Self-organization for community resilience in an invisible agricultural community

This study investigates how self-organizing efforts by residents of informal settlements, primarily migrant and informal farmworkers, shape community resilience in Majes, a water-scarce irrigation district in the Atacama Desert of Peru. We collected 45 semi-structured interviews with residents and authorities in Majes and analyzed findings through a framework of self-organizing. Analyses revealed that self-organizing by residents of informal settlements incorporated the three components of White’s theory of Community Agency and Community Resilience, which contends that marginalized communities increase resilience by fostering a commons praxis, practicing a prefigurative politics, and developing opportunities for economic autonomy. We also found that residents self-organized into associations to increase access to resources, resulting in increased resilience. However, certain fees, corruption, and undemocratic decision-making processes can be detrimental to self-organizing. Results expand existing theories of self-organization and community resilience by highlighting how residents of informal settlements in agricultural spaces collectively organize to increase their resilience. Findings also begin to reframe narratives that describe migrants and farmworkers as powerless in the face of water scarcity, climate change, and other social-ecological risks

Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning

A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization from existing datasets followed by fast online fine-tuning with limited interaction. However, existing offline RL methods tend to behave poorly during fine-tuning. In this paper, we study the fine-tuning problem in the context of conservative offline RL methods and we devise an approach for learning an effective initialization from offline data that also enables fast online fine-tuning capabilities. Our approach, calibrated Q-learning (Cal-QL), accomplishes this by learning a conservative value function initialization that underestimates the value of the learned policy from offline data, while also ensuring that the learned Q-values are at a reasonable scale. We refer to this property as calibration, and define it formally as providing a lower bound on the true value function of the learned policy and an upper bound on the value of some other (suboptimal) reference policy, which may simply be the behavior policy. We show that a conservative offline RL algorithm that also learns a calibrated value function leads to effective online fine-tuning, enabling us to take the benefits of offline initializations in online fine-tuning. In practice, Cal-QL can be implemented on top of the conservative Q learning (CQL) for offline RL within a one-line code change. Empirically, Cal-QL outperforms state-of-the-art methods on 9/11 fine-tuning benchmark tasks that we study in this paper. Code and video are available at https://nakamotoo.github.io/projects/Cal-QLComment: project page: https://nakamotoo.github.io/projects/Cal-Q

Linking migration to community resilience in the receiving basin of a large-scale water transfer project

Large-scale water transfer projects (LWTPs) transfer water to urban and agricultural areas. The Majes-Siguas canal, established in 1983, is an LWTP that created a thriving agricultural area through irrigating the Majes district in the Atacama Desert of Peru. Like other LWTP receiving basins, the project has attracted an influx of migrants who work on the farms. At the same time, the Majes LWTP is the district’s only source of water and has an aging infrastructure which presents significant risks. While many studies critically analyze the consequences of LWTPs in water supply basins, few evaluate the resilience of communities living in LWTP receiving basins. In this study, we ask: what factors stifle or enable resilience of the agricultural community in the Majes-Siguas receiving basin? In 2019, we conducted semi-structured interviews with migrant and residents and water authorities, collected and reviewed historical documents, and conducted participant observations. Using this data, we analyze community resilience by identifying perceived risks, stressors, and vulnerabilities among and between groups of agricultural actors, their adaptations, and their perceptions of water management organizations’ responses. Results show that a single source of water, differential vulnerabilities between groups of agricultural actors, and limited organizational responsiveness stifled community resilience, while communal pooling and self-organization enabled community resilience. Attention to increasing inclusion of migrants in water management decision-making, addressing differential water and land rights, and cultivating space for migrant self-organization could enable the agricultural community to be more resilient

Centering Community Voices in Mining Governance

Peru has shifted away from centralized mining management to governance among government, companies, and communities. Various mechanisms facilitate community participation, including the mining canon, dialogues, and corporate social responsibility programs. Even with these laws and mechanisms, mining pollution and conflicts continue. In this study, we ask: how do communities perceive and participate in mining governance? And what are some alternative ways, driven by community priorities, to address governance in mining contexts? We collected 53 semi-structured with agricultural actors in two Peruvian districts with mining activity and analyzed those perspectives through the lens of community-centered governance. Our analyses revealed how centering community priorities in data collection and analysis illuminates context-specific factors that shape community attitudes toward mining and highlights community-driven approaches to addressing mining governance. Such community driven approaches could include integrating understandings of local livelihoods and historical contexts, implementing transparent participatory processes, and improving laws to give communities decision-making power over mining development

Smart Start: Rituximab, Lenalidomide, and Ibrutinib in Patients With Newly Diagnosed Large B-Cell Lymphoma

