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TACO is an open image dataset for litter detection and segmentation, which is growing through crowdsourcing. Firstly, this paper describes this dataset and the tools developed to support it. Secondly, we report instance segmentation performance using Mask R-CNN on the current version of TACO. Despite its small size (1500 images and 4784 annotations), our results are promising on this challenging problem. However, to achieve satisfactory trash detection in the wild for deployment, TACO still needs much more manual annotations. These can be contributed using: http://tacodataset.org/

BACKGROUND AND OBJECTIVES: Transfusion-associated circulatory overload (TACO) is a serious transfusion complication resulting in respiratory distress. The study's objective was to assess TACO occurrence and potential risk factors among elderly Medicare beneficiaries (ages 65 and older) in the inpatient setting during 2011. MATERIALS AND METHODS: This retrospective claims-based study utilized Medicare administrative databases in coordination with Centers for Medicare & Medicaid Services. Transfusions were identified by recorded procedure and revenue centre codes, while TACO was ascertained via ICD-9-CM diagnosis code. We evaluated TACO diagnosis code rates overall and by age, gender, race, number of units and blood components transfused. Multivariate logistic regression analyses were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Among 2,147,038 inpatient transfusion stays for elderly in 2011, 1340 had TACO diagnosis code, overall rate of 62·4 per 100,000 stays. TACO rates increased significantly with age and units transfused (P < 0·0001). After adjustment for confounding, significantly higher odds of TACO were found for women vs. men (OR = 1·40, 95% CI 1·26-1·60), White people vs. non-White people (OR = 1·38, 95% CI 1·20-1·62) and persons with congestive heart failure (OR = 1·61, 95% CI 1·44-1·88), chronic pulmonary disease (OR = 1·19, 95% CI 1·08-1·32) and different anaemias. CONCLUSION: Our study identified largest number of potential TACO cases to date and showed a substantial increase in TACO occurrence with age and number of units transfused. The study suggested increased TACO risk in elderly with congestive heart failure, chronic pulmonary disease and anaemias. Overall, study shows importance of large administrative databases as an additional epidemiological tool.

Many advanced Learning from Demonstration (LfD) methods consider the decomposition of complex, real-world tasks into simpler sub-tasks. By reusing the corresponding sub-policies within and between tasks, they provide training data for each policy from different high-level tasks and compose them to perform novel ones. Existing approaches to modular LfD focus either on learning a single high-level task or depend on domain knowledge and temporal segmentation. In contrast, we propose a weakly supervised, domain-agnostic approach based on task sketches, which include only the sequence of sub-tasks performed in each demonstration. Our approach simultaneously aligns the sketches with the observed demonstrations and learns the required sub-policies. This improves generalisation in comparison to separate optimisation procedures. We evaluate the approach on multiple domains, including a simulated 3D robot arm control task using purely image-based observations. The results show that our approach performs commensurately with fully supervised approaches, while requiring significantly less annotation effort.

The surge of artificial intelligence, particularly large language models, has driven the rapid development of large-scale machine learning clusters. Executing distributed models on these clusters is often constrained by communication overhead, making efficient utilization of available network resources crucial. As a result, the routing algorithm employed for collective communications (i.e., collective algorithms) plays a pivotal role in determining overall performance. Unfortunately, existing collective communication libraries for distributed machine learning are limited by a fixed set of basic collective algorithms. This limitation hinders communication optimization, especially in modern clusters with heterogeneous and asymmetric topologies. Furthermore, manually designing collective algorithms for all possible combinations of network topologies and collective patterns requires heavy engineering and validation efforts. To address these challenges, this paper presents Tacos, an autonomous synthesizer capable of automatically generating topology-aware collective algorithms tailored to specific collective patterns and network topologies. Tacos is highly flexible, synthesizing an All-Reduce algorithm for a heterogeneous 128-NPU system in just 1.08 seconds, while achieving up to a 4.27× performance improvement over state-of-the-art synthesizers. Additionally, Tacos demonstrates better scalability with polynomial synthesis times, in contrast to NP-hard approaches which only scale to systems with tens of NPUs. Tacos can synthesize for 40K NPUs in just 2.52 hours.

