Smartphone and Wearable Device-Based Digital Phenotyping to Understand Substance use and its Syndemics

Digital phenotyping is a process that allows researchers to leverage smartphone and wearable data to explore how technology use relates to behavioral health outcomes. In this Research Concepts article, we provide background on prior research that has employed digital phenotyping; the fundamentals of how digital phenotyping works, using examples from participant data; the application of digital phenotyping in the context of substance use and its syndemics; and the ethical, legal and social implications of digital phenotyping. We discuss applications for digital phenotyping in medical toxicology, as well as potential uses for digital phenotyping in future research. We also highlight the importance of obtaining ground truth annotation in order to identify and establish digital phenotypes of key behaviors of interest. Finally, there are many potential roles for medical toxicologists to leverage digital phenotyping both in research and in the future as a clinical tool to better understand the contextual features associated with drug poisoning and overdose. This article demonstrates how medical toxicologists and researchers can progress through phases of a research trajectory using digital phenotyping to better understand behavior and its association with smartphone usage

Report to the President for year ended June 30, 2025, Vice Provost for the Arts

This report contains the following sections: Artfinity arts festival, Arts Initiatives, Center for Art, Science & Technology (CAST), the List Visual Arts Center, the MIT Museum, Administrative Initiatives, Finances and Funding, and Personnel

The COVID-19 effect on the Paris agreement

The pandemic and efforts to control it are causing sharp reductions in global economic activity and associated fossil energy use, with unknown influence on longer-term efforts to limit greenhouse gas emissions under the Paris Climate Agreement. To explore this effect, estimates of economic recession and recovery in near-term months are extended to cover a return to full employment in future years, to be compared with an estimate of growth had COVID-19 not occurred. On the assumption that the Paris emissions pledges for 2020 will be met in any case, projection of global emissions with and without the pandemic show that, through its growth impact alone, it will yield only a small effect on emissions in 2030 and beyond. Other COVID legacies may include residual influences in patterns of consumption and travel, and the direction of recovery funds to low carbon investments. Most important, however, will be the effect of the economic shocks on the willingness of nations to meet (or augment) their existing Paris emissions pledges. The main effect of the pandemic on the threat of climate change, therefore, will be not its growth impact but its influence on national commitments to action

Neutronic Performance and Thermal Hydraulic Analysis of the MIT Reactor Fission Converter Experimental Facility Using High-Density U-10Mo Low-Enriched Uranium Fuel Elements

The MITR fission converter (FC) is a core-driven subcritical assembly at the MIT Nuclear Reactor Laboratory, located on the MIT campus in Cambridge, MA. The assembly is made of eleven partially-depleted MITR-II fuel elements in a separate cooling tank attached to the side of the core-tank graphite reflector. The FC serves to boost the thermal flux from the core and send a hardened neutron spectrum to an irradiation target, providing a fission energy flux spectrum without the need to put a sample inside the core tank. It was previously used for boron-neutron capture therapy clinical trials before its decommissioning in the 2010s. Recently, it has been modified from a medical beamline to a general-use engineering and materials testing facility. The new FC-based experimental facility has roughly one cubic meter of empty space downstream intended to contain large experiments, called the m³. This work is a safety and performance study aimed at quantifying the impact of modifying the facility’s geometry as part of the FC’s recommissioning, as well as the impact of changing its fuel from HEU to LEU fuel as part of the MITR LEU conversion project. Neutronics and thermal hydraulics analysis of the renovated facility have been performed using the codes MCNP5 and STAT7, respectively. This analysis quantified the FC’s k_eff, power distribution, multi-group neutron flux, and conditions which cause onset of nucleate boiling (ONB). It was determined that the FC assembly will remain subcritical (k _eff < 0.9) and low power (≤200 kW) under a wide range of performance conditions, including with both types of fuel and a variety of materials on the target-side of the FC tank. The HEU-fueled FC is expected to require no changes to the limiting safety system settings (LSSS) outlined in the original technical specifications document. The LEU fuel is expected to increase the FC performance, but as a tradeoff, will require minor changes to the LSSS setpoints to maintain margin to ONB under the most limiting thermal-hydraulic conditions. Additionally, this study evaluates the feasibility of using the FC for in-assembly fuel experiments, particularly as a pathway for testing the new LEU fuel elements at low power. This study indicated that this proposed FC configuration with one LEU and ten HEU elements is feasible and maintains wide safety margins.S.M