PURPOSE: Chemoimmunotherapy for patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL) is largely unchanged for decades. Both preclinical models and clinical data suggest the combination of lenalidomide and ibrutinib may have synergy in DLBCL, particularly in the non-germinal center B-cell-like subset. METHODS: We enrolled 60 patients with newly diagnosed non-germinal center B-cell-like DLBCL in this investigator-initiated, single-arm phase II trial of rituximab, lenalidomide, and ibrutinib (RLI) with the sequential addition of chemotherapy (ClinicalTrials.gov identifier: NCT02636322). Patients were treated with rituximab 375 mg/m RESULTS: The median age was 63.5 years (range, 29-83 years) with 28% age 70 years or older. The revised international prognostic index identified 42% as high risk, and 62% were double expressor of MYC and BCL2 protein. The ORR after two cycles of RLI was 86.2%, and the complete response rate at the end of RLI-chemotherapy was 94.5%. With a median follow-up of 31 months, the progression-free survival and overall survival were at 91.3% and 96.6% at 2 years, respectively. CONCLUSION: Smart Start is the first study, to our knowledge, to treat newly diagnosed DLBCL with a targeted therapy combination before chemotherapy. RLI produced a high ORR, and RLI with chemotherapy resulted in durable responses. This establishes the potential for developing biologically driven and noncytotoxic first-line therapies for DLBCL

Stroke Severity Versus Dysphagia Screen as Driver for Post-stroke Pneumonia

Background and Purpose: Post-stroke pneumonia is a feared complication of stroke as it is associated with greater mortality and disability than in those without pneumonia. Patients are often kept “Nil By Mouth” (NBM) after stroke until after receiving a screen for dysphagia and declared safe to resume oral intake. We aimed to assess the proportional contribution of stroke severity and dysphagia screen to pneumonia by borrowing idea from coalition game theory on fair distribution of marginal profit (Shapley value).Method: Retrospective study of admissions to the stroke unit at Monash Medical Center in 2015. Seventy-five percent of data were partitioned into training set and the remainder (25%) into validation set. Variables associated with pneumonia (p < 0.1) were entered into Shapley value regression and conditional decision tree analysis.Results: In 2015, there were 797 admissions and 617 patients with ischemic and hemorrhagic stroke (age 69.9 ± 16.2, male = 55.0%, National Institute of Health Stroke Scale/NIHSS 8.1 ± 7.9). The frequency of pneumonia was 6.6% (41/617). In univariable analyses NIHSS, time to dysphagia screen, Charlson comorbidity index (CCI), and age were significantly associated with pneumonia but not weekend admission. Shapley value regression showed that the largest contributor to the model was stroke severity (72.8%) followed by CCI (16.2%), dysphagia screen (3.8%), and age (7.2%). Decision tree analysis yielded an NIHSS threshold of 14 for classifying people with (27% of 75 patients) and without pneumonia (2.5% of 308 patients). The area under the ROC curve for training data was 0.83 (95% CI 0.75–0.91) with no detectable difference between the training and test data (p = 0.4). Results were similar when dysphagia was exchanged for the variable dysphagia screen.Conclusion: Stroke severity status, and not dysphagia or dysphagia screening contributed to the decision tree model of post stroke pneumonia. We cannot exclude the chance that using dysphagia screen in this cohort had minimized the impact of dysphagia on development of pneumonia

Quantitative Whole Body Biodistribution of Fluorescent-Labeled Agents by Non-Invasive Tomographic Imaging

When small molecules or proteins are injected into live animals, their physical and chemical properties will significantly affect pharmacokinetics, tissue penetration, and the ultimate routes of metabolism and clearance. Fluorescence molecular tomography (FMT) offers the ability to non-invasively image and quantify temporal changes in fluorescence throughout the major organ systems of living animals, in a manner analogous to traditional approaches with radiolabeled agents. This approach is best used with biotherapeutics (therapeutic antibodies, or other large proteins) or large-scaffold drug-delivery vectors, that are minimally affected by low-level fluorophore conjugation. Application to small molecule drugs should take into account the significant impact of fluorophore labeling on size and physicochemical properties, however, the presents studies show that this technique is readily applied to small molecule agents developed for far-red (FR) or near infrared (NIR) imaging. Quantification by non-invasive FMT correlated well with both fluorescence from tissue homogenates as well as with planar (2D) fluorescence reflectance imaging of excised intact organs (r2 = 0.996 and 0.969, respectively). Dynamic FMT imaging (multiple times from 0 to 24 h) performed in live mice after the injection of four different FR/NIR-labeled agents, including immunoglobulin, 20–50 nm nanoparticles, a large vascular imaging agent, and a small molecule integrin antagonist, showed clear differences in the percentage of injected dose per gram of tissue (%ID/g) in liver, kidney, and bladder signal. Nanoparticles and IgG1 favored liver over kidney signal, the small molecule integrin-binding agent favored rapid kidney and bladder clearance, and the vascular agent, showed both liver and kidney clearance. Further assessment of the volume of distribution of these agents by fluorescent volume added information regarding their biodistribution and highlighted the relatively poor extravasation into tissue by IgG1. These studies demonstrate the ability of quantitative FMT imaging of FR/NIR agents to non-invasively visualize and quantify the biodistribution of different agents over time

