TITER: predicting translation initiation sites by deep learning.

MotivationTranslation initiation is a key step in the regulation of gene expression. In addition to the annotated translation initiation sites (TISs), the translation process may also start at multiple alternative TISs (including both AUG and non-AUG codons), which makes it challenging to predict TISs and study the underlying regulatory mechanisms. Meanwhile, the advent of several high-throughput sequencing techniques for profiling initiating ribosomes at single-nucleotide resolution, e.g. GTI-seq and QTI-seq, provides abundant data for systematically studying the general principles of translation initiation and the development of computational method for TIS identification.MethodsWe have developed a deep learning-based framework, named TITER, for accurately predicting TISs on a genome-wide scale based on QTI-seq data. TITER extracts the sequence features of translation initiation from the surrounding sequence contexts of TISs using a hybrid neural network and further integrates the prior preference of TIS codon composition into a unified prediction framework.ResultsExtensive tests demonstrated that TITER can greatly outperform the state-of-the-art prediction methods in identifying TISs. In addition, TITER was able to identify important sequence signatures for individual types of TIS codons, including a Kozak-sequence-like motif for AUG start codon. Furthermore, the TITER prediction score can be related to the strength of translation initiation in various biological scenarios, including the repressive effect of the upstream open reading frames on gene expression and the mutational effects influencing translation initiation efficiency.Availability and implementationTITER is available as an open-source software and can be downloaded from https://github.com/zhangsaithu/titer [email protected] or [email protected] informationSupplementary data are available at Bioinformatics online

Using predictive analysis to improve invoice-to-cash collection

Using predictive analysis to improve invoice-to-cash collectio

FHPM: Fine-grained Huge Page Management For Virtualization

As more data-intensive tasks with large footprints are deployed in virtual machines (VMs), huge pages are widely used to eliminate the increasing address translation overhead. However, once the huge page mapping is established, all the base page regions in the huge page share a single extended page table (EPT) entry, so that the hypervisor loses awareness of accesses to base page regions. None of the state-of-the-art solutions can obtain access information at base page granularity for huge pages. We observe that this can lead to incorrect decisions by the hypervisor, such as incorrect data placement in a tiered memory system and unshared base page regions when sharing pages. This paper proposes FHPM, a fine-grained huge page management for virtualization without hardware and guest OS modification. FHPM can identify access information at base page granularity, and dynamically promote and demote pages. A key insight of FHPM is to redirect the EPT huge page directory entries (PDEs) to new companion pages so that the MMU can track access information within huge pages. Then, FHPM can promote and demote pages according to the current hot page pressure to balance address translation overhead and memory usage. At the same time, FHPM proposes a VM-friendly page splitting and collapsing mechanism to avoid extra VM-exits. In combination, FHPM minimizes the monitoring and management overhead and ensures that the hypervisor gets fine-grained VM memory accesses to make the proper decision. We apply FHPM to improve tiered memory management (FHPM-TMM) and to promote page sharing (FHPM-Share). FHPM-TMM achieves a performance improvement of up to 33% and 61% over the pure huge page and base page management. FHPM-Share can save 41% more memory than Ingens, a state-of-the-art page sharing solution, with comparable performance

GaVe: A webcam-based gaze vending interface using one-point calibration

Gaze input, i.e., information input via eye of users, represents a promising method for contact-free interaction in human-machine systems. In this paper, we present the GazeVending interface (GaVe), which lets users control actions on a display with their eyes. The interface works on a regular webcam, available on most of today's laptops, and only requires a short one-point calibration before use. GaVe is designed in a hierarchical structure, presenting broad item cluster to users first and subsequently guiding them through another selection round, which allows the presentation of a large number of items. Cluster/item selection in GaVe is based on the dwell time, i.e., the time duration that users look at a given Cluster/item. A user study (N=22) was conducted to test optimal dwell time thresholds and comfortable human-to-display distances. Users' perception of the system, as well as error rates and task completion time were registered. We found that all participants were able to quickly understand and know how to interact with the interface, and showed good performance, selecting a target item within a group of 12 items in 6.76 seconds on average. We provide design guidelines for GaVe and discuss the potentials of the system

Holo-Relighting: Controllable Volumetric Portrait Relighting from a Single Image

At the core of portrait photography is the search for ideal lighting and viewpoint. The process often requires advanced knowledge in photography and an elaborate studio setup. In this work, we propose Holo-Relighting, a volumetric relighting method that is capable of synthesizing novel viewpoints, and novel lighting from a single image. Holo-Relighting leverages the pretrained 3D GAN (EG3D) to reconstruct geometry and appearance from an input portrait as a set of 3D-aware features. We design a relighting module conditioned on a given lighting to process these features, and predict a relit 3D representation in the form of a tri-plane, which can render to an arbitrary viewpoint through volume rendering. Besides viewpoint and lighting control, Holo-Relighting also takes the head pose as a condition to enable head-pose-dependent lighting effects. With these novel designs, Holo-Relighting can generate complex non-Lambertian lighting effects (e.g., specular highlights and cast shadows) without using any explicit physical lighting priors. We train Holo-Relighting with data captured with a light stage, and propose two data-rendering techniques to improve the data quality for training the volumetric relighting system. Through quantitative and qualitative experiments, we demonstrate Holo-Relighting can achieve state-of-the-arts relighting quality with better photorealism, 3D consistency and controllability.Comment: CVPR202

