How Hard Is Deciding Trivial Versus Nontrivial in the Dihedral Coset Problem?

We study the hardness of the dihedral hidden subgroup problem. It is known that lattice problems reduce to it, and that it reduces to random subset sum with density > 1 and also to quantum sampling subset sum solutions. We examine a decision version of the problem where the question asks whether the hidden subgroup is trivial or order two. The decision problem essentially asks if a given vector is in the span of all coset states. We approach this by first computing an explicit basis for the coset space and the perpendicular space. We then look at the consequences of having efficient unitaries that use this basis. We show that if a unitary maps the basis to the standard basis in any way, then that unitary can be used to solve random subset sum with constant density >1. We also show that if a unitary can exactly decide membership in the coset subspace, then the collision problem for subset sum can be solved for density >1 but approaching 1 as the problem size increases. This strengthens the previous hardness result that implementing the optimal POVM in a specific way is as hard as quantum sampling subset sum solutions

Project RISE: Recognizing Industrial Smoke Emissions

Industrial smoke emissions pose a significant concern to human health. Prior works have shown that using Computer Vision (CV) techniques to identify smoke as visual evidence can influence the attitude of regulators and empower citizens to pursue environmental justice. However, existing datasets are not of sufficient quality nor quantity to train the robust CV models needed to support air quality advocacy. We introduce RISE, the first large-scale video dataset for Recognizing Industrial Smoke Emissions. We adopted a citizen science approach to collaborate with local community members to annotate whether a video clip has smoke emissions. Our dataset contains 12,567 clips from 19 distinct views from cameras that monitored three industrial facilities. These daytime clips span 30 days over two years, including all four seasons. We ran experiments using deep neural networks to establish a strong performance baseline and reveal smoke recognition challenges. Our survey study discussed community feedback, and our data analysis displayed opportunities for integrating citizen scientists and crowd workers into the application of Artificial Intelligence for social good.Comment: Technical repor

MUJER EN EL BOSQUE CON UN PERRO A SUS PIES [Material gráfico]

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte, 201

Complexity in Lipid Membrane Composition Induces Resilience to Aβ42 Aggregation.

The molecular origins of Alzheimer's disease are associated with the aggregation of the amyloid-β peptide (Aβ). This process is controlled by a complex cellular homeostasis system, which involves a variety of components, including proteins, metabolites, and lipids. It has been shown in particular that certain components of lipid membranes can speed up Aβ aggregation. This observation prompts the question of whether there are protective cellular mechanisms to counterbalance this effect. Here, to address this issue, we investigate the role of the composition of lipid membranes in modulating the aggregation process of Aβ. By adopting a chemical kinetics approach, we first identify a panel of lipids that affect the aggregation of the 42-residue form of Aβ (Aβ42), ranging from enhancement to inhibition. We then show that these effects tend to average out in mixtures of these lipids, as such mixtures buffer extreme aggregation behaviors as the number of components increases. These results indicate that a degree of quality control on protein aggregation can be achieved through a mechanism by which an increase in the molecular complexity of lipid membranes balances opposite effects and creates resilience to aggregation

Two human metabolites rescue a C. elegans model of Alzheimer's disease via a cytosolic unfolded protein response.

Age-related changes in cellular metabolism can affect brain homeostasis, creating conditions that are permissive to the onset and progression of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. Although the roles of metabolites have been extensively studied with regard to cellular signaling pathways, their effects on protein aggregation remain relatively unexplored. By computationally analysing the Human Metabolome Database, we identified two endogenous metabolites, carnosine and kynurenic acid, that inhibit the aggregation of the amyloid beta peptide (Aβ) and rescue a C. elegans model of Alzheimer's disease. We found that these metabolites act by triggering a cytosolic unfolded protein response through the transcription factor HSF-1 and downstream chaperones HSP40/J-proteins DNJ-12 and DNJ-19. These results help rationalise previous observations regarding the possible anti-ageing benefits of these metabolites by providing a mechanism for their action. Taken together, our findings provide a link between metabolite homeostasis and protein homeostasis, which could inspire preventative interventions against neurodegenerative disorders

Optimization of a small molecule inhibitor of secondary nucleation in α-synuclein aggregation

Parkinson’s disease is characterised by the deposition in the brain of amyloid aggregates of α-synuclein. The surfaces of these amyloid aggregates can catalyse the formation of new aggregates, giving rise to a positive feedback mechanism responsible for the rapid proliferation of α-synuclein deposits. We report a procedure to enhance the potency of a small molecule to inhibit the aggregate proliferation process using a combination of in silico and in vitro methods. The optimized small molecule shows potency already at a compound:protein stoichiometry of 1:20. These results illustrate a strategy to accelerate the optimisation of small molecules against α-synuclein aggregation by targeting secondary nucleation

Isolation and Characterization of Antibiotic Resistant Bacteria from Swiftlet Feces in Swiftlet Farm Houses in Sarawak, Malaysia

There is a growing concern on the occurrence of antimicrobial resistance. Development of multiple antibiotic resistant bacteria has overtaken new drug development and threatened the patients with untreatable infections. This study was conducted to isolate and characterize the antibiotic resistant bacteria from swiftlet farm houses located in various places including Kota Samarahan, Semarang, Saratok, Betong, Sarikei, Sibu, Sepinang, Maludam, Miri, and Kuching inSarawak, Malaysia.Five fecessamples were collected randomly from each site. One gram of the fecessample was diluted in 9 mLof 0.85% normalsaline solution. The diluted sample was plated on TrypticaseSoy agar plates and incubated at 37±1 °C for 24 h. A total of 500 bacteria isolates were then identified using 16S rRNA analysis method. Disc diffusion method was then used to confirm the resistant phenotypes of these isolates. The results showed that the means of the bacterial colony count were significantly -1 different (p<0.05) from one another, with the highestlog CFU g (9.22±0.72) found in KotaSamarahan and the 10 -1 lowest log CFU g (6.03±0.62) in Betong. Besides, the isolated bacteria were identified as 96% Gram positive 10 bacteria and 4% Gram negative bacteria. The isolated bacteria were highly resistant to penicillin G (36.80±23.87%), ampicillin (28.60±17.13%), and rifampicin (16.90±13.70%). Thus, swiftlet feces are good reservoirfor a range of antibiotic resistant bacteria whichmay pose a potential health hazard to human