BackgroundAdrenal myelolipoma is an uncommon, benign, and hormonally non-functioning tumor that is composed of mature adipose tissue and normal hematopoietic tissue. Most cases to date are asymptomatic or have epigastric pain. Acute hemorrhage is the most dramatic manifestation of adrenal myelolipoma; though, it is a rare entity. Hemorrhagic shock due to adrenal myelolipoma, to our knowledge, was much less mentioned so far. Persistent bleeding and uncontrollable hypotension are considered to be absolute indications for immediate surgical operation.Case presentationHerein we presented a 32-year-old male patient with initial symptoms of nausea, vomiting, and epigastric pain progressing to altered consciousness and hypotension during ER course. Hemorrhagic shock due to a giant adrenal myelolipoma, R’t was diagnosed. Emergent exploratory laparotomy was executed, and en bloc excision of tumor was done.ConclusionAdrenal myelolipoma might be diagnosed as a adjunction to other main causes of illness; furthermore, adrenal myelolipoma could be asymptomatic in lifetime. In our case, however, manifesting as hemorrhage shock was challenging to diagnose step by step; instead, maintaining vital organs perfusion and identifying bleeding sources were to be done. Management of myelolipoma should be done on a case-to-case basis.

Premenarchal girls with adnexal torsion more commonly had a benign ovarian tumor or no underlying abnormality as an etiology; ovarian malignancy was rare. In the management of these cases, detorsion and adnexal conservation surgery should be considered in cases with adnexa appearing to be ischemic or hemorrhagic infarction.

smffsrw Eumck 9S I'& m ?rdl!am, N-Y. 8-10 My 1S98 TIUSpaw wasXICZ!SC2 for PITSC.UOO. by M SPE program CommMecfollowing rcwmv d mtormafmn coma.mcil m m abstacl submmd by fix UJIIMS) Con@msd k paiw, = pnxscntcd. have nor M rcncwaf by IFC Socmy C4 Pcfmlcum Sngmetrs ad am subjectw ccwmdionby tits author(s) Tk mafuul. s p'=cnlcd. k w rMXSSWIlyrefled anyp.niuon d LIWSCCI q d PefIvkum Sngincers. iu ofiixcrs.or mcmbm pap @'escmdm SPS mc$ungsarc subjccfm pubhcauonreview by Sdifcmal Commmccsd k SOL%%Y of Mmfeum Fnglnars SkCUOIUCI'CPNXSUCULNI, 6Mribuuon, u sfomgcof v P Cf IhL$fuF$ fff C9M* PUIPOSCJ wilhoul * milfcn COIISCJM of M SocEty d Peuukum %icecrs s pmhlbttd Pcnnuswn IQ!qwc&e m pnnl s I'CSUKUXI IOm ab4mct d rimmat tbam300 WWC&IllusuuIcmsmay mx bempwd TSKw must cmaainconspimwwacknowkd.gmcnt of + ad by wfwm tic papmwu PWCMCXIWrite Iibrdan, SPS, P O Sax 833836, Richadson.TX 75083-3036, L!S A fax 01-9?2.952+435 AbstractAzimuthal variation in shear wave speed and existence of two shear wave arrivals are commonly assumed to be diagnostic of fractured rock, These properties derive from a representation of fractured rock as an effective anisotropic medium. Work on effects of single fractures suggests that energy can be partitioned in ways that are not captured by the effective medium representation.Single fractures cause frequency dependent reflections, refractions and group time delays in plane waves and can trap energy as interface waves. These effects can result in distinctive seismic signatures in multiply fractured rock.

We analyzed a data set containing functional brain images from 6 healthy controls and 196 individuals with Parkinson's disease (PD), who were divided into five stages according to illness severity. The goal was to predict patients' PD illness stages by using their functional brain images. We employed the following prediction approaches: multivariate statistical methods (linear discriminant analysis, support vector machine, decision tree, and multilayer perceptron [MLP]), ensemble learning models (random forest [RF] and adaptive boosting), and deep convolutional neural network (CNN). For statistical and ensemble models, various feature extraction approaches (principal component analysis [PCA], multilinear PCA, intensity summary statistics [IStat], and Laws' texture energy measure) were employed to extract features, the synthetic minority over‐sampling technique was used to address imbalanced data, and the optimal combination of hyperparameters was found using a grid search. For CNN modeling, we applied an image augmentation technique to increase and balance data sizes over different disease stages. We adopted transfer learning to incorporate pretrained VGG16 weights and architecture into the model fitting, and we also tested a state‐of‐the‐art machine learning model that could automatically generate an optimal neural architecture. We found that IStat consistently outperformed other feature extraction approaches. MLP and RF were the analytic approaches with the highest prediction accuracy rate for multivariate statistical and ensemble learning models, respectively. Overall, the deep CNN model with pretrained VGG16 weights and architecture outperformed other approaches; it captured critical features from imaging, effectively distinguished between normal controls and patients with PD, and achieved the highest classification accuracy.

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