This study aimed to elucidate the similarities and differences between amyloid-forming corpora amylacea (CA) in the prostate and lung, examine the nature of CAs in cystic tumors of the atrioventricular node (CTAVN), and clarify the distinctions between amyloid-forming CA and spheroid-type amyloid deposition. We conducted proteomics analyses using liquid chromatography–tandem mass spectrometry with laser microdissection and immunohistochemistry to validate the characteristics of CAs in the lung and prostate. Our findings revealed that CAs in these organs primarily consist of common proteins (β2-microglobulin and lysozyme) and locally produced proteins. Moreover, we observed a discrepancy between the histopathological and proteomic analysis results in CTAVN-associated CAs. In addition, while the histopathological appearance of amyloid-forming CAs and spheroid-type amyloid deposits were nearly identical, the latter deposition lacked β2-microglobulin and lysozyme, and exhibited evident destruction of surrounding tissue. A literature review further supported these findings. These results suggest that amyloid-forming CAs in the lung and prostate are formed through a shared mechanism, serving as waste containers (wasteosomes) and/or storage for excess proteins (functional amyloids). In contrast, we hypothesize that while amyloid-forming CA and spheroid-type amyloid deposits are formed, in part, through common mechanisms, the latter are pathological.

The morphology and maturation process of gonads of 70 Burmeister´s porpoises, with body lengths ranging 135–183 cm (n= 34 females) and 64.5-182 cm (n= 36 males) were described. Samples were collected in six ports of central and northern Peru in 1987-1999. In the field, females were classified as immature, mature (resting, lactating, pregnant) and males as immature, pubescent and mature based on, respectively, the presence of ovarian corpora and the relative quantity of semen in cut epididymides. The ovaries of P. spinipinnis are ovoid or bean-shaped and flattened, with corpora modifying surface appearance. In the laboratory, ovaries were examined macro- and microscopically, measured, weighed and sliced in 1-3 mm sections. The number of corpora ovarica (lutea, albicantia, atretica) found in each ovary as well as their macroscopic and microscopic characteristics were documented in some detail. The follicles, their oocytes and nucleus were measured. Follicular development in P. spinipinnis is predominantly left-sided, but occurs in both ovaries in 16.3% of females, mainly in those with numerous corpora. Macroscopically, several corpora atretica with luteinization had characteristics similar to those of corpora albicantia, making microscopic determination essential. Inconclusive evidence of recent ovulation was found in January. Two of three immature females showed good follicular development in March and April. The body length at 50% sexual maturity in females was estimated at 152.7 cm. There was no evidence of reproductive senescence. The testes of P. spinipinnis are elongated and cylindrical. Of the 36 males examined macroscopically, 7 were immature, 5 pubescent and 24 mature. The histological analysis determining the presence and abundance of Sertoli cells, spermatogonia, spermatocytes, spermatids and spermatozoa, the relative amount of interstitial tissue, the elongation and mean diameter of the seminiferous tubules and the relative size of the lumen allowed to confidently determine sexual maturity status. The field evaluation of maturity based on the presence of sperm in the epididymides is a useful but, in 8.3% of cases, not exact method. For males the body length at 50% sexual maturity was estimated at 157 cm. No histological evidence of male reproductive seasonality was found. Spermatogenesis was perceptible year-round and tubule diameters had non-specific variations for each month.

Due to the substantial growth of internet users and its spontaneous access via electronic devices, the amount of electronic contents is growing enormously in recent years through instant messaging, social networking posts, blogs, online portals, and other digital platforms. Unfortunately, the misapplication of technologies has boosted with this rapid growth of online content which leads to the rise in suspicious activities. People misuse the web media to disseminate malicious activity, perform the illegal movement, abuse other people, and publicize suspicious contents on the web. The suspicious contents usually available in the form of text, audio or video, whereas text contents have been used in most of the cases to perform suspicious activities. Thus, one of the most challenging issues for NLP researchers is to develop a system that can identify suspicious text efficiently from the specific contents. In this paper, a Machine Learning (ML)-based classification model is proposed (hereafter called STD) to classify Bengali text into non-suspicious and suspicious categories based on its original contents. A set of ML classifiers with various features has been used on our developed corpus, consisting of 7000 Bengali text documents where 5600 documents used for training and 1400 documents used for testing. The performance of the proposed system is compared with the human baseline and existing ML techniques. The SGD classifier `tf-idf’ with the combination of unigram and bigram features are used to achieve the highest accuracy of 84.57%.

With the influence of Big Data culture on qualitative data collection, acquisition, and processing, it is becoming increasingly important that social scientists understand the complexity underlying data collection and the resulting models and analyses. Systematic approaches for creating computationally tractable models need to be employed in order to create representative, specialized reference corpora subsampled from Big Language Data sources. Even more importantly, any such method must be tested and vetted for its reproducibility and consistency in generating a representative model of a particular population in question. This article considers and tests one such method for Big Language Data downsampling of digitally-accessible language data to determine both how to operationalize this form of corpus model creation, as well as testing whether the method is reproducible. Using the U.S. Nuclear Regulatory Commission's public documentation database as a test source, the sampling method's procedure was evaluated to assess variation in the rate of which documents were deemed fit for inclusion or exclusion from the corpus across four iterations. The findings of this study indicate that such a principled sampling method is viable, thus necessitating the need for an approach for creating language-based models that account for extralinguistic factors and linguistic characteristics of documents.