Letters of recommendation are a common tool used in graduate admissions. Most admissions systems require three letters for each applicant, burdening both letter writers and admissions committees with a heavy work load that may not be time well-spent. Most applicants do not have three research advisors who can comment meaningfully on research readiness, adding a large number of letters that are not useful. Ideally, letters of recommendation will showcase the students' promise for a research career, but in practice, the letters often do not fulfill this purpose. As a group of early and mid-career faculty who write dozens of letters every year for promising undergraduates, we are concerned and overburdened by the inefficiencies of the current system. In this open letter to the AAS Graduate Admissions Task Force, we offer an alternative to the current use of letters of recommendation: a portfolio submitted by the student, which highlights e.g., a paper, plot, or presentation that represents their past work and readiness for grad school, uploaded to a centralized system used by astronomy and astrophysics PhD programs. While we argue that we could eliminate letters in this new paradigm,
This paper presents the design and implementation of a Flying Light Speck (FLS) to illuminate English letters. The FLS uses its onboard camera and computing to localize and follow a trajectory to illuminate a letter. We evaluate the illuminations quantitatively and qualitatively. The latter is based on an IRB approved human subject study with 20 participants. The obtained results show a 42 to 56 millimeter error that impacts the detection of letters. A key finding is that the order in which the illumination of letters is presented to subjects has a significant effect on detection duration.
We study the impact of generative AI on labor market signaling using the introduction of an AI-powered cover letter writing tool on a large online labor platform. Our data track both access to the tool and usage at the application level. Difference-in-differences estimates show that access to the tool increased textual alignment between cover letters and job posts and raised callback rates. Time spent editing AI-generated cover letter drafts is positively correlated with hiring success. After the tool's introduction, the correlation between cover letters' textual alignment and callbacks fell by 51%, consistent with what theory predicts if the AI technology reduces the signal content of cover letters. In response, employers shifted toward alternative signals, including workers' prior work histories.
Microorganisms are ubiquitous in nature, and microbial activities are closely intertwined with the entire life cycle system and human life. Developing novel technologies for the detection, characterization and manipulation of microorganisms promotes their applications in clinical, environmental and industrial areas. Over the last two decades, terahertz (THz) technology has emerged as a new optical tool for microbiology. The great potential originates from the unique advantages of THz waves including the high sensitivity to water and inter-/intra-molecular motions, the non-invasive and label-free detecting scheme, and their low photon energy. THz waves have been utilized as a stimulus to alter microbial functions, or as a sensing approach for quantitative measurement and qualitative differentiation. This review specifically focuses on recent research progress of THz technology applied in the field of microbiology, including two major parts of THz biological effects and the microbial detection applications. In the end of this paper, we summarize the research progress and discuss the challenges currently faced by THz technology in microbiology, along with potential solutions. We also
Mathematical models are increasingly a part of microbiological research. Here, we share our perspective on how modeling advances the discipline by: (i) enforcing logical consistency, (ii) enabling quantitative prediction, (iii) extracting hidden parameters from data, and (iv) generating intuitive understanding. We map a spectrum of modeling frameworks, from whole-cell simulations to minimal logistic growth equations, and provide interactive examples for some common frameworks. Building on this overview, we outline pragmatic criteria for choosing an appropriate level of description to capture phenomena of interest. Finally, we present a case study in modeling of microbial ecosystems from our own work to illustrate how mechanistic modeling can yield generalizable intuition. This perspective aims to be an introductory roadmap for integrating mathematical modeling into experimental microbiology.
Advancements in artificial intelligence (AI) have transformed many scientific fields, with microbiology and microbiome research now experiencing significant breakthroughs through machine learning applications. This review provides a comprehensive overview of AI-driven approaches tailored for microbiology and microbiome studies, emphasizing both technical advancements and biological insights. We begin with an introduction to foundational AI techniques, including primary machine learning paradigms and various deep learning architectures, and offer guidance on choosing between traditional machine learning and sophisticated deep learning methods based on specific research goals. The primary section on application scenarios spans diverse research areas, from taxonomic profiling, functional annotation \& prediction, microbe-X interactions, microbial ecology, metabolic modeling, precision nutrition, clinical microbiology, to prevention \& therapeutics. Finally, we discuss challenges in this field and highlight some recent breakthroughs. Together, this review underscores AI's transformative role in microbiology and microbiome research, paving the way for innovative methodologies an
The study of microorganisms, or microbiology, has demonstrated significant development since its inception and is currently a key field of biological sciences that has a huge impact on modern society and scientific research. Over the centuries, this discipline has undergone significant changes, shaping our understanding of infectious diseases and food safety. Starting from the simplest observations of microscopic organisms such as bacteria, viruses, fungi and protozoa, and ending with modern molecular and genomic research methods. This article describes a brief historical path of microbiology development. The heuristic, morphological, physiological, immunological, and molecular genetic stages are the main periods into which the development of this science is traditionally divided, despite the lack of full-fledged and precise boundaries between them.
