EXPRESS:Spatial Organisation in the Human Mind as a Function of the Distance Between Stimuli.

Studies investigating serial order in working memory have shown that participants from Western cultures are faster at responding to items presented at the beginning of a sequence using their left hand and faster at responding to items at the end with their right hand. This is known as the spatial positional association of response codes (SPoARC) effect. The SPoARC effect provides evidence that recently presented information is spatially organised in the cognitive system along a horizontal axis. This study investigated the flexibility of spatialisation by testing the effect that distance between items presented on a screen has on the magnitude of the SPoARC effect. It was hypothesised that by increasing the distance between items on a screen a larger SPoARC effect would be found. We used three conditions: central, narrow, and wide. In central, four random letters were presented sequentially at the centre of the screen, in narrow the letters were presented from left to right on the screen, wide was the same as narrow but the separation between the letters was larger. Participants consisted of 64 adults aged 18-55 years old. Participants were presented with four random letters, followed by single probe letter, participants had to indicate, by pressing a key on a normal keyboard, if the probe had been in the sequence. We analysed the data with multilevel modelling. We found evidence for the SPoARC effect in all three conditions. But no evidence that the effect varied between conditions.

Optimized experimental designs to best detect spatial positional association of response codes in working memory.

The SPoARC (Spatial Positional Association of Response Codes) effect refers to spatialization of information in working memory. Among the potential factors that could influence how order is mapped onto a mental space during the recognition process, we selected the following two factors: i) the type of stimuli, in particular their verbal vs. visual aspects and ii) the number of probes. In this study, 137 participants memorized sequences of either words or pictures and subsequently performed a recognition test for which they responded using lateralized keys. For half of the participants, only one probe was presented after each sequence, whereas the other half was administered several probes. A significantly greater number of participants presented a SPoARC using a single probe. We discuss that spatialization is best detected when the sequence is scanned only once. Results also showed no difference between the two types of stimuli (i.e., verbal vs. visual). This finding raises the question of the respective roles of verbalization and visualization in the SPoARC.

A SPoARC of Music: Musicians Spatialize Melodies but not All-Comers.

Recent studies on the spatial positional associated response codes (SPoARC) effect have shown that when Western adults are asked to keep in mind sequences of verbal items, they mentally spatialize them along the horizontal axis, with the initial items being associated with the left and the last items being associated with the right. The origin of this mental line is still debated, but it has been theorized that it necessitates specific spatial cognitive structures to emerge, which are built through expertise. This hypothesis is examined by testing for the first time whether Western individuals spatialize melodies from left to right and whether expertise in the musical domain is necessary for this effect to emerge. Two groups (musicians and non-musicians) of participants were asked to memorize sequences of four musical notes and to indicate if a subsequent probe was part of the sequence by pressing a "yes" key or a "no" key with the left or right index finger. Left/right-hand key assignment was reversed at mid-experiment. The results showed a SPoARC effect only for the group of musicians. Moreover, no association between pitch and hand responses was observed in either of the two groups. These findings suggest a crucial role of expertise in the SPoARC effect.

TMBleR: a bioinformatic tool to optimize TMB estimation and predictive power.

MOTIVATION: Tumor mutational burden (TMB) has been proposed as a predictive biomarker for immunotherapy response in cancer patients, as it is thought to enrich for tumors with high neoantigen load. TMB assessed by whole-exome sequencing is considered the gold standard but remains confined to research settings. In the clinical setting, targeted gene panels sampling various genomic sizes along with diverse strategies to estimate TMB were proposed and no real standard has emerged yet. RESULTS: We provide the community with TMBleR, a tool to measure the clinical impact of various strategies of panel-based TMB measurement. AVAILABILITY AND IMPLEMENTATION: R package and docker container (GPL-3 Open Source license): https://acc-bioinfo.github.io/TMBleR/. Graphical-user interface website: https://bioserver.ieo.it/shiny/app/tmbler. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Combined Transfer Learning and Test-Time Augmentation Improves Convolutional Neural Network-Based Semantic Segmentation of Prostate Cancer from Multi-Parametric MR Images.

