TGFβ/Smad3 regulates proliferation and apoptosis through IRS-1 inhibition in colon cancer cells.

In this study, we have uncovered a novel crosstalk between TGFβ and IGF-1R signaling pathways. We show for the first time that expression and activation of IRS-1, an IGF-1R adaptor protein, is decreased by TGFβ/Smad3 signaling. Loss or attenuation of TGFβ activation leads to elevated expression and phosphorylation of IRS-1 in colon cancer cells, resulting in enhanced cell proliferation, decreased apoptosis and increased tumor growth in vitro and in vivo. Downregulation of IRS-1 expression reversed Smad3 knockdown-mediated oncogenic phenotypes, indicating that TGFβ/Smad3 signaling inhibits cell proliferation and increases apoptosis at least partially through the inhibition of IRS-1 expression and activation. Additionally, the TGFβ/Smad3/IRS-1 signaling axis regulates expression of cyclin D1 and XIAP, which may contribute to TGFβ/Smad3/IRS-1-mediated cell cycle progression and survival. Given that loss of TGFβ signaling occurs frequently in colon cancer, an important implication of our study is that IRS-1 could be a potential therapeutic target for colon cancer treatment

Unsupervised Adaptation from Repeated Traversals for Autonomous Driving

For a self-driving car to operate reliably, its perceptual system must generalize to the end-user's environment -- ideally without additional annotation efforts. One potential solution is to leverage unlabeled data (e.g., unlabeled LiDAR point clouds) collected from the end-users' environments (i.e. target domain) to adapt the system to the difference between training and testing environments. While extensive research has been done on such an unsupervised domain adaptation problem, one fundamental problem lingers: there is no reliable signal in the target domain to supervise the adaptation process. To overcome this issue we observe that it is easy to collect unsupervised data from multiple traversals of repeated routes. While different from conventional unsupervised domain adaptation, this assumption is extremely realistic since many drivers share the same roads. We show that this simple additional assumption is sufficient to obtain a potent signal that allows us to perform iterative self-training of 3D object detectors on the target domain. Concretely, we generate pseudo-labels with the out-of-domain detector but reduce false positives by removing detections of supposedly mobile objects that are persistent across traversals. Further, we reduce false negatives by encouraging predictions in regions that are not persistent. We experiment with our approach on two large-scale driving datasets and show remarkable improvement in 3D object detection of cars, pedestrians, and cyclists, bringing us a step closer to generalizable autonomous driving.Comment: Accepted by NeurIPS 2022. Code is available at https://github.com/YurongYou/Rote-D

Pre-Training LiDAR-Based 3D Object Detectors Through Colorization

Accurate 3D object detection and understanding for self-driving cars heavily relies on LiDAR point clouds, necessitating large amounts of labeled data to train. In this work, we introduce an innovative pre-training approach, Grounded Point Colorization (GPC), to bridge the gap between data and labels by teaching the model to colorize LiDAR point clouds, equipping it with valuable semantic cues. To tackle challenges arising from color variations and selection bias, we incorporate color as "context" by providing ground-truth colors as hints during colorization. Experimental results on the KITTI and Waymo datasets demonstrate GPC's remarkable effectiveness. Even with limited labeled data, GPC significantly improves fine-tuning performance; notably, on just 20% of the KITTI dataset, GPC outperforms training from scratch with the entire dataset. In sum, we introduce a fresh perspective on pre-training for 3D object detection, aligning the objective with the model's intended role and ultimately advancing the accuracy and efficiency of 3D object detection for autonomous vehicles

Chemical Explosion, Covid-19, and Environmental Justice: insights From Low-Cost air Quality Sensors

OBJECTIVES: to examine the impact of the Intercontinental Terminals Company (ITC) fire and COVID-19 on airborne particulate matter (PM) concentrations and the PM disproportionally affecting communities in Houston using low-cost sensors. METHODS: We compared measurements from a network of low-cost sensors with a separate network of monitors from the Environmental Protection Agency (EPA) in the Houston metropolitan area from Mar 18, 2019, to Dec 31, 2020. Further, we examined the associations between neighborhood-level sociodemographic status and air pollution patterns by linking the low-cost sensor data to EPA environmental justice screening and mapping systems. FINDINGS: We found increased PM levels during ITC fire and pre-COVID-19, and lower PM levels after the COVID-19 lockdown, comparable to observations from the regulatory monitors, with higher variations and a greater number of locations with high PM levels detected. In addition, the environmental justice analysis showed positive associations between higher PM levels and the percentage of minority, low-income population, and demographic index. IMPLICATION: Our study indicates that low-cost sensors provide pollutant measures with higher spatial variations and a better ability to identify hot spots and high peak concentrations. These advantages provide critical information for disaster response and environmental justice studies. SYNOPSIS: We used measurements from a low-cost sensor network for air pollution monitoring and environmental justice analysis to examine the impact of anthropogenic and natural disasters

Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions

Advances in perception for self-driving cars have accelerated in recent years due to the availability of large-scale datasets, typically collected at specific locations and under nice weather conditions. Yet, to achieve the high safety requirement, these perceptual systems must operate robustly under a wide variety of weather conditions including snow and rain. In this paper, we present a new dataset to enable robust autonomous driving via a novel data collection process - data is repeatedly recorded along a 15 km route under diverse scene (urban, highway, rural, campus), weather (snow, rain, sun), time (day/night), and traffic conditions (pedestrians, cyclists and cars). The dataset includes images and point clouds from cameras and LiDAR sensors, along with high-precision GPS/INS to establish correspondence across routes. The dataset includes road and object annotations using amodal masks to capture partial occlusions and 3D bounding boxes. We demonstrate the uniqueness of this dataset by analyzing the performance of baselines in amodal segmentation of road and objects, depth estimation, and 3D object detection. The repeated routes opens new research directions in object discovery, continual learning, and anomaly detection. Link to Ithaca365: https://ithaca365.mae.cornell.edu/Comment: Accepted by CVPR 202

