Sweets for my sweet: modulation of the limbic system drives salience for sweet foods after deep brain stimulation in Parkinson’s disease

BackgroundAn increase in body weight is observed in the majority of patients with Parkinson’s disease (PD) who undergo deep brain stimulation (DBS) of the subthalamic nucleus (STN) although the mechanisms are unclear.ObjectivesTo identify the stimulation-dependent effects on reward-associated and attention-associated neural networks and to determine whether these alterations in functional connectivity are associated with the local impact of DBS on different STN parcellations.MethodsWe acquired functional task-related MRI data from 21 patients with PD during active and inactive STN DBS and 19 controls while performing a food viewing paradigm. Electrode placement in the STN was localised using a state-of-the-art approach. Based on the 3D model, the local impact of STN DBS was estimated.ResultsSTN DBS resulted in a mean improvement of motor function of 22.6%±15.5% (on medication) and an increase of body weight of ~4 kg within 2 years of stimulation. DBS of the limbic proportion of the STN was associated with body weight gain and an increased functional connectivity within the salience network and at the same time with a decreased activity within the reward-related network in the context of sweet food images.ConclusionsOur findings indicate increased selective attention for high-caloric foods and a sweet food seeking-like behaviour after DBS particularly when the limbic proportion of the STN was stimulated.

Impact of Urbanization and Industrial Structure on Carbon Emissions: Evidence from Huaihe River Eco-Economic Zone

Rapid urbanization in China greatly contributes to carbon emissions, while the industrial structure greatly contributes to changes in the variation of carbon emissions. This research addresses the impact of urbanization and industrial structure on carbon emissions from 2010 to 2018, by focusing on the Huaihe River Eco-economic Zone, which is an important economic corridor along the north–south division of China. Although many studies have focused on investigating the impact of urbanization or industrial structure on carbon emissions, few studies further addressed an analysis of the impact of both on carbon emissions, using multiple measurement models. This paper reveals the holistic and local impact of industrial structure and urbanization on carbon emissions, by integrating a threshold regression model with geographically weighted regression. The results are as follows: (1) From a holistic point of view, industrial structure and urbanization had both, single threshold, and double threshold effects on carbon emissions in the Huaihe River Eco-economic Zone. (2) From a regional perspective, the coefficients of industrial structure on carbon emissions were all positive, but the rate of increase gradually slowed down. The coefficients of urbanization on carbon emissions were all negative, reaching a maximum value of negative effect in 2013. Understanding the holistic and local impact of urbanization and industrial structure on carbon emissions provides governments with differentiated and forward-looking suggestions for mitigating carbon emissions in the Huaihe River Eco-economic Zone.

Interplay between mobility, multi-seeding and lockdowns shapes COVID-19 local impact

Assessing the impact of mobility on epidemic spreading is of crucial importance for understanding the effect of policies like mass quarantines and selective re-openings. While many factors affect disease incidence at a local level, making it more or less homogeneous with respect to other areas, the importance of multi-seeding has often been overlooked. Multi-seeding occurs when several independent (non-clustered) infected individuals arrive at a susceptible population. This can lead to independent outbreaks that spark from distinct areas of the local contact (social) network. Such mechanism has the potential to boost incidence, making control efforts and contact tracing less effective. Here, through a modeling approach we show that the effect produced by the number of initial infections is non-linear on the incidence peak and peak time. When case importations are carried by mobility from an already infected area, this effect is further enhanced by the local demography and underlying mixing patterns: the impact of every seed is larger in smaller populations. Finally, both in the model simulations and the analysis, we show that a multi-seeding effect combined with mobility restrictions can explain the observed spatial heterogeneities in the first wave of COVID-19 incidence and mortality in five European countries. Our results allow us for identifying what we have called epidemic epicenter: an area that shapes incidence and mortality peaks in the entire country. The present work further clarifies the nonlinear effects that mobility can have on the evolution of an epidemic and highlight their relevance for epidemic control.

The 2019–2020 upsurge of the desert locust and its impact in Pakistan

The recent upsurge of the desert locust Schistocerca gregaria (Forskål, 1775) has had an impact on East Africa and the Middle East as far as India. It has affected and slowed down many aspects of the Pakistani economy. Swarms of locusts have infested many areas and caused immense damage to all types of crops. Both farmers and economists are concerned and are trying to get the most up-to-date information on the best strategy to manage this pest. This paper is an attempt to (i) provide insight into the dynamics of this upsurge internationally as well as in the various regions of Pakistan, (ii) briefly assess its local impact and locust control measures, and (iii) clarify the role of the various stakeholders in the management, both nationally and internationally, suggesting various improvements for the future.

Why Does COVID-19 Affect Some Cities More than Others?: Evidence from the First Year of the Pandemic in Brazil

This paper investigates what explains the variation in impacts of COVID-19 across Brazilian cities. I assemble data from over 2,500 cities on COVID-19 cases and deaths, population mobility, and local policy responses. I study how these outcomes correlate with pre-pandemic local characteristics, drawing comparisons with existing US estimates when possible. As in the United States, the connections between city characteristics and outcomes in Brazil can evolve over time, with some early correlations fading as the pandemic entered a second wave. Population density is associated with greater local impact of the disease in both countries. However, in contrast to the United States, the pandemic in Brazil took a greater toll in cities with higher income levels consistent with the fact that higher incomes correlate with greater mobility in Brazil. Socioeconomic vulnerabilities, such as the presence of slums and high residential crowding, correlate with higher death rates per capita. Cities with such vulnerabilities in Brazil suffered higher COVID-19 death rates despite their residents' greater propensity to stay home. Policy responses do not appear to drive these connections.

Extremely Preterm Infant Admissions Within the SafeBoosC-III Consortium During the COVID-19 Lockdown

Objective: To evaluate if the number of admitted extremely preterm (EP) infants (born before 28 weeks of gestational age) differed in the neonatal intensive care units (NICUs) of the SafeBoosC-III consortium during the global lockdown when compared to the corresponding time period in 2019.Design: This is a retrospective, observational study. Forty-six out of 79 NICUs (58%) from 17 countries participated. Principal investigators were asked to report the following information: (1) Total number of EP infant admissions to their NICU in the 3 months where the lockdown restrictions were most rigorous during the first phase of the COVID-19 pandemic, (2) Similar EP infant admissions in the corresponding 3 months of 2019, (3) the level of local restrictions during the lockdown period, and (4) the local impact of the COVID-19 lockdown on the everyday life of a pregnant woman.Results: The number of EP infant admissions during the first wave of the COVID-19 pandemic was 428 compared to 457 in the corresponding 3 months in 2019 (−6.6%, 95% CI −18.2 to +7.1%, p = 0.33). There were no statistically significant differences within individual geographic regions and no significant association between the level of lockdown restrictions and difference in the number of EP infant admissions. A post-hoc analysis based on data from the 46 NICUs found a decrease of 10.3%in the total number of NICU admissions (n = 7,499 in 2020 vs. n = 8,362 in 2019).Conclusion: This ad hoc study did not confirm previous reports of a major reduction in the number of extremely pretermbirths during the first phase of the COVID-19 pandemic.Clinical Trial Registration:ClinicalTrial.gov, identifier: NCT04527601 (registered August 26, 2020), https://clinicaltrials.gov/ct2/show/NCT04527601.