CMB Spectral Distortion Constraints on Thermal Inflation

Thermal inflation is a second epoch of exponential expansion at typical energy scales $V^{1/4} \sim 10^{6 \sim 8} \mathrm{GeV}$. If the usual primordial inflation is followed by thermal inflation, the primordial power spectrum is only modestly redshifted on large scales, but strongly suppressed on scales smaller than the horizon size at the beginning of thermal inflation, $k > k_{\rm b} = a_{\rm b} H_{\rm b}$. We calculate the spectral distortion of the cosmic microwave background generated by the dissipation of acoustic waves in this context. For $k_{\rm b} \ll 10^3 \mathrm{Mpc}^{-1}$, thermal inflation results in a large suppression of the $\mu$-distortion amplitude, predicting that it falls well below the standard value of $\mu \simeq 2\times 10^{-8}$. Thus, future spectral distortion experiments, similar to PIXIE, can place new limits on the thermal inflation scenario, constraining $k_{\rm b} \gtrsim 10^3 \mathrm{Mpc}^{-1}$ if $\mu \simeq 2\times 10^{-8}$ were found.Comment: 18 pages, 7 figure

Redox Regulation Facilitates Optimal Peptide Selection by MHC Class I during Antigen Processing

SummaryActivated CD8+ T cells discriminate infected and tumor cells from normal self by recognizing MHC class I-bound peptides on the surface of antigen-presenting cells. The mechanism by which MHC class I molecules select optimal peptides against a background of prevailing suboptimal peptides and in a considerably proteolytic ER environment remained unknown. Here, we identify protein disulfide isomerase (PDI), an enzyme critical to the formation of correct disulfide bonds in proteins, as a component of the peptide-loading complex. We show that PDI stabilizes a peptide-receptive site by regulating the oxidation state of the disulfide bond in the MHC peptide-binding groove, a function that is essential for selecting optimal peptides. Furthermore, we demonstrate that human cytomegalovirus US3 protein inhibits CD8+ T cell recognition by mediating PDI degradation, verifying the functional relevance of PDI-catalyzed peptide editing in controlling intracellular pathogens. These results establish a link between thiol-based redox regulation and antigen processing

Facile fabrication of two-dimensional inorganic nanostructures and their conjugation to nanocrystals

Nanocomposites of two-dimensional (2D) inorganic nanosheets and inorganic nanocrystals are fabricated. Freestanding atomically flat gamma-AlOOH nanosheets (thickness <1 nm) are synthesized from a one-pot hydrothermal reaction. The freestanding and binder-free film composed of the gamma-AlOOH nanosheets is fabricated by sedimentation. Because they have positive zeta potentials in the pH range below ca. 9.3, the gamma-AlOOH nanosheets can function as positively charged 2D inorganic matrices in a broad pH range. By solution phase (pH 7.0) mixing of the gamma-AlOOH nanosheets (zeta potential: 30.7 +/- 0.8 mV) and inorganic nanocrystals with negative surface charge, including Au nanoparticles, Au nanorods, CdSe quantum dots, CdSe/CdS/ZnS quantum dots and CdSe nanorods, the nanocomposites are self-assembled via electrostatic interactions. Negatively charged inorganic nanostructures with a wide range of chemical compositions, shapes, sizes, surface ligands and adsorbates can be used as building blocks for gamma-AlOOH nanocomposites. Adsorption densities of inorganic nanocrystals on the nanocomposites can be controlled by varying concentrations of nanocrystal solutions. Nanocomposite films containing alternating layers of gamma-AlOOH and nanocrystals are obtained by a simple drop casting method.close3

Influence of Estimated Glomerular Filtration Rate on Clinical Outcomes in Patients with Acute Ischemic Stroke Not Receiving Reperfusion Therapies

