The Impact of Terrestrial Radiation on FPGAs in Data Centers

Field programmable gate arrays (FPGAs) are used in large numbers in data centers around the world. They are used for cloud computing and computer networking. The most common type of FPGA used in data centers are re-programmable SRAM-based FPGAs. These devices offer potential performance and power consumption savings. A single device also carries a small susceptibility to radiation-induced soft errors, which can lead to unexpected behavior. This article examines the impact of terrestrial radiation on FPGAs in data centers. Results from artificial fault injection and accelerated radiation testing on several data-center-like FPGA applications are compared. A new fault injection scheme provides results that are more similar to radiation testing. Silent data corruption (SDC) is the most commonly observed failure mode followed by FPGA unavailable and host unresponsive. A hypothetical deployment of 100,000 FPGAs in Denver, Colorado, will experience upsets in configuration memory every half-hour on average and SDC failures every 0.5–11 days on average.

A Comprehensive Survey on Interoperability for IIoT: Taxonomy, Standards, and Future Directions

In the era of Industry 4.0, the Internet-of-Things (IoT) performs the driving position analogous to the initial industrial metamorphosis. IoT affords the potential to couple machine-to-machine intercommunication and real-time information-gathering within the industry domain. Hence, the enactment of IoT in the industry magnifies effective optimization, authority, and data-driven judgment. However, this field undergoes several interoperable issues, including large numbers of heterogeneous IoT gadgets, tools, software, sensing, and processing components, joining through the Internet, despite the deficiency of communication protocols and standards. Recently, various interoperable protocols, platforms, standards, and technologies are enhanced and altered according to the specifications of the applicability in industrial applications. However, there are no recent survey papers that primarily examine various interoperability issues that Industrial IoT (IIoT) faces. In this review, we investigate the conventional and recent developments of relevant state-of-the-art IIoT technologies, frameworks, and solutions for facilitating interoperability between different IIoT components. We also discuss several interoperable IIoT standards, protocols, and models for digitizing the industrial revolution. Finally, we conclude this survey with an inherent discussion of open challenges and directions for future research.

Uncovering variability in children's concepts and conceptual change

Capturing the structure and development of human conceptual knowledge is a challenging but fundamental task in Cognitive Science. The most prominent approach to uncovering these concepts is Multidimensional scaling (MDS), which has provided insight into the structure of human perception and conceptual knowledge. However, MDS usually requires participants to produce large numbers of similarity judgments, leading to prohibitively long experiments for most developmental research. Furthermore, MDS provides a single psychological space, tailored to a fixed set of stimuli. In contrast, we present a method that learns psychological spaces flexibly and generalizes to novel stimuli. In addition, our approach uses a simple, developmentally appropriate task, which allows for short and engaging developmental studies. We evaluate the feasibility of our approach on simulated data and find that it can uncover the true structure even when the data consists of aggregations of diverse categorizers. We then apply the method to data from the World Color Survey and find that it can discover language-specific color organization. Finally, we use the method in a novel developmental experiment and find age-dependent differences in conceptual spaces for fruit categories. These results suggest that our method is robust and widely applicable in developmental tasks with children as young as four years old.

A deep learning model for breast ductal carcinoma in situ classification in whole slide images

The pathological differential diagnosis between breast ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) is of pivotal importance for determining optimum cancer treatment(s) and clinical outcomes. Since conventional diagnosis by pathologists using microscopes is limited in terms of human resources, it is necessary to develop new techniques that can rapidly and accurately diagnose large numbers of histopathological specimens. Computational pathology tools which can assist pathologists in detecting and classifying DCIS and IDC from whole slide images (WSIs) would be of great benefit for routine pathological diagnosis. In this paper, we trained deep learning models capable of classifying biopsy and surgical histopathological WSIs into DCIS, IDC, and benign. We evaluated the models on two independent test sets (n=1,382, n=548), achieving ROC areas under the curves (AUCs) up to 0.960 and 0.977 for DCIS and IDC, respectively.