A saúde mental é o fator mais importante que influencia a qualidade de vida de idosos deixados para trás quando as famílias emigram da China rural

OBJECTIVES: to investigate the quality of life and the associated factors on left behind elderly in rural China. METHOD: the research was conducted cluster sampling to select 456 elderly left behind when family members migrated out of rural China to participate in a cross-sectional study by completing a general data questionnaire and Quality of Life questionnaire. RESULTS: 91.5% of the elderly requested psychological counseling and education. For the elderly, scores for mental health (39.56±13.73) were significantly lower compared with Chinese standard data (61.6±13.7) (POBJETIVOS: investigar la calidad de vida y los factores asociados a los adultos mayores que se quedan en las zonas rurales de China. MÉTODO: la investigación se realizó por medio de muestreo por conglomerados para seleccionar 456 adultos mayores que se quedaron cuando los miembros de la familia emigraron de zonas rurales de China, para participar en un estudio de corte transversal, completando un cuestionario de datos generales y cuestionario de calidad de vida. RESULTADOS: el 91.5% de los adultos mayores solicitó asistencia psicológica y educación. Para los adultos mayores, las puntuaciones de salud mental (39.56±13.73) fueron significativamente más bajos en comparación con los datos estándar de China (61.6±13.7) (pOBJETIVOS: investigar a qualidade de vida e fatores associados de idosos deixados para trás na China rural. MÉTODO: foi realizada amostragem por conglomerado para selecionar 456 idosos deixados para trás quando os membros da família emigram da China rural. Este é um estudo transversal com preenchimento de um questionário de dados gerais e de qualidade de vida. RESULTADOS: 91,5% dos idosos convidados solicitaram aconselhamento e educação psicológicos. Para os idosos, os escores de saúde mental (39,56±13,73) foram significativamente menores em comparação aos dados padrões chineses (61,6±13,7) (

2,2′-(Heptane-1,7-di­yl)dibenz­imidazo­lium chloride nitrate monohydrate

In the title compound, C21H26N4 2+·Cl−·NO3 −·H2O, the organic cations, anions and water mol­ecules are linked through N—H⋯Cl, N—H⋯O, N—H⋯N and O—H⋯Cl hydrogen bonds, forming a three-dimensional framework, assisted by C—H⋯π inter­actions

Sliding Window Based Feature Extraction and Traffic Clustering for Green Mobile Cyberphysical Systems

Both the densification of small base stations and the diversity of user activities bring huge challenges for today’s heterogeneous networks, either heavy burdens on base stations or serious energy waste. In order to ensure coverage of the network while reducing the total energy consumption, we adopt a green mobile cyberphysical system (MCPS) to handle this problem. In this paper, we propose a feature extractionmethod using sliding window to extract the distribution feature of mobile user equipment (UE), and a case study is presented to demonstrate that the method is efficacious in reserving the clustering distribution feature. Furthermore, we present traffic clustering analysis to categorize collected traffic distribution samples into a limited set of traffic patterns, where the patterns and corresponding optimized control strategies are used to similar traffic distributions for the rapid control of base station state. Experimental results show that the sliding window is more superior in enabling higher UE coverage over the grid method. Besides, the optimized control strategy obtained from the traffic pattern is capable of achieving a high coverage that can well serve over 98% of all mobile UE for similar traffic distributions

A NEW POLICY FOR THE SERVICE REQUEST ASSIGNMENT PROBLEM WITH MULTIPLE SEVERITY LEVEL, DUE DATE AND SLA PENALTY SERVICE REQUESTS

We study the problem of assigning multiple severity level service requests to agents in an agent pool. Each severity level is associated with a due date and a penalty, which is incurred if the service request is not resolved by the due date. Motivated by Van Meighem (2003), who shows the asymptotic optimality of the Generalized Longest Queue policy for the problem of minimizing the due date dependent expected delay costs when there is a single agent, we develop a class of Index-based policies that is a generalization of the Priority First-Come-First-Serve, Weighted Shortest Expected Processing Time and Generalized Longest Queue policy. In our simulation study of an assignment system of a large technology firm, the Index-based policy shows an improvement of 0-20 % over the Priority First-Come-First-Serve policy depending upon the load conditions.