Background: We aimed to determine whether estimated glomerular filtration rate (eGFR) is an independent predictor of clinical outcomes in patients with acute ischemic stroke not treated with reperfusion therapy. Methods: A total of 1420 patients with acute ischemic stroke from a hospital-based stroke registry were included in this study. Patients managed with intravenous thrombolysis or endovascular reperfusion therapy were excluded. The included patients were categorized into five groups according to eGFR, as follows: ≥90, 60–89, 45–59, 30–44, and <30 mL/min/1.73 m2. The effects of eGFR on functional outcome at discharge, in-hospital mortality, neurologic deterioration, and hemorrhagic transformation were evaluated using logistic regression analyses. Results: In univariable logistic regression analysis, reduced eGFR was associated with poor functional outcome at discharge (p < 0.001) and in-hospital mortality (p = 0.001), but not with neurologic deterioration and hemorrhagic transformation. However, no significant associations were observed between eGFR and any clinical outcomes in multivariable analysis after adjusting for clinical and laboratory variables. Conclusions: Reduced eGFR was associated with poor functional outcomes at discharge and in-hospital mortality but was not an independent predictor of short-term clinical outcomes in patients with acute ischemic stroke who did not undergo reperfusion therapy

Statistical Modeling of Sea Ice Concentration Using Satellite Imagery and Climate Reanalysis Data in the Barents and Kara Seas, 1979–2012

Extensive sea ice over Arctic regions is largely involved in heat, moisture, and momentum exchanges between the atmosphere and ocean. Some previous studies have been conducted to develop statistical models for the status of Arctic sea ice and showed considerable possibilities to explain the impacts of climate changes on the sea ice extent. However, the statistical models require improvements to achieve better predictions by incorporating techniques that can deal with temporal variation of the relationships between sea ice concentration and climate factors. In this paper, we describe the statistical approaches by ordinary least squares (OLS) regression and a time-series method for modeling sea ice concentration using satellite imagery and climate reanalysis data for the Barents and Kara Seas during 1979–2012. The OLS regression model could summarize the overall climatological characteristics in the relationships between sea ice concentration and climate variables. We also introduced autoregressive integrated moving average (ARIMA) models because the sea ice concentration is such a long-range dataset that the relationships may not be explained by a single equation of the OLS regression. Temporally varying relationships between sea ice concentration and the climate factors such as skin temperature, sea surface temperature, total column liquid water, total column water vapor, instantaneous moisture flux, and low cloud cover were modeled by the ARIMA method, which considerably improved the prediction accuracies. Our method may also be worth consideration when forecasting future sea ice concentration by using the climate data provided by general circulation models (GCM)