Politics is making us sick: The negative impact of political engagement on public health during the Trump administration

Objectives To quantify the effect of politics on the physical, psychological, and social health of American adults during the four-year span of the Trump administration. Methods A previously validated politics and health scale was used to compare health markers in nationally representative surveys administered to separate samples in March 2017 (N = 800) and October 2020 (N = 700). Participants in the 2020 survey were re-sampled approximately two weeks after the 2020 election and health markers were compared to their pre-election baselines. Results Large numbers of Americans reported politics takes a significant toll on a range of health markers—everything from stress, loss of sleep, or suicidal thoughts to an inability to stop thinking about politics and making intemperate social media posts. The proportion of Americans reporting these effects stayed stable or slightly increased between the spring of 2017 and the fall of 2020 prior to the presidential election. Deterioration in measures of physical health became detectably worse in the wake of the 2020 election. Those who were young, politically interested, politically engaged, or on the political left were more likely to report negative effects. Conclusions Politics is a pervasive and largely unavoidable source of chronic stress that exacted significant health costs for large numbers of American adults between 2017 and 2020. The 2020 election did little to alleviate those effects and quite likely exacerbated them.

A Clinical Case of COVID-19-Associated Pulmonary Aspergillosis (CAPA), Illustrating the Challenges in Diagnosis (Despite Overwhelming Mycological Evidence)

The COVID-19 pandemic has resulted in large numbers of patients requiring critical care management. With the established association between severe respiratory virus infection and invasive pulmonary aspergillosis (7.6% for COVID-19-associated pulmonary aspergillosis (CAPA)), the pandemic places a significant number of patients at potential risk from secondary invasive fungal disease. We described a case of CAPA with substantial supporting mycological evidence, highlighting the need to employ strategic diagnostic algorithms and weighted definitions to improve the accuracy in diagnosing CAPA.

Single-chain tandem macrocyclic peptides as a scaffold for growth factor and cytokine mimetics

Mimetics of growth factors and cytokines are promising tools for culturing large numbers of cells and manufacturing regenerative medicine products. In this study, we report single-chain tandem macrocyclic peptides (STaMPtides) as mimetics in a new multivalent peptide format. STaMPtides, which contain two or more macrocyclic peptides with a disulfide-closed backbone and peptide linkers, are successfully secreted into the supernatant by Corynebacterium glutamicum-based secretion technology. Without post-secretion modification steps, such as macrocyclization or enzymatic treatment, bacterially secreted STaMPtides form disulfide bonds, as designed; are biologically active; and show agonistic activities against respective target receptors. We also demonstrate, by cell-based assays, the potential of STaMPtides, which mimic growth factors and cytokines, in cell culture. The STaMPtide technology can be applied to the design, screening, and production of growth factor and cytokine mimetics.

A Single Cisterna Magna Injection of AAV Leads to Binaural Transduction in Mice

Viral-mediated gene augmentation, silencing, or editing offers tremendous promise for the treatment of inherited and acquired deafness. Inner-ear gene therapies often require a safe, clinically useable and effective route of administration to target both ears, while avoiding damage to the delicate structures of the inner ear. Here, we examined the possibility of using a cisterna magna injection as a new cochlear local route for initiating binaural transduction by different serotypes of the adeno-associated virus (AAV2/8, AAV2/9, AAV2/Anc80L65). The results were compared with those following canalostomy injection, one of the existing standard inner ear local delivery routes. Our results demonstrated that a single injection of AAVs enables high-efficiency binaural transduction of almost all inner hair cells with a basal-apical pattern and of large numbers of spiral ganglion neurons of the basal portion of the cochlea, without affecting auditory function and cochlear structures. Taken together, these results reveal the potential for using a cisterna magna injection as a local route for binaural gene therapy applications, but extensive testing will be required before translation beyond mouse models.

Archerfish number discrimination

Debates have arisen as to whether non-human animals actually can learn abstract non-symbolic numerousness or whether they always rely on some continuous physical aspect of the stimuli, covarying with number. Here we investigated archerfish (Toxotes jaculatrix) non-symbolic numerical discrimination with accurate control for co-varying continuous physical stimulus attributes. Archerfish were trained to select one of two groups of black dots (Exp. 1: 3 vs. 6 elements; Exp. 2: 2 vs. 3 elements); these were controlled for several combinations of physical variables (elements’ size, overall area, overall perimeter, density and sparsity), ensuring that only numerical information was available. Generalization tests with novel numerical comparisons (2 vs. 3, 5 vs. 8 and 6 vs. 9 in Exp. 1; 3 vs. 4, 3 vs. 6 in Exp. 2) revealed choice for the largest or smallest numerical group according to the relative number that was rewarded at training. None of the continuous physical variables, including spatial frequency, were affecting archerfish performance. Results provide evidence that archerfish spontaneously use abstract relative numerical information for both small and large numbers when only numerical cues are available.