Miracle-dz/UniFAR-FTUs huggingface dataset:
Biodiversity and local interaction complexity promote sustainable fisheries in large food webs

On a global scale, fisheries harvest an estimated 96 million tonnes of fish biomass annually, making them one of the most important drivers of marine ecosystem biodiversity. Yet little is known about the interactions between fisheries and the dynamics of complex food webs in which the harvested species are embedded. We have developed a synthetic model that combines resource economics with complex food webs to examine the direct effects of fishing on exploited species and the indirect impact on other species in the same community. Our model analyses show that the sensitivity of the targeted species increases with its trophic level and decreases with its local interaction complexity (i.e. its number of interactions with prey, predators, and competitors). In addition, we also document a strikingly positive effect of community species richness on the resilience of the harvested species to this disturbance. The indirect effects on other species show specific patterns of spreading across trophic modules that differ systematically from how other disturbances spread across ecological networks. While these results call for further research on how human resource exploitation in general and fishery in particular affect ecological dynamics and biodiversity in naturally complex systems, they also allow for some cautious conclusions. Taken together, our results suggest that the sustainability concerning fishery yield and ecosystem integrity can be maximised by focusing the harvest on low trophic level species with a high local interaction complexity in high biodiversity ecosystems. In this sense, our complex network approach offers a promising avenue for integrating the necessities of generating economic revenue with the protection of natural biodiversity.
On the variation of the ionospheric potential due to large‐scale radioactivity enhancement and solar activity

Sensitivity of the global electric circuit (GEC) to variations of atmospheric conductivity and current sources is analyzed and discussed. When the undisturbed exponential conductivity profile is assumed all over the Earth, the most substantial changes in the ionospheric potential (IP) are caused by conductivity perturbations inside thunderstorms; if, in addition, conductivity reduction inside thunderstorms and nonelectrified clouds is assumed, the IP becomes less sensitive to conductivity perturbations; besides, the IP is even more sensitive to source current variations than to conductivity. Current source and voltage source descriptions of GEC generators are compared; it is shown that the IP variation may critically depend on the chosen description. As an application, the IP variation due to nuclear weapons testing is studied; it is shown that neither local nor global increase of conductivity in the stratosphere could alone explain the observed 40% IP increase in the 1960s; at the same time this increase might be accounted for by a 40% increase in the source current density or a 46% reduction of the conductivity inside thunderstorms, provided that it was not reduced initially. The IP variation due to solar activity and, in particular, due to solar modulation of galactic cosmic ray flux is also discussed and modeled, which required an adequate parameterization of the rate of atmospheric ion pair production over the solar cycle. It is estimated that the maximum IP variation on the scale of the solar cycle does not exceed 5% of the mean value, unless source current perturbations are taken into account.
A novel histological staging of hippocampal sclerosis that is evident in gray matter loss in vivo

Hippocampal sclerosis of aging (HS) is defined by end‐stage histological findings, strongly associated with limbic‐predominant age‐related TAR DNA‐binding protein 43 (TDP‐43) encephalopathy (LATE). We aimed to characterize features of early HS to refine the understanding of its role within combined pathology.
Internal limiting membrane disruption facilitates engraftment of transplanted human stem cell derived retinal ganglion cells

Retinal ganglion cell (RGC) death causes irreversible vision loss in patients with glaucoma and other forms of optic neuropathy because the mammalian retina and optic nerve lack endogenous regenerative capacity. RGC transplantation and optic nerve regeneration represent a potential translational approach to vision restoration in glaucoma secondary to RGC loss. Functional RGC replacement requires that 1) donor RGCs integrate into the recipient retina and receive synaptic input from afferent bipolar and amacrine cells and that 2) donor RGCs extend their axons and establish synaptic connections to appropriate neurons in the brain. Here, in an effort to address retinal integration, we demonstrate that the internal limiting membrane (ILM) acts as a physical barrier to the integration of transplanted human stem cell-derived RGCs (hRGCs) into the recipient retina following intravitreal transplantation in vivo. To circumvent the ILM barrier, we intravitreally injected the nonspecific protease pronase-E in immunosuppressed adult C57BL/6J mice prior to transplantation of hRGCs. Separately, we also transplanted hRGCs into adult Lama1nmf223 mice, which harbor a point mutation in their Lama-α1 gene that causes developmental ILM dysgenesis. We assessed donor hRGC survival and engraftment using 3D reconstructions of confocal z-stacks in retinal flatmounts. Migration of surviving donor RGC somas into the recipient RGC layer significantly increased after proteolytic or developmental ILM disruption. Moreover, lamination of dendritic arbors into the recipient inner plexiform layer was observed exclusively following ILM disruption. To assess the clinical translatability of this finding, we transplanted hRGCs onto postmortem organotypic human retinal explant cultures and observed significantly increased engraftment following proteolytic ILM digestion. These findings enhance our understanding of the barriers faced by transplanted hRGCs in the adult murine and human retina and provide an avenue for clinically translatable regenerative medicine approaches to vision restoration in optic neuropathy.
Evaluating hippocampal replay without a ground truth

