Recent advances in understanding the structural and functional evolution of FtsH proteases

The FtsH family of proteases are membrane-anchored, ATP-dependent, zinc metalloproteases. They are universally present in prokaryotes and the mitochondria and chloroplasts of eukaryotic cells. Most bacteria bear a single ftsH gene that produces hexameric homocomplexes with diverse house-keeping roles. However, in mitochondria, chloroplasts and cyanobacteria, multiple FtsH homologues form homo and heterocomplexes with specialised functions in maintaining photosynthesis and respiration. The diversification of FtsH homologues combined with selective pairing of FtsH isomers is a versatile strategy to enable functional adaptation. In this article we summarise recent progress in understanding the evolution, structure and function of FtsH proteases with a focus on the role of FtsH in photosynthesis and respiration

3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration

In this paper, we propose the 3DFeat-Net which learns both 3D feature detector and descriptor for point cloud matching using weak supervision. Unlike many existing works, we do not require manual annotation of matching point clusters. Instead, we leverage on alignment and attention mechanisms to learn feature correspondences from GPS/INS tagged 3D point clouds without explicitly specifying them. We create training and benchmark outdoor Lidar datasets, and experiments show that 3DFeat-Net obtains state-of-the-art performance on these gravity-aligned datasets.Comment: 17 pages, 6 figures. Accepted in ECCV 201

A Modified Sequence-to-point HVAC Load Disaggregation Algorithm

This paper presents a modified sequence-to-point (S2P) algorithm for disaggregating the heat, ventilation, and air conditioning (HVAC) load from the total building electricity consumption. The original S2P model is convolutional neural network (CNN) based, which uses load profiles as inputs. We propose three modifications. First, the input convolution layer is changed from 1D to 2D so that normalized temperature profiles are also used as inputs to the S2P model. Second, a drop-out layer is added to improve adaptability and generalizability so that the model trained in one area can be transferred to other geographical areas without labelled HVAC data. Third, a fine-tuning process is proposed for areas with a small amount of labelled HVAC data so that the pre-trained S2P model can be fine-tuned to achieve higher disaggregation accuracy (i.e., better transferability) in other areas. The model is first trained and tested using smart meter and sub-metered HVAC data collected in Austin, Texas. Then, the trained model is tested on two other areas: Boulder, Colorado and San Diego, California. Simulation results show that the proposed modified S2P algorithm outperforms the original S2P model and the support-vector machine based approach in accuracy, adaptability, and transferability

A Comparison between the Zero Forcing Number and the Strong Metric Dimension of Graphs

The \emph{zero forcing number}, $Z(G)$, of a graph $G$ is the minimum cardinality of a set $S$ of black vertices (whereas vertices in $V(G)-S$ are colored white) such that $V(G)$ is turned black after finitely many applications of "the color-change rule": a white vertex is converted black if it is the only white neighbor of a black vertex. The \emph{strong metric dimension}, $sdim(G)$, of a graph $G$ is the minimum among cardinalities of all strong resolving sets: $W \subseteq V(G)$ is a \emph{strong resolving set} of $G$ if for any $u, v \in V(G)$, there exists an $x \in W$ such that either $u$ lies on an $x-v$ geodesic or $v$ lies on an $x-u$ geodesic. In this paper, we prove that $Z(G) \le sdim(G)+3r(G)$ for a connected graph $G$, where $r(G)$ is the cycle rank of $G$. Further, we prove the sharp bound $Z(G) \leq sdim(G)$ when $G$ is a tree or a unicyclic graph, and we characterize trees $T$ attaining $Z(T)=sdim(T)$. It is easy to see that $sdim(T+e)-sdim(T)$ can be arbitrarily large for a tree $T$; we prove that $sdim(T+e) \ge sdim(T)-2$ and show that the bound is sharp.Comment: 8 pages, 5 figure

MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations

This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of load profiles in one shot. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads to enable the generation of realistic synthetic load profiles in large quantity for meeting the emerging need in distribution system planning. The novelty and uniqueness of the MultiLoad-GAN framework are three-fold. First, it generates a group of load profiles bearing realistic spatial-temporal correlations in one shot. Second, two complementary metrics for evaluating realisticness of generated load profiles are developed: statistics metrics based on domain knowledge and a deep-learning classifier for comparing high-level features. Third, to tackle data scarcity, a novel iterative data augmentation mechanism is developed to generate training samples for enhancing the training of both the classifier and the MultiLoad-GAN model. Simulation results show that MultiLoad-GAN outperforms state-of-the-art approaches in realisticness, computational efficiency, and robustness. With little finetuning, the MultiLoad-GAN approach can be readily extended to generate a group of load or PV profiles for a feeder, a substation, or a service area.Comment: Submitted to IEEE Transactions on Smart Gri

Learning and Matching Multi-View Descriptors for Registration of Point Clouds

Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space. The correspondence problem is generally addressed by the design of discriminative 3D local descriptors on the one hand, and the development of robust matching strategies on the other hand. In this work, we first propose a multi-view local descriptor, which is learned from the images of multiple views, for the description of 3D keypoints. Then, we develop a robust matching approach, aiming at rejecting outlier matches based on the efficient inference via belief propagation on the defined graphical model. We have demonstrated the boost of our approaches to registration on the public scanning and multi-view stereo datasets. The superior performance has been verified by the intensive comparisons against a variety of descriptors and matching methods

Artemisinin resistance in Plasmodium falciparum malaria.