PURPOSE: Multiparametric MRI (mp-MRI) is a widely used tool for diagnosing and staging prostate cancer. The purpose of this study was to evaluate whether transfer learning, unsupervised pre-training and test-time augmentation significantly improved the performance of a convolutional neural network (CNN) for pixel-by-pixel prediction of cancer vs. non-cancer using mp-MRI datasets. METHODS: 154 subjects undergoing mp-MRI were prospectively recruited, 16 of whom subsequently underwent radical prostatectomy. Logistic regression, random forest and CNN models were trained on mp-MRI data using histopathology as the gold standard. Transfer learning, unsupervised pre-training and test-time augmentation were used to boost CNN performance. Models were evaluated using Dice score and area under the receiver operating curve (AUROC) with leave-one-subject-out cross validation. Permutation feature importance testing was performed to evaluate the relative value of each MR contrast to CNN model performance. Statistical significance (p<0.05) was determined using the paired Wilcoxon signed rank test with Benjamini-Hochberg correction for multiple comparisons. RESULTS: Baseline CNN outperformed logistic regression and random forest models. Transfer learning and unsupervised pre-training did not significantly improve CNN performance over baseline; however, test-time augmentation resulted in significantly higher Dice scores over both baseline CNN and CNN plus either of transfer learning or unsupervised pre-training. The best performing model was CNN with transfer learning and test-time augmentation (Dice score of 0.59 and AUROC of 0.93). The most important contrast was apparent diffusion coefficient (ADC), followed by Ktrans and T2, although each contributed significantly to classifier performance. CONCLUSIONS: The addition of transfer learning and test-time augmentation resulted in significant improvement in CNN segmentation performance in a small set of prostate cancer mp-MRI data. Results suggest that these techniques may be more broadly useful for the optimization of deep learning algorithms applied to the problem of semantic segmentation in biomedical image datasets. However, further work is needed to improve the generalizability of the specific model presented herein.

Bootstrapping the visuospatial bootstrapping effect and testing its spatialisation.

When sequences of digits are visually presented within a numerical keypad on a screen, memory span increases, this effect was named visuospatial bootstrapping. The aim of the first experiment was to know if this effect could emerge without presenting a keypad on the screen. For this purpose, a three-phase experiment was designed. During phase 1, the immediate serial recall of two groups of participants was compared (pre-training): the first group saw sequences of one-digit numbers displayed on a screen within a keypad (the keypad group) whereas the second group heard the (same) sequences (the auditive group). During phase 2, all participants underwent a training session to help them visualise in their mind a keypad. Finally, in the third phase, participants were tested again with an immediate serial recall task (post-training). Results showed that both groups had comparable performance in post-training indicating that the visuospatial bootstrapping could be obtained without displaying a numerical keypad. The second experiment also involved a keypad group and an auditive group and was designed to investigate their spatial representation. Results showed that both groups spatialised the digits following the keypad spatial configuration: digits 1-4-7 were associated to left, 2-5-8 to middle and 3-6-9 to right.

Machine learning-based reclassification of germline variants of unknown significance: The RENOVO algorithm.

The increasing scope of genetic testing allowed by next-generation sequencing (NGS) dramatically increased the number of genetic variants to be interpreted as pathogenic or benign for adequate patient management. Still, the interpretation process often fails to deliver a clear classification, resulting in either variants of unknown significance (VUSs) or variants with conflicting interpretation of pathogenicity (CIP); these represent a major clinical problem because they do not provide useful information for decision-making, causing a large fraction of genetically determined disease to remain undertreated. We developed a machine learning (random forest)-based tool, RENOVO, that classifies variants as pathogenic or benign on the basis of publicly available information and provides a pathogenicity likelihood score (PLS). Using the same feature classes recommended by guidelines, we trained RENOVO on established pathogenic/benign variants in ClinVar (training set accuracy = 99%) and tested its performance on variants whose interpretation has changed over time (test set accuracy = 95%). We further validated the algorithm on additional datasets including unreported variants validated either through expert consensus (ENIGMA) or laboratory-based functional techniques (on BRCA1/2 and SCN5A). On all datasets, RENOVO outperformed existing automated interpretation tools. On the basis of the above validation metrics, we assigned a defined PLS to all existing ClinVar VUSs, proposing a reclassification for 67% with >90% estimated precision. RENOVO provides a validated tool to reduce the fraction of uninterpreted or misinterpreted variants, tackling an area of unmet need in modern clinical genetics.

