Betting against sentiment? Seemingly unrelated anomalies and the low-risk effect

The negative CAPM alphas of high-beta and high-variance stocks are attributable to an unaccounted factor in the CAPM. We use eight seemingly unrelated anomalies to construct a composite factor in the spirit of the optimal orthogonal portfolio (FOP). Accounting for FOP re-establishes a positive relation between beta and average returns in time series regressions as well as cross-sectional and explains the negative alphas of high-beta and high-variance stocks. To analyze economic drivers behind FOP, we perform a horse race between leverage constraints, investor sentiment, and disagreement. Our results highlight investor sentiment as the most promising explanation for the low-risk effect

Acoustic estimation of the manatee population and classification of call categories using artificial intelligence

The population sizes of manatees in many regions remain largely unknown, primarily due to the challenging nature of conducting visual counts in turbid and inaccessible aquatic environments. Passive acoustic monitoring has shown promise for monitoring manatees in the wild. In this study, we present an innovative approach that leverages a convolutional neural network (CNN) for the detection, isolation and classification of manatee vocalizations from long-term audio recordings. To improve the effectiveness of manatee call detection and classification, the CNN works in two phases. First, a long-term audio recording is divided into smaller windows of 0.5 seconds and a binary decision is made as to whether or not it contains a manatee call. Subsequently, these vocalizations are classified into distinct vocal classes (4 categories), allowing for the separation and analysis of signature calls (squeaks). Signature calls are further subjected to clustering techniques to distinguish the recorded individuals and estimate the population size. The CNN was trained and validated using audio recordings from three different zoological facilities with varying numbers of manatees. Three different clustering methods (community detection with two different classifiers and HDBSCAN) were tested for their suitability. The results demonstrate the ability of the CNN to accurately detect manatee vocalizations and effectively classify the different call categories. In addition, our study demonstrates the feasibility of reliable population size estimation using HDBSCAN as clustering method. The integration of CNN and clustering methods offers a promising way to assess manatee populations in visually challenging and inaccessible regions using autonomous acoustic recording devices. In addition, the ability to differentiate between call categories will allow for ongoing monitoring of important information such as stress, arousal, and calf presence, which will aid in the conservation and management of manatees in critical habitats

The Bacteroidetes Aequorivita sp. and Kaistella jeonii Produce Promiscuous Esterases With PET-Hydrolyzing Activity

Certain members of the Actinobacteria and Proteobacteria are known to degrade polyethylene terephthalate (PET). Here, we describe the first functional PET-active enzymes from the Bacteroidetes phylum. Using a PETase-specific Hidden-Markov-Model- (HMM-) based search algorithm, we identified several PETase candidates from Flavobacteriaceae and Porphyromonadaceae. Among them, two promiscuous and cold-active esterases derived from Aequorivita sp. (PET27) and Kaistella jeonii (PET30) showed depolymerizing activity on polycaprolactone (PCL), amorphous PET foil and on the polyester polyurethane Impranil® DLN. PET27 is a 37.8 kDa enzyme that released an average of 174.4 nmol terephthalic acid (TPA) after 120 h at 30°C from a 7 mg PET foil platelet in a 200 μl reaction volume, 38-times more than PET30 (37.4 kDa) released under the same conditions. The crystal structure of PET30 without its C-terminal Por-domain (PET30ΔPorC) was solved at 2.1 Å and displays high structural similarity to the IsPETase. PET30 shows a Phe-Met-Tyr substrate binding motif, which seems to be a unique feature, as IsPETase, LCC and PET2 all contain Tyr-Met-Trp binding residues, while PET27 possesses a Phe-Met-Trp motif that is identical to Cut190. Microscopic analyses showed that K. jeonii cells are indeed able to bind on and colonize PET surfaces after a few days of incubation. Homologs of PET27 and PET30 were detected in metagenomes, predominantly aquatic habitats, encompassing a wide range of different global climate zones and suggesting a hitherto unknown influence of this bacterial phylum on man-made polymer degradation

Localize animal sound events reliably (LASER): a new software for sound localization in zoos

