Oral Interview with Carmen Smith

Last semester, I had a class with Carmen, a woman from Mérida, México that moved to Cincinnati ten years ago. Since the moment I met her, she inspired me and now she still inspires me. She is a Spanish teacher and I want to be a Spanish teacher also. I learned a lot about her and from her during that semester and she became like a friend and role model to me. I knew that she moved to the United States many years ago, but I wanted to know her story. I have another class with her this semester and I had to opportunity to talk with her before class one day. She moved to the United States to look for a new life and future. She needed to leave her family, home, and friends in Mexico and I can imagine that it was not easy. I want to share her story of challenges, hard work, success, and love

A Graph-Kernel Method for Re-identification

Re-identification, that is recognizing that an object appearing in a scene is a reoccurrence of an object seen previously by the system (by the same camera or possibly by a different one) is a challenging problem in video surveillance. In this paper, the problem is addressed using a structural, graph-based representation of the objects of interest. A recently proposed graph kernel is adopted for extending to this representation the Principal Component Analyisis (PCA) technique. An experimental evaluation of the method has been performed on two video sequences from the publicly available PETS2009 database

Reproductive system of the genus Crasiella (Gastrotricha, Macrodasyida)

Crasiella diplura from Sweden and Crasiella sp. from Italy were studied alive and with transmission electron microscopy (TEM). The two species are simultaneous hermaphrodites and share the same reproductive system lay-out: paired ovaries extend along the posterior part of the intestine and join mid-dorsally, while bilateral,club-shaped testes lie at the sides of the anterior gut, extending as deferentia that fuse on the mid-ventral plane and open into a single pore; gametes mature in a caudocephalicand centripetal direction. The bulky, sac-like, frontal organ is lined by a simple epithelium and lies dorsolaterally to the intestine, on the left side of the body.The spindle-shaped caudal organ is musculo-glandular and is located ventrolaterally to the gut on the right side. It is characterized by the presence of a roughly Y-shapedinternal channel that opens into two pores close to each other, which function for the intake and outlet of the (auto)sperm, respectively. The spermatozoa, which arepeculiar and similar in the two species, are characterized by a long and complex acrosome consisting of four ultrastructurally distinct regions, three of which findequivalence in other gastrotrich species. The flagellum lacks a striated cylinder. Anatomy and ultrastructure enable us to hypothesize a modality of sperm transfer in Crasiellathat is similar to that observed in Macrodasys

Faunistics and zoogeographical overview of the Mediterranean and Black Sea marine Gastrotricha.

The paper provides an updated overview of the knowledge regarding the marine gastrotrich fauna of 10 Mediterranean and 2 Black Sea countries. Taxonomic account and species distribution come from published records, including electronically disseminated information, as well as original data from 18 localities (16 western and 2 levantine), investigated by the authors between 1990 and 2002. Since the description of the first marine Mediterranean gastrotrich, Emydasys agaso Claparede, 1867, discovered in the Gulf of Naples (Italy), 273 additional species, from 417 locations have been recorded in the basin so far. Of the gastrotrichs found, 143 species, in 24 genera and 6 families, belong to the order Macrodasyida and 131 species, in 11 genera and 3 families, belong to the order Chaetonotida; these statistics include several species as of yet not described. The number of species per location is variable, ranging from 1-37, with a global mean of 8.04 \uf0b1 6.69 spp/location. Egypt and Israel show mean values well above the average, with 12.8 and 10.8 spp/loc. respectively, whereas mean values for Algeria, Tunisia, Bulgaria, Croatia, and France are below average (1- 5.43 spp /location); data for Cyprus, Greece, Romania and Italy are within the average value. Acanthodasys aculeatus, recorded in 28% of the investigated location is the most frequently found macrodasyidan; Halichaetonotus aculifer, also recorded in 28% of the investigated localities, is the most common chaetonotidan whereas. Data analysis indicated substantial differences among countries, regarding sampling effort and, consequently, faunistic knowledge. In comparison with the generally good information concerning the Italian fauna (177 species from 246 localities), gastrotrichs from other Mediterranean nations are much less known; along with Italy, only few other Nations have been investigated to a sufficient extent (i.e., Greece: 44 sampled localities, 77 recorded species; France: 37 L, 70 spp; Egypt: 28 L, 81 spp; Israel: 15 L, 55 spp; Cyprus: 9 L, 41 spp), whereas investigation carried out in several other countries can be considered, to a variable degree, incomplete (i.e., Algeria: 16 investigated localities and 11 species found; Romania: 7 L, 30 spp; Tunisia: 5 L, 2 spp; Bulgaria: 3 L, 11 spp; Croatia: 1 L, 12 spp, Spain: 1 L, 3 sp, Malta; 2L, 2 spp.), or nil (e.g., Morocco, Libya, Turkey, Albania). All marine gastrotrich families and most marine genera have representatives in the Mediterranean fauna, notable absences regard the genera Desmodasys, Dinodasys, Planodasys, Prostobuccantia and perhaps Pseudoturbanella; on the other hand representatives of two genera, Emydasys and Dendropodola, are unknown elsewhere in the world except the western Mediterranean. At species level, about 140 taxa have so far been found only in the Mediterranean area, whereas the remainder have some north-Atlantic connections, including several considered as regional cosmopolitans (i.e., reported from at least two Oceans). Within the Mediterranean, the species geographic distribution does not appear to be homogeneous, with several taxa restricted to either the western or to the levantine basin; yet, Italy, Egypt and Israel show the highest numbers of endemic species, 70, 24 and 14 respectively, whereas only one species seems restricted to the Black Sea. A cluster analysis using the Bray-Curtis presence-absence similarity value to examine the relationship between 11 geographic areas, shows a clear separation between the Black Sea and the Mediterranean regions and within this, the Algerian-Tunisian region is separated from and the remaining 9 areas. The latter appear arranged in two main clusters, one including the southern Levantine regions (i.e., Egypt, Israel and Cyprus), the other containing regions of the north-western basin, the Adriatic sea and the two Greek sub-regions. In this cluster, the region comprising coastal France and Formentera (Spain) appears separated from the one containing, arranged into two distinct subsets, the Italian and the Greek areas. However, due to the paucity of information, data from key-areas is urged to confirm or disprove our current perception about numbers and distribution of gastrotrich taxa within the Mediterranean and the Black Sea