This essay examines the history of the taco in Mexico and the United States as a way of shifting the focus of "McDonaldization" from technology to ethnicity. It begins with the origins of the taco in Mexico to show that it was a product of modernity rather than an ancient tradition transformed by Yankee ingenuity. It then examines patent records, cookbooks, and archival sources to demonstrate that all aspects of the Mexican American taco, including the pre-fried taco shell, were actually invented within the ethnic community. Indeed, new forms of tacos were one of the many ways in which ethnic women mediated the boundaries between Mexican family traditions and U.S. cultural citizenship. These sources also refute corporate hagiography attributing the fast food taco to Glen Bell, founder of Taco Bell. Finally, using GIS to map taco shops against tract-level census data, the essay concludes that non-ethnic fast food chains succeeded by marketing tacos as a form of exoticism or safe danger within the segregated landscape of 1950s Los Angeles.

Preface Introduction A Tale of Two Tacos Part I Proto-Tacos Chapter 1. Maize and the Making of Mexico Chapter 2. Burritos in the Borderlands Part II National Tacos Chapter 3. From the Pastry War to Parisian Mole Chapter 4. The Rise and Fall of the Chili Queens Chapter 5. Inventing the Mexican American Taco Part III Global Tacos Chapter 6. The First Wave of Global Mexican Chapter 7. The Blue Corn Bonanza Conclusion The Battle of the Taco Trucks Notes Select Bibliography Index

Long noncoding RNAs (lncRNAs) are primarily regulated by their cellular localization, which is responsible for their molecular functions, including cell cycle regulation and genome rearrangements. Accurately identifying the subcellular location of lncRNAs from sequence information is crucial for a better understanding of their biological functions and mechanisms. In contrast to traditional experimental methods, bioinformatics or computational methods can be applied for the annotation of lncRNA subcellular locations in humans more effectively. In the past, several machine learning-based methods have been developed to identify lncRNA subcellular localization, but relevant work for identifying cell-specific localization of human lncRNA remains limited. In this study, we present the first application of the tree-based stacking approach, TACOS, which allows users to identify the subcellular localization of human lncRNA in 10 different cell types. Specifically, we conducted comprehensive evaluations of six tree-based classifiers with 10 different feature descriptors, using a newly constructed balanced training dataset for each cell type. Subsequently, the strengths of the AdaBoost baseline models were integrated via a stacking approach, with an appropriate tree-based classifier for the final prediction. TACOS displayed consistent performance in both the cross-validation and independent assessments compared with the other two approaches employed in this study. The user-friendly online TACOS web server can be accessed at https://balalab-skku.org/TACOS.

INTRODUCTION: Intravenous corticosteroids are the mainstay of treatment of patients hospitalized with acute severe ulcerative colitis (ASUC). However, 30%-40% of the patients are refractory to corticosteroids. We investigated whether addition of tofacitinib to corticosteroids improved the treatment responsiveness in patients with ASUC. METHODS: This single-center, double-blind, placebo-controlled trial randomized adult patients with ASUC (defined by the Truelove Witts severity criteria) to receive either tofacitinib (10 mg thrice daily) or a matching placebo for 7 days while continuing intravenous corticosteroids (hydrocortisone 100 mg every 6 hours). The primary end point was response to treatment (decline in the Lichtiger index by >3 points and an absolute score <10 for 2 consecutive days without the need for rescue therapy) by day 7. The key secondary outcome was the cumulative probability of requiring initiation of infliximab or undergoing colectomy within 90 days following randomization. All analyses were performed in the intention-to-treat population. RESULTS: A total of 104 patients were randomly assigned to a treatment group (53 to tofacitinib and 51 to placebo). At day 7, response to treatment was achieved in 44/53 (83.01%) patients receiving tofacitinib vs 30/51 (58.82%) patients receiving placebo (odds ratio 3.42, 95% confidence interval 1.37-8.48, P = 0.007). The need for rescue therapy by day 7 was lower in the tofacitinib arm (odds ratio 0.27, 95% confidence interval 0.09-0.78, P = 0.01). The cumulative probability of need for rescue therapy at day 90 was 0.13 in patients who received tofacitinib vs 0.38 in patients receiving placebo (log-rank P = 0.003). Most of the treatment-related adverse effects were mild. One patient, receiving tofacitinib, developed dural venous sinus thrombosis. DISCUSSION: In patients with ASUC, combination of tofacitinib and corticosteroids improved treatment responsiveness and decreased the need for rescue therapy.

