Statistical methods of translating microarray data into clinically relevant diagnostic information in colorectal cancer.

In this paper we show the usefulness of Hotelling's T2 statistic for monitoring batch processes in both Phase I and Phase II operations. Discussions of necessary adaptations, such as in the formulas for computing the statistic and its distribution, are included. In a Phase I operation, where the focus is on detecting and removing outliers, consideration is given to batch processes where the batch observations are taken from either a common multivariate normal distribution or a series of multivariate normal distributions with different mean vectors. In a Phase II operation, where the monitoring of future observations is of primary concern, emphasis is placed on the application of the T2 statistic using a known or estimated in-control mean vector. A variety of data sets taken from different types of industrial batch processes are used to illustrate these techniques.

This paper describes two BASIC computer programs that calculate Hotelling's T2 statistic either for one sample or for two samples. Output of the program includes the Mahalanobis distance D2, the F ratio associated with T2, and its probability level.

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better.

Multivariate control charts for monitoring captopril stability

Radiotherapy is a very effective adjuvant treatment for rectal cancer with little side effects. Its killing effect on tumor cells seems to be more profound than the effect on normal tissue. The molecular events caused by irradiation are mainly analyzed in in vitro and animal models; investigations on human material are rare. In the current study, we analyzed the effects of irradiation on gene expression in normal and tumor tissue of rectal cancer patients. Normal and carcinoma tissue of patients from a randomized clinical trial of the benefits of preoperative radiotherapy were analyzed using the Affymetrix Human Cancer Gene Chip. Preoperative radiotherapy was given within 5 days prior to surgery. Results for normal tissue and tumor were compared to investigate the radiation-related differences between normal and tumor cells. We clustered the differentially expressed genes based on their functional annotation. Results were compared with immunohistochemical and literature data. The majority of the investigated cancer-related genes remained unchanged by irradiation (92% in tumor tissue and 93% in normal tissue). The differentially expressed genes varied between tumor and normal tissue except for maspin and IL-8. Both in tumor and normal tissue, differentially expressed genes were present related to cell signaling and cycle control, apoptosis and cell survival and tissue response and repair. However, the spectrum of affected genes was totally different. Pre-existing differences in gene expression between normal tissue and tumor tissue might explain the differences in their responses to radiation. This change in response may explain the clinical beneficial effect of radiotherapy on tumor cells (low local recurrence rate) and the less severe effects on normal tissue (minor side effects).

In this work, a derivative‐free batch‐to‐batch optimization method is proposed. In order to conquer the difficulties in building a first principal model, a local batch‐wise unfolded PLS (BW‐PLS) model is used to accurately describe the concerned region, and the first principal model based dynamic optimization problem is transformed into a static one. The just‐in‐time (JIT) modelling method is employed to dynamically update the local BW‐PLS model upon request, and the nonlinearity and abrupt changes from one batch run to another can be effectively resolved. Then the proposed local BW‐PLS model with JIT modelling method is integrated into the trust‐region framework. Not only can the issue of plant‐model mismatch be dealt with, but also the computation of the experimental gradients can be avoided. In addition, taking the advantages of PLS regression, the Hotelling's T2 statistic is utilized as a hard constraint to ensure the reliability of the optimal solution. Extension to handle soft inequality constraints is also included in this work. Finally, the efficacy of the proposed batch‐to‐batch optimization method is illustrated via a toy example and a simulated cobalt oxalate synthesis process under different operating conditions, and satisfied optimization performances were obtained.

Efficiencies in repeated-measurements experiments are studied by the use of F statistics when appropriate and of Hotelling's T2 statistics otherwise. Bounds on the asymptotic relative efficiencies of these statistics are given and conditions are found under which one test dominates another uniformly. Similar comparisons are made between T2 and the Lawley-Hotelling T2(0) test in the analysis of repeated vector measurements. It is found that the T2 test may be considerably more efficient than its competitors, but that its efficiency cannot be substantially less. These findings are illustrated numerically by examples from the literature. It is shown further that F and T2 tests are exact for all unimodal distributions constant on ellipsoids, and that efficiency comparisons among F tests are preserved under scale mixtures of multidimensional Gaussian laws.

