Oxidation of tertiary homoallylic alcohols by thallium trinitrate: fragmentation vs. ring contraction

The oxidation of tertiary homoallylic alcohols with thallium trinitrate (TTN) was investigated. The alcohols bearing an allylic methyl group lose a molecule of acetone via a fragmentation reaction that leads to isomeric secondary allylic alcohols as major products, together with their corresponding acetylated derivatives. On the other hand, treating analogous tertiary alcohols without the allylic methyl group with TTN gives indans, through a ring contraction reaction.A oxidação de álcoois homoalílicos terciários com trinitrato de tálio (TTN) foi investigada. Os álcoois que possuem uma metila na posição alílica perdem uma molécula de acetona via uma reação de fragmentação, levando a uma mistura de álcoois alílicos isoméricos como principais produtos, juntamente com os correspondentes derivados acetilados. Por outro lado, o tratamento com TTN de álcoois terciários análogos, sem a metila na posição alílica, fornece indanos, através de uma reação de contração de anel.981988Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

Tuberculous Lymphadenitis in Southern Italy: Clinical Aspects and Treatment Perspectives

Lymph node tuberculosis (TB) represents the most common form of extrapulmonary TB that affects most often laterocervical lymph nodes. The disease, favored by states of immunosuppression, manifests itself by usually unilateral swelling, which may be associated with skin changes (hyperaemia, infiltration or fistula) and poor systemic symptoms. Our study included 75 patients arrived at the hospital “V. Monaldi” of Naples from 2008 to 2012, in order to evaluate some aspects of clinical, diagnostic and therapeutic characteristics of tuberculous lymphadenopathy. All patients were subjected to clinical, microbiological and radiological findings, surgical biopsy (alternatively, fine needle aspiration cytology, FNAC) of lymph nodes affected by pathology, antituberculosis chemotherapy for six months and subsequent followup. 49% patients were female and 51% male (mean age 44.5 years). 50.7% of patients were immunocompromised (6.7% of HIV-positive cases, 4% with type II diabetes mellitus, 16% of drug users, 6.7% treated with immunosuppressive drugs, 4% affected by malnutrition, 12% homeless). The laterocervical lymphadenopathy was unilateral in 70.7% of cases, smaller than 2 cm in 66.7%, single in 62.7%, with normal overlying skin in 72%. 9.3% of cases showed pulmonary involvement. 68% of cases excisional biopsy was performed for microbiological and histopathological examination and in 32% patients FNAC (integrated with excisional biopsy in more than half of the cases). Lymph node tuberculosis is still a problem of high clinical significance. Diagnosis requires excisional biopsy for differential diagnosis from other forms of adenopathy; treatment utilises a multi-agent chemotherapy scheme that allows a good cure rate, with few side effects. © 2014 Bentham Science Publisher

Exploring the fine-grained analysis and automatic detection of irony on Twitter

To push the state of the art in text mining applications, research in natural language processing has increasingly been investigating automatic irony detection, but manually annotated irony corpora are scarce. We present the construction of a manually annotated irony corpus based on a fine-grained annotation scheme for irony that allows to identify different irony types. We conduct a series of binary classification experiments for automatic irony recognition using a support vector machine exploiting a varied feature set and a deep learning approach making use of an LSTM network and (pre-trained) word embeddings. Evaluation on a held-out corpus shows that the SVM model outperforms the neural network approach and benefits from combining lexical, semantic and syntactic information sources. A qualitative analysis of the classification output reveals that the classifier performance may be further enhanced by integrating implicit sentiment information and context- and user-based features