Q-Switched 2 Micron Solid-State Lasers and Their Applications

In this chapter, we overview the Q-switched 2 μm solid-state laser development achieved in recent years, including flash- and diode-pumped solid-state lasers based on active and passive modulators. In summary, active Q-switching is still the first choice for obtaining large pulse energy at 2 μm currently, while passive Q-switching based on saturable absorbers (SAs), especially the newly emerging broadband low-dimension nanomaterial, is becoming promising approach in generating Q-switched 2 μm lasers specially with high repetition rate, although the output power, pulse duration, and pulse energy needs further enhancement. Besides, some important applications of 2 μm lasers, such as medicine, laser radar, and infrared directional interference, have also been introduced in brief

Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion

Point clouds scanned by real-world sensors are always incomplete, irregular, and noisy, making the point cloud completion task become increasingly more important. Though many point cloud completion methods have been proposed, most of them require a large number of paired complete-incomplete point clouds for training, which is labor exhausted. In contrast, this paper proposes a novel Reconstruction-Aware Prior Distillation semi-supervised point cloud completion method named RaPD, which takes advantage of a two-stage training scheme to reduce the dependence on a large-scale paired dataset. In training stage 1, the so-called deep semantic prior is learned from both unpaired complete and unpaired incomplete point clouds using a reconstruction-aware pretraining process. While in training stage 2, we introduce a semi-supervised prior distillation process, where an encoder-decoder-based completion network is trained by distilling the prior into the network utilizing only a small number of paired training samples. A self-supervised completion module is further introduced, excavating the value of a large number of unpaired incomplete point clouds, leading to an increase in the network's performance. Extensive experiments on several widely used datasets demonstrate that RaPD, the first semi-supervised point cloud completion method, achieves superior performance to previous methods on both homologous and heterologous scenarios

Object Level Depth Reconstruction for Category Level 6D Object Pose Estimation From Monocular RGB Image

Recently, RGBD-based category-level 6D object pose estimation has achieved promising improvement in performance, however, the requirement of depth information prohibits broader applications. In order to relieve this problem, this paper proposes a novel approach named Object Level Depth reconstruction Network (OLD-Net) taking only RGB images as input for category-level 6D object pose estimation. We propose to directly predict object-level depth from a monocular RGB image by deforming the category-level shape prior into object-level depth and the canonical NOCS representation. Two novel modules named Normalized Global Position Hints (NGPH) and Shape-aware Decoupled Depth Reconstruction (SDDR) module are introduced to learn high fidelity object-level depth and delicate shape representations. At last, the 6D object pose is solved by aligning the predicted canonical representation with the back-projected object-level depth. Extensive experiments on the challenging CAMERA25 and REAL275 datasets indicate that our model, though simple, achieves state-of-the-art performance.Comment: 19 pages, 7 figures, 4 table

Identifying Unexpected Therapeutic Targets via Chemical-Protein Interactome

Drug medications inevitably affect not only their intended protein targets but also other proteins as well. In this study we examined the hypothesis that drugs that share the same therapeutic effect also share a common therapeutic mechanism by targeting not only known drug targets, but also by interacting unexpectedly on the same cryptic targets. By constructing and mining an Alzheimer's disease (AD) drug-oriented chemical-protein interactome (CPI) using a matrix of 10 drug molecules known to treat AD towards 401 human protein pockets, we found that such cryptic targets exist. We recovered from CPI the only validated therapeutic target of AD, acetylcholinesterase (ACHE), and highlighted several other putative targets. For example, we discovered that estrogen receptor (ER) and histone deacetylase (HDAC), which have recently been identified as two new therapeutic targets of AD, might already have been targeted by the marketed AD drugs. We further established that the CPI profile of a drug can reflect its interacting character towards multi-protein sets, and that drugs with the same therapeutic attribute will share a similar interacting profile. These findings indicate that the CPI could represent the landscape of chemical-protein interactions and uncover “behind-the-scenes” aspects of the therapeutic mechanisms of existing drugs, providing testable hypotheses of the key nodes for network pharmacology or brand new drug targets for one-target pharmacology paradigm

Distribution, degradation and dynamics of dissolved organic carbon and its major compound classes in the Pearl River estuary, China