Prediction & Model Evaluation for Space-Time Data

Evaluation metrics for prediction error, model selection and model averaging on space-time data are understudied and poorly understood. The absence of independent replication makes prediction ambiguous as a concept and renders evaluation procedures developed for independent data inappropriate for most space-time prediction problems. Motivated by air pollution data collected during California wildfires in 2008, this manuscript attempts a formalization of the true prediction error associated with spatial interpolation. We investigate a variety of cross-validation (CV) procedures employing both simulations and case studies to provide insight into the nature of the estimand targeted by alternative data partition strategies. Consistent with recent best practice, we find that location-based cross-validation is appropriate for estimating spatial interpolation error as in our analysis of the California wildfire data. Interestingly, commonly held notions of bias-variance trade-off of CV fold size do not trivially apply to dependent data, and we recommend leave-one-location-out (LOLO) CV as the preferred prediction error metric for spatial interpolation.Comment: 15 pages, 5 figure

Ribosomal Proteins RPS11 and RPS20, Two Stress-Response Markers of Glioblastoma Stem Cells, Are Novel Predictors of Poor Prognosis in Glioblastoma Patients.

Glioblastoma stem cells (GSC) co-exhibiting a tumor-initiating capacity and a radio-chemoresistant phenotype, are a compelling cell model for explaining tumor recurrence. We have previously characterized patient-derived, treatment-resistant GSC clones (TRGC) that survived radiochemotherapy. Compared to glucose-dependent, treatment-sensitive GSC clones (TSGC), TRGC exhibited reduced glucose dependence that favor the fatty acid oxidation pathway as their energy source. Using comparative genome-wide transcriptome analysis, a series of defense signatures associated with TRGC survival were identified and verified by siRNA-based gene knockdown experiments that led to loss of cell integrity. In this study, we investigate the prognostic value of defense signatures in glioblastoma (GBM) patients using gene expression analysis with Probeset Analyzer (131 GBM) and The Cancer Genome Atlas (TCGA) data, and protein expression with a tissue microarray (50 GBM), yielding the first TRGC-derived prognostic biomarkers for GBM patients. Ribosomal protein S11 (RPS11), RPS20, individually and together, consistently predicted poor survival of newly diagnosed primary GBM tumors when overexpressed at the RNA or protein level [RPS11: Hazard Ratio (HR) = 11.5, p<0.001; RPS20: HR = 4.5, p = 0.03; RPS11+RPS20: HR = 17.99, p = 0.001]. The prognostic significance of RPS11 and RPS20 was further supported by whole tissue section RPS11 immunostaining (27 GBM; HR = 4.05, p = 0.01) and TCGA gene expression data (578 primary GBM; RPS11: HR = 1.19, p = 0.06; RPS20: HR = 1.25, p = 0.02; RPS11+RPS20: HR = 1.43, p = 0.01). Moreover, tumors that exhibited unmethylated O-6-methylguanine-DNA methyltransferase (MGMT) or wild-type isocitrate dehydrogenase 1 (IDH1) were associated with higher RPS11 expression levels [corr (IDH1, RPS11) = 0.64, p = 0.03); [corr (MGMT, RPS11) = 0.52, p = 0.04]. These data indicate that increased expression of RPS11 and RPS20 predicts shorter patient survival. The study also suggests that TRGC are clinically relevant cells that represent resistant tumorigenic clones from patient tumors and that their properties, at least in part, are reflected in poor-prognosis GBM. The screening of TRGC signatures may represent a novel alternative strategy for identifying new prognostic biomarkers