Microsoft Word - etrij.oct2011.0661

In this paper, we propose an efficient soft-output signal detection method for spatially multiplexed multiple-input multiple-output (MIMO) systems. The proposed method is based on the ordered successive interference cancellation (OSIC) algorithm, but it significantly improves the performance of the original OSIC algorithm by solving the error propagation problem. The proposed method combines this enhanced OSIC algorithm with a multiple-channel-ordering technique in a very efficient way. As a result, the log likelihood ratio values can be computed by using a very small set of candidate symbol vectors. The proposed method has been synthesized with a 0.13-μm CMOS technology for a 4×4 16-QAM MIMO system. The simulation and implementation results show that the proposed detector provides a very good solution in terms of performance and hardware complexity. Keywords: MIMO, OSIC, K-best, QRD-M, QRM-MLD. Manuscript received Oct. 1, 2010; revised Mar. 9, 2011; accepted Mar. 23, 2011. Tae Ho Im (phone: +82 10 2971 4008, email: [email protected]), Insoo Park (email: [email protected]), Hyun Jong Yoo (email: [email protected]), Sungwook Yu (email: [email protected]), and Yong Soo Cho (corresponding author, email: [email protected]) are with the School of Electrical and Electronic Engineering, Chung-Ang University, Seoul, Rep. of Korea. http://dx.doi.org/10.4218/etrij.11.0110.0574 I. Introduction Multiple-input multiple-output (MIMO) communication systems have received tremendous attention because of their high spectral efficiency and near-capacity performance. As a result, MIMO has become a key component in several wireless communication standards, including LTE-Advanced and IEEE 802.16m Multiple antennas can be used to improve the reception reliability by sending the same data (spatial diversity) or to increase data rates by sending different data (spatial multiplexing) As a result, most recent works have focused on the detection methods that are based on tree searches, which achieve nearoptimal performance but involve significantly less complexity than the original ML method The rest of this paper is organized as follows. In section II, the proposed method is explained and compared with the Kbest method. Sections III and IV compare the proposed method with the K-best method in terms of performance and hardware complexity. Finally, the conclusions are given in section V. II. Algorithm In a MIMO system with N T transmit antennas and N R receiver antennas, the transmitted signal and the received signal are related as where r is the received symbol vector, x is the transmitted symbol vector, and z is an independent and identically distributed (i.i.d.) complex zero-mean Gaussian noise. The element h ij of the N R ×N T matrix H represents the complex transfer function from the j-th transmit antenna to the i-th receive antenna, and all h ij 's are i.i.d. complex zero-mean Gaussian with a variance of 0.5 per dimension. For spatial multiplexing, the entries of x are chosen independently from a set Ω of complex-valued constellation points with B bits per symbol (that is, B=log 2 |Ω|). In this paper, we assume that perfect synchronization and perfect channel estimation are achieved at the receiver side. Thus, it is assumed that temporal signal interference does not exist. The column ordering of the matrix H is important, and there have been several methods to obtain the optimal (column) ordering By applying the QR-decomposition (QRD) on the matrix H, we obtain H=QR, where Q is an N R ×N T unitary matrix and R is an N T ×N T upper triangular matrix. Then, multiplying both [ ] ( ) ( ) where H Q z has the same statistical characteristics as the original noise vector z. Since the upper triangular matrix R is more tractable than the original channel matrix H, many MIMO detection methods use (2) instead of (1) Each H i is QR-decomposed, and y i is obtained as can be seen from lines 3 and 4 in As can be expected, the ESIC algorithm requires more computation and hardware. It, however, serves as an efficient solution to the error propagation problem since the candidate vector which is severely affected by the error propagation problem is not likely to have the minimum SED. The performance improvement far outweighs the hardware overhead, as can be seen in sections III and IV. More importantly, this ESIC algorithm can be efficiently combined with a multiple-channel-ordering technique for soft-output MIMO systems, as will be explained shortly in this section. For a soft decoding system, the likelihood ratio (LLR) computation (or estimation) is required for each bit of the decoded symbol vectors in addition to the SED computation where b ij represents the j-th bit in the i-th symbol. Since the direct computation of ( ) where the sets ( 1) ij (1) ij X include all the symbol vectors whose j-th bit in the i-th symbol are -1 and +1, respectively. This approximation greatly reduces complexity at a cost of slight performance degradation ( 1) ij − X and (1) ij X satisfy ( 1) (1) ij ij , ( 1) . ij ij − ∪ = S S S For example, the K-best method usually uses the survivor candidates in the last stage as the subset S. Although this reduces the complexity very much, it has a problem in that (1) ij S may be empty for some i and j. For example, it is possible that b 11 in every symbol vector in S happens to be −1, which makes it impossible to compute the second term in (5). Although there have been several attempts to overcome this kind of problem, these solutions use extra hardware to estimate the LLR values or use some constant values, which causes performance degradation [12], On the other hand, the proposed method makes use of the multiple channel matrices to solve this problem. The outer forloop in , , , ( 1, 1), ( 1,1), (1, 1), (1,1) . As can be seen in the figure, there are 4 (=N T ) groups, where each group has 4 (=|Ω|) candidate vectors. Thus, there are 16 (that is, N T ×|Ω|) candidate vectors. For each candidate vector, the SED value is calculated, and then used for the LLR update in (5). The