We investigated the distribution, degradation and dynamics of organic carbon and its major compound classes, carbohydrates and amino acids, based upon a cruise in the Pearl River estuary in April 2007. Dissolved oxygen (DO), nutrients, particulate organic carbon (POC), chlorophyll a (Chl a), dissolved organic carbon (DOC), total dissolved carbohydrates (TCHO, including monosaccharides, MCHO, and polysaccharides, PCHO) as well as total dissolved amino acids (TAA, both dissolved free, DFAA, and combined components. DCAA) were measured along a salinity gradient. Community respiration and biodegradable DOC were also determined via both short term (within 3 days) and long term (lasting 30 days) incubation. DOC, MCHO, TCHO, DFAA and TAA concentrations were high in the upper reach of the Pearl River estuary and decreased rapidly downstream. Anthropogenic sewage input appeared to be an important source of the DOC pool in the upper estuary. DOC distribution was non-conservative during the estuarine mixing, showing a net consumption of DOC in the upper reach and in the low salinity (S<20) region of the Pearl River estuary. Changes in the relative compositions of carbohydrates (MCHO vs. PCHO) and amino acids (DFAA vs. DCAA) along the salinity gradient further indicated that different processes (biodegradation, flocculation, and phytoplankton production) had different influences on distributions of organic compound classes in this estuarine system. Our one-month incubation experiment further revealed that a substantial portion (15-45%) of DOC from the estuary was biodegradable. Bacterial respiration rates were much higher (0.12-5.8 mu mol O-2 L-1 h(-1)) than the DOC consumption rates, suggesting that there were other oxygen consumption processes, such as nitrification besides the aerobic respiration of organic matter in the Pearl River estuary, as inferred by the distribution of NH4+ and NO3-. We estimated that 5.3 x 10(8) g C d(-1) of DOC can be exported out from the Lingdingyang Bay (a major subestuary of the Pearl River estuary) to the continental shelf of the South China Sea during this low flow season. (C) 2009 Elsevier B.V. All rights reserved.Natural Science Foundation of China [40576036, 90711005, 40821063

DRAR-CPI: a server for identifying drug repositioning potential and adverse drug reactions via the chemical–protein interactome

Identifying new indications for existing drugs (drug repositioning) is an efficient way of maximizing their potential. Adverse drug reaction (ADR) is one of the leading causes of death among hospitalized patients. As both new indications and ADRs are caused by unexpected chemical–protein interactions on off-targets, it is reasonable to predict these interactions by mining the chemical–protein interactome (CPI). Making such predictions has recently been facilitated by a web server named DRAR-CPI. This server has a representative collection of drug molecules and targetable human proteins built up from our work in drug repositioning and ADR. When a user submits a molecule, the server will give the positive or negative association scores between the user’s molecule and our library drugs based on their interaction profiles towards the targets. Users can thus predict the indications or ADRs of their molecule based on the association scores towards our library drugs. We have matched our predictions of drug–drug associations with those predicted via gene-expression profiles, achieving a matching rate as high as 74%. We have also successfully predicted the connections between anti-psychotics and anti-infectives, indicating the underlying relevance of anti-psychotics in the potential treatment of infections, vice versa. This server is freely available at http://cpi.bio-x.cn/drar/

Strengthening mechanisms in thermomechanically processed NbTi-microalloyed steel

The effect of deformation temperature on microstructure and mechanical properties was investigated for thermomechanically processed NbTi-microalloyed steel with ferrite-pearlite microstructure. With a decrease in the finish deformation temperature at 1348 K to 1098 K (1075 °C to 825 °C) temperature range, the ambient temperature yield stress did not vary significantly, work hardening rate decreased, ultimate tensile strength decreased, and elongation to failure increased. These variations in mechanical properties were correlated to the variations in microstructural parameters (such as ferrite grain size, solid solution concentrations, precipitate number density and dislocation density). Calculations based on the measured microstructural parameters suggested the grain refinement, solid solution strengthening, precipitation strengthening, and work hardening contributed up to 32 pct, up to 48 pct, up to 25 pct, and less than 3 pct to the yield stress, respectively. With a decrease in the finish deformation temperature, both the grain size strengthening and solid solution strengthening increased, the precipitation strengthening decreased, and the work hardening contribution did not vary significantly