A new method proposed to explore the feline's paw bones of contributing most to landing pattern recognition when landed under different constraints

Felines are generally acknowledged to have natural athletic ability, especially in jumping and landing. The adage “felines have nine lives” seems applicable when we consider its ability to land safely from heights. Traditional post-processing of finite element analysis (FEA) is usually based on stress distribution trend and maximum stress values, which is often related to the smoothness and morphological characteristics of the finite element model and cannot be used to comprehensively and deeply explore the mechanical mechanism of the bone. Machine learning methods that focus on feature pattern variable analysis have been gradually applied in the field of biomechanics. Therefore, this study investigated the cat forelimb biomechanical characteristics when landing from different heights using FEA and feature engineering techniques for post-processing of FEA. The results suggested that the stress distribution feature of the second, fourth metacarpal, the second, third proximal phalanx are the features that contribute most to landing pattern recognition when cats landed under different constraints. With increments in landing altitude, the variations in landing pattern differences may be a response of the cat's forelimb by adjusting the musculoskeletal structure to reduce the risk of injury with a more optimal landing strategy. The combination of feature engineering techniques can effectively identify the bone's features that contribute most to pattern recognition under different constraints, which is conducive to the grasp of the optimal feature that can reveal intrinsic properties in the field of biomechanics

1.28 and 5.12 Gbps multi-channel twinax cable receiver ASICs for the ATLAS Inner Tracker Pixel Detector Upgrade

We present two prototypes of a gigabit transceiver ASIC, GBCR1 and GBCR2, both designed in a 65-nm CMOS technology for the ATLAS Inner Tracker Pixel Detector readout upgrade. The first prototype, GBCR1, has four upstream receiver channels and one downstream transmitter channel with pre-emphasis. Each upstream channel receives the data at 5.12 Gbps through a 5 meter AWG34 Twinax cable from an ASIC driver located on the pixel module and restores the signal from the high frequency loss due to the low mass cable. The signal is retimed by a recovered clock before it is sent to the optical transmitter VTRx+. The downstream driver is designed to transmit the 2.56 Gbps signal from lpGBT to the electronics on the pixel module over the same cable. The peak-peak jitter (throughout the paper jitter is always peak-peak unless specified) of the restored signal is 35.4 ps at the output of GBCR1, and 138 ps for the downstream channel at the cable ends. GBCR1 consumes 318 mW and is tested. The second prototype, GBCR2, has seven upstream channels and two downstream channels. Each upstream channel works at 1.28 Gbps to recover the data directly from the RD53B ASIC through a 1 meter custom FLEX cable followed by a 6 meter AWG34 Twinax cable. The equalized signal of each upstream channel is retimed by an input 1.28 GHz phase programmable clock. Compared with the signal at the FLEX input, the additional jitter of the equalized signal is about 80 ps when the retiming logic is o . When the retiming logic is on, the jitter is 50 ps at GBCR2 output, assuming the 1.28 GHz retiming clock is from lpGBT. The downstream is designed to transmit the 160 Mbps signal from lpGBT through the same cable connection to RD53B and the jitter is about 157 ps at the cable ends. GBCR2 consumes about 150 mW when the retiming logic is on. This design was submitted in November 2019.Comment: 7 pages, 15 figure

Exosomes Derived from Dendritic Cells Treated with Schistosoma japonicum Soluble Egg Antigen Attenuate DSS-Induced Colitis

Exosomes are 30–150 nm small membrane vesicles that are released into the extracellular medium via cells that function as a mode of intercellular communication. Dendritic cell (DC)-derived exosomes modulate immune responses and prevent the development of autoimmune diseases. Moreover, Schistosoma japonicum eggs show modulatory effects in a mouse model of colitis. Therefore, we hypothesized that exosomes derived from DCs treated with S. japonicum soluble eggs antigen (SEA; SEA-treated DC exosomes) would be useful for treating inflammatory bowel disease (IBD). Exosomes were purified from the supernatant of DCs treated or untreated with SEA and identified via transmission electron microscopy, western blotting and NanoSight. Acute colitis was induced via the administration of dextran sulfate sodium (DSS) in drinking water (5.0%, wt/vol). Treatment with exosomes was conducted via intraperitoneal injection (i.p.; 50 μg per mouse) from day 0 to day 6. Clinical scores were calculated based on weight loss, stool type, and bleeding. Colon length was measured as an indirect marker of inflammation, and colon macroscopic characteristics were determined. Body weight loss and the disease activity index of DSS-induced colitis mice decreased significantly following treatment with SEA-treated DC exosomes. Moreover, the colon lengths of SEA-treated DC exosomes treated colitis mice improved, and their mean colon macroscopic scores decreased. In addition, histologic examinations and histological scores showed that SEA-treated DC exosomes prevented colon damage in acute DSS-induced colitis mice. These results indicate that SEA-treated DC exosomes attenuate the severity of acute DSS-induced colitis mice more effectively than DC exosomes. The current work suggests that SEA-treated DC exosomes may be useful as a new approach to treat IBD

