PDMSkin – On-Skin Gestures with Printable Ultra-Stretchable Soft Electronic Second Skin

Innovative enabling technologies are key drivers of human augmentation. In this paper, we explore a new, conductive, and configurable material made from Polydimethylsiloxane (PDMS) that is capillary doped with silver particles (Ag) using an immiscible secondary fluid to build ultra-stretchable, soft electronics. Bonding silver particles directly with PDMS enables inherently stretchable Ag-PDMS circuits. Compared to previous work, the reduced silver consumption creates significant advantages, e.g., better stretchability and lower costs. The secondary fluid ensures self-assembling conductivity networks. Sensors are 3D-printed ultra-thin (200%. Therefore, printed circuits can attach tightly onto the body. Due to biocompatibility, devices can be implanted (e.g., open wounds treatment). We present a proof of concept on-skin interface that uses the new material to provide six distinct input gestures. Our quantitative evaluation with ten participants shows that we can successfully classify the gestures with a low spatial-resolution circuit. With few training data and a gradient boosting classifier, we yield 83% overall accuracy. Our qualitative material study with twelve participants shows that usability and comfort are well perceived; however, the smooth but easy to adapt surface does not feel tissue-equivalent. For future work, the new material will likely serve to build robust and skin-like electronics

Towards Respiration Rate Monitoring Using an In-Ear Headphone Inertial Measurement Unit

State-of-the-art respiration tracking devices require specialized equipment, making them impractical for every day at-home respiration sensing. In this paper, we present the first system for sensing respiratory rates using in-ear headphone inertial measurement units (IMU). The approach is based on technology already available in commodity devices: the eSense headphones. Our processing pipeline combines several existing approaches to clean noisy data and calculate respiratory rates on 20-second windows. In a study with twelve participants, we compare accelerometer and gyroscope based sensing and employ pressure-based measurement with nasal cannulas as ground truth. Our results indicate a mean absolute error of 2.62 CPM (acc) and 2.55 CPM (gyro). This overall accuracy is comparable to previous approaches using accelerometer-based sensing, but we observe a higher relative error for the gyroscope. In contrast to related work using other sensor positions, we can not report significant differences between the two modalities or the three postures standing, sitting, and lying on the back (supine). However, in general, performance varies drastically between participants

Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany

The effective reproductive number R$_t$ has taken a central role in the scientific, political, and public discussion during the COVID-19 pandemic, with numerous real-time estimates of this quantity routinely published. Disagreement between estimates can be substantial and may lead to confusion among decision-makers and the general public. In this work, we compare different estimates of the national-level effective reproductive number of COVID-19 in Germany in 2020 and 2021. We consider the agreement between estimates from the same method but published at different time points (within-method agreement) as well as retrospective agreement across eight different approaches (between-method agreement). Concerning the former, estimates from some methods are very stable over time and hardly subject to revisions, while others display considerable fluctuations. To evaluate between-method agreement, we reproduce the estimates generated by different groups using a variety of statistical approaches, standardizing analytical choices to assess how they contribute to the observed disagreement. These analytical choices include the data source, data pre-processing, assumed generation time distribution, statistical tuning parameters, and various delay distributions. We find that in practice, these auxiliary choices in the estimation of R$_t$ may affect results at least as strongly as the selection of the statistical approach. They should thus be communicated transparently along with the estimates

Collaborative nowcasting of COVID-19 hospitalization incidences in Germany

Real-time surveillance is a crucial element in the response to infectious disease outbreaks. However, the interpretation of incidence data is often hampered by delays occurring at various stages of data gathering and reporting. As a result, recent values are biased downward, which obscures current trends. Statistical nowcasting techniques can be employed to correct these biases, allowing for accurate characterization of recent developments and thus enhancing situational awareness. In this paper, we present a preregistered real-time assessment of eight nowcasting approaches, applied by independent research teams to German 7-day hospitalization incidences during the COVID-19 pandemic. This indicator played an important role in the management of the outbreak in Germany and was linked to levels of non-pharmaceutical interventions via certain thresholds. Due to its definition, in which hospitalization counts are aggregated by the date of case report rather than admission, German hospitalization incidences are particularly affected by delays and can take several weeks or months to fully stabilize. For this study, all methods were applied from 22 November 2021 to 29 April 2022, with probabilistic nowcasts produced each day for the current and 28 preceding days. Nowcasts at the national, state, and age-group levels were collected in the form of quantiles in a public repository and displayed in a dashboard. Moreover, a mean and a median ensemble nowcast were generated. We find that overall, the compared methods were able to remove a large part of the biases introduced by delays. Most participating teams underestimated the importance of very long delays, though, resulting in nowcasts with a slight downward bias. The accompanying prediction intervals were also too narrow for almost all methods. Averaged over all nowcast horizons, the best performance was achieved by a model using case incidences as a covariate and taking into account longer delays than the other approaches. For the most recent days, which are often considered the most relevant in practice, a mean ensemble of the submitted nowcasts performed best. We conclude by providing some lessons learned on the definition of nowcasting targets and practical challenges