Metformin for the prevention of diabetes among people with HIV and either impaired fasting glucose or impaired glucose tolerance (prediabetes) in Tanzania: a Phase II randomised placebo-controlled trial

AIMS/HYPOTHESIS: In sub-Saharan Africa (SSA), 5% of adults are living with type 2 diabetes and this is rising sharply, with a greater increase among people with HIV. Evidence on the efficacy of prevention strategies in this cohort is scarce. We conducted a Phase II double-blind placebo-controlled trial that aimed to determine the impact of metformin on blood glucose levels among people with prediabetes (defined as impaired fasting glucose [IFG] and/or impaired glucose tolerance [IGT]) and HIV in SSA. METHODS: Adults (≥18 years old) who were stable in HIV care and found to have prediabetes (IFG and/or IGT) and who were attending hospitals in Dar es Salaam, Tanzania, were randomised to receive sustained-release metformin, 2000 mg daily, or matching placebo between 4 November 2019 and 21 July 2020. Randomisation used permuted blocks. Allocation was concealed in the trial database and made visible only to the Chief Pharmacist after consent was taken. All participants, research and clinical staff remained blinded to the allocation. Participants were provided with information on diet and lifestyle and had access to various health information following the start of the coronavirus disease 2019 (COVID-19) pandemic. Participants were followed up for 12 months. The primary outcome measure was capillary blood glucose measured 2 h following a 75 g glucose load. Analyses were by intention-to-treat. RESULTS: In total, 364 participants (182 in each arm) were randomised to the metformin or placebo group. At enrolment, in the metformin and placebo arms, mean fasting glucose was 6.37 mmol/l (95% CI 6.23, 6.50) and 6.26 mmol/l (95% CI 6.15, 6.36), respectively, and mean 2 h glucose levels following a 75 g oral glucose load were 8.39 mmol/l (95% CI 8.22, 8.56) and 8.24 mmol/l (95% CI 8.07, 8.41), respectively. At the final assessment at 12 months, 145/182 (79.7%) individuals randomised to metformin compared with 158/182 (86.8%) randomised to placebo indicated that they had taken >95% of their medicines in the previous 28 days (p=0.068). At this visit, in the metformin and placebo arms, mean fasting glucose levels were 6.17 mmol/l (95% CI 6.03, 6.30) and 6.30 mmol/l (95% CI 6.18, 6.42), respectively, and mean 2 h glucose levels following a 75 g oral glucose load were 7.88 mmol/l (95% CI 7.65, 8.12) and 7.71 mmol/l (95% CI 7.49, 7.94), respectively. Using a linear mixed model controlling for respective baseline values, the mean difference between the metformin and placebo group (metformin-placebo) was -0.08 mmol/l (95% CI -0.37, 0.20) for fasting glucose and 0.20 mmol/l (95% CI -0.17, 0.58) for glucose levels 2 h post a 75 g glucose load. Weight was significantly lower in the metformin arm than in the placebo arm: using the linear mixed model adjusting for baseline values, the mean difference in weight was -1.47 kg (95% CI -2.58, -0.35). In total, 16/182 (8.8%) individuals had a serious adverse event (Grade 3 or Grade 4 in the Division of Acquired Immunodeficiency Syndrome [DAIDS] adverse event grading table) or died in the metformin arm compared with 18/182 (9.9%) in the placebo arm; these events were either unrelated to or unlikely to be related to the study drugs. CONCLUSIONS/INTERPRETATION: Blood glucose decreased over time in both the metformin and placebo arms during the trial but did not differ significantly between the arms at 12 months of follow up. Metformin therapy was found to be safe for use in individuals with HIV and prediabetes. A larger trial with longer follow up is needed to establish if metformin can be safely used for the prevention of diabetes in people who have HIV. TRIAL REGISTRATION: The trial is registered on the International Standard Randomised Controlled Trial Number (ISRCTN) registry ( www.isrctn.com/ ), registration number: ISCRTN76157257. FUNDING: This research was funded by the National Institute for Health Research using UK aid from the UK Government to support global health research

Influenza D Virus Infection in Feral Swine Populations, United States

Influenza D virus (IDV) has been identified in domestic cattle, swine, camelid, and small ruminant populations across North America, Europe, Asia, South America, and Africa. Our study investigated seroprevalence and transmissibility of IDV in feral swine. During 2012–2013, we evaluated feral swine populations in 4 US states; of 256 swine tested, 57 (19.1%) were IDV seropositive. Among 96 archived influenza A virus–seropositive feral swine samples collected from 16 US states during 2010–2013, 41 (42.7%) were IDV seropositive. Infection studies demonstrated that IDV-inoculated feral swine shed virus 3–5 days postinoculation and seroconverted at 21 days postinoculation; 50% of in-contact naive feral swine shed virus, seroconverted, or both. Immunohistochemical staining showed viral antigen within epithelial cells of the respiratory tract, including trachea, soft palate, and lungs. Our findings suggest that feral swine might serve an important role in the ecology of IDV