It is well known that Charles Hermite kept an intense correspondence with many of the word's leading mathematicians of his time. This paper focuses on Hermite's letters to Francisco Gomes Teixeira, a Portuguese mathematician, who exchanged letters with Hermite for more than twenty years.
Since clinical letters contain sensitive information, clinical-related datasets can not be widely applied in model training, medical research, and teaching. This work aims to generate reliable, various, and de-identified synthetic clinical letters. To achieve this goal, we explored different pre-trained language models (PLMs) for masking and generating text. After that, we worked on Bio\_ClinicalBERT, a high-performing model, and experimented with different masking strategies. Both qualitative and quantitative methods were used for evaluation. Additionally, a downstream task, Named Entity Recognition (NER), was also implemented to assess the usability of these synthetic letters. The results indicate that 1) encoder-only models outperform encoder-decoder models. 2) Among encoder-only models, those trained on general corpora perform comparably to those trained on clinical data when clinical information is preserved. 3) Additionally, preserving clinical entities and document structure better aligns with our objectives than simply fine-tuning the model. 4) Furthermore, different masking strategies can impact the quality of synthetic clinical letters. Masking stopwords has a positive im
Frequency of letters in a symbolic sequence ${\bf u}$ over a finite alphabet is one of the basic characteristics of ${\bf u}$. The notion of $k$-balancedness captures the property that the number of any letter occurring in two arbitrary factors of ${\bf u}$ of equal length differs at most by $k$. For a fixed integer $k$ and alphabet size $d\in \mathbb N$, we discuss possible frequencies of letters in $k$-balanced $d$-ary sequences. For the size $d$ of the alphabet, we introduce the notion of balancedness threshold $BT(d)$ and give an upper bound on it, where $BT(d)$ is the minimum $k$ such that there exists a $k$-balanced sequence over a $d$-letter alphabet for all possible letter frequencies.
This study addresses from the Optimal Experimental Design perspective the use of the isothermal experimentation procedure to precisely estimate the parameters defining models used in predictive microbiology. Starting from a case study set out in the literature, and taking the Baranyi model as the primary model, and the Ratkowsky square-root model as the secondary, D- and c-optimal designs are provided for isothermal experiments, taking the temperature both as a value fixed by the experimenter and as a variable to be designed. The designs calculated show that those commonly used in practice are not efficient enough to estimate the parameters of the secondary model, leading to greater uncertainty in the predictions made via these models. Finally, an analysis is carried out to determine the effect on the efficiency of the possible reduction in the final experimental time.
The Earth possesses many environmental extremes that mimic conditions on extraterrestrial worlds. The stratosphere at 30-40 km altitude closely resembles the surface of Mars in terms of pressure, temperature, and radiation levels (UV, proton, and Galactic cosmic rays). While microbial life in the troposphere is well documented, the true upper limit of Earth's biosphere remains unclear. The stratosphere offers a promising environment to explore microbial survival in such extreme conditions. Despite its significance to astrobiology, this region remains largely unexplored due to difficulties in access and avoiding contamination. To address this, we have developed SAMPLE (Stratospheric Altitude Microbiology Probe for Life Existence), a balloon-borne payload designed to collect dust samples from the stratosphere and return them in conditions suitable for lab analysis. The entire system is novel and designed in-house, with weight- and stress-optimized components. The main payload includes three pre-sterilized sampling trays and a controller that determines altitude and governs tray operation. One tray will remain closed during flight (airborne control) and another on the ground (cleanroo
The Antibiotic Resistance Microbiology Dataset (ARMD) is a de-identified resource derived from electronic health records (EHR) that facilitates research in antimicrobial resistance (AMR). ARMD encompasses big data from adult patients collected from over 15 years at two academic-affiliated hospitals, focusing on microbiological cultures, antibiotic susceptibilities, and associated clinical and demographic features. Key attributes include organism identification, susceptibility patterns for 55 antibiotics, implied susceptibility rules, and de-identified patient information. This dataset supports studies on antimicrobial stewardship, causal inference, and clinical decision-making. ARMD is designed to be reusable and interoperable, promoting collaboration and innovation in combating AMR. This paper describes the dataset's acquisition, structure, and utility while detailing its de-identification process.
In this paper we address the well-known problem of counting the number of $3n$-letter words that can be formed from a three-letter alphabet by decomposing it into four possible cases based on its remainder when divided by three. The solution to the problem also gives us some sums of trinomial coefficients.