During rest and sleep, memory traces replay in the brain. The dialogue between brain regions during replay is thought to stabilize labile memory traces for long-term storage. However, because replay is an internally-driven, spontaneous phenomenon, it does not have a ground truth - an external reference that can validate whether a memory has truly been replayed. Instead, replay detection is based on the similarity between the sequential neural activity comprising the replay event and the corresponding template of neural activity generated during active locomotion. If the statistical likelihood of observing such a match by chance is sufficiently low, the candidate replay event is inferred to be replaying that specific memory. However, without the ability to evaluate whether replay detection methods are successfully detecting true events and correctly rejecting non-events, the evaluation and comparison of different replay methods is challenging. To circumvent this problem, we present a new framework for evaluating replay, tested using hippocampal neural recordings from rats exploring two novel linear tracks. Using this two-track paradigm, our framework selects replay events based on their temporal fidelity (sequence-based detection), and applies a cross-validation using each event’s trajectory discriminability, where sequenceless decoding across both tracks is used to quantify whether the track replaying is also the most likely track being reactivated.
Urbanisation impacts the diversity, coloration, and body size of wild bees in a Mediterranean city

Urbanisation is a growing phenomenon causing the decline of wild bees globally. Yet, bees manage to persist in the urban matrix thanks to islands of vegetation in public parks and private gardens. While we begin to comprehend the impact of urbanisation on bees’ diversity and abundance, our understanding of its impact on the functional diversity of wild bees is limited. Here, we use an integrative approach to investigate the response of wild bees to urbanisation at the community, species, and individual levels. To do so, we sampled wild bees in 24 public parks along an urbanisation gradient in the Mediterranean city of Marseille. We found that species richness and abundance decreased in more urbanised areas, but increased in larger city parks. Moreover, larger individuals within species, but not larger species, were found in larger city parks, suggesting that park size is crucial for the persistence of bees in cities. Interestingly, we show that brighter species were found in parks surrounded by a large amount of impervious surface, highlighting the importance of colour traits in the response to environmental changes. Finally, our results revealed that larger species, but not larger individuals, were also more colourful. In summary, our study not only confirmed that urbanisation negatively impacts community-level traits, but that it also affects species’ coloration and individuals’ body size, thus improving our understanding of the functional response of wild bees to urbanisation. We suggest that increasing park size may compensate for the negative effects of urbanisation on wild bees.
Predictive coding during action observation revealed by human electrocorticographic activity

Predictive coding is a theoretical framework that has received much attention for its ability to generate testable hypotheses on how multiple brain regions integrate information during cognitive functions. Given relatively large sensorimotor delays, during social interactions, predicting the behavior of others is crucial to enable joint actions or provide competitive advantages. The action observation network (AON) has been extensively studied, but how information is integrated across its main nodes remains poorly understood. Here we leverage the high spatial and temporal resolution of intracranial Electrocorticography (ECoG), to characterize how the key nodes of the AON - including precentral, supramarginal and visual areas - exchange information. We found more top-down beta oscillation from precentral to supramarginal contacts during the observation of predictable actions while more bottom-up gamma oscillation from visual to supramarginal contacts were measured for unpredictable actions. These results, in line with predictive coding, provide critical evidence towards an understanding of how nodes of the AON integrate information to process the actions of others.
Mass transport in water waves