Prospective Validation of the Italian Alliance Against Cancer Lung Panel in Patients With Advanced Non-Small-Cell Lung Cancer.

BACKGROUND: The deeper knowledge of non-small-cell lung cancer (NSCLC) biology and the discovery of driver molecular alterations have opened the era of precision medicine in lung oncology, thus significantly revolutionizing the diagnostic and therapeutic approach to NSCLC. In Italy, however, molecular assessment remains heterogeneous across the country, and numbers of patients accessing personalized treatments remain relatively low. Nationwide programs have demonstrated that the creation of consortia represent a successful strategy to increase the number of patients with a molecular classification. PATIENTS AND METHODS: The Alliance Against Cancer (ACC), a network of 25 Italian Research Institutes, has developed a targeted sequencing panel for the detection of genomic alterations in 182 genes in patients with a diagnosis of NSCLC (ACC lung panel). One thousand metastatic NSCLC patients will be enrolled onto a prospective trial designed to measure the sensitivity and specificity of the ACC lung panel as a tool for molecular screening compared to standard methods. RESULTS AND CONCLUSION: The ongoing trial is part of a nationwide strategy of ACC to develop infrastructures and improve competences to make the Italian research institutes independent for genomic profiling of cancer patients.

Spatialization in working memory: can individuals reverse the cultural direction of their thoughts?

A recent study based on the SPoARC effect (spatial position association response codes) showed that culture heavily shapes cognition and more specifically the way thought is organized; when Western adults are asked to keep in mind a sequence of colors, they mentally organize them from left to right, whereas right-to-left reading/writing adults spatialize them in the opposite direction. Here, we investigate if the spontaneous direction of spatialization in Westerners can be reversed. Lists of five consonants were presented auditorily at a rate of 3 s per item, participants were asked to mentally organize the memoranda from right to left. Each list was followed by a probe. Participants had to indicate whether the probe was part of the sequence by pressing a "yes" key or a "no" key with the left or right index finger. Left/right-hand key assignment was switched after half of the trials were completed. The results showed a reverse SPoARC effect that was comparable in magnitude to the spontaneous left-to-right SPoARC effect found in a previous study. Overall, our results suggest that individuals can reverse the cultural direction of their thoughts.

The development of working memory spatialization revealed by using the cave paradigm in a two-alternative spatial choice.

When Western participants are asked to keep in mind a sequence of verbal items, they tend to associate the first items to the left and the last items to the right. This phenomenon, known as the spatial-positional association response codes effect, has been interpreted as showing that individuals spatialize the memoranda by creating a left-to-right mental line with them. One important gap in our knowledge concerns the development of this phenomenon: when do Western individuals start organizing their thought from left to right? To answer this question, 274 participants in seven age groups were tested (kindergarten, Grades 1, 2, 3, 4, and 5, and adults). We used a new protocol meant to be child-friendly, which involves associating two caves with two animals using a two-alternative spatial forced choice. Participants had to guess in which cave a specific animal could be hidden. Results showed that it is from Grade 3 on that participants spatialize information in working memory in a left-to-right fashion like adults.

About the interplay between internal and external spatial codes in the mind: implications for serial order.

Previous studies have shown that serial order in verbal working memory is spontaneously coded along the horizontal axis (i.e., the spatial positional association response codes (SPoARC) effect), with the initial items being associated with left and the last items being associated with right. These studies have led to the idea that when the cognitive system is confronted with a sequence of items processed verbally and semantically, it internally generates a spatial coordinate frame onto which memoranda can be bound to maintain their serial information. In this study, the interplay between internal and external spatial codes in the mind was investigated by testing the flexibility of the SPoARC effect. A verbal Sternberg probe detection task was used in which the displayed direction of the items during encoding (centrally, from left-to-right and from right-to-left) and the presentation rate (1- and 5-s/item) were manipulated. SPoARC effects were found in all conditions but were reversed in the right-to-left presentation condition. Follow-up analyses revealed no evidence of any spatial cost for the reversal; moreover, it was not influenced by the presentation rates. These findings suggest that space can be flexibly recruited for the spontaneous coding of serial order. The theoretical implications of these observations are discussed.

Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study.

Dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is a method of temporal imaging that is commonly used to aid in prostate cancer (PCa) diagnosis and staging. Typically, machine learning models designed for the segmentation and detection of PCa will use an engineered scalar image called Ktrans to summarize the information in the DCE time-series images. This work proposes a new model that amalgamates the U-net and the convGRU neural network architectures for the purpose of interpreting DCE time-series in a temporal and spatial basis for segmenting PCa in MR images. Ultimately, experiments show that the proposed model using the DCE time-series images can outperform a baseline U-net segmentation model using Ktrans. However, when other types of scalar MR images are considered by the models, no significant advantage is observed for the proposed model.

Explaining the SPoARC and SNARC effects with knowledge structures: An expertise account.

Two proposals have been put forward to account conjointly for the spatial-numerical association of response codes (SNARC) effect and the spatial-positional association of response codes (SPoARC) effect: the working memory account and the dual account. Here, on the basis of experimental and theoretical knowledge acquired in the field of expert memory, we propose an alternative account-named the expertise account-that explains both effects through the acquisition and use of knowledge structures (a generalization of "chunks," "retrieval structures," and "templates"), which have been used extensively in expert memory theory. These knowledge structures can be of two types: nonslotted or slotted schemas. We suggest that the SNARC effect can be explained via the use of nonslotted schemas, and the SPoARC effect via slotted schemas. We conclude our article by presenting the broader implications of our framework for working memory in general, when considering knowledge structures.

Cetuximab for treating non-small cell lung cancer.

INTRODUCTION: Epidermal Growth Factor Receptor (EGFR)-dependent signaling plays a crucial role in epithelial cancer biology, and dictated the development of several targeting agents. The mouse-human chimeric antibody Cetuximab was among the first to be developed. After about two decades of clinical research it has gained a significant place in the management of advanced colorectal and head and neck cancers, whereas its development in non small cell lung cancer (NSCLC) has not led to a place in routine clinical practice, because of marginal clinical benefit despite statistically significant Phase III trials. Recent data from ongoing trials suggest that more careful selection based on molecular markers may identify good responders. Areas covered: In this article, the authors review the literature concerning basic science studies identifying EGFR as a therapeutic target, pharmacological development of Cetuximab, its pharmacodynamics and pharmacokinetics, and clinical trials on Cetuximab in NSCLC, focusing on recent findings on putative predictive biomarkers. Expert opinion: Cetuximab currently has no role in NSCLC treatment outside of research settings. We argue that failure to identify a predictive biomarker early on has hampered its chances to enter routine practice. Although recent research suggests benefit in highly selected patient subsets, its potential impact is severely dampened by lack of regulatory body approval and the emergence of competitors for the same niches.

Compression in Working Memory and Its Relationship With Fluid Intelligence.

Working memory has been shown to be strongly related to fluid intelligence; however, our goal is to shed further light on the process of information compression in working memory as a determining factor of fluid intelligence. Our main hypothesis was that compression in working memory is an excellent indicator for studying the relationship between working-memory capacity and fluid intelligence because both depend on the optimization of storage capacity. Compressibility of memoranda was estimated using an algorithmic complexity metric. The results showed that compressibility can be used to predict working-memory performance and that fluid intelligence is well predicted by the ability to compress information. We conclude that the ability to compress information in working memory is the reason why both manipulation and retention of information are linked to intelligence. This result offers a new concept of intelligence based on the idea that compression and intelligence are equivalent problems.

Spatialization in working memory is related to literacy and reading direction: Culture "literarily" directs our thoughts.

The ability to maintain arbitrary sequences of items in the mind contributes to major cognitive faculties, such as language, reasoning, and episodic memory. Previous research suggests that serial order working memory is grounded in the brain's spatial attention system. In the present study, we show that the spatially defined mental organization of novel item sequences is related to literacy and varies as a function of reading/writing direction. Specifically, three groups (left-to-right Western readers, right-to-left Arabic readers, and Arabic-speaking illiterates) were asked to memorize random (and non-spatial) sequences of color patches and determine whether a subsequent probe was part of the memorized sequence (e.g., press left key) or not (e.g., press right key). The results showed that Western readers mentally organized the sequences from left to right, Arabic readers spontaneously used the opposite direction, and Arabic-speaking illiterates showed no systematic spatial organization. This finding suggests that cultural conventions shape one of the most "fluid" aspects of human cognition, namely, the spontaneous mental organization of novel non-spatial information.

