TechMiner: Extracting Technologies from Academic Publications

In recent years we have seen the emergence of a variety of scholarly datasets. Typically these capture ‘standard’ scholarly entities and their connections, such as authors, affiliations, venues, publications, citations, and others. However, as the repositories grow and the technology improves, researchers are adding new entities to these repositories to develop a richer model of the scholarly domain. In this paper, we introduce TechMiner, a new approach, which combines NLP, machine learning and semantic technologies, for mining technologies from research publications and generating an OWL ontology describing their relationships with other research entities. The resulting knowledge base can support a number of tasks, such as: richer semantic search, which can exploit the technology dimension to support better retrieval of publications; richer expert search; monitoring the emergence and impact of new technologies, both within and across scientific fields; studying the scholarly dynamics associated with the emergence of new technologies; and others. TechMiner was evaluated on a manually annotated gold standard and the results indicate that it significantly outperforms alternative NLP approaches and that its semantic features improve performance significantly with respect to both recall and precision

A quantitative comparison of cognitive performance and patient-reported symptoms in preoperative lower-grade glioma patients from two Dutch Hospitals

Background Protocols for assessment of (neuro)psychological outcomes in lower-grade glioma patients vary between hospitals. This potentially complicates generalization of these outcomes. We compared standardized scores on tests of two frequently impaired cognitive domains (attention and executive functioning (EF)), and two relevant patient-reported outcomes (PROs; depression and fatigue) of two neuro-oncological hospitals that use different measurement instruments. Material and Methods Data were used from preoperative assessments of patients with (IDH-mut) WHO grade II/III glioma tested between 2007 and 2021 at Amsterdam UMC (AMS) or at Elisabeth-Tweesteden Hospital Tilburg (ETZ). AMS patients were referred for (neuro)psychological assessment based on physician and patient preference (paper and pencil tests), whereas all ETZ patients routinely undergo screening (computerized tests). To compare scores of the different attention and EF tests we converted patients’ performances to z-scores based on normative data. For cognitive performance, we compared scores of different cognitive flexibility tests (CST vs SAT), processing speed tests (SDC vs LDMT), and Stroop tests (Stroop I and Stroop III). PROs included the CES-D vs HADS-D and the CIS-fatigue vs MVI-general fatigue (AMS vs ETZ, resp.). Differences were tested using Fisher's, χ 2, and Mann-Whitney U tests. Results Assessments were done median 4 weeks (AMS, n=97, range 19-0 weeks) and 1 day (ETZ, n=106; range 14-0 days) preoperatively. Age, sex, tumor location and histology were comparable between cohorts (p>0.05), but the AMS cohort showed significantly more grade III tumors (36% vs 16%) and more awake surgeries (84% vs 46%). Z-scores measuring attention and EF (n=94 and n=95, AMS vs ETZ) were not significantly different (CST vs SAT, percentage with a disorder (z <-1.5SD) 15% vs 13%; SDC vs LDMT 13% vs 14%; Stroop I 11% vs 18%; Stroop III 13% vs 16% at AMS and ETZ, resp.). Percentages of patients with possible depression (CES-D≥16, n=88 and HADS-D≥8, n=106) did not differ significantly between hospitals (28% vs 26%), nor did percentages of patients with severe fatigue (CIS-fatigue≥35, n=88 and MVI-general fatigue (z <-1.5SD), n=38, 42% vs 24% at AMS and ETZ, resp.). Conclusion Standardized scores of glioma patients on cognitive domains (attention and EF) and PROs (depression and fatigue) did not differ between two centers with slightly different samples using different testing protocols. This cautiously suggests that study findings on cognitive functioning and symptoms could be generalized. For research purposes, conjoint use of pooled populations for outcome evaluation could be explored with different samples from other centers using different instruments

Epilepsy is related to theta band brain connectivity and network topology in brain tumor patients

