Mitochondrial dysfunction generates aggregates that resist lysosomal degradation in human breast cancer cells

Disrupting functional protein homeostasis is an established therapeutic strategy for certain tumors. Ongoing studies are evaluating autophagy inhibition for overcoming chemotherapeutic resistance to such therapies by neutralizing lysosomal pH. New and sensitive methods to monitor autophagy in patients are needed to improve trial design and interpretation. We report that mitochondrial-damaged breast cancer cells and rat breast tumors accumulate p53-positive protein aggregates that resist lysosomal degradation. These aggregates were localized to enzymatically-active autolysosomes that were degrading autophagosomes and the autophagic receptor proteins TAX1BP1 and NDP52. NDP52 was identified to associate with aggregated proteins and knocking down NDP52 led to the accumulation of protein aggregates. TAX1BP1 was identified to partly localize with aggregates, and knocking down TAX1BP1 enhanced aggregate formation, suppressed autophagy, impaired NDP52 autophagic degradation and induced cell death. We propose that quantifying aggregates and autophagic receptors are two potential methods to evaluate autophagy and lysosomal degradation, as confirmed using primary human tumor samples. Collectively, this report establishes protein aggregates and autophagy receptors, TAX1BP1 and NDP52, as potential endpoints for monitoring autophagy during drug development and clinical studies

Univalent Foundations and the UniMath Library

We give a concise presentation of the Univalent Foundations of mathematics outlining the main ideas, followed by a discussion of the UniMath library of formalized mathematics implementing the ideas of the Univalent Foundations (section 1), and the challenges one faces in attempting to design a large-scale library of formalized mathematics (section 2). This leads us to a general discussion about the links between architecture and mathematics where a meeting of minds is revealed between architects and mathematicians (section 3). On the way our odyssey from the foundations to the "horizon" of mathematics will lead us to meet the mathematicians David Hilbert and Nicolas Bourbaki as well as the architect Christopher Alexander

Health service quality in 2929 facilities in six low-income and middle-income countries: a positive deviance analysis

BACKGROUND: Primary care is of insufficient quality in many low-income and middle-income countries. Some health facilities perform better than others despite operating in similar contexts, although the factors that characterise best performance are not well known. Existing best-performance analyses are concentrated in high-income countries and focus on hospitals. We used the positive deviance approach to identify the factors that differentiate best from worst primary care performance among health facilities across six low-resource health systems. METHODS: This positive deviance analysis used nationally representative samples of public and private health facilities from Service Provision Assessments of the Democratic Republic of the Congo, Haiti, Malawi, Nepal, Senegal, and Tanzania. Data were collected starting June 11, 2013, in Malawi and ending Feb 28, 2020, in Senegal. We assessed facility performance through completion of the Good Medical Practice Index (GMPI) of essential clinical actions (eg, taking a thorough history, conducting an adequate physical examination) according to clinical guidelines and measured with direct observations of care. We identified hospitals and clinics in the top decile of performance (defined as best performers) and conducted a quantitative, cross-national positive deviance analysis to compare them with facilities performing below the median (defined as worst performers) and identify facility-level factors that explain the gap between best and worst performance. FINDINGS: We identified 132 best-performing and 664 worst-performing hospitals, and 355 best-performing and 1778 worst-performing clinics based on clinical performance across countries. The mean GMPI score was 0.81 (SD 0.07) for the best-performing hospitals and 0.44 (0.09) for the worst-performing hospitals. Among clinics, mean GMPI scores were 0.75 (0.07) for the best performers and 0.34 (0.10) for the worst performers. High-quality governance, management, and community engagement were associated with best performance compared with worst performance. Private facilities out-performed government-owned hospitals and clinics. INTERPRETATION: Our findings suggest that best-performing health facilities are characterised by good management and leaders who can engage staff and community members. Governments should look to best performers to identify scalable practices and conditions for success that can improve primary care quality overall and decrease quality gaps between health facilities. FUNDING: Bill & Melinda Gates Foundation

High Energy Particles from Monopoles Connected by Strings

Monopole-antimonopole pairs connected by strings and monopole-string networks with $N>2$ strings attached to each monopole can be formed at phase transitions in the early universe. In such hybrid defects, monopoles accelerate under the string tension and can reach ultrarelativistic Lorentz factors, $\gamma\gg 1$. We study the radiation of gauge quanta by accelerating monopoles. For monopoles with a chromomagnetic charge, we also discuss the high-energy hadron production through emission of virtual gluons and their subsequent fragmentation into hadrons. The relevant parameter for gauge boson radiation is $M/a$, where $M$ is the boson mass and $a$ is the proper acceleration of the monopole. For $M\ll a$, the gauge bosons can be considered as massless and the typical energy of the emitted quanta is $E\sim\gamma a$. In the opposite limit, $M\gg a$, the radiation power is exponentially suppressed and gauge quanta are emitted with a typical energy $E\sim\gamma M$ in a narrow range $\Delta E/E\sim (a/M)^{1/2}$. Cosmological monopole-string networks can produce photons and hadrons of extremely high energies. For a wide range of parameters these energies can be much greater than the Planck scale.Comment: 28 pages, ReVTex, 5 postscript figures. Minor changes, some references added. Submitted to Phys. Rev.

Gravitating global defects: the gravitational field and compactification

We give a prescription to add the gravitational field of a global topological defect to a solution of Einstein's equations in an arbitrary number of dimensions. We only demand that the original solution has a O(n) invariance with n greater or equal 3. We will see that the general effect of a global defect is to introduce a deficit solid angle. We also show how the same kind of scalar field configurations can be used for spontaneous compactification of "n" extra dimensions on an n-sphere.Comment: Uses revte

Forest fire susceptibility and risk mapping using social/infrastructural vulnerability and environmental variables

Forests fires in northern Iran have always been common, but the number of forest fires has been growing over the last decade. It is believed, but not proven, that this growth can be attributed to the increasing temperatures and droughts. In general, the vulnerability to forest fire depends on infrastructural and social factors whereby the latter determine where and to what extent people and their properties are affected. In this paper, a forest fire susceptibility index and a social/infrastructural vulnerability index were developed using a machine learning (ML) method and a geographic information system multi-criteria decision making (GIS-MCDM), respectively. First, a forest fire inventory database was created from an extensive field survey and the moderate resolution imaging spectroradiometer (MODIS) thermal anomalies product for 2012 to 2017. A forest fire susceptibility map was generated using 16 environmental variables and a k-fold cross-validation (CV) approach. The infrastructural vulnerability index was derived with emphasis on different types of construction and land use, such as residential, industrial, and recreation areas. This dataset also incorporated social vulnerability indicators, e.g., population, age, gender, and family information. Then, GIS-MCDM was used to assess risk areas considering the forest fire susceptibility and the social/infrastructural vulnerability maps. As a result, most high fire susceptibility areas exhibit minor social/infrastructural vulnerability. The resulting forest fire risk map reveals that 729.61 ha, which is almost 1.14% of the study areas, is categorized in the high forest fire risk class. The methodology is transferable to other regions by localisation of the input data and the social indicators and contributes to forest fire mitigation and prevention planning