Recent reports have indicated that cholesterol-dependent association of tryptophan-aspartate containing coat protein (TACO) plays a crucial role in the entry/survival of Mycobacterium tuberculosis within human macrophages. Keeping this in view, the present study explored whether the molecules that have the ability to downregulate TACO gene transcription could also restrict entry/survival of mycobacteria within human macrophages. The study revealed that chenodeoxycholic acid (CDCA), either alone or in combination with retinoic acid (RA), had the inherent capacity to downregulate TACO gene transcription in a dose-dependent fashion. This result was in conformity with the existence of a functional FXR/RXR binding site analyzed in the regulatory region of the TACO gene. Furthermore, we demonstrate that the entry and intracellular survival of M. tuberculosis is significantly restricted in THP-1 macrophages exposed to CDCA/RA. On the basis of these findings, we propose that the CDCA/RA-dependent pathway may open a new possibility for the treatment of tuberculosis.

Recent evidence suggests that persistence of Helicobacter pylori can be explained, at least in part, by the failure of macrophages to kill bacteria. The fate of type 1 H. pylori strain LC11, which expresses the cag pathogenicity island (PAI) and the vacuolating cytotoxin, and type 2 strain LC20, which lacks both these virulence factors, was determined following infection of the murine macrophage cell line RAW 264.7 or the human macrophage-like cell line THP-1. Helicobacter pylori strain LC11 displayed enhanced survival in macrophages in comparison with strain LC20 (4.0 +/- 0.2 versus 2.1 +/- 0.6 log CFU ml-1, P < 0.01) at 24 h. Phagosomes containing strain LC11 showed reduced co-localization with LysoTracker Red, higher levels of expression of the early endosome marker EEA1 expression and lower expression of the late endosome/lysosome marker LAMP1 relative to internalized strain LC20, both at 2 h and 24 h. These findings indicate that, in contrast to strain LC20, strain LC11 resides in a compartment with early endosome properties and does not fuse with lysosomes. In addition, phagosomes containing LC11 recruited and retained a higher percentage of TACO (coronin 1) protein in comparison with phagosomes containing strain LC20. Furthermore, IFN-gamma stimulation facilitated maturation of phagosomes containing strain LC11 in association with the release of TACO and a reduction in bacterial survival. We have demonstrated through the use of isogenic cagA-, cagE-/picB- and vacA- mutant strains, that VacA plays a significant role in the interruption of the phagosome maturation. Taken together, these results indicate that, following phagocytosis, H. pylori strains expressing the vacuolating cytotoxin arrest phagosome maturation in association with the retention of TACO.

Abstract — In this paper, an efficient linear time algorithm TACO is proposed for the first time to minimize the worst case clock skew in the presence of on-chip thermal variation. TACO, while tries to minimize the worst case clock skew, also attempts to minimize the clock tree wirelength by building up merging diamonds in a bottom-up manner. As an output, TACO provides balanced merging points and the modified clock routing paths to minimize the worst case clock skew under thermal variation. Experimental results on a set of standard benchmarks show 50- 70 % skew reduction with less than 0.6 % wirelength overhead. I.

Taco Bell Corporation has approximately 6,490 company-owned, licensed, and franchised locations in 50 states and a growing international market. Worldwide yearly sales are approximately $4.6 billion. In 1988, Taco Bell introduced six core-menu items for the reduced price of 59 cents and offered free drink refills. Taco Bell has since continued to change and innovate. Its new strategy meant restructuring the business to become more efficient and cost-effective. To do this, the company relied on an integrated set of operations research models, including forecasting to predict customer arrivals, simulation to determine the optimum labor required to provide desired customer service, and optimization to schedule and allocate crew members to minimize payroll. Through 1997, these models have saved over $53 million in labor costs.

