Abstract Cuttlefish are an important global fisheries resource, and their demand is placing increasing pressure on populations in many areas, necessitating conservation measures. We reviewed evidence from case studies spanning Europe, Africa, Asia, and Australia encompassing diverse intervention methods (fisheries closures, protected areas, habitat restoration, fishing-gear modifications, promoting egg survival, and restocking), and we also discuss the effects of pollution on cuttlefish. We conclude: (1) spatio-temporal closures need to encompass substantial portions of a species’ range and protect at least one major part of their life cycle; (2) fishing-gear modifications have the potential to reduce unwanted cuttlefish capture, but more comprehensive trials are needed; (3) egg survival can be improved by diverting and salvaging from traps; (4) existing lab rearing and restocking may not produce financially viable results; and (5) fisheries management policies should be regularly reviewed in light of rapid changes in cuttlefish stock status. Further, citizen science can provide data to reduce uncertainty in empirical assessments. The information synthesized in this review will guide managers and stakeholders to implement regulations and conservation initiatives that increase the productivity and sustainability of fisheries interacting with cuttlefish, and highlights gaps in knowledge that need to be addressed.

The COVID-19 pandemic has significantly changed the mental health care. Treating psychiatric patients with COVID-19 poses multiple challenges in the inpatient psychiatric setting in terms of mitigating transmission of the virus. Gracie Square Hospital, a freestanding psychiatric hospital located in New York City, devoted a unit to treating COVID-19 patients requiring inpatient psychiatric treatment. This paper describes our experiences and challenges while managing the psychiatric COVID-19 unit that may serve as a model for other health care facilities during the COVID-19 pandemic.

The COVID-19 pandemic has significantly changed the mental health care. Treating psychiatric patients with COVID-19 poses multiple challenges in the inpatient psychiatric setting in terms of mitigating transmission of the virus. Gracie Square Hospital, a freestanding psychiatric hospital located in New York City, dedicated a unit for treating COVID-19 patients requiring inpatient psychiatric treatment. We faced different challenges including treatment refusal, difficulty complying with safety precautions due to psychosis, agitated behavior, and staff psychological well-being. We considered reformation of protocols, expansion of the use of technology, development of a supportive platform, and standardization of clinical practice. This paper describes our strategies to manage the challenges while providing acute psychiatric treatment to COVID-19 patients.

Searchable abstracts of presentations at key conferences on calcified tissues ISSN 2052-1219 (online)

We describe here some early results of the FRESH (Freshman Research Engagement in the Sciences) program. A program with the goal to expose freshman to an ongoing research project during the academic year to promote student growth and improve retention in the STEM disciplines. Freshmen worked with a faculty mentor and were also chaperoned by a more senior student researcher in order that they learn lab techniques and the capacity to work independently. Participants were fully engaged in a research project (performing experiments, analyzing and discussing results), not a classic classroom projects, but discovery based projects. By bringing students into the research lab at this early stage, our aim was to improve retention by allowing science students to actually act as scientists, providing an enhanced experience over the usual freshman survey course content. Of the 13 students in two cohorts who joined the program as freshmen, 12 are still in their major and have co‐authored over 20 different papers and conference presentations to date. Based on these initial successes, we have modified our approach, tracking qualified applicants who we were unable to fund to serve as our control group in order to study the impact of the FRESH approach on student success. Support or Funding Information This work was supported by grants from the Indiana Space Grant Consortium (INSGC). This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Background and Aim: Environmental impacts from carcass management are a significant concern globally.Despite a history of costly, ineffective, and environmentally damaging carcass disposal efforts, large animal carcass disposal methods have advanced little in the past decade.An outbreak today will likely be managed with the same carcass disposal techniques used in the previous decades and will likely result in the same economic, health, and environmental impacts.This article overviews the results of one field test that was completed in Virginia (United States) using the aboveground burial (AGB) technique and the disposal of 111 foot-and-mouth disease (FMD) infected sheep in Tunisia using a similar methodology. Materials and Methods:Researchers in the United States conducted a field test to assess the environmental impact and effectiveness of AGB in decomposing livestock carcasses.The system design included a shallow trench excavated into native soil and a carbonaceous base placed on the bottom of the trenches followed by a single layer of animal carcasses.Excavated soils were subsequently placed on top of the animals, and a vegetative layer was established.A similar methodology was used in Tunisia to manage sheep infected with FMDs, Peste des Petits Ruminants virus, and Bluetongue Virus. Results:The results of the field test in the United States demonstrated a significant carcass degradation during the 1-year period of the project, and the migration of nutrients below the carcasses appears to be limited thereby minimizing the threat of groundwater contamination.The methodology proved practical for the disposal of infected sheep carcasses in Tunisia. Conclusions:Based on the analysis conducted to date, AGB appears to offer many benefits over traditional burial for catastrophic mortality management.Ongoing research will help to identify limitations of the method and determine where its application during large disease outbreaks or natural disasters is appropriate.