Two low-power optical data transmission ASICs for the ATLAS Liquid Argon Calorimeter readout upgrade

A serializer ASIC and a VCSEL driver ASIC are needed for the front-end optical data transmission in the ATLAS liquid argon calorimeter readout phase-I upgrade. The baseline ASICs are the serializer LOCx2 and the VCSEL driver LOCld, designed in a 0.25-{\mu}m Silicon-on-Sapphire (SoS) CMOS technology and consumed 843 mW and 320 mW, respectively. Based on a 130-nm CMOS technology, we design two pin-to-pin-compatible backup ASICs, LOCx2-130 and LOCld-130. Their power consumptions are much lower then of their counterparts, whereas other performance, such as the latency, data rate, and radiation tolerance, meet the phase-I upgrade requirements. We present the design of LOCx2-130 and LOCld-130. The test results of LOCx2-130 are also presented.Comment: 12 pages, 12 figure

A Reference Proteomic Database of Lactobacillus plantarum CMCC-P0002

Lactobacillus plantarum is a widespread probiotic bacteria found in many fermented food products. In this study, the whole-cell proteins and secretory proteins of L. plantarum were separated by two-dimensional electrophoresis method. A total of 434 proteins were identified by tandem mass spectrometry, including a plasmid-encoded hypothetical protein pLP9000_05. The information of first 20 highest abundance proteins was listed for the further genetic manipulation of L. plantarum, such as construction of high-level expressions system. Furthermore, the first interaction map of L. plantarum was established by Blue-Native/SDS-PAGE technique. A heterodimeric complex composed of maltose phosphorylase Map3 and Map2, and two homodimeric complexes composed of Map3 and Map2 respectively, were identified at the same time, indicating the important roles of these proteins. These findings provided valuable information for the further proteomic researches of L. plantarum

A Revenue Maximization Approach for Provisioning Services in Clouds

With the increased reliability, security, and reduced cost of cloud services, more and more users are attracted to having their jobs and applications outsourced into IAAS data centers. For a cloud provider, deciding how to provision services to clients is far from trivial. The objective of this decision is maximizing the provider’s revenue, while fulfilling its IAAS resource constraints. The above problem is defined as IAAS cloud provider revenue maximization (ICPRM) problem in this paper. We formulate a service provision approach to help a cloud provider to determine which combination of clients to admit and in what Quality-of-Service (QoS) levels and to maximize provider’s revenue given its available resources. We show that the overall problem is a nondeterministic polynomial- (NP-) hard one and develop metaheuristic solutions based on the genetic algorithm to achieve revenue maximization. The experimental simulations and numerical results show that the proposed approach is both effective and efficient in solving ICPRM problems

A Cross-Entropy-Based Admission Control Optimization Approach for Heterogeneous Virtual Machine Placement in Public Clouds

Virtualization technologies make it possible for cloud providers to consolidate multiple IaaS provisions into a single server in the form of virtual machines (VMs). Additionally, in order to fulfill the divergent service requirements from multiple users, a cloud provider needs to offer several types of VM instances, which are associated with varying configurations and performance, as well as different prices. In such a heterogeneous virtual machine placement process, one significant problem faced by a cloud provider is how to optimally accept and place multiple VM service requests into its cloud data centers to achieve revenue maximization. To address this issue, in this paper, we first formulate such a revenue maximization problem during VM admission control as a multiple-dimensional knapsack problem, which is known to be NP-hard to solve. Then, we propose to use a cross-entropy-based optimization approach to address this revenue maximization problem, by obtaining a near-optimal eligible set for the provider to accept into its data centers, from the waiting VM service requests in the system. Finally, through extensive experiments and measurements in a simulated environment with the settings of VM instance classes derived from real-world cloud systems, we show that our proposed cross-entropy-based admission control optimization algorithm is efficient and effective in maximizing cloud providers’ revenue in a public cloud computing environment