Integrated dual-laser photonic chip for high-purity carrier generation enabling ultrafast terahertz wireless communications

Photonic generation of Terahertz (THz) carriers displays high potential for THz communications with a large tunable range and high modulation bandwidth. While many photonics-based THz generations have recently been demonstrated with discrete bulky components, their practical applications are significantly hindered by the large footprint and high energy consumption. Herein, we present an injection-locked heterodyne source based on generic foundry-fabricated photonic integrated circuits (PIC) attached to a uni-traveling carrier photodiode generating high-purity THz carriers. The generated THz carrier is tunable within the range of 0-1.4 THz, determined by the wavelength spacing between the two monolithically integrated distributed feedback (DFB) lasers. This scheme generates and transmits a 131 Gbits-1 net rate signal over a 10.7-m distance with -24 dBm emitted power at 0.4 THz. This monolithic dual-DFB PIC-based THz generation approach is a significant step towards fully integrated, cost-effective, and energy-efficient THz transmitters

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

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

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Comparisons Between COVID-19 Stigma and Other Stigmas: Distinct in Explicit Attitudes and Similar in Implicit Process

Since the outbreak of COVID-19, the public stigma associated with COVID-19 has emerged. To better understand the COVID-19 stigma, the present research conducted three studies on 1,493 Chinese participants from the outbreak to the recovery period of the COVID-19 pandemic to examine the psychological mechanisms of COVID-19 stigma by comparing it with other disease-related stigmas in terms of their explicit and implicit processes. Study 1 and Study 2 jointly demonstrated that the public endorsed more stigma toward the COVID-19 related people (i.e., the COVID-19 patients) relative to the other disease-related people (i.e., the SARS patients, people with flu) in multiple explicit aspects, including emotional, motivational, cognitive, and social processing. Using the implicit association test (IAT), Study 3 found no significant difference in the implicit measures of the COVID-19 vs. the SARS groups, which further revealed that the pandemic stigmas (i.e., COVID-19 and SARS) were similar at the implicit level. These findings suggest common (implicit level) but distinct (explicit level) psychological processes of the pandemic-related stigmas, which provide reference to policymakers in formulating suitable interventions to deal with COVID-19 stigma and a newly generated potential stigma and provide psychological support for the public in the future

Trait Empathy Modulates Patterns of Personal and Social Emotions During the COVID-19 Pandemic

The COVID-19 pandemic has caused profound consequences on people's personal and social feelings worldwide. However, little is known about whether individual differences in empathy, a prosocial trait, may affect the emotional feelings under such threat. To address this, we measured 345 Chinese participants' personal emotions (e.g., active, nervous), social emotions (i.e., fearful and empathetic feelings about various social groups), and their empathy traits during the COVID-19 pandemic. Using the representational similarity analysis (RSA), we calculated the pattern similarity of personal emotions and found the similarity between the positive and negative emotions was less in the high vs. low empathy groups. In addition, people with high (vs. low) empathy traits were more likely to have fearful and sympathetic feelings about the disease-related people (i.e., depression patients, suspected COVID-19 patients, COVID-19 patients, flu patients, SARS patients, AIDS patients, schizophrenic patients) and showed more pattern dissimilarity in the two social feelings toward the disease-related people. These findings suggest a prominent role of trait empathy in modulating emotions across different domains, strengthening the polarization of personal emotions as well as enlarging social feelings toward a set of stigmatized groups when facing a pandemic threat

Electroencephalogram of Happy Emotional Cognition Based on Complex System of Music and Image Visual and Auditory

People are ingesting various information from different sense organs all the time to complete different cognitive tasks. The brain integrates and regulates this information. The two significant sensory channels for receiving external information are sight and hearing that have received extensive attention. This paper mainly studies the effect of music and visual-auditory stimulation on electroencephalogram (EEG) of happy emotion recognition based on a complex system. In the experiment, the presentation was used to prepare the experimental stimulation program, and the cognitive neuroscience experimental paradigm of EEG evoked by happy emotion pictures was established. Using 93 videos as natural stimuli, fMRI data were collected. Finally, the collected EEG signals were removed with the eye artifact and baseline drift, and the t-test was used to analyze the significant differences of different lead EEG data. Experimental data shows that, by adjusting the parameters of the convolutional neural network, the highest accuracy of the two-classification algorithm can reach 98.8%, and the average accuracy can reach 83.45%. The results show that the brain source under the combined visual and auditory stimulus is not a simple superposition of the brain source of the single visual and auditory stimulation, but a new interactive source is generated

Reinvestigating the phylogeny of Myriapoda with more extensive taxon sampling and novel genetic perspective

Background There have been extensive debates on the interrelationships among the four major classes of Myriapoda—Chilopoda, Symphyla, Diplopoda, and Pauropoda. The core controversy is the position of Pauropoda; that is, whether it should be grouped with Symphyla or Diplopoda as a sister group. Two recent phylogenomic studies separately investigated transcriptomic data from 14 and 29 Myriapoda species covering all four groups along with outgroups, and proposed two different topologies of phylogenetic relationships. Methods Building on these studies, we extended the taxon sampling by investigating 39 myriapods and integrating the previously available data with three new transcriptomic datasets generated in this study. Our analyses present the phylogenetic relationships among the four major classes of Myriapoda with a more abundant taxon sampling and provide a new perspective to investigate the above-mentioned question, where visual genes’ identification were conducted. We compared the appearance pattern of genes, grouping them according to their classes and the visual pathways involved. Positive selection was detected for all identified visual genes between every pair of 39 myriapods, and 14 genes showed positive selection among 27 pairs. Results From the results of phylogenomic analyses, we propose that Symphyla is a sister group of Pauropoda. This stance has also received strong support from tree inference and topology tests

Perception of strong social norms during the COVID-19 pandemic is linked to positive psychological outcomes

AbstractSocial norms can coordinate individuals and groups during collective threats. Pandemic-related social norms (e.g., wearing masks, social distancing) emerged to curb the spread of COVID-19. However, little is known about the psychological consequences of the emerging norms. We conducted three experiments cross-culturally, during the early period of the COVID-19 pandemic in China (Study 1), the recovery period in China (Study 2), and the severe period in the United States and Canada (Study 3). Across the three studies, we first distinguished the opposite effects of social norms and risk perception on individuals’ psychological characteristics during the COVID-19 pandemic and further revealed that individuals who perceived stronger pandemic norms reported a lower level of COVID-19 risk perception, which in turn would be associated with?fewer negative emotions, lower?pressure, more positive emotions, higher levels of trusts, and more confidence in fighting against COVID-19. Our findings show that perceived tighter social norms are linked to beneficial psychological outcomes. This research helps governments, institutions, and individuals understand the mechanism and benefits of social norms during the pandemic, thereby facilitating policy formulation and better responses to social crises