Can spatialisation be extended to episodic memory and open sets?

The SPoARC effect (Spatial Positional Associated Response Codes) has only been observed in working memory (WM) using closed sets. It is interpreted as showing that individuals spatialise to-be-remembered items in a left-to-right fashion, using spatialisation as context. Given that context is crucial for episodic memory (EM), we tested if this effect could be observed in EM by using 15-word lists taken from an open set. After each list, 30 probes were sequentially displayed to test recognition. The left/right-hand key assignment for yes/no answers was varied. No SPoARC effect was observed. However, as all previous SPoARC experiments had used short lists and closed sets, it was not possible to know if this absence of SPoARC was due to the open set feature or the length of the lists. A second experiment was thus run using open sets and short 5-word lists, which do not necessitate EM to be remembered. A SPoARC effect was observed indicating that Experiment 1 result was due to the involvement of supra-span lists and that SPoARC effects do not extend to EM with open sets. Experiment 2 also enabled us to generalise the SPoARC effect to open sets in WM for the first time.

Precision Trial Drawer, a Computational Tool to Assist Planning of Genomics-Driven Trials in Oncology.

PURPOSE: Trials that accrue participants on the basis of genetic biomarkers are a powerful means of testing targeted drugs, but they are often complicated by the rarity of the biomarker-positive population. Umbrella trials circumvent this by testing multiple hypotheses to maximize accrual. However, bigger trials have higher chances of conflicting treatment allocations because of the coexistence of multiple actionable alterations; allocation strategies greatly affect the efficiency of enrollment and should be carefully planned on the basis of relative mutation frequencies, leveraging information from large sequencing projects. METHODS: We developed software named Precision Trial Drawer (PTD) to estimate parameters that are useful for designing precision trials, most importantly, the number of patients needed to molecularly screen (NNMS) and the allocation rule that maximizes patient accrual on the basis of mutation frequency, systematically assigning patients with conflicting allocations to the drug associated with the rarer mutation. We used data from The Cancer Genome Atlas to show their potential in a 10-arm imaginary trial of multiple cancers on the basis of genetic alterations suggested by the past Molecular Analysis for Personalised Therapy (MAP) conference. We validated PTD predictions versus real data from the SHIVA (A Randomized Phase II Trial Comparing Therapy Based on Tumor Molecular Profiling Versus Conventional Therapy in Patients With Refractory Cancer) trial. RESULTS: In the MAP imaginary trial, PTD-optimized allocation reduces number of patients needed to molecularly screen by up to 71.8% (3.5 times) compared with nonoptimal trial designs. In the SHIVA trial, PTD correctly predicted the fraction of patients with actionable alterations (33.51% [95% CI, 29.4% to 37.6%] in imaginary v 32.92% [95% CI, 28.2% to 37.6%] expected) and allocation to specific treatment groups (RAS/MEK, PI3K/mTOR, or both). CONCLUSION: PTD correctly predicts crucial parameters for the design of multiarm genetic biomarker-driven trials. PTD is available as a package in the R programming language and as an open-access Web-based app. It represents a useful resource for the community of precision oncology trialists. The Web-based app is available at https://gmelloni.github.io/ptd/shinyapp.html.

Transcriptional activation of RagD GTPase controls mTORC1 and promotes cancer growth.

The mechanistic target of rapamycin complex 1 (mTORC1) is recruited to the lysosome by Rag guanosine triphosphatases (GTPases) and regulates anabolic pathways in response to nutrients. We found that MiT/TFE transcription factors-master regulators of lysosomal and melanosomal biogenesis and autophagy-control mTORC1 lysosomal recruitment and activity by directly regulating the expression of RagD. In mice, this mechanism mediated adaptation to food availability after starvation and physical exercise and played an important role in cancer growth. Up-regulation of MiT/TFE genes in cells and tissues from patients and murine models of renal cell carcinoma, pancreatic ductal adenocarcinoma, and melanoma triggered RagD-mediated mTORC1 induction, resulting in cell hyperproliferation and cancer growth. Thus, this transcriptional regulatory mechanism enables cellular adaptation to nutrient availability and supports the energy-demanding metabolism of cancer cells.