be used for cosmology. The uncertainties on cosmological parameters derived from these large samples of SNe Ia will be entirely dominated by the systematic uncertainties of distance measurements to SNe Ia. In this dissertation, we discuss several different methods of improving the systematic uncertainties in distance measurements to SNe Ia.This dissertation is split into three main chapters each discussing how to improve a different aspect of distance measurements to SNe Ia. In Chapter 2, we examine how instrumental calibration can affect these distance measurements, and discuss a new anomalous behaviour of CCD readout electronics related to the binary encoding of pixel values that affects most astronomical instruments currently in use. For the Nearby Supernova Factory, this anomaly introduces a dispersion in the measured B-band/U-band magnitudes of 0.11 mag/0.51 mag for the faintest 20% of measurements.Another major source of systematic uncertainty in distance measurements to SNe Ia is intrinsic variation of the SNe Ia. In Chapter 3, we develop a new method of parametrizing SNe Ia using manifold learning to generate a non-linear decomposition of the intrinsic diversity of their spectra near maximum light. We identify regions of the parameter space of SNe Ia where previous standardization methods such as SALT2 have biases of up to 0.3 mag, and show how correlations between host galaxy properties and distance estimates are greatly reduced when standardizing SNe Ia using our new parametrization.Finally, in Chapter 4, we discuss how upcoming surveys such as LSST will need to rely on photometric classification to identify the majority of the transients that they discover, which means that samples of SNe Ia used for cosmology will be contaminated with other types of transients. We developed a set of techniques for photometric classification to address the fact that spectroscopic subsamples used for training classifiers are typically highly biased compared to the full samples of transients and variables that will be discovered. Using these techniques, we built a photometric classifier that won the PLAsTiCC photometric classification challenge out of 1,094 competing teams

Live American Sign Language Letter Classification with Convolutional Neural Networks

This project is centered around building a neural network that is able to recognize ASL letters in images, particularly within the scope of a live video feed. Initial testing results came up short of expectations when both the convolutional network and VGG16 transfer learning approaches failed to generalize in settings of different backgrounds. The use of a pre-trained hand joint detection model was then adopted with the produced joint locations being fed into a fully-connected neural network. The results of this approach exceeded those of prior methods and generalized well to a live video feed application.Comment: 10 pages, 10 figure