BACKGROUND: Artemisinin-based combination therapies are the recommended first-line treatments of falciparum malaria in all countries with endemic disease. There are recent concerns that the efficacy of such therapies has declined on the Thai-Cambodian border, historically a site of emerging antimalarial-drug resistance. METHODS: In two open-label, randomized trials, we compared the efficacies of two treatments for uncomplicated falciparum malaria in Pailin, western Cambodia, and Wang Pha, northwestern Thailand: oral artesunate given at a dose of 2 mg per kilogram of body weight per day, for 7 days, and artesunate given at a dose of 4 mg per kilogram per day, for 3 days, followed by mefloquine at two doses totaling 25 mg per kilogram. We assessed in vitro and in vivo Plasmodium falciparum susceptibility, artesunate pharmacokinetics, and molecular markers of resistance. RESULTS: We studied 40 patients in each of the two locations. The overall median parasite clearance times were 84 hours (interquartile range, 60 to 96) in Pailin and 48 hours (interquartile range, 36 to 66) in Wang Pha (P<0.001). Recrudescence confirmed by means of polymerase-chain-reaction assay occurred in 6 of 20 patients (30%) receiving artesunate monotherapy and 1 of 20 (5%) receiving artesunate-mefloquine therapy in Pailin, as compared with 2 of 20 (10%) and 1 of 20 (5%), respectively, in Wang Pha (P=0.31). These markedly different parasitologic responses were not explained by differences in age, artesunate or dihydroartemisinin pharmacokinetics, results of isotopic in vitro sensitivity tests, or putative molecular correlates of P. falciparum drug resistance (mutations or amplifications of the gene encoding a multidrug resistance protein [PfMDR1] or mutations in the gene encoding sarco-endoplasmic reticulum calcium ATPase6 [PfSERCA]). Adverse events were mild and did not differ significantly between the two treatment groups. CONCLUSIONS: P. falciparum has reduced in vivo susceptibility to artesunate in western Cambodia as compared with northwestern Thailand. Resistance is characterized by slow parasite clearance in vivo without corresponding reductions on conventional in vitro susceptibility testing. Containment measures are urgently needed. (ClinicalTrials.gov number, NCT00493363, and Current Controlled Trials number, ISRCTN64835265.

Biomolecular condensates undergo a generic shear-mediated liquid-to-solid transition.

Membrane-less organelles resulting from liquid-liquid phase separation of biopolymers into intracellular condensates control essential biological functions, including messenger RNA processing, cell signalling and embryogenesis1-4. It has recently been discovered that several such protein condensates can undergo a further irreversible phase transition, forming solid nanoscale aggregates associated with neurodegenerative disease5-7. While the irreversible gelation of protein condensates is generally related to malfunction and disease, one case where the liquid-to-solid transition of protein condensates is functional, however, is that of silk spinning8,9. The formation of silk fibrils is largely driven by shear, yet it is not known what factors control the pathological gelation of functional condensates. Here we demonstrate that four proteins and one peptide system, with no function associated with fibre formation, have a strong propensity to undergo a liquid-to-solid transition when exposed to even low levels of mechanical shear once present in their liquid-liquid phase separated form. Using microfluidics to control the application of shear, we generated fibres from single-protein condensates and characterized their structural and material properties as a function of shear stress. Our results reveal generic backbone-backbone hydrogen bonding constraints as a determining factor in governing this transition. These observations suggest that shear can play an important role in the irreversible liquid-to-solid transition of protein condensates, shed light on the role of physical factors in driving this transition in protein aggregation-related diseases and open a new route towards artificial shear responsive biomaterials

Hand-grip strength is a simple and effective outcome predictor in esophageal cancer following esophagectomy with reconstruction: a prospective study

<p>Abstract</p> <p>Background</p> <p>Surgery for esophageal cancer usually carries considerable complication and mortality rate. Adequate preoperative evaluation is mandatory to decrease complication rate. Hand-grip strength is a useful measure to assess the extent of aging, nutrition and patient's overall condition. Because preoperative nutrition state and physiologic aging process play important roles in postoperative recovery, we would like to know if hand-grip strength is an adequate tool for such evaluation.</p> <p>Material and methods</p> <p>From January 1st, 2007 to December 31, 2008, there was 68 cases underwent esophagectomy with reconstruction due to esophageal cancer in our hospital. After excluding 7 patients of incomplete data and loss of follow-up, there were 61 patients included in the study.</p> <p>Results</p> <p>There were 54 men and 7 women. The mean age is 60.7. Most of patients had squamous cell carcinoma. Patient with weak hand-grip strength prior to operation had exceedingly high rates of complication and mortality within 6 months after operation. Compared to other risk factors, low grip strength has highest relative risks for both mortality and morbidity.</p> <p>Conclusion</p> <p>Because test for hand-grip strength is cheap, not time-consuming and has high predictive value, it may be included in routine preoperative evaluation.</p