Quantum anti-Zeno effect without wave function reduction

We study the measurement-induced enhancement of the spontaneous decay (called quantum anti-Zeno effect) for a two-level subsystem, where measurements are treated as couplings between the excited state and an auxiliary state rather than the von Neumann's wave function reduction. The photon radiated in a fast decay of the atom, from the auxiliary state to the excited state, triggers a quasi-measurement, as opposed to a projection measurement. Our use of the term "quasi-measurement" refers to a "coupling-based measurement". Such frequent quasi-measurements result in an exponential decay of the survival probability of atomic initial state with a photon emission following each quasi-measurement. Our calculations show that the effective decay rate is of the same form as the one based on projection measurements. What is more important, the survival probability of the atomic initial state which is obtained by tracing over all the photon states is equivalent to the survival probability of the atomic initial state with a photon emission following each quasi-measurement to the order under consideration. That is because the contributions from those states with photon number less than the number of quasi-measurements originate from higher-order processes.Comment: 7 pages, 3 figure

Increased collagen synthesis rate during wound healing in muscle

Wound healing in muscle involves the deposition of collagen, but it is not known whether this is achieved by changes in the synthesis or the degradation of collagen. We have used a reliable flooding dose method to measure collagen synthesis rate in vivo in rat abdominal muscle following a surgical incision. Collagen synthesis rate was increased by 480% and 860% on days 2 and 7 respectively after surgery in the wounded muscle compared with an undamaged area of the same muscle. Collagen content was increased by approximately 100% at both day 2 and day 7. These results demonstrate that collagen deposition during wound healing in muscle is achieved entirely by an increase in the rate of collagen synthesis

Fast fabrication of micropillar arrays using a combination of laser and FIB for micromechanical compression tests

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Large Scale Reasoning Using Allen's Interval Algebra

This paper proposes and evaluates a distributed, parallel approach for reasoning over large scale datasets using Allen's Interval Algebra (IA). We have developed and implemented algorithms that reason over IA networks using the Spark distributed processing framework. Experiments have been conducted by deploying the algorithms on computer clusters using synthetic datasets with various characteristics. We show that reasoning over datasets consisting of millions of interval relations is feasible and that our implementation scales effectively. The size of the IA networks we are able to reason over is far greater than those found in previously published works

Costs associated with febrile neutropenia in solid tumor and lymphoma patients - an observational study in Singapore.

BackgroundThe primary objective was to describe the total direct inpatient costs among solid tumor and lymphoma patients with chemotherapy-induced febrile neutropenia (FN) and the factors that were associated with higher direct cost. The secondary objective was to describe the out-of-pocket patient payments and the factors that were associated with higher out-of-pocket patient payments.MethodsThis was a single-center observational study conducted at the largest cancer center in Singapore. All of the adult cancer patients hospitalized due to FN from 2009 to 2012 were studied. The primary outcomes were the total hospital cost and the out-of-pocket patient payments (adjusted by government subsidy) per FN episode. Univariate analysis and multiple linear regression were conducted to identify the factors associated with higher FN costs.ResultsThree hundred and sixty seven adult cancer patients were documented with FN-related hospitalizations. The mean total hospital cost was US$4,193 (95$3,779-4,607) and the mean out-of-pocket patient payment was US$2,230 (95$1,976-2,484), per FN episode. The factors associated with a higher total hospital cost were longer length of stay, severe sepsis, and lymphoma as underlying cancer. The out-of-pocket patient payment was positively associated with longer length of stay, severe sepsis, lymphoma diagnosed as underlying cancer, the therapeutic use of granulocyte colony-stimulating factor (GCSF), the private ward class, and younger patients.ConclusionsThe total hospital cost and out-of-pocket patient payments of FN management in lymphoma cases were substantial compared with other solid tumors. Factors associated with a higher FN management cost may be useful for developing appropriate strategies to reduce the cost of FN for cancer patients

Machine learning-based classification of rock discontinuity trace: SMOTE oversampling integrated with GBT ensemble learning

This paper presents a hybrid ensemble classifier combined synthetic minority oversampling technique (SMOTE), random search (RS) hyper-parameters optimization algorithm and gradient boosting tree (GBT) to achieve efficient and accurate rock trace identification. A thirteen-dimensional database consisting of basic, vector, and discontinuity features is established from image samples. All data points are classified as either “trace” or “non-trace” to divide the ultimate results into candidate trace samples. It is found that the SMOTE technology can effectively improve classification performance by recommending an optimized imbalance ratio of 1:5 to 1:4. Then, sixteen classifiers generated from four basic machine learning (ML) models are applied for performance comparison. The results reveal that the proposed RS-SMOTE-GBT classifier outperforms the other fifteen hybrid ML algorithms for both trace and non-trace classifications. Finally, discussions on feature importance, generalization ability and classification error are conducted for the proposed classifier. The experimental results indicate that more critical features affecting the trace classification are primarily from the discontinuity features. Besides, cleaning up the sedimentary pumice and reducing the area of fractured rock contribute to improving the overall classification performance. The proposed method provides a new alternative approach for the identification of 3D rock trace

Movement variability in stroke patients and controls performing two upper limb functional tasks: a new assessment methodology

