Studying patterns of use of transport modes through data mining - Application to U.S. national household travel survey data set

Data collection activities related to travel require large amounts of financial and human resources to be conducted successfully. When available resources are scarce, the information hidden in these data sets needs to be exploited, both to increase their added value and to gain support among decision makers not to discontinue such efforts. This study assessed the use of a data mining technique, association analysis, to understand better the patterns of mode use from the 2009 U.S. National Household Travel Survey. Only variables related to self-reported levels of use of the different transportation means are considered, along with those useful to the socioeconomic characterization of the respondents. Association rules potentially showed a substitution effect between cars and public transportation, in economic terms but such an effect was not observed between public transportation and nonmotorized modes (e.g., bicycling and walking). This effect was a policy-relevant finding, because transit marketing should be targeted to car drivers rather than to bikers or walkers for real improvement in the environmental performance of any transportation system. Given the competitive advantage of private modes extensively discussed in the literature, modal diversion from car to transit is seldom observed in practice. However, after such a factor was controlled, the results suggest that modal diversion should mainly occur from cars to transit rather than from nonmotorized modes to transi

Transurethral radiofrequency collagen denaturation for the treatment of women with urinary incontinence

Peer reviewedPublisher PD

Role for Akt3/Protein Kinase Bγ in Attainment of Normal Brain Size

Studies of Drosophila and mammals have revealed the importance of insulin signaling through phosphatidylinositol 3-kinase and the serine/threonine kinase Akt/protein kinase B for the regulation of cell, organ, and organismal growth. In mammals, three highly conserved proteins, Akt1, Akt2, and Akt3, comprise the Akt family, of which the first two are required for normal growth and metabolism, respectively. Here we address the function of Akt3. Like Akt1, Akt3 is not required for the maintenance of normal carbohydrate metabolism but is essential for the attainment of normal organ size. However, in contrast to Akt1−/− mice, which display a proportional decrease in the sizes of all organs, Akt3−/− mice present a selective 20% decrease in brain size. Moreover, although Akt1- and Akt3-deficient brains are reduced in size to approximately the same degree, the absence of Akt1 leads to a reduction in cell number, whereas the lack of Akt3 results in smaller and fewer cells. Finally, mammalian target of rapamycin signaling is attenuated in the brains of Akt3−/− but not Akt1−/− mice, suggesting that differential regulation of this pathway contributes to an isoform-specific regulation of cell growth

Elongation Factor TFIIS Prevents Transcription Stress and R-Loop Accumulation to Maintain Genome Stability

Although correlations between RNA polymerase II (RNAPII) transcription stress, R-loops, and genome instability have been established, the mechanisms underlying these connections remain poorly understood. Here, we used a mutant version of the transcription elongation factor TFIIS (TFIISmut), aiming to specifically induce increased levels of RNAPII pausing, arrest, and/or backtracking in human cells. Indeed, TFIISmut expression results in slower elongation rates, relative depletion of polymerases from the end of genes, and increased levels of stopped RNAPII; it affects mRNA splicing and termination as well. Remarkably, TFIISmut expression also dramatically increases R-loops, which may form at the anterior end of backtracked RNAPII and trigger genome instability, including DNA strand breaks. These results shed light on the relationship between transcription stress and R-loops and suggest that different classes of R-loops may exist, potentially with distinct consequences for genome stability.Cancer Research UK FC001166UK Medical Research Council FC001166Wellcome Trust FC001166European Research Council 693327, ERC2014 AdG669898Ministerio de Economía y Competitividad BFU2013-42918-P, BFU2016-75058-

Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering

Prompting and in-context learning (ICL) have become efficient learning paradigms for large language models (LLMs). However, LLMs suffer from prompt brittleness and various bias factors in the prompt, including but not limited to the formatting, the choice verbalizers, and the ICL examples. To address this problem that results in unexpected performance degradation, calibration methods have been developed to mitigate the effects of these biases while recovering LLM performance. In this work, we first conduct a systematic analysis of the existing calibration methods, where we both provide a unified view and reveal the failure cases. Inspired by these analyses, we propose Batch Calibration (BC), a simple yet intuitive method that controls the contextual bias from the batched input, unifies various prior approaches, and effectively addresses the aforementioned issues. BC is zero-shot, inference-only, and incurs negligible additional costs. In the few-shot setup, we further extend BC to allow it to learn the contextual bias from labeled data. We validate the effectiveness of BC with PaLM 2-(S, M, L) and CLIP models and demonstrate state-of-the-art performance over previous calibration baselines across more than 10 natural language understanding and image classification tasks.Comment: ICLR 2024. 9 pages, 9 figures, 3 tables (22 pages, 11 figures, 11 tables including references and appendices

Defining and distinguishing infant behavioral states using acoustic cry analysis: is colic painful?

BackgroundTo characterize acoustic features of an infant's cry and use machine learning to provide an objective measurement of behavioral state in a cry-translator. To apply the cry-translation algorithm to colic hypothesizing that these cries sound painful.MethodsAssessment of 1000 cries in a mobile app (ChatterBabyTM). Training a cry-translation algorithm by evaluating >6000 acoustic features to predict whether infant cry was due to a pain (vaccinations, ear-piercings), fussy, or hunger states. Using the algorithm to predict the behavioral state of infants with reported colic.ResultsThe cry-translation algorithm was 90.7% accurate for identifying pain cries, and achieved 71.5% accuracy in discriminating cries from fussiness, hunger, or pain. The ChatterBaby cry-translation algorithm overwhelmingly predicted that colic cries were most likely from pain, compared to fussy and hungry states. Colic cries had average pain ratings of 73%, significantly greater than the pain measurements found in fussiness and hunger (p < 0.001, 2-sample t test). Colic cries outranked pain cries by measures of acoustic intensity, including energy, length of voiced periods, and fundamental frequency/pitch, while fussy and hungry cries showed reduced intensity measures compared to pain and colic.ConclusionsAcoustic features of cries are consistent across a diverse infant population and can be utilized as objective markers of pain, hunger, and fussiness. The ChatterBaby algorithm detected significant acoustic similarities between colic and painful cries, suggesting that they may share a neuronal pathway