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Abstract Gene regulatory networks specify the gene expression patterns needed for traits to develop. Differences in these networks can result in phenotypic differences between organisms. Although loss-of-function genetic screens can identify genes necessary for trait formation, gain-of-function screens can overcome genetic redundancy and identify loci whose expression is sufficient to alter trait formation. Here, we leveraged transgenic lines from the Transgenic RNAi Project at Harvard Medical school to perform both gain- and loss-of-function CRISPR/Cas9 screens for abdominal pigmentation phenotypes. We identified measurable effects on pigmentation patterns in the Drosophila melanogaster abdomen for 21 of 55 transcription factors in gain-of-function experiments and 7 of 16 tested by loss-of-function experiments. These included well-characterized pigmentation genes, such as bab1 and dsx, and transcription factors that had no known role in pigmentation, such as slp2. Finally, this screen was partially conducted by undergraduate students in a Genetics Laboratory course during the Spring semesters of 2021 and 2022. We found this screen to be a successful model for student engagement in research in an undergraduate laboratory course, that can be readily adapted to evaluate the effect of hundreds of genes on many different Drosophila traits, with minimal resources. 

Abstract We present 0.6–3.2 pc resolution mid-infrared (MIR) JWST images at 7.7μm (F770W) and 21μm (F2100W) covering the main star-forming regions of two of the closest star-forming low-metallicity dwarf galaxies, NGC 6822 and Wolf–Lundmark–Melotte (WLM). The images of NGC 6822 reveal filaments, edge-brightened bubbles, diffuse emission, and a plethora of point sources. By contrast, most of the MIR emission in WLM is pointlike, with a small amount of extended emission. Compared to solar-metallicity galaxies, the ratio of 7.7μm intensity ( $I_{\nu }^{\mathrm{F770W}}$), tracing polycyclic aromatic hydrocarbons (PAHs), to 21μm intensity ( $I_{\nu }^{\mathrm{F2100W}}$), tracing small, warm dust grain emission, is suppressed in these low-metallicity dwarfs. Using Atacama Large Millimeter/submillimeter Array CO(2–1) observations, we find that detected CO intensity versus $I_{\nu }^{\mathrm{F770W}}$at ≈2 pc resolution in dwarfs follows a similar relationship to that at solar metallicity and lower resolution, while the CO versus $I_{\nu }^{\mathrm{F2100W}}$relationship in dwarfs lies significantly below that derived from solar-metallicity galaxies at lower resolution, suggesting more pronounced destruction of CO molecules at low metallicity. Finally, adding in Local Group L-Band Survey 21 cm Hiobservations from the Very Large Array, we find that $I_{\nu }^{\mathrm{F2100W}}$and $I_{\nu }^{\mathrm{F770W}}$versus total gas ratios are suppressed in NGC 6822 and WLM compared to solar-metallicity galaxies. In agreement with dust models, the level of suppression appears to be at least partly accounted for by the reduced galaxy-averaged dust-to-gas and PAH-to-dust mass ratios in the dwarfs. Remaining differences are likely due to spatial variations in dust model parameters, which should be an exciting direction for future work in local dwarf galaxies. 

Existing machine learning inference-serving systems largely rely on hardware scaling by adding more devices or using more powerful accelerators to handle increasing query demands. However, hardware scaling might not be feasible for fixed-size edge clusters or private clouds due to their limited hardware resources. A viable alternate solution is accuracy scaling, which adapts the accuracy of ML models instead of hardware resources to handle varying query demands. This work studies the design of a high-throughput inferenceserving system with accuracy scaling that can meet throughput requirements while maximizing accuracy. To achieve the goal, this work proposes to identify the right amount of accuracy scaling by jointly optimizing three sub-problems: how to select model variants, how to place them on heterogeneous devices, and how to assign query workloads to each device. It also proposes a new adaptive batching algorithm to handle variations in query arrival times and minimize SLO violations. Based on the proposed techniques, we build an inference-serving system called Proteus and empirically evaluate it on real-world and synthetic traces. We show that Proteus reduces accuracy drop by up to 3× and latency timeouts by 2-10× with respect to baseline schemes, while meeting throughput requirements. 