It was shown by Stokes that in a water wave the particles of fluid possess, apart from their orbital motion, a steady second-order drift velocity (usually called the mass-transport velocity). Recent experiments, however, have indicated that the mass-transport velocity can be very different from that predicted by Stokes on the assumption of a perfect, non-viscous fluid. In this paper a general theory of mass transport is developed, which takes account of the viscosity, and leads to results in agreement with observation. Part I deals especially with the interior of the fluid. It is shown that the nature of the motion in the interior depends upon the ratio of the wave amplitude a to the thickness d of the boundary layer: when a2/d2 is small the diffusion of vorticity takes place by viscous ‘conduction’; when a2/d2 is large, by convection with the mass-transport velocity. Appropriate field equations for the stream function of the mass transport are derived. The boundary layers, however, require separate consideration. In part II special attention is given to the boundary layers, and a general theory is developed for two types of oscillating boundary: when the velocities are prescribed at the boundary, and when the stresses are prescribed. Whenever the motion is simple-harmonic the equations of motion can be integrated exactly. A general method is described for determining the mass transport throughout the fluid in the presence of an oscillating body, or with an oscillating stress at the boundary. In part III, the general method of solution described in parts I and II is applied to the cases of a progressive and a standing wave in water of uniform depth. The solutions are markedly different from the perfect-fluid solutions with irrotational motion. The chief characteristic of the progressivewave solution is a strong forward velocity near the bottom. The predicted maximum velocity near the bottom agrees well with that observed by Bagnold.
Transcription regulation models and their application to human disease research (עם תקציר בעברית ושער נוסף: מודלים לבקרת שעתוק ויישומם במחקר של מחלות אדם.)

Living cells are constantly responding to internal and external signals. From the simplest yeast that adapts to changes in temperature, to a stem cell in the human body that decides to which type of cell to differentiate. A major part of the cellular response to signals is production of proteins according to the cell’s needs. The first step in protein production is transcription of genes to intermediate mRNA molecules, from which the proteins are translated. In the past decade, technological developments enabled measuring of mRNA expression levels of all the genes in the cell, providing a profile of cellular expression at a given time point. Comparison of such profiles under different conditions (e.g., before and after a drug treatment) provides a useful insight into which proteins play a role in these conditions (e.g., in response to the drug). Gene transcription is tightly regulated by a complex multilayer mechanism. An important part of this mechanism are transcription factors proteins that bind the DNA and activate or repress transcription. The binding of transcription factors is specific — each factor regulates a set of genes which are often recognized by specific DNA sequences near the gene or in distant enhancer regions. A fascinating question which I explore here, is which transcription factors play a role in different human conditions and diseases, and what are their target genes. Identifying the key transcription factors and their targets is crucial for understanding the molecular background of diseases and paves the road for their therapeutics. Tremendous efforts have been invested in developing computational models that map transcription factors to their targets. These models, typically represented as a network of interactions between genes or proteins, highly differ in their underling mathematical assumptions. One such model, called Module Networks, is a probabilistic entity based on Bayesian Networks, which uses expression levels to identify modules of genes that share a similar expression profile and other genes that regulate them.
Semi-supervised natural language acquisition (למידה מפוקחת למחצה של שפה טבעית על ידי מחשב.; למידה מפקחת למחצה של שפה טבעית על ידי מחשב.)

Natural Language processing (NLP) is a field that combines linguistics, cognitive science, statistical machine learning and other computer science areas in order to compile intelligent computer systems that can understand human languages. NLP has various applications, among which are machine translation, question answering and search engines. The field of NLP has, in the past two decades, come to simultaneously rely on and challenge the field of machine learning. Statistical methods now dominate NLP, and have moved the field forward substantially, opening up new possibilities for the exploitation of data in developing NLP components and applications. Many state of the art natural language algorithms are based on supervised learning techniques. In this type of learning, a corpus consisting of texts annotated by human experts is compiled and used to train a learning algorithm. While supervised learning has made substantial contribution to NLP, it faces some significant challenges. Many fundamental NLP tasks, such as syntactic parsing, part-of-speech (POS) tagging and machine translation, involve structured prediction and sequential labeling. For such kind of tasks, compiling annotated corpora is costly and error prone due to the complex nature of annotation. I refer to this challenge as the annotation bottleneck. A closely related challenge is that of domain adaptation. Supervised algorithms usually perform well when the training data and the data for which they should provide predictions (the test data) are drawn from similar domains. When an algorithm trained with data from one domain is to provide predictions for data taken from a substantially different domain, its performance markedly degrades. Creating corpora for every test domain is not feasible due to the aforementioned annotation bottleneck. Supervised natural language learning is also challenged by methodological problems. Annotation schemes for tasks such as syntactic parsing are often based on arbitrary decisions. Such schemes often provide a detailed description of certain structures while addressing others only briefly. Many applications would benefit from different annotation decisions. This work focuses on developing machine learning techniques that deal with these challenges. Its main theme is utilizing the plentiful amounts of raw text available nowadays, for creating state of the art algorithms that use little to no manually annotated data. Cases where little amounts of manually annotated text is utilized are referred to as semi-supervised learning; while cases where only raw text is used are known as unsupervised learning. This work explores semi-supervised and unsupervised techniques for structured
From unsupervised to semi-supervised learning : algorithms and applications (מלמידה לא-מפוקחת ללמידה מפוקחת-למחצה.)