Nonlinear PCA for process monitoring using the local approach

An omnibus permutation test of the overall null hypothesis can be used to assess the association of an entire ensemble of genetic markers with disease in case-control studies. In this approach, p-values for univariate marker-specific Armitage trend tests are combined to form a scalar statistic, which is then used in a permutation test to determine an overall p-value. Two previously described competing methods utilize either a standard two-sample Hotelling's T2 statistic or a global U statistic that is a weighted sum of univariate U statistics. In contrast to Hotelling's test, omnibus tests are much less sensitive to missing data, and utilize all available data. In contrast to the global U test, omnibus tests do not require that the direction of the effects of the individual markers on the risk of disease be correctly specified in advance; in fact, any combination of one- and two-sided univariate tests can be used. Simulations show that, even under circumstances favoring the competing tests (no missing data; direction of effects known), omnibus permutation tests based on Fisher's combining function or the Anderson-Darling statistic typically have power comparable to or greater than Hotelling's and the global U tests.

Condition monitoring and fault detection are of critical importance in modern industrial processes for efficient and productive machine operation. Early and accurate detection of faults facilitates in the efficient and secure operation of the plant by preventing losses due to machinery breakdown. In this study, we present a novel and rapid fault detection methodology using an integrated Industrial PC. The approach is based on integrating existing hardware components and software libraries for efficient application of machine learning algorithms to an industrial process. The Industrial PC is integrated within the fieldbus system of a Bulk Good Laboratory Plant providing superior network security as compared to an external connection to a cloud platform. Additionally, the Industrial PC provides predominantly better numerical computation functionality as compared to traditional PLC environment due to the availability of machine learning libraries in high-level languages. The fault detection procedure is accomplished with the Principal Component Analysis dimensionality reduction algorithm along with Hotelling's T2 statistic. A variety of sensor fault cases pertinent to the Bulk Good Laboratory Plant are analysed and experimental results show that all fault cases were detected with low detection delays. The time required to detect faults reflects the real-time capabilities of the system in an industrial scenario.

A common technique employed in flatwater kayak and canoe races is "wash riding", in which a paddler positions his/her boat on the wake of a leading boat and, at a strategic moment, drops off the wake to sprint ahead. It was hypothesized that this manoeuver was energy efficient, analogous to drafting in cycling. To study this hypothesis, minute ventilation (VE), heart rate (HR) and oxygen consumption (VO2) were measured in 10 elite male kayak paddlers (age = 25 +/- 6.5 yrs, height = 183.6 +/- 4.4 cm, mass = 83.9 +/- 6.1 kg) during steady-state exercise at a standardized velocity in conditions of "wash riding" (WR) and "non-wash riding" (NWR). The data were collected in field conditions using a portable telemetric metabolic system (Cosmed K2). Statistical analysis of the mean values for VE, VO2 and HR was performed using the Hotelling's T2 statistic and revealed significant (p < 0.05) differences between the WR and NWR trials for all three dependent variables. Mean values for VE (l/min) were WR = 113 +/- 16.5, NWR = 126.3 +/- 15.7; for VO2 (l/min) were WR = 3.22 +/- 0.32, NWR = 3.63 +/- 0.3; and for HR (bpm) were WR = 167 +/- 9.9, NWR = 174 +/- 8.0. It was concluded that wash riding during kayak paddling confers substantial metabolic savings at the speeds tested. This has implications for the design of training programs and competitive strategies for flatwater distance kayak racing.

