Complete Genome Sequence of Mycobacterium fortuitum subsp. fortuitum Type Strain DSM46621

Mycobacterium fortuitum is a member of the rapidly growing nontuberculous mycobacteria (NTM). It is ubiquitous in water and soil habitats, including hospital environments. M. fortuitum is increasingly recognized as an opportunistic nosocomial pathogen causing disseminated infection. Here we report the genome sequence of M. fortuitum subsp. fortuitum type strain DSM46621

Clinical peri-implant outcomes, technical complications, and patient satisfaction with single vs. splinted crown supported implants in the anterior mandible region of diabetic individuals

OBJECTIVE: To compare the clinical and radiographic peri-implant parameters around narrow diameter implants (NDI) supported single (NDISCs) and splinted crowns (NDISPs) in the anterior maxilla of non-diabetics and type 2 diabetes mellitus patients (T2DM). MATERIALS AND METHODS: The clinical and radiographic parameters of NDISC and NDISP were assessed in the anterior mandibular jaw of T2DM and non-diabetic individuals. Plaque index (PI), bleeding on probing (BoP), probing depth (PD) and crestal bone levels were recorded. Technical complications and patient satisfaction were also assessed. ANOVA (one-way analysis of variance) was used to compare the inter-group means of clinical indices and radiographic bone loss while Shapiro-Wilk was used to compute the normal distribution of dependent variables. A p-value of less than 0.05 was considered significant. RESULTS: Sixty-three patients (35 males and 28 females) were part of the study out of which 32 were non-diabetics and 31 were T2DM patients. A total of 188 implants (124 NDISCs and 64 NDISPs) having moderately roughened topography were used for the study. The mean glycated hemoglobin in the non-diabetic group was 4.3 while that in the T2DM group was 7.9 with an average diabetic history of 8.6 years. Peri-implant parameters, including PI, BoP, and PD, were comparable between the single crown and splinted crown groups. However, there was a statistically significant difference in PI, BoP, and PD when a comparison was made between the non-diabetes and T2DM groups (p<0.05). An overall 88% of the patients were satisfied with the esthetics of the crowns while 75% of the subjects were satisfied with the function of the crowns. CONCLUSIONS: Narrow diameter implants of both types had satisfactory clinical and radiographic outcomes within non-diabetic and diabetic individuals. However, clinical and radiographic parameters were worse in type 2 diabetes mellitus patients when compared to non-diabetics

PROBLEM FOR EFFECTIVE FACILITIES PLANNING: LAYOUT OPTIMIZATION THEN SIMULATION, OR VICE VERSA?

It is widely accepted that simulation is an integral part of any effective facilities planning or layout study. Traditional approaches claim that layout optimization produces strategic results and therefore should precede simulation analysis, which focuses on operational issues. On the other hand, more recent studies suggest that running simulation models prior to conducting layout optimization produces more realistic layouts. In this paper, we contrast these two paradigms, with respect to the general assumptions and the types of applications that advocates from each paradigm have used to support their claim. In addition, we propose guidelines on which approach to pursue according to the layout study objectives and the characteristics of the system under consideration

Breast Cancer Detection in the Equivocal Mammograms by AMAN Method

Breast cancer is a primary cause of human deaths among gynecological cancers around the globe. Though it can occur in both genders, it is far more common in women. It is a disease in which the patient’s body cells in the breast start growing abnormally. It has various kinds (e.g., invasive ductal carcinoma, invasive lobular carcinoma, medullary, and mucinous), which depend on which cells in the breast turn into cancer. Traditional manual methods used to detect breast cancer are not only time consuming but may also be expensive due to the shortage of experts, especially in developing countries. To contribute to this concern, this study proposed a cost-effective and efficient scheme called AMAN. It is based on deep learning techniques to diagnose breast cancer in its initial stages using X-ray mammograms. This system classifies breast cancer into two stages. In the first stage, it uses a well-trained deep learning model (Xception) while extracting the most crucial features from the patient’s X-ray mammographs. The Xception is a pertained model that is well retrained by this study on the new breast cancer data using the transfer learning approach. In the second stage, it involves the gradient boost scheme to classify the clinical data using a specified set of characteristics. Notably, the experimental results of the proposed scheme are satisfactory. It attained an accuracy, an area under the curve (AUC), and recall of 87%, 95%, and 86%, respectively, for the mammography classification. For the clinical data classification, it achieved an AUC of 97% and a balanced accuracy of 92%. Following these results, the proposed model can be utilized to detect and classify this disease in the relevant patients with high confidence

Erratum: De Novo Variants Disrupting the HX Repeat Motif of ATN1 Cause a Recognizable Non-progressive Neurocognitive Syndrome (The American Journal of Human Genetics (2019) 104(3) (542–552), (S0002929719300138), (10.1016/j.ajhg.2019.01.013))

(The American Journal of Human Genetics 104, 542–552; March 7, 2019) In the original version of this article published on March 7, 2019, Łukasz Jaremko's name was unfortunately misspelled as Łukas Jaremko. It appears correctly here and online. The Journal and the authors apologize for this error