Assessment of heterogeneity amongaAssessment of Heterogeneity Among Participants in the Parkinson’s Progression Markers Initiative Cohort Using α-Synuclein Seed Amplification: A Cross-Sectional Study participants in the Parkinson\u27s Progression Markers Initiative cohort using α-synuclein seed amplification: a cross-sectional study

BACKGROUND: Emerging evidence shows that α-synuclein seed amplification assays (SAAs) have the potential to differentiate people with Parkinson\u27s disease from healthy controls. We used the well characterised, multicentre Parkinson\u27s Progression Markers Initiative (PPMI) cohort to further assess the diagnostic performance of the α-synuclein SAA and to examine whether the assay identifies heterogeneity among patients and enables the early identification of at-risk groups. METHODS: This cross-sectional analysis is based on assessments done at enrolment for PPMI participants (including people with sporadic Parkinson\u27s disease from LRRK2 and GBA variants, healthy controls, prodromal individuals with either rapid eye movement sleep behaviour disorder (RBD) or hyposmia, and non-manifesting carriers of LRRK2 and GBA variants) from 33 participating academic neurology outpatient practices worldwide (in Austria, Canada, France, Germany, Greece, Israel, Italy, the Netherlands, Norway, Spain, the UK, and the USA). α-synuclein SAA analysis of CSF was performed using previously described methods. We assessed the sensitivity and specificity of the α-synuclein SAA in participants with Parkinson\u27s disease and healthy controls, including subgroups based on genetic and clinical features. We established the frequency of positive α-synuclein SAA results in prodromal participants (RBD and hyposmia) and non-manifesting carriers of genetic variants associated with Parkinson\u27s disease, and compared α-synuclein SAA to clinical measures and other biomarkers. We used odds ratio estimates with 95% CIs to measure the association between α-synuclein SAA status and categorical measures, and two-sample 95% CIs from the resampling method to assess differences in medians between α-synuclein SAA positive and negative participants for continuous measures. A linear regression model was used to control for potential confounders such as age and sex. FINDINGS: This analysis included 1123 participants who were enrolled between July 7, 2010, and July 4, 2019. Of these, 545 had Parkinson\u27s disease, 163 were healthy controls, 54 were participants with scans without evidence of dopaminergic deficit, 51 were prodromal participants, and 310 were non-manifesting carriers. Sensitivity for Parkinson\u27s disease was 87·7% (95% CI 84·9-90·5), and specificity for healthy controls was 96·3% (93·4-99·2). The sensitivity of the α-synuclein SAA in sporadic Parkinson\u27s disease with the typical olfactory deficit was 98·6% (96·4-99·4). The proportion of positive α-synuclein SAA was lower than this figure in subgroups including LRRK2 Parkinson\u27s disease (67·5% [59·2-75·8]) and participants with sporadic Parkinson\u27s disease without olfactory deficit (78·3% [69·8-86·7]). Participants with LRRK2 variant and normal olfaction had an even lower α-synuclein SAA positivity rate (34·7% [21·4-48·0]). Among prodromal and at-risk groups, 44 (86%) of 51 of participants with RBD or hyposmia had positive α-synuclein SAA (16 of 18 with hyposmia, and 28 of 33 with RBD). 25 (8%) of 310 non-manifesting carriers (14 of 159 [9%] LRRK2 and 11 of 151 [7%] GBA) were positive. INTERPRETATION: This study represents the largest analysis so far of the α-synuclein SAA for the biochemical diagnosis of Parkinson\u27s disease. Our results show that the assay classifies people with Parkinson\u27s disease with high sensitivity and specificity, provides information about molecular heterogeneity, and detects prodromal individuals before diagnosis. These findings suggest a crucial role for the α-synuclein SAA in therapeutic development, both to identify pathologically defined subgroups of people with Parkinson\u27s disease and to establish biomarker-defined at-risk cohorts. FUNDING: PPMI is funded by the Michael J Fox Foundation for Parkinson\u27s Research and funding partners, including: Abbvie, AcureX, Aligning Science Across Parkinson\u27s, Amathus Therapeutics, Avid Radiopharmaceuticals, Bial Biotech, Biohaven, Biogen, BioLegend, Bristol-Myers Squibb, Calico Labs, Celgene, Cerevel, Coave, DaCapo Brainscience, 4D Pharma, Denali, Edmond J Safra Foundation, Eli Lilly, GE Healthcare, Genentech, GlaxoSmithKline, Golub Capital, Insitro, Janssen Neuroscience, Lundbeck, Merck, Meso Scale Discovery, Neurocrine Biosciences, Prevail Therapeutics, Roche, Sanofi Genzyme, Servier, Takeda, Teva, UCB, VanquaBio, Verily, Voyager Therapeutics, and Yumanity