Holographic approach to a minimal Higgsless model

In this work, following an holographic approach, we carry out a low energy effective study of a minimal Higgsless model based on SU(2) bulk symmetry broken by boundary conditions, both in flat and warped metric. The holographic procedure turns out to be an useful computation technique to achieve an effective four dimensional formulation of the model taking into account the corrections coming from the extra dimensional sector. This technique is used to compute both oblique and direct contributions to the electroweak parameters in presence of fermions delocalized along the fifth dimension.Comment: Latex file, 23 page

Machine Learning Models Accurately Model Ozone Exposure during Wildfire Events

&nbsp; Epidemiologists use prediction models to downscale (i.e., interpolate) air pollution exposure where monitoring data is insufficient. This study compares machine learning prediction models for ground-level ozone during wildfires, evaluating the predictive accuracy of ten algorithms on the daily 8-hour maximum average ozone during a 2008 wildfire event in northern California. Models were evaluated using a leave-one-location-out cross-validation (LOLO CV) procedure to account for the spatial and temporal dependence of the data and produce more realistic estimates of prediction error. LOLO CV avoids both the well-known overly optimistic bias of k-fold cross-validation on dependent data and the conservative bias of evaluating prediction error over a coarser spatial resolution via leave-k-locations-out CV. Gradient boosting was the most accurate of the ten machine learning algorithms with the lowest LOLO CV estimated root mean square error (0.228) and the highest LOLO CV R&circ;2 (0.677). Random forest was the second best performing algorithm with an LOLO CV R&circ;2 of 0.661. The LOLO CV estimates of predictive accuracy were less optimistic than 10-fold CV estimates for all ten models. The difference in estimated accuracy between the 10-fold CV and LOLO CV was greater for more flexible models like gradient boosting and random forest. The order of estimated model accuracy depended on the choice of evaluation metric, indicating that 10-fold CV and LOLO CV may select different models or sets of covariates as optimal, which calls into question the reliability of 10-fold CV for model (or variable) selection. These prediction models are designed for interpolating ozone exposure, and are not suited to inferring the effect of wildfires on ozone or extrapolating to predict ozone in other spatial or temporal domains. This is demonstrated by the inability of the best performing models to accurately predict ozone during 2007 southern California wildfires.</p

Use of Mg/Ca modified biochars to take up phosphorus from acid-extract of incinerated sewage sludge ash (ISSA) for fertilizer application

Recovery of phosphorus (P) from incinerated sewage sludge ash (ISSA) by biochar is an attractive solution for mitigating the P scarcity and transferring waste to resources. This work used Mg/Ca-modified biochars to take up P from the acid-extract from ISSA at low pH (<2), which simplified the previous P recycling process. The hypothesis is to produce a P-enriched post-sorption biochar that can be directly applied as a P fertilizer. Wastes of peanut shell and sugarcane bagasse were used to synthesize Mg/Ca-modified biochars at pyrolysis temperatures of 450 °C, 700 °C and 850 °C. Preliminary results indicated Mg-modified sugarcane bagasse biochar pyrolysed at 700 °C produced optimal P-absorption. This biochar was positively charged and had a high specific surface area (1440 m2/g), consistent with a layered porous structure. The optimal biochar showed rapid adsorption of P which could be described by the pseudo-second-order model. Successful adsorption of P from the acid-extract by the optimal biochar was mainly due to chemical precipitation and its adsorption capacity is 129.79 mg P/g biochar