Existing machine learning inference-serving systems largely rely on hardware scaling by adding more devices or using more powerful accelerators to handle increasing query demands. However, hardware scaling might not be feasible for fixed-size edge clusters or private clouds due to their limited hardware resources. A viable alternate solution is accuracy scaling, which adapts the accuracy of ML models instead of hardware resources to handle varying query demands. This work studies the design of a high-throughput inferenceserving system with accuracy scaling that can meet throughput requirements while maximizing accuracy. To achieve the goal, this work proposes to identify the right amount of accuracy scaling by jointly optimizing three sub-problems: how to select model variants, how to place them on heterogeneous devices, and how to assign query workloads to each device. It also proposes a new adaptive batching algorithm to handle variations in query arrival times and minimize SLO violations. Based on the proposed techniques, we build an inference-serving system called Proteus and empirically evaluate it on real-world and synthetic traces. We show that Proteus reduces accuracy drop by up to 3× and latency timeouts by 2-10× with respect to baseline schemes, while meeting throughput requirements. 

Abstract We present the Local GroupL-Band Survey, a Karl G. Jansky Very Large Array (VLA) survey producing the highest-quality 21 cm and 1–2 GHz radio continuum images to date, for the six VLA-accessible, star-forming, Local Group galaxies. Leveraging the VLA’s spectral multiplexing power, we simultaneously survey the 21 cm line at high 0.4 km s⁻¹velocity resolution, the 1–2 GHz polarized continuum, and four OH lines. For the massive spiral M31, the dwarf spiral M33, and the dwarf irregular galaxies NGC 6822, IC 10, IC 1613, and the Wolf–Lundmark–Melotte Galaxy, we use all four VLA configurations and the Green Bank Telescope to reach angular resolutions of <5″ (10–20 pc) for the 21 cm line with <10²⁰cm⁻²column density sensitivity, and even sharper views (<2″; 5–10 pc) of the continuum. Targeting these nearby galaxies (D ≲ 1 Mpc) reveals a sharp, resolved view of the atomic gas, including 21 cm absorption, and continuum emission from supernova remnants and Hiiregions. These data sets can be used to test theories of the abundance and formation of cold clouds, the driving and dissipation of interstellar turbulence, and the impact of feedback from massive stars and supernovae. Here, we describe the survey design and execution, scientific motivation, data processing, and quality assurance. We provide a first look at and publicly release the wide-field 21 cm Hidata products for M31, M33, and four dwarf irregular targets in the survey, which represent some of the highest-physical-resolution 21 cm observations of any external galaxies beyond the LMC and SMC. 

Neutral atomic gas (H I) effectively traces galactic dynamics across mid to large galactocentric radii. However, its limitations in observing small-scale changes within the central few kiloparsecs, coupled with the often observed H Ideficit in galactic centers, necessitates the use of molecular gas emission as a preferred tracer in these regions. Understanding the dynamics of both neutral atomic and molecular gas is crucial for a more complete understanding of how galaxies evolve, funnel gas from the outer disk into their central parts, and eventually form stars. In this work we aim to quantify the dynamics of both, the neutral atomic and molecular gas, in the nearby spiral galaxies NGC 1512, NGC 4535, and NGC 7496 using new MeerKAT H Iobservations together with ALMA CO (2-1) observations from the PHANGS collaboration. We use the analysis tool^3DBarolo to fit tilted ring models to the H Iand CO observations. A combined approach of using the H Ito constrain the true disk orientation parameters before applying these to the CO datasets is tested. This paper sets expectations for the results of the upcoming high-resolution H Icoverage of many galaxies in the PHANGS-ALMA sample using MeerKAT or VLA, to establish a robust methodology for characterizing galaxy orientations and deriving dynamics from combing new H Iwith existing CO data. 

