Lifeboat Foundation

3 dollars and cents arguments that definitively prove the need for people-oriented, walk-friendly places.

Simply put, we need a reliable and secure energy grid. Two ways to ensure continuous electricity regardless of the weather or an unforeseen event are by using distributed energy resources (DER) and microgrids. DER produce and supply electricity on a small scale and are spread out over a wide area. Rooftop solar panels, backup batteries, and emergency diesel generators are examples of DER. While traditional generators are connected to the high-voltage transmission grid, DER are connected to the lower-voltage distribution grid, like residences and businesses are.

Microgrids are localized electric grids that can disconnect from the main grid to operate autonomously. Because they can operate while the main grid is down, microgrids can strengthen grid resilience, help mitigate grid disturbances, and function as a grid resource for faster system response and recovery.

After decades of inertial confinement fusion research, a yield of more than 1.3 megajoules (MJ) was achieved at Lawrence Livermore National Laboratory’s (LLNL’s) National Ignition Facility (NIF) for the first time on Aug. 8, 2021, putting researchers at the threshold of fusion gain and achieving scientific ignition.

On the one-year anniversary of this historic achievement, the scientific results of this record experiment have been published in three peer-reviewed papers: one in Physical Review Letters and two in Physical Review E (See papers one and two). More than 1,000 authors are included in the Physical Review Letters paper to recognize and acknowledge the many individuals who have worked over many decades to enable this significant advance.

“The record shot was a major scientific advance in fusion research, which establishes that fusion ignition in the lab is possible at NIF,” said Omar Hurricane, chief scientist for LLNL’s inertial confinement fusion program. “Achieving the conditions needed for ignition has been a long-standing goal for all inertial confinement fusion research and opens access to a new experimental regime where alpha-particle self-heating outstrips all the cooling mechanisms in the fusion plasma.”

On June 27th 2022, Yann LeCun, one of the godfathers of artificial intelligence and Head of AI at Meta released his vision on how to build autonomous AI systems. Here is the link to the paper.

First of all, I really suggest you to read this paper. As mentioned in the prologue, the text is written with as little jargon as possible. It uses as little mathematical prior knowledge as possible to appeal to readers with various backgrounds. It’s essentially a vision of what might direct the research efforts at Meta and elsewhere in the industry.

When you start reading the paper, quite quickly, you realize that this vision is very ambitious and futuristic. After all, Yann is describing an autonomous and polyvalent AI system.

Clean energy accounted for more than two-thirds of the new US electrical generating capacity added during the first six months of 2022, according to data recently released by the Federal Energy Regulatory Commission (FERC).

Wind (5,722 megawatts) and solar (3,895 MW) provided 67.01% of the 14,352 MW in utility-scale (that is, greater than 1 MW) capacity that came online during the first half of 2022.

Additional capacity was provided by geothermal (26 MW), hydropower (7 MW), and biomass (2 MW). The balance came from natural gas (4,695 MW) and oil (5 MW). No new capacity was reported for 2022 from either nuclear power or coal.

Molecule has potential in redox reactions, as it’s highly soluble in organic solvents and easily stored in a glovebox.

An artificial intelligence (AI) algorithm to detect subtle brain abnormalities that cause epileptic seizures has been developed. The abnormalities, known as focal cortical dysplasias (FCDs), can often be treated with surgery but are difficult to visualize on an MRI. The new algorithm is expected to give physicians greater confidence in identifying FCDs in patients with epilepsy.

The work, which was part of the Multicentre Epilepsy Lesion Detection (MELD) project, appeared in Brain “Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study.” Konrad Wagstyl, PhD, and Sophie Adler, PhD, both from University College London, led an international team of researchers on the work.

To develop the algorithm, the team quantified features of the brain cortex—such as thickness and folding—in more than 1,000 patient MRI scans from 22 epilepsy centers around the world. They then trained the algorithm on examples labeled by expert radiologists as either being healthy or having FCD.

To evaluate the accuracy of the models²⁸, we sampled from the BNN or deep ensemble to determine their uncertainty predictions (Extended Data Fig. 3a, b). Correct predictions are oriented toward the lower and higher range of the output, representing greater certainty about samples’ states, whereas incorrect predictions tend towards the 0.5 threshold. We can therefore assume higher confidence in a model’s predictions by removing the predictions in the middle using thresholds. We evaluated a range of thresholds with several models (Extended Data Fig. 3c–f), which show a substantial increase in accuracy due to the ambiguous samples being discarded, including the ensemble of normalized models reaching accuracy of 97.2%. A similar approach was applied to other models, including the IR and RS models (Extended Data Fig. 3g, h), raising accuracy by 10–15%, although this reduces the number of cells considered.