This thesis is comprised of two parts. In the first part we consider algorithms and applications of unsupervised and semi-supervised learning. In the second part we apply generalized belief propagation, an approximate inference algorithm from the field of graphical models, to an electrical engineering system. The thesis is organized in the following chapters: Chapter 2 Unsupervised learning: We present the typical cut criterion using probabilistic undirected graphical models. Apart from providing accurate and efficient clustering solutions, the probabilistic framework allows us to learn a task-relevant affinity function later used in clustering. We also present an application of the typical cut criterion to the computer vision problems of image segmentation and perceptual grouping. Chapter 3 Semi-supervised learning:We consider two types of semi-supervised scenarios, in which the (limited) supervision is either provided as a partially labelled data set, or in the form of equivalence constraints. In the first scenario we present an extension of the typical cut clustering criterion. The second scenario considers equivalence constraint, i.e. pairs of data points that are known to originate from the same class or from different classes, although the classes themselves are unknown. We present two algorithms which incorporate equivalence constraints – the relevant component analysis algorithm and an extension of the EM algorithm for estimating Gaussian mixture models. Chapter 4 Generalized belief propagation – An engineering application:We apply generalized belief propagation, to an engineering application of two-dimensional channels. The performance of generalized belief propagation in this real-life application almost coincides with the best theoretically possible result. On the the hand, the performance of standard belief propagation in this case is poor. The research in this thesis appeared in the published papers [A-F], and in [3,22].
Distance functions: theory, algorithms and applications (פונקציות מרחק.)

Distance functions are at the core of numerous computer vision and machine learning tasks. For example, a multiple view geometry application (such as homography estimation), often includes a descriptor matching step. First, small image patches are selected. Then, descriptors (vectors) are computed to describe these patches. To decide if two descriptors (from two different images) are from the same real-world location the descriptors are compared using a distance function. The choice of the distance function determines both the accuracy and the speed of the method. Distance functions are also used for image retrieval and determine both the percent of similar images returned and the time it takes to return them. In machine learning, knearest neighbor classification performance is effected by the chosen distance function. This thesis introduces several new distance functions and investigates their properties. Special emphasis is on practical applicability with theoretical insights. The thesis presents efficient algorithms to compute several distances. The distances are designed to be robust to common image noise such as occlusion, geometrical transforms, light changes and non-rigid deformations. On the other hand, they are designed to be as distinctive as possible. Our proposed methods have been successfully used both by computer vision researchers and by researchers in other fields. The success of the methods in other fields is probably because the noise characteristics in those fields are similar to image noise characteristics. The first and second parts of the thesis are devoted to the Earth Mover’s Distance (EMD) [1]. The EMD is a generalization of the transportation distance (also known as Monge-Kantorovich, Mallows, 1 Wasserstein and match distance) for non-normalized histograms. We introduce a new generalization of the transportation distance for non-normalized histograms called, ÊMD. Unlike the classic EMD, ÊMD is a metric even when the histograms are non-normalized. Additionally, in practice, ÊMD is often better than EMD. We also show that ÊMD is a generalization of the L1 distance. Histograms of oriented gradient descriptors are ubiquitous tools in numerous computer vision tasks. Unfortunately, the distances that are used to match such descriptors do not take into account the special structure of these histograms. One problem with these distances is that they only compare corresponding bins of histograms. Although there are cross-bin distances that take into account the amount of similarity between all bins, they are not adequate to the histograms of oriented gradients that are computed from images. Additionally, most cross-bin distances are too slow. We present a
From gene expression to molecular pathways (מביטוי גנים למסלולים מולקולריים.)

Molecular networks involving interacting proteins, RNA, and DNA molecules, underlie the major functions of living cells. DNA microarrays probe how the gene expression changes to perform complex coordinated tasks in adaptation to a changing environment at a genome-wide scale. In this dissertation we address the challenge of reconstructing molecular pathways and gene regulation from gene expression data. Our goal is to automatically infer regulatory relations between genes, as well as other types of molecular interactions. To answer this challenge, we develop probabilistic graphical models of the biological system. We offer three such models and algorithms to automatically learn these from gene expression data. Our models and learning algorithms are based on the assumption that statistical correlation might indicate molecular or genetic interaction. We offer systematic evaluation for each of the methods presented culminating in experimental validation of novel predictions, automatically generated by one of our models.