The molecular basis of β-thalassemia (β-thal) syndromes have been well documented, while the spectrum of mutations causing δ-thalassemia (δ-thal) has not been well characterized. δ-Thalassemia has no clinical symptoms but its coinheritance with heterozygous β-thal may cause misdiagnosis, especially in countries with a high prevalence of β-thal where prevention programs have been implemented. The coinheritance of β- and δ-globin mutations in India is not common. This association may interfere with correct diagnosis and genetic counseling of β-thal in screening programs. Here we report two families showing borderline Hb A2 levels belonging to the Koli Community, indigenous to the Saurashtra Province of Gujarat, India. They were referred to us for thalassemia molecular screening as they had children clinically presenting before 2 years of age and requiring regular blood transfusions. Interestingly, both families carried a novel δ-globin gene mutation at codon 100 (C > T) linked to a polyadenylation (polyA) site [AATAAA > A(-AATAA)] 5 bp deletional β-thal mutation, never before reported in the Indian population. This report highlights the importance of considering δ-globin gene analysis during β-thal screening to avoid false-negative results in the detection of at-risk couples. It also highlights how incomplete diagnosis of a borderline or normal Hb A2 level may lead to the probable birth of a β-thal major (β-TM) child. This has important implications in prenatal diagnosis.

The millimeter wave (mmWave) bands have attracted considerable attention for high precision localization applications due to the ability to capture high angular and temporal resolution measurements. This paper explores mmWave-based positioning for a target localization problem where a fixed target broadcasts mmWave signals and a mobile robotic agent attempts to capture the signals to locate and navigate to the target. A three-stage procedure is proposed: First, the mobile agent uses tensor decomposition methods to detect the multipath channel components and estimate their parameters. Second, a machine-learning trained classifier is then used to predict the link state, meaning if the strongest path is line-of-sight (LOS) or non-LOS (NLOS). For the NLOS case, the link state predictor also determines if the strongest path arrived via one or more reflections. Third, based on the link state, the agent either follows the estimated angles or uses computer vision or other sensor to explore and map the environment. The method is demonstrated on a large dataset of indoor environments supplemented with ray tracing to simulate the wireless propagation. The path estimation and link state classification are also integrated into a state-of-the-art neural simultaneous localization and mapping (SLAM) module to augment camera and LIDAR-based navigation. It is shown that the link state classifier can successfully generalize to completely new environments outside the training set. In addition, the neural-SLAM module with the wireless path estimation and link state classifier provides rapid navigation to the target, close to a baseline that knows the target location.

Since the start of the COVID-19 pandemic, technology enthusiasts have pushed for digital contact tracing as a critical tool for breaking the COVID-19 transmission chains. Motivated by this push, many countries and companies have created apps that enable digital contact tracing with the goal to identify the chain of transmission from an infected individual to others and enable early quarantine. Digital contact tracing applications like AarogyaSetu in India, TraceTogether in Singapore, SwissCovid in Switzerland, and others have been downloaded hundreds of millions of times. Yet, this technology hasn't seen the impact that we envisioned at the start of the pandemic. Some countries have rolled back their apps, while others have seen low adoption [12, 17]. Therefore, it is prudent to ask what the technology landscape of contact-tracing looks like and what are the missing pieces. We attempt to undertake this task in this paper. We present a high-level review of technologies underlying digital contact tracing, a set of metrics that are important while evaluating different contact tracing technologies, and evaluate where the different technologies stand today on this set of metrics. Our hope is two fold: (a) Future designers of contact tracing applications can use this review paper to understand the technology landscape, and (b) Researchers can identify and solve the missing pieces of this puzzle, so that we are ready to face the rest of the COVID-19 pandemic and any future pandemics. A majority of this discussion is focused on the ability to identify contact between individuals. The questions of ethics, privacy, and security of such contact tracing are briefly mentioned but not discussed in detail.

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Understanding the mobility of humans and their devices is a fundamental problem in mobile computing. While there has been much work on empirical analysis of human mobility using mobile device data, prior work has largely assumed devices to be independent and has not considered the implications of modern Internet users owning multiple mobile devices that exhibit correlated mobility patterns. Also, prior work has analyzed mobility at the spatial scale of the underlying mobile dataset and has not analyzed mobility characteristics at different spatial scales and its implications on system design. In this paper, we empirically analyze the mobility of modern Internet users owning multiple devices at multiple spatial scales using a large campus WiFi dataset. First, our results show that mobility of multiple devices belonging to a user needs to be analyzed and modeled as a group, rather than independently, and that there are substantial differences in the correlations exhibited by device trajectories across users that also need to be considered. Second, our analysis shows that the mobility of users shows different characteristics at different spatial scales such as within and across buildings. Third, we demonstrate the implications of these results by presenting generative models that highlight the importance of considering the spatial scale of mobility as well as multi-device mobility. More broadly, our empirical results point to the need for new modeling research to fully capture the nuances of mobility of modern multi-device users.