To better understand the development of the senescent phenotype and how nuclear morphology changes over time, we analyzed human fibroblasts induced to senescence by 10 Gy IR and imaged at days 10, 17, 24 and 31. The predictor identifies senescence at all four times points with probability that increases from days 10 to 17 but declines by day 31 (Extended Data Fig. 4a). Interestingly, examining the probability distribution of the predictor it was apparent that a growing peak of nonsenescent cells appear after day 17, suggesting that a small number of cells were able to escape senescence induction and eventually overgrow the senescent cells (Extended Data Fig. 4b). Indeed, when investigating markers of proliferation, we see that over the time course, PCNA declines until day 17, after which the expression starts to return (Extended Data Fig. 4c). p21^Cip1 follows an inverse pattern with stain intensity increasing initially and then declining slightly by day 31 (Extended Data Fig. 4D). We also saw a decrease in DAPI intensity for days 10 and 17, indicating senescence, but a reversion to control level by day 31 (Extended Data Fig. 4e). To confirm that the predictor accurately determined senescence even 31 days after IR, we evaluated if markers of proliferation and senescence correlated with predicted senescence. Accordingly, cells with predicted senescence had higher p21^Cip1 levels, lower PCNA and lower DAPI intensities and vice versa (Extended Data Fig. 4f–h). Morphologically, area and aspect are higher for predicted senescence, whereas convexity is lower (Extended Data Fig. 4i–k). Finally, a simple nuclei count confirms growth, following IR treatment (Extended Data Fig. 4l). Overall, the senescence predictor captures the state during development in agreement with multiple markers and morphological signs.

Senescent cells are associated with the appearance of persistent nuclear foci of the DNA damage markers γH2AX and 53BP1 (refs. ^31,32). Our base data set including control, RS and IR lines were examined for damage foci using high-content microscopy, where we found the mean count for controls to be below 1 for each marker, whereas RS had 4.0 γH2AX and 2.0 53BP1 foci and IR had 3.4 γH2AX and 3.0 53BP1 foci (Fig. 4a, b and Extended Data Fig. 5a). We calculated the Pearson correlation between predicted senescence and γH2AX and 53BP1 foci counts and found that across all conditions, there is a moderately strong correlation of around 0.5 (Fig. 4c). This association is also visible when simply plotting foci counts and senescence prediction, which shows predicted senescence flipping from low to high, along with shifts in foci counts (Extended Data Fig. 5b). Our feature reduction masked internal nuclear structure, but it is nonetheless notable that senescence prediction correlates with foci count. We also compared the correlation between predicted senescence and area, where we see a correlation of around 0.5. In sum, there is a considerable correlation between foci counts and senescence.

In this review, we undertake a critical appraisal of eight published studies providing first evidence that a history of attention-deficit/hyperactivity disorder (ADHD) may increase risk for the later-life development of a neurodegenerative disease, in particular Lewy body diseases (LBD), by up to five-fold. Most of these studies have used data linked to health records in large population registers and include impressive sample sizes and adequate follow-up periods. We identify a number of methodological limitations as well, including potential diagnostic inaccuracies arising from the use of electronic health records, biases in the measurement of ADHD status and symptoms, and concerns surrounding the representativeness of ADHD and LBD cohorts. Consequently, previously reported risk associations may have been underestimated due to the high likelihood of potentially missed ADHD cases in groups used as “controls”, or alternatively previous estimates may be inflated due to the inclusion of confounding comorbidities or non-ADHD cases within “exposed” groups that may have better accounted for dementia risk. Prospective longitudinal studies involving well-characterized cases and controls are recommended to provide some reassurance about the validity of neurodegenerative risk estimates in ADHD.

Attention-deficit hyperactivity disorder (ADHD) is a psychiatric disorder beginning in childhood that is characterized by core symptoms of inattention, impulsivity, and hyperactivity (Biederman and Faraone, 2005; American Psychiatric Association, 2013; Faraone et al., 2015). Diagnostic criteria require symptoms to present in early childhood, before age 12, and cause impairment in daily activities in more than one setting (e.g., home, school, social environment, and/or interpersonal relationships; American Psychiatric Association, 2013). Although it is largely considered a childhood disorder, 40–60% of cases of ADHD persist into adulthood (Culpepper and Mattingly, 2010; Michielsen et al., 2012; Volkow and Swanson, 2013; Asherson et al., 2016), and the overall prevalence of adult ADHD ranges from 2 to 4% (Kieling and Rohde, 2012; Fayyad et al., 2017).

ADHD may persist into later life as well. Roughly 3% of adults over age 50 suffer from significant symptoms of attention-deficit/hyperactivity disorder (ADHD; Michielsen et al., 2012; Kooij et al., 2016), often presenting as executive dysfunction (e.g., absent-mindedness) and memory impairments (e.g., forgetfulness or difficulty learning new things; Rosler et al., 2010; Thorell et al., 2017; Callahan et al., 2021). These symptoms overlap with those of early neurodegenerative disease (Ivanchak et al., 2012; Pollack, 2012; Goodman et al., 2016; Callahan et al., 2017), and it is currently unclear whether ADHD is associated with an increased neurodegenerative risk, or if it is being misdiagnosed due to symptom overlap (Callahan et al., 2017). Clarifying this issue is crucial to reduce dementia misdiagnoses, and to guide treatment, which will differ depending on whether the disease course is assumed to be neurodegenerative or not.