BackgroundMany studies have documented the abnormal concentrations of major/trace elements in serum or malignant tissues of patients, but very few works systematically tested the concentrations of elements in tumor tissues in comparison with paired adjacent normal tissues from the same patients.MethodsTumor and adjacent normal lung tissues were obtained from 93 patients with previously untreated NSCLC, and 43 patients whose tumor and paired normal lung tissues reached 200 mg or more were selected for measurement of the elements' concentrations using an inductively coupled plasma‐atomic emission spectrometer.ResultsWe found that the concentrations of the 52 elements varied from 0.4 ng/g tissue (Lu, Pd, and Tm) to 1 658 000 ng/g (Na), 1 951 000 ng/g (P), and 2 495 000 ng/g (K). Thirty eight of the 52 (73.1%) elements showed approximately equal concentrations in tumor and adjacent normal lung tissues of the patients. The concentrations of nine elements (K, P, Mg, Zn, Rb, Cu, Se, Cs, and Tl) in tumor samples were significantly higher than their paired normal lung tissues, and five elements (Na, Fe, Cr, Cd, and Ge) exhibited decreased concentrations in cancer samples compared to counterpart normal lung tissues. Low Fe in tumor samples was associated with smoking history, whereas low Cr was associated with histology (squamous cell carcinoma) of the patients.ConclusionsOur results demonstrate that measurement of elements’ concentrations in both cancer and paired normal tissues is important to get insights into the roles of these elements in carcinogenesis, and therapeutic approaches to normalize the elements are warranted to treat NSCLCs.

Microsatellite alteration in histologically normal lung tissue of patients with non-small cell lung cancer

Prostaglandins support progression of colorectal cancer by several mechanisms. This conclusion is based on epidemiological and drug intervention long-term studies or retrieved from animal and cell culture experiments. The aim of the present study was to map receptor and enzyme expression for prostanoid metabolism in the presence of high or low PGE2 content within colon cancer tissue at primary tumor operation and after short-term preoperative provision of non-steroidal anti-inflammatory drug (NSAID). Twenty-three unselected patients with colon cancer were randomly selected to receive indomethacin (NSAID) or sham treatment for 3 days before surgery. Normal colon and tumor tissue were collected at operation for RNA extraction. Tissue PGE2 levels were measured by radioimmunoassay. Gene expression was quantified by microarray and real-time PCR. COX-1 expression increased proportionally to COX-2 expression in colon cancer tissue from untreated patients. Indomethacin reduced PGE2 content in normal and tumor tissue with subsequently decreased IP, HPGD and PPARgamma receptor expression in both tumor and normal colon tissue, while subtype EP1-4 receptors were not significantly influenced by indomethacin treatment. MPGES-1 expression was not related to overall PGE2 content in tumor and colon tissue, but decreased significantly in normal tissue during indomethacin exposure. Reduction of tumor tissue PGE2 was related to significant alteration in expression of several hundred genes indicating decreased cell cycling and increased apoptosis during indomethacin treatment, probably related to upregulation of acute phase reactants in tumor tissue. Increased prostanoid activity in colon cancer tissue is related to cross-talk between tumor and stroma cells.

Certain directed oncologic therapies seek to take advantage of the fact that tumors are typically more susceptible to directed therapeutic agents than normal tissue owing to their extensive networks of poorly formed, leaky vasculature. If differences between the vascularity of normal and tumor tissues could be quantified, patients could be selected for or excluded from directed treatments on the basis of this difference. However, angiographic imaging techniques such as digital subtraction angiography (DSA) yield two-dimensional data that may be inadequate for this task. As a first step, the authors evaluated the feasibility of using a commercial implementation of flat panel computed tomography (FPCT) to quantify differences in enhancement of a simulated tumor compared with normal tissue based on differences in CT number measured in precontrast and postcontrast scans. To evaluate the FPCT scanner studied, the authors scanned several phantoms containing simulated normal and tumor tissues. In the first experiment, the authors used an anthropomorphic phantom containing inclusions representing normal, tumor, and bone tissue to evaluate the constancy of CT numbers in scans repeated at clinically relevant intervals of 1 and 3 min. The authors then scanned gelatin phantoms containing dilutions of iodinated contrast to evaluate the accuracy of relative contrast enhancement measurements for a clinical FPCT system. Data were analyzed using widely available software. CT numbers measured in identical locations were constant over both scan intervals evaluated. Measured relative contrast enhancement values were accurate compared with known relative contrast enhancement values. Care must be taken to avoid artifacts in reconstructed images when placing regions of interest. Despite its limitations, FPCT in the interventional laboratory can be used to quantify relative contrast enhancement in phantoms. This is accomplished by measuring CT number in simulated tumor and normal tissue on precontrast and postcontrast scans. This information opens the door for refinement of technique in an effort to use such a technique to plan directed therapies.