PURPOSE: To determine if pre-season measures of trunk control differ between athletes who sustain a low back or lower extremity injury over the season versus athletes who do not. METHODS: Prior to ...the start of the season 41 male NCAA Division I lacrosse athletes (20+ or -1.5 years, 1.81+ or -0.05 m, and 83.8+ or -7.4 kg) performed trunk extension endurance and left and right side bridge endurance for time. Trunk extension to side bridge symmetry ratios were expressed as the percentage of the left or right bridge over extension. RESULTS: 17 players sustained a lower extremity or low back injury over the course of one season. Side bridge ratio was significantly different between those that experienced a low back injury and those that did not (injured= 0.74 non-injured = 0.90 p=0.004), but the low incidence rate (low back injury =3) limits this finding.
Mass spectrometry is the tool of choice for sequencing peptides and determining the sites of posttranslational modifications; however, this bottom-up approach lacks in providing global information ...about the modification states of proteins including the number and types of isoforms and their stoichiometry. Recently, various techniques and mass spectrometers, such as high-field Fourier Transform Ion Cyclotron Resonance (FTICR) mass spectrometers, have been developed to study intact proteins (top-down proteomics). While the protein molecular mass and the qualitative and quantitative information about protein isoforms can be revealed by FTICR-MS analysis, their primary structure (including the identification of modifications and their exact locations in the amino acid sequence) can directly be determined using the MS/MS capability offered by the FTICR mass spectrometer. The distinct advantage of top-down methods are that modifications can be determined for a specific protein isoform rather than for peptides belonging to one or several isoforms. In this chapter, we describe different top-down proteomic approaches enabled by high-field (7, 9.4, and 12 T) FTICR mass spectrometers, and their applicability to answer biological and biomedical questions. We also describe the use of the free flow electrophoresis (FFE) to separate proteins prior to top-down mass spectrometric characterization.
Matrilysin is a matrix metalloprotease that is overexpressed in cancer cells of epithelial origin and in normal tissues during events involving matrix remodeling such as the cycling endometrium. We ...previously observed that inflamed ductule and acinar epithelia in the prostate also overexpress matrilysin. The presence of infiltrating macrophages in these areas prompted us to determine if factors secreted from monocytes could induce matrilysin expression in a human prostatic cell line. Conditioned media collected from the monocyte cell line THP-1 following lipopolysaccharide treatment substantially induced matrilysin protein and mRNA expression in LNCaP prostate carcinoma cells. Matrilysin expression in LNCaP cells was also induced by recombinant interleukin (IL)-1 (50 pm), but not by equimolar concentrations of recombinant tumor necrosis factor-α or IL-6. The matrilysin-inducing activity of THP-1 conditioned medium was completely abrogated by preincubation with a neutralizing antibody to IL-1β. Transient transfection analyses with a chimeric human matrilysin promoter-chloramphenicol acetyltransferase reporter construct demonstrated that IL-1β activates transcription through the matrilysin promoter in LNCaP cells. This is the first report of matrilysin induction by an inflammatory cytokine in a cell line of epithelial origin, and the results suggest a potential mechanism for the overexpression of matrilysin in inflamed ducts and glands of the prostate.
Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU) --- ...deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X -- 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X -- 80X higher. Moreover, using the CPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.
Matrilysin is a matrix metalloprotease that is overexpressed in cancer cells of epithelial origin and in normal tissues during
events involving matrix remodeling such as the cycling endometrium. We ...previously observed that inflamed ductule and acinar
epithelia in the prostate also overexpress matrilysin. The presence of infiltrating macrophages in these areas prompted us
to determine if factors secreted from monocytes could induce matrilysin expression in a human prostatic cell line. Conditioned
media collected from the monocyte cell line THP-1 following lipopolysaccharide treatment substantially induced matrilysin
protein and mRNA expression in LNCaP prostate carcinoma cells. Matrilysin expression in LNCaP cells was also induced by recombinant
interleukin (IL)-1 (50 p m ), but not by equimolar concentrations of recombinant tumor necrosis factor-α or IL-6. The matrilysin-inducing activity of
THP-1 conditioned medium was completely abrogated by preincubation with a neutralizing antibody to IL-1β. Transient transfection
analyses with a chimeric human matrilysin promoter-chloramphenicol acetyltransferase reporter construct demonstrated that
IL-1β activates transcription through the matrilysin promoter in LNCaP cells. This is the first report of matrilysin induction
by an inflammatory cytokine in a cell line of epithelial origin, and the results suggest a potential mechanism for the overexpression
of matrilysin in inflamed ducts and glands of the prostate.
Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit ...(TPU)---deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN). The heart of the TPU is a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS) and a large (28 MiB) software-managed on-chip memory. The TPU's deterministic execution model is a better match to the 99th-percentile response-time requirement of our NN applications than are the time-varying optimizations of CPUs and GPUs (caches, out-of-order execution, multithreading, multiprocessing, prefetching, ...) that help average throughput more than guaranteed latency. The lack of such features helps explain why, despite having myriad MACs and a big memory, the TPU is relatively small and low power. We compare the TPU to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the same datacenters. Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand. Despite low utilization for some applications, the TPU is on average about 15X - 30X faster than its contemporary GPU or CPU, with TOPS/Watt about 30X - 80X higher. Moreover, using the GPU's GDDR5 memory in the TPU would triple achieved TOPS and raise TOPS/Watt to nearly 70X the GPU and 200X the CPU.
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