This paper describes the ALISA tool, which implements a lightly supervised method for sentence-level alignment of speech with imperfect transcripts. Its intended use is to enable the creation of new speech corpora from a multitude of resources in a language-independent fashion, thus avoiding the need to record or transcribe speech data. The method is designed so that it requires minimum user intervention and expert knowledge, and it is able to align data in languages which employ alphabetic scripts. It comprises a GMM-based voice activity detector and a highly constrained grapheme-based speech aligner. The method is evaluated objectively against a gold standard segmentation and transcription, as well as subjectively through building and testing speech synthesis systems from the retrieved data. Results show that on average, 70% of the original data is correctly aligned, with a word error rate of less than 0.5%. In one case, subjective listening tests show a statistically significant preference for voices built on the gold transcript, but this is small and in other tests, no statistically significant differences between the systems built from the fully supervised training data and the one which uses the proposed method are found.

Vocoding of speech is a standard part of statistical parametric speech synthesis systems. It imposes an upper bound of the naturalness that can possibly be achieved. Hybrid systems using parametric models to guide the selection of natural speech units can combine the benefits of robust statistical models with the high level of naturalness of waveform concatenation. Existing hybrid systems use Hidden Markov Models (HMMs) as the statistical model. This paper demonstrates that the superiority of Deep Neural Network (DNN) acoustic models over HMMs in conventional statistical parametric speech synthesis also carries over to hybrid synthesis. We compare various DNN and HMM hybrid configurations, guiding the selection of waveform units in either the vocoder parameter domain, or in the domain of embeddings (bottleneck features).

To automatically build, from scratch, the language processing component for a speech synthesis system in a new language, a purified text corpora is needed where any words and phrases from other languages are clearly identified or excluded. When using found data and where there is no inherent linguistic knowledge of the language/languages contained in the data, identifying the pure data is a difficult problem. We propose an unsupervised language identification approach based on Latent Dirichlet Allocation where we take the raw n-gram count as features without any smoothing, pruning or interpolation. The Latent Dirichlet Allocation topic model is reformulated for the language identification task and Collapsed Gibbs Sampling is used to train an unsupervised language identification model. In order to find the number of languages present, we compared four kinds of measure and also the Hierarchical Dirichlet process on several configurations of the ECI/UCI benchmark. Experiments on the ECI/MCI data and a Wikipedia based Swahili corpus shows this LDA method, without any annotation, has comparable precisions, recalls and F-scores to state of the art supervised language identification techniques.

We investigate two wavelet-based decomposition strategies of the f0 signal and their usefulness as a secondary task for speech synthesis using multi-task deep neural networks (MTL-DNN). The first decomposition strategy uses a static set of scales for all utterances in the training data. We propose a second strategy, where the scale of the mother wavelet is dynamically adjusted to the rate of each utterance. This approach is able to capture f0 variations related to the syllable, word, clitic-group, and phrase units. This method also constrains the wavelet components to be within the frequency range that previous experiments have shown to be more natural. These two strategies are evaluated as a secondary task in multi-task deep neural networks (MTL-DNNs). Results indicate that on an expressive dataset there is a strong preference for the systems using multi-task learning when compared to the baseline system.