Characteristics of two polarized groups in online social networks’ controversial discourse

In today’s interconnected world, online social networks play a pivotal role in facilitating global communication. These platforms often host discussions on contentious topics such as climate change, vaccines, and war, leading to the formation of two distinct groups: deniers and believers. Understanding the characteristics of these groups is crucial for predicting information flow and managing the diffusion of information. Moreover, such understanding can enhance machine learning algorithms designed to automatically detect these groups, thereby contributing to the development of strategies to curb the spread of disinformation, including fake news and rumors. In this study, we employ social network analysis measures to extract the characteristics of these groups, conducting experiments on three large-scale datasets of over 22 million tweets. Our findings indicate that, based on network science measures, the denier (anti) group exhibits greater coherence than the believer (pro) group

Global Bioenergy Availability

In efforts to decarbonize and mitigate climate change impacts, some sectors require unprecedented levels of technological innovation to substantially reduce greenhouse gas emissions. This is especially true for air transportation and maritime shipping, where vehicle electrification is currently infeasible due to limitations in available energy storage technologies. The use of biomass to produce readily substitutable (“drop-in”) and low-carbon liquid fuels presents a lever to reduce emissions along the lifecycle of the fuel. The potential to scale-up production of such fuels depends on establishing a robust supply chain for biogenic feedstocks and on building and operating cost-competitive, high-throughput biorefineries. The ultimate ‘ceiling’ for displacing fossil fuels with bio-based fuels is set by the supply of primary biomass. Existing estimates of biomass potential are heterogenous, ranging from 2 to 1200 EJ/yr for energy crops and 8-215 EJ/yr for biomass sourced as waste or residue. We provide a summary of current understanding and outline the underlying assumptions to evaluate whether these conditions are realistic, sustainable, and comprise a future which is worth pursuing. Motifs within the analysis point towards sizable barriers limiting the development of bioenergy to cover future energy demand, such as the logistics of collection and the implications of large-scale land use. For the case of agricultural residues, where such challenges can be partially mitigated, we conservatively estimate a global potential of 18±15 EJ/yr. for 2050, compared to a median estimate from meta-analysis of 27.5 EJ/yr. We dive into this apparent discrepancy, identifying multiple sources of uncertainty. Overall, our findings show that bio-energy alone is unlikely to cover the needs of decarbonizing tough-to-decarbonize transportation modes such as maritime shipping and aviation in a sustainable way

An implantable piezoelectric ultrasound stimulator (ImPULS) for deep brain activation

Precise neurostimulation can revolutionize therapies for neurological disorders. Electrode-based stimulation devices face challenges in achieving precise and consistent targeting due to the immune response and the limited penetration of electrical fields. Ultrasound can aid in energy propagation, but transcranial ultrasound stimulation in the deep brain has limited spatial resolution caused by bone and tissue scattering. Here, we report an implantable piezoelectric ultrasound stimulator (ImPULS) that generates an ultrasonic focal pressure of 100 kPa to modulate the activity of neurons. ImPULS is a fully-encapsulated, flexible piezoelectric micromachined ultrasound transducer that incorporates a biocompatible piezoceramic, potassium sodium niobate [(K,Na)NbO3]. The absence of electrochemically active elements poses a new strategy for achieving long-term stability. We demonstrated that ImPULS can i) excite neurons in a mouse hippocampal slice ex vivo, ii) activate cells in the hippocampus of an anesthetized mouse to induce expression of activity-dependent gene c-Fos, and iii) stimulate dopaminergic neurons in the substantia nigra pars compacta to elicit time-locked modulation of nigrostriatal dopamine release. This work introduces a non-genetic ultrasound platform for spatially-localized neural stimulation and exploration of basic functions in the deep brain

