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We re-analyzed the data from a recent large-scale study that reported strong correlations between DNA signatures of microbial organisms and 33 different cancer types and that created machine-learning predictors with near-perfect accuracy at distinguishing among cancers. We found at least two fundamental flaws in the reported data and in the methods: (i) errors in the genome database and the associated computational methods led to millions of false-positive findings of bacterial reads across all samples, largely because most of the sequences identified as bacteria were instead human; and (ii) errors in the transformation of the raw data created an artificial signature, even for microbes with no reads detected, tagging each tumor type with a distinct signal that the machine-learning programs then used to create an apparently accurate classifier. Each of these problems invalidates the results, leading to the conclusion that the microbiome-based classifiers for identifying cancer presented in the study are entirely wrong. These flaws have subsequently affected more than a dozen additional published studies that used the same data and whose results are likely invalid as well. Recent reports showing that human cancers have a distinctive microbiome have led to a flurry of papers describing microbial signatures of different cancer types. Many of these reports are based on flawed data that, upon re-analysis, completely overturns the original findings. The re-analysis conducted here shows that most of the microbes originally reported as associated with cancer were not present at all in the samples. The original report of a cancer microbiome and more than a dozen follow-up studies are, therefore, likely to be invalid.

Otitis externa (OE) is a very common disease in Australia. It is associated with swimming and exposure to water. Typically, treatment consists of aural toileting and the use of topical antimicrobial drops. Antimicrobial treatment is empiric, and most Australian guidelines advise the use of dexamethasone/framycetin/gramicidin as first-line therapy. This study aimed to identify the most prevalent pathogens implicated in OE in Queensland, Australia, and determine if there was any variability with the season, proximity to a coastline, age, gender and First Nations status. The primary pathogen cultured, the specimen type, the date of collection and the patient demographics were retrieved from microbiology swabs sent from hospitals to Pathology Queensland. Multivariate analysis was performed on the swabs. Pseudomonas aeruginosa was the most prevalent pathogen cultured in the external ear in Queensland, at 37.9%. In inland regions, Staphylococcus aureus was the most prevalent organism. Children were three-fold less likely to have OE resulting from a fungal pathogen. The use of targeted antimicrobials against Pseudomonas aeruginosa in coastal regions during summer is sensible. Due to the low burden of fungal disease in children, there should be a high threshold for the commencement of antifungal ear drops.

In this analysis, the objective is to forecast the stock prices of property and casualty insurance in 2022. This industry is known to be relatively stable and resilient to economic downturns. The data utilizes weekly adjusted closing prices of Progressive Insurance from 2019 to 2021 to form the training set. Three different models were created to predict weekly adjusted closing prices for 2022. The methods used were the LSTM and GRU recurrent neural network models, as well as the ARIMA time series analysis. Based on the results, the GRU method achieved the lowest RMSE due to its ability to avoid overfitting and does not rely on the assumption of stationarity.

Within the labor relations paradigm, employee voice is broadly defined as the ways and means through which employees 'have a say' and influence organizational issues at work. Whilst we know much about employee voice in the Anglo-American (developed) world, we know much less about how employee voice operates in emerging economies. This volume explores the nature of employee voice in four emerging economies: Argentina, China, India and South Korea. The volume brings together an internationally renowned group of contributors who are experts in their field and an authority on their countries, to combine cutting edge research and theory in this essential exploration of voice in emerging economies.  This volume identifies, inter alia, novel forms and channels of employee voice, new institutional and informal actors, new challenges to social dialogue and representation in emerging economies, and, the importance of cultural norms in predicting employee voice behaviors. The volume therefore provides a timely challenge to the predominant assumptions that underline the nature, operation and effectiveness of employee voice in the Western world.

The genetic tractability, well-mapped circuitry, and diverse behavioral repertoire of the nematode make it an ideal model for physiological and behavioral studies. A wide range of methods has been developed for analyzing behaviors, evolving with advances in technology such as videography and computer-assisted analysis. However, unlike organisms with distinct body features such as limbs and wings, the contour of is rather uniform, posing unique challenges for automated analyses of behavior. Here, we introduce -an open-source, artificial intelligence (AI)-based platform we recently developed-to the research community. We trained deep learning models in capable of automatically categorizing and quantifying multiple user-defined parameters of worm locomotion behavior in multi-worm videos with high accuracy. Furthermore, we demonstrated their efficacy in quantifying locomotion changes in aging worms. Our work offers a cost-effective, user-accessible, and comprehensive approach to behavioral analysis in .

The ability to sense and avoid noxious environments is essential for animal survival; yet, how this is achieved at the behavioral, neuronal, and molecular levels is not well understood. Here, we use larvae as a model to investigate how animals sense and avoid cold temperatures. By implementing custom-built thermoelectric devices capable of delivering rapid and precise thermal stimuli, we find that cold delivered to the larval head evokes robust escape behavioral responses. We identify a group of head-located cold-sensitive neurons as necessary and sufficient for such avoidance responses. We further demonstrate that the kainate-type glutamate receptor acts as a novel cold sensor required for head cold sensitivity. Knockdown of in head cold-sensing neurons suppresses their cold sensitivity. Heterologous expression of confers cold sensitivity. Our results show that larvae have evolved the capacity to detect and avoid cold temperatures through a previously uncharacterized cold-sensing mechanism.