In this experiment, we tested the hypothesis that adult-child differences in cue weighting are influenced by adult-child differences in knowledge of (a) the relative predictability of wordinitial vs. word-final consonants, and (b) of the relationship between predictability and acoustic salience/distinctiveness. We tested our hypothesis using synthetic speech continua with formant transitions varying from /edi/ to /ebi/, which listeners were encouraged to hear as either “Abe E/Ade E” (VC#V context) or as “A bee/A dee” (V#CV context). We tested the extent to which changes in formant transitions influence /d/ vs. /b/ categorisation. Results show that adults were more influenced by transitions cueing word-initial consonants (less predictable in English) than by transitions cueing word-final consonants (more predictable in English), whereas children showed a more balanced pattern, with marginally more influence of transitions cueing word-final consonants. Results are consistent with the view that adults have learned more about the relative predictability of word-initial vs. word-final consonants and have learned that acoustic cues to the less-predictable initial consonants are more distinctive. They therefore weight these cues more heavily than less-distinctive, more contextually predictable, word-final cues.

Abstract Tight gas/tight oil reservoirs require fracture stimulation to achieve commercial rates of hydrocarbons. Fracturing operations involve pumping considerable volumes of proppant and water/gel into the reservoir. Rapid cleanup of fracturing fluids and residual proppant ensures the desired goals of the stimulation operations are achieved: enhancing the flow capacity of the well while minimizing the risk of proppant damage to surface equipment. It is normal for significant quantities of proppant and frac fluid to flow back after opening the well for cleanup. Frac fluid often contains broken cross-linked gel, which must be flowed back to ensure cleanup of the fracture and minimize plugging of the fracture face and the proppant pack. In North America, it is not unusual for frac fluid recovery to be only a small fraction of the amount of injected fluid. Frac fluid recovery of 5 to 30 percent is not unusual and 50 percent is often considered excellent. In the Sultanate of Oman, BP is currently achieving 50 to 90 percent frac fluid recovery, while pressure transient analysis indicates post-frac skin damage figures of -6 or better, indicating excellent stimulation effectiveness. Critically, no proppant has flowed through to the surface facility. These cleanup procedures have been conducted in wells with a variety of frac types including 450,000 to 1,000,000 lb cross-linked gel fracs and multiple 17,000 bbl slick water fracs. An analysis of the procedures used to achieve these results will be presented in this paper. It indicates that allowing the frac to close and opening the well on a moderate choke, with re-direction of the post-frac fluid through effective sand management systems, followed by flowing the well at a managed drawdown against the reservoir has achieved excellent results. These results underpinned the decision to move forward in this multi-billion dollar development project.

The Continuous Wavelet Transform (CWT) has been re- cently proposed to model f0 in the context of speech synthe- sis. It was shown that systems using signal decomposition with the CWT tend to outperform systems that model the signal di- rectly. The f0 signal is typically decomposed into various scales of differing frequency. In these experiments, we reconstruct f0 with selected frequencies and ask native listeners to judge the naturalness of synthesized utterances with respect to natural speech. Results indicate that HMM-generated f0 is compara- ble to the CWT low frequencies, suggesting it mostly generates utterances with neutral intonation. Middle frequencies achieve very high levels of naturalness, while very high frequencies are mostly noise.

We propose a representation of f0 using the Continuous Wavelet Transform (CWT) and the Discrete Cosine Transform (DCT). The CWT decomposes the signal into various scales of selected frequencies, while the DCT compactly represents complex contours as a weighted sum of cosine functions. The proposed approach has the advantage of combining signal decomposition and higher-level representations, thus modeling low-frequencies at higher levels and high-frequencies at lower-levels. Objective results indicate that this representation improves f0 prediction over traditional short-term approaches. Subjective results show that improvements are seen over the typical MSD-HMM and are comparable to the recently proposed CWT-HMM, while using less parameters. These results are discussed and future lines of research are proposed.