National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021

We compare forecasts of weekly case and death numbers for COVID-19 in Germany and Poland based on 15 different modelling approaches. These cover the period from January to April 2021 and address numbers of cases and deaths one and two weeks into the future, along with the respective uncertainties. We find that combining different forecasts into one forecast can enable better predictions. However, case numbers over longer periods were challenging to predict. Additional data sources, such as information about different versions of the SARS-CoV-2 virus present in the population, might improve forecasts in the future

Escherichia coli, an Intestinal Microorganism, as a Biosensor for Quantification of Amino Acid Bioavailability

In animal diets optimal amino acid quantities and balance among amino acids is of great nutritional importance. Essential amino acid deficiencies have negative impacts on animal physiology, most often expressed in sub-optimal body weight gains. Over supplementation of diets with amino acids is costly and can increase the nitrogen emissions from animals. Although in vivo animal assays for quantification of amino acid bioavailability are well established, Escherichia coli-based bioassays are viable potential alternatives in terms of accuracy, cost, and time input. E. coli inhabits the gastrointestinal tract and although more abundant in colon, a relatively high titer of E. coli can also be isolated from the small intestine, where primary absorption of amino acids and peptides occur. After feed proteins are digested, liberated amino acids and small peptides are assimilated by both the small intestine and E. coli. The similar pattern of uptake is a necessary prerequisite to establish E. coli cells as accurate amino acid biosensors. In fact, amino acid transporters in both intestinal and E. coli cells are stereospecific, delivering only the respective biological l-forms. The presence of free amino- and carboxyl groups is critical for amino acid and dipeptide transport in both biological subjects. Di-, tri- and tetrapeptides can enter enterocytes; likewise only di-, tri- and tetrapeptides support E. coli growth. These similarities in addition to the well known bacterial genetics make E. coli an optimal bioassay microorganism for the assessment of nutritionally available amino acids in feeds

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Why are different estimates of the effective reproductive number so different? A case study on COVID-19 in Germany.

The effective reproductive number Rt has taken a central role in the scientific, political, and public discussion during the COVID-19 pandemic, with numerous real-time estimates of this quantity routinely published. Disagreement between estimates can be substantial and may lead to confusion among decision-makers and the general public. In this work, we compare different estimates of the national-level effective reproductive number of COVID-19 in Germany in 2020 and 2021. We consider the agreement between estimates from the same method but published at different time points (within-method agreement) as well as retrospective agreement across eight different approaches (between-method agreement). Concerning the former, estimates from some methods are very stable over time and hardly subject to revisions, while others display considerable fluctuations. To evaluate between-method agreement, we reproduce the estimates generated by different groups using a variety of statistical approaches, standardizing analytical choices to assess how they contribute to the observed disagreement. These analytical choices include the data source, data pre-processing, assumed generation time distribution, statistical tuning parameters, and various delay distributions. We find that in practice, these auxiliary choices in the estimation of Rt may affect results at least as strongly as the selection of the statistical approach. They should thus be communicated transparently along with the estimates

Collaborative nowcasting of COVID-19 hospitalization incidences in Germany.

Real-time surveillance is a crucial element in the response to infectious disease outbreaks. However, the interpretation of incidence data is often hampered by delays occurring at various stages of data gathering and reporting. As a result, recent values are biased downward, which obscures current trends. Statistical nowcasting techniques can be employed to correct these biases, allowing for accurate characterization of recent developments and thus enhancing situational awareness. In this paper, we present a preregistered real-time assessment of eight nowcasting approaches, applied by independent research teams to German 7-day hospitalization incidences during the COVID-19 pandemic. This indicator played an important role in the management of the outbreak in Germany and was linked to levels of non-pharmaceutical interventions via certain thresholds. Due to its definition, in which hospitalization counts are aggregated by the date of case report rather than admission, German hospitalization incidences are particularly affected by delays and can take several weeks or months to fully stabilize. For this study, all methods were applied from 22 November 2021 to 29 April 2022, with probabilistic nowcasts produced each day for the current and 28 preceding days. Nowcasts at the national, state, and age-group levels were collected in the form of quantiles in a public repository and displayed in a dashboard. Moreover, a mean and a median ensemble nowcast were generated. We find that overall, the compared methods were able to remove a large part of the biases introduced by delays. Most participating teams underestimated the importance of very long delays, though, resulting in nowcasts with a slight downward bias. The accompanying prediction intervals were also too narrow for almost all methods. Averaged over all nowcast horizons, the best performance was achieved by a model using case incidences as a covariate and taking into account longer delays than the other approaches. For the most recent days, which are often considered the most relevant in practice, a mean ensemble of the submitted nowcasts performed best. We conclude by providing some lessons learned on the definition of nowcasting targets and practical challenges