There is growing interest in U.S. cities to shift resources towards community-led solutions to crime and disorder. However, there is a simultaneous need to provide community organizations with access to real-time data to facilitate decision making, to which only the police normally have access. In this work we present a low-cost gunshot detection system with localization that has been developed for community-based violence interruption. The distributed real-time gunshot detection sensor network is linked to a mobile phone-based alert and tasking system for exclusive use by civilian gang interventionists. Here we present details on the system architecture and gunshot detection model, which consists of an Audio Spectrogram Transformer (AST) neural network. We then combine gradient maps of the input to the AST for time of arrival identification with a Bayesian maximum a posteriori estimation procedure to identify the location of gunshots. We conduct several experiments using simulated data, open data from the commercial ShotSpotter detection system in Pittsburgh, and data collected using our devices during live-fire experiments at the Indianapolis Metropolitan Police Department (IMPD) gun firing range. We then discuss potential applications of the system and directions for future research. 

Measuring the properties of the cold neutral medium (CNM) in low-metallicity galaxies provides insight into heating and cooling mechanisms in early Universe-like environments. We report detections of two localized atomic neutral hydrogen (Hi) absorption features in NGC 6822, a low-metallicity (0.2 Z⊙) dwarf galaxy in the Local Group. These are the first unambiguous CNM detections in a low-metallicity dwarf galaxy outside the Magellanic Clouds. The Local Group L-Band Survey (LGLBS) enabled these detections due to its high spatial (15 pc for Hi emission) and spectral (0.4 km s−1) resolution. We introduce LGLBS and describe a custom pipeline to search for Hi absorption at high angular resolution and extract associated Hi emission. A detailed Gaussian decomposition and radiative transfer analysis of the NGC 6822 detections reveals five CNM components, with key properties: a mean spin temperature of 32±6 K, a mean CNM column density of 3.1×1020 cm−2, and CNM mass fractions of 0.33 and 0.12 for the two sightlines. Stacking non-detections does not reveal low-level signals below our median optical depth sensitivity of 0.05. One detection intercepts a star-forming region, with the Hi absorption profile encompassing the CO (2−1) emission, indicating coincident molecular gas and a depression in high-resolution Hi emission. We also analyze a nearby sightline with deep, narrow Hi self-absorption dips, where the background warm neutral medium is attenuated by intervening CNM. The association of CNM, CO, and Hα emissions suggests a close link between the colder, denser Hi phase and star formation in NGC 6822. 

The property of star formation rate (SFR) is tightly connected to the amount of dense gas in molecular clouds. However, it is not fully understood how the relationship between dense molecular gas and star formation varies within galaxies and in different morphological environments. Most previous studies have typically been limited to kiloparsec-scale resolution such that different environments could not be resolved. In this work, we present new ALMA observations of HCN(1−0) at 260 pc scale to test how the amount of dense gas and its ability to form stars varies with environmental properties. Combined with existing CO(2−1) observations from ALMA and Hαfrom MUSE, we measured the HCN/CO line ratio, a proxy for the dense gas fraction, and SFR/HCN, a proxy for the star formation efficiency of the dense gas. We find a systematic > 1 dex increase (decreases) of HCN/CO (SFR/HCN) towards the centre of the galaxy, and roughly flat trends of these ratios (average variations < 0.3 dex) throughout the disc. While spiral arms, interarm regions, and bar ends show similar HCN/CO and SFR/HCN, on the bar, there is a significantly lower SFR/HCN at a similar HCN/CO. The strong environmental influence on dense gas and star formation in the centre of NGC 4321, suggests either that clouds couple strongly to the surrounding pressure or that HCN emission traces more of the bulk molecular gas that is less efficiently converted into stars. Across the disc, where the ISM pressure is typically low, SFR/HCN is more constant, indicating a decoupling of the clouds from their surrounding environment. The low SFR/HCN on the bar suggests that gas dynamics (e.g. shear and streaming motions) can have a large effect on the efficiency with which dense gas is converted into stars. In addition, we show that HCN/CO is a good predictor of the mean molecular gas surface density at 260 pc scales across environments and physical conditions.