The SSPACE Astrobiology Payload (SAP) series, starting with the SAP-1 project is designed to conduct in-situ microbiology experiments in low earth orbit. This payload series aims to understand the behaviour of microbial organisms in space, particularly those critical for human health, and the corresponding effects due to microgravity and solar/galactic radiation. SAP-1 focuses on studying Bacillus clausii and Bacillus coagulans, bacteria beneficial to humans. It aims to provide a space laboratory for astrobiology experiments under microgravity conditions. The hardware developed for these experiments is indigenous and tailored to meet the unique requirements of autonomous microbiology experiments by controlling pressure, temperature, and nutrition flow to bacteria. A rotating platform, which forms the core design, is innovatively utilised to regulate the flow and mixing of nutrients with dormant bacteria. The technology demonstration models developed at SSPACE have yielded promising results, with ongoing efforts to refine, adapt for space conditions, and prepare for integration with nanosatellites or space modules. The anticipated payload will be compact, approximately 1U in size (1
Deterministic and nondeterministic finite automata with translucent letters were introduced by Nagy and Otto more than a decade ago as Cooperative Distributed systems of a kind of stateless restarting automata with window size one. These finite state machines have a surprisingly large expressive power: all commutative semi-linear languages and all rational trace languages can be accepted by them including various not context-free languages. While the nondeterministic variant defines a language class with nice closure properties, the deterministic variant is weaker, however it contains all regular languages, some non-regular context-free languages, as the Dyck language, and also some languages that are not even context-free. In all those models for each state, the letters of the alphabet could be in one of the following categories: the automaton cannot see the letter (it is translucent), there is a transition defined on the letter (maybe more than one transitions in nondeterministic case) or none of the above categories (the automaton gets stuck by seeing this letter at the given state and this computation is not accepting). State-deterministic automata are recent models, where the
We propose a geometrical approach to generate symbol letters of amplitudes/integrals in planar $\mathcal{N}=4$ Super Yang-Mills theory, known as {\it Schubert problems}. Beginning with one-loop integrals, we find that intersections of lines in momentum twistor space are always ordered on a given line, once the external kinematics $\mathbf{Z}$ is in the positive region $G_+(4,n)$. Remarkably, cross-ratios of these ordered intersections on a line, which are guaranteed to be positive now, nicely coincide with symbol letters of corresponding Feynman integrals, whose positivity is then concluded directly from such geometrical configurations. In particular, we reproduce from this approach the $18$ multiplicative independent algebraic letters for $n=8$ amplitudes up to three loops. Finally, we generalize the discussion to two-loop Schubert problems and, again from ordered points on a line, generate a new kind of algebraic letters which mix two distinct square roots together. They have been found recently in the alphabet of two-loop double-box integral with $n\geq9$, and they are expected to appear in amplitudes at $k+\ell\geq4$.
In a recent work (Dick et al, arXiv:2310.06187), we considered a linear stochastic elasticity equation with random Lamé parameters which are parameterized by a countably infinite number of terms in separate expansions. We estimated the expected values over the infinite dimensional parametric space of linear functionals ${\mathcal L}$ acting on the continuous solution $\vu$ of the elasticity equation. This was achieved by truncating the expansions of the random parameters, then using a high-order quasi-Monte Carlo (QMC) method to approximate the high dimensional integral combined with the conforming Galerkin finite element method (FEM) to approximate the displacement over the physical domain $Ω.$ In this work, as a further development of aforementioned article, we focus on the case of a nearly incompressible linear stochastic elasticity equation. To serve this purpose, in the presence of stochastic inhomogeneous (variable Lamé parameters) nearly compressible material, we develop a new locking-free symmetric nonconforming Galerkin FEM that handles the inhomogeneity. In the case of nearly incompressible material, one known important advantage of nonconforming approximations is that th
In the recent paper by Teys [JETP Letters 105 (8), 477-483 (2017)], an atomic model for the Si(331) reconstructed surface (hereby referred to as T-model) was proposed on the basis of high-resolution scanning tunneling microscopy (STM) images. While detailing the virtues against previous and abandoned models, the author avoids any reference to the rather distinct 8P-model advocated few weeks earlier by Zhachuk and Teys [R. Zhachuk, S. Teys, Phys. Rev. B 95, 041412 (2017)], casting doubts to his own work. Formulated that way, findings from Ref. [JETP Letters 105 (8), 477-483 (2017)] leave readers of JETP Letters with a partial and confusing view of the problem, and above all, leaves the observations open to ambiguous interpretation. The 8P-model is also based on STM measurements, and unlike the T-model, passed through the scrutiny of first-principles calculations. The present comment reconciles Ref. [JETP Letters 105 (8), 477-483 (2017)] with the literature by supplementing the discussion with a missing and critical account on the stability and electronic structure of the T- versus 8P-models of Si(331).
Motivated by reformulating Yangian invariants in planar ${\cal N}=4$ SYM directly as $d\log$ forms on momentum-twistor space, we propose a purely algebraic problem of determining the arguments of the $d\log$'s, which we call "letters", for any Yangian invariant. These are functions of momentum twistors $Z$'s, given by the positive coordinates $α$'s of parametrizations of the matrix $C(α)$, evaluated on the support of polynomial equations $C(α) \cdot Z=0$. We provide evidence that the letters of Yangian invariants are related to the cluster algebra of Grassmannian $G(4,n)$, which is relevant for the symbol alphabet of $n$-point scattering amplitudes. For $n=6,7$, the collection of letters for all Yangian invariants contains the cluster ${\cal A}$ coordinates of $G(4,n)$. We determine algebraic letters of Yangian invariant associated with any "four-mass" box, which for $n=8$ reproduce the $18$ multiplicative-independent, algebraic symbol letters discovered recently for two-loop amplitudes.