MS1Connect: a mass spectrometry run similarity measure

Interpretation of newly acquired mass spectrometry data can be improved by identifying, from an online repos- itory, previous mass spectrometry runs that resemble the new data. However, this retrieval task requires comput- ing the similarity between an arbitrary pair of mass spectrometry runs. This is particularly challenging for runs acquired using different experimental protocols. We propose a method, MS1Connect, that calculates the simi- larity between a pair of runs by examining only the intact peptide (MS1) scans, and we show evidence that the MS1Connect score is accurate. Specifically, we show that MS1Connect outperforms several baseline methods on the task of predicting the species from which a given proteomics sample originated. In addition, we show that MS1Connect scores are highly correlated with similarities computed from fragment (MS2) scans, even though this data is not used by MS1Connect.

Prolactin action is necessary for parental behavior in male mice

Parental care is critical for successful reproduction in mammals. In comparison to maternal care, the neuroendocrine mechanisms supporting paternal care are less well-studied. Laboratory mice show a mating-induced suppression of infanticide (normally observed in virgins) and onset of paternal behavior. Using this model, we sought to investigate whether the hormone prolactin plays a role in paternal behavior, as it does for maternal behavior. First, using c-fos immunoreactivity in Prlr-IRES-Cre-tdtomato reporter mouse sires, we show that the circuitry activated during paternal interactions contains prolactin-responsive neurons, including the medial preoptic area, bed nucleus of the stria terminalis, and medial amygdala. To evaluate whether prolactin action is required for the establishment and display of paternal behavior, we conditionally deleted the prolactin receptor (Prlr) from 3 distinct cell types: glutamatergic, GABAergic, and CaMKIIα-expressing forebrain neurons. Prlr-deletion from CaMKIIα-expressing forebrain neurons, but not from glutamatergic or GABAergic cells, resulted in a profound effect on paternal behavior, as none of these males completed the pup retrieval task. Finally, although sires do not show an acute increase in circulating prolactin levels in response to pups, pharmacological blockade of prolactin-release at the time of pup exposure resulted in failure to retrieve pups, similar to when the Prlr was deleted from CaMKIIα neurons, with prolactin administration rescuing this behavior. Taken together, our data show that paternal behavior in sires is dependent on basal levels of circulating prolactin acting at the Prlr on CaMKIIα-expressing neurons. These new data in male mice demonstrate that prolactin has a similar action in both sexes to promote parental care.

A Research Paradigm to Investigate Event-Related Shame and Guilt: Preliminary Findings

Our aim was to study effects of different psychotherapeutic treatment components on event-related emotions and psychological symptoms. In this pilot study, we wanted to evaluate if our audiotaped memory retrieval task (MRT) is able to elicit event-related emotions. Also, we made a first attempt to compare the effects of two standardized mini-interventions based on IRRT and PE on event-related shame and guilt, general distress and affective state.

Fine-Grained Visual Textual Alignment for Cross-Modal Retrieval Using Transformer Encoders

Despite the evolution of deep-learning-based visual-textual processing systems, precise multi-modal matching remains a challenging task. In this work, we tackle the task of cross-modal retrieval through image-sentence matching based on word-region alignments, using supervision only at the global image-sentence level. Specifically, we present a novel approach called Transformer Encoder Reasoning and Alignment Network (TERAN). TERAN enforces a fine-grained match between the underlying components of images and sentences (i.e., image regions and words, respectively) to preserve the informative richness of both modalities. TERAN obtains state-of-the-art results on the image retrieval task on both MS-COCO and Flickr30k datasets. Moreover, on MS-COCO, it also outperforms current approaches on the sentence retrieval task. Focusing on scalable cross-modal information retrieval, TERAN is designed to keep the visual and textual data pipelines well separated. Cross-attention links invalidate any chance to separately extract visual and textual features needed for the online search and the offline indexing steps in large-scale retrieval systems. In this respect, TERAN merges the information from the two domains only during the final alignment phase, immediately before the loss computation. We argue that the fine-grained alignments produced by TERAN pave the way toward the research for effective and efficient methods for large-scale cross-modal information retrieval. We compare the effectiveness of our approach against relevant state-of-the-art methods. On the MS-COCO 1K test set, we obtain an improvement of 5.7% and 3.5% respectively on the image and the sentence retrieval tasks on the Recall@1 metric. The code used for the experiments is publicly available on GitHub at https://github.com/mesnico/TERAN .

Deep Learning Triplet Ordinal Relation Preserving Binary Code for Remote Sensing Image Retrieval Task

As satellite observation technology rapidly develops, the number of remote sensing (RS) images dramatically increases, and this leads RS image retrieval tasks to be more challenging in terms of speed and accuracy. Recently, an increasing number of researchers have turned their attention to this issue, as well as hashing algorithms, which map real-valued data onto a low-dimensional Hamming space and have been widely utilized to respond quickly to large-scale RS image search tasks. However, most existing hashing algorithms only emphasize preserving point-wise or pair-wise similarity, which may lead to an inferior approximate nearest neighbor (ANN) search result. To fix this problem, we propose a novel triplet ordinal cross entropy hashing (TOCEH). In TOCEH, to enhance the ability of preserving the ranking orders in different spaces, we establish a tensor graph representing the Euclidean triplet ordinal relationship among RS images and minimize the cross entropy between the probability distribution of the established Euclidean similarity graph and that of the Hamming triplet ordinal relation with the given binary code. During the training process, to avoid the non-deterministic polynomial (NP) hard problem, we utilize a continuous function instead of the discrete encoding process. Furthermore, we design a quantization objective function based on the principle of preserving triplet ordinal relation to minimize the loss caused by the continuous relaxation procedure. The comparative RS image retrieval experiments are conducted on three publicly available datasets, including UC Merced Land Use Dataset (UCMD), SAT-4 and SAT-6. The experimental results show that the proposed TOCEH algorithm outperforms many existing hashing algorithms in RS image retrieval tasks.

Text-Image Retrieval With Salient Features

In recent years, deep learning has achieved remarkable results in the text-image retrieval task. However, only global image features are considered, and the vital local information is ignored. This results in a failure to match the text well. Considering that object-level image features can help the matching between text and image, this article proposes a text-image retrieval method that fuses salient image feature representation. Fusion of salient features at the object level can improve the understanding of image semantics and thus improve the performance of text-image retrieval. The experimental results show that the method proposed in the paper is comparable to the latest methods, and the recall rate of some retrieval results is better than the current work.

Scalable supervised online hashing for image retrieval

Online hashing methods aim to learn compact binary codes of the new data stream, and update the hash function to renew the codes of the existing data. However, the addition of new data streams has a vital impact on the retrieval performance of the entire retrieval system, especially the similarity measurement between new data streams and existing data, which has always been one of the focuses of online retrieval research. In this paper, we present a novel scalable supervised online hashing method, to solve the above problems within a unified framework. Specifically, the similarity matrix is established by the label matrix of the existing data and the new data stream. The projection of the existing data label matrix is then used as an intermediate term to approximate the binary codes of the existing data, which not only realizes the semantic information of the hash codes learning but also effectively alleviates the problem of data imbalance. In addition, an alternate optimization algorithm is proposed to efficiently make the solution of the model. Extensive experiments on three widely used datasets validate its superior performance over several state-of-the-art methods in terms of both accuracy and scalability for online retrieval task.