Locating a vocalizing animal can be useful in many fields of bioacoustics and behavioral research, and is often done in the wild, covering large areas. In zoos, however, the application of this method becomes particularly difficult, because, on the one hand, the animals are in a relatively small area and, on the other hand, reverberant environments and background noise complicate the analysis. Nevertheless, by localizing and analyzing animal sounds, valuable information on physiological state, sex, subspecies, reproductive state, social status, and animal welfare can be gathered. Therefore, we developed a sound localization software that is able to estimate the position of a vocalizing animal precisely, making it possible to assign the vocalization to the corresponding individual, even under difficult conditions. In this study, the accuracy and reliability of the software is tested under various conditions. Different vocalizations were played back through a loudspeaker and recorded with several microphones to verify the accuracy. In addition, tests were carried out under real conditions using the example of the giant otter enclosure at Dortmund Zoo, Germany. The results show that the software can estimate the correct position of a sound source with a high accuracy (median of the deviation 0.234 m). Consequently, this software could make an important contribution to basic research via position determination and the associated differentiation of individuals, and could be relevant in a long-term application for monitoring animal welfare in zoos

Introducing the Software CASE (Cluster and Analyze Sound Events) by Comparing Different Clustering Methods and Audio Transformation Techniques Using Animal Vocalizations

Unsupervised clustering algorithms are widely used in ecology and conservation to classify animal sounds, but also offer several advantages in basic bioacoustics research. Consequently, it is important to overcome the existing challenges. A common practice is extracting the acoustic features of vocalizations one-dimensionally, only extracting an average value for a given feature for the entire vocalization. With frequency-modulated vocalizations, whose acoustic features can change over time, this can lead to insufficient characterization. Whether the necessary parameters have been set correctly and the obtained clustering result reliably classifies the vocalizations subsequently often remains unclear. The presented software, CASE, is intended to overcome these challenges. Established and new unsupervised clustering methods (community detection, affinity propagation, HDBSCAN, and fuzzy clustering) are tested in combination with various classifiers (k-nearest neighbor, dynamic time-warping, and cross-correlation) using differently transformed animal vocalizations. These methods are compared with predefined clusters to determine their strengths and weaknesses. In addition, a multidimensional data transformation procedure is presented that better represents the course of multiple acoustic features. The results suggest that, especially with frequency-modulated vocalizations, clustering is more applicable with multidimensional feature extraction compared with one-dimensional feature extraction. The characterization and clustering of vocalizations in multidimensional space offer great potential for future bioacoustic studies. The software CASE includes the developed method of multidimensional feature extraction, as well as all used clustering methods. It allows quickly applying several clustering algorithms to one data set to compare their results and to verify their reliability based on their consistency. Moreover, the software CASE determines the optimal values of most of the necessary parameters automatically. To take advantage of these benefits, the software CASE is provided for free download

Time series cluster analysis reveals individual assignment of microbiota in captive tiger (Panthera tigris) and wildebeest (Connochaetes taurinus)

Abstract Fecal microbiota variability and individuality are well studied in humans and also in farm animals (related to diet‐ or disease‐specific influences), but very little is known for exotic zoo‐housed animals. This includes a wide range of species that differ greatly in microbiota composition and variation. For example, herbivorous species show a very similar and constant fecal microbiota over time, whereas carnivorous species appear to be highly variable in fecal microbial diversity and composition. Our objective was to determine whether species‐specific and individual‐specific clustering patterns were observed in the fecal microbiota of wildebeest (Connochaetes taurinus) and tigers (Panthera tigris). We collected 95 fecal samples of 11 animal individuals that were each sampled over eight consecutive days and analyzed those with Illumina MiSeq sequencing of the V3–V4 region of the 16SrRNA gene. In order to identify species or individual clusters, we applied two different agglomerative hierarchical clustering algorithms – a community detection algorithm and Ward's linkage. Our results showed that both, species‐specific and individual‐specific clustering is possible, but more reliable results were achieved when applying dynamic time warping which finds the optimal alignment between different time series. Furthermore, the bacterial families that distinguish individuals from each other in both species included daily occurring core bacteria (e.g., Acidaminococcaceae in wildebeests or Clostridiaceae in tigers) as well as individual dependent and more fluctuating bacterial families. Our results suggest that while it is necessary to consider multiple consecutive samples per individual, it is then possible to characterize individual abundance patterns in fecal microbiota in both herbivorous and carnivorous species. This would allow establishing individual microbiota profiles of animals housed in zoos, which is a basic prerequisite to quickly detect deviations and use microbiome analysis as a non‐invasive and cost‐effective tool in animal welfare