BACKGROUND: Cooperative binding of transcription factor (TF) dimers to DNA is increasingly recognized as a major contributor to binding specificity. However, it is likely that the set of known TF dimers is highly incomplete, given that they were discovered using ad hoc approaches, or through computational analyses of limited datasets. RESULTS: Here, we present TACO (Transcription factor Association from Complex Overrepresentation), a general-purpose standalone software tool that takes as input any genome-wide set of regulatory elements and predicts cell-type-specific TF dimers based on enrichment of motif complexes. TACO is the first tool that can accommodate motif complexes composed of overlapping motifs, a characteristic feature of many known TF dimers. Our method comprehensively outperforms existing tools when benchmarked on a reference set of 29 known dimers. We demonstrate the utility and consistency of TACO by applying it to 152 DNase-seq datasets and 94 ChIP-seq datasets. CONCLUSIONS: Based on these results, we uncover a general principle governing the structure of TF-TF-DNA ternary complexes, namely that the flexibility of the complex is correlated with, and most likely a consequence of, inter-motif spacing.

Transfusion-related acute lung injury (TRALI) and transfusion-associated circulatory overload (TACO) are the leading causes of transfusion-related morbidity and mortality. These adverse events are characterized by acute pulmonary edema within 6 hours of a blood transfusion and have historically been difficult to study due to underrecognition and nonspecific diagnostic criteria. However, in the past decade, in vivo models and clinical studies utilizing active surveillance have advanced our understanding of their epidemiology and pathogenesis. With the adoption of mitigation strategies and patient blood management, the incidence of TRALI and TACO has decreased. Continued research to prevent and treat these severe cardiopulmonary events is focused on both the blood component and the transfusion recipient.

Taco complex templation based on the bis(m-phenylene)-32-crown-10/paraquat recognition motif is used to develop a general method for preparing mechanically interlocked molecules. A [2]rotaxane and a [2]catenane were synthesized in high yields by a ring-closing metathesis reaction, which was owed to the impactful template effect. Due to the high symmetry of (5,5')-difunctional bis(m-phenylene)-32-crown-10 derivatives, this taco complex templated synthesis has potential to be a tempting method to solve a symmetry-based problem in the fabrication of complicated mechanically interlocked structures.

Contrastive learning has been widely used to train transformer-based vision-language models for video-text alignment and multi-modal representation learning. This paper presents a new algorithm called Token-Aware Cascade contrastive learning (TACo) that improves contrastive learning using two novel techniques. The first is the token-aware contrastive loss which is computed by taking into account the syntactic classes of words. This is motivated by the observation that for a video-text pair, the content words in the text, such as nouns and verbs, are more likely to be aligned with the visual contents in the video than the function words. Second, a cascade sampling method is applied to generate a small set of hard negative examples for efficient loss estimation for multi-modal fusion layers. To validate the effectiveness of TACo, in our experiments we finetune pretrained models for a set of downstream tasks including text-video retrieval (YouCook2, MSR-VTT and ActivityNet), video action step localization (CrossTask), video action segmentation (COIN). The results show that our models attain consistent improvements across different experimental settings over previous methods, set-ting new state-of-the-art on three public text-video retrieval benchmarks of YouCook2, MSR-VTT and ActivityNet.

The translocation and assembly module (TAM) plays a role in the transport and insertion of proteins into the bacterial outer membrane. TamB, a component of this system spans the periplasmic space to engage with its partner protein TamA. Despite efforts to characterize the TAM, the structure and mechanism of action of TamB remained enigmatic. Here we present the crystal structure of TamB amino acids 963–1,138. This region represents half of the conserved DUF490 domain, the defining feature of TamB. TamB963-1138 consists of a concave, taco-shaped β sheet with a hydrophobic interior. This β taco structure is of dimensions capable of accommodating and shielding the hydrophobic side of an amphipathic β strand, potentially allowing TamB to chaperone nascent membrane proteins from the aqueous environment. In addition, sequence analysis suggests that the structure of TamB963-1138 is shared by a large portion of TamB. This architecture could allow TamB to act as a conduit for membrane proteins.

This paper presents TACO (Toolchain for Automated Control and Optimization), which is a Modelica-based automated toolchain for model predictive control (MPC) of building systems. Its goal is to significantly reduce the engineering expertise and the time investment required for applying MPC to buildings. TACO is based on JModelica. Modifications compared to JModelica are discussed and the implementation of our custom MPC problem formulation is presented. The implementation is verified using two example models and is benchmarked with respect to accuracy and computation time. These results show that the computation time can be reduced significantly using the toolchain options, while only slightly reducing the controller optimality.