Background: In the evaluation of upper limb impairment post stroke there remains a gap between detailed kinematic analyses with expensive motion capturing systems and common clinical assessment tests. In particular, although many clinical tests evaluate the performance of functional tasks, metrics to characterise upper limb kinematics are generally not applicable to such tasks and very limited in scope. This paper reports on a novel, user-friendly methodology that allows for the assessment of both signal magnitude and timing variability in upper limb movement trajectories during functional task performance. In order to demonstrate the technique, we report on a study in which the variability in timing and signal magnitude of data collected during the performance of two functional tasks is compared between a group of subjects with stroke and a group of individually matched control subjects. Methods: We employ dynamic time warping for curve registration to quantify two aspects of movement variability: 1) variability of the timing of the accelerometer signals' characteristics and 2) variability of the signals' magnitude. Six stroke patients and six matched controls performed several trials of a unilateral ('drinking') and a bilateral ('moving a plate') functional task on two different days, approximately 1 month apart. Group differences for the two variability metrics were investigated on both days. Results: For 'drinking from a glass' significant group differences were obtained on both days for the timing variability of the acceleration signals' characteristics (p = 0.002 and p = 0.008 for test and retest, respectively); all stroke patients showed increased signal timing variability as compared to their corresponding control subject. 'Moving a plate' provided less distinct group differences. Conclusion: This initial application establishes that movement variability metrics, as determined by our methodology, appear different in stroke patients as compared to matched controls during unilateral task performance ('drinking'). Use of a user-friendly, inexpensive accelerometer makes this methodology feasible for routine clinical evaluations. We are encouraged to perform larger studies to further investigate the metrics' usefulness when quantifying levels of impairment

Selective scattering between Floquet-Bloch and Volkov states in a topological insulator

The coherent optical manipulation of solids is emerging as a promising way to engineer novel quantum states of matter. The strong time periodic potential of intense laser light can be used to generate hybrid photon-electron states. Interaction of light with Bloch states leads to Floquet-Bloch states which are essential in realizing new photo-induced quantum phases. Similarly, dressing of free electron states near the surface of a solid generates Volkov states which are used to study non-linear optics in atoms and semiconductors. The interaction of these two dynamic states with each other remains an open experimental problem. Here we use Time and Angle Resolved Photoemission Spectroscopy (Tr-ARPES) to selectively study the transition between these two states on the surface of the topological insulator Bi2Se3. We find that the coupling between the two strongly depends on the electron momentum, providing a route to enhance or inhibit it. Moreover, by controlling the light polarization we can negate Volkov states in order to generate pure Floquet-Bloch states. This work establishes a systematic path for the coherent manipulation of solids via light-matter interaction.Comment: 21 pages, 6 figures, final version to appear in Nature Physic

Genome-Wide Characterization of Menin-Dependent H3K4me3 Reveals a Specific Role for Menin in the Regulation of Genes Implicated in MEN1-Like Tumors

Inactivating mutations in the MEN1 gene predisposing to the multiple endocrine neoplasia type 1 (MEN1) syndrome can also cause sporadic pancreatic endocrine tumors. MEN1 encodes menin, a subunit of MLL1/MLL2-containing histone methyltransferase complexes that trimethylate histone H3 at lysine 4 (H3K4me3). The importance of menin-dependent H3K4me3 in normal and transformed pancreatic endocrine cells is unclear. To study the role of menin-dependent H3K4me3, we performed in vitro differentiation of wild-type as well as menin-null mouse embryonic stem cells (mESCs) into pancreatic islet-like endocrine cells (PILECs). Gene expression analysis and genome-wide H3K4me3 ChIP-Seq profiling in wild-type and menin-null mESCs and PILECs revealed menin-dependent H3K4me3 at the imprinted Dlk1-Meg3 locus in mESCs, and all four Hox loci in differentiated PILECs. Specific and significant loss of H3K4me3 and gene expression was observed for genes within the imprinted Dlk1-Meg3 locus in menin-null mESCs and the Hox loci in menin-null PILECs. Given that the reduced expression of genes within the DLK1-MEG3 locus and the HOX loci is associated with MEN1-like sporadic tumors, our data suggests a possible role for menin-dependent H3K4me3 at these genes in the initiation and progression of sporadic pancreatic endocrine tumors. Furthermore, our investigation also demonstrates that menin-null mESCs can be differentiated in vitro into islet-like endocrine cells, underscoring the utility of menin-null mESC-derived specialized cell types for genome-wide high-throughput studies

Microbial functional diversity covaries with permafrost thaw-induced environmental heterogeneity in tundra soil.

Permafrost soil in high latitude tundra is one of the largest terrestrial carbon (C) stocks and is highly sensitive to climate warming. Understanding microbial responses to warming-induced environmental changes is critical to evaluating their influences on soil biogeochemical cycles. In this study, a functional gene array (i.e., geochip 4.2) was used to analyze the functional capacities of soil microbial communities collected from a naturally degrading permafrost region in Central Alaska. Varied thaw history was reported to be the main driver of soil and plant differences across a gradient of minimally, moderately, and extensively thawed sites. Compared with the minimally thawed site, the number of detected functional gene probes across the 15-65 cm depth profile at the moderately and extensively thawed sites decreased by 25% and 5%, while the community functional gene β-diversity increased by 34% and 45%, respectively, revealing decreased functional gene richness but increased community heterogeneity along the thaw progression. Particularly, the moderately thawed site contained microbial communities with the highest abundances of many genes involved in prokaryotic C degradation, ammonification, and nitrification processes, but lower abundances of fungal C decomposition and anaerobic-related genes. Significant correlations were observed between functional gene abundance and vascular plant primary productivity, suggesting that plant growth and species composition could be co-evolving traits together with microbial community composition. Altogether, this study reveals the complex responses of microbial functional potentials to thaw-related soil and plant changes and provides information on potential microbially mediated biogeochemical cycles in tundra ecosystems