LLR update function in ( 1) ij − S and (1) ij S are non-empty for all i and j. In other words, by combining the ESIC algorithm and multiple orderings in an efficient way, the MESIC method can generate the LLR values for all the bits without using any LLR estimation methods. It is possible to solve the empty set problem by adding some candidate vectors such as {1, 1,…, 1} and {-1, -1,…, -1}. However, this does not guarantee a good decoding performance since it is very likely that the SED value by an arbitrary vector is very high. On the other hand, the candidate vectors in the proposed method are obtained by the ESIC method as can be seen from It is very important to note that, in the proposed MESIC method, the orders of the multiple channel matrices can be fixed without knowing the channel condition. On the other hand, the OSIC and the K-best methods require the channel information to obtain the optimal ordering for the single channel matrix, which requires additional computation or hardware H h h h h H h h h h H h h h h H h h h h Although the three graphs are not identical, it can be seen that the frame error rate (FER) difference is very small. This is mainly because there is no empty set problem in the optimal ordering case or the fixed/worst ordering cases. The situation is similar for a soft decoding system as can be seen in III. Performance Comparison Although the MESIC method shows better performance than the ESIC method, the difference (in the hard decoding case) is too small to justify the computational overhead incurred by the MESIC method. Figures 6 and 7 show the FER comparison for the MIMO system in As explained in the previous section, this is mainly because the LLR values for all the bits are obtained efficiently in the proposed MESIC method. Although the ML search (max-log) method shows the best performance, it uses (4) instead of It should be emphasized that the MESIC method does not require more candidate vectors than the K-best method in order to achieve the performance improvement. On the contrary, the MESIC method generally requires a smaller number of candidate vectors. As can be seen from section II, the number of candidate vectors in the K-best method is K×|Ω|, whereas the number of candidate vectors in the proposed method is N T ×|Ω|. In most cases, the value for K is the same as (or just a little bit smaller than) |Ω|, whereas N T is usually much smaller than |Ω|. As can be seen in the figures, the MESIC method shows good performance (especially in the soft-decoding MIMO systems) regardless of the constellation sizes. Although the K-best method shows slightly better performance in the hard decoding case, it requires a much larger number of candidate vectors than the MESIC method (especially in the 64-QAM system) as can be seen from The multiple-QRD unit is basically composed of 4 QRD units. Update Q j Inner product Column norm Division by norm The ESIC unit performs the enhanced SIC operation, as described from line 6 to line 10 in The proposed detectors have been designed and synthesized with a 0.13-μm CMOS technology. Unfortunately, the proposed methods are not based on the Kbest method, and there are no K values for the MESIC and ESIC methods. For a more direct comparison, we implemented not only the proposed ESIC and MESIC algorithms, but also the K-best and the OSIC algorithms. The last 4 columns of As might be expected, the ESIC method occupies more area than the OSIC method as it requires multiple (that is, 4) OSIC blocks. The area of the ESIC method, however, is only 169% (not 400%) because the other blocks, including the QRD block, can be shared. On the other hand, the performance improvement is significant as can be seen in Although the area required by the MESIC method is nearly four times as large as that required by the ESIC method, it is still based on the simple OSIC algorithm, and as a result, it requires less area than the K-best method. It should be noted that the K-best method requires large sorting blocks that select K minimum numbers among K×|Ω| candidates, whereas the MESIC method requires small sorting blocks that select the minimum number (that is, one minimum number) among N T or |Ω| candidates. Thus, considering the results in Figs. 6 and 7, it can be said that the MESIC method is a very good solution for soft decoding systems. In V. Conclusion Although the ESIC method is based on the OSIC method, it shows significantly better performance because it solves the error propagation problem inherent in the original OSIC algorithm. By efficiently combining the ESIC method with a multiple-channel-ordering technique, the MESIC method can obtain all the LLR values without using an LLR estimation method, and thus it shows very good FER performance. The MESIC method also requires a small number of candidate symbol vectors and small sorting blocks. As a result, the MESIC method is very efficient in terms of both performance and area, especially in soft decoding systems. Reference

Complex Permittivity Measurements of Steel Fiber-Reinforced Cementitious Composites Using a Free-Space Reflection Method with a Focused Beam Lens Horn Antenna

To measure the electromagnetic properties of steel fiber-reinforced concrete (SFRC) in the X-band, 1-port measurements were performed using a lens horn antenna in a free-space measurement system. Free-space 1-port calibration with translations of the position of the reflector regarding the characteristics of the focused beam lens horn antenna was applied. The intrinsic impedance and complex permittivity of the SFRC were obtained from the measured reflection characteristics. The steel fiber content increased and the electromagnetic properties of the SFRC gradually changed from a dielectric to a conductor, even in very low frequencies compared to the plasma frequencies of general metal, which are optical frequencies. This is considered to be the plasmon effect of the metallic structure formed by the steel fiber. This result is applicable for analyses of the electromagnetic phenomenon of large structures with fiber content