<p>Abstract</p> <p>Background</p> <p>Both epilepsy patients and brain tumor patients show altered functional connectivity and less optimal brain network topology when compared to healthy controls, particularly in the theta band. Furthermore, the duration and characteristics of epilepsy may also influence functional interactions in brain networks. However, the specific features of connectivity and networks in tumor-related epilepsy have not been investigated yet. We hypothesize that epilepsy characteristics are related to (theta band) connectivity and network architecture in operated glioma patients suffering from epileptic seizures. Included patients participated in a clinical study investigating the effect of levetiracetam monotherapy on seizure frequency in glioma patients, and were assessed at two time points: directly after neurosurgery (t1), and six months later (t2). At these time points, magnetoencephalography (MEG) was recorded and information regarding clinical status and epilepsy history was collected. Functional connectivity was calculated in six frequency bands, as were a number of network measures such as normalized clustering coefficient and path length.</p> <p>Results</p> <p>At the two time points, MEG registrations were performed in respectively 17 and 12 patients. No changes in connectivity or network topology occurred over time. Increased theta band connectivity at t1 and t2 was related to a higher total number of seizures. Furthermore, higher number of seizures was related to a less optimal, more random brain network topology. Other factors were not significantly related to functional connectivity or network topology.</p> <p>Conclusions</p> <p>These results indicate that (pathologically) increased theta band connectivity is related to a higher number of epileptic seizures in brain tumor patients, suggesting that theta band connectivity changes are a hallmark of tumor-related epilepsy. Furthermore, a more random brain network topology is related to greater vulnerability to seizures. Thus, functional connectivity and brain network architecture may prove to be important parameters of tumor-related epilepsy.</p

Public health and valorization of genome-based technologies: a new model

<p>Abstract</p> <p>Background</p> <p>The success rate of timely translation of genome-based technologies to commercially feasible products/services with applicability in health care systems is significantly low. We identified both industry and scientists neglect health policy aspects when commercializing their technology, more specifically, Public Health Assessment Tools (PHAT) and early on involvement of decision makers through which market authorization and reimbursements are dependent. While Technology Transfer (TT) aims to facilitate translation of ideas into products, Health Technology Assessment, one component of PHAT, for example, facilitates translation of products/processes into healthcare services and eventually comes up with recommendations for decision makers. We aim to propose a new model of valorization to optimize integration of genome-based technologies into the healthcare system.</p> <p>Methods</p> <p>The method used to develop our model is an adapted version of the Fish Trap Model and the Basic Design Cycle.</p> <p>Results</p> <p>We found although different, similarities exist between TT and PHAT. Realizing the potential of being mutually beneficial justified our proposal of their relative parallel initiation. We observed that the Public Health Genomics Wheel should be included in this relative parallel activity to ensure all societal/policy aspects are dealt with preemptively by both stakeholders. On further analysis, we found out this whole process is dependent on the Value of Information. As a result, we present our LAL (Learning Adapting Leveling) model which proposes, based on market demand; TT and PHAT by consultation/bi-lateral communication should advocate for relevant technologies. This can be achieved by public-private partnerships (PPPs). These widely defined PPPs create the innovation network which is a developing, consultative/collaborative-networking platform between TT and PHAT. This network has iterations and requires learning, assimilating and using knowledge developed and is called absorption capacity. We hypothesize that the higher absorption capacity, higher success possibility. Our model however does not address the phasing out of technology although we believe the same model can be used to simultaneously phase out a technology.</p> <p>Conclusions</p> <p>This model proposes to facilitate optimization/decrease the timeframe of integration in healthcare. It also helps industry and researchers to come to a strategic decision at an early stage, about technology being developed thus, saving on resources, hence minimizing failures.</p

‘Functional Connectivity’ Is a Sensitive Predictor of Epilepsy Diagnosis after the First Seizure

Background: Although epilepsy affects almost 1 % of the world population, diagnosis of this debilitating disease is still difficult. The EEG is an important tool for epilepsy diagnosis and classification, but the sensitivity of interictal epileptiform discharges (IEDs) on the first EEG is only 30–50%. Here we investigate whether using ‘functional connectivity ’ can improve the diagnostic sensitivity of the first interictal EEG in the diagnosis of epilepsy. Methodology/Principal Findings: Patients were selected from a database with 390 standard EEGs of patients after a first suspected seizure. Patients who were later diagnosed with epilepsy (i.e. $two seizures) were compared to matched nonepilepsy patients (with a minimum follow-up of one year). The synchronization likelihood (SL) was used as an index of functional connectivity of the EEG, and average SL per patient was calculated in seven frequency bands. In total, 114 patients were selected. Fifty-seven patients were diagnosed with epilepsy (20 had IEDs on their EEG) and 57 matched patients had other diagnoses. Epilepsy patients had significantly higher SL in the theta band than non-epilepsy patients. Furthermore, theta band SL proved to be a significant predictor of a diagnosis of epilepsy. When only those epilepsy patients without IEDs were considered (n = 74), theta band SL could predict diagnosis with specificity of 76 % and sensitivity of 62%. Conclusion/Significance: Theta band functional connectivity may be a useful diagnostic tool in diagnosing epilepsy