Quantum error correction is essential for achieving practical quantum computing but has a significant computational overhead. Among fault-tolerant (FT) gate operations, non-Clifford gates, such as \\(T\\), are particularly expensive due to their reliance on magic state distillation. These costly \\(T\\) gates appear frequently in FT circuits as many quantum algorithms require arbitrary single-qubit rotations, such as \\(R_x\\) and \\(R_z\\) gates, which must be decomposed into a sequence of \\(T\\) and Clifford gates. In many quantum circuits, \\(R_x\\) and \\(R_z\\) gates can be fused to form a single \\(U3\\) unitary. However, existing synthesis methods, such as Gridsynth, rely on indirect decompositions, requiring separate \\(R_z\\) decompositions that result in a threefold increase in \\(T\\) count. This work presents TensoR-based Arbitrary unitary SYNthesis (trasyn), a novel FT synthesis algorithm that directly synthesizes arbitrary single-qubit unitaries, avoiding the overhead of separate \\(R_z\\) decompositions. By leveraging tensor network-based search, our approach enables native \\(U3\\) synthesis, reducing the \\(T\\) count, Clifford gate count, and approximation error. Compared to Gridsynth-based circuit synthesis, for 187 representative benchmarks, our design reduces the T count by up to 3.5\\(\\times\\), and Clifford gates by 7\\(\\times\\), resulting in up to 4\\(\\times\\) improvement in overall circuit infidelity.

To evaluate a quantum circuit on a quantum processor, one must find a mapping from circuit qubits to processor qubits and plan the instruction execution while satisfying the processor's constraints. This is known as the qubit mapping and routing (QMR) problem. High-quality QMR solutions are key to maximizing the utility of scarce quantum resources and minimizing the probability of logical errors affecting computation. The challenge is that the landscape of quantum processors is incredibly diverse and fast-evolving. Given this diversity, dozens of papers have addressed the QMR problem for different qubit hardware, connectivity constraints, and quantum error correction schemes by a developing a new algorithm for a particular context. We present an alternative approach: automatically generating qubit mapping and routing compilers for arbitrary quantum processors. Though each QMR problem is different, we identify a common core structure-device state machine-that we use to formulate an abstract QMR problem. Our formulation naturally leads to a compact domain-specific language for specifying QMR problems and a powerful parametric algorithm that can be instantiated for any QMR specification. Our thorough evaluation on case studies of important QMR problems shows that generated compilers are competitive with handwritten, specialized compilers in terms of runtime and solution quality.

It is our ability to respond to pathogens, and often more importantly, revert back to homeostasis which allows us to survive. Tubby-like protein 4 (TULP4) is a novel putative immunoregulatory protein, which may be pertinent for this reversal process. Many bacterially induced mortalities are not due solely to an overwhelming pathogenic load, but to sepsis. Sepsis caused by gram negative bacteria is often a result of the host’s response to lipopolysaccharide (LPS). TULP4 may be significant for the inhibition of the host’s immune response to the LPS stimulated Toll-like receptor 4 (TLR4) signaling cascade. This dissertation explores both where and mechanistically how TULP4 implements this regulation of the innate inflammatory response. Chapter 1 introduces what is currently known about TULP4, TLR innate immune signaling and the ubiquitin proteasome system that is known to regulate it. Chapter 2 defines TULP4 as an innate immune inhibitor and explores where within the TLR4-mediated NF?B signaling cascade TULP4 functions whereas, Chapter 3 examines the ubiquitin ligase mechanism and target of TULP4’s inhibition. Finally, Chapter 4 discusses future experiments which will better clarify TULP4’s inhibitory role and associated potential therapeutic strategies for treatment of immune related conditions.

Near-term quantum computers are expected to work in an environment where each operation is noisy, with no error correction. Therefore, quantum-circuit optimizers are applied to minimize the number of noisy operations. Today, physicists are constantly experimenting with novel devices and architectures. For every new physical substrate and for every modification of a quantum computer, we need to modify or rewrite major pieces of the optimizer to run successful experiments. In this paper, we present QUESO, an efficient approach for automatically synthesizing a quantum-circuit optimizer for a given quantum device. For instance, in 1.2 minutes, QUESO can synthesize an optimizer with high-probability correctness guarantees for IBM computers that significantly outperforms leading compilers, such as IBM's Qiskit and TKET, on the majority (85%) of the circuits in a diverse benchmark suite. A number of theoretical and algorithmic insights underlie QUESO: (1) An algebraic approach for representing rewrite rules and their semantics. This facilitates reasoning about complex symbolic rewrite rules that are beyond the scope of existing techniques. (2) A fast approach for probabilistically verifying equivalence of quantum circuits by reducing the problem to a special form of polynomial identity testing. (3) A novel probabilistic data structure, called a polynomial identity filter (PIF), for efficiently synthesizing rewrite rules. (4) A beam-search-based algorithm that efficiently applies the synthesized symbolic rewrite rules to optimize quantum circuits.