Visual Acuity Outcomes and Influencing Factors in a Cohort of UK Real-World Diabetic Macular Oedema Patients During the First Two Years of Anti-VEGF Treatment

Background/Objectives: The visual acuity (VA) outcomes after the first and second years of anti-vascular endothelial growth factor (anti-VEGF) treatment in patients with diabetic macular oedema (DMO) were evaluated, and the factors associated with treatment success were investigated. Methods: Using Medisoft electronic medical records (UK), this retrospective cohort study analysed VA outcomes, changes, and determinants in DMO patients at year 1 and year 2 after initial anti-VEGF injection. Descriptive analysis examined baseline demographics and clinical characteristics, while regression models were used to assess associations between these factors and changes in VA. Results: 728 DMO patients (1035 eyes) treated with anti-VEGFs (ranibizumab, aflibercept, or bevacizumab) at the Northern Ireland Mater Macular Clinic from 2008 to 2021 were evaluated. The mean age was 64.5 (SD 12.8) years, and 59.6% were male. In the first year, the median annual injection number and interval were 6.0 (IQR 5.0&ndash;8.0) and 6.1 weeks (IQR 5.4&ndash;7.8), respectively, and in the second year, they were 3.0 (IQR 2.0&ndash;5.0) and 10.0 weeks (IQR 6.5&ndash;20.1). In the first two treatment years, 83.4% and 79.8% of eyes had improved/stable VA (ISVA) respectively. The injection number, interval, baseline VA, age, and proliferative diabetic retinopathy (PDR) significantly impacted VA outcomes. Conclusions: Our study confirms the effectiveness of anti-VEGF treatments in improving or maintaining vision for DMO patients, consistent with previous real-world clinical data. An elder age, a better baseline VA, low annual injection numbers (&lt;5), and less frequent injection intervals (&ge;12 weeks) were negatively associated with ISVA success in the first two years. These findings have implications for managing patient expectations, allocating resources, and understanding DMO clinical management

The Response Regulator OmpR Negatively Controls the Expression of Genes Implicated in Tilimycin and Tilivalline Cytotoxin Production in Klebsiella oxytoca

Klebsiella oxytoca toxigenic strains represent a critical health threat, mainly due to their link to antibiotic-associated hemorrhagic colitis. This serious condition results from the bacteria’s ability to produce tilimycin and tilivalline cytotoxins. Our research highlights the pivotal role of OmpR, a key regulator within the EnvZ/OmpR two-component system, in controlling the virulence factors associated with K. oxytoca. Our findings strongly indicate that OmpR is a repressor of the aroX and npsA genes, the first genes of aroX and NRPS operons, respectively, which are indispensable for producing these enterotoxins. Notably, in the absence of OmpR, we observe a significant increase in cytotoxic effects on Caco-2 cells. These observations identify OmpR as a crucial negative transcription regulator for both operons, effectively managing the release of these cytotoxins. This research deepens our understanding of the mechanisms of toxigenic K. oxytoca and opens promising avenues for targeting OmpR for new therapeutic interventions. By focusing on this innovative approach, we can develop more effective solutions to combat this pressing health challenge, ultimately improving patient outcomes against this pathogen

The Robust Malware Detection Challenge and Greedy Random Accelerated Multi-Bit Search

AISec’20, November 13, 2020, Virtual Event, USATraining classifiers that are robust against adversarially modified examples is becoming increasingly important in practice. In the field of malware detection, adversaries modify malicious binary files to seem benign while preserving their malicious behavior. We report on the results of a recently held robust malware detection challenge. There were two tracks in which teams could participate: the attack track asked for adversarially modified malware samples and the defend track asked for trained neural network classifiers that are robust to such modifications. The teams were unaware of the attacks/defenses they had to detect/evade. Although only 9 teams participated, this unique setting allowed us to make several interesting observations. We also present the challenge winner: GRAMS, a family of novel techniques to train adversarially robust networks that preserve the intended (malicious) functionality and yield high-quality adversarial samples. These samples are used to iteratively train a robust classifier. We show that our techniques, based on discrete optimization techniques, beat purely gradient-based methods. GRAMS obtained first place in both the attack and defend tracks of the competition